ethicalabs/Research-Intent-Collab huggingface dataset:
Antimicrobial stewardship: community pharmacists’ antibiotic dispensing practices, knowledge, and perception regarding antibiotics and antibiotic resistance

Abstract                                   Objectives                   Antimicrobial resistance is a major global health threat, and the inappropriate use of antibiotics is a key driver of this phenomenon. Community pharmacists play a crucial role in the responsible use of antibiotics and in promoting antimicrobial stewardship. This study assessed the knowledge and perception of community pharmacists regarding antibiotics and antibiotic resistance as well as their antibiotic dispensing practices.                                                   Methods                   This was a cross-sectional survey conducted among 126 community pharmacists in Ibadan, with the use of a self-administered paper questionnaire. The questionnaire contained Likert-type 8-item knowledge, 5-item perception, and 18-item dispensing practice scales. The scaled score was graded as adequate and inadequate knowledge, and good or poor dispensing practices. Data were analysed using SPSS (version 25) and results are presented using descriptive statistics.                                                   Key findings                   Adequate knowledge of antibiotics and antibiotic resistance was demonstrated by 89 (70.6%) of the community pharmacists. However, 60 (47.6%) reported that antibiotics were not classified as prescription-only medications in Nigeria and that there were no regulations governing their sale and dispensing. Majority, 118 (93.7%), agreed that it is important to conduct culture and sensitivity tests before prescribing antibiotics to patients. Only 21 (16.7%) of the respondents agreed that pharmacists should always dispense antibiotics only when a prescription is presented. In all, 115 (91.3%) community pharmacists had poor antibiotic dispensing practices.                                                   Conclusion                   Most of the community pharmacists possess adequate knowledge of antibiotics and antibiotic resistance; nonetheless, a significant portion of them exhibits poor antibiotic dispensing practices.
Physics of liquid crystals in cell biology

The last decade has witnessed a rapid growth in understanding of the pivotal roles of mechanical stresses and physical forces in cell biology. As a result an integrated view of cell biology is evolving, where genetic and molecular features are scrutinized hand in hand with physical and mechanical characteristics of cells. Physics of liquid crystals has emerged as a burgeoning new frontier in cell biology over the past few years, fueled by an increasing identification of orientational order and topological defects in cell biology, spanning scales from subcellular filaments to individual cells and multicellular tissues. Here, we provide an account of most recent findings and developments together with future promises and challenges in this rapidly evolving interdisciplinary research direction.
The Utilization and Limitation of Genetic Information in Insurance: Improvement Plan of China’s“Genetic Non-discrimination Act”

According to current Chinese law, insurance companies cannot use genetic information for medical, disease, disability, care and medical accident insurance. Nevertheless, this approach has not effectively solved the problem of “genetic discrimination” in China. Genetic information plays a vital role in insurance. The theory that genetic information can be used in insurance is based on risk classification and price equilibrium principle. Adverse selection can be avoided by dividing genetic information into different risk classes. This is an inevitable requirement of the insurer's right to know. For the consideration of social and public interests, commercial insurance should undertake a reasonable social relief function, but it is necessary to clarify the limitations of the social relief function of commercial insurance, and commercial insurance and social insurance cannot be equated. In terms of the characteristics of genetic information itself, there is no substantial difference between it and general medical information. Insurance covers uncertain risks, and with the progress of technology, the accuracy of genetic testing has also been improved. Therefore, theoretically speaking, it is not necessary to completely prohibit insurers from using genetic information in the insurance field. Therefore, in the future, China's legislation on genetic discrimination should be changed from completely prohibiting the use of genetic information by insurers to conditionally restricting the use of genetic information by insurers. First of all, the way to obtain genetic information by the insurer should be clarified, and the insurer should be prohibited from forcing the insured to carry out predictive genetic testing. The insurance applicant only must inform the specific genetic testing results. Secondly, it is necessary to set conditions for the insurer to use genetic information. The insurer can only use genetic information in personal insurance that meets a certain amount of money, and the use of genetic information must meet legal procedures and obtain the consent of the insured. In addition, it is also necessary to strengthen the regulation of the gene testing industry, formulate strict market access rules, and establish a gene consultant system to make up for the inferior position of the insured in the profession. Finally, improve the rescue of the insured, let professionals participate in the settlement of disputes, and increase the severity of punishment for the illegal use of genetic information by insurance companies.
Erratum: "The Lateral Trigger Probability function for the Ultra-High Energy Cosmic Ray Showers detected by the Pierre Auger Observatory"

Este documento es una errata de "The Lateral Trigger Probability function for the Ultra-High Energy Cosmic Ray showers detected by the Pierre Auger Observatory" (ver documento relacionado).
Hydrogeological Controls on Groundwater Contamination from Petroleum Operations: Contaminant Migration, Aquifer Vulnerability, and Assessment Methods

Abstract Petroleum hydrocarbons enter groundwater from every stage of the oil and gas supply chain, drilling, pipeline transport, refining, storage, and retail distribution, and persist in aquifer systems as free-phase, dissolved, residual, and vapor-phase contaminants for decades after the initial release. How severely a given release affects groundwater depends less on spill volume or operation type than on the hydrogeological properties of the receiving aquifer. This narrative review evaluates the controls that govern that relationship, drawing on field evidence published between 2010 and 2025 from Scopus, Web of Science, and Google Scholar. Four domains are addressed: contaminant migration and fate, including multiphase flow behavior and natural source zone depletion rates measured across 40 petroleum sites at a median of 9,540 L/ha/yr; aquifer vulnerability assessment, comparing DRASTIC, GOD, and physically based transport models across alluvial, karst, and fractured settings; hydrogeological controls on contamination severity, with depth to the water table, lithology, fracture connectivity, and water table fluctuation identified as the dominant factors; and field assessment and monitoring methods, from standard hydrochemical indicators through compound-specific isotope analysis and geophysical imaging. Index-based vulnerability frameworks provide useful regional screening but produce vulnerability estimates differing by a factor of four when multiple models are applied to identical input data, and none account for the temporal evolution driven by biodegradation. Site characterization that pairs index screening with multi-parameter field investigation, hydrochemistry, redox profiling, isotopic analysis, and geophysical spatial coverage, would bring routine practice closer to the level of understanding that the available science already supports.&nbsp;
Health Care Quality in End-of-Life Care: Promoting Palliative Care in the Intensive Care Unit

Seminal articles published in the late 1990s instigated not only an intense interest in health care quality but also a new era of research into quality end-of-life care, particularly in intensive care units (ICUs). ICUs can improve health care quality at the end of life by better using palliative care services and palliative care-related principles. This article details how the interest in health care quality has spurred a similar interest in end-of-life and palliative care in ICUs, defines palliative care and describes how it improves health care quality, and highlights barriers to the incorporation of palliative care in ICUs.
X-ray Binaries in External Galaxies

X-ray appearance of normal galaxies is mainly determined by X-ray binaries powered by accretion onto a neutron star or a stellar mass black hole. Their populations scale with the star-formation rate and stellar mass of the host galaxy and their X-ray luminosity distributions show a significant split between star-forming and passive galaxies, both facts being consequences of the dichotomy between high- and low-mass X-ray binaries. Metallicity, IMF and stellar age dependencies, and dynamical formation channels add complexity to this picture. The numbers of high-mass X-ray binaries observed in star-forming galaxies indicate quite high probability for a massive star to become an accretion powered X-ray source once upon its lifetime. This explains the unexpectedly high contribution of X-ray binaries to the Cosmic X-ray Background, of the order of $\sim 10\%$, mostly via X-ray emission of faint star-forming galaxies located at moderate redshifts which may account for the unresolved part of the CXB. Cosmological evolution of the $L_X-{\rm SFR}$ relation can make high-mass X-ray binaries a potentially significant factor in (pre)heating of intergalactic medium in the early Universe.
The Impact of Climate and Land Use Change on Greek Centipede Biodiversity and Conservation

Centipedes (Chilopoda, Myriapoda) are crucial soil predators, yet their vulnerability to climate and land use change remains unexplored. We assess the impact of these drivers on Greek centipedes, identify current and future biodiversity hotspots, and evaluate the effectiveness of the Natura 2000 Network of protected areas for their conservation. We used an updated species occurrence database of Greek centipedes, derived from literature reviews and museum collections, and evaluated database completeness and geographic sampling biases. Species Distribution Models were employed to predict future distribution shifts under climate and land use change scenarios. Biodiversity hotspots were identified based on species richness (SR) and corrected-weighted endemism (CWE) metrics. We overlapped SR and CWE metrics against the Natura 2000 Network to assess its effectiveness. We found that sampling effort is highly heterogeneous across Greece. All species are projected to experience range contractions, particularly in the 2080s, with variation across scenarios and taxa. Current biodiversity hotspots are concentrated in the south Aegean islands and mainland mountain ranges, where areas of persistent high biodiversity are also projected to occur. The Natura 2000 Network currently covers 52% of SR and 44% of CWE hotspots, with projected decreases in SR coverage but increases in CWE coverage. Our work highlights the vulnerability of Greek centipedes to climate and land use change and reveals conservation shortfalls within protected areas. We identify priority areas for future field surveys, based on sampling bias and survey completeness assessments, and highlight the need for further research into mechanisms driving centipede responses to global change.
MUST-CNN: A Multilayer Shift-and-Stitch Deep Convolutional Architecture for Sequence-based Protein Structure Prediction

Predicting protein properties such as solvent accessibility and secondary structure from its primary amino acid sequence is an important task in bioinformatics. Recently, a few deep learning models have surpassed the traditional window based multilayer perceptron. Taking inspiration from the image classification domain we propose a deep convolutional neural network architecture, MUST-CNN, to predict protein properties. This architecture uses a novel multilayer shift-and-stitch (MUST) technique to generate fully dense per-position predictions on protein sequences. Our model is significantly simpler than the state-of-the-art, yet achieves better results. By combining MUST and the efficient convolution operation, we can consider far more parameters while retaining very fast prediction speeds. We beat the state-of-the-art performance on two large protein property prediction datasets.
First ‘One-Health’ study on genome-wide comparison of multidrug-resistant Escherichia coli from Human-Animal-Aquaculture-Environment continuum: a collective effort from six institutes of India

Summary:                   Antibiotic-resistant bacteria are emerging threat to the global public health. Estimates suggests &gt;10 million deaths by 2050 due to AMR alone. Six Indian institute collaborated along with 4 UK universities to address the issue of antimicrobial resistance (AMR) through One Health approach under NEOSTAR project jointly funded by Department of Biotechnology (DBT), Government of India and UK Research and Innovation (UKRI), United Kingdom. To address a question “Dose AMR in livestock contributes to AMR in people? A pilot study was conducted which is a collaborative effort to collect archival microbial isolates of                   Escherichia coli                   ,                   Klebsiella                   spp.,                   Staphylococcus aureus                   and coagulase negative                   S. aureus                   (CoNS). The current study deals with                   E. coli                   isolates collected from ICAR-NIVEDI, ICAR-CIFT, ICAR-IVRI, ICAR-RCNEH, IIT-Delhi and GMCH and were subjected to QC analysis at ICAR-NIVEDI’s BSL2+ facility, these isolates were further segregated into 5 components viz., Animal origin (including aquaculture) (n=50) food of animal origin (n=30), Human (community settings) (n=3), Hospital origin (n=15) and Environment origin (n=14). A total of 112                   E. coli                   isolates sequenced for whole genome through Illumina’s HiSeq 2500 platform at Sangers Institute, UK and paired end libraries were generated for all the 112 isolates were assembled and subjected to genome characterization and gene mining through various bioinformatics tools revealed the frequency of resistance, co-resistance, and resistant genes are high and similar across Human-Animal-Aquaculture-Environment continuum in India. This emphasizes the need to collaborate and mitigate antibiotic resistance with a ‘One Health’ approach.
YY1 in hematopoietic stem cells: epigenetic regulation, chromatin architecture, and aging

Purpose of Review                     Yin Yang 1 (YY1) is a multifunctional transcription factor (TF) with established roles in lymphocyte development. More recently, its functions as a Polycomb group (PcG) protein and chromatin structural regulator in the hematopoietic system have gained increasing attention. This mini-review summarizes emerging insights into the epigenetic and architectural roles of YY1 in fetal and adult hematopoietic stem cells (HSCs), extending beyond its classical transcriptional activity. We also discuss how altered YY1 function contributes to HSC aging and hematopoietic decline.                                                           Recent Findings                     Recent studies show that YY1 is essential for maintaining HSC quiescence, self-renewal, and engraftment during both fetal and adult hematopoiesis. Mechanistically, YY1 regulates hematopoiesis through PcG-dependent pathways and by shaping higher-order chromatin organization. It mediates long-range chromatin interactions, cooperates with cohesin and CTCF to organize three-dimensional genome architecture, and coordinates transcriptional and metabolic programs critical for stem cell maintenance. Notably, age-associated reductions in YY1 activity are linked to functional impairment of HSCs, implicating YY1 in hematopoietic aging.                                                           Summary                     YY1 acts as a TF, chromatin organizer, and PcG protein in hematopoiesis. Defining its roles in normal and aging HSCs may reveal mechanisms of hematopoietic decline and inform strategies to preserve blood system function.
Strategic HR Activities to Enhance the Plant Productivity Through Inventory, Logistics and Supply Chain Management

The steel manufacturing industries have become the most significant growth catalysts for the Indian economy. Some of the most difficulties these companies face are the strategic planning and execution of the Human Resources in the productive field of Inventory, Logistic and Supply Chain which are actually meant for the increase in the productivity of the plant.Aligning everyone in this organization with the strategy is one of the most important things that can be done beyond formulating and implementing great strategies. The significance of the study is to determine & analyze the manpower planning, recruitment, training, compensation & retention towards the increase in plant productivity. Also from the operational point of view, to determine the performance based Logistic & Supply Chain Management, work break down structure of Logistic Management, importance of SIMILAR concept in the production & manufacturing industry. A clear statement of problem is what should be the proper manpower planning, productivity of HR, logistic support cycle, responsibility & maintainability of logistic, effective & efficient use of HR, raw material handling etc. towards the plant’s growth in terms of economy & output. Secondary data was first studied and the strategy followed by the talent acquisition team and production team is analyzed, then awareness of it is identified sending the questionnaire to the specific employees.
High-dimensional flow law parameter calibration and uncertainty quantification over Antarctic ice shelves: a variational Bayesian approach using deep learning

The flow and deformation of glacier ice in response to stress is often described using Glen’s Flow Law, a power-law relation that compactly represents ice rheology with a prefactor, A, and stress exponent, n. For natural ice, these parameters (and the parameters subsumed within them) come with large uncertainties that have not been robustly quantified with observations. Modern remote sensing technologies that collect data with finer resolutions and broader coverage provide us with an opportunity to robustly calibrate these rheological parameters for certain environments. Here, we utilize publicly available observations of ice sheet surface velocity and elevation acquired with remote sensing platforms to calibrate the flow law parameters over select Antarctic ice shelves. We build upon recent work that used remote sensing observations to quantify the relationship between ice stress and strain rate in extensional flow to infer an exponent of n = 4.1 +/- 0.4 for Antarctica. Here, we model two-dimensional flow and perform parameter calibration by constructing and training physics-informed neural networks (PINNs) to learn spatially-varying A and uniform n for each ice shelf. We cast the parameter estimation problem as a neural network optimization problem through minimization of a cost function that includes both data reconstruction errors and momentum balance residuals derived from the 2D shallow-shelf approximation. Additionally, we formulate the networks to predict spatially-varying uncertainties for A by using variational inference techniques, which approximate Bayesian inference (traditionally a computationally-intensive procedure) as an additional optimization objective. Finally, we demonstrate the use of time-dependent surface velocities, which are becoming increasingly more available over the ice sheets, to independently constrain the stress exponent n, confirming the appropriateness of n = 4 derived from previous work. Overall, calibration of these parameters with robust uncertainties are critical for placing observational constraints on prognostic ice flow model parameters and to improve our understanding of flow and fracture processes on ice shelves in Antarctica.
Characterization of Big Data Stream Processing Pipeline

In recent years there has been a surge in applications focusing on streaming data to generate insights in real-time. Both academia, as well as industry, have tried to address this use case by developing a variety of Stream Processing Engines (SPEs) with a diverse feature set. On the other hand, Big Data applications have started to make use of High-Performance Computing (HPC) which possess superior memory, I/O, and networking resources compared to typical Big Data clusters. Recent studies evaluating the performance of SPEs have focused on commodity clusters. However, exhaustive studies need to be performed to profile individual stages of a stream processing pipeline and how best to optimize each of these stages to best leverage the resources provided by HPC clusters. To address this issue, we profile the performance of a big data streaming pipeline using Apache Flink as the SPE and Apache Kafka as the intermediate message queue. We break the streaming pipeline into two distinct phases and evaluate percentile latencies for two different networks, namely 40GbE and InfiniBand EDR (100Gbps), to determine if a typical streaming application is network intensive enough to benefit from a faster interconnect. Moreover, we explore whether the volume of input data stream has any effect on the latency characteristics of the streaming pipeline, and if so how does it compare for different stages in the streaming pipeline and different network interconnects. Our experiments show an increase of over 10x in 98 percentile latency when input stream volume is increased from 128MB/s to 256MB/s. Moreover, we find the intermediate stages of the stream pipeline to be a significant contributor to the overall latency of the system.
MIND MEETS MACHINE: REVOLUTIONIZING MENTAL HEALTH WITH CHATBOTS AND DIGITAL PHENOTYPING

The COVID-19 pandemic highlighted an urgent need for scalable mental health solutions, leading to the rise of chatbots and digital phenotyping as innovative interventions. This study examines how intelligent chatbots like Ethika leverage digital phenotyping to provide personalized mental health support by analyzing user data such as screen time and app usage. These systems address mental health disparities, particularly in underserved areas, by offering accessible, real-time recommendations and resources. Despite their potential, ethical concerns regarding data privacy remain critical for building trust in these technologies. This research underscores the transformative potential of chatbots in advancing remote mental health care while emphasizing the need for ethical safeguards.
THE ROLE OF SUSTAINABLE AGRICULTURAL DEVELOPMENT IN ENSURING FOOD SECURITY

The article examines the issues of increasing the sustainability of agricultural producers as the main factor in ensuring food security in the context of instability of international relations and markets for means of production and food. This problem is studied at the international, state, regional and local levels. For this purpose, the criteria and corresponding systems of indicators are substantiated, as well as indicators of the state of food security at various levels of management: federal, regional, local. The features of their application are shown depending on natural and climatic conditions and changes in ecological systems, on the technogenic impact of man on the environment, on the territorial location and specialization of agricultural production, its material and technical base and staffing.