Visual exploration of complex data helps interpretation, especially in the case of omics data analysis in life sciences and clinical research. Generating and analysing omics data requires bioinformatic skills, while they are interpreted by domain experts, for whom parsing large and complex data structures may be challenging. However, outcomes of visual analytics are often difficult to quantify, which emphasises precision and reproducibility, especially for research on human diseases. Here, we propose a workflow combining a reproducible computational environment with a dedicated visualisation functionality for systems biology diagrams. By linking the Galaxy with the MINERVA Platform, we visualise and explore COVID-19 transcriptomic data to demonstrate the utility of our workflow. Visualised data recapitulate findings of the original publication and offer new insights about the COVID-19 pathology. Our results offer a blueprint for quick prototyping of computational workflows that facilitate communication and exploration of complex data in biomedical research.

Neurodegeneration is a major problem in neuroscience and medicine as it causes and worsens many neurological and psychiatric disorders. It involves gradual loss of neurons at different levels of the brain, affecting cognition, emotion, behavior, consciousness, and attention. Understanding the causes and consequences of neurodegeneration is crucial for identifying the risk factors, biomarkers, and treatments. However, the current therapies are mostly symptomatic and ineffective. Therefore, new and innovative methods, such as noninvasive brain stimulation (NIBS), are needed to modulate brain activity and plasticity in a safe and reversible manner. This field is rapidly developing, with a focus on exploring new clinical applications, mechanisms, and combinations of NIBS and drugs. This special issue covers the latest progress and challenges in the study of neurodegeneration in cognitive impairment and mood disorders from the experimental, clinical, and translational perspectives. It addresses the complexity and diversity of these conditions, the lack of disease-modifying treatments, the ethical and practical issues of conducting research in vulnerable and diverse populations, the integration and interpretation of data from multiple sources and modalities, and the development and validation of novel methods such as NIBS, artificial intelligence, and drug repurposing. Collaborative efforts among various stakeholders are needed to improve our knowledge and treatment of neurodegeneration underlying cognitive impairment and mood disorders.

Involving industrial waste into economic circulation, in particular flotation waste from coal enrichment, is an urgent task. The coal preparation waste of ArcelorMittal Temirtau JSC was studied with a view to the possibility of using it as an alternative substitute for coke breeze in the sintering process. During a thermoanalytical study on a synchronous thermal analyzer STA 449 NETZSCH in dynamic mode with a heating rate of 10, 20, 30 °C/min in an air flow of 30 cm³/min in the temperature range 30–1000 °C, we determined the lower heat of combustion of carbon-containing material, change mass during calcination, change and rate of change in the mass of the sample, thermal effect of the process, the temperature of the beginning, end and maximum development of thermal effects. It has been established that the greatest mass loss is observed in the temperature range 580 °C- 610 °C, which is about 65–67% of the total mass of the organic component of coal. The maximum burnout rate corresponds to a temperature of 540–563 °C. The lowest calorific value of the studied waste was 15.9 MJ/kg. The results of thermal analytical research and technical analysis substantiate the possibility of using flotation waste in a mixture of up to 10% with coke breeze for combustion in the sintering layer for the production of sinter. Analysis of the composition of flotation waste showed that they have a high specific surface area (56.8∙10³ m²/kg), are highly dispersible, soggy, and capable of swelling, and the predominant amount of clay substance makes them a plastic material. The possibilities of using coal flotation waste as an alternative substitute for coke breeze have been studied after the regimes of its preparation for sintering have been developed and a rational regime of layer-by-layer combustion in the sintering layer has been established. The optimal ratio of coke and OPC in the fuel mixture of the sintering charge was determined.

This paper explores the transformative impact of computational pharmaceutics, integrating AI, multi-scale modeling, quantum computing, AI-driven drug discovery, and blockchain frameworks. Molecular dynamics simulations provide insights into atomic behaviors crucial for drug design. Quantum computing reduces computational costs for simulating protein-drug interactions. AI and Graph Neural Networks accelerate drug discovery and personalized medicine. Blockchain frameworks facilitate secure pharmacogenetic data sharing. These advancements revolutionize drug development, offering unprecedented efficiency, innovation, and personalized treatments.

The intricate architecture of dendritic arborization is fundamental to the formation and functionality of neural networks, serving as the primary site for synaptic integration and signal propagation. This study presents a pioneering computational model that simulates dendritic growth dynamics, employing exponential and logarithmic scaling over an extended developmental period of 720 days. This model offers a valuable tool for investigating the implications of diverse dendritic growth patterns on neural development, synaptic connectivity, and ultimately, cognitive functions. The computational framework incorporates biologically plausible parameters, allowing for the systematic exploration of dendritic branching patterns and their potential impact on neuronal information processing. By simulating exponential and logarithmic growth scales, the model captures the inherent complexity and diversity of dendritic morphologies observed in various neuronal populations across different brain regions. The findings from this study hold significant implications for our understanding of neural circuit assembly, synaptic integration, and the potential functional consequences of aberrant dendritic growth patterns observed in neurodevelopmental disorders. Furthermore, the model's ability to simulate extended developmental trajectories over a prolonged period of 720 days offers insights into the dynamic interplay between dendritic growth and synaptic pruning, which is crucial for the refinement and optimization of neural networks.

Aileen Oeberst and Roland Imhoff’s remarkable paper (Perspectives on Psychological Science 2023; 18: 1464-87). should be compulsory reading for all intelligence analysts. It offers a tantalisingly parsimonious explanation comprising the combination of strongly held prior 'beliefs' coupled with subsequent 'belief-consistent information processing' (akin to confirmation bias) - an explanation that simplifies understanding of a vast array of cognitive biases, and thereby suggests that all of these might be attenuated through the application of accessible analytical and assessment practices. What this means for intelligence analysis will be determined by the explicit and implicit 'beliefs' (or, more generally, ‘value[s]’) attributed to any evidence available, and any insight generated, to reduce decision-makers’ uncertainty and unsubstantiated certainty, and thereby offer them future advantage. Analysts are well aware that different sources of evidence (whether empirical, theoretical or entirely speculative) can assign different types and amounts of ‘prior value’ to the information available for intelligence analysis and assessment. Acknowledging such value as a potential driver of subsequent confirmation bias should help analysts guard against any inherent tendency to preference evidence solely on the basis that this was initially considered most ‘valuable’. Instead they should subject all evidence and all insight – regardless of perceived ‘value’ – to a consistent battery of systematic, rigorous and robust evaluation.

Continual learning, a crucial facet of machine learning, involves the perpetual acquisition of valuable insights from incoming data, sans the necessity for full dataset access. Esteemed as a fundamental goal in artificial intelligence, continual learning grapples with an enduring challenge—catastrophic forgetting. A proficient model must exhibit adaptability for new data assimilation and robustness to retain existing knowledge. Class-incremental learning (CIL) aids the gradual integration of knowledge from newly introduced classes, forming a universal classifier. However, directly training the model with fresh class instances triggers a problem—forgetting distinguishing features of prior classes, causing a performance decline. Addressing such issues in machine learning, this survey aims to delineate significant challenges, outcomes, and recent advancements, including our contributions to CIL techniques, especially in image classification.

Researchers and organizations are pursing 100 Day Vaccine Development to reduce the impact of any new pandemic-causing virus. They propose monitoring viral emergence and developing vaccines for discovered viruses which would undergo safety testing in humans. If one of these viruses emerges, phase III trials would then take place. There are two problems with this approach - first, phase III trials are too long for the 100-day goal and second, the virus that emerged might not have been discovered in their surveillance. An alternative approach would be to first develop vaccines for viral families similar to the pan coronavirus vaccines being proposed for next generation COVID vaccines. Phase III trials for them would be run even if a virus in the family wasn’t having an outbreak. If a virus in a family for which a vaccine has been developed has an outbreak and the phase III trialed vaccine is not effective against it, develop a booster based on the phase III trialed vaccine. Since booster development and approval takes less than 100 days, the 100 Day Vaccine Development goal would be met.

Accurate classification of objects in 3D point clouds is a significant problem in several applications, such as autonomous navigation and augmented/virtual reality scenarios, which has become a research hot spot. In this paper, we presented a deep learning strategy for 3D object classification in augmented reality. The proposed approach is a combination of the GRU and LSTM. LSTM networks learn longer dependencies well, but due to the number of gates, it takes longer to train; on the other hand, GRU networks have a weaker performance than LSTM, but their training speed is much higher than GRU, which is The speed is due to its fewer gates. The proposed approach used the combination of speed and accuracy of these two networks. The proposed approach achieved an accuracy of 0.99 in the 4,499,0641 points dataset, which includes eight classes (unlabeled, man-made terrain, natural terrain, high vegetation, low vegetation, buildings, hardscape, scanning artifacts, cars). Meanwhile, the traditional machine learning approaches could achieve a maximum accuracy of 0.9489 in the best case.

Abstract: Brassinosteroids (BR) are essential plant hormones that play a crucial role in regulating plant growth and development, as well as their resistance to abiotic and biotic stresses, including pathogen infections. In the context of plant im-munity, BR influence various processes such as the expression of genes related to pathogen defense, the production of antimicrobial compounds, and the re-inforcement of the plant cell structure. The aim of work is understand the role of BR in plant defense against abiotic and biotic stresses. BR are involved in the regulation of metabolic processes, water economy, and photosynthesis, contributing to increased plant resistance to abiotic stresses. The study discusses various mechanisms of BR action on plant immunity, including the regulation of gene expression related to defense, activation of the plant defense system, in-teraction with other plant hormones, regulation of water and salt balance, and influence on photosynthetic processes. In the context of plant immunity, BR influence various processes, including the expression of genes related to pathogen defense, the production of antimicrobial compounds, and the reinforcement of the plant cell structure. Additionally, BR are involved in the regulation of metabolic processes, water economy, and photosynthesis, con-tributing to increased plant resistance to abiotic and biotic stresses. The paper emphasizes the importance of further research on the role and mechanisms of action of BR in plant immunity, which may contribute to the development of more effective strategies for the use of BR in agriculture, horticulture, and plant protection.

The Universe is treated as an unbound thermodynamic system in which two forces of Nature compete: Attractive gravity loss and repulsive entropic gain. Entropic gain, although not presently considered a force of Nature, is otherwise well known and can be expressed with the gas laws. The Universe at the time of last scattering was all gas. Its instant gain is expressed as gas pressure. When the Gibbs equation is properly applied to this unbound gas, the resulting atomic Hubble parameter is found to be exclusively dependent on baryon density, giving a constant and perpetual two-to-one gain/loss ratio. Instant loss to gravity cannot offset gas pressure in this “thermal equation”. By contrast, the Friedmann equation, a ΛCDM foundation, treats baryon mass as accreted, having little if any entropic gain. These two equations are actually equivalent. Expanding accreted matter at its comoving critical density is herein shown to behave exactly like a freely expanding gas. However, neither equation includes all of today’s Universal kinetic energy, and cannot by themselves fully account for observed stellar movement. The present paper proposes the plausible existence of highly energetic or “suprathermal” free electrons which comprise about half of the intergalactic medium’s kinetic total. It is this suprathermal energy which Λ expresses. The fluid equation, another ΛCDM foundation, eliminates entropic gain from the Gibbs equation. Additionally, rest mass’s Einstein energy E=Mc² is treated as a thermal variable. These two assumptions, isoentropy and energy conflation, led astronomers to populate Λ as they followed the prior art.

Bio-absorbable stents are a novel tool which relieve stenosis and then resorb as the patient grows, avoiding obligatory re-intervention. This case describes the novel use of an iron bio-absorbable stent in a child with surgically repaired Tetralogy of Fallot who post-operatively developed a severely stenotic right ventricular outflow tract.

This article encapsulates an exploration of seizure dynamics through the lens of computational modeling in epilepsy. Starting with a foundational understanding of the FitzHugh-Nagumo model, asimplified representation of neuronal activity, we try to understand the intricacies of epileptic seizures. We methodically demonstrated the transition from normal neuronal activity to a seizure-like state by altering key parameters in the model, such as external current and coupling strength. This was followed by an extension of the model to a network of neurons, simulating the complex interactions and synchronization patterns indicative of seizure propagation. Numerical simulations were conducted to visualize the impact of varying coupling strengths on network dynamics, offering insights into the mechanisms of seizure initiation and spread. The study was complemented by a discussion on the implications of these findings for understanding epilepsy, highlighting the bridging of theoretical models with clinical understanding. Our approach not only illuminates the potential of computational models in epilepsy research but also underscores the significance of interdisciplinary collaboration in advancing our comprehension of neurological disorders. Through this article, we aim to provide a nuanced perspective on the modeling of epileptic seizures, offering a valuable resource for researchers and clinicians in the field.

The therapeutic, attenuative, and morphological effects of proprietary neural stem cell-derived extracellular vesicles (NSC EVs, AB126) produced by Aruna Bio, Inc. were assessed in a range of in vitro and in vivo models of stroke. We emphasize that modulation of neuroinflammation is a key target and suggest further studies to advance our understanding of the mechanisms of action and ultimately how best to dose and monitor the benefits of AB126 in stroke patients.

In recent years the field of computer science has experienced great changes, due to the incredible advances in the field of artificial intelligence. Deep Learning models are responsible for most of them since the biggest milestones occurred in 2012 when AlexNet won the image classification challenge called ImageNet. These models have demonstrated great performances in different types of complex tasks like image restoration, medical diagnosis or object recognition. Their disadvantages are related to their high data dependency, which forces experts in the field to follow a precise methodology to obtain accurate models. In this paper, we describe a complete workflow that begins with the management of the raw data until the in-depth interpretation of the performance of the models. This should be taken as a high-level consultation document describing good practices that should be applied. Apart from the step-by-step methodology, we present different use cases that correspond to the two main problems of the field: classification and regression.

A proof is given showing the Collatz Conjecture is true for all positive integers. The proof was possible once the perspective of the proof was changed from looking at the pattern of the positive integers to looking at the conjecture rules. The conjecture rule for even numbers organizes all positive integers into unique sets and the rule for odd numbers interconnects the unique sets into dendritic pathways to “1.” Infinite loops, other than the minor 4-2-1 loop after reaching “1,” and pathways to infinity are shown to be mathematically impossible. The proof predicted a general equation that shows all positive integers ultimately reach a final value of “1," and calculates the values and locations of the odd positive integers during the iterations for each tested positive integer.

Mediterranean Diet (MD), rich in minimally processed plant foods and in monounsaturated fats, but low in saturated fats, meat and dairy products, represents one of the most studied diets for cardiovascular health. It has been shown, from both observational and randomized controlled trials, that MD reduces body weight, improves cardiovascular disease surrogates such as waist-to-hip ratio, lipids, and inflammation markers, and even prevents the development of fatal and non-fatal cardiovascular disease, diabetes, obesity, and other diseases. However, it is unclear whether it offers cardiovascular benefits from its individual components or as a whole. Furthermore, limitations in the methodology of studies and meta-analyses have raised some concerns on its potential cardiovascular benefits. MD is also associated with characteristic changes in the intestinal microbiota, mediated through its constituents. These include increased growth of species producing short-chain fatty acids, such as Clostridium leptum and Eubacterium rectale, increased growth of Bifidobacteria, Bacteroides and Faecalibacterium prausnitzii species, and reduced growth of Firmicutes and Blautia species. Such changes are known to be favorably associated with inflammation, oxidative status, and overall metabolic health. This review will focus on the effects of MD on cardiovascular health through its action on gut microbioma.

Sechium compositum (Cucurbitaceae) is a wild species that is distributed in the Soconusco region, Chiapas, Mexico, and the border with Guatemala. This species has an intangible biochemical value resulting from the pharmacological relevance of its secondary metabolites. However, as a consequence of the lack of knowledge about its importance, it is being displaced from its habitat at an accelerated rate, incurring the risk of genetic loss. Therefore, an in vitro culture protocol with two experimental phases was evaluated to propagate, conserve, and regenerate this species. The first phases considered the multiplication of seedlings, adding seven concentrations (0.1, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2 mg mL-1) of 6-benzylaminopurine (BA) and thidiazuron (TDZ) and evaluating the number of buds, shoots, and seedling height. The best multiplication response was recorded with 0.1, 0.2, 0.4, and 1.0 mg L-1 of BA and 0.1 mg L-1 of TDZ, as well as the MS base culture medium. With 0.1 mg L-1 of BA, 52-mm height, 1.36 shoots, and 9.22 buds were obtained, surpassing the MS control (MS culture medium alone); likewise, twice more roots (80%) and a 14% reduction in the bud structure were packed in 50 repetitions. The second phase consisted of inducing callus formation from stem and leaf explants through the addition of 0.5, 1.0, and 2.0 mg L-1 of TDZ and 2,4-Dichlorophenoxyacetic acid (2,4-D) to the medium. The results demonstrated that the induction of callogenesis in S. compositum has a better start from the stem in a medium with 2.0 mg L-1 of 2,4-D, whose value was 4.89 —equivalent to 97.8% callus formation around the explant. The addition of 500 mg L-1 of polyvinylpyrrolidone (PVP) is also suggested to reduce oxidation. This protocol represents a significant advance in the conservation, multiplication, and callogenesis of S. compositum and contributes to its rescue and revaluation in the face of the danger of extinction.

IBD, encompassing CD and UC, are characterized by chronic gastrointestinal inflammation due to abnormal immune responses to gut microflora. The bidirectional communication between the gut and the brain, known as the GBA, is disrupted in IBD, leading to neurobiological imbalances and affective symptoms. Systemic inflammation in IBD affects the brain's inflammatory response system, hormonal axis, and BBB integrity, influencing gut microbiota and clinical responses to probiotics. This review aims to explore the links between dysregulations in the GBA, serum biomarkers, and the development of MCI and NDDs. The objective is to identify potential correlations and propose future research directions to understand the impact of altered microbiomes and intestinal barrier functions on NDDs. Studies suggest a potential link between IBD and extraintestinal complications such as MCI and NDDs. Mechanisms include systemic inflammation, BBB dysfunction, autoimmune responses, GBA dysfunction, medication effects, and comorbidities. Serum biomarkers, including vitamin D3, C-reactive protein, high-sensitivity C-reactive protein, vitamin B12, homocysteine, serum amyloid, neuron-specific enolase, neurofilament light chain, S100 proteins, brain-derived neurotrophic factor, IL-6, LP-2 associated phospholipase, prostaglandin E2, IL-1β, and TNF-α, have been investigated for their potential to predict MCI but current results are lacking clarity.

A hybrid organic molecules as ligands and inorganic NCs in this research combines the chemical properties of organic semiconductors with the chemical stability of inor- ganic nanoparticles like PbS. It is possible to improve the ligand exchange and PbS-NCs' distribution by adding Amino phynel anchor group to TIPs-pentacene and Carboxylic anchor group to Tetraphenylporphyrin molecules. By adding anchor groups to one or two sides of these molecules, they would convert the molecules to ecient ligands to obtain superlattice coupled organic-inorganic nanostrcuture semicondcutor layers (COINs). Adding on side group would cap the PbS nanoparticles while adding two an- chor group would play a linker role between two PbS NCs. This morophology enhance- ment paves the way to manipulate the physical properties of the COINs and fabricate solar cells. Singlet ssion phenomena demonstrates pentacene molecule's advantage for high eciency in organic solar cells and its ability to overcome the Schockley-Queisser limit. Ultraviolet photo-emission spectroscopy (UPS) determines the highest occupied molecular orbital (HOMO) and the work function of COINs, while the lowest unoccu- pied molecular orbital (LUMO) of COINs were calculated subsequently by adding both the exciton binding energy and the optical band gap to the measured HOMO energy. The current versus voltage characteristics and impedance spectroscopy were studied for these molecules as well.

To stabilize the Earth’s climate a large-scale transition is needed to non-carbon emitting renewable energy technologies like wind and solar energy. Although these renewable energy sources are now lower-cost than fossil fuels, their inherent intermittency make them unable to supply a constant load without storage. To address these challenges rechargeable electric batteries are currently the most promising option; however, their high capital costs limit current deployment velocities. To both reduce the cost as well as improve performance 3D printing technology has emerged as a promising solution. This literature review provides the state-of-the-art enhancements of battery properties with 3D printing including efficiency, mechanical stability, energy and power density, customizability and sizing, production process efficiency, material conservation and environmental sustainability as well as the progress in solid-state batteries. The principles, advantages, limitations, and recent advancements associated with the most common types of 3D printing are reviewed focusing on their contributions to the battery field. 3D printing batteries offer design flexibility, material flexibility, reduces pack weight, minimizes material waste, increases the range of applications and has the potential to reduce costs. As 3D printing technologies become more accessible, the prospect of cost-effective production for customized batteries is extremely promising.

o estudo visa explicar os efeitos diferentes entre as equações de multiplicação, soma, divisão e subtração. Nesse sentido, as equações de multiplicação e soma desenvolvem um novo elemento ou fato, enquanto isso, as equações de divisão expressar informações dentro do elemento ou fato, a subtração é a seleção de elemento ou fato Alem disso, para uma equação de multiplicação e soma resulta no novo elemento ou fato é necessario dependência de conceitos físicos específicos, sem alteração extrema, pois resultaria na alteração do elemento ou fato. vale ressaltar, a importância de valores fixos na equação para descreve a natureza específica do elemento ou fato. O estudo possibilita o entendimento das equações em aberta(sem solução) na matematica e fisica, além de compreender equações existentes. O estudo faz referência à teoria da necessidade obrigatória e teoria da diferenças entre elementos.

Lake sediment records are of great importance for understanding the evolution of watershed environments. Various studies have been carried out to constrain the depositional ages of lake sediments and examine their physical, chemical, and biological characteristics, aiming to construct historical vegetation, environment and climate patterns in Chinese lake watersheds. In this study, we obtained relevant literature on lake sediment records by searching the key word 'age-depth' from the databases – Web of Science and China National Knowledge Infrastructure (CNKI, the largest Chinese academic database). We analyzed the literature based on its type (published in a Chinese/English journal or as a Master’s/PhD thesis), period of publication, journal (if published in a journal), key authors, study area, dating scale, and main aims. The results suggest that the lakes in the plateau regions are the most popular research topic typically covering 100–200 years (short-term) and 500–30,000 years (long-term). The literature focuses on a wide range of topics, from past environmental evolution in the watershed to lake ecology, and provides a solid foundation for better understanding regional climate change and the preservation of lake environments and ecosystems. In the future, resulting data from environmental reconstruction with lake sediments will be needed to integrate with emerging information processing technologies (e.g., artificial intelligence and meta-analysis), to disentangle the complex interplay between Earth surface processes and global climate change; furthermore, strengthening interdisciplinary collaboration will deepen our comprehension of man-land relationship and promote the sustainable management of lake ecosystems in the context of global climate change.

Wearable Biosensor Technology (WBT) has emerged as a transformative tool in the educational system over the past decade. This systematic review encompasses a comprehensive analysis of WBT utilization in educational settings over a 10-year span (2012-2022), highlighting both the evolution of this field and its integration to address challenges in education by integrating technology to solve specific educational challenges, such as enhancing student engagement, monitoring stress and cognitive load, improving learning experiences, and providing real-time feedback for both students and educators. By exploring these aspects, the review sheds light on the potential implications of WBT for the future of learning. A rigorous and systematic search of major academic databases, including Google Scholar and Scopus, was conducted in accordance with PRISMA guidelines. Relevant studies were selected based on predefined inclusion and exclusion criteria. The articles selected were assessed for methodological quality and bias using established tools. The process of data extraction and synthesis followed a structured framework. Key findings include the shift from theoretical exploration to practical implementation with the EEG being the predominant measurement, aiming to explore mental states, physiological constructs, and teaching effectiveness. Wearable biosensors are significantly impacting the educational field, serving as an important resource for educators and a tool for students. Their application has the potential to transform and optimize academic practices through sensors that capture biometric data, enabling the implementation of metrics and models to understand the development and performance of students and professors in an academic environment, as well as to gain insights into the learning process.

The concept of vulnerable carotid plaques is pivotal in understanding the pathophysiology of is-chemic stroke secondary to large-artery atherosclerosis. At the macroscopic evaluation, vulnera-ble plaques are characterized by one or more of the following features: microcalcification, neo-vascularization, lipid-rich necrotic core (LRNC), intraplaque hemorrhage (IPH), thin fibrous caps, plaque surface ulceration, huge dimension determining stenosis, and plaque rupture. Recognizing these macroscopic characteristics is crucial to estimating the risk of cerebrovascular events, also in case of non-significant (less than 50%) stenosis. Inflammatory biomarkers, such as cytokines and adhesion molecules, lipid-related markers like oxidized low-density lipoprotein (LDL), and proteolytic enzymes capable of degrading extracellular matrix components are among the key molecules that will be scrutinized for their associative roles in plaque instability. Through their quantification and evaluation, these biomarkers reveal intricate molecular cross-talking govern-ing plaque inflammation, rupture potential, and thrombogenicity. Current evidence demonstrates that plaque vulnerability phenotypes are multiple and heterogeneous and are associated with as many highly complex molecular pathways that determine the activation of an immune-mediated cascade that culminates in thromboinflammation. This narrative review provides a comprehen-sive analysis of current knowledge on molecular biomarkers expressed by symptomatic carotid plaques. It explores the association of these biomarkers with the structural and compositional at-tributes that characterize vulnerable plaques

This review aims to explore the intricate relationship between epigenetic mechanisms, stress, and affective disorders, focusing on how early life experiences and coping mechanisms contribute to susceptibility to mood disorders. Epigenetic factors play a crucial role in regulating gene expression without altering the DNA (deoxyribonucleic acid) sequence, and recent research has revealed associations between epigenetic changes and maladaptive responses to stress or psychiatric disorders. A scoping review of 33 studies employing the PRISMA-S (Preferred Reporting Items for Systematic Reviews and Meta-Analyses – Statement) guidelines investigates the role of stress-induced epigenetic mechanisms and coping strategies in affective disorders' occurrence, development, and progression. The analysis encompasses various stress factors, including childhood trauma, work-related stress, and dietary deficiencies, alongside epigenetic changes such as DNA methylation and altered gene expression. Findings indicate that specific stress-related genes frequently exhibit epigenetic changes associated with affective disorders. Moreover, the review examines coping mechanisms in patients with bipolar disorder and major depressive disorder, revealing mixed associations between coping strategies and symptom severity. While active coping is correlated with better outcomes, emotion-focused coping may exacerbate depressive or manic episodes. Overall, this review underscores the complex interplay between genetic predisposition, environmental stressors, coping mechanisms, and affective disorders. Understanding these interactions is essential for developing targeted interventions and personalized treatment strategies for individuals with mood disorders. However, further research is needed to elucidate specific genomic loci involved in affective disorders and the clinical implications of coping strategies in therapeutic settings.

The classification and summarization of online reviews using machine learning and deep learningbased approaches are the subject of this systematic literature review. Consumers, companies, and researchers can now find vital information from online reviews. Automatically categorizing and summarizing these reviews can offer useful information and facilitate decision-making. The purpose of this study is to offer a thorough overview of the current methodology, approaches, and developments in this area. The review concentrates on machine learning-based methods for categorization and summarizing tasks, highlighting their advantages, drawbacks, and new developments. This systematic review provides insights into the most recent methodologies and indicates prospective directions for further investigation by synthesizing the data from a wide range of pertinent research publications

We discuss three key points: defining mathematically the notions of phase, symmetry breaking, topological defects and their stability in a formal language of homotopy theory and fiber bundle formalism; postulating and proving two theorems for successive symmetry breaking scenarios in condensed matter physics and cosmology, and demonstrating application of theorems to superfluid Helium 3 and Kibble-Lazarides-Shafi (KLS) walls, supporting and mathematically justifying the results of [Volovik, 2020].

The Eurasian grapevine (Vitis vinifera L.) is the most widely grown horticultural crop in the world and is important for the economy of many countries. In the wine production chain, grape varieties play an important role as they directly influence the authenticity and classification of the product. Identifying the different grape varieties is therefore fundamental for quality control and inspection activities, as well as for regulating production. Currently, ampelography and molecular analysis are the main approaches to identifying grape varieties. However, both methods have limitations. Ampelography is subjective and prone to errors and is experiencing enormous difficulties as ampelographers are increasingly scarce. On the other hand, molecular analyses are very demanding in terms of cost and time. In this scenario, Deep Learning (DL) and Machine Learning (ML) methods have emerged as a classification alternative to deal with the scarcity of ampelographs and avoid molecular analyses. In this study, the most recent and current methods for identifying grapevine varieties using DL classification-based approaches are presented through a systematic literature review. The classification pipeline of the 31 studies found in the literature was described, highlighting its pros and cons. Most of the studies used DL-based models trained with leaf images acquired in a controlled environment at a maximum distance of 1.2 metres to classify grape varieties. In addition, there is a large gap between practical applications and the datasets used: a great lack of varieties, limited data acquired in the field and a lack of tests on plants under adverse conditions. Potential directions for improving this area of research were also presented.

Cultural tourism in Tanzania presents a diverse tapestry of traditions, varied ethnic groups, and historical accounts. This article aims to pinpoint areas for further exploration in the cultural tourism sector of Tanzania and analyze their potential impacts. The study conducted a systematic review of the literature following established inclusion criteria as well as the Preferred Reporting Items for Systematic Reviews and meta-analysis guidelines. A total of 21 articles focused on Tanzania Cultural Heritage Tourism from sources such as Google Scholar and web databases were examined, revealing several research gaps, including theoretical, contextual and methodological aspects. Furthermore, the research offers insights into theoretical implications and recommends avenues for future studies that could advance the field by addressing these knowledge gaps providing valuable direction to new researchers interested in exploring these specific areas as well as practitioners seeking a comprehensive understanding of various aspects related to Tanzania's cultural heritage tourism

Abstract: Social media has become a vital tool in ensuring productivity among Undergraduates, as studies have shown an increased number of users globally. This study assessed the impact of social media usage on undergraduates’ academic performance and working memory. Participants from three different universities such as the University of Georgia (UG), Eastern European University (EEU), and Batumi State University (BSU) completed this study using online google forms. UG and EEU are in the capital city, and BSU is in Batumi City. The study questionnaires were approved by the institutional review board before the data collection. Descriptive analyses were assessed for all study populations, characteristics, frequencies, percentages, means, and standard deviations using Statistical Package for Social Science (SPSS Inc., Chicago, IL, version 25.0 for Windows) software. 41.3% were male students, as most of the participants were foreign students living and studying in Georgia. Male students had the risk of ‘conflict’ with families and friends because of social media. Female students are at increased risk of using social media as an ‘escape’ from negative feelings. 89.7% of students that perform physical activities, considering their academic workload had the risk of preoccupation complaints. 87.9% felt dissatisfied regularly because they wanted to spend more time on social media. Utilization of social media differed based on the characteristics of the participants such as age, gender, and geography. We observed the increase in prevalence of social media addiction, its effect on academic performance and working memory among undergraduate students.

The application of smartphone-based color evaluation of passive sampling devices for gases has only been sparsely reported. The present review aims to compile available publications with respect to the configuration of the passive samplers, conditions of smartphone photographing, analytical procedure for color detection and quantification (including calibration processes), and their application to different target gases. The performance of the methods - whenever available - is presented regarding the analytical specifications selectivity, sensitivity, and limit of detection in comparison with other color evaluation methods of passive samplers. Practical aspects like requirements of instrumentation and ease of use will be outlined in view of the potential employment in education and citizen science projects. In one section of the review the inconsistent terminology of passive and diffusive sampling is discussed in order to clarify the distinction of information obtained from the uptake of the passive samplers between gas-phase concentration and the accumulated deposition flux of gaseous analytes. Colorimetric gas sensors are included in the review when applied in passive sampling configurations and evaluation is performed with smartphone-based color evaluation. Differences in the analytical procedures employed after the passive sampling step and prior to the detection of the colored compounds are also presented.

Critically ill patients treated in the intensive care unit (ICU) can present many abdominal conditions that need a prompt diagnosis and timely treatment because of their general frailty. Clinical evaluation and diagnostic tools like ultrasound or CT scan are not reliable or feasible in these patients. Bedside laparoscopy (BSL) is a minimally invasive procedure that allows surgeons to assess the abdominal cavity directly in the ICU, thus avoiding unnecessary exploratory laparotomy or incidence related to intra-hospital transfer. We conducted a review of the literature to summarize the state-of-the-art of BSL. Medline, The Cochrane Central Register of Controlled Trials (CENTRAL), and Scopus databases were utilized to identify all relevant publications. Indications, contraindications, technical aspects, and outcomes are discussed. The procedure is safe, feasible, and effective. When other diagnostic tools fail to diagnose or exclude an intra-abdominal condition in ICU patients, BSL should be preferred over exploratory laparotomy.

This study aimed to review the dietary risk factors for root caries prevalence in older adults, from socially active people to frail patients. A comprehensive search strategy was used to select studies from PubMed and Scopus databases. Two evalua-tors performed data extraction, screening, and quality assessment independently. Only studies written in English were included. Caries is very common in the elderly, as there are several aggravating factors such as the decrease of the ability to self-care and, by extension, insufficient oral hygiene, a carious diet, limited exposure to fluoride, xero-stomia, gingival recession, limited access to dental care. Root caries is prevalent in the elderly due to gingival recession and root exposure to the oral environment. Dietary risk factors significantly affect root caries prevalence in older adults, including high intake of sugars and an alteration of their composition preference in this age group. Caries risk appears more significant in frail, standardized patients who feed with even softer food or supplements.

Continuous technological progress places significant demands on the materials used in increasingly modern devices. An important parameter is often the long-term thermal resistance of the material. The use of heat-resistant polymer materials worked well in technologically advanced products. An economically justified direction in searching for new materials is the area of polymer nanocomposite materials. It is necessary to appropriately select both the polymer matrix and the nanofillers best able to demonstrate the synergistic effect. A promising area of exploration for such nanocomposites is the use of organosilicon polymers, which results from the unique properties of these polymers related to their structure. This review presents the results of the analysis of the most important literature reports regarding organosilicon polymer nanocomposites with increased thermal resistance. Particular attention was paid to modification methods of silicone nanocomposites focused on the increase of their thermal resistance related to the modification of siloxane molecular structure and by making nanocomposites using inorganic additives, carbon nanomaterials. Attention was also paid to such important issues as the influence of the dispersion of additives in the polymer matrix on the thermal resistance of silicone nanocomposites and the possibility of modifying the polymer matrix and permanently introducing nanofillers thanks to the presence of various reactive groups. The thermal stability mechanism of these nanocomposites was also analysed.

In the dynamic landscape of the telecommunications industry, understanding and mitigating customer churn is paramount for maintaining competitive advantage and sustaining revenue streams. This comprehensive literature review delves into the intricacies of customer churn prediction, a crucial aspect in fortifying the bond between telecom businesses and their customers. The review methodically analyzes 11 articles published between 2000 and March 2024, spotlighting the strategic role of Customer Relationship Management and the prevalence of high churn rates that necessitate a shift from customer acquisition to retention strategies. Furthermore, the review navigates through the challenges inherent in churn prediction, including the complexities of managing large, imbalanced, and noisy datasets and the intricacies involved in model selection and optimization. Despite these challenges, the paper highlights the superior capability of machine learning techniques in handling complex datasets to provide accurate predictions, albeit acknowledging their practical complexities. The main aim of the study is to provide an overview, serving as a knowledge base for researchers interested in machine learning techniques within the realm of telecommunications.

Introduction Blunt carotid injuries (BCI) in pediatric trauma are quite rare. Due to the low number of cases, only a few reports and studies have been conducted on this topic. This review will discuss how frequent BCI/ BCVI on pediatric patients after blunt trauma is, what routine diagnostics look like, if a CT/ CTA scan on pediatric patients after blunt trauma is always necessary and if there are any negative health effects? Methods This literature review includes reviews, systematic reviews, case reports and original studies in english language between 1999 and 2020 that have dealt with pediatric blunt trauma and the diagnostics of BCI and BCVI. Furthermore, publications on the risk of radiation exposure on children were included in the study. For literature research, Medline (PubMed) and the Cochrane library were used. Results Pediatric BCI/ BCVI, shows an overall incidence between 0.03 – 0.5% of confirmed BCI/ BCVI cases due to pediatric blunt trauma. 1.1 – 3.5% of pediatric blunt trauma patients underwent CTA to detect BCI. Only 0.17 – 1.2% of all CTA scans shows a positive diagnosis for BCI. In children, the median volume CT dose index on a non-contrast head CT is 33 milligray, a computed tomography angiography needs at least 138 mGy. A cumulative doses of about 50 mGy almost triples the risk of leukemia, and doses of about 60 mGy triples the risk of brain cancer. Discussion Knowing that a BCI could have extensive neurologic consequences for children, it is necessary to evaluate routine pediatric diagnostics after blunt trauma. Computed tomography scans (CT) and computed tomography angiography (CTA) are mostly used in routine BCI diagnostics. However, since radiation exposure in children should be as low as reasonably achievable, it should be asked if other diagnostic methods could be used to identify risk groups. Trauma guidelines and clinical scores like the McGovern score are reflect established BCI screening options, as well as using duplex ultrasound.

Meat serves as a vital component of the human diet, offering a rich source of protein and essential trace elements. China stands as both a significant producer and consumer of meat on a global scale, leading the world in terms of total meat production and consumption. As the standard of living within the country continues to rise, there is a growing emphasis on the nutritional value and safety of meat consumption. The tenderness of meat is a critical factor influencing its overall quality. Traditionally, tenderness has been assessed through shear force testing, a method that is inherently destructive, inefficient, and results in considerable sample wastage. In light of these drawbacks, non-destructive testing techniques have emerged as a preferred alternative, promising greater accuracy, efficiency, and convenience without compromising the integrity of the samples. This paper delves into the application of five advanced non-destructive testing technologies in the realm of meat tenderness assessment. These include near-infrared spectroscopy, hyperspectral imaging, Raman spectroscopy, airflow optical fusion detection, and nuclear magnetic resonance detection. Each technology is scrutinized for its respective strengths and limitations, providing a comprehensive overview of their current utility and potential for future development. Moreover, the integration of these techniques with the latest advancements in artificial intelligence (AI) technology is explored. The fusion of AI with non-destructive testing offers a promising avenue for the development of more sophisticated, rapid, and intelligent systems for meat tenderness evaluation. This integration is anticipated to significantly enhance the efficiency and accuracy of quality assessment in the meat industry, ensuring a higher standard of safety and nutritional value for consumers. The paper concludes with a set of technical recommendations aimed at guiding the future direction of non-destructive, AI-enhanced meat tenderness detection, thereby contributing to the ongoing improvement of the meat industry in China and beyond.

Cotton is the most ubiquitous and profitable fiber with diverse industrial and domestic applications. Grown in over 100 countries, it has a global market value of about $40 billion and employs over 350 million people from fields to textile mills, contributing about 7% of total labor-force recruitment in developing economies. Cotton has an indeterminate growth habit and an extensive tap root system affected by soil physicochemical and environmental conditions. There is a consensus among experts that conventional cotton cultivation still has a long way to attain sustainability, which is essential if cotton will maintain its competitive edge over other natural and synthetic fibers like hemp, polyesters, and rayon. Despite several efforts already committed to growing cotton sustainably, sustainability has eluded cotton cultivation globally because of the intense farm inputs (freshwater, pesticides, and heavy-duty equipment) needs, especially in developing economies. Some of the technological advancements towards achieving the goal of sustainable cotton cultivation include the development of new varieties, improved irrigation and mulching and precision agriculture techniques, application of remote sensing and Unmanned Aerial Systems (UAS) combined with image processing, and the introduction of autonomous and multi-purpose robotic platforms for growing and harvesting cotton. This review attempts to evaluate the successes already achieved by stakeholders in moving cotton towards sustainable production and identify areas where efforts are still needed to reach sustainable production and improved profitability goals for cotton with projections for future research directions.

Multi-rotor drones, a kind of unmanned equipment which is widely used in military, commercial consumption and other fields, has been developed very rapidly in recent years. However, their short flight time has hindered the expansion of their application range. This can be addressed by utilizing hydrogen fuel cells, which exhibit high energy density, strong adaptability to ambient temperature, and no pollution emissions, as the power source. Accordingly, the application of hydrogen fuel cells as the power source in multi-rotor drones is a promising technology that has attracted significant research attention. This paper summarizes the development process of hydrogen fuel cell mul-ti-rotor drones and analyzes the key obstacles that need to be addressed for the further development of hydrogen fuel cell multi-rotor drones, including structural lightweight, hydrogen storage methods, energy management strategies. Additionally, prospects for the future development of hydrogen fuel cell multi-rotor drones are presented.

Heterometallic rare-earth clusters have garnered much attention because of their interest-ing solid-state structures and versatile applications in catalysis and material and polymer chemistry over the last four decades. Particular interest is rare earth–transition metal co-ordination compounds that behave as single-molecule magnets (SMMs) because of their potential use in information storage, spintronic devices, and magnetic refrigeration sys-tems. Another implementation involves using lanthanide clusters in luminescent-based sensing for environmental protection and security screening or in photocatalytic energy conversion reactions. Heterometallic rare earth complexes offer the promise of catalytic performance of many organic reactions due to their high oxygenophilicity, Lewis acidity, high coordination numbers, and ability to change the coordination environment quickly. Therefore, they are commonly applied as catalysts in asymmetric synthesis, oxidation re-actions, and CO2 conversions or as initiators in ring-opening polymerization and copoly-merization of cyclic monomers. They are also attractive single-source molecular precur-sors for functional inorganic materials. This review focused on the unique features and properties of the heterometallic rare earth complexes, which are closely correlated with their molecular structure. Our summary is a valuable resource, providing researchers with an insightful exploration of the heterometallic rare-earth complexes that demonstrated multimetallic synergy and cooperativity effects in various applications.

Agriculture is an industry that heavily relies on the environment. The increasing frequency of ex-treme disasters attributable to climate change has adversely affected agricultural land and dis-rupted the stability of the supply of agricultural products. Therefore, we implemented a research program focused on current strategies for adapting to agricultural meteorological disasters. The primary goal was to actively promote disaster prevention information and techniques through information platforms and applications and to provide refined forecasting and an early warning system to enhance preparedness for and recovery from agricultural disasters. This initiative can transform passive mitigation into dynamic action by disseminating crucial information to farmers. We integrated meteorological data and crop characteristics to develop disaster prevention strate-gies for the predisaster, during-disaster, and postdisaster stages to empower farmers to implement autonomous disaster prevention, ensuring their profitability and minimizing the impact of disas-ters. This paper presents an agricultural disaster prevention system involving cross-disciplinary collaboration to establish an early warning system for crop disasters. However, the effectiveness of the early warning system relies on the availability of complete meteorological and crop infor-mation, necessitating high-density agricultural meteorological stations, refined weather forecast services, historical crop damage data, and ongoing research and development on disaster preven-tion technology. In the system, a diversified social media interface is used to provide local farmers with weather forecast information. When impending disasters are identified, the platform acti-vates disaster prevention measures to reduce agricultural losses. This article paper introduces Taiwan’s agricultural disaster prevention system as a complete disaster prevention strategy.

Metabolic-associated fatty liver disease (MASLD) is not only associated with traditional cardiovascular risk factors such as T2DM and obesity, but it is also an independent risk factor for the development of cardiovascular disease. MASLD has been shown to be independently related to endothelial dysfunction and atherosclerosis. MASLD is characterized by a chronic proinflammatory response that, in turn, may induce a prothrombotic state. Several mechanisms such as endothelial and platelet dysfunction, changes in the coagulation cascade, decreased fibrinolytic activity can contribute to induce the prothrombotic state. Platelets are players and addresses of metabolic dysregulation; obesity and insulin resistance are related to platelet hyperactivation. Furthermore, platelets can exert a direct effect on liver cells, particularly through the release of mediators from granules. Growing data in literature support the use of antiplatelet agent as a treatment for MASLD. The use of antiplatelets drugs seems to have potential clinical implication for the prevention of HCC patients with MASLD, since platelets contribute to fibrosis progression and cancer development. This review aims to summarize the main data on the role of platelets in the pathogenesis of MAFLD and its main complications such as cardiovascular events and the development of liver fibrosis. Furthermore, we will examine the role of antiplatelet therapy not only in the prevention and treatment of cardiovascular events but also as a possible anti-fibrotic and anti-tumor agent.

Antimicrobial resistance (AMR) poses a critical global health threat, leading to approximately 1.27 million deaths annually from infectious diseases resistant to existing antibiotics. Factors such as antibiotic misuse and environmental contamination have accelerated AMR, with the COVID-19 pandemic exacerbating the issue due to increased antibiotic usage. However, the development of new antibiotics has stagnated, with only a few approved in recent years, and many facing resistance. Among the priority pathogens identified by the WHO, Pseudomonas aeruginosa stands out as a significant concern due to its multidrug resistance and virulence. P. aeruginosa infections are challenging to treat due to intrinsic and acquired resistance mechanisms, as well as its ability to form biofilms. Additionally, P. aeruginosa produces a plethora of virulence factors, including proteases, siderophores, and toxins, further complicating treatment. Quorum sensing (QS) systems play a crucial role in regulating bacterial physiology and virulence factor expression. P. aeruginosa employs three QS systems, making it an attractive target for quorum quenching (QQ) strategies. Enzymes such as AHL lactonases, acylases, and oxidoreductases have shown promise in inhibiting QS, thus reducing virulence without impacting bacterial growth. Furthermore, natural QS inhibitors produced by various organisms offer potential therapeutic alternatives. Despite promising results in vitro, translating QQ strategies to clinical settings remains challenging. Nevertheless, QQ represents a novel approach in combating multidrug-resistant infections and warrants further investigation as a complementary therapeutic strategy alongside traditional antibiotics. This review summarizes enzymes with Qurorum Quenching activity evaluated against the pathogen P. aeruginosa and their potential applications in combination therapies.

Efficient heat management and control in optical devices, facilitated by advanced thermo-optic materials, is critical for many applications such as photovoltaics, thermal emitters, mode-locked lasers, and optical switches. Here, we investigate the thermo-optic properties of 2D graphene oxide (GO) films by precisely integrating them onto microring resonators (MRRs) with control over the film thicknesses and lengths. We characterize the refractive index, extinction coefficient, thermo-optic coefficient, and thermal conductivity of for the GO films with different layer numbers and degrees of reduction, including reversible reduction and enhanced optical bistability induced by photo-thermal effects. Experimental results show that the thermo-optic properties of 2D GO films vary widely with the degree of reduction, with significant polarization anisotropy, enabling efficient polarization sensitive devices. The versatile thermo-optic response of 2D GO substantially expands the scope of functionalities and devices that can be engineered, making it promising for a diverse range of thermo-optic applications.

Pregnancy is a complex biological process which is associated with significant metabolic and hemodynamic changes of the maternal body which allow a normal fetal growth. The inability to adapt to these changes can affect the cardiovascular health. During pregnancy, women may experience mood swings, anxiety and emotional ambivalence. These symptoms can lead to stress and have a negative impact on the mental well-being of expectant mothers. It is crucial to know the aspects that can influence the development of cardiovascular problems among pregnant women in order to manage these cases properly. The current narrative review explores and summarizes the evidence regarding the impact of stress and anxiety on cardiovascular health in pregnant women. Understanding the physiological adaptations that the body undergoes throughout pregnancy is crucial for educating and supporting individuals as well as healthcare professionals. This enables them to recognize any risk factors or pathological conditions that may exacerbate pregnant women's health. In order to reduce maternal morbidity and mortality from cardiovascular disease, a psychological and cardiovascular risk factor screening that is done either before or during pregnancy may be able to identify situations that call for particular medical and/or psychological therapies.

Infection with the malaria parasite Plasmodium, remains a major cause of illness and death world-wide. The development of evasion strategies by the parasite from host immunity and its adaptability to antimalarial drugs has raised the urgency for developing new strategies to combat this disease. One promising avenue is targeting telomeres – sequences found at chromosome termini– and telomerase – the enzyme that catalysis synthesis of telomeres using an intrinsic RNA template. As telomeres shorten critically, cells undergo replicative senescence or apoptosis. Plasmodium, lacking telomerase activity in its human stages, exploits alternative mechanisms, accelerating telomere shortening, aging, and immune evasion. Chemical telomerase inhibitors show promise as antimalarials. In this review, we examine the potential of targeting this system in malaria therapy, with telomerase inhibitors offering a novel approach. A systematic search on PubMed using Boolean techniques identified 246 relevant articles from 1985 to March 31, 2024. After filtering, 43 articles met inclusion criteria, supplemented by snowball sampling. The telomerase inhibitor drug strategy is widely applicable against cancer. However, given the drugability of telomerase and the absolute requirement for active telomere maintenance in the vast majority of parasites including plasmodium, telomerase and telomere maintenance could well represent the Achilles heel of the parasite

Incorrect sitting posture, characterized by asymmetrical or uneven positioning of the body, often leads to spinal misalignment and muscle tone imbalance. Prolonged maintenance of such postures can adversely impact well-being and contribute to the development of spinal deformities and musculoskeletal disorders. In response, smart-sensing chairs equipped with cutting-edge sensor technologies have been introduced as a viable solution for real-time detection, classification, and monitoring of sitting postures, aiming to mitigate the risk of musculoskeletal disorders and promote overall health. This comprehensive literature review evaluates the current body of research on smart-sensing chairs, with a specific focus on the strategies used for posture detection and classification, as well as the effectiveness of different sensor technologies. A meticulous search across MDPI, IEEE, and Google Scholar databases yielded 34 pertinent studies that utilize non-invasive methods for posture monitoring. The analysis reveals that Force Sensing Resistors (FSR) are the predominant sensors utilized for posture detection, whereas Convolutional Neural Networks (CNN) and Artificial Neural Networks (ANN) are the leading machine learning models for posture classification. However, it was observed that CNNs and ANNs do not outperform traditional statistical models in terms of classification accuracy, due to constrained size and lack of diversity within training datasets. These datasets often fail to comprehensively represent the array of human body shapes and musculoskeletal configurations. Moreover, this review identifies a significant gap in the evaluation of user feedback mechanisms, essential for alerting users to their sitting posture and facilitating corrective adjustments.

With the increasing interest in CM as a potential source of nutrients and bioactive compounds, there arises a necessity to delve into methods for its processing and drying. Over recent decades, a significant amount of research has been conducted on the chemical composition of CM and its potential health benefits. Consequently, this study is directed towards examining CM drying technology, including an analysis of the influence of different drying methods on its chemical composition and nutri-tional properties. The objective of this review is to systematically summarize the available literature on CM drying processes and their impact on its chemical composition. Special attention is devoted to comparing various drying methods such as freeze-drying, spray drying, and freeze-drying, as well as investigating factors affecting the quality of dried CM. To achieve this goal, we scrutinize a series of studies published between 2003 and 2022 to identify optimal drying methods that promote the maximum preservation of CM's nutritional components and bioactive compounds. Additionally, we explore possibilities for reducing drying time to enhance production efficiency. This research is grounded in the analysis of data from diverse scientific research databases, including Scopus and Google Scholar, encompassing a systematic review of scientific literature and a comparative analysis of the obtained results.

In this comprehensive review, we delve into the realm of intention recognition within the context of digital forensics and cybercrime. The rise of cybercrime has become a major concern for individuals, organizations, and governments worldwide. Digital forensics is a field that deals with the investigation and analysis of digital evidence in order to identify, preserve, and analyze information that can be used as evidence in a court of law. Whereas, Intention recognition is a subfield of artificial intelligence that deals with the identification of agents’ intentions based on their actions and change of states. In the context of cybercrime, intention recognition can be used to identify the intentions of cybercriminals and even to predict their future actions. Employing a meticulous six-step systematic review approach, we curated research articles from reputable journals and categorized them into three distinct modeling approaches: logic-based, classical machine learning-based, and deep learning-based. Notably, intention recognition has transcended its historical confinement to network security, now addressing critical challenges across various subdomains, including social engineering attacks, AI black box vulnerabilities, and physical security. While deep learning emerges as the dominant paradigm, its inherent lack of transparency poses unique challenges in the digital forensics landscape. We advocate for hybrid solutions that blend deep learning’s power with interpretability. Furthermore, we propose the creation of a comprehensive taxonomy to precisely define intention recognition, paving the way for future advancements in this pivotal field.

In this paper, we study the constrained minimization problem for an energy functional which is related to the following Kirchhoff type equation \begin{equation*} -\Big(\eta+b\big(\int_{\R^{3}}|\nabla u|^{2}dx\big)^{s}\Big)\Delta u+V(x)u=\mu u +\lambda|u|^{p}u,\end{equation*} where $b$ is a positive constant, parameters $\eta\geq0, \lambda>0$, exponents $s>0$, $0

Ti and its alloys are the most commonly used materials for biomedical applications. However, bacterial infection after implant placement is still one of the significant rising complications. Therefore, the application of the antimicrobial agents into implant surfaces to prevent implant-associated infection has attracted lots of attention. Scientific papers have shown that inorganic antibacterial metal element (e.g. Ag, Cu, Zn) can be introduced to implant surfaces with the addition of metal nanoparticles or metallic compounds into electrolyte via micro-arc oxidation (MAO) technology. In this review, the effects of the composition and concentration of electrolyte and process parameters (e.g. voltage, current density, oxidation time) on morphological characteristics (e.g. surface morphology, bonding strength), antibacterial ability and biocompatibility of MAO antimicrobial coating were discussed in detail. Anti-infection and osseo-integration can be simultaneously accomplished with the selection of the proper antibacterial elements and operating parameters. Besides, MAO assisted by magnetron sputtering (MS) to endow Ti-based implant materials with superior antibacterial ability and biocompatibility was also discussed. Finally, the development trend of MAO technology in the future was forecasted.

Elizabethkingia anophelis is an opportunistic pathogen causing lifethreatening infections in humans, particularly in immunocompromised patients, neonates and elderly. Infections caused by this species are exceptionally challenging to treat due to its multiresistance to antimicrobials. We report a case of central line-associated bloodstream infection by E. anophelis in a 2.5-year-old girl with acute lymphoblastic leukemia successfully treated with a combination of piperacillin/tazobactam and amikacin. The literature was also reviewed on paediatric infections caused by E. anophelis. Accurate identification with MALDI-TOF, or using molecular techniques, is crucial because of varying susceptibilities among species. Early, accurate diagnosis and prompt effective treatment optimize outcomes.

Tumid Lupus Erythematosus has been the subject of heated debate as to its correct nosographic classification. In particular, its definition has changed over time, depending on the different studies performed. In this review we address the initial definition of TLE, the changes that have taken place in the understanding and placement within the classification of Lupus Erythematosus (LE), and focus on clinical, histopathological, immunophenotypical and differential diagnosis aspects.

This study explores novel techniques to improve the detection accuracy of skarn iron deposits using advanced image processing methodologies. Leveraging the capabilities of Aster image, Band Ratio (BR) images, and Principal Component Analysis (PCA), alongside the power of 3D Convolutional Neural Networks (3D-CNN), the research aims to enhance the precision and efficiency of ore detection in complex geological environments. The proposed method employs a specific 3D-CNN architecture, accepting input as a 7×7×C image patch, where C represents the combined number of selected Aster image bands, Principal Component (PC) bands, and computed BR images. To evaluate the accuracy of the proposed method, five distinct image bands combinations, including the proposed bands combination, were tested and evaluated based on the overall accuracy (OA), average accuracy (AA), and kappa coefficient. The results demonstrated that while the incorporation of BR images alongside Aster bands initially seemed promising, it introduced significant confusion in certain classifications, leading to unexpected mis-classification rates. Surprisingly, utilizing solely Aster bands as input parameters yielded higher accuracy rates (OA= 93.13%, AA = 91.96%, Kappa = 90.91%) compared to scenarios involving the integration with band ratios (OA = 87.02%, AA = 79.15, and Kappa = 82.60%) or the integration of BR images to PC bands (OA= 87.78%, AA = 82.39%, Kappa = 83.81%). However, the amalgamation of Aster bands with selected PC bands showed slight improvements in accuracy (OA= 94.65%, AA = 92.93%, Kappa = 93.45%), although challenges in accurately classifying certain features persisted. Ultimately, the proposed combination of Aster bands, PC bands, and BR images (proposed bands combination) presented the most visually appealing and statistically accurate results (OA = 96.95%, AA= 94.87%, and Kappa = 95.93%), effectively addressing misclassifications observed in the other combinations. These findings underscore the synergistic contributions of each of the Aster bands, PC bands, and BR images, with Aster bands proving pivotal for optimal skarn classification, PC bands enhancing intrusions classification accuracy, and BR images strengthening wall rock classification accuracy. In conclusion, the proposed combination of input image bands emerges as a robust and comprehensive methodology, demonstrating unparalleled accuracy in the remote sensing detection of skarn iron minerals.

Introduction Livestock abortigenic agents, which are microorganisms that lead to premature foetal death and expulsion before completion of the gestation period, are common in Africa and Asia. Abortion events cause economic losses by lowering reproduction (and hence herd/flock sizes) and effects on milk production. Despite the importance of livestock production for food security and livelihoods of millions of the world’s poorest communities, very little is known about the scale, magnitude or causes of livestock abortion in Africa. The aim of this review was to determine the current status of the burden of livestock abortion and surveillance measures adopted for livestock abortigenic pathogens in Africa and Asia, and to explore feasible surveillance technologies. Methodology A systematic literature search was conducted using Preferred Reporting of Systematic Reviews and Meta-analyses (PRISMA) guidelines in four databases for studies published between 1 Jan 1990 and 31 July 2021 that reported epidemiological surveys of livestock abortigenic pathogens in cattle, goats and sheep in Africa and Asia including; Brucella spp., Neospora caninum, Toxoplasma gondii, Rift valley fever virus, Coxiella burnetii, Chlamydia, Leptospira and Bovine viral Diarrhoea Virus. A meta-analysis was used to estimate the species-specific prevalence of the abortigenic diseases and the region where they were detected. Results In the systematic literature search, 48 full papers were included which in total included 50 species-specific surveillance reports from Africa and 19 from Asia. Adjusted median seroprevalence calculations estimated Brucella at 6.85% (range 1.2-11.6) of 9071 sheep, 3.35% (range 0.90-5.40) of 17,007 goats, 8.95% (range 0.50-63.60) of 171,733 cattle, Neospora at 6.80% (range 6.80 -6.80) of 555 sheep, 10.80 (range 10.80-10.80) of 185 goats, 12.65% (range 3.40- 25.60) of 3775 cattle, Toxoplasma at 27.50% (range 1.40 – 75.90) of 2284 sheep, 32.0% (range 20.00- 64.80) of 1226 goats, 7.50% (range 7.50 - 7.50) of 174 cattle, Coxiella at 9.20 (range 9.20 – 9.20) of 184 sheep, 24.20% (range 24.20-24.20) of 91 goats, 13.80% (range 13.80-13.80) of 217 cattle, Rift valley fever virus at 7.70 (2.40-40.00) of 874 sheep, 20.95 (range 2.50-40.00) of 547 goats, 7.45% (range 3.60-11.30) of 309 cattle, Bovine viral diarrhea virus at 78.90 (range 78.90 – 78.90) of 398 cattle, Leptospira at 70.50 (range 70.50 – 70.50) of 373 cattle and Chlamydia at 6.60 (6.60-6.60) of 803 sheep. We found that most studies, 45 (89%) used serological surveys, 1 (2%) used molecular and 1 (2%) reported to have used Mobile-phone based surveillance approach. Three studies (6.25%) of the 48 included were embedded in the national surveillance programs of the respective countries they were conducted, majority 89% were stand-alone cross-sectional studies. Conclusion In conclusion, livestock abortigenic pathogens are still a burden in many African and Asian countries.

Introduction: Cleft lip with or without cleft palate (CL/P) is the most common congenital face anomaly in the world of multifactorial etiology. Objective: To determine the prevalence and characteristics of cleft palate. To identify associated risk factors to guide prevention and/or prenatal detection for early intervention and better health outcomes. To assess the rehabilitation protocols after operations in cleft palate. Design: Retrospective descriptive and clinical study. Patients: One hundred and three children with cleft palate treated in the Department of Head and Neck Surgery Clinic for Children and Young Adults, Department of Clinical Pediatrics, University of Warmia and Mazury, were included in the study. Methods: Records evaluation was conducted regarding sex, cleft type, mother occupation, parents education, histories of cleft in any of parents. The data were analyzed using R (R core team 2021) and the ordinal logistic regression analyses. Results: Children of stressed mothers during pregnancy had 9.4 times higher odds of having bilateral cleft. While there was no evidence that children sex, order of birth, and body mass as well as risk of maternal toxicity at work or reporting infections or drugs toxicity had a significant effect on the dependent variable. Conclusions: Children of parents with cleft history and having low educated mothers had higher probability of bilateral cleft palate. Children of stressed mothers during pregnancy had higher odds of having bilateral cleft. In literature, there is a lack of reports about rehabilitation in different operations methods in children with cleft palates.

In recent decades, as eutrophication in inland and coastal waters (ICWs) has increased due to anthropogenic activities and global warming, so has the need for timely monitoring. Compared with traditional sampling and laboratory analysis methods, satellite remote sensing technology can provide macro-scale, low-cost, and near real-time water quality monitoring services. The Geostationary Ocean Color Imager (GOCI), integrated onboard the Communication Ocean and Meteorological Satellite (COMS) from the Republic of Korea, was the first geostationary ocean color observation satellite and was operational from April 1, 2011 to March 31, 2021. Over ten years, GOCI has observed oceans, coastal waters, and inland waters within its 2,500 km×2,500 km target area centered on the Korean Peninsula. The most attractive feature of GOCI, compared with other commonly used watercolor sensors, was its high temporal resolution (1h, eight times daily from 0 UTC to 7 UTC), providing the opportunity to monitor the quality of ICWs, where optical properties can change rapidly throughout the day. This systematic review aims to comprehensively review GOCI features and applications in ICWs, analyzing progress in atmospheric correction algorithms and water quality monitoring. Analyzing 123 articles from the Web of Science and China National Knowledge Infrastructure (CNKI) through a bibliometric quantitative approach, we examined GOCI’s strength and performance with different processing methods. These articles reveal that GOCI played an essential role in monitoring the ecological health of ICWs in its observation area in East Asia. GOCI has led the way to a new era of geostationary ocean satellites, providing new technical means for monitoring water quality in oceans, coastal zones, and inland lakes. We also discuss the challenges encountered by geostationary satellites in monitoring water quality and provide suggestions for improvements.

Gene regulation is vital for cellular function and homeostasis, involving diverse mechanisms controlling specific gene products and contributing to tissue-specific gene expression. Dysregulation leads to diseases, emphasizing the need for understanding these mechanisms. Non-coding regions, notably enhancers and super-enhancers (SEs), act as crucial gene regulators, orchestrating transcriptional activity via transcription factors (TFs). SEs, with high activity, are pivotal in cell identity and disease progression. Current computational approaches focus on individual regulators like TFs and miRNAs, neglecting SE interactions. We emphasize incorporating SEs into models to enhance understanding. Experimental studies have recently linked miRNAs and TFs to SEs. Chromatin immunoprecipitation followed by sequencing (ChIP-Seq) provides valuable SE information. In this review, we categorize computational methods leveraging TF and miRNA data into three areas and explore challenges integrating enhancers/SEs. These include unraveling indirect regulatory networks, identifying network motifs, and enriching pathway identification by dissecting gene regulators, including SEs. We hypothesize that addressing these challenges will enhance our understanding of gene regulation, aiding in identifying therapeutic targets and disease biomarkers. We believe that understanding the role of ChIP-Seq data in predicting the effect of miRNA on gene regulation is crucial to construct statistical/computational model that would tackle these challenges.

Depressive disorder in childhood and adolescence is a highly prevalent mood disorder that tends to recur throughout life. Untreated mood disorders can adversely impact a patient's quality of life and cause socioeconomic loss. Thus, an accurate diagnosis and appropriate treatment is crucial. However, until now, diagnoses and treatments are conducted according to clinical symptoms. Objective and biological validation are lacking. This may result in a poor outcome for patients with depressive disorder. Research has been conducted to identify the biomarkers that are related to depressive disorder. Cumulative evidence has revealed that certain immunologic biomarkers in-cluding brain-derived neurotrophic factor (BDNF) and cytokines, gastrointestinal biomarkers, hormones, oxidative stress, and certain hypothalamus-pituitary axis biomarkers are associated with depressive disorder. This article reviews the biomarkers related to the diagnosis and treatment of pediatric depressive disorders. To date, clinical biomarker tests are not yet available for diagnosis or for the prediction of treatment prognosis. However, cytokines such as Interleukin-2, interfer-on-gamma, tumor necrosis factor-alpha, and BDNF have shown significant results in previous studies of pediatric depressive disorder. These biomarkers have the potential to be used for diagnosis, prognostic assessment, and group screening for those at high risk.

Polycystic Ovary Syndrome (PCOS) is the most common endocrine disorder that affects women of reproductive age representing the primary cause of anovulatory infertility. The nonapeptide oxytocin (OT) plays an important role in cognitive, emotional, and reproductive functions in human beings. Oxytocin receptors are expressed in several body parts including the ovaries. Despite this, the possible role played by oxytocin in symptoms of PCOS is not clear. The present systematic review aimed at understanding, the presence of possible oxytocin level alterations in PCOS, the connection between alterations of OT levels and the symptoms of PCOS, and the effect of oxytocin administration in PCOS. After a systematic search in the principal databases, seven studies, five human and two animal, were included. Most of the studies highlighted the role played by oxytocin in fertility issues related to PCOS. Studies that analyzed oxytocin basal levels in PCOS women agreed that PCOS is associated with a reduction in the serum level of oxytocin. Two studies found that in PCOS the serum levels of oxytocin were lower than healthy controls. One human and one animal study agreed about lower levels of PCOS, confirming a possible implication of a dysfunction of OT, in the pathogenesis of PCOS.

We have carried out a systematic review of the scientific literature published on PM and, more specifically, on its presence in fish and seafood of interest to the population from Mediterranean area. Given its preference and wide acceptability in the scientific community, we followed the PRISMA. The potential functional and clinical toxicity in humans due to MP in fish must be considered by the chemical composition of the microplastics the presence of PM and its additives, associated contaminants and pathogens in fish and shellfish, as well as the possible effects on human health, requires improving legislation on evaluation and providing a basis for consumer protection.

Hybrid materials are a recent addition in the field of Restorative Dentistry for computer-assisted design/ computer-assisted manufacturing (CAD/CAM) indirect restorations. The long–term clinical success of modern dental restorative materials follows a multifactorial pattern. Among the characteristics, affecting the longevity of a restoration, mechanical properties and physicο – chemical interactions are of utmost importance. While numerous researchers constantly evaluate the mechanical properties, the biological background of resin–based CAD/CAM biomaterials is scarcely investigated and, therefore, less described in the literature. This review aims to analyze the biofilm formation on the surfaces of novel hybrid, resin–based CAD/CAM materials and evaluate the methodological protocols followed to assess microbial growth. It is demonstrated that the surface structure, the composition and the finishing and polishing procedures on the surface of a dental restorative material influence the initial bacterial adhesion; however most studies focus on in vitro protocols, whereas in vivo and/or in situ research of microbiomics in CAD/CAM restorative materials is lacking, obstructing in that manner the accurate understanding of the bioadhesion phenomenon in the oral cavity.

Every subset S of a Cartesian spaces Rd, endowed with differen- tial structure C∞(S) generated by restrictions to S of functions in C∞(Rd), has a canonical partition M(S) by manifolds, which are or- bits of the family X(S) of all derivations of C∞(S) that generate local one-parameter groups of local diffeomorphisms of S. This partition satisfies the frontier condition, Whitney’s conditions A and B. If M(S) is locally finite, then it satisfies all definitions of stratification of S. This result extends to Hausdorff locally Euclidean differential spaces.

Most research on colostrum quality and colostrum management focuses on maternal IgG transfer to the newborn calf. However, in addition to being a source of antibodies, colostrum has other components that influence passive immune transfer, nutrition, gut maturation, and thermoregulation. In this review, the constituents in the bovine colostrum, as well as their variation, are explored and it is discussed the biological role of colostrum on the newborn calf. Protein is the nutrient in higher concentration and fat is the nutrient with the most variation. There is a pronounced amplitude in the total solids content of colostrum, but the average concentration does not vary much between studies (24.7%). Bovine colostrum major components can vary with several factors, such as the region, season, breed, dry period length, and nutritional aspects of the dam. Bioactive components, cells, and microbiome variability are less understood, besides IgG. Regardless, they play crucial distinct roles in the physiological development of the newborn. However, a multi-interactive effect between these components, cells, and microorganisms is more likely to occur. Recently researched components in colostrum like miRNA, nucleo(t)sides, the milk fat globule membrane, oligosaccharides, minor proteins, and the microbiome are examples of promising interest areas of study.

The Na+/K+ ATPase is a protein involved in the active transport of ions across the cellular membrane. Ouabain is a cardiotonic glycoside that, by inhibiting the Na+/K+ pump, interferes with cell processes mediated directly by the pump, but also indirectly influences other cellular processes, such as cellular cycle and proliferation, growth, cell differentiation, angiogenesis, migration, adhesion and invasion. We used the SK-BR-3 breast cancer cell line, mesenchymal stem cells (MSCs), and tumor-associated fibroblasts (TAFs) in vitro for determining the changes induced by Ouabain in concentration of 10-5 M, 10-6 M, 10-8 M, 10-9 M on these cellular types, employing specific methods: SEM for morphological evaluation; immunocytochemistry, flowcytometry to assess immunophenotypical markers expression, cell cycle and Annexin V/PI assay for cellular proliferation and viability testing; flowchamber assay to evaluate functional adhesion behavior; RT-PCR for Na+/K+ ATPase subunits molecular expression. Being indicative of the pro-apoptotic and inhibitory effect of Ouabain on tumor invasion and metastasis, the results support the addition of Ouabain to the oncological therapeutic arsenal, trailing the “repurposing drugs” approach.

Recent years have witnessed significant advances in reinforcement learning (RL), which has registered remarkable success in solving various sequential decision-making problems in machine learning. Most of the successful RL applications, e.g., the games of Go and Poker, robotics, and autonomous driving, involve the participation of more than one single agent, which naturally fall into the realm of multi-agent RL (MARL), a domain with a relatively long history, and has recently re-emerged due to advances in single-agent RL techniques. Although, typically, the communication protocol between agents is manually specified and not altered during training, recently, some papers have shown signs of trying to emerge a communication between agents on the one hand and, on the other hand, to understand what is exchanged between agents. So, there is a growing body of literature on this topic which includes qualitative and quantitative studies and the ones that apply mixed methods. This study presents the scoping review of the methodological strategies undertaken in a total of 16 research articles. The results present the critical appraisal of quantitative methods in terms of validity and reliability and for qualitative methods considering four trustworthiness factors. In the end, relevant insights are further explored with implications and reflections on how they can benefit one's research in the field.

This paper presents a review on numerical and experimental methods to predict fracture of ad-vanced high strength steels (AHSS). AHSS are widely processed through sheet metal forming processes. Although being an excellent choice when lightweight, high strength and ductility are critical factors, their multi-phase microstructure accentuates forming challenges. To accurately model forming behavior, necking based failure criteria as well as direct fracture models require improvements. As a necking based failure model, the conventional forming limit diagram/curve (FLD/FLC) presents limitations such as in estimating direct fracture (surface cracks, edge cracks, shear cracks), as well as deformation histories under nonlinear strain paths. Thus, significant re-search efforts are being made towards the needs of advanced fracture constitutive models capa-ble of predicting fracture scenarios without necking, more frequently observed in the realm of AHSS. Scientific community research is divided into several directions aiming at improving the forming and fracture behavior accuracy of parts subjected to sheet metal forming operations. In this review paper, a comprehensive overview on ductile fracture modeling is presented. Firstly, the FLD/FLC limitations modeling fracture behavior in sheet metal forming operations are stud-ied. Followed by the recent trends in constitutive material modeling. Afterwards, material char-acterization methods advancements to cover a broad range of stress states are comprised. Final-ly, damage and fracture models predicting failure in AHSS are investigated. This review paper supplies relevant information on the current issues the sheet metal forming community is chal-lenged with due to the trend towards AHSS employment in the automotive industry.

Keywords: Innovation ecosystems, Economic sustainability, Hydrocarbon-based economies, Economic diversification, Qualitative approach

Waste glycerol can be subjected to various processing operations, including purification and re-fining, to obtain glycerol of appropriate purity. In addition, alternative methods of utilising waste glycerol are also being sought, e.g. by converting it into other valuable chemical products or biofuels. Therefore, various technologies are being developed to utilise this type of waste effectively and sustainably. The production of value-added products from waste glycerol is an important issue that determines the improvement of the economic viability of biofuel production and corresponds to the model of a waste-free and emission-free circular economy. This paper characterised the mechanisms and evaluated the efficiency of existing methods of microbiological utilisation of waste glycerol into liquid biofuels, including biodiesel, bioethanol and biobutanol, and identified further directions for the production of value-added products. In addition, a bibliographical analysis of publications re-lated to the production of liquid fuels and economically valuable products from glycerol was carried out, the progress of research and application work was assessed and directions for future research were identified.

Conventional cancer clinical trials can be time-consuming and expensive, often yielding results with limited applicability to real-world scenarios and presenting challenges for patient participation. Real-world data (RWD) studies offer a promising solution to address evidence gaps and provide essential information about the effects of cancer treatments in real-world settings. Defining RWD based on its original intent, which is the collection of data at the point of care for clinical purposes, can distinguish it from conventional clinical trial data. RWD can be generated using experimental designs that resemble those used in conventional clinical trials, but with several advantages, such as improved efficiency and a better balance between internal and external validity. RWD can support pharmacovigilance, insights into disease progression, and the development of external control groups. Prospective RWD collection enables evidence generation based on pragmatic clinical trials (PCTs) that support randomized study designs and expand clinical research to the point of care. To ensure the quality of real-world studies, it is crucial to implement auditable data abstraction methods and develop new incentives to capture clinically relevant data electronically at the point of care. The treatment landscape is constantly evolving, with the integration of front-line immune checkpoint inhibitors (ICIs), either alone or in combination with chemotherapy, affecting subsequent treatment lines. Real-world effectiveness and safety in underrepresented populations, such as the elderly, patients with poor performance status (PS), hepatitis, or human immunodeficiency virus-infected patients, are still largely unexplored. Similarly, the cost-effectiveness and sustainability of these innovative agents are important considerations in the real world.

Keywords: mHealth; peritoneal dialysis; functionality; usability

Abstract: Rice is one of the most important food crops in sub-Saharan Africa. Climate change, variability, and economic globalization threaten to disrupt rice value chains across the subcontinent, undermining their important role in economic development, food security, and poverty reduction. This paper maps existing research on the vulnerability of rice value chains, synthesizes the evidence and the risks posed by climate change and economic globalization, and discusses agriculture and rural development policies and their relevance for the vulnerability of rice value chains in sub-Saharan Africa. Important avenues for future research are identified. These include the impacts of multiple, simultaneous pressures on rice value chains, the effects of climate change and variability on parts of the value chain other than production, and the forms and extent to which different development policies hinder or enhance the resilience of rice value chains in the face of climatic and other pressures.

Over the past couple of decades, Global positioning system (GPS) technology has been utilized to collect large-scale data from travel surveys. As the precise spatiotemporal characteristics of travel could be provided by GPS devices, the issues of traditional travel survey, such as misreporting and non-response, could be addressed. Considering the defects of dedicated GPS devices (e.g., need much money to buy devices, forget to take devices to collect data, limit the simple size because of the number of devices, etc.), and the phenomenon that the smartphone is becoming one of necessities of life, there is a great chance for the smartphone to replace dedicated GPS devices. Although, several general reviews have been done about smartphone-based GPS travel survey in the literature review section in some articles, a systematic review from smartphone-based GPS data collection to travel mode detection has none. The included studies were searched from six databases. The purpose of this review is to critically assess the current literature on the existing methodologies of travel mode detection based on GPS raw data collected by smartphones. Meanwhile, according to the systematic comparison among different methods from data-preprocessing to travel mode detection, this paper could carefully provide the Strengths and Weaknesses of existing methods. Furthermore, it is the crucial step to develop the methodologies and applications of GPS raw data collected by smartphones.

Brain is the most complex organ in the human body and it is divided into two hemispheres - left and right hemispheres. Left hemisphere is responsible for control of right side of our body whereas right hemisphere is responsible for control of left side of our body. Brain image segmentation from different neuroimaging modalities is one of the important parts in clinical diagnostic tools. Neuroimaging based digital imagery generally contain noise, inhomogeneity, aliasing artifacts, and orientational deviations. Therefore, accurate segmentation of brain images is a very difficult task. However, the development of accurate segmentation of brain images is very important and crucial for a correct diagnosis of any brain related diseases. One of the fundamental segmentation tasks is to identify and segment inter-hemispheric fissure/mid-sagittal plane, which separate the two hemispheres of the brain. Moreover, the symmetric/asymmetric analyses of left and right hemispheres of brain structures are important for radiologists to analyze diseases such as Alzheimer's, Autism, Schizophrenia, Lesions and Epilepsy. Therefore, in this paper we have analyzed the existing computational techniques used to find brain symmetric/asymmetric analysis in various neuroimaging techniques (MRI/CT/PET/SPECT), which are utilized for detecting various brain related disorders.

The review explores the therapeutic potential of mangrove plants as sources of bioactive compounds, particularly focusing on their anti-inflammatory properties. Mangroves, found in tropical and sub-tropical tidal coasts, exhibit unique ecological and biological activities. The review compiles and examines 136 research findings published between 1982 and 2018, with 41 studies specifically addressing the anti-inflammatory properties of mangroves in Indian coastal regions, including the Andaman and Nicobar Islands. It summarizes the chemical structures, molecular weights, formulas, solvents, and biological activities of these mangroves, as reported by various researchers over time. The data underscore the significant potential of tropical Indian mangroves in anti-inflammatory applications. However, a notable research gap exists regarding the extraction of bioactive compounds from mangrove species in the Andaman and Nicobar Islands compared to other regions such as Gujarat, Maharashtra, Goa, Karnataka, Kerala, Tamil Nadu, Andhra Pradesh, Odisha, West Bengal, and Puducherry. Overall, the compilation provides valuable insights into mangrove-based studies for drug discovery research, emphasizing the need for further exploration and exploitation of these natural resources.

The term Conjugated Linoleic Acid (CLA) refers generically to a class of positional and geometric conjugated dienoic isomers of linoleic acid. Among the isomers of linoleic acid cis9, trans11-CLA (c9, t11-CLA) and trans10, cis12-CLA (t10, c12-CLA) are found to be bio-logically active isomers, and they occur naturally in milk, dairy products and meat from ruminants. In addition, some vegetables and some sea-foods have also been reported to contain CLA. Although the CLA levels in these natural sources are insufficient to confer the essential health benefits, anti-carcinogenic or anti-cancer effects are of current interest. In the rumen, CLA is an intermediate of isomerization and the biohydrogenation process of linoleic acid to stearic acid conducted by ruminal microorganisms. In addition to rumen bacteria, some other bacteria, such as Propionibacterium, Bifidobacterium and some lactic acid bacteria (LAB) are also capable of producing CLA. In this regard, Lactiplantibacillus plantarum (formerly Lactobacillus plantarum) has demon-strated the ability to produce CLA isomers from linoleic acid by multiple enzymatic activi-ties, including hydration, dehydration, and isomerization. L. plantarum is one of the most versatile species of LAB and the bacterium is widely used in the food industry as a micro-bial food culture. The studies reviewed in this article reveal how, in the production of some fermented foods, the use of appropriate strains of L. plantarum, as a starter or additional culture, can be considered a critical factor in the design of new CLA-enriched functional foods.

Hydrogen has a leading role in accomplishing a net zero green-house gas emission future. Today’s challenge is producing green hydrogen to cover the fuel demand of transportation and industry, to gain independence from fossil fuels. This review’s goal is to demonstrate critically the existing methods of biomass treatment and assess their ability to scale up. Biomass is an excellent hydrogen carrier and biomass-derived processes are the main target for hydrogen production as it is an innovative pathway to green hydrogen production. Comparing the existing processes, thermochemical treatment is found to be far more evolved than biological or electrochemical treatment especially with regard to scaling prospects.

Fungal infections caused by Candida spp. are responsible for high hospital morbidity and mortality rates. Therefore, there is a continuous search for new antifungal medicines, particularly ones with anti-Candida activity. This review analyzed articles published between 2002 and 2023 considering the anti-Candida activity of chemical compounds identified or isolated in plants of the Anacardiaceae family. In addition, the in-silico prediction of the isolated compounds described was performed. The analysis of 35 studies showed that extracts, essential oils, and compounds from the anti-Candida activity were frequently determined in vitro using the minimum inhibitory concentration (MIC). The most commonly tested species were Candida albicans, C. tropicalis, C. parapsilosis, C. glabrata, C. krusei, and C. guilliermondii, respectively. Essential oils were the most used form (37% of the studies). The isolated compounds with antifungal effects include cardanol, estragole, trans-anethole, β-caryophyllene, myrcene, catechin-3-o-rhamnoside, β-sitosterol-3-O-glucoside, 24Z-isomasticadienolic acid, oleanolic acid, and oleanolic aldehyde. The in-silico evaluation of those isolated compounds revealed the compounds' drug-likeness and possible antifungal activity. However, some of them showed high toxicity. In conclusion, compounds from the Anacardiaceae family show promise for developing new therapeutic antifungal drugs, especially in combination with conventional antifungals.

Compressed carbon dioxide energy storage (CCES) offers several benefits over other existing energy storage systems, including ease of liquefaction, high energy storage density, and environmental friendliness. As a result, the research progress, economic and technological feasibility, and system operation of the CCES system are all discussed in depth in this study. The system evaluation method is summarized and the compressed carbon dioxide storage is analyzed, and the performance optimization direction of the compressed carbon dioxide energy storage technology is discussed. When the overall performance of a transcritical CCES system, a supercritical CCES system and a liquid CCES system are compared, it is discovered that the supercritical CCES system has better thermodynamic characteristics and a simpler system configuration, making it suitable for large-scale development and use. The goal of the CCES system's future development is to create a design with an optimum compression and expansion ratio, a more precise analysis and system model, and multi-field coupling. This review's discussion serves as a guide for the best design and use of the CCES system.

Timely and accurate information on tree species is crucial for the sustainable management of natural resources, forest inventory, biodiversity detection, and carbon stock calculation. The advancement of remote sensing technology and artificial intelligence has facilitated the acquisition and analysis of remote sensing data, resulting in more precise and effective classification of tree species. Multimodal remote sensing data and deep learning seem to be the current tree species classification research mainstream, whether or not. The current review on the remote sensing data and deep learning tree species classification methods perspectives to analyze the unimodal and multimodal remote sensing data and classification methods in this realm is missing. To bridge the gap, we search for major trends in the remote sensing data and tree species classification methods, provide a detailed overview of classic deep learning-based methods for tree species classification, and discuss the limitations.

Abstract: Wildfires in forest ecosystems exert substantial ecological, economic, and social impacts. The effectiveness of fire management hinges on precise pre‐fire risk assessments to inform mitigation efforts. This study aims to investigate the relationship between predictions from pre‐fire risk assessments and outcomes observed through post‐fire burn severity analyses. In this study, forest fire risk was assessed with the Fuzzy Analytical Hierarchy Process, in which fire behavior factors were used as input. The degree of burn was determined using the Random Forest method using 11519 training points and 400 test points on Sentinel‐2 satellite images in three different classes. According to the results obtained from 266 selected test points located within the forest boundaries, all primary factors showed an increased effect in areas with high burn severity. Climate, in particular, emerged as the most influential factor, accounting for 52% of the overall impact. However, in cases of high fire severity, climate proved to be the most effective risk factor, ac‐counting for 67%. It followed by topography with 50% accuracy at high fire intensity. In the risk assessment is based on the FAHP method, climate was assigned the highest weight value among other factors with 32.2%. Topography emerged as the second most effective risk factor with 27 %. To evaluate the results more comprehensively both visually and statistically, two regions with different stand canopy characteristics were selected within the study area. While high burn severity had the highest accuracy in Case 1 area, moderate burn severity had the highest in Case 2 area. During the days when the fire continued, the direction of spread was obtained from MODIS images. In this way, the fire severity depending on the direction of fire progression was also interpreted. Through an analysis of various case studies and existing literature, the research underscores both the strengths and limitations inherent in predicting forest fire behavior‐based pre‐fire risk assessments. Furthermore, it emphasizes the necessity of continuous improvement to increase the success of forest fire management.

Automated charging technologies are becoming increasingly important in the electrification of heavy road freight transport, especially in combination with autonomous driving. This study provides a comprehensive analysis of automated charging technologies for electric heavy-duty vehicles (HDVs). It encompasses the entire spectrum of feasible technologies, including static and dynamic approaches, with each charging technology evaluated for its advantages, potentials, challenges, and technology readiness level (TRL). Static conductive charging methods such as charging robots, underbody-couplers or pantographs show good potential, with pantographs being the most mature option. These technologies are progressing towards higher TRLs, with a focus on standardization and adaptability. While static wireless charging is operational for some prototype solutions, it encounters challenges related to implementation and efficiency. Dynamic conductive charging through an overhead contact line or contact rails holds promise for high-traffic HDV routes with the overhead contact line being the most developed option. Dynamic wireless charging, although facing efficiency challenges, offers potential for seamless integration into roads and minimal wear and tear. Battery swapping is emerging as a practical solution to reduce downtime for charging, with varying levels of readiness across different implementations. To facilitate large-scale deployment further standardization efforts are required. The study emphasizes the necessity for continued research and development to enhance efficiency, decrease costs, and ensure seamless integration into existing infrastructures. Technologies that achieve this best will have the highest potential to significantly contribute to the creation of an efficiently automated and environmentally friendly transport sector.

Over the years, the need for autonomous systems such as drones, also known as unmanned aerial vehicles (UAVs), has significantly increased in popularity across several industries, including the military. The Internet of Battlefield Things (IoBT) represents a modern technological advancement that significantly enhances the operational efficacy of defense systems. It establishes an integrated military force by connecting individuals to intelligent technology in drones, radios, equipment, antennas and ground stations. This is achieved through the utilization of cloud and edge computing, sensors, mobile devices, embedded systems, and IoT-based systems. However, IoBT security is significantly compromised by enemies that exploit the vulnerabilities and malicious intrusions that specifically target the external environment or to obtain sensitive data. In addition, a comprehensive understanding of cyberattacks and countermeasures is essential for assuring the security of IoBT. On the other hand, as cyber threats get more complicated, trust becomes increasingly important for the security of digital systems. Within this context, the zero trust architecture contends that the security of a complex network is inherently vulnerable to internal and external threats. In defense, UAV security system applications, for example, the primary purpose of the zero trust architecture is to limit the vulnerability of military UAV systems to cyberattacks, hence reducing the possibility of data breaches and illegal entry. This study conducts a systematic review aimed at identifying different aspects of cybersecurity strategies for protecting UAV systems within the IoBT domain. Furthermore, zero trust architecture is presented in the study as an effective prospective solution to the security issues that develop in defense UAV systems.

Due to environmental concerns, natural refrigerants and their use in refrigeration and air conditioning systems are receiving more attention from manufacturers, end users and the scientific community. The study of heat transfer and pressure drop is essential for accurate design and more energy efficient cycles using natural refrigerants. The aim of this work is to provide an overview of the latest outcomes related to heat transfer and pressure drop correlations for ammonia, propane, isobutane and propylene and to investigate the current state of the art in terms of operating conditions. Available data on the existing correlations of heat transfer coefficient and pressure drop for natural refrigerants have been collected through a systematic search. Whenever possible, validity intervals are given for each correlation and the error is quantified. It is the intention of the authors that this could be a valuable support for researchers and an aid to design, with particular reference to heat pumps. A procedure based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement was adopted, and the Scopus database was used to query the relevant literature. A total of 135 publications qualified for inclusion in the survey; 34 articles report experimental investigations for not usual geometric conditions. Of the 101 selected papers related to usual geometric conditions, N = 50 deal only with HTC, N = 16 deal only with pressure drop and the remainder (N = 35) analyse both HTC and pressure drop. Among the 85 HTC papers (N = 53) deal with the evaporating condition, N = 30 with condensation and only N = 2 with the heat transfer correlations under both conditions. Most of the 101 articles concern propane and isobutane. The high temperatures are less widely investigated.

The pharmaceutical industry is one of the largest pollutants and generator of toxic solvent resi-dues for the environment and human beings. In the last decade, there has been a huge evolution in the design and development of new environmentally friendly products and processes, in re-sponse to the global environmental concern and the growing demand for products with quality ingredients and manufacturing processes aimed at achieving sustainability. Consumers are looking for products with the benefits of natural products, with sustainable and environmentally friendly formulations with few synthetic molecules as possible. Thus, based on the origins of the pharmaceutical industry, it is important to return to Nature and enjoy the benefits that natural products bring to us. Deep eutectic solvents appear as an alternative to toxic solvents commonly used to extract bioactive compounds. These solvents are considered green solvents because they are made up of natural compounds such as sugars, amino acids, and carboxylic acids. In addition, they are simple to prepare, with an atomic economy of 100%, with attractive physicochemical properties. This paper is a comprehensive review in which is described the main properties, characteristics, and production methods of deep eutectic solvents as well as its application to extract from natural sources bioactive compounds with pharmaceutical interest.

This paper explores the views of a group of young men aged 16–18, studying towards a Level 1 diploma in music in the North East area of England. It uses the conceptualization of “social haunting” to focus our discussions about how Level 1 learners articulated their experiences in the context of their learning journeys and perceived marginalization. The research took place at a large Further Education (FE) college and included a series of five “research encounters” with the group of Level 1 learners. These encounters included a range of activities, resulting in a variety of data being collected, including several activity worksheets, structured and unstructured focus group interviews, and questionnaires. This paper focusses on the outcomes of a single research activity that focused on exploring the topic of marginalization. We present a discussion on social haunting as one of the emerging findings from this research encounter, outlining how the reality of the social and cultural legacy of the UK’s industrial past can shape today’s youth and their futures. We will talk about the young men’s views of marginalization, their school-to-work transitions, the formation of masculinity in post-industrial communities and the role of Further Education in it. The paper concludes by discussing some insights for practice and future research.

This Systematic Review examines studies focusing on tooth bleaching and its effect on healthy enamel or incipient caries and bacterial adhesion. The aim is to explore the impact of different bleaching agents on incipient caries lesions and healthy enamel. Randomized controlled trials, clinical trials, observational studies, and in vitro studies that compared at least two groups were included. A comprehensive search strategy was used to select studies from Pubmed and Scopus databases. Two evaluators performed data extraction, screening, and quality assessment independently. Only studies written in English were included. From 968 initial records, 28 studies were selected for a full-text evaluation. 7 studies were classified as cluster 1 (bacterial adherence on teeth), 12 studies as cluster 2 (no bacteria involved), 4 studies as cluster 3 (no teeth deployment), and 5 clinical studies were cluster 4. Of the selected studies, 6 (21.43%) supported increased bacterial attachment capacity and cariogenic dynamics, 4 (14.29%) decreased adhesion and cariogenic activity, 7 (25%) no difference, and 11 (39.28%) followed a differentiated methodological approach and could not be categorized. Due to different methodology strategies used in the studies of all clusters, the risk of bias was high. Thus, we cannot reach a confident conclusion.

Monitoring of clients living with HIV encompasses a specific array of activities that routinely check on various aspects of ART following its initiation depending on the availability of resources in that particular state, among other factors as per guidelines from the World Health Organisation. This is a significant practice as it promotes positive health outcomes for patients and efficient utilisation of ART regimens to reduce resistance to the medication – hence it is a measure to guard avoidable costs. It also forms a basis on which decisions are made from. It can be done clinically by assessing for signs e.g., skin rash and symptoms e.g., nausea and vomiting, checking vital organ function and measuring weights across all populations, or can be also be done by assessing specimens for their respective viral loads, white blood cell count, pregnancy status or other infections such as hepatitis B. There have not been any special indications required for one to be eligible for monitoring during ART in developing countries; all qualify for routine monitoring even though some may be more frequent than others in cases of comorbidities such as renal failure where medications can be attributed to exacerbating the condition.

Background: Mental capacity is a fundamental aspect that enables patients to fully par-ticipate in various healthcare procedures. To assist healthcare professionals (HCPs) in assessing patients' capacity, especially in the mental health field, several standardized tools have been developed. These tools include the MacArthur Competence Assessment Tool for Treatment (MacCAT-T), the MacArthur Competence Assessment Tool for Clinical Research (MacCAT-CR), and the Competence Assessment Tool for Psychiatric Advance Directives (CAT-PAD). The core dimensions explored by these tools include Understanding, Appreciation, Reasoning, and Expression of a choice. Objective: This meta-analysis aimed to investigate potential differences in deci-sion-making capacity within the healthcare context among groups of patients with bi-polar disorder (BD) and schizophrenia spectrum disorder (SSD). Methods: A systematic search was conducted on Medline/Pubmed, and Scopus. Addi-tionally, Google Scholar was manually inspected, and a manual search of emerging re-views and reference lists of the retrieved papers was performed. Eligible studies were specifically cross-sectional, utilizing standardized assessment tools, and involving pa-tients diagnosed with BD and SSD. Data from the studies were independently extracted and pooled using random-effect models. Hedges g was used as a measure for outcomes. Results: Six studies were identified, with three studies using the MacCAT-CR, two studies the MacCAT-T, and one the CAT-PAD. The participants included 189 individuals with BD and 324 individuals with SSD. The meta-analysis revealed that patients with BD performed slightly better, and the difference was statistically significant, compared to patients with SSD, particularly in the domain of Appreciation (ES=0.23, 95% CI: 0.01 to 0.04, p=0.037). There was no statistically significant difference between the two groups for Understanding (ES=0.09, 95% CI: - 0.10 to 0.27, p=0.352), Reasoning (ES=0.18, 95% CI: - 0.12 to 0.47, p=0.074), and Expression of a choice (ES = 0.23, 95% CI: - 0.01 to 0.48, p=0.60). In the sensitivity analysis, furthermore, when considering only studies involving patients in symptomatic remission, the difference for Appreciation also resulted in non-significant (ES=0.21, 95% CI: -0.04 to 0.46, p=0.102). (ES=0.21, 95% CI: -0.04 to 0.46, p=0.102). Conclusions: These findings indicate that there are no significant differences between patients with BD and SSD during remission phases, while differences are minimal during acute phases. The usefulness of standardized assessment of capacity at any stage of the illness should be considered, both for diagnostic-therapeutic phases and for research and advance directives. Further studies are necessary to understand the reasons for the overlap in capacity between the two diagnostic categories compared in this study.

We will apply Pascal’s hydrostatic pressure law to black holes and also to the Hubble sphere. Haug has recently demonstrated how hydrostatic pressure in water can be utilized to determine the Planck length. One can conceptualize the energy in the Hubble sphere as a type of superfluid, and if this is correct, then Blaise Pascal’s hydrostatic pressure formula may also be applicable to such a superfluid, even within the Hubble sphere. By employing the same method on the Hubble sphere and considering it as a Schwarzschild black hole with the critical Friedmann mass, we can estimate the Planck length based on its hypothetical hydrostatic pressure.

The development of plant-based meat analogues has become a significant challenge for the food industry in recent years due to the increasing demand for sustainable and healthier proteins in the context of a global protein transition. Plant-based meat analogues imitate the visual, textural, and chemical properties of traditional meat products and are required to closely resemble meat to appeal to consumers. In addition, consumers demand natural, clean-label, and nutritional and healthy products. To address these challenges, the food industry must develop highly healthy, nutritious, and E-number-free meat analogue products. Understanding the functionality of each ingredient and its role in the food matrix is crucial to being a key player in the innovation of the meat analogues market. This review provides updated information on the primary ingredients utilized for the development of plant-based burger meat alternatives and their functionality. The key components of meat analogue burgers are outlined, including plant proteins, binding agents, fats and oils, flavorings, colorings, preservatives, fortificants, and clean-label considerations.

Preschoolers with disabilities and their caregivers have been neglected in health and social service provision in most low-income countries and arguably too in low resourced areas of more affluent nations. Yet as this rapid review of published literature identifies, there are low-cost, evidence-based strategies to address their needs that can be implemented in communities by local people. Five key features of the necessary supports are examined. First the leadership functions required to create and implement the support services. Second, the family-centred, home-based support provided to caregivers and the personnel undertaking this form of support. Third, providing opportunities for peer support to flourish and encouraging the formation of advocacy groups across families. Fourth, mobilizing the support of significant groups within the community; notably traditional healers and leaders; health services and poverty alleviation initiatives. Fifth, devising ways in which preschool educational opportunities can be offered to children as a prelude to their inclusion in primary schools. The review serves a further purpose. It provides an example of how public health researchers and academics could achieve more rapid implementation of evidence-based knowledge into existing and new support services through dissemination to community practitioners.

In the field of delta-sigma modulators, reducing system power consumption without sacrificing accuracy has become a challenge. The summing circuit, as the main part of the cascade-of-integrator-in-feedforward delta-sigma modulator, consumes a significant amount of power in active summing. On the other hand, passive summing circuits have low power consumption but can cause attenuation in the summing results. This article proposes a low-power multi-bit delta-sigma modulator based on passive and attenuationless summation scheme. The summation circuit achieves multiplication of the voltage signal carried on the summation capacitor through bidirectional sampling technique, compensating for the inherent attenuation caused by passive summation, thus eliminating the need for active OTA to achieve perfect summation. A 2-order delta-sigma modulator based on 4-bit 1-order passive noise-shaping (NS) SAR quantizer is designed to verify the scheme. By combining a passive NS SAR as the 4-bit quantizer and the third-stage integrator of the system, and incorporating an energy-efficient cascode floating inverter amplifier (FIA) structure as the core active OTA, these elements have significantly enhanced the energy-efficiency of the modulator. Simulation results show that at a supply voltage of 1.2 V and a bandwidth of 20 kHz, the SNDR reaches 102.62 dB, power consumption is only 148.32 μW, and Schreier figure-of-merit (FoM) of SNDR is 183.92 dB.

Background: Schistosome egg deposition in pregnant women may affect the placenta of infected mothers and cause Placental Schistosomiasis (PS). Histopathological examination of placental tissue is an inadequate detection method due to low sensitivity. So far, there has not been any sys-tematic review on PS. Methods: We conducted a systematic literature search on PubMed and in-cluded relevant secondary literature found in the citations of the primarily included publications. Results: We found a total of 8 publications describing PS with a total of 92 cases, describing egg deposition of dead and/or viable eggs and worms of S. haematobium and S. mansoni in placental tissue. One cross-sectional study investigating the prevalence of PS and its association to adverse birth outcomes, found 22% of placentas to be infested using a maceration technique but only < 1% using histologic examination. Additionally, no direct link to deleterious pregnancy outcomes could be shown. Conclusions: PS is a highly unattended and underdiagnosed condition in en-demic populations, due to a lack of awareness as well as low sensitivity of histopathological examinations. However, PS may play an important role in mediating or reinforcing adverse birth outcomes (ABO) such as fetal growth restriction (FGR) in maternal schistosomiasis, possibly by placental inflammation.

Background: Autism spectrum disorder (ASD) is a persistent neurodevelopment disorder frequently co-occurring with Attention-Deficit/Hyperactivity Disorder (ADHD) and behavior-related disorders. While behavioral therapy is the first-line option to manage ASD core symptoms, pharmacological therapy is sometimes needed to treat acute problems, such as agitation and aggressive behaviors. Recent guidelines recommend neuroleptics to reduce psychomotor agitation in people with ASD. However, since children with ASD are often drug-resistant, alternative treatments are often justified. Reports from literature showed that intra-venous valproate (IV-VPA) can be effective in reducing agitation in psychiatric patients, with lower frequency of adverse events compared to convention-al treatments. However, since findings are occasionally inconsistent, IV-VPA is not an approved option in clinical psychiatry. We aim at improving the knowledge on the IV-VPA treatment option in emergency psychiatry in the pediatric age. Methods: We report the case of a 11-year-old boy suffering from a complex neurodevelopmental condition, who experienced a psychotic episode with severe aggressive and disruptive behaviors, successfully treated with IV-VPA. Besides, we conducted an up-dated literature review on this topic. Conclusion: In our case, first-line therapies proved to be ineffective. On the contrary, IV-VPA led to a safe and prompt clinical success, in line with other reports. Based on our literature review, IV-VPA can be highly effective and can reduce the risk of adverse events that frequently occur with the use of high-dose standard medications in emergency psychiatry.

For 80 years high-intensity focused ultrasound (HIFU) has been a significant area for medical research. It is a non-invasive procedure which causes cell death in a highly localised area at a defined distance from the transducer. Since 2000, the clinical indications have broadened considerable as the technology has improved. This review looks back at the history of HIFU and the current clinical indications.

Children are inherently curious about everything they encounter, making the early years a perfect time for STEAM (science, technology, engineering, arts, and math) investigations. An outdoor learning environment can influence STEAM concept formation of preschoolers with intentional design that offer STEAM learning affordances. Despite the rising interest in early STEAM education, there is still limited literature on how the outdoor environment may influence STEAM learning behaviors of preschoolers (3-5 years old). This scoping review intends to evaluate the existing knowledge regarding the physical factors that contribute to STEAM learning affordances in an outdoor environment for children aged 3 to 5. The review included studies from the last twenty years. This scoping review was conducted in accordance with the criteria outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR). For this scoping review, 843 citations were discovered across four databases (JSTOR, Scopus, EBSCOhost, and Web of Science), ProQuest, and Google Scholar, and 31 articles were considered eligible for inclusion. Through the synthesis of information from those 31 studies, a list of STEAM learning behaviors of children and STEAM activity-supportive settings were identified that may positively influence preschoolers' STEAM concept development.

Dirac's Large Number Hypothesis (LNH), proposed in 1937, has captivated the scientific community with its exploration of profound correlations between cosmic and atomic scales. This hypothesis delves into the intricate interplay between the infinitesimally small and the vastly large, offering potential insights into the fundamental nature of the universe. As we continue to uncover the mysteries of the cosmos, the LNH oscillates on the precipice of scientific acceptance—neither fully validated nor entirely dismissed. In this review paper, we embark on a comprehensive journey through Dirac's LNH, shedding light on its theoretical underpinnings, its implications for universe models, and its resonance with the Anthropic Principle. We delve into the possibilities of variable gravitational constants and continuous mass creation, inviting further exploration into the intricacies of our cosmic symphony. By embracing the ongoing quest for understanding, we endeavor to unravel the profound harmonies that connect the infinitesimal with the infinite, contributing to the symphony of knowledge in theoretical physics and cosmology.

Prostate cancer is the second most frequent malignancy (after lung cancer) in men worldwide. Prostate tissue biopsies are usually graded using scores according to the Gleason grading system. This Gleason score is the most popular prognostic marker that reveals the potential aggressiveness of the disease. However, inter-observer variability in rating by human assessors is a major limiting factor. Such variability could lead to missing a severe case or suggesting unnecessary treatments. This study explores the discriminative ability of artificial intelligence (deep learning models) for Gleason score assessment. The study was designed to use whole-slide images of digitized H&E-stained biopsies of prostate tissues to automate the grading process and provide a remotely accessible clinical decision support system. Custom convolutional neural network architectures were trained on 10,616 images of prostate tissues. Gaussian filters were applied to pre-process the images and improve model performance. Transfer learning was applied to train eight machine learning architectures namely: Xception, VGG16 & 19, ResNet101, MobileNet, DenseNet121, EfficientNetB5 & B7. Efficient NetB7 had the best performance 85.2% compared with ground-truth classification by experts. Performance improves as more data is available. The model was deployed and hosted as a web application API on Google cloud service to ensure remote access. Tissue biopsy images can be uploaded and the corresponding Gleason score recovered immediately. This system reduces diagnostics turnaround time, increase throughput, and compensate for limited skills, especially in low resource settings.