ethicalabs/Research-Intent-Collab huggingface dataset:
Integrative Pathway Analysis of Metabolic Signature in Bladder Cancer: A Linkage to The Cancer Genome Atlas Project and Prediction of Survival

We used targeted mass spectrometry to study the metabolic fingerprint of urothelial cancer and determine whether the biochemical pathway analysis gene signature would have a predictive value in independent cohorts of patients with bladder cancer.Pathologically evaluated, bladder derived tissues, including benign adjacent tissue from 14 patients and bladder cancer from 46, were analyzed by liquid chromatography based targeted mass spectrometry. Differential metabolites associated with tumor samples in comparison to benign tissue were identified by adjusting the p values for multiple testing at a false discovery rate threshold of 15%. Enrichment of pathways and processes associated with the metabolic signature were determined using the GO (Gene Ontology) Database and MSigDB (Molecular Signature Database). Integration of metabolite alterations with transcriptome data from TCGA (The Cancer Genome Atlas) was done to identify the molecular signature of 30 metabolic genes. Available outcome data from TCGA portal were used to determine the association with survival.We identified 145 metabolites, of which analysis revealed 31 differential metabolites when comparing benign and tumor tissue samples. Using the KEGG (Kyoto Encyclopedia of Genes and Genomes) Database we identified a total of 174 genes that correlated with the altered metabolic pathways involved. By integrating these genes with the transcriptomic data from the corresponding TCGA data set we identified a metabolic signature consisting of 30 genes. The signature was significant in its prediction of survival in 95 patients with a low signature score vs 282 with a high signature score (p = 0.0458).Targeted mass spectrometry of bladder cancer is highly sensitive for detecting metabolic alterations. Applying transcriptome data allows for integration into larger data sets and identification of relevant metabolic pathways in bladder cancer progression.
Highlighting the role of agriculture and geospatial technology in food security and sustainable development goals

AbstractFood security is a global challenge that aligns with several Sustainable Development Goals (SDGs), including SDG 1 ‐ “No Poverty”, SDG 2 ‐ “Zero Hunger,” SDG 3 ‐ “Good Health and Well‐being,” SDG 13 ‐ “Climate Action,” and SDG 15 ‐ “Life on Land.”. To effectively address this issue, a convergence of agriculture and technology is crucial, incorporating precision agriculture, sustainable agriculture, bio‐economy and advanced technologies such as machineries, Artificial intelligence‐meachine learning and geospatial technology. Recent trends in food security worldwide have witnessed the adoption of technological advancements. However, it is important to consider biodiversity when implementing and adopting technological advancements. The integration of technology in agricultural practices aim to reduce chemical usage in farms, while increased production remains the main objective of smart farming and Precision Agriculture. By minimizing the use or implementing localized approaches for chemicals on farms, we can preserve soil‐faunal diversity, which is at risk along with targets due to excessive applications of pesticides, herbicides, and insecticides. Geospatial technology plays a significant role by leveraging remotely sensed images acquired through satellites, aerial platforms, and GPS‐tagged drones to enhance agricultural practices, improve crop growth and increase crop yields. Through geospatial technology, we can map and monitor soil and crop conditions, and effectively conserve, protect, and manage biodiversity on a global scale. The applications of remote sensing technology consider ecological and environmental parameters, soil factors, crop conditions, and plant‐soil diversity to optimize yields and agricultural productivity. Additionally, the evaluation of various agricultural practices such as sustainable agriculture, bio‐economy, using technology can contribute to increased yields, profitability, and biodiversity conservation as central components of these practices. This study emphasizes that achieving the SDG objectives is attainable through agriculture, which has direct and indirect connections to all SDGs. Geospatial technology plays a vital role in supporting the objectives at spatial and temporal scales by facilitating agricultural practices, ensuring food security, preserving biodiversity, monitoring soil and crop conditions, and promoting sustainable development.
CO$_2$ sequestration hybrid solver using isogeometric alternating-directions and collocation-based robust variational physics informed neural networks (IGA-ADS-CRVPINN)

This paper presents the hybrid solver for a $CO_2$ sequestration problem. The solver uses the IGA-ADS (IsoGeometric Analysis Alternating Directions solver) to compute the saturation scalar field update using the explicit method, and CRVPINN (Collocation-based Robust Variational Physics Informed Neural Networks solver) to compute the pressure scalar field. The study focuses on simulating the physical behavior of $CO_2$ in porous structures, excluding chemical reactions. The mathematical model is based on Darcy's Law. The CRVPINN is pretrained on the initial pressure configuration, and the time step pressure updates require only 100 iterations of the Adam method per time step. We compare our hybrid IGA-ADS solver, coupled with the CRVPINN method, with a baseline of the IGA-ADS solver coupled with the MUMPS direct solver. Our hybrid solver is over 3 times faster on a single computational node from the ARES cluster of ACK CYFRONET. Future work includes extensive testing, inverse problem solving, and potential application to $H_2$ storage problems.
Spatiotemporal chromatin dynamics - A telltale of circadian epigenetic gene regulation

Over the course of evolution, nature has forced organisms under selection pressure to hardwire an internal time keeping device that defines 24 h of a daily cycle of physiological and behavioral rhythms, known as circadian rhythms. At the cellular level, the cycle is governed by significant fractions of transcriptomes, which are under the control of transcriptional and translational feedback loop of clock genes. Intriguingly, this feedback loop is regulated at multiple stratums such as at the transcriptional and translational levels, which direct a cell towards producing a robust rhythm by sustaining the repeated stoichiometry of protein products. Moreover, with the advent of state of the art paradigms, epigenetic regulation of circadian rhythms has been becoming more evident at present time. Light-induced recurring fluctuations in chromatin acetylation concurrent with the binding of RNA Pol II and integration of miRNAs monitor the chromatin modifiers or clock genes expression to drive temporal rhythmicity. Furthermore, CLOCK protein intrinsic histone acetyl transferase activity, the interaction of CLOCK-BMAL-1 with HAT enzymes, and the involvement of many histone deacetylases also maintain the rhythmic protein profile. Additionally, the critical role of the rhythmic methylation pattern of clock genes in battery of cancer and metabolic disorders also defines its importance. Therefore, in this review, we focused on accumulating all the present data available on epigenetics and circadian rhythms. Interestingly, we also gathered evidence from the available literature pinpointing towards the dynamic nature of chromatin architecture governed by long and short-range regulatory elements DNA contacts arising daily, that was thought to be steady otherwise.
The Causes of Their Death Appear (Unto Our Shame Perpetual): Why Root Cause Analysis Is Not the Best Model for Error Investigation in Mental Health Services

Objectives             Root cause analysis (RCA) is the model of accident investigation mandated by the New South Wales Ministry of Health in Australia to review all serious incidents. Anecdotal evidence suggests that the RCA model is not suitable for reviewing adverse events in mental health services such as suicides and homicides. The aims of the research were to evaluate the impact of RCA on improving patient outcomes in a regional mental health service in New South Wales, Australia, and to discover whether the RCA model is the most appropriate model in mental health.                                   Methods             The literature on RCA was reviewed. The effectiveness of implementation of RCA recommendations was evaluated using quantitative and qualitative data.                                   Results             Suicides and homicides, which constitute 90% of serious critical incidents in mental health, do not always lend themselves well to identification of a root cause. There is evidence for low clinician engagement in the recommendations that arise from RCAs and a poor level of evidence that implementation of RCA recommendations results in safer patient care. Most troubling is the finding that RCAs frequently make recommendations that repeat existing policy or previous recommendations.                                   Conclusions             Although the RCA model offers a formal and systematic approach to the review of serious critical incidents in mental health, it is not the model of best fit. Only 65% of recommendations made through RCA reviews are implemented within 12 months.
Pedestrian Attribute Recognition: A New Benchmark Dataset and A Large Language Model Augmented Framework

Pedestrian Attribute Recognition (PAR) is one of the indispensable tasks in human-centered research. However, existing datasets neglect different domains (e.g., environments, times, populations, and data sources), only conducting simple random splits, and the performance of these datasets has already approached saturation. In the past five years, no large-scale dataset has been opened to the public. To address this issue, this paper proposes a new large-scale, cross-domain pedestrian attribute recognition dataset to fill the data gap, termed MSP60K. It consists of 60,122 images and 57 attribute annotations across eight scenarios. Synthetic degradation is also conducted to further narrow the gap between the dataset and real-world challenging scenarios. To establish a more rigorous benchmark, we evaluate 17 representative PAR models under both random and cross-domain split protocols on our dataset. Additionally, we propose an innovative Large Language Model (LLM) augmented PAR framework, named LLM-PAR. This framework processes pedestrian images through a Vision Transformer (ViT) backbone to extract features and introduces a multi-embedding query Transformer to learn partial-aware features for attribute classification. Significantly, we enhance this framework with LLM for ensemble learning and visual feature augmentation. Comprehensive experiments across multiple PAR benchmark datasets have thoroughly validated the efficacy of our proposed framework. The dataset and source code accompanying this paper will be made publicly available at \url{https://github.com/Event-AHU/OpenPAR}.
Using Artificial Intelligence to Enhance Myelodysplastic Syndrome Diagnosis, Prognosis, and Treatment

Myelodysplastic syndromes represent a group of hematological neoplastic diseases caused by defective stem cells causing cytopenia and abnormal hematopoiesis. More than 30% of myelodysplastic syndrome cases develop into acute myeloid leukemia. An analysis of bone marrow samples, peripheral blood smears, multiparametric flow cytometry data, and clinical patient information is part of the current, time-consuming, and labor-intensive work up for myelodysplastic syndromes. Nowadays, clinical biomedical research has been transformed by the advent of artificial intelligence, specifically machine learning. Artificial intelligence (AI) can improve risk assessment and diagnosis, as well as boost the precision of clinical outcome prediction and illness classification. Algorithms based on artificial intelligence may be potentially helpful in discovering new needs for myelodysplastic syndrome-affected patients, choosing treatment and assessing minimal residual disease. In this review, we seek to identify the primary mechanisms and uses of artificial intelligence in myelodysplastic syndrome, pointing out its advantages and disadvantages while discussing the possible benefits of using AI pipelines in a therapeutic setting.
Requirements for marine protected areas to conserve the biodiversity of rocky reef fishes

Abstract This study describes spatial patterns in the biodiversity (species, assemblages) of rocky reef fishes at a spatial scale relevant to management, and compared the outcomes for this biodiversity from alternative procedures for selecting marine protected areas (MPAs) and from the selection of MPAs for fisheries‐related objectives. The study area included 104 species in two assemblage types; 36 species and 14 species occurred only in one or two locations respectively. MPAs selected by hotspot richness, greedy richness complementarity, and summed irreplaceability included similar percentages of species and significantly more species than randomly selected MPAs. A combined species‐assemblage selection ensured representation of assemblage diversity. Representation of all species and assemblage types required 92% of locations. MPAs chosen using density of all fishes or density of exploitable fishes as selection criteria included fewer species (than MPAs selected using species identity) and the percentage of species accumulated did not differ from a random selection. Use of an established MPA as the seed for an expanded network was inefficient, leading to additional locations being required and an accumulation of species that did not differ from a random selection. The smallest MPA network that fulfilled multiple management objectives (representation of assemblage diversity and majority of species, population viability, support for fisheries, connectivity) required 30% of the surveyed locations. This study concluded that: MPAs selected without the benefit of data on intra‐habitat variation in species assemblages will be unrepresentative; the upper range of currently promoted targets for MPA establishment (i.e. 30%) should be regarded as a minimum for biodiversity conservation; MPAs selected for fisheries‐related reasons may not provide expected benefits for the remainder of the fish assemblage.  Copyright © 2006 John Wiley &amp; Sons, Ltd.
Investigating the relationship between social responsibility of human resources management and organizational citizenship behavior of employees and responsible leadership of private companies in Fars province

The purpose of this research is to investigate the relationship between the social responsibility of human resources management and the organizational citizenship behavior of employees of private companies in Fars province with the moderating role of responsible leadership. The statistical population of this research is more than 100 thousand people and the statistical sample using Morgan 's table is 384 people. Information is collected by library, field and internet methods. In this research, using correlation and regression methods, with spss software and structural equations with smart-pls software, the relationships between research variables are tested. The results of the research showed that responsible leadership is one of the most emerging and compensated leadership styles to promote the credibility of an organization and maintain the sustainable development of the organization and society. Organizational citizenship behavior describes employee behavior related to environmental protection that is not controlled by any organization 's formal reward system. Basically, it is the voluntary behavior of employees that is directed towards environmental protection, and it represents an operational supplement for people 's environmental protection behavior and organizations ' green growth approaches. Responsible leadership positively and significantly affects organizational citizenship behavior. Psychological ownership plays a mediating role in the relationship between responsible leadership and organizational citizenship behavior, environmental commitment of employees plays a moderating role between responsible leadership and psychological ownership and organizational citizenship behavior. Responsible leadership creates a sense of responsibility and encourages employees to adopt environmental protection behavior.
MetaStore: High-Performance Metagenomic Analysis via In-Storage Computing

Metagenomics has led to significant advancements in many fields. Metagenomic analysis commonly involves the key tasks of determining the species present in a sample and their relative abundances. These tasks require searching large metagenomic databases containing information on different species' genomes. Metagenomic analysis suffers from significant data movement overhead due to moving large amounts of low-reuse data from the storage system to the rest of the system. In-storage processing can be a fundamental solution for reducing data movement overhead. However, designing an in-storage processing system for metagenomics is challenging because none of the existing approaches can be directly implemented in storage effectively due to the hardware limitations of modern SSDs. We propose MetaStore, the first in-storage processing system designed to significantly reduce the data movement overhead of end-to-end metagenomic analysis. MetaStore is enabled by our lightweight and cooperative design that effectively leverages and orchestrates processing inside and outside the storage system. Through our detailed analysis of the end-to-end metagenomic analysis pipeline and careful hardware/software co-design, we address in-storage processing challenges for metagenomics via specialized and efficient 1) task partitioning, 2) data/computation flow coordination, 3) storage technology-aware algorithmic optimizations, 4) light-weight in-storage accelerators, and 5) data mapping. Our evaluation shows that MetaStore outperforms the state-of-the-art performance- and accuracy-optimized software metagenomic tools by 2.7-37.2$\times$ and 6.9-100.2$\times$, respectively, while matching the accuracy of the accuracy-optimized tool. MetaStore achieves 1.5-5.1$\times$ speedup compared to the state-of-the-art metagenomic hardware-accelerated tool, while achieving significantly higher accuracy.
Applications of wireless sensor networks in the real-time ambient air pollution monitoring and air quality in metropolitan cities — A survey

Advanced studies in the technology of sensor network and wireless communications have contributed a wide range of applications of wireless sensor network in environmental monitoring and pollution control. Pollution detection and pollution control are the major research issue in today's world. We discuss here a detailed study on ambient air pollution monitoring and air quality in metro cities using sensor nodes in a wireless sensor network. This paper mainly focusses on how a sensor network is used in a wide range to monitor the levels of air pollutants such as CO2, NO2, SO2, PM, NH3 and other toxic gases present in an urban ambient place where a major human activities and industrial activities takes place. Role of WSN in air pollution monitoring involves deployment of sensor nodes in a large scale at very low cost and to collect the real-time data and produce accurate results. This paper carries a detailed survey work on major contribution of several authors towards air pollution monitoring and determines the air pollutants in several methods using sensor network technology. This work is helpful to forecast and send the warning to the public.
The African Court of Human and Peoples' Rights : expectations, progress and prospects

Diplomarbeit Karl-Franzens-Universität Graz 2016 R8497
EFFECTIVENESS OF STRUCTURED TEACHING PROGRAM ON KNOWLEDGE AND ATTITUDE REGARDING EFFECT OF SOCIAL MEDIA ON MENTAL HEALTH AMONG ADOLESCENTS IN A SELECTED HIGHER SECONDARY SCHOOL OF AIZAWL, MIZORA

Introduction: Today is the age of information and technology, in which social media became an integral part of adolescent's life. Social media have significant effects on the society especially on adolescents. Title of the study: A study to assess the effectiveness of structured teaching program on knowledge and attitude regarding effect of social media on mental health among young adolescents in a selected higher secondary school. Objectives: To assess the level of knowledge among adolescents' usage of social media impact regarding mental illness, to evaluate the effectiveness of planned teaching program on mental illness and to find out the association between the pre-test knowledge scores with a selected variables. Methodology: Pre-experimental one group pre-test post-test research design was adopted. 50 samples were selected using non-probability convenience sampling technique. Result: The estimated z score is -9.075 and -2.658 for knowledge and attitude respectively.The difference in knowledge and attitude score are significant at of 0.05 level of significance. Chi square (2) value findings show no significant association between pre-test knowledge scores with selected variables.Conclusion: The study revealed that the structured teaching programme was effective
Quantum-Inspired DNA Computing: A Four-Dimensional Nucleotide State Space Framework with Superposition, Entanglement, and Error Correction

We present a comprehensive quantum-inspired DNA computing framework implemented in IcsDNA using the InChroSil Virtual Machine [11], a production-ready DNA computing platform. Our approach extends the classical four-letter nucleotide alphabet {A, G, C, T} into a four-dimensional complex Hilbert space ℋ! , enabling superposition states, entanglement between nucleotide pairs, and quantum gate operations adapted to the biological domain. The framework introduces novel adaptations of fundamental quantum computing primitives, including Pauli gates, Hadamard transforms, rotation operators, and Bell-state generation, to a quaternary (base-4) nucleotide encoding. We further implement quantum-inspired algorithms for DNA sequence alignment, pattern matching via Grover-like amplitude amplification, Fourier analysis of genomic sequences, and quantum error correction for DNA storage integrity. The system provides both a high-level C++ object-oriented stack and a C API for integration into bioinformatics pipelines. Experimental evaluation demonstrates the applicability of quantum-inspired techniques to probabilistic genomic analysis, DNA-based cryptographic key generation, and fault-tolerant DNA data storage with decoherence simulation.