ethicalabs/Research-Intent-Collab huggingface dataset:
Polarizable force fields for molecular dynamics simulations of biomolecules

Molecular dynamics simulations are well established for the study of biomolecular systems. Within these simulations, energy functions known as force fields are used to determine the forces acting on atoms and molecules. While these force fields have been very successful, they contain a number of approximations, included to overcome limitations in computing power. One of the most important of these approximations is the omission of polarizability, the process by which the charge distribution in a molecule changes in response to its environment. Since polarizability is known to be important in many biochemical situations, and since advances in computer hardware have reduced the need for approximations within force fields, there is major interest in the use of force fields that include an explicit representation of polarizability. As such, a number of polarizable force fields have been under development: these have been largely experimental, and their use restricted to specialized researchers. This situation is now changing. Parameters for fully optimized polarizable force fields are being published, and associated code incorporated into standard simulation software. Simulations on the hundred‐nanosecond timescale are being reported, and are now within reach of all simulation scientists. In this overview, I examine the polarizable force fields available for the simulation of biomolecules, the systems to which they have been applied, and the benefits and challenges that polarizability can bring. In considering future directions for development of polarizable force fields, I examine lessons learnt from non‐polarizable force fields, and highlight issues that remain to be addressed. WIREs Comput Mol Sci 2015, 5:241–254. doi: 10.1002/wcms.1215This article is categorized under:  Structure and Mechanism &gt; Computational Biochemistry and Biophysics Molecular and Statistical Mechanics &gt; Molecular Mechanics Molecular and Statistical Mechanics &gt; Molecular Dynamics and Monte-Carlo Methods
Probabilistic Hierarchical Forecasting with Deep Poisson Mixtures

Hierarchical forecasting problems arise when time series have a natural group structure, and predictions at multiple levels of aggregation and disaggregation across the groups are needed. In such problems, it is often desired to satisfy the aggregation constraints in a given hierarchy, referred to as hierarchical coherence in the literature. Maintaining coherence while producing accurate forecasts can be a challenging problem, especially in the case of probabilistic forecasting. We present a novel method capable of accurate and coherent probabilistic forecasts for time series when reliable hierarchical information is present. We call it Deep Poisson Mixture Network (DPMN). It relies on the combination of neural networks and a statistical model for the joint distribution of the hierarchical multivariate time series structure. By construction, the model guarantees hierarchical coherence and provides simple rules for aggregation and disaggregation of the predictive distributions. We perform an extensive empirical evaluation comparing the DPMN to other state-of-the-art methods which produce hierarchically coherent probabilistic forecasts on multiple public datasets. Comparing to existing coherent probabilistic models, we obtain a relative improvement in the overall Continuous Ranked Probability Score (CRPS) of 11.8% on Australian domestic tourism data, and 8.1% on the Favorita grocery sales dataset, where time series are grouped with geographical hierarchies or travel intent hierarchies. For San Francisco Bay Area highway traffic, where the series' hierarchical structure is randomly assigned, and their correlations are less informative, our method does not show significant performance differences over statistical baselines.
Modeling and Solving Transportation Problems as a Decision Support Tool in Logistics Process Design and Optimization

This paper provides a comprehensive overview of classical and extended transportation problem-solving methods. It introduces well-known algorithms such as the North-West Corner Method, Least Cost Method, Vogel’s Approximation Method, and the Simplex Method. The paper also presents real-world application areas, including Home delivery of e-commerce packages, transportation of medical equipment and pharmaceuticals, transportation of fresh food products. Practical examples illustrate how these models function in real industrial contexts. Beyond the classical transportation model, the study explores its extensions by incorporating capacity and time constraints. These extended models are analyzed to understand their impact on cost and solution structure. The findings highlight the trade-offs between model complexity and operational feasibility in real-world transportation planning.
The Feasibility of Issuing Central Bank Digital Currency

Central banks all around the world are now concentrating heavily on Central Bank Digital Currency (CBDC) in recent years. CBDC is often characterized as a digital d by central representation of a nation's sovereign currency that is issued by a central bank and supportegovernment credit. Many countries and regions' central banks and monetary authorities constantly monitor fintech advances and investigate the digital form of fiat money, which is moving from theory to reality. But implementing it is complicate d by several legal, technological, and political issues . It is hoped that through this article, people can have a more accurate understanding of the current development of the central bank’s digital currency. It also has a specific reference significance for selecting the operation mode of the digital currency.
Empirical Evaluation of State Event Fault Tree and Fault Tree combined with Markov Chains for the Safety Analysis of Dynamic Embedded Systems

Most innovation in the automotive industry is driven by embedded systems. They make usage of dynamic adaption to environmental changes or component/subsystem failures for remaining safe. Following this evolution, fault tree analysis techniques have been extended with concept for dynamic adaptation but resulting techniques like state event fault tree analysis, are not widely used in practice. In this report we present the results of a controlled experiment that analyze these two techniques (State Events Fault Trees and Faul trees combined with markov chains) with regard to their applicability and efficiency in modeling dynamic behavior of dynamic embedded systems. The experiment was conducted with students of the TU Kaiserslautern to modeli different safety aspects of an ambient assisted living system. The main results of the experiment show that SEFTs where more easy and effective to use.
Fuzzy Reliability Analyzer: Quantitative Assessment of Risk Events in the Construction Industry Using Fuzzy Fault-Tree Analysis

Fault trees are deductive techniques constructed by taking a system failure event and deconstructing it into its root causes (basic events, gate events). Fault trees can be solved qualitatively, by determining minimal cut sets, and quantitatively, by calculating the probability of occurrence of the risk event. In conventional fault-tree analysis (FTA), the probability of occurrence for all basic events must be assessed in order to allow for quantitative fault-tree analysis. However, conducting quantitative fault-tree analysis, especially in construction projects, entails several difficulties owing to the lack of sufficient data, leading to an approximation of the probability of occurrence for some basic events. Assuming probabilities for any basic event will add further uncertainty to the analysis, resulting in a potentially questionable end result. To overcome the challenge of assessing probabilities, this paper presents a comprehensive framework in which experts can use linguistic terms rather than numerals to assess the probability of occurrence of basic events. Fuzzy arithmetic operations are used to perform quantitative fault-tree analysis. Fuzzy Reliability Analyzer (FRA) was developed to automate both qualitative and quantitative FTA. The method presented is demonstrated via a case study to quantify the probability of failure of horizontal directional drilling (HDD) in meeting project objectives. Fourteen minimal cut sets were identified and the fuzzy probability (FPro) of the top event (TE) was calculated. The proposed approach offers the advantage of allowing experts to express themselves linguistically to assess the probability of occurrence of basic events, which is more appropriate for the construction domain. In addition, the proposed method offers the risk analyst the advantage of ranking basic events according to their level of contribution to the probability of the risk event, which can help in establishing more effective risk response strategies.
The Impacts of Health Insurance Coverage on Access to Healthcare in Children Entering Kindergarten

To examine access to healthcare and health outcomes for kindergartners as they relate to insurance status and type. For the 2008, 2009, and 2010 school years, surveys were distributed to parents with a child entering kindergarten in the state of Nevada. Surveys asked parents to provide information about their child concerning their insurance status, routine medical care, medical conditions, and health behaviors. Compared to their insured peers, uninsured kindergartners were less likely to have had a check-up in the previous 12 months (p < .001; OR 6.14; 95 % CI 5.77-6.53), have a primary physician (p < .001; OR 14.32; 95 % CI 13.49-15.20), or have seen a dentist (p < .001; OR 3.93; 95 % CI 3.70-4.16), and were more likely to have a reported unmet medical need (p < .001; OR 2.60; 95 % CI 2.19-3.07). Additionally, compared to children with private insurance, those children with public insurance were less likely to have had a check-up (p < .001; OR 1.73; 95 % CI 1.59-1.89), have a primary care provider (p < .001; OR 3.87; 95 % CI 3.55-4.21), and were more likely to have unmet medical needs (p < .001; OR 2.27; 95 % CI 1.83-2.81). For children in early development-a deeply critical period-insurance status and type are predictors of important access to healthcare variables.
The Shaping of the Precautionary Principle by European Courts: From Scientific Uncertainty to Legal Certainty

The aim of this study is to illustrate the role played by EC Courts in shaping the controversial precautionary principle which allows the adoption of protective measures in situations of scientific uncertainty. Called upon reviewing the legality of national and Community measures inspired by a precautionary approach, both the European Court of Justice (ECJ) and the Court of First Instance (CFI) contributed to development of the precautionary principle. They did not simply lay the ground for the recognition of the principle in the EC legal order, by turning a mere state of mind into a general principle of Community law, but they also defined the conditions triggering its invocation. In short, the EC courts' case law has been crucial in determining when, how and by whom the precautionary principle may be relied upon in Community legal order.
What is nature conservation for the Portuguese public? Understanding the commonsensical image of nature to engage the general public in conservation efforts and to improve the communication between science and society

Abstract                The protection of nature and biodiversity has been gaining importance in public opinion. Nevertheless, the implementation of conservation policies has experienced some public resistance, along with low levels of public involvement. This lack of public interest in and support to conservation may be related to citizens’ images of nature.                In this article, we examine the commonsensical image of nature and its conservation according to two dimensions: cognitive beliefs and normative ethics. This image of nature was examined through a series of focus-group discussions with members of the general public in the Lisbon Metropolitan Area – Portugal and reflects the commonsensical perceptions of what nature conservation is.                The results suggest that, although the participants were not familiar with scientific terminology, they hold rich, mental and social representations about nature and biodiversity. We present a conceptual framework that reflects participants’ overall image of nature as the basis of human life and, above all, as a source of multiple goods that ensure human beings’ survival, which characterizes the common-sense view of nature as largely anthropocentric.                Our work provides policy-makers, planners and managers with a framework that reflects lay people’s perceptions of nature and biodiversity conservation that are instrumental in identifying conservation priorities, appropriate goals and suitable measures while ensuring communication between science and society.
Preserving Judicial Independence in India: A Constitutional Critique of the Appointment Process

The exercise of judicial power of a court of law is hinges for constitutional democracy and rule of law. This study critically analyzes the framework of judicial appointment in India from an executive overshadowed perspective to a judge-centered collegium paradigm constructed through judicial activism. The study focuses on the constitutional provisions, prominent constitutional legislations including, the First, Second and Third Judges Cases and the NJAC judgment, alongside judicial doctrines like the Basic Structure Doctrine and exposes how freedom of judicial independence has been guaranteed and protected. The paper posits that important decision-making disabilities apply at any stage in restrictive scope but primary bestowing authority to the judicial arms is compulsory, the existing collegium system is incapable of rational systems to accountability devoid of representation or ease of access, and transparent processes. Relying primarily on doctrinal and cross-national examinations, this analysis reveals independent yet responsible notions of governance in India and demonstrates the sovereign pressures in assuming such powers. The study suggests measures aimed at strengthening accusations of judicial independence and impartiality while defending institutional reputation, credibility, and democratic soundness of the mechanism in its controls.
Impact of AlphaFold on Structure Prediction of Protein Complexes: The CASP15-CAPRI Experiment

We present the results for CAPRI Round 54, the 5th joint CASP-CAPRI protein assembly prediction challenge. The Round offered 37 targets, including 14 homo-dimers, 3 homo-trimers, 13 hetero-dimers including 3 antibody-antigen complexes, and 7 large assemblies. On average ~70 CASP and CAPRI predictor groups, including more than 20 automatics servers, submitted models for each target. A total of 21941 models submitted by these groups and by 15 CAPRI scorer groups were evaluated using the CAPRI model quality measures and the DockQ score consolidating these measures. The prediction performance was quantified by a weighted score based on the number of models of acceptable quality or higher submitted by each group among their 5 best models. Results show substantial progress achieved across a significant fraction of the 60+ participating groups. High-quality models were produced for about 40% for the targets compared to 8% two years earlier, a remarkable improvement resulting from the wide use of the AlphaFold2 and AlphaFold-Multimer software. Creative use was made of the deep learning inference engines affording the sampling of a much larger number of models and enriching the multiple sequence alignments with sequences from various sources. Wide use was also made of the AlphaFold confidence metrics to rank models, permitting top performing groups to exceed the results of the public AlphaFold-Multimer version used as a yard stick. This notwithstanding, performance remained poor for complexes with antibodies and nanobodies, where evolutionary relationships between the binding partners are lacking, and for complexes featuring conformational flexibility, clearly indicating that the prediction of protein complexes remains a challenging problem.
Dike: Deep Reinforcement Learning For Function Scheduling in SLO-targeted Serverless Edge Computing

Serverless computing is regarded as a good match for distributed edge infrastructures. However, bringing the function-as-a-service model to a highly dynamic, distributed, and heterogeneous pool of resources has its fair amount of challenges. Allocation of functions to the proper resource is an essential operation that avoids over-provisioning and the relative waste of computational resources. In this work, we propose Dike, a bi-level function scheduling and resource allocation framework designed to meet end-to-end latency SLOs (service-level objectives). Dike leverages deep reinforcement learning to balance resource provi- sioning and monetary cost by incorporating both composite cost and SLO violations into the reward. Extensive simulations with real-world production workloads demonstrate the superiority of Dike. Experimental results show that the proposed algorithms approximate the results of state-of-the-art ILP solver within a factor of 1.08 while dramatically reducing the scheduling time
From Physics to Biology by Extending Criticality and Symmetry Breakings

Symmetries play a major role in physics, in particular since the work by E. Noether and H. Weyl in the first half of last century. Herein, we briefly review their role by recalling how symmetry changes allow to conceptually move from classical to relativistic and quantum physics. We then introduce our ongoing theoretical analysis in biology and show that symmetries play a radically different role in this discipline, when compared to those in current physics. By this comparison, we stress that symmetries must be understood in relation to conservation and stability properties, as represented in the theories. We posit that the dynamics of biological organisms, in their various levels of organization, are not just processes, but permanent (extended, in our terminology) critical transitions and, thus, symmetry changes. Within the limits of a relative structural stability (or interval of viability), variability is at the core of these transitions.
Central Bank Digital Currency: A Comparative Analysis in the Context of Design and Protocol Mechanisms

The ecosystem of crypto money that started with Bitcoin and the attempts of big technology companies to establish their own payment systems began to threaten the monopoly rights of Central Banks on money. The ultimate response of the Central Bank towards these developments is the central bank digital currency (CBDC). In this study, a comparison of an alternative protocol used in blockchain networks with respect to efficiency, security, and privacy that is compatible with the CBDC literature will be provided. The intention of this study is to analyze the function of protocol and design features in the “new financial architecture”. In this context, we delve into the operation of the protocols mentioned in the CBDC design. Within the scope of this research, data were collected by using semi-structured interview technique with blockchain technology experts and subjected to comparative analysis. According to the findings obtained by the comparative analysis method, the use of the Proof of Authority (POA) protocol in the CBDC design can completely eliminate individual privacy by giving an opportunity to the central banks to oversee the entire financial system. In the absence of the offline payment (OPS) protocol, financial inclusion, one of the objectives of the CBDC, may become dysfunctional. Using variations of Proof of Work (PoW) and Proof of Stake (PoS) protocols might give rise to scaling problems. A hybrid protocol can be created by considering the strengths of the protocols used in CBDC designs. A strong protocol structure can significantly increase the functionality of the CBDC in the context of monetary policy. CBDC protocols are frequently discussed in the literature regarding their efficiency and security dimensions. We assume that the privacy dimension should also be included in the discussion.
Climate Change Litigation and Environmental Law Enforcement in South Africa: A Scholarly Review

Climate change litigation has emerged as a significant tool for addressing environmental issues in South Africa, alongside traditional regulatory mechanisms. A systematic literature review approach was employed to analyse case studies, legislative frameworks, and judicial decisions related to climate change litigation in South Africa. The analysis reveals a growing trend of climate change-related lawsuits filed against both governmental and non-governmental entities, with a notable increase in successful cases over the past decade. While climate change litigation has demonstrated potential as an effective tool for environmental enforcement, it is crucial to enhance its legal ecosystem through improved judicial training and harmonization of laws across different jurisdictions. The government should invest in fostering a robust legal framework that supports climate change litigation while ensuring fair access to justice for all stakeholders. Enhanced public education initiatives are also recommended to foster greater understanding of environmental issues. The empirical specification follows $Y=\beta_0+\beta^\top X+\varepsilon$, and inference is reported with uncertainty-aware statistical criteria.
Modeling Networks of Glycolysis, Overall Energy Metabolism and Drug Metabolism under a Systems Biology Approach

This chapter presents systems biology as a tool to understand perturbations in mammalian metabolism caused by disease and medicine. Like the empirical and sometimes serendipitous methods of drug discovery and evaluation that came before it, the molecular-target approach of modeling metabolism of energy and drugs is increasingly recognized as only a partial—and often too simple—answer to biological questions that are more complex than was fully appreciated until relatively recently. The realization that all biochemical phenomena within an organism have important and varying influence on each other depending of many variables is too late to be applied to metabolic modeling and drug discovery and development. A systems biology approach incorporates both the micro- and macro-views of organism function in health and disease. Improved models of such function are most likely to help break the logjam of stalled progress in developing more effective treatments in a wide variety of diseases. Evolving are more accurate models in vivo, ex vivo and in silico, and combinations as interactive and dynamic in change as the biologic systems they represent.
Altered and allele-specific open chromatin landscape reveals epigenetic and genetic regulators of innate immunity in COVID-19

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection causes severe COVID-19 in some patients and mild COVID-19 in others. Dysfunctional innate immune responses have been identified to contribute to COVID-19 severity, but the key regulators are still unknown. Here, we present an integrative single-cell multi-omics analysis of peripheral blood mononuclear cells from hospitalized and convalescent COVID-19 patients. In classical monocytes, we identified genes that were potentially regulated by differential chromatin accessibility. Then, sub-clustering and motif-enrichment analyses revealed disease condition-specific regulation by transcription factors and their targets, including an interaction between C/EBPs and a long-noncoding RNA LUCAT1, which we validated through loss-of-function experiments. Finally, we investigated genetic risk variants that exhibit allele-specific open chromatin (ASoC) in COVID-19 patients and identified a SNP rs6800484-C, which is associated with lower expression of CCR2 and may contribute to higher viral loads and higher risk of COVID-19 hospitalization. Altogether, our study highlights the diverse genetic and epigenetic regulators that contribute to COVID-19.