: On earth, plants play the most important part. Every organ of a plant plays a vital role in the ecological field as well as the medicinal field. But on the whole earth there are several species of plants are available. Different plants have different diseases. Therefore it is needed to identify the plants and their diseases to prevent loss. Now to identify the plants and their diseases manually is very time consuming. In this research an automatic plant and their disease detection system is proposed. For experimental purposes, high-quality leaf images are accepted for training and testing. For detecting the healthy and diseased area in a leaf, region-based and color-based region thresholding techniques were used. For feature selection Histogram Oriented Gradient (HOG) and Local Binary Pattern (LBP) method were applied. Finally for classification two-class and multi-class Support Vector Machine (SVM) was used. It is observed that both feature selection processes with SVM give 99% accuracy. Finally to understand the automated system a graphical user interface was created for all users.

Carotenoid and carotenoid esters profiles of peel, pulp and whole fruit tissues of astringent persimmon (Diospyrus kaki Thunb., var. Rojo Brillante) have been characterized in detail and quantified for the first time. Carotenoids were determined by HPLC-PDA-MS/MS (APCI+), using a reverse phase C30 column. A total of 38 carotenoids were identified and quantified, corresponding to 21 free carotenoids (13 xanthophylls and 8 hydrocarbon carotenes) and a total of 17 carotenoid esters. The qualitative profiles are very similar among tissues, differing only in the carotenoids concentration. The most important identified free xanthophylls were (all-E)-β-cryptoxanthin, (all-E)-antheraxanthin, (all-E)-lutein, (all-E)-zeaxanthin and (all-E)-violaxanthin . Hydrocarbon carotenoids found were (all-E)-β-carotene, (all-E)-α-carotene, (9Z)-β-carotene, (13Z)-β-carotene, (9Z)-α-carotene, and lycopene. The most abundant carotenoid esters were (all-E)-lutein-3-O-palmitate, (all-E)-zeaxanthin myristate, (all-E)-zeaxanthin palmitate and (all-E)-cryptoxanthin laurate. Processing by high pressures produced no regular effect on persimmon carotenoids and pasteurization affected negatively the content of all carotenoids from all studied persimmon tissues. This work will contribute to the development of scientific research about the bioaccessibity and bioavailabity of each individual free or esterified persimmon carotenoids in order to a better understanding of the carotenoid compounds impact in human health.

Face routing has been adopted in wireless sensor networks (WSNs) where topological changes occur frequently or maintaining full network information is difficult. For message forwarding in networks, a planar graph is used to prevent looping, and because long edges are removed by planarization and the resulting planar graph is composed of short edges, messages are forwarded along multiple nodes connected by them even though they can be forwarded directly. To solve this, face routing using information on all nodes within 2-hop range was adopted to forward messages directly to the farthest node within radio range. However, as the density of the nodes increases, network performance plunges because message transfer nodes receive and process increased node information. To deal with this problem, we propose a new face routing using the planar graphs of neighboring nodes to improve transfer efficiency. It forwards a message directly to the farthest neighbor and reduces loads and processing time by distributing network graph construction and planarization to the neighbors. It also decreases the amount of location information to be transmitted by sending information on the planar graph nodes rather than on all neighboring nodes. Simulation results show that it significantly improves transfer efficiency.

Distributional word vector representation orword embedding has become an essential ingredient in many natural language processing (NLP) tasks such as machine translation, document classification, information retrieval andquestion answering. Investigation of embedding model helps to reduce the feature space and improves textual semantic as well as syntactic relations.This paper presents three embedding techniques (such as Word2Vec, GloVe, and FastText) with different hyperparameters implemented on a Bengali corpusconsists of180 million words. The performance of the embedding techniques is evaluated with extrinsic and intrinsic ways. Extrinsic performance evaluated by text classification, which achieved a maximum of 96.48% accuracy. Intrinsic performance evaluatedby word similarity (e.g., semantic, syntactic and relatedness) and analogy tasks. The maximum Pearson (ˆr) correlation accuracy of 60.66% (Ssˆr) achieved for semantic similarities and 71.64% (Syˆr) for syntactic similarities whereas the relatedness obtained 79.80% (Rsˆr). The semantic word analogy tasks achieved 44.00% of accuracy while syntactic word analogy tasks obtained 36.00%

A novel stationary phase for affinity separations is presented. This material is based on sintered borosilicate glass readily available as semi-finished filter plates with defined porosity and surface area. The material shows fast binding kinetics and excellent long-term stability under real application conditions due to lacking macropores and high mechanical rigidity. The glass surface can be easily modified with standard organosilane chemistry to immobilize selective binders or other molecules used for biointeraction. In this paper, the manufacturing of the columns and their respective column holders by 3D printing is shown in detail. The model system protein A/IgG was chosen as an example to examine the properties of such monolithic columns under realistic application conditions. Several specifications, such as (dynamic) IgG capacity, pressure stability, long-term performance, productivity, non-specific binding, and peak shape, are presented. It could be shown that due to the very high separation speed, 250 mg antibody per hour and column can be collected, which surpasses the productivity of most standard columns of the same size. The total IgG capacity of the shown columns is around 4 mg (5.5 mg/mL), which is sufficient for most tasks in research laboratories. The cycle time of an IgG separation can be less than 1 minute. Due to the glass material's excellent pressure resistance, these columns are compatible with standard HPLC systems. This is usually not the case with standard affinity columns, limited to manual use or application in low-pressure systems. The use of a standard HPLC system also improves the ability for automation, which enables the purification of hundreds of cell supernatants in one day. The sharp peak shape of the elution leads to an enrichment effect, which might increase the concentration of IgG by a factor of 3. The final concentration of IgG can be around 7.5 mg/mL without the need for an additional nanofiltration step. The purity of the IgG was > 95% in one step and nearly 99% with a second polishing run.

RNA polymerase II (RNAPII) frequently transcribes non-protein coding DNA sequences in eukaryotic genomes into long non-coding RNA (lncRNA). Here, we focus on the impact of the act of lncRNA transcription on nearby functional DNA units. Distinct molecular mechanisms linked to the position of lncRNA relative to the coding gene illustrate how non-coding transcription controls gene expression. We review the biological significance of the act of lncRNA transcription on DNA processing, highlighting common themes, such as mediating cellular responses to environmental changes. This review presents the background in chromatin signaling to appreciate examples in different organisms where we can interpret functions of non-coding DNA through the act of RNAPII transcription.

Clean production of renewable fuels is a great challenge of our scientific community. Iridium complexes have demonstrated a superior catalytic activity in the water oxidation (WO) reaction, which is a crucial step in water splitting process. Herein we have used a defective zirconium MOF with UiO-66 structure as support of a highly active Ir complex based on EDTA with formula [Ir(HEDTA)Cl]Na. The defects are induced by the partial substitution of tereftalic acid with smaller formiate groups. Anchoring of the complex occurs through a post-synthetic exchange of formiate anions, coordinated at the zirconium clusters of the MOF, with the free carboxylate group of the [Ir(HEDTA)Cl]-complex. The modified material was tested as heterogenous catalyst for the WO reaction by using Cerium Ammonium Nitrate as sacrificial agent. Although TOF and TON values are comparable to those of other iridium heterogenized catalysts, the MOF exhibits iridium leaching not limited at the first catalytic run, as usually observed, suggesting a lack of stability of the hybrid system under strong oxidative conditions.

The mental workload induced by a Web page is essential for improving the user’s browsing experience. However, continuously assessing the mental workload during a browsing task is challenging. In order to face this issue, this paper leverages the correlation between stimuli and physiological responses, which are measured with high-frequency, non-invasive psychophysiological sensors during very short span windows. An experiment was conducted to identify levels of mental workload through the analysis of pupil dilation measured by an eye-tracking sensor. In addition, a method was developed to classify real-time mental workload by appropriately combining different signals (electrodermal activity (EDA), electrocardiogram, photoplethysmography (PPG), electroencephalogram (EEG), temperature and eye gaze) obtained with non-invasive psychophysiological sensors. The results show that the Web browsing task involves on average four levels of mental workload. Also, by combining EEG with the PPG and EDA, the accuracy of the classification reaches 95.73 %.

Recent years have seen the growing adoption of non-relational data models for representing diverse, incomplete data. Among these, the RDF graph-based data model has seen ever-broadening adoption, particularly on the Web. This adoption has prompted the standardization of the SPARQL query language for RDF, as well as the development of a variety of local and distributed engines for processing queries over RDF graphs. These engines implement a diverse range of specialized techniques for storage, indexing, and query processing. A number of benchmarks, based on both synthetic and real-world data, have also emerged to allow for contrasting the performance of different query engines, often at large scale. This survey paper draws together these developments, providing a comprehensive review of the techniques, engines and benchmarks for querying RDF knowledge graphs.

The voice contains elementary social communication cues, conveying speech, as well as paralinguistic information pertaining to the emotional state and the identity of the speaker. In contrast to vocal-speech and vocal-emotion processing, voice-identity processing has been less explored. This seems surprising, given the day-to-day significance of person recognition by voice. A valuable approach to unravel how voice-identity processing is accomplished is to investigate people who have a selective deficit in recognising voices. Such a deficit has been termed phonagnosia. In the present chapter, we provide a systematic overview of studies on phonagnosia and how they relate to current neurocognitive models of person recognition. We review studies that have characterised people who suffer from phonagnosia following brain damage (i.e. acquired phonagnosia) and also studies, which have examined phonagnosia cases without apparent brain lesion (i.e. developmental phonagnosia). Based on the reviewed literature, we emphasise the need for a careful behavioural characterisation of phonagnosia cases by taking into consideration the multistage nature of voice-identity processing and the resulting behavioural phonagnosia subtypes.

Calibrating ecological models or making decisions with them is an optimisation problem with challenging methodological issues. Depending on the optimisation formulation, there may be a large variety of optimisation configurations (e.g. multiple objectives, constraints, stochastic criteria) and finding a single acceptable solution may be difficult. The challenges are exacerbated by the high computational cost and the non linear or elusive mathematical properties that increased with the complexity of numerical models. From the feedbacks of practitioners, the need for a guideline for conducting optimisation of complex models has emerged. In this context, we propose a practical guide for the complex model optimisation process, covering both calibration and decision-making. The guide sets out the workflow with recommendations for each step based on existing tools and methods usually scattered throughout the literature. This guide is accompanied with an ODDO template (Overview, Design, Details of Optimisation) to standardise the published description of model-based optimisation and suggests research directions.

Human Perrault syndrome (PRLTS) is defined by autosomal recessive inheritance with primary ovarian insufficiency and early hearing loss. Most PRLTS disease proteins modulate mitochondrial transcription or translation. Among the genetic causes are ClpP mutations, which trigger also complete azoospermia, whose cellular and molecular underpinnings are unknown. Here, the ClpP-null mouse model was studied by global transcriptomics, proteomics, RT-qPCR, immunoblots, tissue fractionation, testis histology, and was crossed with STING/IFNAR mutants. Spermatogenesis showed accumulated early spermatocytes, versus deficits of desynapsis and kinetochore factors; excess Dazl/Stra8 and acetylSMC3, versus deficient SHCBP1L, were molecular correlates. Spermiogenesis showed few round spermatids, tsHMG/TFAM in elongated spermatids was absent; transcripts for tail/acrosome factors were downregulated from start. Nuclear anomalies included a failed Rec8 induction, early BRDT deficiency, histone H3 cleavage, and cGAMP increase, among antiviral responses typical of ClpP-mutants. However, deletion of downstream innate immune signals STING/IFNAR failed to reestablish fertility. As mitochondrial triggers, we observed accumulation of ClpX, with PTCD1, POLDIP2, GRSF1, ALKBH7, DNAJA3, AURKAIP1, VWA8, and Perrault proteins ERAL1, PEO1, HARS2, partially showing nuclear redistribution. ClpP-depletion is known to cause extra-mitochondrial release of mispacked mtDNA/mtRNA/protein complexes. Now we define nuclear inflammatory responses and meiotic arrest as consequences, similar to observations in mito-mice and mutator-mice.

Synthetic Aperture RADAR (SAR) is a radar imaging technique in which the relative motion of the sensor is used to synthesize a very long antenna and obtain high spatial resolution. Standard SAR raw data processing techniques assume uniform motion of the satellite (or aerial vehicle) and a fixed antenna beam pointing sideway orthogonally to the motion path, assumed rectilinear. Despite SAR data processing is a well established imaging technology that has become fundamental in several fields and applications, in this paper a novel approach has been used to exploit coherent illumination, demonstrating the possibility of extracting a large part of the ancillary data information from the raw data itself, to be used in the focusing procedure. In this work an effort has been carried out to try to focus the raw SAR complex data matrix without the knowledge of anyof the parameters needed in standard focusing procedures as Range Doppler (RD) algorithm, ω - K algorithm and Chirp Scaling (CS) algorithm. All the literature references regarding the algorithms needed to obtain a precise image from raw data use such parameters that refer both to the SAR system acquisition geometry and its radiometric specific parameters. In [12], authors introduced a preliminary work dealing with this problem and able to obtain, in the presence of a strong point scatterer in the observed scene, good quality images, if compared to the standard processing techniques. In this work the proposed technique is described and performances parameters are extracted to compare the proposed approach to RD.

A quantum time-dependent spectrum analysis, or simply, quantum spectral analysis (QSA) is presented in this work, and it’s based on Schrödinger’s equation. In the classical world, it is named frequency in time (FIT), which is used here as a complement of the traditional frequency-dependent spectral analysis based on Fourier theory. Besides, FIT is a metric which assesses the impact of the flanks of a signal on its frequency spectrum - not taken into account by Fourier theory and let alone in real time. Even more, and unlike all derived tools from Fourier Theory (i.e., continuous, discrete, fast, short-time, fractional and quantum Fourier Transform, as well as, Gabor) FIT has the following advantages, among others: a) compact support with excellent energy output treatment, b) low computational cost, O(N) for signals and O(N2) for images, c) it does not have phase uncertainties (i.e., indeterminate phase for a magnitude = 0) as in the case of Discrete and Fast Fourier Transform (DFT, FFT, respectively). Finally, we can apply QSA to a quantum signal, that is, to a qubit stream in order to analyze it spectrally.

Climacteric fruits are characterized by a dramatic increase in autocatalytic ethylene production, which is accompanied by a spike in respiration, at the onset of ripening. The change in the mode of ethylene production from autoinhibitory to auto-stimulatory is known as the system 1 (S1) to system 2 (S2) transition. Existing physiological models explain the basic and overarching genetic, hormonal, and transcriptional regulatory mechanisms governing the S1 to S2 transition of climacteric fruit. However, the links between ethylene and respiration, the two main factors that characterize the respiratory climacteric, have been largely understudied at the molecular level. Results of recent studies indicate that the AOX respiratory pathway may play an important role in mediating cross talk between ethylene response, carbon metabolism, ATP production, and ROS signaling during climacteric ripening. New genomic, metabolic, and epigenetic information sheds light on the interconnectedness of ripening-associated metabolic pathways, necessitating expanding the current, ethylene-centric physiological models. Understanding points at which ripening responses can be manipulated may reveal key, speciesand cultivar-specific targets for regulation of ripening enabling superior strategies for reducing postharvest wastage.

Current theories of autoimmunity are diverse, sometimes contradictory, and suffer from incompleteness. Although substantial evidence exists that adaptive and innate immunity, sex, genetic predisposition, and the microbiome all play essential roles in autoimmune disease etiologies and pathogenesis, and that antigen processing is altered during disease induction, no existing theory integrates all of these factors through a single, coherent mechanism. In an attempt to focus the field on the need to elucidate such an integrative mechanism, I propose one possibility here that, if nothing else, helps to identify the nature of the problems that need to be addressed. My theory is that autoimmune diseases are induced by normal immunological responses to unique pairs of complementary antigens, at least one of which is a molecular mimic of a host target. Each antigen in the complementary pair induces a complementary immune response (T or B cell); although each immune response is idiotypic in origin, the antigenic complementarity results in what appears to be an idiotype-anti-idiotype relationship between the responses. Additionally, because of the antigenic complementarity, each immune response mimics one of antigens, abrogating the distinction between self and non-self. If at least one of the antigens mimics a host antigen, then the resulting immunological civil war spreads to a host tissue. Complementary antigens also alter antigen processing so that antigens that would normally be proteolytically digested are presented by the major histocompatibility complex (MHC) to T and B cell receptors inducing a cross-reactive immune response. The resulting civil war is supported by the innate immune system due to the complementarity of the initiating antigens.. Complementary antigens stimulate synergistic toll-like receptors (TLR) and/or nucleotide-binding oligomerization receptors (NOD) resulting in up-regulation of cytokine production and further stimulation of the adaptive immune response. Because the immune responses (e.g., antibodies) mimic the initiating antigens, this synergistic activation of innate immunity becomes chronic. Additionally, TLR and NOD function are highly sensitive to sex hormones, some becoming up-regulated and some down-regulated in the presence of either testosterone or estrogens. This sensitivity explains how sex modifies susceptibility to autoimmune diseases. Genetic mutations in TLR, NOD and MHC further alter antigen presentation and the degree to which antigens stimulate an immune response explaining how genetics also modifies susceptibility. Finally, sex hormones also alter the host microbiome, which in turn modulates autoimmune disease risk by shaping the immunological nature of self and by mediating susceptibility to microbial infection. Moreover, it appears that the microbiome camouflages itself from the immune system by mimicking the host antigenic repertoire; the mimicry between the antigens of the microbiome and the host results in selective attacks on microbiome constituents concomitant with any autoimmune attack on host tissues. This antigenic complementarity theory thereby integrates all major elements known to affect, or be affected by, autoimmune diseases and provides a set of testable implications.

Post-viral fatigue syndrome (PVFS) encompasses a wide range of complex neuroimmune disorders of unknown cause characterized by disabling post-exertional fatigue, myalgia and joint pain, cognitive impairments, unrefreshing sleep, autonomic dysfunction, and neuropsychiatric symptoms. It includes myalgic encephalomyelitis, also known as chronic fatigue syndrome (ME/CFS), fibromyalgia (FM), and more recently post-COVID-19 condition (Long COVID). To date, there are no definitive clinical case criteria and no FDA-approved pharmacological therapies for PVFS. Given the current lack of effective treatments, there is a need to develop novel therapeutic strategies for these disorders. Mitochondria, the cellular organelles responsible for tissue energy production, have recently garnered attention in research into PVFS due to their crucial role in cellular bioenergetic metabolism in these conditions. Accumulating literature has identified a link between mitochondrial dysfunction and low-grade systemic inflammation in ME/CFS, FM, and Long COVID. To address this issue, this article aimed to critically review the evidence relating to mitochondrial dysfunction in the pathogenesis of these disorders; in particular, to evaluate the effectiveness of coenzyme Q10 supplementation on chronic fatigue and pain symptoms as a novel therapeutic strategy for the treatment of PVFS.

Low-temperature processed ITO thin films offer the potential of overcoming the doping limit by suppressing the equilibrium of compensating oxygen interstitial defects. To elucidate this potential, electrical properties of Sn-doped In₂O₃ (ITO) thin films are studied in dependence on film thickness. In-operando conductivity and Hall effect measurements during annealing of room temperature deposited films with different film thickness in different environments allow to discriminate between the effects of crystallization, grain growth, donor activation and oxygen diffusion on carrier concentrations and mobilities. At 200 °C, a control of carrier concentration by oxygen incorporation or extraction is only possible for very thin films. The electrical properties of thicker films deposited at room temperature are mostly affected by grain size. The remaining diffusivity of compensating oxygen defects at 200 °C is sufficient to screen the high Fermi level induced by deposition of Al₂O₃ using atomic layer deposition (ALD), which disables the use of defect modulation doping at this temperature. The results indicate that achieving higher carrier concentrations requires a control of the oxygen pressure during deposition in combination with seed layers to enhance crystallinity or the use of near room temperature ALD.

Ongoing COVID-19 Pandemic has resulted into massive damage to various platforms of global economy which has caused disruption to human livelihood. Natural Language Processing has been extensively used in different organizations to categorize sentiments, perform recommendation, summarizing information and topic modelling. This research aims to understand the non-medical impact of COVID-19 on global economy by leveraging the natural language processing methodology. This methodology comprises of text classification which includes topic modelling on unstructured COVID-19 media articles dataset provided by Anacode. Like other Natural Language Processing algorithms, Latent Dirichlet allocation (LDA) and Non-negative matrix factorization (NMF) has been proposed to classify the media articles dataset in order to analyze COVID-19 pandemic impacts in the different sectors of global economy. Model Accuracy was examined based on the coherence and perplexity score which came out to be 0.51 and -10.90 using LDA algorithm. Both the LDA and NMF algorithm identified similar prevalent topics that was impacted by COVID-19 pandemic in multiple sectors of economy. Through intertopic distance map visualization produced by LDA algorithm, it can be reciprocated that general industries which includes children schooling, parental care, and family gatherings had the major impact followed by business sector and the financial industry.

Connexins are tetraspan transmembrane proteins that form gap junctions and facilitate direct intercellular communication, a critical feature for the development, function and homeostasis of tissues and organs. In addition, a growing number of gap junction-independent functions are being ascribed to these proteins. The connexin gene family is under extensive regulation at the transcriptional and post-transcriptional level, and undergoes numerous modifications at the protein level, including phosphorylation, which ultimately affects their trafficking, stability and function. Here, we summarize these key regulatory events, with emphasis on how these affect their multi-functionality in health and disease.

If the world’s countries seriously tackle the climate targets agreed in Paris, their citizens are likely to experience substantial changes in production, consumption and employment. We present a long-run post-Keynesian model for studying the potential implications of a major transition on macroeconomic stability and employment. It is a demand-led model in which firms have considerable but not absolute freedom to administer prices, while household consumption exhibits inertia. Firms continually seek input-saving technological improvements that, in the aggregate, tie technological progress to firms' cost structure. Together with firm pricing strategies and wage setting, the productivities of different inputs determine the functional income distribution. Saving and investment, and production and purchase of consumption goods, are undertaken by different economic actors, driven by income and capacity utilization, with the possibility that productive capacity exceeds, or falls short of, effective demand. The model produces business cycles and long waves driven by technological change. We present results for a “downshifting” scenario in which households voluntarily withdraw labor and discuss the implications of downshifting for stability, growth, and employment. We contrast the downshifting scenario with ones in which households reduce consumption without withdrawing from the labor pool.

For centuries, crop plants have represented the basis of the daily human diet. Among them, cereals and legumes, accumulating oils, proteins and carbohydrates in their seeds, distinctly dominate modern agronomic practice. Indeed, these plants play an essential role in the food industry and fuel production. Therefore, the seeds of crop plants are intensively studied by food chemists, biologists, biochemists, and nutritional physiologists. Accordingly, not only seed development and germination, but also age- and stress-related alterations in seed vigor, longevity, nutritional value and safety can be addressed by a broad panel of analytical, biochemical and physiological methods. Currently, functional genomics is one of the most powerful tools, giving direct access to characteristic metabolic changes, accompanying plant development, senescence and response to biotic or environmental stress. Among individual methodological platforms, proteomics represents one of the most effective ones, giving access to cellular metabolism at the level of proteins. Here we discuss the main methodological approaches employed by seed proteomics in the context of physiological changes related to seed development, ageing and response to environmental stress.

Nowadays, and with the mechanization of life, speech processing has become so crucial for the interaction between humans and machines. Deep neural networks require a database with enough data for training. The more features are extracted from the speech signal, the more samples are needed to train these networks. Adequate training of these networks can be ensured when there is access to sufficient and varied data in each class. If there is not enough data; it is possible to use data augmentation methods to obtain a database with enough samples. One of the obstacles to developing speech emotion recognition systems is the Data sparsity problem in each class for neural network training. The current study has focused on making a cycle generative adversarial network for data augmentation in a system for speech emotion recognition. For each of the five emotions employed, an adversarial generating network is designed to generate data that is very similar to the main data in that class, as well as differentiate the emotions of the other classes. These networks are taught in an adversarial way to produce feature vectors like each class in the space of the main feature, and then they add to the training sets existing in the database to train the classifier network. Instead of using the common cross-entropy error to train generative adversarial networks and to remove the vanishing gradient problem, Wasserstein Divergence has been used to produce high-quality artificial samples. The suggested network has been tested to be applied for speech emotion recognition using EMODB as training, testing, and evaluating sets, and the quality of artificial data evaluated using two Support Vector Machine (SVM) and Deep Neural Network (DNN) classifiers. Moreover, it has been revealed that extracting and reproducing high-level features from acoustic features, speech emotion recognition with separating five primary emotions has been done with acceptable accuracy.

Endometriosis is an oestrogen-dependant reproductive disease, with genetic, vascular, neural, inflammatory and auto-immune characteristics. There are many theories about the etiology of endometriosis, however, all of these theories have limitations and do not explain all the locations that endometriosis is found or types of patients with endometriosis. The objective of this paper is to postulate the hypothesis that endometriosis is caused by Maternal Microchimerism, the presence of maternal cells in the fetus. A literature review was conducted, analysing the characteristics, current etiological theories of endometriosis, theory limitations and relationship of maternal microchimerism and endometriosis. At time of writing, there was no literature on maternal microchimerism and endometriosis. These results suggest that Maternal Microchimerism could be a cause of endometriosis. This could account for the genetic and auto-immune characteristics seen in people with endometriosis, inducing a micro-environment for vascular, neural and epigenetic changes. This could also account for account for endometriosis seen in non-menstruating patients, such as men, fetuses and post-menopausal women and endometriosis found in non-peritoneal locations. If the hypothesis of Maternal Microchimerism is correct, endometriosis could be considered a pregnancy-related disease that could affect all humans, changing the accepted demographics of patients and potentially new diagnostic techniques and treatment options for patients with endometriosis. Further studies are needed to test this hypothesis.

The principal aim of a spectral observer is twofold: the reconstruction of a signal of time via state estimation and the decomposition of such a signal into the frequencies that make it up. A spectral observer can be catalogued as an online algorithm for time-frequency analysis because is a method that can compute on the fly the Fourier transform (FT) of a signal, without having the entire signal available from the start. In this regard, this paper presents a novel spectral observer with an adjustable constant gain for reconstructing a given signal by means of the recursive identification of the coefficients of a Fourier series. The reconstruction or estimation of a signal in the context of this work means to find the coefficients of a linear combination of sines a cosines that fits a signal such that it can be reproduced. The design procedure of the spectral observer is presented along with the following applications: (1) the reconstruction of a simple periodical signal, (2) the approximation of both a square and a triangular signal, (3) the edge detection in signals by using the Fourier coefficients, (4) the fitting of the historical Bitcoin market data from 2014-12-01 to 2018-01-08 and (5) the estimation of a input force acting upon a Duffing oscillator. To round out this paper, we present a detailed discussion about the results of the applications as well as a comparative analysis of the proposed spectral observer vis-à-vis the Short Time Fourier Transform (STFT), which is a well-known method for time-frequency analysis.

Abstract: Understanding social media networks and group interactions are crucial to the ad-vancement of linguistic and cultural behaviour. This includes the manner in which people ac-cessed advice on health, especially during the global lockdown periods. Some people turned to social media to access information on health where most activities were curtailed with isolation rules, especially for older generations. Facebook public pages, groups and verified profiles, using "senior citizen health", "older generations", and "healthy living" keywords were analysed over a 12-month period to analyse the engagement promoting good mental health. CrowdTangle was used to source English language status updates, photo and video sharing information which resulted in an initial 116,321 posts and 6,462,065 interactions Data analysis and visualisation were used to explore large datasets, including natural language processing for “Message” content discovery, word frequency and correlational analysis and co-word clustering. Preliminary results indicate strong links to healthy aging information shared on social media which showed correla-tions to global daily confirmed case and daily death totals. The results can be used to identify public concerns early on and address mental health issues in the senior generation on Facebook.

This paper presents a novel micro-segmented genetic algorithm (μsGA) to identify the best solution for the locomotion of a quadruped robot designed on a rectangular ABS plastic platform. We compare our algorithm with three similar algorithms found in the specialized literature: a standard genetic algorithm (GA), a micro-genetic algorithm (μGA), and a micro artificial immune system (μAIS). The quadruped robot prototype guarantees the same conditions for each test. The platform was developed using 3D printing for the structure and can accommodate the mechanisms, sensors, servomechanisms as actuators. It also has an internal battery and a multicore embedded system (mES) to process and control the robot locomotion. This research proposes a μsGA that segments the individual into specific bytes. μGA techniques are applied to each segment to reduce the processing time; the same benefits as the GA are obtained, while the use of a computer and the high computational resources characteristic of the GA are avoided. This is the reason why some research in robot locomotion is limited to simulation. The results show that the performance of μsGA is better than the three other algorithms (GA, μGA and AIS). The processing time was reduced using a mES architecture that enables parallel processing, meaning that the requirements for resources and memory were reduced. This research solves the problem of continuous locomotion of a quadruped robot, and gives a feasible solution with real performance parameters using a μsGA bio-micro algorithm and a mES architecture.

The nature of contemporary Spatial Data Infrastructures lies in the provision of geospatial information in an on-demand fashion. Though recent applications identified the need to react to real-time information in a time-critical way. In particular, research efforts in the field of geospatial Internet of Things have identified substantial gaps in this context, ranging from a lack of standardization for event-based architectures to the meaningful handling of real-time information as ''events''. This manuscript presents work in the field of Event-driven Spatial Data Infrastructures with a particular focus on sensor networks and the devices capturing in-situ measurements. The current landscape of Spatial Data Infrastructures is outlined and used as the basis for identifying existing gaps that retain certain geospatial applications from using real-time information. We present a selection of approaches - developed in different research projects - to overcome these gaps. Being designed for specific application domains, these approaches share commonalities as well as orthogonal solutions and can build the foundation of an overall Event-driven Spatial Data Infrastructure.

Developing efficient Artificial Intelligence (AI)-enabled system to substitute human role in non-destructive testing is an emerging topic of considerable interest. In this study, we propose a novel impact-echo analysis system using online machine learning, which aims at achieving near-human performance for assessment of concrete structures. Current computerized impact-echo systems commonly employ lab-scale data to validate the models. In practice, however, the echo patterns can be far more complicated due to varying geometric shapes and materials of structures. To deal with a large variety of unseen data, we propose a sequential treatment for echo characterization. More specifically, the proposed system can adaptively update itself to approaching human performance in impact-echo data interpretation. To this end, a two-stage framework has been introduced, including echo feature extraction and the model updating scheme. Various state-of-the-art online learning algorithms have been reviewed and evaluated for the task. To conduct experimental validation, we collected 10,940 echo instances from multiple inspection sites with each sample had been annotated by human experts with healthy/defective condition labels. The results demonstrated that the proposed scheme achieved favorable echo pattern classification accuracy with high efficiency and low computation load.

The sentiment of a word varies based on its context of usage: the words used around it and the part-of-speech it is used as. This paper proposes a technique to suggest the sentiment of a word by combining its part-of-speech and the semantic similarities of its co-occurrences with both context-specific and pre-trained embeddings to achieve powerful and fast results. A study was conducted across domains and sub-domains to measure variance of sentiment by switching domains and switching context within the same domain. Re-scoring a commonly used polarity lexicon showed that 10% of words changed scores while switching domains and 8% changed scores within domains while switching context. Part of Speech analysis on 65,353 commonly used sentiment lexicons showed that 81% of sentiment bearing (non-neutral) lexicons were of the tags NN (Common Noun), JJ (Adjective) or NNS (Proper Noun).

Abstract: COVID-19 Long Haulers, an estimated 3% to 12% of people infected globally with coronavirus having latter devasting symptoms 12 weeks after the initial infection is on the rise. We conducted a collaborative study with the long covid patient organization in Greece in order to estimate the prevalence, symptoms and problems that adult long haulers experience and then propose a management plan for these patients. Symptoms were obtained from 208 patients using unstructured qualitative free text entries in an anonymized online questionnaire. The majority of respondents (68.8%) were not hospitalized and had been diagnosed more than six months ago with lingering symptoms (66,8%). Eighteen different symptoms (fatigue, tachycardia, shortness of breath, parosmia etc) were mentioned in both hospitalized and community patients. Interestingly, patients with initial mild symptoms suffer from the same persistent symptoms as those who were hospitalized. Awareness of long covid sequelae seems to be low even among medical doctors. Treatment options incorporating targeted rehabilitation programs are either not available or still excluded from the management plan of long covid patients. Since long COVID is a multi-systemic entity, we propose a holistic interventional approach using a multidisciplinary medical team in order to securely and effectively diagnose and treat these specific patients. Academic and medical community must collaborate with long covid patients’ organizations so as to provide personalized medicine.

The emergence of the COVID-19 pandemic has propelled the use of alcohol-based hand sanitizers to the fore as a SARS-CoV-2 control measure. To be effective these products must comply with relevant quality parameters such as alcohol concentration, methanol limits and purity. The current study was designed to determine the quality of alcohol-based hand sanitizer products in the Nairobi metropolitan area. For this purpose, 74 commercially marketed samples were collected and subjected to analysis by gas chromatography. Only three samples (4.1%) complied with the regulatory specifications for alcohol content, methanol limits and pH. Five samples (6.8%) complied with the specification for alcohol content but did not meet methanol or pH limits. A total of 44 (59.5%) samples had methanol levels that exceeded threshold limits. Eleven samples (14.9%) were found with methanol substitution (i.e., methanol, instead of ethanol or isopropanol, was the main alcohol component). The results show that users of alcohol-based hand sanitizers are being exposed to substandard and falsified products which in addition to being non-efficacious pose harm due to unacceptable levels of toxic impurities. Regular, routine post-market surveillance is needed to prevent such products from reaching the market.

Backward electromagnetic waves are extraordinary waves with contra-directed phase velocity and energy flux. Unusual properties of the coherent nonlinear optical coupling of the phase-matched ordinary and backward electromagnetic waves with contra-directed energy fluxes are described which enable greatly-enhanced frequency and propagation direction conversion, parametrical amplification, as well as control of shape of the light pulses. Extraordinary transient processes that emerge in such metamaterials in pulsed regimes are described. The results of the numerical simulation of particular plasmonic metamaterials with hyperbolic dispersion are presented, which prove the possibility to match phases of such coupled guided ordinary and backward electromagnetic waves. Particular properties of the outlined processes in the proposed metamaterial are demonstrated through numerical simulations. Potential applications include ultra-miniature amplifiers, frequency changing reflectors, modulators, pulse shapers, and remotely actuated sensors.

Sensors available on mobile devices allow the automatic identification of Activities of Daily Living (ADL). This paper describes an approach for the creation of a framework for the identification of ADL, taking in account several concepts, including data acquisition, data processing, data fusion, pattern recognition, and machine learning. These concepts can be mapped in a module of the framework, including the use and creation of several algorithms. For the development of a framework that works in several conditions, e.g., without Internet connection, these algorithms should take in account the hardware and software limitations of the mobile devices to run all main tasks locally. The main purpose of this paper is related to the presentation the sensors, algorithms, and architecture of the proposed approach.

Understanding the role of risk regions identified by genome-wide association studies (GWAS) have made considerable progress lately referred to the post-GWAS era. Annotation of the genes to the GWAS and fine-mapped functional variants, and understanding their biological pathway/gene networks enrichments is expected to give rich dividend by elucidating the mechanisms underlying prostate cancer. To this aim, we compiled and analysed currently available post-GWAS data on genes identified through GWAS and validated through experimental studies in prostate cancer to investigate molecular biological pathways enriched for assigned functional genes. The results highlight some well-known cancer signalling pathways, antigen presentation process and enrichment in cell growth and development gene networks suggesting prostate cancer may result from the accumulation of the effects of functional variants through multiple gene sets and pathways. The upstream analysis identifies critical transcription factors, which supplements the results regarding the regulatory role of the post-GWAS genes. We also identified the common genes between post-GWAS and three well-annotated prostate cancer Oncomine data in patient samples in order to uncover possible main genes in prostate cancer development/progression. Post-GWAS generated knowledge of gene networks and pathways, if analysed further and targeted appropriately, will have an important impact on clinical management of the disease.

This article described the technology of determining earthquake epicenter with radar remote sensing on the example of Sentinel-1A/B. To determine the epicenter of the earthquake, the Earth's crust displacements were analyzed using radar remote sensing data obtained for the ascending and descending flight orbits. Coordinates of Earthquake epicenters were found according to line-of-sight displacement images via its maximum value. Displacement of the Earth's crust was obtained by processing in the GMTSAR package in the VirtualBox virtual machine of the Linux Ubuntu 16.04 operation system. Two earthquakes that occurred in 2020 were studied to determine the accuracy of finding epicenters using the ascending and descending orbits Sentinel-1A/B. These earthquakes occurred in Western Xizang, China, and Doganyol, Turkey. The maximum deviation from the officially registered epicenter coordinates was 15.38 km for Doganyol and 3.2 km for the Western Xizang earthquake. The negative displacement was 90 mm for Doganyol and 50 mm for Western Xizang.

Social media gives researchers an invaluable opportunity to gain insight into different facets of human life.Researchers put a great emphasis on categorizing the socioeconomic status (SES) of individuals to help predict various findings of interest. Forum uses, hashtags and so on are common tools of conversations grouping. On the other hand, crowdsourcing is a concept that involves gathering intelligence to group online user community based on common interest. This paper provides a mechanism to look at writings on social media and group them based on their academic background. We build upon earlier work where we analyzed online forum posts from various geographical regions in the USA and Canada and characterized the readability scores of such users. Specifically, we collected 1000 tweets from the members of the US Senate and computed the Flesch-Kincaid readability score for the Senators. Comparing the Senators’ tweets to the ones from average citizens, we note the following. 1) US Senators’ readability based on their tweets rate is much higher affirming the gap between the academic performance of US Senators and their average citizen, and 2) the immense difference among average citizen’s score compared to those of US Senators is attributed to the wide spectrum of academic attainment.

Historical maps constitute unique sources of retrospective geographic information. Recently, several map archives containing map series covering large spatial and temporal extents have been systematically scanned and made available to the public. The geographic information contained in such data archives allows extending geospatial analysis retrospectively beyond the era of digital cartography. However, given the large data volumes of such archives and the low graphical quality of older map sheets, the processes to extract geographic information need to be automated to the highest degree possible. In order to understand the salient characteristics, data quality variation, and potential challenges in large-scale information extraction tasks, preparatory analytical steps are required to efficiently assess spatio-temporal coverage, approximate map content, and spatial accuracy of such georeferenced map archives across different cartographic scales. Such preparatory steps are often neglected or ignored in the map processing literature but represent highly critical phases that lay the foundation for any subsequent computational analysis and recognition. In this contribution we demonstrate how such preparatory analyses can be conducted using classical analytical and cartographic techniques as well as visual-analytical data mining tools originating from machine learning and data science, exemplified for the United States Geological Survey topographic map and Sanborn fire insurance map archives.

The modern diet, which consists of food produced with high level of industrial processing, is associated with an increased risk of the development of lifestyle diseases. Current nutritional science is, however, focused on chemical composition of food, and not on the type and degree of processing used during the food production. Here, we take a fresh perspective on the relationship between the extent and type of food processing, and evaluate its impact on consumer health. We argue that the preference for consumption of minimally processed foods, and restriction of ultra-processed foods should be an essential part of a healthy lifestyle, disease prevention, and even treatment. We also present a simple, user-friendly consumer guide, which is intended to be used as a practical, ready-to-go identifier of ultra-processed foods.

The deep neural network - BERT model (Bidirectional Encoder Representations from Transformers) and the stocks cumulative abnormal return is used in this article to analyze the sentiment of financial texts. The proposed approach, unlike those used so far, does not require the creation of dictionaries, takes into account the broad context of words and their meaning in financial texts, eliminates the problem of ambiguity of words in various contexts, does not require manual labelling of data and is free from the subjective assessment of the researcher. The sentiment of financial texts in the meaning presented in this paper is directly related to the market reaction to the information contained in these texts. For texts belonging to one of the two classes (positive or negative) with the highest probability the BERT model gives the results of predictions with a precision level of 62.38% for the positive class and 55% for the negative class. The results at this level can be used in event study, market efficiency research, investment strategy development or support of investment analysts using fundamental analysis.

Time recordings of impulse-type oscillation responses are short and highly transient. These characteristics may complicate the usage of classical spectral signal processing techniques for a) describing the dynamics and b) deriving discriminative features from the data. However, common model identification and validation techniques mostly rely on steady-state recordings, characteristic spectral properties and non-transient behavior. In this work, a recent method, which allows reconstructing differential equations from time series data, is extended for higher degrees of automation. With special focus on short and strongly damped oscillations, an optimization procedure is proposed that fine-tunes the reconstructed dynamical models with respect to model simplicity and error reduction. This framework is analyzed with particular focus on the amount of information available to the reconstruction, noise contamination and non-linearities contained in the time series input. Using the example of a mechanical oscillator, we illustrate how the optimized reconstruction method can be used to identify a suitable model and to extract features from uni-variate and multivariate time series recordings in an engineering-compliant environment. Moreover, the determined minimal models allow for identifying the qualitative nature of the underlying dynamical systems as well as testing for the degree and strength of non-linearity. The reconstructed differential equations would then be potentially available for classical numerical studies, such as bifurcation analysis. These results represent a physically interpretable enhancement of data-driven modeling approaches in structural dynamics.

Grading of rice grains has gain attentions due its requirement of quality assessment during import or export. Rice grain quality depends on milling operation, where rice hull is removed with a huller system followed by whitening operation. In such process, adjustment of rollers, control, and operation is important in terms of quality of milled rice. Especially, the basmati rice needed more quality assurance as it is not parboiled rice and exported globally with a high product value. In this present work, the basic problem of quality assessment in rice industry is addressed with digital image processing based technique. Machine vision and digital image processing provide an alternative with the automated, nondestructive, cost-effective, and fast approach as compared with traditional method which is done manually by human inspectors. A model of quality grade testing and identification is built based on morphological features using digital image processing and knowledge based adaptive neuro-fuzzy inference system (ANFIS). The qualities of rice kernels are determined with the help of shape descriptors and geometric features using the sample images of milled rice. The adopted technique has been tested on a sufficient number of training images of basmati rice grain. The proposed method gives a promising result in an evaluation of rice quality with 100% classification accuracy for broken and whole grain. The milling efficiency is also assessed using the ratio between head rice and broken rice percentage and it is 77.27% for the test sample. The overall results of the adopted methodology are promising in terms of classification accuracy and efficiency.

Crowdsourced Data (CSD) generated by citizens is becoming more popular as its potential utilisation in many applications is increasing due to its currency and availability. However, the quality of CSD, including its relevance, is often questioned as the data is not generated by professionals nor follows standard data collection procedures. The quality of CSD can be assessed according to a range of attributes including its relevance. Information relevance has been explored through using in Geographic Information Retrieval (GIR) techniques to identify relevant information. This research tested a relevance assessment approach for CSD by adapting relevance assessment techniques available in the GIR domain. The thematic and geographic relevance were assessed using the Term Frequency-Inverse Document Frequency (TF-IDF), Vector Space Model (VSM) and Natural Language Processing (NLP) techniques. The thematic and geographic specificities of the queries were calculated as 0.44 and 0.67 respectively, which indicates the queries used were more geographically specific than thematically specific. The Spearman's rho value of 0.62 indicated that the final ranked relevance lists showed reasonable agreement with a manually classified list and confirmed the potential of the approach for CSD relevance assessment for other possible crowdsourced data analysis.

Open pit mining is a common activity in the Yucatan peninsula for the extraction of limestone. This mining is known under the generic name of quarries, and regionally as sascaberas (sascab=white soil in Mayan language). These areas are characterized by the total removal of the natural vegetation cover and soil in order to have access to the calcareous material. The present study shows the composition and structure of the vegetation in five quarries after approximately ten years of abandonment, and the conserved vegetation near to each one of the quarries in southeastern Quintana Roo. Using a canonical correspondence analysis (CCA), the distribution of the species was determined in relation to the edaphic variables: soil depth, percentage of organic matter (OM), cationic exchange capacity (CEC), pH and texture. 26 families, 46 genera and 50 species were recorded in the quarries and 25 families, 45 genera and 47 species were recorded in the conserved areas. The dominant species in the quarries belong to the families Poaceae, Fabaceae, Rubiaceae and Anacardiaceae. The quarries with higher values of OM (1.63%), CEC (24.05 Cmol/kg), depth (11 cm) and sand percentage (31.33%) include the following species like Lysiloma latisiliquum, Metopium brownei and Bursera simaruba which are commonly found in secondary forests. On the other hand, quarries with lower values of OM (0.39%), CEC (16.58 Cmol/kg) and depth (5.02), and higher percentage of silt (42.44%) were dominated by herbaceous species belonging to the Poaceae family and by Borreria verticillata, which are typical in disturbed areas of southeastern Mexico. In all cases, the pH was slightly alkaline due to the content of calcium carbonate (CaCO3), characteristic of the soils of the region. The edaphic variables are significantly correlated with the development and distribution of vegetation, and with the structure of the communities.

The effects of transcranial magnetic stimulations (TMS) show that the human brain is impacted by some magnetic fields (EMFs). Moreover, after a delay, it produces potentials that reveal a subsequent processing of this impact. The human brain might also be sensitive to very weak magnetic fields of extremely low frequencies (vwEMFelf). Namely, to the vwEMelf produced by the brain of other persons when they process visual stimuli. In effect, two studies report that the event-related brain potentials (ERPs) that are evoked by presenting a picture to a participant can be modulated by simultaneously presenting a picture to a partner. To confirm it here, we followed most of the methods of these studies. We recorded the ERPs evoked by presenting, at each trial, the photograph of a face. Simultaneously and, most importantly, privately, we presented a partner with the same or with a different face photograph. ERPs of participants were found to depend on that sameness (p0.001), unbeknownst to them. These joint processing effects (JPEs), confirm a sensitivity of the human brain to the vwEMFelf produced by other brains.

The flow behavior of Fe-6.5wt.%Si alloys during hot compression was investigated at temperatures 650–950 °C and strain rates 0.01–10 s^-1. The results showed that the flow stress depended distinctly on the deformation temperatures and strain rates. The flow stress and work hardening rate increased with the decrease of temperature and the increase of strain rate. The activation energy under all the deformation conditions was calculated to be 410 kJ/mol. The constitutive equation with hyperbolic sine function and Zener–Hollomon parameter was developed. The peak stress, critical stress, and steady-state stress could be represented as σ=A+Bln(Z/A). Dynamic recrystallization occurred under the deformation conditions where the values of Z were lower than 10²⁰. Processing maps were established to optimize the processing parameters. The power dissipation efficiency decreased in the high temperature and low strain rate region, increased in the high temperature and high strain rate region, and remained unchanged in other regions with the increase of true strain. Furthermore, the unstable area expanded. The true strain of 0.7 was the optimum reduction according to the processing map. Based on the analysis of surface quality, microstructures, and ordered structures, the optimized processing parameters for the Fe-6.5wt.%Si alloys were the temperature and strain rate of higher than 900 °C and 0.01–10 s^-1, respectively, or 800–900 °C and lower than 0.4 s^-1, respectively.

Precision agriculture is a technology used by farmers to help food sustainability amidst growing population. One of the tools of precision agriculture is yield monitoring, which helps a farmer manage his production. Yield monitoring is usually done during harvest, however it could also be done early in the growing season. Early prediction of yield, specifically for fruit trees, aids the farmer in the marketing of their product and assists in managing production logistics such as labor requirement and storage needs. In this study, a machine vision system is developed to estimate fruit yield early in the season. The machine vision system uses a color camera to capture images of fruit trees during the full bloom period. An image segmentation algorithm based on an artificial neural network was developed to recognize and count the blossoms on the tree. The artificial neural network segmentation algorithm uses color information and position as input. The resulting correlation between the blossom count and the actual number of fruits on the tree shows the potential of this method to be used for early prediction of fruit yield.

Perception and motor interaction with physical surroundings can be analyzed by the changes in probability laws governing two possible outcomes of neuronal activity, namely the presence or absence of spikes (binary states). Perception and motor interaction with physical environment are accounted partly by the reduction in entropy within the probability distributions of binary states of neurons in distributed neural circuits, given the knowledge about the characteristics of stimuli in physical surroundings. This reduction in the total entropy of multiple pairs of circuits in networks, by an amount equal to the increase of mutual information among them, occurs as sensory information is processed successively from lower to higher cortical areas or between different areas at the same hierarchical level but belonging to different networks. The increase in mutual information is partly accounted by temporal coupling as well as synaptic connections as proposed by Bahmer and Gupta [1]. We propose that robust increases in mutual information, measuring the association between the characteristics of sensory inputs and neural circuits connectivity patterns, are partly responsible for perception and successful motor interactions with physical surroundings. It is also argued that perception from a sensory input is the result of networking of many circuits to a common circuit that primarily processes the given sensory input.

Land surface albedo determines the splitting of downwelling solar radiation into components which are either reflected back to the atmosphere or absorbed by the surface. Land surface albedo is an important variable for the climate community and therefore was defined by the Global Climate Observing System (GCOS) as an Essential Climate Variable (ECV). Within the scope of the Satellite Application Facility for Land Surface Analysis (LSA SAF) of EUMETSAT, a near-real time (NRT) daily albedo product was developed in the last decade from observations provided by the SEVIRI instrument on board the geostationary satellites of the Meteosat Second Generation (MSG) series. In this study we present a new collection of albedo satellite products based on the same satellite data. The MSG Ten-day ALbedo (MTAL) product incorporates MSG observations over 31 days with a frequency of NRT production of 10 days. The MTAL collection is more dedicated to climate analysis studies compared to the daily albedo that was initially designed for the weather prediction community. For this reason, a homogeneous reprocessing of MTAL was done in 2018 to generate a Climate Data Record (CDR). The resulting product is called MTAL-R and has been made available to the community in addition to the NRT version of the MTAL product which has been available for several years. The retrieval algorithm behind the MTAL products comprises three distinct modules: one for atmospheric correction, one for daily inversion of a semi-empirical model of the bidirectional reflectance distribution function, and one for monthly composition that also determines surface albedo values. In this study the MTAL-R CDR is compared to ground surface measurements and concomitant albedo products collected by sensors on-board polar-orbiting satellites (SPOT-VGT and MODIS). We show that MTAL-R meets the quality requirements if MODIS or SPOT-VGT are considered as reference. This work leads to 14 years of production of geostationary land surface albedo products with a guaranteed continuity in the LSA SAF for the future years with the forthcoming third generation of European geostationary satellites.

Aphasia is one of the most socially disabling post-stroke deficits. Although traditional therapies have been shown to induce adequate clinical improvement, aphasic symptoms often persist. Therefore, new rehabilitation techniques which act as a substitute or as an adjunct to traditional approaches are urgently needed. The present review provides an overview of the efficacy and safety of the most innovative approaches which have been proposed over the last twenty years. First, we examined the effectiveness of the pharmacological approach, principally used as an adjunct to language therapy, reporting the mechanism of action of each single drug for the recovery of aphasia. Results are conflicting but promising. Secondly, we discussed the application of Virtual Reality (VR) which has been proved to be useful since it potentiates the ecological validity of the language therapy by using virtual contexts which simulate real-life everyday contexts. Finally, we focused on the use of Transcranial Direct Current Stimulation (tDCS), both discussing its applications at the cortical level and highlighting a new perspective, which considers the possibility to extend the use of tDCS over the motor regions. Although the review revels an extraordinary variability among the different studies, substantial agreement has been reached on some general principles, such as the necessity to consider tDCS only as an adjunct to traditional language therapy.

As shown earlier, the algorithmic complexity, like Shannon information and Boltzmann entropy, tends to increase in accordance with the general law of complification. However, the algorithmic complexity of most material systems does not reach its maximum, i.e. chaotic state, due to the various laws of nature that create certain structures. The complexity of such structures is very different from the algorithmic complexity, and we intuitively feel that its maximal value should be somewhere between order and chaos. I propose a formula for calculation such structural complexity, which can be called - structuredness. The structuredness of any material system is determined by structures of three main types: stable, dissipative, and post-dissipative. The latter are defined as stable structures created by dissipative ones, directly or indirectly. Post-dissipative structures, as well as stable, can exist for an unlimited time, but at the micro level only, without energy influx. The appearance of such structures leads to the “ratchet” process, which determines the structuregenesis in non-living and, especially, in living systems. This process allows systems with post-dissipative structures to develop in the direction of maximum structuring due to the gradual accumulation of these structures, even when such structuring contradicts the general law of complification.

The main purpose of bovine colostrum, being the milk secreted by a cow after giving birth, is to transfer passive immunity to the calf. The calves have an immature immune system as they lack immunoglobulins (Igs). Subsequently, the supply of good quality bovine colostrum is required. The quality of colostrum is classified by low bacterial counts and adequate Ig concentrations. Bacterial contamination can contain a variety of human pathogens or high counts of spoilage bacteria, which becomes more challenging with emerging use of bovine colostrum as food and food supplements. There is also a growing risk for the spread of zoonotic diseases originating from bovines. For this reason, processing based on heat treatment or other feasible techniques are required. This review provides an overview of literature on the microbial quality of bovine colostrum and processing methods to improve its microbial quality and keep its nutritional values as food. The highlights of this review are: high quality colostrum is a valuable raw material in food products and supplements, the microbial safety of bovine colostrum is increased using appropriate processing-suitable effective heat treatment, which does not destroy the high nutrition value of colostrum, the heat treatment processes are cost-effective compared to other methods, and heat treatment can be performed in both small- and large-scale production.

Sustainable agriculture is essential to provide food security for a growing world population without further sacrificing the integrity of the environment. To make progress towards agricultural sustainability we must consider ecological and socioeconomic processes within the agricultural socio-ecosystem and involve stakeholders in the research process. We propose an innovative experimental approach for examining how natural regulation of ecosystems may provide an alternative to increasing external inputs in agriculture while improving the socio-economic welfare of farmers. These “social-ecological experiments” go further to participatory action research by not only involving stakeholders in the research process but also by manipulating simultaneously socioeconomic and ecological processes under real field conditions to give a faster route to sustainability. Social-ecological experiments are undertaken in real field conditions, explicitly involving stakeholders, and help untangle the drivers of social-ecological dynamics under various land management and farming practices. Social-ecological experiments are distinct from adaptive management and scenario-planning approaches as they highlight the interactions between ecological and social processes, manipulate the social and ecological processes shaping the system and show causal links between patterns and processes. As an example, we describe a social-ecological experiment for reducing herbicide use. Social-ecological experiments offer great opportunities for increasing stakeholders’ acceptance of environmental policies implemented through adaptive management. These experiments may help to identify management practices that optimize multiple objectives, deliver a portfolio of ecosystem services and satisfy key stakeholders.

The detection of seismic events at regional and teleseismic distances is critical to Nuclear Treaty Monitoring. Traditionally, detecting regional and teleseismic events has required the use of an expensive multi-instrument seismic array; however in this work, we present DeepPick, a novel seismic detection algorithm capable of array-like performance from a single trace. We achieve this directly, by training our single-trace detector against labeled events from an array catalog, and by utilizing a deep temporal convolutional neural network. The training data consists of all arrivals in the International Seismological Centre Catalog for seven seismic arrays over a five year window from 1 Jan 2010 to 1 Jan 2015, yielding a total training set of 608,362 detections. The test set consists of the same seven arrays over a one year window from 1 Jan 2015 to 1 Jan 2016. We report our results by training the algorithm on six of the arrays and testing it on the seventh, so as to demonstrate the transportability and generalization of the technique to new stations. Detection performance against this test set is outstanding. Fixing a type-I error rate of 1%, the algorithm achieves an overall recall rate of 73% on the 141,095 array beam picks in the test set, yielding 102,394 correct detections. This is more than 4 times the 23,259 detections found in the analyst-reviewed single-trace catalogs over the same period, and represents an 8dB improvement in detector sensitivity over current methods. These results demonstrate the potential of our algorithm to significantly enhance the effectiveness of the global treaty monitoring network.

Three patients with multi-year histories of relapsing and remitting Lyme disease and associated co-infections despite extended antibiotic therapy were each given double dose dapsone combination therapy (DDD CT) for a total of 7-8 weeks. At the completion of therapy, all three patients major Lyme symptoms remained in remission for a period of 25-30 months. In conclusion, Double dose dapsone therapy could represent a novel and effective anti-infective strategy in chronic Lyme disease/PTLDS, especially in those individuals who have failed regular dose dapsone combination therapy (DDS CT) or standard antibiotic protocols. A randomized, blinded, placebo-controlled trial is warranted to evaluate the efficacy of DDD CT in those individuals with chronic Lyme disease/PTLDS.

In recent years, demand has been increasing for target detection and tracking from aerial imagery via drones using onboard powered sensors and devices. We propose a very effective method for this application based on a deep learning framework. A state-of-art embedded hardware system empowers small flying robots to carry out the real-time onboard computation necessary for object tracking. Two types of embedded modules were developed: one is designed using a Jetson TX or AGX Xavier, and the other is based on an Intel Neural Compute Stick. These are suitable for real-time onboard computing power on small flying drones with limited space. A comparative analysis of current state-of-art deep-learning-based multi-object detection algorithms was carried out utilizing the designated GPU-based embedded computing modules to obtain detailed metric data about frame rates as well as the computation power. We also introduce an effective target tracking approach for moving objects. The algorithm for tracking moving objects is based on the extension of simple online and real-time tracking. It was developed by integrating a deep-learning-based association metric approach (Deep SORT), which uses a hypothesis tracking methodology with Kalman filtering and a deep-learning-based association metric. In addition, a guidance system that tracks the target position using a GPU-based algorithm is introduced. Finally, we demonstrate the effectiveness of the proposed algorithms by real-time experiments with a small multi-rotor drone.

Automated Electrocardiogram (ECG) processing is an important technique which helps in identifying abnormalities in the heart before any formal diagnosis. This research presents a real-time and lightweight R-assisted feature extraction algorithm and a heartbeat classification scheme which achieves highly accurate abnormality detection. In the proposed algorithm, we extract fifteen features from each heartbeat taken from raw Lead-II ECG signals. The features carry medically valuable information such as locations, amplitude and energy of ECG waves (P, Q, R, S, T waves) which are then used for detection of any abnormality that might be present in the heartbeat using various classification algorithms. We have used four popular databases from Physionet and extracted ten thousand ECG signals from each for training the models and benchmarking results. Four classification models i.e. Naïve Bays, k-Nearest Neighbor, Neural Network, Decision Tree were used for abnormality detection validating the efficiency of the system.

We employ the solution concept of ex post Nash equilibrium to predict the interaction of a finite number of agents competing in a finite number of basic games simultaneously. The competition is called a multi-game. For each agent, a specific weight, considered as private information, is allocated to each basic game representing its investment in that game and the utility of each agent for any strategy profile is the weighted sum, i.e., convex combination, of its utilities in the basic games. Multi-games can model decision making in multi-environments in a variety of circumstances, including decision making in multi-markets and decision making when there are both material and social utilities for agents as, we propose, in the Prisoner's Dilemma and the Trust Game. Given a set of pure Nash equilibria, one for each basic game in a multi-game, we construct a pure Bayesian Nash equilibrium for the multi-game. We then focus on the class of so-called uniform multi-games in which each agent is constrained to play in all games the same strategy from an action set consisting of a best response per game. Uniform multi-games are equivalent to multi-dimensional Bayesian games where the type of each agent is a finite dimensional vector with non-negative components. A notion of pure type-regularity for uniform multi-games is developed and it is shown that a multi-game that is pure type-regular on the boundary of its type space has a pure ex post Nash equilibrium which is computed in constant time with respect to the number of the types and is independent of prior probability distributions. We then develop an algorithm, linear in the number of types of the agents, which tests if a multi-game is pure type-regular on the boundary of its type space in which case it returns a pure ex post Nash equilibrium for the multi-game.

The current urban environment is faced with the potential threat of frequent natural disasters, and the sustainable development of post-disaster community has become a global issue. As an intrinsic motivation influencing the social interaction and capital operation of community, social network is an important mechanism promoting such sustainable development. However, the difference in social network caused by different member structure, spatial arrangement and management mechanism of post-disaster communities in different reconstruction modes has influenced such sustainable development process. Therefore, reasonable selection of reconstruction mode is crucial. This paper applied analytic hierarchy process to comprehensively measure and compare the social network strength in post-disaster communities in the four reconstruction modes adopted by the government of China, i.e. unified planning and unified construction, unified planning and independent construction, in situ reconstruction and relocation resettlement, with communities after the 2008 Sichuan earthquake in Chengdu, China as study objects from the perspective of three social relations (the relations between residents and residents, residents and managers, and residents and servers). The results showed that strong connections are generally presented in the social network of post-disaster communities in unified planning modes, that the strength is significantly higher than that of those in non-unified planning modes, and that the strength of UPIC communities is the highest. Meanwhile, government intervention, residents’ free participation and market operation are positively correlated to government trust, community interaction and community service respectively. The positive impact of government intervention is the most significant, but it has a peak value. No government management and excessive government intervention will exert negative impacts. The coordination of government, society and market is the key contents of post-disaster community reconstruction. The reconstruction modes based on “government leadership, resident participation and market operation” may become a feasible path for such sustainable development.

This paper has two major parts. In the first part histogram equalization for the image enhancement was implemented without using the built-in function in MATLAB. Here, at first, a color image of a rat was chosen and the image was transformed into a grayscale image. After this conversion, histogram equalization was implemented on the grayscale image. Later on, in the same image for each RGB channel, histogram equalization was implemented to observe the effect of histogram equalization on each channel. In the end, the histogram equalization was implemented to this specific color image of a rat. In the second part, for the grayscale image in part 1, the desired histogram of another colored image of a rat was introduced and histogram specification was implemented on the original colored image.

Recommender systems have been applied in a wide range of domains such as e-commerce, media, banking, and utilities. This kind of system provides personalized suggestions based on large amounts of data in order to increase user satisfaction. These suggestions help client select products, while organizations can increase the consumption of a product. In the case of social data, sentiment analysis can help gain better understanding of a user’s attitudes, opinions and emotions, which is beneficial to integrate in recommender systems for achieving higher recommendation reliability. On the one hand, this information can be used to complement explicit ratings given to products by users. On the other hand, sentiment analysis of items that can be derived from online news services, blogs, social media or even from the recommender systems themselves is seen as capable of providing better recommendations to users. In this study, we present and evaluate a recommendation approach that integrates sentiment analysis into collaborative filtering methods. The recommender system proposal is based on an adaptive architecture, which includes improved techniques for feature extraction and deep learning models based on sentiment analysis. The results of the empirical study performed with two popular datasets show that sentiment–based deep learning models and collaborative filtering methods can significantly improve the recommender system’s performance.

Neutron computed tomography (nCT) has been established at many major neutron sources worldwide, using high-end equipment requiring major investment and development. Many older and smaller reactors would be capable of doing nCT as well, but cannot afford the investment before feasibility is proven. We have developed a compact low-cost but high-quality detection system using a new cooled CMOS camera that can either be fully integrated into a sophisticated setup, or used with a rudimentary CT control and motion system to quickly evaluate feasibility of neutron CT at a given beam line facility. Exchanging the scintillation screen makes it feasible for X-rays as well, even for visible light (and transparent samples) using a matte screen. The control system uses a hack to combine motion control with existing imaging software so it can be used to test several dozen different cameras without writing specific drivers. Freeware software can do reconstruction and 3D imaging.

The violation of privacy, others people or personal, is a very current problem, which concerns not only on the web but also in private life. In the years 1990 it was expected that nowadays, that any routine operation was carried out "manually", and it would be performed through mobile phones or personal computers. The problem pertains the distribution network that allows to share and bring together information and as result the network becomes unsafe, if subjected to attacks. Nowaday we put personal information on web because otherwise we are seen as “weak”. This work aims to measure and analyze how much information are shared by users of a pre-established social network and it is carried out through a set of algorithms techniques of machine learning.

The processed tomato is one of the major food products of Italy and characterized today many Italian Regions, in northern and southern Italy, even though the firms in the industry have had difficulties, in recent years, due to an increase in the cost of raw materials. Furthermore, tomato processing firms are often characterized by significant investment in fixed assets and working capital, with an consequent increase in equity and/or debt financing, with consequent increasing of the risk of bankruptcy, as happens to many firms in the sector in recent years. The aim of the research is than to analyze the risk of bankruptcy of tomato processing companies applying financial ratios. To achieve this goal, the research consider the annual data of a sample of 17 tomato processing firms operating in the Inter Regional Interprofessional Organization “OI Pomodoro da Industria Nord Italia”; firms’ data are divided into still-active (not-distressed) and failed (distressed) firms, with the aim of analyzing the differences between financial data and management practices of the two groups of firms to evaluate the sustainability of the business cycle. The company data suggest that larger firms, with adequate financial structure, have been able to withstand the tomato market crisis in recent years, where distressed firms are on average smaller and suffer from a higher recourse to debt capital with lower profit margins than not-distressed firms. The research could then be applied by entrepreneurs, managers, bankers and public operator to define good practices of management to be achieved and measured with financial ratios even in order reduce the risk of distress for firms operating in tomato sector.

The dominance of social media has added to the channels of bullying to perpetrators. Unfortunately, cyberbullying (CB) is the most prevalent phenomenon in today’s cyber world and is a severe threat to the mental and physical health of citizens. This opens the need to develop a robust system to prevent bullying content from online forums, blogs, and social media platforms to manage the impact in our society. Several machine learning (ML) algorithms have been proposed for this purpose. However, their performances are not consistent due to high class imbalance issue and generalisation. In recent years, large language models (LLM) like BERT and RoBERTa have achieved state of the art (SOTA) results in several natural language processing (NLP) tasks. Unfortunately, the LLMs have not been applied extensively. In our paper, we explored the use of these models for cyberbullying (CB) detection. We have prepared a new dataset (D2) from existing studies (Formspring and Twitter). Our experimental results for dataset D1 and D2 showed RoBERTa outperformed other models.

Despite the development of technologies, modern methods of disposal of end-of-life tires most often represent either the incineration in cement kilns or the destruction of tires in special landfills, showing a lack of sustainable recycling of this valuable material. The fundamental role of recycling is evident, and the development of high-efficiency processes represents a priority for the European market. Therefore, investigation of end-of-life rubber processing methods is of high importance for manufacturers and recyclers of rubber materials. In this paper, methods of processing of end-of-life tires are reviewed in order to obtain rubber crumb, which can later be used in the production of new industrial rubber goods and composite The processes of separation end-of-life tire into fractions by type of materials using mechanical processing methods along with mechanochemical and mechanical processes of processing the materials of used tires in order to obtain crumb rubber of various fractions and chemical reactivity are considered.

Numerous goods and services are now offered through online platforms due to the recent growth of online transactions like e-commerce. Users have trouble locating the product that best suits them from the numerous products available in online shopping. Many studies in deep learning-based recommender systems (RSs) have focused on the intricate relationships between the attributes of users and items. Deep learning techniques have used consumer or item-related traits to improve the quality of personalized recommender systems in many areas, such as tourism, news, and e-commerce. Various companies, primarily e-commerce, utilize sentiment analysis to enhance product quality and effectively navigate today's business environment. Customer feedback regarding a product is gathered through sentiment analysis, which uses contextual data to split it into separate polarities. The explosive rise of the e-commerce industry has resulted in a large body of literature on e-commerce from different perspectives. Researchers have made an effort to categorize the recommended future possibilities for e-commerce study as the field has grown. There are several challenges in e-commerce, such as fake reviews, frequency of user reviews, advertisement click fraud, and code-mixing. In this review, we introduce an overview of the preliminary design for e-commerce. Second, the concept of deep learning, e-commerce, and sentiment analysis are discussed. Third, we represent different versions of the commercial dataset. Finally, we explain various difficulties facing RS and future research directions.

Software engineers from all over the world solve independently a lot of similar problems. In this condition the problem of code or even better architecture reusing becomes an issue of the day. In this paper two phase approach to determining the functional and structural likenesses of software projects is proposed. This approach combines two methods of artificial intelligence: natural language processing techniques with a novel method for comparing software projects based on ontological representation of their architecture automatically obtained from the projects source code. Additionally several similarity metrics are proposed to estimate similarity between projects.

The long-term laboratory aspects of the effects of COVID-19 on liver function are still not well understood. Therefore, this study aimed to evaluate the hepatic clinical-laboratory profile of patients with up to 20 months of long-term COVID-19. A total of 243 patients of both sexes aged 18 years or older hospitalised in the acute phase of COVID-19 were included in this study. Liver function analysis was performed. Changes were identified in the mean levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST), lactate dehydrogenase (LDH), gamma-glutamyl transferase (GGT), and ferritin. Inflammatory markers such as ferritin > 300 U/L were observed in the group that presented more changes in liver function markers (ALT, AST, and GGT). Age ≥ 60 years, male sex, AST > 25 U/L, and GGT ≥ 50 or 32 U/L were associated with ALT > 29 U/L. There was a correlation between ALT and AST, LDH, GGT, and ferritin. Our findings suggest that ALT and AST levels may be elevated in patients with long-term COVID, especially in those hospitalised in the acute phase. In addition, ALT > 29 U/L was associated with other markers of liver injury, such as LDH, GGT, and ferritin.

Background and Objectives: Due to huge demand and availability of Banana, innovative cost effective method is necessary to promote and smoothen the banana production among farmers commercially mitigating the demand. Method and Materials: In this study, we feed cow dung mixture along with Urea, TSP, MoP, water to the distal part of rachis after cutting down male bud as soon as the female flowers matured into fruits (T1). The effect of this method was then compared with two control groups; one with the same strategy except fertilizer applied on root following ring method (C1, Positive control) and another was male flower untouched without applying fertilizer on rachis or root (C2, Negative control). Results and Conclusion: T1 showed more than double increase in length than controls. In the same way, in case of shape (diameter), T1 (0.46 cm) showed twice as better growth in the C1 (0.22 cm) and C2 (0.18 cm). Trend analysis showed the test group T1 curve is much steeper than the control groups suggesting faster growth rate than the other two. Finally, the cost of fertilizers for T1 per plant was estimated 0.091 USD while for positive control C1 it was 2.9 USD. This study shows an approach to be effective and economic comparing to traditional method of fertilizer application, which can be adapted as a new method of banana production.

Documenting cultural heritage by using artificial intelligence (AI) is crucial for preserving the memory of the past and a key point for future knowledge. However, modern AI technologies make use of statistical learners that lead to self-empiricist logic, which, unlike human minds, use learned non-symbolic representations. Nevertheless, it seems that it is not the right way to progress in AI. If we want to rely on AI for these tasks, it is essential to understand what lies behind these models. Among the ways to discover AI there are the senses and the intellect. We could consider AI as an intelligence. Intelligence has an essence, but we do not know whether it can be considered “something” or “someone”. Important issues in the analysis of AI concern the structure of symbols -operations with which the intellectual solution is carried out- and the search for strategic reference points, aspiring to create models with human-like intelligence. For many years, humans, seeing language as innate, have carried out symbolic theories. Everything seems to have skipped with the advent of Machine Learning. In this paper, after a long analysis of history, the rule-based and the learning-based vision, we propose KERMIT as a unit of investigation for a possible meeting point between the different learning theories. Finally, we propose a new vision of knowledge in AI models based on a combination of rules, learning and human knowledge.

There exists an implicit potential limitation in every physical discoveries that has been implemented and understood. However, the limitations can be bounded within a safe limit to prevent any constructing theory to be free from errors. As, it’s the inert nature of the humans, to go far beyond the scope of experimental findings in order to pursue any studies with the sole help of logical reasoning and mathematics, the argument can be prevailed in the form of WEAK Clampdown Effect & STRONG Clampdown Effect. More, the theories are constructed out of physical nature, more the theory gets hypothetical without any finding evidence, but that does or doesn’t actually justify the phenomenon, that too with the more increment of KARDASHEV Scale, more moderate ways of experimentation got developed curbing down the limitations within the human limit of ‘ERRORS’, that does can be neglected by approximation. Relationship being cross-judgmental on the basis of the computational limits and calculation accuracy, leading to a soft singularity, as a warning, that if computer powers cannot be checked on the basis of error approximations, then this may lead to the hitting of a hard singularity, that in phase with the forbidden gap (or after the optimum limit that arises at the core constraints of nature) to prevent the computation being carried off with respect to super-intelligence machines that are cognitive capability oriented future computers responsible for self growth & reproduction with more improvement algorithm, restricting all forms of humanity & constraints the human growth by virtue of limiting capacities of the humans as compared to computers.

We report an easy-to-implement device for SERS-based detection of various analytes dissolved in water droplets at trace concentrations. The device combines an analyte-enrichment system and SERS-active sensor site, both produced via inexpensive and high-performance direct fs-laser printing. Fabricated on a surface of water-repellent polytetrafluoroethylene substrate as an arrangement of micropillars, the analyte-enrichment system supports evaporating water droplet in the Cassie-Baxter superhydrophobic state, thus ensuring delivery of the dissolved analyte molecules towards the hydrophilic SERS-active site. The efficient pre-concentration of the analyte onto the sensor site based on densely-arranged spiky plasmonic nanotextures results in its subsequent label-free identification by means of SERS spectroscopy. Using the proposed device, we demonstrate reliable SERS-based fingerprinting of various analytes, including common organic dyes and medical drugs at ppb concentrations. The proposed device is believed to find applications in various areas, including label-free environmental monitoring, medical diagnostics, and forensics.

Brillouin-Enhanced Four-Wave-Mixing techniques, which couple four optical beams through Brillouin nonlinearity, have gained popularity in the 1980's largely owing to their phase conjugation properties. Experiments were mainly conducted in liquid cells. The interest in Brillouin-Enhanced Four-Wave-Mixing has reawakened in the 2000's, following the quest for dynamically reconfigurable gratings in optical fibers. Termed Brillouin Dynamic Grating this time around, it is, in fact, an acoustic wave, optically generated by stimulated Brillouin scattering process between two pump waves. The acoustic wave either carries the coherent information encoded by the pump beams, or in the case of sensing applications, its properties are determined by the environmental parameters. This information, in turn, is imparted to the third phase-matched optical probe wave through the elasto-optic effect. Over the last decade, this mechanism allowed for the realization of many all-optical signal processing functions and has proven instrumental in distributed sensing applications. This paper describes the basics, as well as the state of the art, of BDG-based applications in optical fibers. It also surveys the efforts being done to carry over these concepts to the photonic chip level.

Abstract: Spatially explicit, fine-grained datasets describing historical urban extents are rarely available prior to the era of operational remote sensing. However, such data are necessary to better understand long-term urbanization and land development processes and for the assessment of coupled nature-human systems, e.g., the dynamics of the wildland-urban interface. Herein, we propose a framework that jointly uses remote sensing derived human settlement data (i.e., the Global Human Settlement Layer, GHSL) and scanned, georeferenced historical maps to automatically generate historical urban extents for the early 20th century. By applying unsupervised color segmentation to the historical maps, spatially constrained to the urban extents derived from the GHSL, our approach generates historical settlement extents for seamless integration with the multi-temporal GHSL. We apply our method to study areas in countries across four continents, and evaluate our approach for two U.S. study sites against historical settlement extents derived from the Historical Settlement Data Compilation for the US, HISDAC-US, achieving Area-under-the-Curve values >0.9. Our results are largely in agreement with model-based urban areas from the HYDE database, and demonstrate that the integration of remote sensing derived observations and historical cartographic data sources opens up new, promising avenues for assessing urbanization, and long-term land cover change in countries where historical maps are available.

Image processing is a field of which its popularity increases and continues and, that grows dynamically with new technologies. Nowadays, image processing finds itself in use in almost every field. One of these uses is undoubtedly in the field of medicine, where diagnosis and treatment planning are made from images and, which is constantly changing with newly developed techniques. Of course, the most important factor in using this so widely in the medical field is the acquisition of images on every medical field. With the help of these images, the complaints can be seen more easily and the doctor can follow a path in the treatment of the disease. In our study, we used the Region Growing segmentation method to detect the intestinal mass. This study compares the area determined by the specialist with the area obtained with the segmentation process and, it is seen that the created software system can be used as an auxiliary system to specialist doctors.

Many aging adults experience some form of hearing problems that may arise from auditory peripheral damage. However, it has been increasingly acknowledged that hearing loss is not only a dysfunction of the auditory periphery but results from changes within the entire auditory system, from periphery to cortex. Damage to the auditory periphery is associated with an increase in neural activity at various stages throughout the auditory pathway. Here, we review neurophysiological evidence of hyperactivity, auditory perceptual difficulties that may result from hyperactivity, and outline open conceptual and methodological questions related to the study of hyperactivity. We suggest that hyperactivity alters all aspects of hearing – including spectral, temporal, spatial hearing – and, in turn, impairs speech comprehension when background sound is present. By focusing on the perceptual consequences of hyperactivity and the potential challenges of investigating hyperactivity in humans, we hope to bring animal and human electrophysiologists closer together to better understand hearing problems in older adulthood.

As overeating, overweight and obesity remain public health concerns, it is crucial to design satiety-enhancing foods that suppress appetite and lower snack intake. Existing research identifies oro-sensory targets to promote satiation and satiety within the “satiety cascade” yet it remains unclear as to whether it is ‘chewing’ or ‘oral lubrication’ that might amplify satiation signals.Here we have combined techniques from experimental psychology, food material science and mechanical engineering to measure the role of chewing and lubrication using novel, model foods as preloads on subjective appetite and intake of a salty snack. Three mint flavoured hydrogels were engineered to vary in their texture (fracture stress) and lubrication (inverse of friction coefficient) properties, and a control group received a mint tea. Results showed that snack intake was suppressed by 32% after eating the low chewing/high lubricating preload as compared to the high chewing/low lubricating preload (p<0.05). No other significant effects were found for snack intake. Hunger ratings decreased from t1 to t3 (p<0.05), however differences between conditions were subtle and not significant. Thus, this proof-of-concept study demonstrates that manipulating oral lubrication is a promising new construct to reduce snack intake that merits future research in the oro-sensory satiety domain.

With the increasing adoption of building integrated photovoltaics (BIPV), concerns arise about potential glare. While recommended criteria to assess glare exist, it is challenging to apply these in the spatial and temporal domains and communicate the complex data to planning authorities and clients. This paper presents a new computational workflow using annual daylight simulation, material modelling using bi-directional scattering distribution functions (BSDFs) and image-based postprocessing to obtain 3-dimensional renderings of cumulative annual irradiance and glare duration on the built environment. The annual daylight simulation considers relevant sun positions in high temporal resolution (15-minute timesteps) and measured BSDFs to model different PV materials. The postprocessing includes a relative irradiance visualisation comparing the impact of a proposed PV proportional to a reference material. It also includes a new spatio-temporal workflow to assess the glare duration based on recommended thresholds. This workflow is demonstrated with a case study of a proposed PV roof for a church, assessing the glare potential of two different PV materials. The visualisations indicate glare durations well below the thresholds with satinated PVs, and in noncritical zones outside observer positions with standard PVs. Thus the proposed PV roof does not cause any disturbing glare.

Advanced image processing will be crucial for emerging technologies such as autonomous driving, where the requirement to quickly recognize and classify objects under rapidly changing, poor visibility environments in real time will be needed. Photonic technologies will be key for next-generation signal and information processing, due to their wide bandwidths of 10’s of Terahertz and versatility. Here, we demonstrate broadband real time analog image and video processing with an ultrahigh bandwidth photonic processor that is highly versatile and reconfigurable. It is capable of massively parallel processing over 10,000 video signals simultaneously in real time, performing key functions needed for object recognition, such as edge enhancement and detection. Our system, based on a soliton crystal Kerr optical micro-comb with a 49GHz spacing with >90 wavelengths in the C-band, is highly versatile, performing different functions without changing the physical hardware. These results highlight the potential for photonic processing based on Kerr microcombs for chip-scale fully programmable high-speed real time video processing for next generation technologies.

The 3D reconstruction method using RGB-D camera has a good balance in hardware cost, point cloud quality and automation. However, due to the limitation of inherent structure and imaging principle, the acquired point cloud has problems such as a lot of noise and difficult registration. This paper proposes a three-dimensional reconstruction method using Azure Kinect to solve these inherent problems. Shoot color map, depth map and near-infrared image of the target from six perspectives by Azure Kinect sensor. Multiply the 8-bit infrared image binarization with the general RGB-D image alignment result provided by Microsoft to remove ghost images and most of the background noise. In order to filter the floating point and outlier noise of the point cloud, a neighborhood maximum filtering method is proposed to filter out the abrupt points in the depth map. The floating points in the point cloud are removed before generating the point cloud, and then using the through filter filters out outlier noise. Aiming at the shortcomings of the classic ICP algorithm, an improved method is proposed. By continuously reducing the size of the down-sampling grid and the distance threshold between the corresponding points, the point clouds of each view are continuously registered three times, until get the complete color point cloud. A large number of experimental results on rape plants show that the point cloud accuracy obtained by this method is 0.739mm, a complete scan time is 338.4 seconds, and the color reduction is high. Compared with a laser scanner, the proposed method has considerable reconstruction accuracy and a significantly ahead of the reconstruction speed, but the hardware cost is much lower and it is easy to automate the scanning system. This research shows a low-cost, high-precision 3D reconstruction technology, which has the potential to be widely used for non-destructive measurement of crop phenotype.

There is growing recognition in research and policy of a mental health crisis among Canada’s paramedics but despite this, epidemiological surveillance of the problem is in its infancy. Just weeks before the emergence of the COVID-19 pandemic, we surveyed paramedics from a single, large, urban paramedic service in Ontario, Canada to assess for symptom clusters consistent with Post-Traumatic Stress Disorder (PTSD), major depressive disorder, and generalized anxiety disorder and to identify potential risk factors for each. In total, we received 589 completed surveys (a 97% completion rate) and found that 11% screened positive for PTSD, 15% for depression, and 15% for anxiety, with 1 in 4 active-duty paramedics screening positive for any of the three as recently as February 2020. In adjusted analyses, the risk of a positive screen varied as a function of employment classification, gender, self-reported resilience, and previous experience as a member of the service’s peer support team. Our findings support the position that paramedics screen positive for mental disorders at high rates – a problem likely to have worsened since the onset of the COVID-19 pandemic. We echo the calls of researchers and policymakers for urgent action to support paramedic mental health in Canada.

The growth of demand for metallic minerals has faced with the need for new techniques and improving technologies for all mine life cycle operations. Nowadays, the exploitation of old tailings and mine wastes facilities could represent a solution to this demand, with economic and environmental advantages. W-Sn Panasqueira Mine has been operating for more than 100 years. Its first processing plant “Rio” was located near Zêrere river being the mineral processing residues deposited on the top hillside on the margin of this river in Cabeço do Pião tailings dam. The lack of maintenance and monitoring of this enormous structure in the last twenty years represents high risks to the environment and population of the surrounding region. The re-mining of the tailings by hydrometallurgical methods was considered, in order to satisfy these two enounced conditions - metals demand and environmental risk, aiming for the sale of the metal to pay the environmental intervention. Field samples campaign allowed collecting data and results from laboratory tests driving to use regression optimization. The re-mining solution was studied, taking into account the technical, economic, social, and environmental aspects.

The aim of this study was to examine brightness effect, which is the perceptual property of visual stimuli, on brain responses obtained during visual processing of these stimuli. For this purpose, brain responses of the brain to changes in brightness were explored comparatively using different emotional images (pleasant, unpleasant and neutral) with different luminance levels. Moreover, electroencephalography recordings from 12 different electrode sites of 31 healthy participants were used. The power spectra obtained from the analysis of the recordings using short time Fourier transform were analyzed, and a statistical analysis was performed on features extracted from these power spectra. Statistical findings obtained from electrophysiological data were compared with those obtained from behavioral data. The results showed that the brightness of visual stimuli affected the power of brain responses depending on frequency, time and location. According to the statistically verified findings, the distinctive effect of brightness occurred in the parietal and occipital regions for all the three types of stimuli. Accordingly, the increase in the brightness of pleasant and neutral images increased the average power of responses in the parietal and occipital regions whereas the increase in the brightness of unpleasant images decreased the average power of responses in these regions. However, the increase in brightness for all the three types of stimuli reduced the average power of frontal and central region responses (except for 100-300 ms time window for unpleasant stimuli). The statistical results obtained for unpleasant images were found to be in accordance with the behavioral data. The results also revealed that the brightness of visual stimuli could be represented by changing the activity power of the brain cortex. The main contribution of this research was to comprehensively examine brightness effect on brain activity for images with different emotional content and different frequency bands at different time windows of visual processing for different brain regions. The findings emphasized that the brightness of visual stimuli should be viewed as an important parameter in studies using emotional image techniques such as image classification, emotion evaluation and neuro-marketing.

Exposure to adverse childhood experiences (ACE) has been found to have a profound negative impact on multiple child outcomes, including academic achievement, social cognition patterns, and behavioral adjustment. However, these links have yet to be examined in preschool children that are already experiencing behavior or social-emotional problems. Thus, the present study examined the links between the caregiver's and the child's exposure to ACE and multiple child and caregiver's outcomes in a sample of 30 preschool children enrolled in a Therapeutic Nursery Program (TNP). Children are typically referred to this TNP due to significant delays in their social emotional development that often result in difficulty functioning in typical childcare, home, and community settings. Analyses revealed some contradictory patterns that may be specific to this clinical sample. Children with higher exposure to ACE showed more biased social information processing patterns and their caregivers reported lower child social skills than caregivers of children with less exposure, however their inhibitory control levels were higher (better control) and staff reported that these children exhibited better social skills as well as better approaches to learning than children with less exposure. No such contradictions were found in relation to the caregiver's exposure to ACE, as it was positively associated with a number of negative child and caregiver outcomes.

Manual visual inspections typically conducted after an earthquake are high-risk, subjective, and time-consuming. Delays from inspections often exacerbate the social and economic impact of the disaster on affected communities. Rapid and autonomous inspection using images acquired from unmanned aerial vehicles offer the potential to reduce such delays. Indeed, a vast amount of re-search has been conducted toward developing automated vision-based methods to assess the health of infrastructure at the component and structure level. Most proposed methods typically rely on images of the damaged structure, but seldom consider how the images were acquired. To achieve autonomous inspections, methods must be evaluated in a comprehensive end-to-end manner, incorporating both data acquisition and data processing. In this paper, we leverage recent advances in computer generated imagery (CGI) to construct a 3D synthetic environment for simulation of post-earthquake inspections that allows for comprehensive evaluation and valida-tion of autonomous inspection strategies. A critical issue is how to simulate and subsequently render the damage in the structure after an earthquake. To this end, a high-fidelity nonlinear finite element model is incorporated in the synthetic environment to provide a representation of earthquake-induced damage; this finite element model, combined with photo-realistic rendering of the damage, is termed herein a physics-based graphics models (PBGM). The 3D synthetic en-vironment with PBGMs provide a comprehensive end-to-end approach for development and validation of autonomous post-earthquake strategies using UAVs, including: (i) simulation of path planning of virtual UAVs and image capture under different environmental conditions; (ii) au-tomatic labeling of captured images, potentially providing an infinite amount of data for training deep neural networks; (iii) availability of the ground truth damage state from the results of the finite-element simulation; and (iv) direct comparison of different approaches to autonomous as-sessments. Moreover, the synthetic data generated has the potential to be used to augment field datasets. To demonstrate the efficacy of PBGMs, models of reinforced concrete moment-frame buildings with masonry infill walls are examined. The 3D synthetic environment employing PBGMs is shown to provide an effective testbed for development and validation of autonomous vision-based post-earthquake inspections that can serve as an important building block for ad-vancing autonomous data to decision frameworks.

Named herein “CytroCell”, the cellulosic material obtained via hydrodynamic cavitation of citrus processing waste in water is cellulose of low crystallinity, high porosity, good water holding capacity and good dispersibility in water. These properties, here demonstrated for the first time for lemon and grapefruit CytroCell, open the route to mass scale production of a useful functional material from a cheap and abundant biowaste. The process, indeed, does not require any pre-treatment of the raw material, and does not use acid, alkali, chemical oxidants or enzymes.

Background: This study aims to investigate the effects of visual neurofeedback stimulation on the brain activity in overweight cases. The neuroscience studies indicated the personal decision about eating under the impact of environmental factors such as (visually, smelling, tasting) is related to neural activity of the prefrontal lobe of the brain. Therefore, there were many attempts to modify the food intake behavior in overweight cases through the stimulation of the prefrontal cortex. However, the empirical viewing of EEG-neurofeedback experiments has not explicated the details about the effect of the EEG-NF, the specificity of positive treatment effects remains in a challenging scope.Methods: This study is a cue-exposure EEG-NF experiment to verify the hypothesis of effecting the EEG-NF on the electrical activity of PFC and modifying the general symptoms of food intake behavior in overweight cases. Twenty-four individuals were recruited as participants for this study. These participants were assigned randomly into two groups; the EX-Group (N=12) enrolled in 8 sessions of the EEG-NF experiment, and the C-Group (N=12) was listed in no EEG-NF sessions. The participants provided researchers with a self-report questionnaire relating to their observation of general symptoms of food intake behavior, and EEG signals recordings into the pre and posts stimulation phase. The power spectral density (PSD) method was applied for EEG parameters extraction.Results: The results of a two-way analysis of variance (ANOVA) explained that a significant variation in variables between the two groups after the EEG-NF experiment. The analysis of the quantitative variables indicated that the effect of EEG-NF experiment was a significant decrement in EEG power bands which significantly influenced changing the median of self-report questionnaire responses that is related to general symptoms of food intake behavior.Conclusions: This study provides preliminary support for the therapeutic potential of EEG-NF experiment that targets the prefrontal cortex, to influence neural processes underlying food intake behavior in overweight cases.

Smoking during pregnancy remains one of the most significant risk factors for poor birth outcomes. The Loma Linda University Health Comprehensive Tobacco Treatment Program (CTTP) is the largest maternal tobacco cessation program in San Bernardino County – the largest county in the contiguous US. CTTP is an eight week, incentivized, behavioral intervention for tobacco cessation for pregnant smokers. As part of program evaluation, we used a retrospective cohort design to assess smoking abstinence and to identify predictors of relapse/smoking after enrollment. Among CTTP cohort enrollees (n=233) from 2012-2013 we found: 1) a 28.4% 8 week point prevalence abstinence (PPA), and at a median of 9 months of follow-up after the PPA, 81.6% continued to indicate tobacco cessation, and 2) a high rate of relapse or loss to follow-up (56%). Our modeling of relapse/smoking after enrollment indicated that this was higher in young mothers, non-Hispanic mothers (White, Black), first/third trimester mothers, and rural mothers. The evaluation identified efficacy of the behavioral model at 8 weeks, but that relapse/smoking was occurring in disparity populations. Formative research is needed to expand the programmatic outreach and enrollment of mothers wanting to quit smoking.

Sentinel-1A/B radar remote sensing data were applied for the first time to determine the sixth nuclear test, its underground explosion h-bomb location and affected zone in North Korea, on September 3, 2017. Location of epicenters nuclear test were found according to line-of-sight displacement images via its maximum value. Line-of-sight displacement images were obtained by processing in the GMTSAR package in the VirtualBox virtual machine of the Linux Ubuntu 16.04 operation system. In this research, three scenes Sentinel-B data with descending orbits were considered, one after and two before the event (the nuclear test date) scene were used.

Role identity theory describes the purpose and meaning in life that comes, in part, from occupying social roles. While robustly linked to health and well-being, this may become, however, when an individual is unable to fulfil the perceived requirements of an especially salient role in the way that they believe they should. Amid high rates of mental illness among public safety personnel, we interviewed a purposely selected sample of 21 paramedics from a single service in Ontario, Canada to explore incongruence between an espoused and able-to-enact paramedic role identity. Situated in an interpretivist epistemology, and using successive rounds of thematic analysis, we developed a framework for role identity dissonance wherein chronic, identity-relevant disruptive events cause emotional and psychological distress. While some participants were able to recalibrate their sense of self and understanding of the role, for others, this dissonance was irreconcilable, contributing to disability and lost time from work. In addition to contributing a novel perspective on paramedic mental health and well-being, our work also offers a modest contribution to the theory in using the paramedic context as an example to consider identity disruption through chronic workplace stress.

Plastid gene expression involves many post-transcriptional maturation steps resulting in a complex transcriptome composed of multiple isoforms. Although short read RNA-seq has considerably improved our understanding of the molecular mechanisms controlling these processes, it is unable to sequence full-length transcripts. This information is however crucial when it comes to understand the interplay between the various steps of plastid gene expression. Here, the study of the Arabidopsis leaf plastid transcriptome using Nanopore sequencing showed that many splicing and editing events were not independent but co-occurring. For a given transcript, maturation events also appeared to be chronologically ordered with splicing happening after most sites are edited.

We propose an asynchronous acoustic chirp slope keying to map bit sequences on single or multiple bands without preamble or error correction coding on the physical layer. Details of the implementation are disclosed and discussed, the performance verified on laboratory scale in a pool measurement, as well as simulated for a channel containing Rayleigh fading and Additive White Gaussian Noise. For time-bandwidth products of 50 in single band mode, a raw data rate of 100~bit/s is simulated to achieve bit error rates below 0.001 for signal-to-noise ratios above -6~dB. In dual-band mode and a data rate of 200~bit/s, this bit error level was achieved for signal-to-noise ratios above 0~dB for time-bandwidth product of 25. The packet error rates follow this behavior with an offset of 1~dB.

Aggregated alpha-synuclein (-synuclein) is the main component of Lewy bodies (LBs), Lewy neurites (LNs), and glial cytoplasmic inclusions (GCIs), which are pathological hallmarks of idiopathic Parkinson’s disease (IPD) and multiple system atrophy (MSA), respectively. Initiating factors that culminate in forming LBs/LNs/GCIs remain elusive. Several species of -synuclein exist, including phosphorylated and nitrated forms. It is unclear which -synuclein post-translational modifications (PTMs) appear within aggregates throughout disease pathology. Herein we aimed to establish the predominant synuclein PTMs in post-mortem IPD and MSA pathology using immunohistochemistry. We examined the patterns of three -synuclein PTMs (pS87, pS129, nY39) simultaneously in pathology-affected regions of 15 PD, 5 MSA, 6 neurologically normal controls. All antibodies recognized LBs, LNs, and GCIs, albeit to a variable extent. pS129 -synuclein antibody was particularly immunopositive for LNs and synaptic dot-like structures followed by nY39 -synuclein antibody. GCIs, neuronal inclusions, and small threads were positive for nY39 -synuclein in MSA. Quantification of the LB scores revealed that pS129 -synuclein was the dominant and earliest -synuclein PTM followed by nY39 -synuclein, while lower amounts of pSer87 -synuclein appeared later in disease progression in PD. These results may have implications for novel biomarker and therapeutic developments.

Knowledge processing is an important feature of intelligence in general and artificial intelligence in particular. To develop computing systems working with knowledge, it is necessary to elaborate means of working with knowledge representations (as opposed to data) because knowledge is an abstract structure. There are different forms of knowledge representations derived from data. One of the basic forms is called a schema. The goal of this paper is the development of theoretical and practical tools for processing schemas. To achieve this goal, we use schema representations elaborated in the mathematical theory of schemas and use structural machine as a powerful theoretical tool for modeling parallel and concurrent computational processes. We describe the schema of autopoietic machines as physical realizations of structural machines. An autopoietic Machine is a technical system capable of regenerating, reproducing and maintaining itself by production, transformation and destruction of its components and the networks of processes downstream contained in them. We present the theory and practice of designing and implementing autopoietic machines as information processing structures integrating both symbolic computing and neural networks. Autopoietic machines use knowledge structures containing the behavioral evolution of the system and its interactions with the environment to maintain stability by counteracting fluctuations.

It has recently been shown that, contrary to the wide belief that a shift-enabled condition (necessary for any shift-invariant filter to be representable by a graph shift matrix) can be ignored because any non-shift-enabled matrix can be converted to a shift-enabled matrix, such a conversion in general may not hold for a directed graph with non-symmetric shift matrix. This paper extends this prior work, focusing on undirected graphs where the shift matrix is generally symmetric. We show that while, in this case, the shift matrix can be converted to satisfy the original shift-enabled condition, the converted matrix is not associated with the original graph, that is, it does not capture anymore the structure of the graph signal. We show via a counterexample, that a non-shift-enabled matrix cannot be converted to a shift-enabled one and still maintain the topological structure of the underlying graph, which is necessary to facilitate localized signal processing.

The effectiveness of forest resources depends on the comprehensiveness and rationality of their consumption and processing into finished products. This article discusses a problem of utilising solid fir wood greenery residues generated during the industrial production of essential oils. Bioconversion is considered to be the most promising utilization method. The objective of this research was to study the chemical composition of bioconversion products of fir wood greenery-based substrates. The РР-3.2 strain of Pleurotus pulmonarius (Fr.) Quél was used as a biodestructor. In the process of bioconversion, the contents of polysaccharides and lignin substances is reduced to 38 and 28 % respectively. Up to 20 % of protein accumulates in bioconversion products of fir wood greenery. The amount of nucleic acids is not more than 1.5 g per 1 kg, the contents of heavy metals, such as mercury, cadmium, arsenic, lead, do not exceed maximum permissible concentration standards. The substrate weight loss reaches 15 %. When fallen leaves and post-extraction poplar bud residues are added to the substrate, the substrate-destroying activity of fungi rises, as well as the protein content increases by 3 %. The digestibility of products as a result of bioconversion increases 1.6–2.8 times depending on the substrate composition. The obtained data enable to recommend post-fermented substrates based on fir wood greenery and balsam poplar biomass for use as a protein feed additive.

The high demand for liquefied natural gas (LNG) requires more LNG carriers (LNGCs) to be in operation. During transportation, there is a high risk due to the required extremely low temperatures and the explosive nature of LNG cargo. Moreover, when there is a lack of experience in operating old LNGCs, there is a serious concern regarding operational accidents. A systematic maintenance strategy, especially for LNG cargo containment systems, is crucial for maintaining safe LNG transportation at sea. The purpose of this study is to develop preventive LNG cargo containment system maintenance models by using LNGC dock specifications from LNGCs of various ages. The dock specifications from a conventional LNGC repairing dock were analyzed using natural language processing techniques in order to develop preventive maintenance models of the LNG cargo containment system. From these results, and by considering the ship’s age, it was found that for young LNGCs the priority for repair should focus on checking routine consumable spare parts by tank inspections, whereas for older LNGCs the focus should be on tank condition maintenance rather than on other facilities. These results are expected to be useful in the development of a maintenance strategy of preventive LNG cargo containment systems in maritime LNG transportation.

Facing unprecedented global declines in the extent and integrity of ecosystems, the 15^th UN Biodiversity Conference (COP-15) in Kunming, China, presents an opportunity for transformative change. However, a lack of consensus on two key issues – resource mobilization and Access and Benefit Sharing (ABS) associated with Digital Sequence Information (DSI) – risks stalling negotiations for an ambitious ‘Post-2020 Global Biodiversity Framework’ as the next 10-year strategic plan under the Convention on Biological Diversity. We highlight systemic misconceptions concerning the financing of biodiversity and the burden this places on the ABS system. In the context of DSI, we caution that conflating ABS with resource mobilization risks disrupting modern science policy built on open access, with potentially severe ramifications for scientific research and innovation. To resolve these tensions, we call for a recalibration of discussions on ABS in order to maximize the value delivered by biodiversity for all of society, including indigenous peoples and local communities.

Progression of oral squamous cell carcinoma (OSCC) has been associated with an escape of tumor cells from the host immune surveillance due to an increased knowledge of its underlying molecular mechanisms and its modulation by the tumor microenvironment and immune cell repertoire. In this study the expression of HLA class I (HLA-I) antigens and of components of the antigen processing machinery (APM) was analyzed in 160 pathologically classified human papilloma virus (HPV)-negative OSCC lesions and correlated to the intra-tumoral immune cell response, IFN- signaling and to the patients outcome. A heterogeneous, but predominantly lower constitutive protein expression of HLA-I APM components was found in OSCC sections when compared to non-neoplastic cells. Tumoral HLA-I APM component expression was further categorized into the three major phenotypes HLA-Ihigh/APMhigh, HLA-Ilow/APMlow and HLA-Idiscordant high/low/APMhigh. In the HLA-Ihigh/APMhigh group, the highest frequency of intra-tumoral CD8+ T cells and lowest number of CD8+ T cells close to FoxP3+ cells was found. Patients within this group presented the most unfavorable survival, which was significantly evident in stage T2 tumors. Despite a correlation with the number of intra-tumoral CD8+ T cells, tumoral JAK1 expression as a surrogate marker for IFN- signaling was not associated with HLA-I/APM expression. Thus, the presented findings strongly indicate the presence of additional factors involved in the immunomodulatory process of HPV-negative OSCC with a possible tumor-burden-dependent complex network of immune escape mechanisms beyond HLA-I/APM components and T cell infiltration in this tumor entity.