ethicalabs/Research-Intent-Collab huggingface dataset:
Effect of occupational safety and health education received during schooling on the incidence of workplace injuries in the first 2 years of occupational life: a prospective study

ObjectivesThis study aimed to determine the effect of occupational safety and health (OSH) education during formal schooling on the incidence of workplace injuries (WIs) in young people starting their careers. We hypothesised that young people who had received OSH education during their schooling would have fewer WIs than those who received no OSH education. Secondary objectives focused on the effect of ‘first aid at work’ training during schooling and the conditions encountered on arrival in the company (occupational hazard information, safety training and job task training) on WI occurrence.DesignProspective cohort study.ParticipantsFrom 2009 to 2012, French apprentices and students at the end of their schooling and starting their careers were included.OutcomesOccurrence of WIs.MethodsAt the time of inclusion, information about school courses and personal characteristics were collected, and subsequent half-yearly contacts gathered information relating to work and personal data. During the 2-year follow-up, WIs were directly reported by participants and were identified by searching the French National Health Insurance Funds’ databases listing compulsory WI declarations.Results755 participants reported holding 1290 jobs. During follow-up, 158 WIs were identified, corresponding to an incident rate of 0.12 (0.10 to 0.14) WIs per full-time worker. Subjects who reported having received OSH education at school had two times less WIs than those declaring not having received OSH education (incidence rate ratio (IRR) 0.51, 0.00 to 0.98). A lower WI risk was observed for participants who received the ‘first aid at work’ training (IRR=0.68, 0.00 to 0.98). The conditions on arrival in company were not associated with WIs occurrence.ConclusionIn France, the OSH education provided to apprentices and students is mostly broader than the specific risks related to future jobs. Our results highlight the advantages of reinforcing this approach.
Topological Signal Processing over Generalized Cell Complexes

Topological Signal Processing (TSP) over simplicial complexes is a framework that has been recently proposed, as a generalization of graph signal processing (GSP), to extend GSP to analyzing signals defined over sets of any order (i.e., not only vertices of a graph) and to capture multiway relations of any order among the data. However, simplicial complexes are required to satisfy the so-called inclusion property, according to which, if a set belongs to the complex, then all its subsets must also belong to the complex. In some applications, this is a severe limitation. To overcome this limit, in this paper we extend TSP to deal with signals defined over cell complexes and we also generalize the concept of cell complexes to include hollow cells. We show that, even if the algebraic formulation does not change significantly, the extension to the generalized cell complexes considerably broadens the number of applications. Most important, the new representation provides a much better trade-off between the complexity of the representation and its accuracy. In addition, we propose a method to infer the structure of the cell complex from data and we propose distributed filtering strategies, including a method to retrieve the sparsest representation of the harmonic component. We quantify the advantages of using cell complexes instead of simplicial complexes, in terms of the complexity/accuracy trade-off, for different applications such image segmentation and recovering of real flows measured on data traffic and transportation networks.
Regenerative Medicine and Stem Cell Therapy: Advancements and Future Potential

The stem cell therapy and regenerative medicine sectors are quickly expanding due to the profound impact of stem cells on treating situations that were previously considered untreatable. Regenerative medicine, as a sophisticated field related to cellular biology, tissue engineering, and genetics, leverages the potential of stem cells to heal, regenerate, or restore tissues and organs. In this article, we focus on the latest breakthroughs in pluripotent and mesenchymal stem cells, the development of induced pluripotent stem cells (iPSCs), CRISPR gene editing, and their impact on developing practical solutions for treating neurodegenerative diseases, cardiovascular diseases, autoimmune diseases, and organ failure. The synergistic effects of new biomaterials, 3D bioprinting, and personalized medicine are advancing the precision of therapies. We analyze international clinical trials, ethical debates, policy issues, and the strategic vision concerning the use of advanced regenerative medicine techniques in targeted healthcare. By integrating novel research with anticipated shifts, we aim to equip professionals in stem cell therapy with research-appropriate comprehension and tools.
An efficient and easy-to-extend Matlab code of the Moving Morphable Component (MMC) method for three-dimensional topology optimization

Explicit topology optimization methods have received ever-increasing interest in recent years. In particular, a 188-line Matlab code of the two-dimensional (2D) Moving Morphable Component (MMC)-based topology optimization method was released by Zhang et al. (Struct Multidiscip Optim 53(6):1243-1260, 2016). The present work aims to propose an efficient and easy-to-extend 256-line Matlab code of the MMC method for three-dimensional (3D) topology optimization implementing some new numerical techniques. To be specific, by virtue of the function aggregation technique, accurate sensitivity analysis, which is also easy-to-extend to other problems, is achieved. Besides, based on an efficient identification algorithm for load transmission path, the degrees of freedoms (DOFs) not belonging to the load transmission path are removed in finite element analysis (FEA), which significantly accelerates the optimization process. As a result, compared to the corresponding 188-line 2D code, the performance of the optimization results, the computational efficiency of FEA, and the convergence rate and the robustness of optimization process are greatly improved. For the sake of completeness, a refined 218-line Matlab code implementing the 2D-MMC method is also provided.
Matrix and tensor comparisons of genomic profiles to predict cancer survival and drug targets

Despite recent large-scale profiling efforts, the best predictor of a glioblastoma (GBM) brain cancer patient's survival remains the patient's age at diagnosis. The best predictor of an ovarian serous cystadenocarcinoma (OV) patient's survival remains the tumor's stage, an assessment - numbering I to IV - of the spread of the cancer. To identify DNA copy-number alterations (CNAs) that might predict GBM or OV patients' survival, we comparatively modeled matched genomic profiles from The Cancer Genome Atlas (TCGA). Generalized singular value decomposition (GSVD) of patient-matched but probe- independent GBM and normal profiles uncovered a previously unknown global pattern of tumor-exclusive co-occurring CNAs that is correlated, and possibly causally related to, GBM patients' survival and response to chemotherapy. This suggests that the GBM survival phenotype is an outcome of its global genotype. The GSVD, formulated as a framework for comparatively modeling two composite datasets, removes from the pattern variations that occur in the normal human genome (e.g., female-specific X chromosome amplification) and experimental variations, without a-priori knowledge of these variations. The pattern is independent of age, and combined with age, makes a better predictor than age alone. The pattern suggests previously unrecognized targets for personalized GBM drug therapy, the kinase TLK2 and the methyltransferase METTL2A. A novel tensor GSVD of patient- and platform-matched OV and normal genomic profiles revealed multiple chromosome arm-wide patterns of CNAs that are correlated with OV patients' survival. These indicate several, previously unrecognized, subtypes of OV. The tensor GSVD is an exact simultaneous decomposition of two high-dimensional datasets arranged in higher-order tensors. The tensor GSVD generalizes the GSVD, which is limited to two second-order tensors, i.e., matrices. The chromosome arm-wide patterns of CNAs are independent of the OV tumor stage. Combined with stage, each of the patterns makes a better predictor than stage alone. We conclude that the GSVD and the novel tensor GSVD can uncover the relations, and possibly causal coordinations, between different recorded aspects of the same medical phenomenon. GSVD and tensor GSVD comparisons can be used to determine one patient's medical status in relation to other patients in a set, and inform the patient's prognosis, and possibly also treatment.
Reconstruction of Solar Extreme-ultraviolet Irradiance Using Ca <scp>II</scp> K Images and SOHO/SEM Data with Bayesian Deep Learning and Uncertainty Quantification

Abstract                Solar extreme-ultraviolet (EUV) irradiance plays a crucial role in heating the Earth’s ionosphere, thermosphere, and mesosphere, affecting atmospheric dynamics over varying time scales. Although significant effort has been spent studying short-term EUV variations from solar transient events, there is little work to explore the long-term evolution of the EUV flux over multiple solar cycles. Continuous EUV flux measurements have only been available since 1995, leaving significant gaps in earlier data. In this study, we propose a Bayesian deep learning model, named SEMNet, to fill the gaps. We validate our approach by applying SEMNet to construct Solar and Heliospheric Observatory/Solar EUV Monitor EUV flux measurements in the period between 1998 and 2014 using Ca II K images from the Precision Solar Photometric Telescope. We then extend SEMNet through transfer learning to reconstruct solar EUV irradiance in the period between 1950 and 1960 using Ca II K images from the Kodaikanal Solar Observatory. Experimental results show that SEMNet provides reliable predictions along with uncertainty bounds, demonstrating the feasibility of Ca II K images as a robust proxy for long-term EUV fluxes. These findings contribute to a better understanding of solar influences on Earth’s climate over extended periods.
The Impact of Green Human Resource Management on Employees’ Eco-Friendly Behavior: The Mediating Roles of Green Transformational Leadership and Green Perceived Organizational Support

This study examines the relationship between Green Human Resource Management (GHRM) and employees’ Eco-Friendly Behavior (EFB), with Green Transformational Leadership (GTFL) and Green Perceived Organizational Support (GPOS) serving as mediating variables. We adopted quantitative research design and surveyed employees in four- and five-star hotels using structured questionnaires. We collected 240 responses through purposive sampling; however, only 110 were valid and met the criteria for further analysis. We analyzed the data using Structural Equation Modeling (SEM) in SmartPLS. The findings reveal that GHRM practices have a positive and significant effect on GPOS and GTFL Furthermore, GTFL has a positive and significant effect on EFB, whereas GPOS does not. Additionally, GTFL has a positive and significant effect on GPOS. These results highlight the critical role of leadership in enhancing GHRM's effectiveness in promoting eco-friendly behavior among employees. This study contributes to the growing body of literature on sustainable human resource management and provides practical implications for the hospitality industry in fostering environmentally responsible behavior.
Disparities in Insurance Coverage, Health Services Use, and Access Following Implementation of the Affordable Care Act: A Comparison of Disabled and Nondisabled Working-Age Adults

The objective of this study was to assess trends in health insurance coverage, health service utilization, and health care access among working-age adults with and without disabilities before and after full implementation of the Affordable Care Act (ACA), and to identify current disability-based disparities following full implementation of the ACA. The ACA was expected to have a disproportionate impact on working-age adults with disabilities, because of their high health care usage as well as their previously limited insurance options. However, most published research on this population does not systematically look at effects before and after full implementation of the ACA. As the US Congress considers new health policy reforms, current and accurate data on this vulnerable population are essential. Weighted estimates, trend analyses and analytic models were conducted using the 1998-2016 National Health Interview Surveys (NHIS) and the 2014 Medical Expenditure Panel Survey. Compared with working-age adults without disabilities, those with disabilities are less likely to work, more likely to earn below the federal poverty level, and more likely to use public insurance. Average health costs for this population are 3 to 7 times higher, and access problems are far more common. Repeal of key features of the ACA, like Medicaid expansion and marketplace subsidies, would likely diminish health care access for working-age adults with disabilities.
Humanitarian Intervention, the Responsibility to Protect, and Sovereignty: Historical and Moral Reflections

Article published in the Michigan State International Law Review.
Metagenomic analysis discloses the impact of intrapartum antimicrobial prophylaxis in the early microbiome

The correct establishment of the intestinal microbiome at the beginning of life plays a key role in infant and later health. This process is affected by several factors, some of them (delivery and feeding type or gestational age) are well-known. However, other factors such as the intrapartum antimicrobial prophylaxis (IAP), in spite of being present in over 30% of total deliveries, have been much less studied. Given the increasingly evident relationship between early microbiota and later disease risk and the rising worries regarding antimicrobial resistances, there is an urgent need to assess the potential adverse consequences of IAP on the establishment of intestinal microbiota. The main objective of this study was to assess the effect of the IAP on intestinal microbiome composition in full-term 30-day-old infants by metagenomics approach. [Methods] Nine one-month-old babies, five of whom were born from mothers receiving IAP, were recruited for this study. With the objective of avoiding possible cofounders, the enrolled cohort of babies were in all cases full-term and vaginally delivered. Their gut microbial DNA was extracted from faecal samples. Illumina shotgun sequencing of total gut DNA was performed, and reconstruction of the microbiomes allowed taxonomic and functional characterization. [Results] Babies whose mother had received IAP showed slight microbiota differences when compared with non IAP-exposed babies. Beneficial microbial groups, such as bifidobacteria and bacteroides, were the most affected. The functional classification of coding reads by means of different databases allowed us to identify differences in the abundance of transporters and enzymes involved in antibiotic resistance in infants exposed to IAP compared with their non-exposed counterparts. [Conclusions] Metagenomics analysis on healthy babies confirms an impact of IAP treatment on the correct establishment of their intestinal microbiota. These observations highlight the need to develop new dietary strategies based on microbial modulation with the objective of minimizing the effect of IAP in the early stage of life
Theoretical Power Spectrum Estimation from Cosmic Microwave Background Data

The cosmic microwave background (CMB) power spectrum is a powerful cosmological probe as it entails almost all the statistical information of the CMB perturbations. Having access to only one sky, the CMB spectrum measured by our experiments is only a realization of the true underlying angular power spectrum. In this paper we use the sparsity of the CMB spectrum to develop a technique that estimates the true underlying CMB power spectrum from data alone. The developed IDL code, TOUSI, for Theoretical pOwer spectrUm using Sparse estImation, will be released with the next version of ISAP.
Interventions to optimize the use of antibiotics in China: A scoping review of evidence from humans, animals, and the environment from a One Health perspective

The overuse and misuse of antibiotics has accelerated the rapid emergence of antibiotic resistance. The aim of the study was to review interventions conducted in China to optimize use of antibiotics in humans, animals, and the environment from a One Health perspective.The literature review for this study was limited to English and Chinese articles published from January 1985 to May 2021. Literature review searches were conducted using Web of Science, Scopus, PubMed and three biomedical databases from China (the Chinese Scientific Journals database, the Wanfang Database, and China National Knowledge Infrastructure). We used Arksey and O'Malley's step-wise methodological framework as the basis for our scoping review.A total of 53 studies met our inclusion criteria, of which 51 (96%) were from human healthcare settings, one from environment health that pertained to rural ponds, and no studies were found that met our criteria on interventions used to improve antibiotic use in animals. For human health, the majority of the research was related to antibiotic intervention programs performed in public institutions, and only one policy assessment study included private institutions. Interventions were classified into four broad categories: 1) Knowledge interventions; 2) decision support; 3) financial incentives; and 4) organizational/management systems. Our findings indicated that combinations of multiple interventions were more effective in promoting the rational use of antibiotics in China.China has made major efforts on improving rational use of antibiotics in the past decades. Most policies or interventions, however, focused mainly on the human health aspect, less effort targeted toward the environment and animal health sectors. For further optimizing use of antibiotics, the cross-disciplinary and coordinated multi-faceted interventions guided by the One Health perspective should be developed and implemented. Meanwhile, the cross-departmental collaborative mechanism leading by the Chinese central government should be further strengthened to play a greater and more active role in fighting against antibiotic resistance wholly.
Abstract 3149: Prediction of metabolic drug targets for cancer patients with poor prognosis by using genome-scale metabolic models and survival analyses

Abstract                Cancer metabolism has been extensively explored to identify biomarkers and novel drug targets for effective cancer treatment. In particular, it is expected that metabolism-derived data somehow reflects unique features associated with a cancer patient’s prognosis. Here, we introduce a computational framework that predicts metabolic drug targets by considering metabolites associated with poor prognosis. For inputs, the computational framework requires genome-scale metabolic models (GEMs) and survival data that represent each of cancer patients with diverse prognoses. GEM is a computational model that allows predicting fluxes of entire metabolic reactions in a patient-specific manner. By using this computational framework, a risk score, which reflects the prognosis of cancer patients, was first established on the basis of metabolites significantly associated with survival. Finally, metabolic reaction targets were predicted that could reduce the risk score (i.e., improving the prognosis) upon their knockdown in silico. In this study, this framework was applied to bladder cancer patients as a proof-of-concept demonstration, and predicted 13 metabolic reaction targets. These metabolic reaction targets were expected to be also effective to bladder cancer patients with poor prognosis. The computational framework developed in this study is distinct from previous drug targeting methods because it also considers unique metabolic features associated with cancer patients with poor prognosis in order to identify drug targets that are effective to both low-risk and high-risk groups of cancer patients.                Citation Format: Sang Mi Lee, GaRyoung Lee, Hyun Uk Kim. Prediction of metabolic drug targets for cancer patients with poor prognosis by using genome-scale metabolic models and survival analyses [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3149.
Meta-Learning Probabilistic Inference For Prediction

This paper introduces a new framework for data efficient and versatile learning. Specifically: 1) We develop ML-PIP, a general framework for Meta-Learning approximate Probabilistic Inference for Prediction. ML-PIP extends existing probabilistic interpretations of meta-learning to cover a broad class of methods. 2) We introduce VERSA, an instance of the framework employing a flexible and versatile amortization network that takes few-shot learning datasets as inputs, with arbitrary numbers of shots, and outputs a distribution over task-specific parameters in a single forward pass. VERSA substitutes optimization at test time with forward passes through inference networks, amortizing the cost of inference and relieving the need for second derivatives during training. 3) We evaluate VERSA on benchmark datasets where the method sets new state-of-the-art results, handles arbitrary numbers of shots, and for classification, arbitrary numbers of classes at train and test time. The power of the approach is then demonstrated through a challenging few-shot ShapeNet view reconstruction task.
Right to equitable access to health care and essential medicines during the Covid-19 pandemic

Artykuł został poświęcony problemowi prawa do sprawiedliwego dostępu do opieki zdrowotnej i podstawowych leków w czasie pandemii COVID-19, zarówno z perspektywy prawa międzynarodowego, jak i krajowego. Powyższe zostało wykonane w odniesieniu do poglądu wyrażonego przez profesora Roberto Andorno. W pracy szczegółowo przeanalizowano prawo biomedyczne, prawa pacjenta oraz zasadę sprawiedliwości i równości. W artykule porównano prawo międzynarodowe i krajowe z przeciwstawnych perspektyw i przedstawiono wyniki przeprowadzonych analiz.