Importance: p53 is an unequivocal tumor suppressor altered in half cancers. The immune system produces systemic p53 autoantibodies (p53 Abs) in many cancer patients. Objective: The focus of this systemic review and meta-analysis is on the prognostic value of p53 Abs expressed in the serum of patients with solid tumors. Data Sources: All the clinical investigations were searched on PubMed, MBase and Cochrane from 1993 reporting the first study until May 2021. Study Selection: Studies were included that met the following criteria: 1) participants with cancer; 2) outcome results expressed in relation to the presence of a p53 antibody; 3) a primary outcome (disease free survival, overall survival or progression free survival) expressed as hazard ratio (HR). The following exclusion criteria were used: 1) insufficient data available to evaluate outcomes; 2) animal studies; 3) studies with less than 10 participants. 1333 potentially relevant articles; studies as duplicates, non-patients studies or reviews were excluded. After viewing the titles and abstracts of the 52 remaining studies, the full texts of 34 studies were retrieved and 12 studies were included in the analysis. Data Extraction and Synthesis: PRISMA guidelines were used for abstracting and assessing data quality and validity by three independent observers. The summary estimates were generated using a fixed-effect model (Mantel–Haenszel method) or a random-effect model (DerSimonian–Laird-method) depending on the absence or presence of heterogeneity (I2). Main Outcome(s) and Measure(s): The primary study outcome was to determine the prognostic value of p53 Abs from a large population size of patients with solid tumors, as determined before data collection. Results: In total 12 clinical studies and of which 2094 patients were included and it was determined that p53-wt Abs expression in the serum significantly correlated with a worse survival of cancer patients (95% CI 1.48 [1.24, 1.77]; p<0.00001). On the contrary, data from literature indicated that there was a potential association between p53-mut Abs antibodies with better survival. Conclusions and Relevance: This is the first meta-analysis proving the diagnostic utility of p53-Abs for cancer patients, predicting a worse outcome. The serum-p53 value (s-p53-value) could be useful for future theranostics.

Polymerase chain reactions are helpful diagnostic methods to accurately quantitate the intensities of infections of various soil-transmitted helminths, especially in the low-intensity infection samples. Method of DNA extraction hugely impacts the outcomes of the diagnostic method. Trichuris trichiura is one of the three major helminths prevalent world-wide and accurate estimation of their loads in stool is affected by the method of DNA extraction. We meta-analyze two studies by dividing them into all together 6 sub-studies. The objectives of the meta-analysis required the two studies to be divided into sub-studies as the different methods of DNA extractions could not be combined. We found that the inclusion of the bead-beating step during DNA extraction significantly increases the sensitivity of the test.

The domestication and subsequent genetic improvement of wheat led to the development of large-seeded cultivated wheat species relative to their smaller-seeded wild progenitors. While increased grain weight (GW) continues to be an important goal of many wheat breeding programs, few genes underlying this trait have been identified despite an abundance of studies reporting quantitative trait loci (QTLs) for GW. Here we perform a QTL analysis for GW using a population of recombinant inbred lines (RILs) derived from the cross between wild emmer wheat accession ‘Zavitan’ and durum wheat variety ‘Svevo’. Identified QTLs in this population were anchored to the recent Zavitan reference genome, along with previously published QTLs for GW in tetraploid wheat. This genome-based, meta-QTL analysis enabled the identification of a locus on chromosome 6A whose introgression from wild wheat positively affects GW. The locus was validated using an introgression line carrying the 6A GW QTL region from Zavitan in a Svevo background, resulting in >8% increase in GW compared to Svevo. Using the reference sequence for the 6A QTL region, we identified a wheat ortholog to OsGRF4, a rice gene previously associated with GW. The coding sequence of this gene (TtGRF4-A) contains four SNPs between Zavitan and Svevo, one of which reveals the Zavitan allele to be rare in a core collection of wild emmer and completely absent from the domesticated emmer genepool. Similarly, another wild emmer accession (G18-16) was found to carry a rare allele of TtGRF4-A that also positively affects GW and is characterized by a unique SNP absent from the entire core collection. These results exemplify the rich genetic diversity of wild wheat, posit TtGRF4-A as a candidate gene underlying the 6A GW QTL, and suggest that the natural Zavitan and G18-16 alleles of TtGRF4-A have potential to increase wheat yields in breeding programs.

In 2012 the Open Geospatial Consortium published GeoSPARQL defining “an RDF/OWL ontology for [spatial] information”, “SPARQL extension functions” for performing spatial operations on RDF data and “RIF rules” defining entailments to be drawn from graph pattern matching. In the 8+ years since its publication, GeoSPARQL has become the most important spatial Semantic Web standard, as judged by references to it in other Semantic Web standards and its wide use for Semantic Web data. An update to GeoSPARQL was proposed in 2019 to deliver a version 1.1 with a charter to: handle outstanding change requests and source new ones from the user community and to “better present” the standard, that is to better link all the standard’s parts and better document &amp; exemplify elements. Expected updates included new geometry representations, alignments to other ontologies, handling of new spatial referencing systems, and new artifact presentation. In this paper, we describe motivating change requests and actual resultant updates in the candidate version 1.1 of the standard alongside reference implementations and usage examples. We also describe the theory behind particular updates, initial implementations of many parts of the standard, and our expectations for GeoSPARQL 1.1’s use.

A recent systematic review for 19 selected articles after searching through to 30 September 2017 showed vitamin D deficiency was associated with ischemic stroke (IS), not hemorrhagic stroke (HS). But a heterogeneity would be introduced with comparing the lowest and highest category of vitamin D. The aim of this article was to conduct an updated meta-analysis (UMA) with searching through to 31 March 2019. An interval collapsing method as information extraction was applied in order to decrease a heterogeneity among studies. Additional articles were selected from cited lists from 19 selected articles using citation discovery tools. Random effect model was applied if I-squared value was over 50%. A funnel plot and Egger’s test were used to detect a publication bias. After 5 new studies were added, the summary RRs [and their 95% confidence intervals] (I-squared value) were 1.52 [1.33–1.74] (0.0%) in IS, and 2.44 [1.34–4.46] (69.7%) in HS. This UMA supported the hypothesis that serum vitamin D deficiency was associated with an increased risk of HS as well as IS. Diverse public health programs against vitamin D deficiency status would be needed for higher risk group, especially elderly people.

Convolutional neural network (CNN), a class of artificial neural network (ANN) is attracting interests of researchers in all research domain. CNN was invented for computer vision. They have also shown to be useful for semantic parsing, sentence modeling and other natural language processing related tasks. Here in this paper we discuss the basics of CNN models and their scope to provide a reference/baseline to the researchers interested in using CNN models in their research.

This paper presents a cooperative parallel tabu search meta-heuristic for the vehicle routing problem with time windows. It is based on the scheme in which several search threads cooperate by asynchronously exchanging information on the best solutions identified. The exchanges are performed through a mechanism called adaptive memory which holds and manages a pool of solutions. This enforces the asynchronous strategy of information exchanges and ensures the independence of the individual search threads. Each of these independent threads implements a tabu search meta-heuristic. Comprehensive computational experiments and comparisons to best known solutions show that the proposed cooperative parallel tabu search algorithm is able to achieve 48 new best solutions on VRPTW benchmark instances.

In this paper, I give readers an idea of what some scholars are interested in, what I found interesting, and what may be of future interest in the philosophy of emotion. I begin with a brief overview of the general topics of interests in the philosophy of emotion. I then discuss what I believe to be some of the most interesting topics in the contemporary discourse, including questions about how philosophy can inform the science of emotion, conceptions of the mind and the mind-body problem, concerns about perception, cognition, and emotion, along with questions about the place of 4E approaches and meta-semantic pluralist approaches in the embodied cognitive tradition. Finally, I discuss the emerging field of cultural evolution, the import of a dual-inheritance theory in this emerging field, and I propose a possible way to integrate the frameworks of dual-inheritance theory and meta-semantic pluralism to demonstrate at least one way in which the philosophy of emotion can contribute to the emerging field of cultural evolution.

Overweight/obesity is one of the most important health problem worldwide. Birth by cesarean section has been shown to influence long-term health outcome including obesity. The aim of this systematic review-meta-analysis is to examine whether cesarean section increases the risk of offspring’ s overweight/obesity. The study follows the PRISMA and MOOSE guidelines. A systematic literature search was con-ducted on Scopus, PubMed, and WoS until December 2020. For inclusion, studies must have re-ported either (I) both Birth by cesarean section and adult (≥ 18 years) offspring BMI, (II) cohort or case–control study design and (III) a risk estimate. Heterogeneity testing was performed using Cochran's Q and I2 statistics. Publication bias was assessed by Egger’s test and Begg’s test. Me-ta-analysis was performed through a random effect model. Eleven studies with a combined population of 180.408 subjects were included in the meta-analysis. The overall analysis (n = 18) yielded a combined risk estimate for overweight/obesity of 1.19 (95% CI 1.08-1.31) and the test of heterogeneity resulted Q=53,37 (I2 = 70,37 %, P≤0•0001). The risk of offspring obesity is 1.23 (95% CI 1.09-1.39) and the test of heterogeneity resulted Q=39.50 (I2= 72,15%, P≤0•0001). Children born by cesarean section have an increased risk of developing obesity in adulthood

Large structures such as wind turbines are subject to environmental factors and varying operational loads which may result in structural damage, making components of these large structures prone to performance and mechanical degradation. The use of high-definition optical vision sensors in digital image correlation (DIC) allow for the application of a non-destructive image registration technique in which it measures finite three-dimensional deformations on surfaces through correlations of a unique pattern or set of unique localized patterns. However, the physical placement of an artificial marker such as a unique speckled pattern on the surface of the structure is time-consuming and often impractical for large structures. Therefore, we propose a novel auto-mated methodology that searches and segments salient and unique regions of an image as well as for all subsequent images to assist in performing efficient displacement measurements for vibrational study and structural health monitoring purposes. Our algorithm is validated on a con-trolled set of images, as well as on a small structure and large real-world wind turbine, which suggests the algorithm’s efficacy without the use of artificial markers for large structural health monitoring.

The research presents the Methodology of Improving the Accuracy in Text Classification in Light of Modelling the Latent Semantic Relations (LSR). The aim of this Methodology is to find the ways of eliminating the Limitations of Discriminant and Probabilistic methods for LSR revealing and customizing the Text Classification Process to the more accurate recognition of the text tonality. This aim should be achieved by using the knowledge about the text’s Hierarchical Semantic Context in the form of Corpora-based Hierarchical Sentiment Dictionary. The main scientific contribution of this research is the following set of approaches to improve the qualitative characteristics of Text Classification process: combination of the Discriminant and Probabilistic methods allowing to decrease the influences of the Limitations of these methods on the LSR revealing process; considering each document as a complex structure allowing to estimate documents integrally by separated classification of topically completed textual component (paragraphs); taking into account the features of Argumentative type of documents (Reviews) allowing to use the author’s subjective evaluation of text tonality for development the Text Classification methodology. Tonality, expressed by the Review’s author, has a significant, but not critical, effect on the qualitative indicators of Sentiment Recognition.

The development of the smart city concept and the inhabitants’ need to reduce travel time, as well as society’s awareness of the reduction of fuel consumption and respect for the environment, lead to a new approach to the classic problem of the Travelling Salesman Problem (TSP) applied to urban environments. This problem can be formulated as “Given a list of geographic points and the distances between each pair of points, what is the shortest possible route that visits each point and returns to the departure point?” Nowadays, with the development of IoT devices and the high sensoring capabilities, a large amount of data and measurements are available, allowing researchers to model accurately the routes to choose. In this work, the purpose is to give solution to the TSP in smart city environments using a modified version of the metaheuristic optimization algorithm TLBO (Teacher Learner Based Optimization). In addition, to improve performance, the solution is implemented using a parallel GPU architecture, specifically a CUDA implementation.

Diabetes mellitus (DM) and obesity account for the highest burden of non-communicable diseases. There is increasing evidence showing therapeutic patient education (TPE) as a clinically and cost-effective solution to improve biomedical and psychosocial outcomes among people with DM and obesity. The present systematic review and meta-analysis presents a critical synthesis of development of TPE interventions for DM and obesity and the efficacy of these interventions across a range of biomedical, psychosocial and psychological outcomes. A total of 54 of these RCTs were identified among patients with obesity and diabetes and were thus, qualitatively synthesized. Out of these, 47 were included into quantitative synthesis. There was substantial heterogeneity in reporting of these outcomes (I²= 88.35%, Q= 317.64), with significant improvement (SMD=0.36, 95% CI: 0.23 to 0.49) noted in biomedical outcomes in the intervention group. The effect sizes were comparable across interventions delivered by different modes and delivery agents. These interventions can be delivered by allied health staff, doctors or electronically as self-help programs, with similar effectiveness (P < 0.001). These interventions should be implemented in healthcare and community settings to improve health of patients suffering from DM and obesity.

Semantic jokes involve resolving an incongruity emerging from wordplay or from violation of world knowledge. Research has shown individuals with autism spectrum disorder (ASD) demonstrate lower performance on humor tasks involving social situations, but less is known about their semantic joke comprehension. This study examined semantic joke comprehension among adolescents with ASD, and its possible relationship to vocabulary size, theory of mind (ToM), and idiom comprehension. Thirty-two adolescents with ASD and 32 typically developed (TD) peers participated. Semantic joke comprehension was assessed via multiple-choice questionnaire and time-limited computer program. Vocabulary, ToM abilities, and idiom comprehension were also tested. Results revealed that adolescents with ASD were as fast in processing semantic jokes as their age- and vocabulary-matched TD peers but less accurate. Age and idiom comprehension contributed significantly to semantic joke comprehension among both groups. As semantic joke comprehension is based on incongruity resolution, the greater difficulties in comprehension among the adolescents with ASD may be due to deficits in simultaneously retaining two alternative interpretations and selecting the relevant one. Like the TD group, semantic joke comprehension among the ASD group appeared more developed with age. Future neuroimaging studies should test semantic brain region involvement in semantic joke comprehension in ASD.

As the amount of sensor data made available online increases, it becomes more difficult for users to identify useful datasets. Semantic web technologies improve discovery with meaningful ontologies, but the decision of suitability remains with the users. The GEO label provides a visual summary of the standardised metadata to aid users in this process. This work presents novel rules for deriving the information for the GEO label's multiple facets, such as user feedback or quality information, based on the Semantic Sensor Network Ontology and related ontologies. It enhances an existing implementation of the GEO label API to generate labels for resources of the Semantic Sensor Web. The prototype is deployed to serverless cloud infrastructures. We find that serverless GEO label generation is capable of handling two evaluation scenarios for concurrent users and burst generation. More real-world semantic sensor descriptions and an integration into large scale discovery platforms are needed to develop the presented solutions further.

An autonomous concrete crack inspection system is necessary for preventing hazardous incidents arising from deteriorated concrete surfaces. In this paper, we represent a concrete crack detection framework to aid the process of automated inspection. The proposed approach employs a deep convolutional neural network architecture for crack segmentation from concrete image. The proposed network alleviates the effect of gradient vanishing problem present in deep neural network architectures. A feature silencing module is incorporated in the crack detection framework, for eliminating unnecessary feature maps from the network. The overall performance of the network significantly improves as a result. Experimental results support the benefit of incorporating feature silencing within a convolutional neural network architecture for improving the network’s robustness, sensitivity, and specificity. An added benefit of the proposed architecture is its ability to accommodate for the trade-off between specificity (positive class detection accuracy) and sensitivity (negative class detection accuracy) with respect to the target application. Furthermore, the proposed framework achieves a high precision rate and processing time than crack detection architectures present in literature.

Deep learning has achieved lots of successes in many fields, but when trainable sample are extremely limited, deep learning often under or overfitting to few samples. Meta-learning was proposed to solve difficulties in few-shot learning and fast adaptive areas. Meta-learner learns to remember some common knowledge by training on large scale tasks sampled from a certain data distribution to equip generalization when facing unseen new tasks. Due to the limitation of samples, most approaches only use shallow neural network to avoid overfitting and reduce the difficulty of training process, that causes the waste of many extra information when adapting to unseen tasks. Euclidean space-based gradient descent also make meta-learner's update inaccurate. These issues cause many meta-learning model hard to extract feature from samples and update network parameters. In this paper, we propose a novel method by using multi-stage joint training approach to post the bottleneck during adapting process. To accelerate adapt procedure, we also constraint network to Stiefel manifold, thus meta-learner could perform more stable gradient descent in limited steps. Experiment on mini-ImageNet shows that our method reaches better accuracy under 5-way 1-shot and 5-way 5-shot conditions.

In recent years, semantic communication has received significant attention from both academia and industry, driven by the growing demands for ultra-low latency and high-throughput capabilities in emerging intelligent services. Nonetheless, a comprehensive and effective theoretical framework for semantic communication has yet to be established. In particular, finding the fundamental limits of semantic communication, exploring the capabilities of semantic-aware networks, or utilizing theoretical guidance for deep learning in semantic communication are very important yet still unresolved issues. In general, the mathematical theory of semantic communication and the mathematical representation of semantics are referred to as semantic information theory. In this paper, we introduce the pertinent advancements in semantic information theory. Grounded in the foundational work of Claude Shannon, we present the latest developments in semantic entropy, semantic rate-distortion, and semantic channel capacity. Additionally, we analyze some open problems in semantic information measurement and semantic coding, providing a theoretical basis for the design of a semantic communication system. Furthermore, we carefully review several mathematical theories and tools and evaluate their applicability in the context of semantic communication. Finally, we shed light on the challenges encountered in both semantic communication and semantic information theory.

Background: Pharmacologic and non-pharmacologic treatments for stroke are essential but could be costly or harmful whereas probiotic has been a promising alternative. This scoping review aimed to synthesize the in vitro and in vivo evidence of probiotics on stroke-related neurological, biochemical, and histochemical outcomes. Method: We searched in PubMed, Embase, and Cochrane Central Register of Clinical up to May 7, 2021, and screened by two independent reviewers. We included the use of probiotics, prebiotics, and symbiotics both in vitro and in vivo for the prevention or treatment of the stroke-related model. Result: Of 6,293 articles, 4,990 passed the initial screen, of which 36 theme-related full-texts were assessed and 13 were included in this review. Probiotics could ameliorate the neurological deficit and show their property as an anti-inflammation and anti-oxidative stress. Histopathologically decreased loss of cerebral volume and inhibition of neuronal apoptosis were found. Conclusion: There are potential cognitive benefits of probiotic supplementation, especially among animal models, on decreasing cerebral volume, increasing neurological score, and decreasing the inflammatory response. However, further investigation is needed to validate these conclusions in various populations.

The level of ambiguity in describing glycan structure has significantly increased with the upsurge of large scale glycomics and glycoproteomics experiments. Consequently, an ontology-based model appears as an appropriate solution for navigating this data. However, navigation is not sufficient and the model should also enable advanced search and comparison. A new ontology with a tree logical structure is introduced to represent glycan structures irrespective of the precision of molecular details. The model heavily relies on the GlycoCT encoding of glycan structures. Its implementation in the GlySTreeM knowledge base was validated with GlyConnect data and benchmarked with the Glycowork library. GlySTreeM is shown to be fast, consistent, reliable and more flexible than existing solutions for matching parts of or whole glycan structures. The model is also well suited to painless future expansion. Availability:https://glyconnect.expasy.org/glystreem/wiki

Software is playing the most important role in recent vehicle innovation, and consequently the amount of software has been rapidly growing last decades. Safety-critical nature of ships, one sort of vehicles, makes Software Quality Assurance (SQA) has gotten to be a fundamental prerequisite. Just-In-Time Software Defect Prediction (JIT-SDP) aims to conduct software defect prediction (SDP) on commit-level code changes to achieve effective SQA resource allocation. The first case study of SDP in maritime domain reported feasible prediction performance. However, we still consider that the prediction model has still rooms for improvement since the parameters of the model are not optimized yet. Harmony Search (HS) is a widely used music-inspired meta-heuristic optimization algorithm. In this article, we demonstrated that JIT-SDP can produce the better performance of prediction by applying HS-based parameter optimization with balanced fitness value. Using two real-world datasets from the maritime software project, we obtained an optimized model that meets the performance criterion beyond baseline of previous case study throughout various defect to non-defect class imbalance ratio of datasets. Experiments with open source software also showed better recall for all datasets despite we considered balance as performance index. HS-based parameter optimized JIT-SDP can be applied to the maritime domain software with high class imbalance ratio. Finally, we expect that our research can be extended to improve performance of JIT-SDP not only in maritime domain software but also in open source software.

This article describes the transition of an undirected active network, used for numerical and logical analysis, and made up of communicating operators and nodes, and being self-extensible, into a network of semantic structures, and the extensions necessary to handle language. The network, called Active Structure, was infused with knowledge in the form of tens of thousands of definitions taken from a human-readable dictionary. Where the dictionary fell short, from circularity, lack of a hierarchical form for its senses and sub senses, or inadequate detail around verbs, the entries were extensively curated. The result is a machine which has access to every meaning of every word in its vocabulary, and needs to manage the right meaning for every use of a word, a task that our unconscious minds now handle. To demonstrate that such a network can aspire to human-level tasks, we have begun loading an Anti-Money Laundering Act so that the machine can devise and test strategies against money launderers. There is a higher goal being aimed at, the breaking of the Four Pieces Limit. That is, the network is not intended to save money on human cognitive labor, but to raise an absurdly low limit on it.

In this paper, we present a method to map information regarding service activity provision residing in governmental portals across European Commission. In order to perform this, we used as a basis the enriched Greek e-GIF ontology, modeling concepts, and relations in one of the two data portals (i.e., Points of Single Contacts) examined, since relevant information on the second was not provided. Mapping consisted in transforming information appearing in governmental portals in RDF format (i.e., as Linked data), in order to be easily exchangeable. Mapping proved a tedious task, since description on how information is modeled in the second Point of Single Contact is not provided and must be extracted in a manual manner.

Semantic interoperability facilitates exchange and access of health data that is being documented in EHRs with various semantic features. In the EU, the regulation proposal of European health data space requires development of semantic interoperability. To achieve a fully integrated EHDS ecosystem leveraging the value of health data, stakeholders need to overcome challenges of implementing common standards and other semantic interoperability features. We aimed to research what scientific evidence is available on developing semantic interoperability. Our research questions focused specifically on key features and approaches for semantic interoperability and on possible benefits of these choices. For that purpose, we performed a systematic literature review by defining our study framework based on previous research. Our results consisted of 10 studies where data models, ontologies, terminologies, classifications, and standards were applied for building interoperability. Through increased access to interoperable patient information, a better quality and outcomes in care can be achieved. Better communication based on easily accessible data is facilitated between health professionals and between clinicians and the patients. When heading towards semantic harmonization as outlined in the EHDS proposal, more experiences and analyses are needed to assess how applicable the chosen solutions are for semantic interoperability of European health care data.

Genome-wide association studies (GWAS) have identified and reproduced thousands of diseases associated loci but many of them are not directly interpretable due to the strong linkage disequilibrium among variants. Transcriptome-wide association studies (TWAS) incorporated expression quantitative trait loci (eQTL) cohorts as reference panel to detect associations with the phenotype at the gene level and were gaining popularity in recent years. For nicotine addiction, several important susceptible genetic variants were identified by GWAS, but TWAS that detected genes associated with nicotine addiction and unveiled the underlying molecular mechanism were still lacking. In this study, we used eQTL data from the Genotype-Tissue Expression (GTEx) consortium as reference panel to conduct tissue specific TWAS on cigarettes per day (CPD) over 13 brain tissues in two large cohorts: UK Biobank (UKBB; N=142,202) and the GWAS &amp; Sequencing Consortium of Alcohol and Nicotine use (GSCAN; N=143,210), and then meta-analyzed the results across tissues while considering the heterogeneity across tissues. We identified three major clusters of genes with different meta-patterns across tissues consistent in both cohorts, including homogenous genes associated with CPD in all brain tissues, partially homogeneous genes associated with CPD in cortex, cerebellum and hippocampus tissues, and lastly the tissue-specific genes associated with CPD in only few specific brain tissues. Downstream enrichment analyses on each gene cluster identified unique biological pathways associated with CPD and provided important biological insights into the regulatory mechanism of nicotine dependence in the brain.

How, if at all, consciousness can be part of the physical universe remains a baffling problem. This article outlines a new, developing philosophical theory of how it could do so, and offers a preliminary mathematical formulation of a physical grounding for key aspects of the theory. Because the philosophical side has radical elements, so does the physical-theory side. The philosophical side is radical, first, in proposing that the productivity or dynamism in the universe that many believe to be responsible for its systematic regularities is actually itself a physical constituent of the universe, along with more familiar entities. Indeed, it proposes that instances of dynamism can themselves take part in physical interactions with other entities, this interaction then being “meta-dynamism” (a type of meta-causation). Secondly, the theory is radical, and unique, in arguing that consciousness is necessarily partly constituted of meta-dynamic auto-sensitivity, in other words it must react via meta-dynamism to its own dynamism, and also in conjecturing that some specific form of this sensitivity is sufficient for and indeed constitutive of consciousness. The article proposes a way for physical laws to be modified to accommodate meta-dynamism, via the radical step of including elements that explicitly refer to dynamism itself. Additionally, laws become, explicitly, temporally non-local in referring directly to quantity values holding at times prior to a given instant of application of the law. The approach therefore implicitly brings in considerations about what information determines states. Because of the temporal non-locality, and also because of the deep connections between dynamism and time-flow, the approach also implicitly connects to the topic of entropy insofar as this is related to time.

The high costs of cancer treatment and lack of investment in health care are significant obstacles to public health on the African continent. The objective of this study was to estimate the financial cost of treating children suffering from cancer in Africa. We conducted a systematic review and meta-analysis of expert opinions between March 2000 and March 2020. The key search terms included ‘cost’, ‘cancer’ and ‘child’; we selected articles that specifically addressed the financial costs of childhood cancer in African countries. Of the 103 articles found, 18 met the inclusion criteria. Cancer care was a heavy financial burden in most of the countries studied, although costs varied from country to country; the average expenditure on healthcare was US$1017.39 ± US$319.1 per year. In countries without a health insurance system, the highest proportion of cancer care costs, 46.6%, was indirect, whereas in countries with a cancer financing system, the direct cost of treatment was low, 53.4%. The cost of treating childhood cancer is high in Africa in relation to the standard of living of individuals residing in this region.

Aim of study:Aim of this meta-analysis is to evaluate post-operative procalcitonin as a marker to predict post- operative infectious complications after pancreatic surgeries.Material and Methods:Systemic literature search was performed using MEDLINE, EMBASE and to identify studies evaluating the diagnostic accuracy of Procalcitonin (PCT) as a predictor for detecting infectious complications on postoperative days (POD) 3 and 5 following pancreatic surgery. A meta-analysis was performed using random effect model and pooled predictive parameters for POD 3 and 5 were derived. Geometric means were calculated for PCT cut offs. Results:6 studies included day 3 PCT analysis, 2 studies included both day 3 and day 5 analysis. Total data of 471 patients were derived. 161 patients developed infectious complications. Pooled sensitivity, specificity, pooled area under curve, diagnostic odds ratio (DOR), positive and negative like hood ratio of day 3 PCT were 74%,79%,0.8453, 11.03,3.17 and 0.31 respectively. Pooled sensitivity, specificity, diagnostic odds ratio (DOR), positive and negative like hood ratio of day 5 PCT were 83%,70%,12.91,2.91 and 0.25 respectively. Geometric means for PCT cut off for day 3 and 5 were 0.80 and 0.43. Conclusion:Postoperative procalcitonin particularly day 3 procalcitonin levels predict post-operative infectious complications following pancreatic surgeries.

This mini-review discusses the importance of microorganisms in various biological processes and the paradigm shift in researchers' perception of microorganisms' biology and genetics. Microorganisms are now understood to interact with their associated microorganisms, and advanced sequencing technology is used to study these interactions. The article highlights the importance of careful study design to efficiently generate and integrate data derived from meta-omics approaches such as (meta)genomics, (meta)transcriptomics, (meta)proteomics, and (meta)metabolomics to fully explore the extent of interactions between host organisms and their associated microbiota. The article also discusses the usefulness of metabarcoding and metagenomics in studying coral holobiont diversity and composition. While metabarcoding can comprehensively explore the genetic diversity of various biological taxa, it has limitations in classifying prokaryotes based on 16S rRNA amplicons. On the other hand, metagenomics generates vast amounts of short reads sequencing data that can be used to examine microbial diversity with greater precision and predict the possible functions of the gene set that is presented in the sample. However, the ability to link species to their functional capabilities is challenging, especially for microbes with uncertain evolutionary relationships, hidden microbes, and microeukaryotes. Finally, the article highlights the potential usefulness of these approaches in conservation biology and molecular and environmental science.

The pervasiveness of cardiovascular disease and physician misdiagnosis creates the urgent need for artificial intelligence models to improve diagnosis accuracy. The first objective of this study was to train machine learning models on publicly available data sets containing simple medical information of patients to diagnose cardiovascular disease. The Multilayer Perceptron (MLP) assembled for this task performed optimally with an F1 score of 0.8968. This prompted the creation of an open-source, automated cardiovascular disease diagnosis tool, powered by the MLP. The second objective of this study was to employ a meta-learning methodology called Local Interpretable Model-Agnostic Explanations (LIME) to understand the impact of different features on the model's diagnosis in the form of marginal probabilities. K-Means Clustering was employed to segment the data into ten clusters, after which each data example was passed through LIME. The resulting histograms depict the complex relationship between feature, cluster, and impact on diagnosis. A series of P-values with contrasting orders of magnitude shows the nuances in the MLP's understanding of patients from different clusters. The results of meta-learning analysis reveal that the most important features for cardiovascular disease diagnosis are fasting blood sugar, type of chest pain, and slope of the ST segment on an electrocardiogram. Future experiments should replicate the novel methodology introduced in this study on data sets containing more specialized medical features in order to gain practical medical insights about different types of cardiovascular disease represented by each cluster. Finally, feature engineering pathways should be explored with consideration of these results to create versatile diagnosis models not only for cardiovascular disease, but adaptable to other diseases as well.

Face swapping is an intriguing and intricate task in the field of computer vision. Currently, most mainstream face swapping methods employ face recognition models to extract identity features and inject them into the generation process. Nonetheless, such methods often struggle to effectively transfer identity information, result in generated results failing to achieve a high identity similarity with the source face. Furthermore, if we can accurately disentangle identity information, we can achieve controllable face swapping, thereby providing more choices to users. In pursuit of this goal, we propose a new face swapping framework (ControlFace) based on the disentanglement of identity information. We disentangle the structure and texture of the source face, encoding and characterizing them in the form of feature embeddings separately. According to the semantic level of each feature representation, we inject them into the corresponding feature mapper and fuse them adequately in the latent space of StyleGAN. Owing to such disentanglement of structure and texture, we are able to controllablely transfer parts of the identity features. Extensive experiments and comparisons with state-of-the-art face swapping methods demonstrate the superiority of our face swapping framework in terms of transferring identity information, producing high-quality face images and controllable face swapping.

Objective: The objective of this review was to establish a modified meta-ethnography framework by integrating the traditional phases with methods from social constructivist grounded theory. The modified version was required to identify methods which can ensure analytic generalisability and honour critical inquiry. Method: A narrative synthesis review was undertaken using 4 electronic databases. Articles were located that described the methods and methodologies of meta-ethnography and social constructivist grounded theory. A synthesis was undertaken which integrated the methods of social constructivist grounded theory. Results: The meta-ethnography was undertaken in 5 phases: (1) The positionality of the researcher and the area of interest identified. (2) Identifying what is relevant. (3) Reading the study and determining how studies are related. (4) The iterative process of idea generation required for theory generation. And (5) Expression of the theory, model, or process. Conclusion: The current study provides a useful tool to support the development of a substantive theory (a theory in one area) which can be used then to consider its application in other areas. This approach will help create new lines of research and aid the explanation of problems in other areas.

This paper investigates if Corporate Social Responsibility (CSR) reports published by a selected group of Nordic companies are aligned with the Global Reporting Initiative (GRI) standards. To achieve this goal, several natural language processing, and text mining techniques were implemented and tested. We extracted strings, corpus, and hybrid semantic similarities from the reports and evaluated the models through the intrinsic assessment methodology. A quantitative ranking score based on index matching was developed to complement the semantic valuation. The final results show that Latent Semantic Analysis (LSA) and Global Vectors for Word Representation (GloVE) are the best methods for our study. Our findings will open the door to the automatic evaluation of sustainability reports which could have a strong impact on the environment.

Interhemispheric and frontoparietal functional connectivity have been reported to increase during explicit information processing. However, it is unclear how and when interhemispheric and frontoparietal functional connectivity interact during explicit semantic processing. Here, we tested the neural coupling hypothesis that explicit semantic processing promotes neural activity in the nondominant right hemispheric areas owing to synchronization with enhanced frontoparietal functional connectivity at later processing stages. We analyzed electroencephalogram data obtained using a semantic priming paradigm, which comprised visual priming and target words successively presented under direct or indirect attention to semantic association. Scalp potential analysis demonstrated that the explicit processing of congruent targets reduced negative event-related potentials, as previously reported. Current source density analysis showed that explicit semantic processing activated the right temporal area during later temporal intervals. Subsequent dynamic functional connectivity and neural coupling analyses revealed that explicit semantic processing increased the correlation between right temporal source activities and frontoparietal functional connectivity in later temporal intervals. These findings indicate that explicit semantic processing increases neural coupling between the interhemispheric and frontoparietal functional connectivity during later processing stages.

Digital Twin Cities (DTCs) play a fundamental role in city planning and management. They allow three-dimensional modeling and simulation of cities. 3D semantic segmentation is the foundation for automatically creating enriched DTCs, as well as their updates. Past studies indicate that prior level fusion approaches demonstrate more promising precisions in 3D semantic segmentation compared to point level fusion, features level fusion, and decision level fusion families. In order to improve point cloud enriched semantic segmentation outcomes, this article proposes a new approach for 3D point cloud semantic segmentation through developing and benchmarking three prior level fusion scenarios. A reference approach based on point clouds and aerial images was proposed to compare it with the different developed scenarios. In each scenario, we inject a specific prior knowledge (geometric features,classified images ,etc) and aerial images as attributes of point clouds into the neural network’s learning pipeline. The objective is to find the one that integrates the most significant prior knowledge and enhances neural network knowledge more profoundly, which we have named the "smart fusion approach". The advanced Deep Learning algorithm "RandLaNet" was adopted to implement the different proposed scenarios and the reference approach, due to its excellent performance demonstrated in the literature. The introduction of some significant features associated with the label classes facilitated the learning process and improved the semantic segmentation results that can be achievable with the same neural network alone. Overall, our contribution provides a promising solution for addressing some challenges, in particular more accurate extraction of semantically rich objects from the urban fabric. An assessment of the semantic segmentation results obtained by the different scenarios is performed based on metrics computation and visual investigations. Finally,the smart fusion approach was derived based on the obtained qualitative and quantitative results.

In heterogeneous networks, the random walks based on meta-path requires prior knowledge and lacks flexibility. And the random walks based on non-meta-path only considers the number of node types, but does not consider the influence of schema and topology between node types in real networks. To solve the above problems, this paper proposes a novel model HNE-RWTIC (Heterogeneous Network Embedding Based on Random Walks of Type & Inner Constraint). Firstly, to realize the flexible walks, we design a Type strategy, which is the node type selection strategy based on the co-occurrence probability of node types. Secondly, to achieve the uniformity of node sampling, we design an Inner strategy, which is the node selection strategy based on the adjacency relationship between nodes. The Type & Inner strategy can realize the random walks based on meta-path, the flexibility of the walks, and can sample the node types and nodes uniformly in proportion. Thirdly, based on the above strategy, a transition probability model is constructed; then, we obtain the nodes embedding based on the random walks and Skip-Gram. Finally, in classification and clustering tasks, we conducted a thorough empirical evaluation of our method on three real heterogeneous networks. Experimental results shown that F1-Score and NMI of HNE-RWTIC outperform state-of-the-art approaches.

The aims of this paper are 1) to explore the type of industry of SMEs that can survive for more than 5 years in ASEAN, and 2) to analyze the success factors of SMEs in ASEAN to be able to survive for more than 5 years. This paper employs a qualitative method, with a literature review and meta-analysis. From 2007 to 2017, data was extracted from Google Scholar, Emerald Insight, Sage Pub, and EBSCO databases. There are 14 factors that influence the success of SMEs in the main sectors, 20 factors in the manufacturing sectors, and 14 factors in the service sectors, depending on the type of industry. For instance: eCommerce, technology, and social media, international oriented, good management system, government support, working condition /environment, skill and competency labour/owner, market penetration/selection, policy, organizational performance, and characteristics, networking with government /supplier / customer, networking with government /supplier /customer, planned /strategy (business /market /operational /organizational). The findings of this literature review reveal several important factors that are critical to the success of SMEs in competing with other industries and can be developed for a longer period.

N-of-1 data are unavoidable in zoological medicine. Accordingly, zoological medicine clinicians and investigators need research techniques that can make use of these data. This article reviews two methodologies for using both observational and experimental N-of-1 data: 1) systematic reviews and meta-analyses of case reports and 2) prospective N-of-1 trials. Systematic reviews of case reports and other observational evidence are formal, unbiased summaries of clinical characteristics of a particular disease-taxon combination. They offer advantages to narrative reviews by minimizing omission of relevant articles, thereby reducing the potential for mischaracterization of the literature. Meta-analyses are extensions of systematic reviews that quantitatively synthesize the data from the included articles. While valuable, systematic reviews and meta-analyses of case reports can have limited interpretations due to publication bias and confounding present in their source materials. In contrast to case reports, N-of-1 trials are prospective study designs that allow clinicians to make strong inferences about the effect of an intervention in a particular patient. They are double-blinded, single patient, multi-crossover studies that are of particular value in fields where it is difficult to recruit sufficient patients for conventional randomized control trials (RCTs), such as zoological medicine. Because they require multiple crossover periods, N-of-1 trials are ideal for evaluating short-acting interventions in patients with somewhat stable chronic diseases, such as osteoarthritis. More complex than conventional therapeutic trials, N-of-1 trials require prior consideration of how to achieve blinding, appropriate placebo controls, quantitative primary outcomes, analysis methods, and ethical approval. Aggregation of N-of-1 trials allows estimation of the average treatment effect across the population with fewer participants than a conventional RCT. While systematic reviews and meta-analyses of case reports can be used to synthesize the observational N-of-1 data already in existence, N-of-1 trials offer an exciting way to prospectively generate strong evidence that will be useful for evidence-based decision-making.

French Guiana, located in the Guiana Shield, is a natural reservoir for many zoonotic pathogens that are of considerable medical or veterinary importance. Until now, there are few data available on the description of parasites circulating in this area, especially on protozoan belonging to the phy-lum Apicomplexa; conversely, the neighbouring countries describe a high parasitic prevalence in animals and humans. Epidemiological surveillance is necessary, as new potentially virulent strains may emerge from these forest ecosystems, such as Amazonian toxoplasmosis. However, there is no standard tool for detecting protozoa in wildlife. In this study, we developed Meat-Borne-Parasite, a high-throughput meta-barcoding workflow for detecting Apicomplexa based on Oxford Nanopore Technologies sequencing platform using the 18S gene of fourteen Apicomplexa-positive samples collected in French Guiana. Sequencing reads were then analysed with MetONTIIME pipeline. Thanks to a scoring rule, we were able to classify as Apicom-plexa-positive 10 samples out of 14 and reveal the presence of co-carriages. The same samples were also sequenced with Illumina platform, for validation purposes. On samples identified as Apicomplexa-positive by both platforms, a strong positive correlation at up to genus level was reported. Overall, the presented workflow represents a reliable method for Apicomplexa detec-tion, which may pave the way for more comprehensive biomonitoring of zoonotic pathogens.

Globally, seagrass meadows provide critical ecosystem services. However, seagrasses are globally degraded at an accelerated rate. The lack of information on seagrass spatial distribution and seagrass health status seriously hinders seagrass conservation and management. Therefore, this study proposes to combine remote sensing big data with a new hybrid machine learning model (RF-SWOA) to predict potential seagrass habitats. The multivariate remote sensing data is used to train the machine learning model, which can improve the prediction accuracy of the model. This study shows that a hybrid machine learning model (RF-SWOA) can predict potential seagrass habitats more accurately and effectively than traditional models. At the same time, it has been shown that the most important factors influencing the potential habitat of seagrass in the Hainan region were the distance from land (38.2%) and the depth of the ocean (25.9%). This paper provides a more accurate machine learning model approach for predicting the distribution of marine species, which can help develop seagrass conservation strategies to restore healthy seagrass ecosystems.

The Swiss Personalized Health Network has developed a national framework for enabling the semantic representation of health data within a Knowledge Graph. This framework has been implemented in all Swiss university hospitals, promoting seamless sharing and integration of clinical routine data with other health-related data, including omics and clinical research data. While research projects often have flexibility in selecting terminologies and specific versions, historical clinical routine data are typically coded for billing or administrative purposes using predefined terminologies in various (sometimes even unknown) versions over time. Some of these terminologies do not adhere to best practices for ontology design, presenting significant challenges to the retrospective re-use of such coded data for research. Common issues with these terminologies include the lack of machine-readable traceability across versions and non-adherence to FAIR principles. Terms from older versions frequently disappear in newer ones, making it challenging to distinguish outdated from invalid terms. Additionally, 'semantic drift' occurs, where the meaning of terms changes across versions. To address these challenges, we have implemented FAIR and historized versions of ATC, CHOP, and ICD-10-GM. We represent each version in RDF using versioned URIs and track meaning changes between versions in a machine-readable way using OWL and RDFS. The integration of these historized terminologies into our quality control framework, based on SHACLs, enables comprehensive data quality control in hospitals and empowers researchers to effectively utilize this data. Our work aims to bridge the gap between health data coded in different terminology versions, ensuring a consistent and reliable semantic representation.

Gross strap muscle invasion (gSMI) in patients with differentiated thyroid cancer (DTC) was defined as high-risk recurrent group in the 2015 American Thyroid Association guidelines. However, controversy persists because several studies suggested gSMI had little effect on disease outcome. Herein, a systematic review and meta-analysis was conducted to investigate impact of gSMI on outcome of DTC. Methods: A systematic search of electronic databases (PubMed, EMBASE, Cochrane Library, and MEDLINE) for studies published until February 2020 was performed. Case-control studies and randomized controlled trials that studied the impact of gSMI on outcome of DTC were included. Results: Six studies (all retrospective studies) involving 13639 patients met final inclusion criteria. Compared with no extrathyroidal extension (ETE), patients with gSMI were associated with increased risk of recurrence (P=0.0004,OR, 1.46; 95% CI: 1.18 to 1.80) and lymph node metastasis (LNM) (P<0.00001,OR 4.19;95% CI. 2.53 to 6.96). For mortality (P=0.34,OR 1.47;95% CI:0.67 to 3.25), ten-year disease-specific survival (P=0.80, OR 0.91;95% CI:0.44 to 1.88) and distant metastasis (DM) (P=0.21, OR 2.94;95% CI. 0.54 to 15.93), there was no significant difference between gSMI and no ETE group. In contrast with maximal ETE, patients with gSMI were associated with decreased risk of recurrence (P<0.0001,OR, 0.58; 95% CI: 0.44 to 0.76) , mortality (P=0.0003,OR 0.20;95% CI:0.08 to 0.48), LNM (P=0.0003,OR 0.64;95% CI. 0.50 to 0.81) and DM (P=0.0009,OR 0.28;95% CI. 0.13 to 0.59). Conclusions: DTC patients with gSMI had a higher risk of recurrence and LNM than those without ETE. However, in contrast with maximal ETE, a much better prognosis was observed in DTC patients with only gSMI. The findings of our meta-analysis provide supportive evidence for the validity of the T category changes in the 8th edition American Joint Committee on Cancer system. The actual impact of gSMI should be re-evaluated and revised in the recurrent risk stratification system in the future.

Systematic reviews and meta-analyses have become an important way to summarize data. The hierarchy of evidence-based medicine confers a high platform to meta-analyses. Hence, it is imperative that the analytical and reporting strategy associated with a meta-analysis be standardized. The modern tradition of reporting and publishing a meta-analysis is riddled with several methodological flaws. The most common of these flaws involve the choice of the model used to conduct the meta-analysis and the way that the heterogeneity of studies is assessed. The use of the I² statistic as a means of detecting within-study heterogeneity is a common methodological flaw encountered in reporting summary statistics.One of the ways to rectify the above flaws is to arm reviewers with an objective way to scrutinize the manuscript. This narrative review enlists six checklists for reviewers to improve the reporting of meta-analyses. The first two checklists address reporting of the risk of bias and assessment of publication bias with the help of a funnel plot. Checklists 3, 4, and 5 address the choice of model selected for the meta-analysis, the method to assess the confidence interval, and the measures of heterogeneity. The final checklist addresses the question asked in the meta-analysis, binary versus an effect size estimate. Although there are many more aspects which can help improve the assessment of a manuscript, these basic checklists can be employed by reviewers without the need for an external statistical analysis to enhance and improve meta-analyses.

This study presents a meta-analysis of studies that investigate the effectiveness of chitosan administration on lifestyle-related disease in murine models. A total of 34 published studies were used to evaluate the effect of chitosan supplementation. The effect sizes for various items after chitosan administration were evaluated using the standardized mean difference. Using Cochran’s Q test, the heterogeneity of effect sizes was assessed, after which a meta-ANOVA and –regression test was conducted to explain the heterogeneity of effect sizes using the mixed-effect model. Publication bias was performed using Egger’s linear regression test. Among the items evaluated, blood triglyceride and HDL-cholesterol showed the highest Q statistics and I2 values, respectively. Other than blood HDL-cholesterol, total cholesterol, and triglyceride in feces, most items evaluated showed a negative effect size with high significance in the fixed- and random-effect model (p<0.0001). In the meta-ANOVA and -regression test, administering chitosan and resistant starch was revealed to be most effective in lowering body weight. In addition, chitosan supplementation proved to be an effective solution for TNF-α inhibition. In conclusion, chitosan has been shown to be somewhat useful in improving symptoms of lifestyle-related disease. Although there are some limitations in the results of this meta-analysis due to the limited number of animal experiments conducted, chitosan administration nevertheless shows promise in enhancing the quality of human life.

Personal Information Management Systems (PIMS) are acquiring a prominent role in the data economy by promoting services that help individuals to have more control over the processing of their personal data, in line with the European data protection law. One of the highlighted solutions in this area is Solid, a new protocol that is decentralizing the storage of data, through the usage of interoperable Web standards and semantic vocabularies, to empower its users to have more control over the processing of data by agents and applications. However, to fulfill this vision and gather widespread adoption, Solid needs to be aligned with the law governing the processing of personal data in Europe, the main piece of legislation being the General Data Protection Regulation (GDPR). To assist with this process, we analyze the current efforts to introduce a policy layer in the Solid ecosystem, in particular, related to the challenge of obtaining consent for processing personal data, focusing on the GDPR. Furthermore, we investigate if, in the context of using personal data for biomedical research, consent can be expressed in advance, discuss the conditions for valid consent and how it can be obtained in this decentralized setting, namely through the matching of privacy preferences, set by the user, with requests for data and whether this can signify informed consent. Finally, we discuss the technical challenges of an implementation that caters to the previously-identified legal requirements.

Cancer survival continues to improve in high-income countries, partly explained by advances in screening and treatment. Previous studies have mainly examined the relationship between individual dietary components and cancer prognosis in tumours with good therapeutic response (breast, colon and prostate cancers). The aim of this review was to assess qualitatively (and quantitatively where appropriate) the associations of dietary patterns and cancer prognosis from published prospective cohort studies, as well as the effect of diet interventions by means of randomized controlled trials (RCT). A systematic search was conducted in PubMed, and a total of 35 prospective cohort studies and 14 RCT published between 2011 and 2021 were selected. Better overall diet quality was associated with improved survival among breast and colorectal cancer survivors; adherence to the Mediterranean diet was associated to lower risk of mortality in colorectal and prostate cancer survivors. A meta-analysis using a random-effects model showed that higher versus lower diet quality was associated with a 23% reduction in overall mortality in breast cancer survivors. There was evidence that dietary interventions, generally combined with physical activity, improved overall quality of life, though most studies were in breast cancer survivors. Further cohort and intervention studies in other cancers are needed to make more specific recommendations.

Recent developments, begun by the ascending spiral of the anticipated endless prospects of ChatGPT, promote artificial intelligence (AI) as an indispensable tool and commodity whose time has come. Yet the sinister specter of a technology that has hidden and unmanageable attributes that might be harmful to society looms in the background. As well as the likelihood that it will never deliver on the purported promise of artificial general intelligence (AGI). Currently the prospects for the development of AI and AGI are more a matter of opinion than based on a consistent methodological approach. Thus the need to take a step back to develop a general framework from which to evaluate current AI efforts, which also permits the determination of the limits to its future prospects as AGI. To gain insight into the development of a general framework, a key question that needs resolution is, what is the connection between human intelligence and machine intelligence? This is the question that needs a response because humans are at the center of AI creation and realize that without an understanding of how we become what we become, we have no chance of finding a solution. This work proposes infoautopoiesis, the self-referential, recursive and interactive process of self-production of information, as the needed general framework. Infoautopoiesis shows how the key ingredient of information is fundamental to an insightful resolution to this crucial question and allows predictions as to the present and future of AGI.

For consciousness to exist, an entity must have prerequisite characteristics and attributes to give rise to it. We explore these “building blocks” of consciousness in detail in this paper, which range from perceptive to computational to higher-order characteristics of an entity’s cognitive architecture. We show how each cognitive attribute is strictly necessary for the emergence of consciousness, and how the building blocks are collectively sufficient for any entity to be classified as being conscious. The list of building blocks is not limited to human or organic consciousness and may be used to classify artificial and organisational conscious entities. We further explore a list of attributes that seem intuitively necessary for consciousness, but on further investigation, are neither required nor sufficient. The building blocks do not represent a theory of consciousness but rather a meta-theory on the emergence and classification of consciousness.

We performed Bayesian network meta-analysis (NMA) to suggest frontline treatments for patients with high PD-L1 expression (at least 50%). A total of 5,237 patients from 22 studies were included in this NMA. In terms of progression-free survival, immune checkpoint inhibitors (ICIs) plus bevacizumab plus chemotherapy had the highest surface under the cumulative ranking curve (SUCRA) value (98.1%), followed by ICI plus chemotherapy (82.9%). In terms of overall survival (OS), dual immunotherapy plus chemotherapy had the highest SUCRA value (79.1%), followed by ICI plus bevacizumab plus chemotherapy (73.4%). However, there was no significant difference of survival outcomes among treatment regimens combined with immunotherapy. Moreover, ICI plus chemotherapy failed to reveal a significant OS superiority to ICI monotherapy (hazard ratio = 0.978, 95% credible internal: 0.771-1.259). In conclusion, this NMA indicates that ICI plus chemotherapy with/without bevacizumab might to be the best options in terms of OS for NSCLC with high PD-L1 expression. Considering there was no significant difference of survival outcomes among treatment regimens incorporating immunotherapy and ICI plus chemotherapy failed to show significant survival benefits over ICI monotherapy, however, ICI monotherapy may be reasonable as first-line treatment for advanced NSCLC with high PD-L1 expression and no targetable aberrations.

Embedding representation models characterize each word as a vector of numbers with a fixed length. These models have been used in tasks involving text classification, such as recommen- dation and question-answer systems. Semantic relationships are words with a relationship between them providing a complete idea to a text. Therefore, it is hypothesized that an embedding model involving semantic relationships will provide better performance for tasks that use them. This paper presents three embedding models based on semantic relations extracted fromWikipedia to classify texts. The synonym, hyponym, and hyperonym semantic relationships were the ones considered in this work since previous experiments have shown that they are the ones that provide the most semantic knowledge. Lexical-syntactic patterns present in the literature were implemented and subsequently applied to the Wikipedia corpus to obtain the semantic relationships present in it. Several semantic relationships are used in different models: synonymy, hyponym-hyperonym, and a combination of the first two. A convolutional neural network was trained for text classification to evaluate the performance of each model. The results obtained were evaluated with the metrics of precision, accuracy, recall, and F1-measure. The best values obtained with the second model were accuracy of 0.79 for the 20-Newsgroup corpus. F1-measure and recall of 0.87 respectively for the Reuters corpus.

Evaluation of market connectedness and asymmetric volatility spillover has recently seen a surge in financial risk analytics and portfolio diversification. We carried out a meta-literature review on connectedness and spillovers, providing solid insight into the research field and robust guidelines for future investigation. The review consists of a quantitative bibliometric analysis of 594 papers and a qualitative content analysis of 77 papers covering 1991 to 2021. The results of the meta-citation analysis shows that Diebold's Spillover index (2007) is the predominant method in most works as far as market connectedness and spillover is concerned. With an extensive review, we answered the following objectives (1) Analyze the most influential authors, journals, and publications, (2) Understand the research streams and most studied streams, (3) Understand the theme structure and thematic evolution, and keyword trends, (4) Examine the pattern of collaboration, and most productive affiliations, (5) Explore future research directions and untapped areas. The content analysis revealed the following important research streams in the current literature (1) Asymmetries in the market connectedness, (2) Influence of Macro Factors in the market connectedness and spillover, (3) the Role of Oil in market spillovers and hedging portfolios, (4) Dynamic cross-market connectedness and spillovers. The ongoing financial turmoil and market advancements make market connectedness a vital research topic; thus, our work would significantly contribute to macroeconomic policymakers, researchers and hedging investors.

A review is here provided on the thermal effects on the optical chirality. To this goal, chiral objects dispersed in an embedding fluid are examined for their magnetoelectric coupling. Archetypal twisted-Omega particles are examined with respect to electron transport, phonon dynamics, and temperature effects. Continuum-mechanical aspect of thermo-elasticity is reviewed along with transverse deformations. A transition temperature delineating a sign flip in the chirality parameter is identified as well.

With the popularity of solar energy in the electricity market, demand arises for data such as precise locations of solar panels for efficient energy planning, management, and distribution. However, this data is not easily accessible and in some cases, information such as precise locations does not exist. Furthermore, existing data sets for training semantic segmentation models of PV installations are limited, and their annotation is time-consuming and labor-intensive. Therefore, for additional remote sensing (RS) data creation, the pix2pix generative adversarial network (GAN) is utilized, enriching the original resampled training data of varying GSDs without compromising its integrity. Experiments done with the DeepLabV3 model, ResNet-50 backbone, and pix2pix GAN architecture were conducted to find the optimal configuration for an accurate RS imagery segmentation model. The result is a fine-tuned solar panel semantic segmentation model, trained using transfer learning and utilizing an optimal amount – 60% of generated RS imagery for additional training data, increasing model accuracy. The findings demonstrate the benefits of using GAN-generated images as additional training data, increasing the size of small data sets, and improving the capabilities of the segmentation model for solar panel detection in RS images.

Symbolic semantic understanding of staff images is an important part in music information retrieval. Due to the complex composition of staff symbols and the strong semantic correlation between symbol spaces, it is difficult to understand the pitch and duration of each note during performances. In this paper, we design a semantic understanding system for optical staff symbols. The system uses the YOLOv5 to implement optical staff’s low-level semantic understanding stage, which understands the pitch and duration in natural scales and other symbols that affect the pitch and duration. The proposed note encoding reconstruction algorithm is used to implement high-level semantic understanding stage. Such algorithm understands the logical, spatial, and temporal relationships between natural scales and other symbols based on music theory, and outputs digital codes for the pitch and duration of main notes during performances. The model is trained with a self-constructed SUSN dataset. Experimental results of YOLOv5 show that the precision is 0.989 and the recall is 0.972. For the system, the error rate is 0.031 and the omission rate is 0.021. The paper concludes by analysing the causes of semantic understanding errors and offers recommendations for further research. The results of this paper provide a method for multimodal music artificial intelligence applications such as notation recognition through listening, intelligent score flipping and automatic performance.

Most natural language processing (NLP) tasks suffer performance degradation when encountering long complex sentences, such as semantic parsing, syntactic parsing, machine translation, and text summarization. Previous works address the issue with an intuition of decomposing complex sentences and linking simple ones, such as RST-style discourse parsing, split-and-rephrase (SPRP), text simplification (TS), simple-sentence-decomposition (SSD), etc. However, these works are not applicable for semantic parsing like abstract meaning representation (AMR) parsing and semantic dependency parsing due to misalignments to semantic relations and unavailabilities to preserve original semantics. Following the same intuition and avoiding deficiencies of previous works, we propose a novel framework, hierarchical clause annotation (HCA), based on the linguistic research of clause hierarchy. With the HCA framework, we annotate a large HCA corpus to explore the potentialities of integrating HCA structural features into semantic parsing with complex sentences. Moreover, we decompose HCA into two subtasks, i.e., clause segmentation and clause parsing, and provide neural baseline models for more silver annotations.

Background: Neuroendocrine tumors (NET) are a rare group of epithelial neoplasm present in gastrointestinal tract (GI) (67.5%), bronchopulmonary tree (25.3-30%), and in 15% of cases, the primary sites cannot be identified. Although endoscopic screening, improvement in pathological techniques, and early detection have shown improvement in NET survival rates, the prognosis of advanced, metastatic, and poorly differentiated NET is very poor. In this study, we aimed to evaluate the effect of Gastrointestinal and pancreatic (GEPs) NETs grade on overall survival. Method: We searched observational studies describing the overall survival or prognostic factors of primary GEP NETs from May 2011 -May 2021 following PRISMA guidelines. Studies describing the effect of primary grade 3 GEP NETs on overall survival were included. Meta-analysis was performed, and pooled hazard ratio and their 95% confidence interval (95% CI) were obtained. The forest plots were created using random-effects models and sensitivity analysis was performed to account for the heterogeneity. Results: Seven studies with 7692 confirmed patients were included. In our meta-analysis grade 3 GEP NET were associated with higher odds of poor survival (pooled HR: 2.73; 95% CI: 1.36–5.47; p = 0.005), with 92% heterogeneity between studies (p < 0.0001). To account for heterogeneity, sensitivity analysis was performed by removing two outlying studies (Fathi et al. and Foubert et al.) on funnel plots. The results after sensitivity analysis did not change and still showed significant association of grade 3 with poor survival (pooled HR: 4.53; 95% CI: 3.54–5.78; p < 0.00001), with no heterogeneity between studies (p = 0.72; I2 = 0%). Conclusion: Our meta-analysis found that grade 3 GEP NETs are associated with poor survival and additional future studies are needed to identify other risk factors associated with poor survival in GEP NETs to improve mortality.

Brain hemispheres develop rather symmetrically except in the case of pathology or intense training. As school experience is a form of training, the current study tested the influence of pedagogy on morphological development through the cortical thickness (CTh) asymmetry index (AI). First, we compared CTh AI of 111 students aged 4 to 18 with 77 adults aged > 20. Second, we investigated CTh AI in the students, as a function of schooling background (Montessori or traditional). At the whole-brain level, CTh AI was not different between the adult and student groups, even when controlling for age. However, the pedagogical experience was found to impact CTh AI in the temporal lobe, within the parahippocampal (PHC) region. The PHC has a functional lateralization, with the right PHC having a stronger involvement in spatiotemporal context encoding, while the left PHC is involved in semantic encoding. We observed CTh asymmetry toward the left PHC for participants enrolled in Montessori schools and toward the right for participants enrolled in traditional schools. As these participants were matched on age, intelligence, home life, and socioeconomic conditions, we interpret this effect found in memory-related brain regions to reflect differences in learning strategies. Pedagogy modulates how new concepts are encoded, with possible long-term effects on knowledge transfer.

Building Information Modelling (BIM) has gained significant relevance in civil engineering, particularly in the infrastructure sector. It offers the potential to increase efficiencies, optimize processes, and enhance sustainability throughout infrastructure projects’ life cycles. BIM consolidates information about asset components into a single model, enabling real-time updates, cost reduction, and improved consistency. Digital twins serve as valuable extensions of BIM in infrastructure, playing a crucial role during operation. By capturing real-time data from sensors and IoT devices, digital twins enabls continuous monitoring, proactive maintenance, efficient energy management, and informed decision-making for optimized operational efficiency. Advancements in 3D point cloud technologies, such as 3D laser scanning, have expanded BIM’s application in operation and maintenance. These technologies rapidly capture accurate and detailed three-dimensional information, prompting the exploration of automated alternatives to manual point cloud modeling. This paper demonstrates the application of supervised machine learning, specifically support vector machines, for analyzing and segmenting 3D point clouds, a crucial step in 3D modeling. Various approaches for semantic segmentation are introduced, investigated, and evaluated using diverse data sets. The results highlight the effectiveness of supervised machine learning techniques in achieving accurate segmentation of 3D point clouds.

Atopic dermatitis (AD) has been extensively investigated for genetic associations utilizing both candidate gene approaches and genome-wide scans. Here, we comprehensively evaluated the available literature to determine the association of candidate genes in AD to gain additional insight in the etiopathogenesis of the disease. We systematically screened all studies that explored the association between polymorphisms and AD risks in cases of European and Asian ancestry and synthesized the available evidence through random-effects meta-analysis. We identified 97 studies that met our inclusion/exclusion criteria that examined 17 candidate loci in Europeans and 14 candidate genes in Asians. We confirmed the significant associations between FLG variants in both European and Asian populations and AD risk, while additional synthesis of available data revealed novel loci mapped to IL18 and TGFB1 genes in Europeans and IL12RB1 and MIF in Asians, that have not yet been identified by genome-wide association studies. Our findings provide comprehensive evidence for AD risk loci in cases of both European and Asian ancestries, validating previous associations as well as revealing novel loci that could imply previously unexplored biological pathways.

Recently, the move from cost-tied to open-access data has led to the mushrooming of research in pursuit of algorithms for estimating aboveground grass biomass (AGGB). Nevertheless, a comprehensive synthesis or direction on the milestones archived or an overview of how these models perform is lacking. This study synthesises the research work from decades of experiments in order to point researchers in the direction of what was done, the challenges faced, as well as how the models perform. A pool of findings from 108 remote sensing-based AGGB studies published from 1972 to 2020 show that about 62% of the remote sensing-based algorithms were tested in the Steppe grasslands, mostly in the temperate climate zone. An uneven annual publication yield was observed with approximately 36% of the research output from Asia whereas countries in the global south yielded few publications (&lt;10%). Optical sensors, particularly MODIS, remain a major source of satellite data for AGGB studies, whilst studies in the global south rarely use active sensors such as Sentinel-1. Optical data tend to produce poor regression accuracies that are highly inconsistent across the studies compared to Radar. Vegetation indices, particularly the Normalised Difference Vegetation Index (NDVI), remain a major predictor variable. Predictor variables such as Sward height, Red edge position and Backscatter coefficients produced slightly consistent accuracies. Deciding on the optimal algorithm for estimating AGGB is daunting due to the lack of overlap in the grassland type, location, sensor types, and predictor variables, signalling the need for further studies around the transferability of remote sensing-based AGGB models.

In response to the growing inspection demand exerted by process automation in component manufacturing, Non-destructive testing (NDT) continues to explore automated approaches that utilize deep learning algorithms for defect identification, including within digital X-ray radiography images. This necessitates a thorough understanding of the implication of image quality parameters on the performance of these deep learning models. This study investigates the influence of two image quality parameters, namely Signal-to-Noise Ratio (SNR) and Contrast-to-Noise Ratio (CNR), on the performance of U-net deep learning segmentation model. Input images were acquired with varying combinations of exposure factors such as kilovoltage, milli-ampere, and exposure time, which altered the resultant quality. The data was sorted into 5 different datasets according to their measured SNR and CNR values. The deep learning model was trained 5 distinct times, utilizing a unique dataset for each training session. Training the model with high CNR values yielded an intersection over Union (IoU) metric of 0.9594 on test data of the same category but drops to 0.5875 when tested on lower CNR test data. The result in this study emphasizes the importance of achieving a balance in training dataset according to the investigated quality parameters, to enhance the performance of deep learning segmentation models in NDT radiography applications.

To systematically assess the effect size of different methods for patients with low back pain. PubMed, Cochrane Library, Web of Science, and Google Scholar. databases were searched in January 2023. This study has been developed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Statement. The key search terms were: “Low back pain”, “Back pain”, “SF-36”, “VAS”, “VASP”, “Roland-Morris”, “Oswestry Disability Index”, and “conservative treatment”. The risk of bias was determined for each randomized trial using the Cochrane Risk of Bias Tool, and the methodological index for non-randomized studies (MINORS). The outcomes included SF-36 Mental, SF-36 Physical, VAS, Roland-Morris, and Oswestry Disability Index. R 4.0.5 software was used, and standardized mean difference (SMD) and 95% confidence intervals (CI) were calculated for continuous outcomes, random model. Twenty-five studies were included. Depending on the outcome being measured, the effect size of different methods in treating low back pain varies from small to large as follows: SF-36 Mental (SMD = 0.39, p &lt; 0.0001), SF-36 Physical (SMD = 0.55, p &lt; 0.0001), VAS (SMD = -0.84, p &lt; 0.0001), Roland-Morris (SMD = -0.45, p &lt; 0.0001), and Oswestry Disability Index (SMD = -0.61, p &lt; 0.0001). Our meta-analysis indicates the positive effects of applying different methods in the treatment of low back pain. PROSPERO registration number: CRD42022371282

Coronavirus Disease-19 (COVID-19) is caused by the infection of severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2). The virus enters host cells through receptor-mediated endocytosis of angiotensin-converting enzyme-2 (ACE2), leading to systemic inflammation, also known as a “cytokine storm,” and neuroinflammation. COVID-19’s upstream regulator, interferon-gamma (IFNG), is downregulated upon the infection of SARS-CoV-2, which leads to the downregulation of ACE2. The neuroinflammation signaling pathway (NISP) can lead to neurodegenerative diseases, such as Parkinson's disease (PD), which is characterized by the formation of Lewy Bodies made of α-Synuclein protein encoded by synuclein alpha (SNCA). This study presents the pathways involved in the downregulation of ACE2 following SARS-CoV-2 infection and its effect on PD progression. Through QIAGEN’s Ingenuity Pathway Analysis, the study identifies the NISP as a top-five canonical pathway/signaling pathway and SNCA as a top-five upstream regulator. Core Analysis was also conducted on the associated molecules between COVID-19 and SNCA to construct a network connectivity map. The Molecule Activity Predictor tool was used to simulate the infection of SARS-CoV-2 by downregulating IFNG, which leads to the predicted activation of SNCA, and subsequently PD, through a dataset of intermediary molecules. Downstream Effect analysis was further used to quantify the downregulation of ACE2 on SNCA activation.

Few-shot fine-grained image classification (FSFGIC) methods refer to machine learning methods which aim to classify images (e.g., bird species, flowers, and airplanes) belonging to subordinate object categories of the same entry-level category with only a few samples. It is worth to note that feature representation learning is used not only to represent training samples, but also to construct classifiers for performing various FSFGIC tasks. In this paper, starting from the definition of FSFGIC, a taxonomy of feature representation learning for FSFGIC is proposed. According to this taxonomy, we discuss key issues on FSFGIC (including data augmentation, local or/and global deep feature representation learning, class representation learning, and task specific feature representation learning). The existing popular datasets and evaluation standards are introduced. Furthermore, a novel classification performance evaluation mechanism is designed with a 0.95 confidence interval for judging whether the classification accuracy obtained by a certain specified method is good or bad. Moreover, current challenges and future trends of feature representation learning on FSFGIC are elaborated.

Although other methods exist to store and manage data in modern information technology, the standard solution are file systems. Therefore keeping well-organized file structures and file system layouts can be key to a sustainable research data management infrastructure. However, file structures alone are lacking several important capabilities for FAIR data management: The two most striking are insufficient visualization of data and inadequate possibilities for searching and getting an overview. Research data management systems (RDMS) can fill this gap, but many do not support the simultaneous use of the file system and the RDMS. This simultaneous use can have many benefits, but keeping data in the RDMS in synchrony with the file structure is challenging. Here, we present concepts that allow to keep file structures and semantic data models (in RDMS) synchronous. Furthermore, we propose a specification in yaml-format that allows for a structured and extensible declaration and implementation of a mapping between the file system and data models used in semantic research data management. Implementing these concepts will facilitate the re-use of specifications for multiple use cases. Furthermore, the specification can serve as a machine-readable and, at the same time, human-readable documentation of specific file system structures. We demonstrate our work using the Open Source RDMS CaosDB.

Background Dietary guidelines typically recommend limiting the intake of fresh red meat, yet evidence linking red meat consumption with adverse health outcomes is inconsistent. Objective To assess the level of evidence from existing systematic reviews and meta-analyses and analyze the association between high consumption of red meat and cancer risk and adverse health outcomes. Methods Eight databases were searched to collect systematic reviews and meta-analyses from database inception to December 2022. Two independent reviewers screened and extracted data and used the AMSTAR 2 tool to evaluate methodological quality of the included studies. Meta-analysis was conducted using STATA 17.0. Results A total of 18 studies were included, of which 5 were rated as high quality, 4 as medium quality, 6 as low quality, and 3 as extremely low quality. Of the 9 SRs/MAs included, primary studies with overlap were identified, and 14 studies were eventually included. The meta-analysis results showed that compared with the lowest intake of red meat, the highest intake of red meat was associated with an increased risk of colorectal cancer (OR 1.21, 95% CI 1.07-1.37), gastric cancer (OR 1.37, 95% CI 1.18-1.59), non-Hodgkin's lymphoma (OR 1.10.95% CI 1.02-1.19), stroke (RR 1.11, 95% CI 1.03-1.20), type 2 diabetes (RR 1.19 95% CI 1.04-1.37), and ischemic heart disease (RR 1.09, 95% CI 1.06-1.12), as well as a negative effect on total cholesterol (WMD 0.264, 95% CI 0.144-0.383), triglycerides (WMD -0.181, 95% CI -0.349 to -0.013), low-density lipoprotein (WMD 0.198, 95% CI 0.065-0.330), and high-density lipoprotein (WMD -0.065, 95% CI -0.109 to -0.020). However, the associations with breast cancer (RR 1.05 95% CI 1.00-1.11), glycemic control (WMD 0.040, 95% CI 0.049-0.129), and changes in body weight status [overweight (β 0.89, 95% CI 0.48-1.64); obesity (β 1.06, 95% CI 0.30-3.71)] were not significant. Conclusions This comprehensive umbrella review suggests that high consumption of red meat may increase the risk of colorectal cancer, gastric cancer, non-Hodgkin lymphoma, stroke, type 2 diabetes, and cardiovascular disease, and that reducing red meat intake is a key modifiable dietary factor for reducing risk. However, there was no significant correlation with breast cancer, kidney stones, age-related eye disease or changes in body weight.

The development of knowledge graphs for water resources as a tool for studying sustainable regional development is a relevant task, as the increasing deterioration of water bodies affects the ecology, economy of the country, as well as the health of the population living near water bodies. Solving this problem requires the development of water resource monitoring methods based on semantic data analysis is required to detect and track sources of pollution and provide recommendations to support decision-making. This research proposes a method of semantic analysis for modeling knowledge graphs and constructing rules that enable the integration of data from heterogeneous sources, semantic compatibility, support for reasoning and inference, facilitate queries and search, and represent knowledge in the field of water resource monitoring. As a result, an ontology based on the SWRL language is created, which enables the expression of complex relationships and derivation of new knowledge, SSN for interaction between the ontology and sensor data, and Time Ontology, which models and analyzes information related to temporal aspects.After populating the ontology and establishing logical connections, SPARQL queries are implemented to retrieve new knowledge about water resources.

(1)Background: Among new anti-angiogenetic agents being developed and everchanging guidelines indications, the question of benefits/safety ratio remains unclear. (2)Methods: we have conducted a systematic review and meta-analysis of 25 randomized controlled trials (15487 patients), evaluating overall survival – OS, progression free survival– PFS and toxicity (grade ≥3 adverse effects, type and number of all adverse effects. (3)Results: analysis showed improvement of pooled-PFS (HR 0.72, 95%CI 0.66–0.78, I2 = 77%, P<0.00001) regardless of treatment settings (first-line - HR 0.83, 95%CI 0.77-0.90, P<0.00001, recurrent cancer – HR 0.61, 95%CI 0.54-0.68, P<0.00001 or maintenance – HR 0.82, 95%CI 0.67-1.00, P=0.04) and type of anti-angiogenetic drug used (VEGF inhibitors, VEGF-R inhibitors or angiopoietin inhibitors). Improved OS was also observed (HR 0.95, 95%CI 0.91–0.99, P=0.02). OS benefits were only observed in recurrent platinum-sensitive or platinum-resistant cancers. Grade≥3 adverse effects were increased across all trials. Anti-angiogenetic therapy increased the risk of hypertension, infection, thromboembolic/ hemorrhagic events, gastro-intestinal perforations but not the risk of wound related issues, anemia or posterior leukoencephalopathy syndrome. (4)Conclusions: Although angiogenesis inhibitors improve PFS, there is little to no OS benefits. Given the high risk of severe adverse reactions a careful selection of patients is required for obtaining the best results possible.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with high heterogeneity and has a prevalence of 0.7% to 3.2% in children. Gut microbiota are a collection of microorganisms that inhabit in human guts, which can produce various metabolites that affect the homeostasis and functions of nervous and endocrine systems. There are many studies on the relationship between various gut microbiota and ASD, but the alteration pattern of microbial profiles in ASD children is not conclusive. In order to more robustly describe the deregulations of gut microbiota compositions in ASD, a meta-analysis was performed. The related investigations from PubMed, Embase and Web of Science were collected and manually reviewed. By procedure, 26 eligible studies until 2023, with a total of 1021 ASD and 951 typically developed children and adolescents, were included for the meta-analysis. RevMan5.4 was used to assess the overall effect of 8 microbes at the phylum level and 19 microbes at the genus level. Results demonstrated relatively up-regulated abundance of Bacteroidetes, Verrucomicrobia, Bacteroides, Clostridium, Dorea and Sutterella, and down-regulated abundance of Proteobacteria, Bifidobacterium, Coprococcus, and Akkermansia in ASD children, indicating partly agreement in the ASD-associated microbes, albeit the heterogeneity of ASD.

Recently, new semantic segmentation and object detection methods have been proposed for direct processing of three-dimensional (3D) LiDAR sensor point clouds. LiDAR can produce highly accurate and detailed 3D maps of natural and man-made environments and is used for sensing in many contexts due to its ability to capture more information and its robustness to dynamic changes in the environment. In addition, the cost of LiDAR sensors has decreased in recent years, which is an important factor for many application scenarios. The challenge with 3D LiDAR sensors like the Velodyne HDL-64E S2 is that they can output large 3D data at up to 1,300,000 points per second, which is difficult to process in real time when applying complex algorithms and models for efficient semantic segmentation. Most existing approaches are either only suitable for relatively small point clouds or rely on computationally intensive sampling techniques to reduce their size. As a result, most of these methods do not work in real-time in realistic field robotics application scenarios, making them unsuitable for practical applications. Systematic point selection is a possible solution to reduce the amount of data to be processed, but although it is memory and computationally efficient, it selects only a small subset of points, which may result in important features being missed. To address this problem, we propose a new approach to semantic segmentation in forestry that uses a systematic sampling method in which the local neighbors of each point are retained to preserve geometric details. Our approach has been shown to process up to 1 million points in a single pass. It outperforms the state of the art in efficient semantic segmentation in large datasets such as Semantic3D. We also present a preliminary study on the performance of LiDAR-only data, i.e., intensity values from LiDAR sensors without RGB values for semi-autonomous robot perception.

Soil is an extremely significant resource for human survival, and agglomerates, as the basic unit of soil structure, not only enhance soil fertility and control the biological validity of nutrients, but also strengthen the soil's erosion resistance. The mass application of fertilizers may have a significant impact on crop growth and soil structure, and the rational application and dispensing of fertilizers will be an urgent issue to be addressed. Therefore, the effect of fertilizer application on the stability of water-stable soil aggregates needs to be studied under different meteorological and soil conditions to draw more general and feasible conclusions. Our Meta-analysis of data from 220 of 56 published studies found that fertilizer application increased mean weight diameter (MWD) by an average of 18% compared to the no-fertilizer treatment. Among the nitrogen (N), phosphate (P), and organic (OM) fertilizer treatments, the organic fertilizer treatment had a greater stimulatory effect on MWD (26%). Among the different fertilizer levels, low level of phosphorus (<40kg·hm-2·yr-1), high level of N (>120kg·hm-2·yr-1), and low level of organic fertilizer (<5000kg·hm-2·yr-1) increased MWD by 19%, 14%, and 41% respectively. Across soil types and land use types, the response to MWD was positive for red soils and paddy fields, and the stimulatory effect of organic fertilizer was more significant compared to chemical fertilizer. The regression model showed that the response ratio of MWD was negatively correlated with the response ratio of soil pH and bulk density (BD), and positively correlated with the response ratio of soil organic carbon (SOC) and microbial mass carbon (MBC). Meanwhile, the PLS-SEM model showed that average annual temperature was the main factor affecting the stability of soil aggregates, and the average annual rainfall is the secondary factor. Therefore, this study found that the long-term use of organic fertilizers in place of some chemical fertilizers was more effective than chemical fertilizer alone. Temperature and rainfall have greater effects on the response of fertilizer to soil aggregate stability.

Digital twins are gaining in popularity for simulating complex natural and urban environments. In this context, accurate segmentation of objects within 3D urban environments is of crucial importance. The aim of this project is to develop a methodology for extracting buildings from textured 3D meshes. To this end, PicassoNet-II, a semantic segmentation architecture is employed. The methodology also incorporates Markov field-based contextual analysis to assess post-segmentation features. In addition, building instantiation is performed using cluster analysis algorithms. Training this model to fit various datasets requires a large amount of annotated data, both from Quebec City, Canada, and from simulated data. Experimental results show that the use of simulated data improves segmentation accuracy, and the DBScan algorithm proves effective in extracting isolated buildings. This project paves the way for improved applications in 3D urban modeling, offering opportunities in fields ranging from urban planning to resource management. The positive results with simulated data reinforce the impact of this research on improving digital models of our ever-changing urban environments.

Semantic interoperability is a foundational concept in modern healthcare, enabling seamless communication and meaningful data exchange across diverse EHR systems. At the heart of this concept lies archetype clinical information models, which serve as standardized templates to structure and encode clinical data, ensuring consistency and shared understanding among various healthcare stakeholders. This review paper delves into the intricacies of Electronic Health Records (EHRs) within the context of semantic interoperability and the pivotal role played by archetype clinical information models. Drawing from a wealth of recent research, this review paper examines the theoretical underpinnings of semantic interoperability. By surveying the literature, it synthesizes the diverse approaches and methods employed to achieve semantic interoperability within EHR systems. Furthermore, this review does not shy away from addressing the significant challenges that persist in this arena. These challenges encompass issues related to data quality, governance, resistance to change, and the scalability and integration hurdles that healthcare organizations encounter in their quest for interoperable EHR systems. Through a comprehensive review of relevant research papers from esteemed sources including PubMed, ScienceDirect, and Taylor & Francis, the paper offers a comprehensive understanding of the approaches and methods employed in this domain. This analysis encompasses a time frame ranging from 2015 to 2023, ensuring an up-to-date assessment of the field. By presenting a well-rounded picture of the field, this review paper offers valuable insights for healthcare stakeholders, researchers, and practitioners. It underscores the enduring importance of semantic interoperability in EHR systems, not only in advancing patient care, clinical decision support, and healthcare operations but also in driving data-driven innovations in the dynamic and ever-evolving medical healthcare sector. This comprehensive examination sets the stage for future advancements in the pursuit of truly interoperable and data-driven healthcare systems.

For highly time-constrained very short-term investors, reading and extracting valuable information from financial news poses significant challenges. The wide range of topics covered in these news articles further compounds the difficulties for investors. The diverse content adds complexity and uncertainty to the text, making it arduous for very short-term investors to swiftly and accurately extract valuable insights. Variations in authors, media sources, and cultural backgrounds also introduce additional complexities. Hence, performing a bull-bear semantic analysis of financial news using text mining technologies can alleviate the volume, time, and energy pressures on very short-term investors while enhancing the efficiency and accuracy of their investment decisions. This study proposes labeling bull-bear words from a futures corpus detection method that extracts valuable information from financial news, allowing investors to understand market trends quickly. Generative AI models are trained to provide real-time bull-bear advice, aiding investors in adapting to market changes and devising effective trading strategies. Experimental results show the effectiveness of various models, with Random Forest and SVMs achieving an impressive 80% accuracy rate. MLP and Deep learning models also perform well. By leveraging these models, the study reduces the time spent reading financial articles, enabling faster decision-making and increasing the likelihood of investment success. Future research can explore the application of this method in other domains and enhance model design for improved predictive capabilities and practicality.

We assume that poetic devices have an implicit goal: producing an overall sound scheme that will induce the reader to associate intended and expressed meaning to the sound of the poem. Sounds may be organized into categories and assigned presumed meaning as suggested by traditional literary studies. In my work, I have extracted automatically the sound grids of all the sonnets by William Shakespeare and have combined them with the themes expressed by their contents. In a first experiment I have computed lexically and semantically based sentiment analysis obtaining an 80% of agreement. In a second experiment sentiment analysis has been substituted by Appraisal Theory thus obtaining a more fine-grained interpretation which in some cases contradicts the first one. The computation for the second poet - regarded by many critics the best of last century - includes both vowels and consonants. In addition, it combines automatic semantically and lexically based sentiment analysis with sound grids. The results produce visual maps that clearly separate poems into three clusters: negative harmony, positive harmony and disharmony where the latter instantiates the need by the poet to encompass the opposites in a desperate attempt to reconcile them.

As we all know, many more dynamic objects appear almost continuously in real world, which are immensely able to impair the performance of the majority of vision-based SLAM systems that are based on the static-world assumption. In order to improve the robustness and accuracy of visual SLAM in the high dynamic environment, a real-time and robust stereo SLAM system for dynamic scenes was proposed. To weaken influences of dynamic content, the moving object detection method was put forward in our visual odometry, then the semantic segmentation network was combined into our stereo SLAM to extract pixel-level contours of dynamic objects. Then influences of dynamic objects were extremely weaken and the performance of our system was increased markedly in dynamic, complex and crowed city spaces. Experiment with both on KITTI Odometry dataset and in a real-life scene, the results show that our method can dramatically decrease the tracking error or drift, improve the robustness and stability of our stereo SLAM in high dynamic outdoor scenarios.

Wireless sensor networks (WSN) are mostly utilized in many applications. Indeed, WSN is composed of sensors to evaluate some physical phenomena. However, the information brought by the sensor still missed if we don't know the exact location of the event. Several localization algorithms have been proposed in order to locate the nodes in WSN. The localization algorithms are categorized into range-based and range-free techniques. The range-based techniques use either distance or time to calculate the coordinates of unknown nodes. Nevertheless, those kinds of techniques need some additional material in computation purpose. Therefore, range-based techniques present an expensive solution for positioning in WSN. Alternatively, range-free techniques may do the same task without involving additional material. But they don’t offer a high precision of localization in comparison with range-based techniques. DVHOP is the most popular range-free technique that uses the hop count method in the localization process. In this work, we propose an improvement of DVHOP localization algorithm to create three improved versions of this algorithm and we have achieved that by adopting two meta-heuristic (simulated annealing, particle swarm optimization) and FMINCON solver dedicated to the optimization in the field of WSN nodes localization. The experimental results obtained in this work show clearly the gain and the good impact of our proposition.

In this work we introduce an end-to-end multi-modal neural network to segment the Gross Tumor Volume (GTV) from 3D-CBCT’s during radiotherapy. We improve the tumor segmentation by using a U-net which takes additional information such as the tumor mask generated at the planning phase along with the CBCT volume. The mask contains relevant information about the tumor’s location and can guide the model to use this knowledge appropriately to give a better prediction. This technique could become an alternative to produce segmentation masks of GTV in CBCT automatically during radiotherapy as in the traditional RT-pipeline, they are not segmented. We have evaluated our model on a dataset of 82 patients who have undergone radiotherapy. We compare the results of registered target volumes from planning CT as mask seed with 2 different types of multi-modal architectures. Our model shows a DSC of 0.706±0.002 with Late Fusion and 0.702±0.015 with Early Fusion using the GTV Mask. The performance of the two models on this mask is similar, so we perform further experiments with different types of masks which suggest that Late Fusion model produces a better segmentation of the tumor than the Early Fusion model. We also provide an ablation study consisting of a single modality U-net and a metric based on the Planning CT mask registration. It indicates a clear advantage of using our model to produce segmentation for this type of imaging.

Abstract: Background: Pain management is one of the main parts of treatments for Temporomandibular Disorders (TMDs). However, there is still a lack of high-quality evidence that compare the overall effects of these non-surgical treatments. The objective of this systematic review is to identify the most potential treatment protocol in dealing with pain caused by TMDs through a mixed comparison of interventions based on network meta-analysis. Methods: A systematic review and network meta-analysis of studies identified by searching PubMed, Embase, Medline, Ovid, and CINAHL. All the included studies should have characteristics that: (1) participants with TMDs of any age; (2) non-surgical treatments; (3) score of Visual Analog Scale (VAS) as the outcome measure; (4) randomized controlled trials. The Cochrane Bias Assessment Toll was used to assess the bias, the CINeMA website was applied to rate the confidence of evidence, and ADDIS software was used to conduct the network meta-analysis. Results: 46 studies were included in this review. The agreement between authors reached a kappa value of 0.78. The results of the network meta-analysis showed that wearable therapy devices are more likely to be the best choice for reducing the pain of patients with myogenic TMDs, whereas a combination of platelet-rich plasma injection and wearable therapy devices is more likely to be the best choice for reducing the pain in a long term after treatment for patients with mix-type TMDs. Moreover, the application of therapy equipment has the most potential in reducing pain in a long term after treatment for patients with articular TMDs. Discussion: Wearable devices have a great potential for pain syndrome caused by TMDs, the mechanism might come from a biomechanical perspective. However, the overall confidence rating of evidence is low. Studies with high quality are still needed in the future. Other: The PROSPERO Registration Number of this systematic review is CRD42021253442.

It is a well-established fact that inadequate Vitamin D (Vit-D) levels have negative effects on the development and progression of malignant diseases, mainly cancer. The purpose of this paper was to elucidate the effects of Vit-D intake and serum 25-hydroxyvitamin-D (25(OH)D) levels on cancer incidence and mortality, the current evidence in this field, and the biases of this evidence using the meta-meta-analysis method. Meta-analyses focusing on Vit-D intake, serum 25(OH)D levels, and cancer risk/mortality were identified. A structured computer literature search was performed in PubMed/Medline, Web of Science (WoS), and Scopus electronic databases using predetermined keyword combinations. Primary and secondary meta-meta-analyses were carried out, combining odds ratios (ORs), risk ratios (RRs), and hazard ratios (HRs) for outcomes reported in selected meta-analyses. A total of 35 eligible meta-analyses assessing the association between Vit-D and cancer incidence and/or mortality were included in this study. In the pooled analysis, higher Vit-D intake and serum 25(OH)D levels were associated with lower cancer risk (OR = 0.93, 95% CI: 0.90-0.96, p < 0.001; OR = 0.80, 95% CI: 0.72-0.89, p < 0.001, respectively) and cancer-related mortality (RR = 0.89, 95% CI: 0.86-0.93, p < 0.001; RR = 0.67, 95% CI: 0.58-0.78, p < 0.001, respectively). When meta-analyses whose primary reports included only randomized controlled trials were pooled, there was no significant association between Vit-D intake and cancer risk (OR = 0.99, 95% CI: 0.97-1.01, p = 0.320). In subgroup analysis, Vit-D consumption was associated with a significant decrease in colorectal and lung cancer incidence (OR = 0.89, 95% CI: 0.83-0.96, p = 0.002; OR = 0.88, 95% CI: 0.83-0.94, p < 0.001, respectively). Taken together, both Vit-D intake and higher 25(OH)D levels may provide remarkable benefits in terms of cancer incidence and mortality, however, careful evaluation according to cancer types is critically important and recommended.

The dynamics of hydrological lateral nutrient fluxes contribute to our understanding of ecological functions related to energy, materials, and organism flows across various spatiotemporal scales. To explore the connectivity between multiple spatial flow processes, we conducted a one-year field measurement to assess lateral hydrologic carbon (C) and nitrogen (N) fluxes over the continental shelf in the Yangtze estuary. We observed a significant correlation between the differences in remote sensing-based estimates of gross primary production (GPP) (∆GPPMODIS) and the differences in eddy covariance (EC) tower-based GPP (∆GPPEC) at both high-elevation and low-elevation sites. Over the course of a year, our predicted daily maximum tidal elevation (TE) closely matched the observed values in the creek, which facilitated the development of theoretical models to simulate biogeochemical cycling processes and aquatic ecosystem functions. Our findings indicate that the studied saltmarsh acts as a net exporter of dissolved total C (DTC) while serving as a net sink for dissolved total N (DTN). Furthermore, there is a significant correlation in the total dissolved stoichiometry of the C/N ratio between imports and exports. These findings highlight the importance of integrating ecological stoichiometric principles to gain a deeper understanding of the complex relationships between physical, chemical, and biological processes, particularly within the context of the meta-ecosystem framework. Additionally, when considering reciprocal hydrological lateral C and N flows, single ecosystem can function both as sources and sinks within the meta-ecosystem framework.

Peanuts, owing to their composition of complex carbohydrates, plant protein, unsaturated fatty acids, and essential minerals (magnesium, iron, zinc, and potassium), hold significant potential as a vital component of the human diet. Additionally, their low water requirements and nitrogen fixation capacity make them an appropriate choice for cultivation in adverse environmental conditions. The germination ability of seeds profoundly impacts the final yield of the crop, assessing seed viability of extreme importance. Conventional methods for assessing seed viability and germination are both time-consuming and costly. To address these challenges, this study investigated Visible-Near Infrared Spectroscopy (Vis/NIR) in the wavelength range of 500-1030 nm as a non-destructive and rapid method to determine the viability of two varieties of peanut seeds: North Carolina-2 (NC-2) and Spanish flower (Florispan). The study subjected the seeds to three levels of artificial aging through heat treatment, involving incubation in a controlled environment at a relative humidity of 85% and a temperature of 50°C over 24-hour intervals. The absorbance spectra noise was significantly mitigated and corrected to a large extent by combining the Savitzky-Golay (SG) and Multiplicative Scatter Correction (MSC) methods. To identify the optimal wavelengths for seed viability assessment, a range of meta-heuristic algorithms were employed, including world competitive contest (WCC), league championship algorithm (LCA), Genetics (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), imperialist competitive algorithm (ICA), learning automata (LA), heat transfer optimization (HTS), forest optimization (FOA), discrete symbiotic organisms search (DSOS), and cuckoo optimization (CUK). These algorithms offer powerful optimization capabilities for effectively extracting relevant wavelength information from spectral data. Results revealed that all the algorithms demonstrated remarkable accuracy in predicting the allometric coefficient of seeds, achieving correlation coefficients exceeding 0.985 and errors below 0.0036, respectively. In terms of execution time, the ICA (2.3635 seconds) and LCA (44.9389 seconds) algorithms exhibited the most and least efficient performance, respectively. Conversely, the FOA and the LCA algorithms excelled in identifying the least number of optimal wavelengths (10 wavelengths). Subsequently, the seeds were classified based on the wavelengths selected by the FOA (10 wavelengths) and (DSOS (16 wavelengths) methods, in conjunction with logistic regression (LR), decision tree (DT), Multilayer Perceptron (MP), support vector machine (SVM), k-nearest neighbor (K-NN), and NaiveBayes (NB) classifiers. The DSOS-DT and FOA-MP methods demonstrated the highest accuracy, yielding values of 0.993 and 0.983, respectively. Conversely, the DSOS-LR and DSOS-KNN methods obtained the lowest accuracy, with values of 0.958 and 0.961, respectively. Overall, our findings demonstrated that Vis/NIR spectroscopy, coupled with variable selection algorithms and learning methods, presents a suitable and non-destructive approach for detecting seed viability.

The fusion of machine learning, biomedical, and bioengineering research offers novel ways to understand, diagnose, and treat various health conditions. However, the complexities of biomedical and bioengineering data, coupled with the intricate process of developing and deploying machine learning solutions, often pose significant challenges to researchers in these fields. We introduce a new Automatic Semantic Machine Learning Microservice Framework (AIMS) that addresses these challenges by automating key aspects of the machine learning pipeline, from data preprocessing to model evaluation. Our proposed framework emphasizes model interpretability, the integration of domain knowledge, and the efficient handling of biomedical data. Furthermore, it incorporates self-supervised knowledge learning strategies, enabling the automatic generation, optimization, and continuous adaptation of machine learning models in response to new tasks and data. We illustrate the potential of our framework through three case studies in biomedical research, showcasing its capacity to streamline research processes, improve scientific exploration quality, and provide a foundation for significant breakthroughs.

Chronic Low Back Pain (CLBP), defined as pain persisting for at least 12 weeks, is a significant public health issue, with prevalence intensifying due to an ageing global population, amassing approximately 619 million cases in 2020 and projected to escalate to 843 million by 2050. Given the multitude of therapeutic modalities available for CLBP, this research sought to ascertain their respective efficacies in ameliorating pain and disability. We specifically scrutinized Multidisciplinary Biopsychosocial Rehabilitation (MBR) among adults enduring non-specific low back pain for over 12 weeks, excluding acute pain conditions and pre-study surgical interventions. MBR is an approach encompassing various modalities, including exercise, behavioral therapies, educational programs, and work conditioning aspects, necessitating a comprehensive Network Meta-Analysis (NMA) to fully grasp the extensive scope of its impact. The constructed network comprised diverse MBR modalities (behavioral, educational, and work conditioning) alongside exercise therapy (ET), minimal intervention, and usual care, serving as controls with pain and disability as outcomes. Through the analysis of 4,619 initially identified studies, 93 were included, encompassing a total of 8,059 participants. The NMA conducted substantiated that both ET and MBR modalities were notably effective in alleviating CLBP, with education-oriented MBR emerging as the most efficacious for pain mitigation and behavior-focused MBR for disability reduction. Nevertheless, the discerned differences amongst the treatments were minimal and uncertain, highlighting that no modality was definitively superior to the others. The analysis revealed low global and local inconsistency within the network, underscoring the reliability of the observed outcomes. Given the intricate nature of CLBP, embodying various facets, our findings advocate for a combined therapeutic approach to optimize treatment efficacy. Nonetheless, the presence of high study heterogeneity and potential biases necessitates cautious interpretation of the results, prompting future research.

The value of artificial intelligence and machine learning applications for use in heritage research is increasingly appreciated [1]. In specific areas, notably remote sensing, data sets have increased in extent and resolution to the point that manual interpretation is problematic and availability of skilled interpreters to undertake such work is limited. Interpretation of geophysical data sets associated with prehistoric submerged landscapes is particularly challenging. Following the last glacial, sea levels rose 120 metres globally, and vast, habitable landscapes were lost to the sea. These landscapes were inaccessible until extensive remote sensing data sets were provided by the offshore energy sector. In this paper, we provide results of a research programme centred on AI applications using data from the southern North Sea. Here, an area of c.188,000 km2 of habitable terrestrial land was inundated between c. 20,000 BP and 7,0000 BP, along with the cultural heritage it contained [2]. As part of this project, machine learning tools were applied to detect and interpret features of archaeological significance from shallow seismic data. The output provides a proof-of-concept model demonstrating verifiable results and the potential for further, more complex leveraging of AI interpretation for the study of submarine palaeolandscapes. Image: Adobe Firefly Generative AI using keywords - marsh landscape deep underwater, auroch skull half-buried in peat

Aims: The aims of this meta-analysis were to assess: the prevalence of Post-Acute COVID-19 sequalae in HIV positive patients; average time of diagnosis; and meta-regress for possible moderators of PACS.Methods: A standard search strategy was used in PubMed, and then later modified according to each specific database to get the best relevant results. These included Medline indexed journals; PubMed Central; NCBI Bookshelf and publishers' Web sites in line with the Preferred Reporting Items for Systematic Review and Meta-Analysis statement. Search terms included “long COVID-19 or post-acute COVID-19 syndrome/sequalae”, “persons living with HIV or HIV. The criteria for inclusion were published clinical articles reporting HIV in association with long COVID-19, further, the average time to an event of post-acute COVID-19 sequelae among primary infected patients with COVID-19. Random-effects model was used. Rank Correlation and Egger's tests were used to ascertain publication bias. Sub-group, sensitivity and meta-regression analysis were conducted. A 95% confidence intervals were presented and a p-value < 0.05 was considered statistically significant. Review Manager 5.4 and comprehensive meta-analysis version 4 (CMA V4) were used for the analysis. The review/trial was PROSPERO registered (CRD42022328509).Results: A total of 43 studies reported post-acute COVID-19 syndrome. Of those, five reported post-acute COVID-19 sequalae in PLHIV. Prevalence of post-acute COVID-19 sequalae was 43.1% (95% CI 20.5% to 68.9%) in persons living with HIV (PLWH). The average time to PACS diagnosis was 4 months at 64% [0.64 (95% CI 0.230, 0.913) (P < 0.0000), I²= 93%] and at one year to PACS diagnosis was at 70 %, however with non-significant correlation (P > 0.05). On comorbidities, asthenia was associated with PACS at 17.6 % [0.176 (95% CI 0.067, 0.385) (P = 0.008), I²= 86%] while fatigue at 82%, however not related with PACS event incidence (P < 0.05). Americas, Asian and European regions showed PACS events rates of 82%, 43% and 19 % respectively (P<0.05) relative to HIV infection. Conclusion: PACS prevalence in PLWH was 43% occurring at an average time of 4 months at 64% and 70 % at 12 months however non-significant with PACS. Asthenia was significantly associated with PACS at 17.6 % while fatigue at 82%, however not related with PACS event incidence. Americas recorded the highest PACS event rates in PLWH.

Topic discovery is finding the main idea of large amounts of textual data. It indicates the recurring topics in the documents, allowing an overview of the texts. Current topic discovery models receive the texts, with or without pre-processing of Natural Language Processing. The processing consists of stopwords removal, text cleaning and normalization (lowercase conversion). A topic discovery model that receives texts with or without processing generates general topics since the input data is many uncategorized texts. The general topics do not offer a detailed overview of the input texts, and manual text categorization is a time-consuming and tedious task. Accordingly, it is necessary to integrate an automatic text classification task in the topic discovery process to obtain specific topics with their top words that contain relevant relationships based on belonging to a class. Text classification performs a word analysis that makes up a document to decide what class or category is being identified; then, integrating the text classification before a topic discovery process will provide latent topics depicted by top words with a high coherence in each topic based on the previously obtained classes. Therefore, this paper exposes a approach that integrates text classification into topic discovery from large amounts of English textual data, such as 20-Newsgroup and Reuters corpora. The text classification is accomplished with a Convolutional Neural Network(CNN) incorporating three embedding models based on semantic relationships. The topic discovery over categorized texts is realized with Latent Dirichlet Analysis(LDA), Probabilistic Latent Semantic Analysis(PLSA), and Latent Semantic Analysis(LSA) algorithms. An evaluation process was performed based on the normalized topic coherence metric. The 20-Newsgroup corpus was classified, and twenty topics with ten top words were discovered for each class, obtaining 0.1723 normalized topic coherence when applying LDA, 0.1622 with LSA, and 0.1716 with PLSA. The Reuters corpus was also classified, obtaining 0.1441 normalized topic coherence when applying the LDA algorithm to obtain 20 topics for each class.

ABSTRACTBackground: Crohn's disease (CD) is a chronic inflammatory bowel disorder with limited treatment options. Mitochondrial dysfunction has been implicated in CD pathogenesis, and mitochondrial-based interventions have emerged as a potential therapeutic avenue. This systematic review aimed to assess the efficacy of mitochondrial-based interventions in the management of CD.Methods: A systematic search of multiple databases was conducted to identify relevant studies in accordance with the PRISMA protocol. Inclusion criteria encompassed studies investigating mitochondrial-targeted interventions in CD patients. Methodological quality and risk of bias assessments were performed. Data synthesis involved both quantitative meta-analysis and qualitative narrative synthesis.Results: A total of 23 studies were included in this review, comprising randomized controlled trials, observational studies, and case reports. Mitochondrial-based interventions encompassed nutraceuticals, probiotics, exercise, and dietary modifications. However, the overall quality of evidence was moderate, primarily due to methodological limitations in some studies.Conclusion: This systematic review suggests that mitochondrial-based interventions hold promise as adjunctive therapies for CD management. They appear to contribute to clinical remission and amelioration of disease activity. Nevertheless, the moderate quality of evidence underscores the need for further well-designed clinical trials to substantiate these findings and delineate optimal intervention strategies.

Jellyfish search (JS) algorithm impersonates the foraging behavior of jellyfish in the ocean. It is a new developed meta-heuristic algorithm that solves complex and real world optimization problems. The global explore capability and robustness of JS are strong, but JS still has great development space in solving complex optimization problems with high dimensions and multiple local optima. Therefore, an enhanced jellyfish search (EJS) algorithm is developed in this study, and three improvements are made: (i) By adding sine and cosine learning factors, the jellyfish can learn from both random individual and best individual during Type B motion in swarm to enhance the optimization capability and convergence speed; (ii) Adding local escape operator can skip local optimal trap and boost the exploitation ability of JS; (iii) Opposition-based learning operator and quasi-opposition learning operator can increase and strengthen the population distribution more diversified, and better individuals are selected from present and new opposition-solution to participates in the next iteration, which can boost the solution’s quality, meanwhile convergence speed is fasted and its precision is increased. In addition, the performance contrast of the developed EJS and some previous outstanding and advanced methods are evaluated on CEC2017, CEC2019 test suite and six real engineering example of case. It is demonstrated that EJS algorithm escaped the trap of local optimum, enhanced the solution’s quality and the calculation speed. What’s more, the practical engineering applications of EJS algorithm also verify its superiority and effectiveness in solving both constrained and unconstrained optimization problems, and it stretched one’s mind for solving such optimization problems.

Multimodal remote sensing data classification can enhance the model’s ability to distinguish land features through multimodal data fusion. In this context, how to help models understand the relationship between multimodal data and target tasks is the focus of researchers. Inspired by human feedback learning mechanisms, causal reasoning mechanisms, and knowledge induction mechanisms, this paper integrates causal learning, reinforcement learning, and meta learning into a unified remote sensing data classification framework and proposed the causal meta-reinforcement learning (CMRL). First, based on feedback learning mechanisms, we have overcome the limitations of traditional implicit optimization of fusion features and customized a reinforcement learning environment for multimodal remote sensing data classification tasks. Through feedback interactive learning between agents and the environment, we help them understand the complex relationships between multimodal data and labels, thereby achieving full mining of multimodal complementary information. Second, based on the causal inference mechanism we designed causal distribution prediction action, classification reward, and causal intervention reward, capturing pure causal factors in multimodal data and cutting off false statistical associations between non-causal factors and class labels. Finally, based on the knowledge induction mechanism, we designed a bi-layer optimization mechanism based on meta-learning. By constructing a meta training task and meta validation task simulation model in the generalization scenario of unseen data, we helped the model induce cross-task shared knowledge, thereby improving its generalization ability for unseen multimodal data. The experimental results on multiple sets of multimodal datasets show that the proposed method achieved state-of-the-art performance in multimodal remote sensing data classification tasks.

Expressive clinical terminologies are of utmost importance for achieving an semantically interoperable data exchange and reuse in healthcare. SNOMED CT, widely respected as the most comprehensive terminology in medicine, provides formal concept definitions based on description logic which not only allows for advanced querying of SNOMED CT-coded data but also for flexibly augmenting its 350,000 concepts. This ability for postcoordination largely increases the expressivity of the terminology but correlates with an intrinsic complexity. Complicated by the current lack of tooling support, postcoordination is widely either ignored or applied in an error-prone way. To help facilitate the adoption of postcoordination, we implemented a web application that guides through the creation of Postcoordinated expressions (PCEs) while ensuring the adherence to syntactic and semantic constraints. Our approach was largely facilitated by making use of the extensive SNOMED CT specifications as well as advanced HL7 FHIR Terminology Services. Qualitative evaluations confirmed the usability of the developed application and the correctness of PCEs created with it.

Background: Gestational diabetes is a common complication during pregnancy that can lead to numerous adverse outcomes. Some studies suggest that probiotics may be used to treat gestational diabetes, however, the results remain controversial. We conducted a systematic review and meta-analysis to evaluate the effect of probiotics on blood glucose and pregnancy outcomes in women with gestational diabetes. Methods: A systematic search of PubMed, Embase, and Cochrane databases was performed (start date to August 22, 2023). Primary outcomes included fasting blood sugar (FBS), fasting serum insulin (FSI), homeostasis model assessment of insulin resistance (HOMA-IR), and quantitative insulin sensitivity check index (QUICKI). Secondary outcomes included pregnancy and neonatal outcomes. Results: 15 articles (n = 1006 women) met the inclusion criteria and were included in the analysis. Compared to a placebo, probiotics can decrease FBS (MD -2.58, 95% CI -4.38 to -0.79, p &lt; 0.01), FSI (MD -2.29, 95% CI -3.40 to -1.18, p &lt; 0.01), HOMA-IR (MD -0.56, 95% CI -0.81 to -0.32, p &lt; 0.01), birthweight (MD -101.20, 95% CI -184.62 to -17.77, p = 0.02), neonatal intensive care unit (NICU) (RR 0.60, 95% CI 0.40 to 0.89, p = 0.01), and hyperbilirubinemia (RR 0.31, 95% CI 0.16 to 0.61, p &lt; 0.01), alongside higher QUICKI (MD 0.01, 95% CI 0.00 to 0.01, p &lt; 0.01). However, no other significant results were obtained. Conclusion: Probiotics may improve blood glucose indicators and reduce neonatal hyperbilirubinemia, NICU admissions, and birth weight in women with GDM.

Gastric cancer (GC) is a highly heterogeneous, complex disease and the fifth most common cancer worldwide (about one million cases and 784 000 deaths worldwide in 2018). CG is lately diagnosed and guarantees a poor prognosis for GC (the 5-year survival rate is less than 20%, but in early detection can reach 90%). This study evaluated the transcriptional profile in tumor gastric samples to find genes highly expressed during tumor establishment and use the related proteins as targets to find new anticancer molecules. Data was collected at Gene Expression Omnibus (GEO) bank to obtain 3 dataset matrices that analyze gastric tumor tissue versus normal gastric tissue, performed microarray using GPL570 platform, and from different sources. The genes found in silico analysis were confirmed in several lines of GC cells by RT-qPCR. The protein data bank was used to find the correspondent proteins. Then a structural-based virtual screening was done to find molecules, and docking analysis was done using the DockThor server. Our results of transcriptomic analysis, together with RT-qPCR, confirm the high expression of the genes AJUBA, FBXL13, CCDC69, CD80 and NOLC1 in GC lines. Based on that, the correspondent proteins were used in SBVS analysis. Five molecules, one each target, MCULE-2386589557-0-6, MCULE-7343047040-0-1, MCULE-5230409338-0-3, MCULE-9178344200-0-1 and MCULE -5881513100-0-29. All molecules have favorable pharmacokinetic, pharmacodynamic and toxicological properties. Molecular docking analysis revealed that the molecules interact with proteins in critical sites for their activity. Using a virtual screening approach, a molecular docking study was performed for proteins encoded by genes that play important roles in cellular functions for carcinogenesis. Combining a systematic collection of public microarray data with a comparative meta-profiling, RT-qPCR, SBVS, and molecular docking analysis provided a suitable approach to find genes involved in GC and work the correspondent protein to strive for new molecules with anticancer properties.

This study aimed to present a systematic meta-analysis examining the association between Helicobacter pylori (H. pylori) infection, hemoglobin A1c levels, and the development of type 1 diabetes mellitus. The initial search identified 451 articles related to the association between H. pylori infection and T1DM. Among them, 14 articles had 2,971 participants meeting the inclusion criteria for advanced meta-analysis. A significant association was observed between H. pylori infection and T1DM, with an odds ratio (OR) of 1.87 (95% confidence interval [CI]: 1.27–2.78, p = 0.002). A subgroup analysis on H. pylori status showed that a longer duration of diabetes diagnosis and higher hemoglobin A1c levels were significantly associated with H. pylori infection (p < 0.001 for both). However, there was no significant association between H. pylori infection and the diagnostic age of diabetes (p = 0.306). These findings contribute to the understanding of the association between H. pylori infection and T1DM, thereby highlighting the potential role of H. pylori in influencing the duration and glycemic control of diabetes.

Objective: This systematic review and meta-analysis compared the patient-reported outcomes of intra-articular facet joint injections of normal saline and selected active substances to identify a more effective agent for treating subacute and chronic low back pain (LBP). Methods: The PubMed, Embase, Scopus, Web of Science, and CENTRAL databases were searched for randomized controlled trials and observational studies published in English. A research quality assessment was performed using ROB2 and ROBINS-I. A meta-analysis was conducted using a random-effects model, and the mean differences (MD) with 95% confidence intervals (CI) in efficacy outcomes, including pain, numbness, disability, and quality of life, were assessed. Results: Of the 2,467 potential studies, three were included (247 patients). The active substances and normal saline had similar therapeutic effects on pain within 1 h, after 1-1.5 months, and after 3-6 months, with MD and 95% CI of 2.43 and -11.61 to 16.50, -0.63 and -7.97 to 6.72, and 1.90 and -16.03 to 19.83, respectively, as well as the quality of life after 1 and 6 months. Conclusion: The short- and long-term clinical effects of intra-articular facet joint injections of normal saline are comparable to those of other active substances in patients with LBP.

The goal of the next generation World Wide Web is machine readability through linked databases. To improve web search, integration, and mining in local languages like Urdu, there is a growing need to develop ontologies and vocabulary in these languages. The majority of people use the web in local languages for agriculture, social media interaction, news, etc. How to create agents for the integration of web data. In our country, the literacy ratio is very low and IT literacy is negligible. More comprehensive information for its target audience is only possible through the World Wide Web. Our first target is to improve and enhance the use of social media and the web in local languages. That will encourage its constructive use in Urdu for society and the economy. The Web in natural languages is the source of income for small and medium enterprises. The semantic web is concerned with linked databases and structured data. In this work, we are focused on some selected ontologies to be translated into natural languages. Expertise in Ontology Engineering helps us in job production. Ontology Engineering has extensive freelancing opportunities. Only if the web is correctly interpreted in regional languages is an economic boost achievable. A standardized foundation for data sharing and reuse on the internet is provided by the Semantic Web. In other words, a group of standards and technology that enables computers to comprehend the semantics (meaning) of material on the Web.

Colorectal cancer (CRC) with KRAS mutation is prone to develop metachronous metastasis. However, effective measures for prediction and prevention of such clinical issue are currently unavailable. In this study, we confirmed that KRAS mutation is a risk factor for CRC metachronous metastasis through meta-analysis and public database analysis. Based on the ICGC-AGRO database, we have constructed a risk scoring model for predicting metachronous metastasis in KRAS-mutant CRC using machine learning, which was validated in public datasets. Furthermore, we discovered that KRAS inhibitors can effectively suppress the migration and invasion capabilities of high risk CRC cells by wound healing assay and transwell experiment, which was validated ex vivo and in vivo using organoids and splenic liver metastatic mouse model. Finally, RNA sequencing, RT-qPCR, and western blots have proven that KRAS inhibitors can suppress the epithelial-mesenchymal transition (EMT) and transforming growth factor beta (TGF-beta) signaling pathway in CRC. Collectively, our study suggests that KRAS inhibitors may inhibit the metastatic ability of CRC by suppressing EMT and the TGF-beta pathway, suggesting their potential for the prevention of metachronous metastasis in CRC with KRAS mutation.

This paper's aim is to extend horizons in symbolic AI. The key to doing so is to establish a scientific basis for knowledge foundations, and specifically for the concept of meaning, and its computation. This led to the creation of the Meaning Computation AI (MCAI). Knowledge structures are at the heart of MCAI. The semantic triple chain, one of such structures, is the subject of this paper. This structure is the core of introducing computational semantic reasoning models across a variety of domains. To efficiently apply knowledge structures to create semantic reasoning applications MCAI contains a development framework consisting of four components. The first component stems from our discovery that knowledge is relational in nature. It can be expressed through implicit relations (which reflect meaning) and semantic relations (which describe how implicit relations act between entities). An algebraical apparatus for relational knowledge, specifically for implicit and semantic relations, represents the first component. Axiomatic models of relational domain ontologies and domain-oriented semantic reasoning models comprise the second and third components. The fourth component contains domain-oriented semantic reasoning engines.

Recently, there has been considerable research on deepfake detection. However, most existing methods face challenges in adapting to the advancements in new generative models within unknown domains. In addition, the emergence of new generative models capable of producing and editing high-quality images, such as Diffusion, Consistency, and LCM, poses a challenge for traditional deepfake training models. These advancements highlight the need for adapting and evolving existing deepfake detection techniques to effectively counter the threats posed by sophisticated image manipulation technologies. In this paper, our objective is to detect deepfake videos in unknown domains using unlabeled data. Specifically, our proposed approach employs Meta Pseudo Labels (MPL) with Supervised Contrastive Learning, so-called SupCon-MPL, allowing the model to be trained on unlabeled images. MPL involves the simultaneous training of both a Teacher model and a Student model, where the Teacher model generates Pseudo Labels utilized to train the Student model. This method aims to enhance the adaptability and robustness of deepfake detection systems against emerging unknown domains. Supervised Contrastive Learning utilizes labels to compare samples within similar classes more intensively, while encouraging greater distinction from samples in dissimilar classes. This facilitates the learning of features in a diverse set of deepfake images by the model, consequently contributing to the performance of deepfake detection in unknown domains. When utilizing the ResNet50 model as the backbone, SupCon-MPL exhibited an improvement of 1.58% in accuracy compared to traditional MPL in known domain detection. Moreover, in the same generation of unknown domain detection, there was a 1.32% accuracy enhancement, while in the detection of post-generation unknown domains, there was an 8.74% increase in accuracy.

About half of the world's population is at risk of dengue infection. Epidemics of dengue fever caused an increased risk of morbidity and mortality in recent years, which led to the exploration of vaccines as a preventive measure. This systematic review and meta-analysis aimed to evaluate the efficacy, immune response, and safety of dengue vaccines in children by analyzing clinical trials. The review followed standard procedures for data extraction using PRISMA guidelines, and searching multiple databases including PubMed, CINAHL, Medline, Health Source, Science Direct, and Academic Search Premiere. Eligible studies involved children (0-17 years). Cochrane Collaboration criteria were used for quality assessment, with thematic data synthesis and meta-analysis. Among 38 selected studies, dengue vaccines showed varying efficacy against all four serotypes. CYD-TDV (Dengvaxia®), and Tekade (TAK-003) vaccines demonstrated strong protection against severe dengue, but their long-term efficacy varied. Vaccines triggered satisfactory immune responses, notably in those previously exposed to dengue. Safety profiles were mostly favorable, noting mild adverse events post-vaccination. Meta-analysis supported vaccine efficacy and immune response, but safety concerns warrant further exploration. In conclusion, dengue vaccines demonstrated promising efficacy and immune response, particularly against severe manifestations.