ethicalabs/Research-Intent-Collab huggingface dataset:
Target Discovery Using Deep Learning-Based Molecular Docking and Predicted Protein Structures With AlphaFold for Novel Antipsychotics

Objective New drugs are needed to treat antipsychotic-resistant schizophrenia, especially those with clozapine-resistant schizophrenia. Atypical antipsychotics have predominantly 5-HT2A and dopaminergic antagonism, but also require investigation of other receptors.Methods In this study, the binding affinities between clozapine, olanzapine, and quetiapine with neuropharmacological, immunological, and metabolic receptors were measured using GNINA (Deep Learning Based Molecular Docking) and AlphaFold (Predicted Protein Structures).Results Through this study, it was determined that these antipsychotics showed high binding affinity to a variety of receptors, such as CB2, 5-HT1BR, NPYR4, and CCR5. Cyclosporin A and everolimus which show high affinities with those receptors could be used for the development of new antipsychotic drugs based on these drugs.Conclusion In the future, the method used in this study will be applied to the development of new antipsychotic drugs, including drug repositioning, and to the discovery of the pathophysiology of schizophrenia.
ANTIMICROBIAL STEWARDSHIP PROGRAMS: IMPLICATIONS FOR RESISTANCE RATES & QUALITY OF CARE IN HOSPITALS

Each year, two million Americans acquire serious infections caused by bacteria that are resistant to antibiotics resulting in significant morbidity, mortality, health care utilization and costs. Despite the recent passage of an antimicrobial stewardship programs (ASP) mandate in California and the Centers for Disease Control and Prevention (CDC) guidelines for a minimum standard ASP in hospitals, literature on the impact of ASPs on antimicrobial resistance (AMR) rates in hospitals is sparse.      The long-term goal of this study is to provide reliable evidence to influence policies and practices to reduce AMR and improve quality and clinical outcomes in hospitals. The overall objective of this study was to investigate the impact of ASP adoption, including the effect of a mandate in California and compliance with the CDC’s 7 core elements on methicillin-resistant Staphylococcus aureus (MRSA) and Clostridioides difficile (C. diff) in acute care hospitals. Then, we investigated their impact on selected quality and clinical outcomes.      In paper 1, we estimated the impact of passing an ASP mandate in California on hospital on MRSA and C. diff rates using 2013-2017 hospital-level data and a difference-in-difference with hospital fixed effects (FE) design. We found that, compared to hospitals in other states, California hospitals had significant (p<0.05) increases of 23%, 30%, and 20% in their MRSA SIR in 2015, 2016 and 2017, respectively. We also observed a 20% (p<0.001) decrease in their C. diff SIR in 2017.      Paper 2 examined the effect of statewide adoption of the CDC’s ASP 7 core components on MRSA and C. diff rates using 2014-2016 data to estimate a state FE model. We found that the percentage of hospitals meeting the CDC’s 7 core elements for ASP between 2014 and 2016 increased in all states. A one percentage point increase in ASP compliance was associated with a 0.3% decrease (p<0.01) in C. diff infections in 2016 relative to 2014. We did not find an effect on MRSA infections.      In paper 3, we measured the association between rates of MRSA/C. diff and quality and clinical outcomes in US acute care hospitals using 2013-2017 hospital-level data and a hospital FE model. We found no association of MRSA or C. diff with 30-day readmissions, length of stay, 30-day mortality and intensive care unit days.      In summary, this study examined the various effects of an ASP state mandate and adoption of the CDC’s 7 core elements, as well as the relationship between AMR and quality and clinical outcomes in hospitals. Our findings help fill important knowledge gaps and can assist policymakers and healthcare administrators make informed decisions on the regulation and implementation of ASPs. Future studies should seek data on hospital-level implementation of specific components of ASP and other resistant bacteria, neither of which is currently available.
Bangladesh's Approach Towards International Criminal Law: A Case Study of International Crimes Tribunal Bangladesh

The International Crimes Tribunal Bangladesh that has been found by the Bangladeshi Government to try war crimes during India Pakistan war of 1971. The tribunal is violating the fair trial rights as guaranteed by Constitution, the International Covenant on Civil and Political Rights and International Humanitarian Law and the standard of the International Crimes Tribunal Bangladesh is far below than that setup by The International Criminal Tribunal for the former Yugoslavia, the International Criminal Tribunal for Rwanda and the International Criminal Court. These irregularities imply serious concern over the proceedings of the said tribunal. Study seeks to describe the International Law about war crimes particularly with respect to fair trial provisions and it compare the proceedings of the Bangladeshi tribunal with the other internationally recognized tribunals.
Neutron-Physical Calculation of a Fresh Zone in the Natural Nuclear Reactor at Oklo

A full-scale computer model of zone 2 of the Oklo reactor with a realistic materials composition is constructed. Modern Monte Carlo programs are used to calculate the multiplication factor, the excess reactivity, and the neutron flux for the fresh reactor zone 2. The calculations are performed in a wide range of variation of the zone parameters: the uranium content in the contemporary Oklo zone is varied from 35 to 55 mass% and the water content from 0.355 to 455 g/cm3. The power effect is determined. The temperature of the fresh zone is found to be 725 ± 55 K, at which the reactor is critical and can operate in a stable manner for a long time. The power of the reactor is maintained by negative feedback. The neutron spectrum, over which the cross section of strong absorbers must be averaged, for example,                   62                   149                 Sm, in order to obtain the most accurate bounds on the possible shift of the samarium resonance as a result of a change in the fundamental constants, is found for 700 K.
PCDTBT: Force Field Parameterization and Properties by Molecular Dynamics Simulation

Conjugated polymers are indispensable building blocks in a variety of organic electronics applications such as solar cells, light-emitting diodes, and field-effect transistors. Poly[N-9'-heptadecanyl-2,7-carbazole-alt-5,5-(4',7'-di-2-thienyl-2',1',3'-benzothiadiazole)] (PCDTBT) is a carbazole-benzothiadiazole-based copolymer with a donor-acceptor structure, consisting of electron-donating and electron-withdrawing subunits and featuring a low band gap. In this work, the General Amber Force Field is extended in two ways, specifically for modeling PCDTBT. First, a set of partial atomic charges is derived that mimic a long chain and adequately describe different conformations that may be encountered in a bulk environment. Second, torsional terms are reparametrized for all dihedral angles in the backbone via ab initio computations. Subsequently, a series of large-scale Molecular Dynamics simulations are employed to construct and equilibrate bulk ensembles of three PCDTBT oligomers using different starting conformations of the oligomer chains. Several structural properties are computed, namely mass density, chain stiffness (through persistence length and Kuhn segment length), and glass transition temperature. Our results are in good agreement with available literature data, demonstrating the suitability of the new parametrization.
Genome-wide analysis to identify a novel distant metastasis-related gene signature predicting survival in patients with gastric cancer

This study designed to identify a potential novel distant metastasis-related gene (DMGs) signature that predicting prognosis in patients with gastric cancer. DMGs was screened by overlapping the differentially expressed genes between M0 and M1 stage, and between tumor and adjacent normal tissue of gastric cancer by analyzing The Cancer Genome Atlas (TCGA) dataset. There were 83 DMGs were identified, the integrative analysis revealed these DMGs were involved in several biological process and pathway. A six-DMGs prognostic signature was developed based on the risk score obtained from Cox analysis. Patients with low risk score presented significantly shorter survival time. This prognostic signature has a moderate predictive value for the overall survival in gastric cancer patients, with an area under curve of 0.604. The DMGs prognostic signature also significantly associated with the overall survival of gastric cancer patients, and showed a better performance for predicting prognosis than traditional clinical indicators. The joint effect of risk score with clinical features could remarkably increased the predictive value as compared with single variable. The results from 60 gastric cancer tissues verified the prognostic value of the six-DMGs prognostic signature. In conclusions, the present study identified a novel six-DMGs prognostic signature that could serve as a biomarker for the prognosis prediction of patients with gastric cancer.
An advanced event tree analysis method for detecting faults in power transformers

Relevance. is improving the efficiency of decision support for the trouble-free operation of responsible electrical equipment requires improved methods and algorithms for fault detection, which is undoubtedly an urgent task.The purpose. is to develop an effective and reliable procedure for the holistic analysis, assessment and prediction of possible malfunctions in oil-filled transformer equipment, as one of the critical assets of an energy company.Methods. When solving problems, the dissolved gas analysis (DGA) method is used as the most informative in terms of the technical condition operational assessment of the transformer and detection of signs of its deviations from the norm, as well as an improved method for constructing and event tree analysis (ETA), which combines the traditional structure of cause-and-effect relationships with components for calculating the equipment health index.Results. The improvement of the event tree analysis method concerns the introduction of a quantitative measure of the importance of cause-effect relationships at each of the hierarchical levels, with the determination of the probabilities of outcomes of each of the initiating events based on a given root event probability. The role of a quantitative measure is performed by a weighting coefficient system obtained using the matrices of paired comparisons of Saati for each ETA level. A logical and probabilistic ETA model has been formed, which allows not only to record the interrelationships between events and the sequence of their occurrence, but also to rank them according to the degree of influence on the final undesirable outcome. Calculations confirming the proposed approach effectiveness have been performed.Conclusion. A logical-probabilistic ETA model has been obtained, which provides, along with the DGA method, a holistic analysis, assessment and prediction of possible malfunctions of oil-filled transformer equipment. An effective procedure has been developed for constructing and analyzing data sets using expert assessments and components for calculating the equipment health index. The possibility of effective application of the developed model is demonstrated by analyzing the causes of monotonous excess of the boundary concentrations of carbon monoxide and carbon dioxide dissolved in the oil of the studied transformers.
Explainable Computational Imaging for Precision Oncology: An Interpretable Deep Learning Framework for Bladder Cancer Histopathology Diagnosis

Bladder cancer represents a significant health problem worldwide, with it being a major cause of death and characterized by frequent recurrences. Effective treatment hinges on early and accurate diagnosis; however, traditional methods are invasive, time-consuming, and subjective. In this research, we propose a transparent deep learning model based on the YOLOv11 structure to not only enhance lesion detection but also give the visual support of the model’s predictions. Five versions of YOLOv11—nano, small, medium, large, and extra large—were trained and tested by us on a comprehensive dataset of hematoxylin and eosin-stained histopathology slides with the inflammation, urothelial cell carcinoma (UCC), and invalid tissue categories. The YOLOv11-large variant turned out to be the best-performing model at the forefront of technology, with an accuracy of 97.09%, precision and recall of 95.47% each, and balanced accuracy of 96.60%. Besides the precision–recall curves (AUPRC: inflammation = 0.935, invalid = 0.852, UCC = 0.958), ROC-AUC curves (overall AUC = 0.972) and risk–coverage analysis (AUC = 0.994) were also used for detailed assessment of the model to confirm its steadiness and trustworthiness. The confusion matrix displayed the highest true positive rates in all classes and a few misclassifications, which mainly happened between inflammation and invalid samples, indicating a possible morphological overlap. Moreover, as supported by a low Expected Calibration Error (ECE), the model was in great calibration. YOLOv11 reaches higher performance while still being computationally efficient by incorporating advanced architectural features like the C3k2 block and C2PSA spatial attention module. This is a step towards the realization of the AI-assisted bladder cancer diagnostic system that is not only reliable and transparent but also scalable, presented in this work.
Robust blue-green urban flood risk management optimised with a genetic algorithm for multiple rainstorm return periods

Flood risk managers seek to optimise Blue-Green Infrastructure (BGI) designs to maximise return on investment. Current systems often use optimisation algorithms and detailed flood models to maximise benefit-cost ratios for single rainstorm return periods. However, these schemes may lack robustness in mitigating flood risks across different storm magnitudes. For example, a BGI scheme optimised for a 100-year return period may differ from one optimised for a 10-year return period. This study introduces a novel methodology incorporating five return periods (T = 10, 20, 30, 50, and 100 years) into a multi-objective BGI optimisation framework. The framework combines a Non-dominated Sorting Genetic Algorithm II (NSGA-II) with a fully distributed hydrodynamic model to optimise the spatial placement and combined size of BGI features. For the first time, direct damage cost (DDC) and expected annual damage (EAD), calculated for various building types, are used as risk objective functions, transforming a many-objective problem into a multi-objective one. Performance metrics such as Median Risk Difference (MedRD), Maximum Risk Difference (MaxRD), and Area Under Pareto Front (AUPF) reveal that a 100-year optimised BGI design performs poorly when evaluated for other return periods, particularly shorter ones. In contrast, a BGI design optimised using composite return periods enhances performance metrics across all return periods, with the greatest improvements observed in MedRD (22%) and AUPF (73%) for the 20-year return period, and MaxRD (23%) for the 50-year return period. Furthermore, climate uplift stress testing confirms the robustness of the proposed design to future rainfall extremes. This study advocates a paradigm shift in flood risk management, moving from single maximum to multiple rainstorm return period-based designs to enhance resilience and adaptability to future climate extremes.
Negotiating Community Engagement and Science in the Federal Environmental Public Health Sector

In this case study, I use ethnographic data to explore how community engagement and science are deployed at the U.S. Agency for Toxic Substances and Disease Registry, with the goal of formulating an understanding of the personalized meanings of science–community relations for key environmental public health experts. In focus is the cultural discourse circulating in the agency that exposes the real concerns, beliefs, and attitudes of these scientists and experts vis‐à‐vis their community engagement experiences. Finally, I propose that critical attention to the place of power relations, knowledge politics, and environmental justice are fundamental to studies of toxic contamination where commitments to community engagement and quality science are joined to form a positive research goal and where attempts are made to improve the conditions of quality environmental public health service.
Collection, usage and privacy of mobility data in the enterprise and public administrations

Human mobility data is a crucial resource for urban mobility management, but it does not come without personal reference. The implementation of security measures such as anonymization is thus needed to protect individuals' privacy. Often, a trade-off arises as such techniques potentially decrease the utility of the data and limit its use. While much research on anonymization techniques exists, there is little information on the actual implementations by practitioners, especially outside the big tech context. Within our study, we conducted expert interviews to gain insights into practices in the field. We categorize purposes, data sources, analysis, and modeling tasks to provide a profound understanding of the context such data is used in. We survey privacy-enhancing methods in use, which generally do not comply with state-of-the-art standards of differential privacy. We provide groundwork for further research on practice-oriented research by identifying privacy needs of practitioners and extracting relevant mobility characteristics for future standardized evaluations of privacy-enhancing methods.
SUPPLY CHAIN MANAGEMENT IN THE SUSTAINABILITY REPORTS: A KEY FACTOR FOR CORPORATE SOCIAL RESPONSIBILITY

A corporate social responsibility begins with good process management that is, managing the sustainability along the supply chain. It comes to managing the flow of materials, information and capital, as well as cooperation between companies in the supply chain, taking into account environmental and social goals, as each organization could influence the reputation and performance of the rest. A tool to inform and communicate this management is through sustainability reports, although, for this it is necessary to improve the way to convey this information. Thereby, in this paper, we propose a model to present the sustainable information of supply chain supported by economic, social and environmental dimensions. In order to develop it, we have performed an analysis of the responsibility reports of different companies to determine the key factors that we must be considered. Lastly, we have contrasted the proposal with a larger sample of companies, testing the quality of information provided using statistical techniques.
Interpretable Multimodal Fusion Model for Bridged Histology and Genomics Survival Prediction in Pan‐Cancer

Abstract                   Understanding the prognosis of cancer patients is crucial for enabling precise diagnosis and treatment by clinical practitioners. Multimodal fusion models based on artificial intelligence (AI) offer a comprehensive depiction of the tumor heterogeneity landscape, facilitating more accurate predictions of cancer patient prognosis. However, in the real‐world, the lack of complete multimodal data from patients often hinders the practical clinical utility of such models. To address this limitation, an interpretable bridged multimodal fusion model is developed that combines histopathology, genomics, and transcriptomics. This model assists clinical practitioners in achieving more precise prognosis predictions, particularly when patients lack corresponding molecular features. The predictive capabilities of the model are validated across 12 cancer types, achieving optimal performance in both complete and missing modalities. The work highlights the promise of developing a clinically applicable medical multimodal fusion model. This not only aids in reducing the healthcare burden on cancer patients but also provides improved assistance for clinical practitioners in precise diagnosis and treatment.
Fostering digital entrepreneurship from startup to scaleup: The role of venture capital funds and angel groups

Abstract   Digital entrepreneurship highly relies on external sources of financing to foster growth. This study aims at investigating how angel groups and venture capital (VC) funds affect growth of digital new ventures in their startup and scaleup phase. To address this aim, we analyzed 372 investment rounds regarding 256 Italian–based new ventures. The key findings are fourfold. First, VC funds positively affect the growth of digital new ventures. Second, digital scaleups, in line with the overall sample of digital new ventures, show a linear path of growth positively correlated with VC funding. Third, the relationship between funding received and growth in digital startups follows an inverted U–shape – with the optimal level within our sample set at 300,000$. Finally, no evidence emerges on the angel groups contribution to the growth of digital new ventures in both startup and scaleup phase. These findings are hence discussed in the light of extant literature on venture financing as well as entrepreneurial bias literature, to provide insight for both researchers and practitioners in digital entrepreneurship.
On the Similarities of Embeddings in Contrastive Learning

Contrastive learning operates on a simple yet effective principle: Embeddings of positive pairs are pulled together, while those of negative pairs are pushed apart. In this paper, we propose a unified framework for understanding contrastive learning through the lens of cosine similarity, and present two key theoretical insights derived from this framework. First, in full-batch settings, we show that perfect alignment of positive pairs is unattainable when negative-pair similarities fall below a threshold, and this misalignment can be mitigated by incorporating within-view negative pairs into the objective. Second, in mini-batch settings, smaller batch sizes induce stronger separation among negative pairs in the embedding space, i.e., higher variance in their similarities, which in turn degrades the quality of learned representations compared to full-batch settings. To address this, we propose an auxiliary loss that reduces the variance of negative-pair similarities in mini-batch settings. Empirical results show that incorporating the proposed loss improves performance in small-batch settings.