ethicalabs/Research-Intent-Collab huggingface dataset:
Explain-seq: an end-to-end pipeline from training to interpretation of sequence-based deep learning models

Abstract                                    Interpreting predictive machine learning models to derive biological knowledge is the ultimate goal of developing models in the era of genomic data exploding. Recently, sequence-based deep learning models have greatly outperformed other machine learning models such as SVM in genome-wide prediction tasks. However, deep learning models, which are black-box models, are challenging to interpret their predictions. Here we represented an end-to-end computational pipeline, Explain-seq, to automate the process of developing and interpreting deep learning models in the context of genomics. Explain-seq takes input as genomic sequences and outputs predictive motifs derived from the model trained on sequences. We demonstrated Explain-seq with a public STARR-seq dataset of the A549 human lung cancer cell line released by ENCODE. We found our deep learning model outperformed gkm-SVM model in predicting A549 enhancer activities. By interpreting our well-performed model, we identified 47 TF motifs matched with known TF PWMs, including ZEB1, SP1, YY1, and INSM1. They are associated with epithelial-mesenchymal transition and lung cancer proliferation and metagenesis. In addition, there were motifs that were not matched in the JASPAR database and may be considered as                   de novo                   enhancer motifs in the A549 cell line.                                                     Availability                                        https://github.com/nsamzhao/Explain-seq                                                                        Contact                                        apboyle@umich.edu                                                                        Supplementary information                   Supplementary data are available as attachment.
Defining and measuring quality palliative and end-of-life care in the intensive care unit

Quality of health care is primarily concerned with the provision of health services that intend to lead to valued health outcomes and are based and driven by evidence. Among other desired health outcomes are patient-and-family-centered values consistent with proficient palliative and end-of-life care in the intensive care unit. The research in palliative and end-of-life care has elucidated important domains for quality care-in general, major targets for improvement are known. However, assessment of quality at local and national levels remains relevant as innovators select where to begin quality improvement efforts and the healthcare system evaluates the efficacy and potential harm from care delivery transformations. In this article, I endeavor to impart a practical framework for quality of end-of-life care assessment with the goal of guiding the selection of initiatives and evaluating cycles of innovation. I will ground this quality evaluation by reviewing palliative and end-of-life care and the known domains for quality palliative care. Although the field has identified candidate indicators for evaluating palliative and end-of-life care in the intensive care unit, future work is needed to operationalize assessment for important aspects of care with valid, reliable, acceptable, efficient, and responsive measures.
Data Mining on Survival Prediction after Chemotherapy for Diffuse Large-B-Cell Lymphoma and Genomics of Metastasis Cancer

This research pertains to the applications of data mining of microarray databases for large-B-cell Lymphoma and metastasis cancer, the latter of which little has been known about the genomic events that regulate the transformation of a tumor into a metastatic phenotype.
Integrating GIS and the Urban Adaptation Assessment Framework for Climate Vulnerability and Resilience in Louisville, Kentucky

Abstract                 Climate change presents an escalating threat to urban environments, where dense populations, aging infrastructure, and entrenched social inequities amplify exposure to climate hazards. Urban centers face compound risks such as flooding and extreme heat, disproportionately impacting socioeconomically marginalized communities. Despite recognition of these challenges, there remains a critical need for integrated, spatially detailed assessments to inform equitable adaptation strategies. This study represents the first application of the Urban Adaptation Assessment (UAA) tool in Louisville, Kentucky, integrating fine-scale spatial data with Geographic Information Systems to rigorously evaluate vulnerability to these dual threats. A multidimensional framework encompassing environmental exposure, social sensitivity, and adaptive capacity was employed, incorporating both equal and context-sensitive weighting schemes to reveal nuanced spatial patterns of risk. Our findings demonstrate a significant convergence of flood and heat vulnerabilities within historically underserved neighborhoods, including Shively, Pleasure Ridge Park, and Newburg, where deteriorating infrastructure, economic precarity, and limited healthcare access heighten climate risk. Notably, critical infrastructure such as hospitals and fire stations is disproportionately situated in these high-risk zones, potentially impeding emergency response effectiveness. This analysis elucidates the intersection of environmental hazards, infrastructure deficits, and social inequities at a granular spatial scale, providing a robust, policy-relevant foundation for advancing equitable urban adaptation strategies.
Comparison of Carbohydrate Force Fields in Molecular Dynamics Simulations of Protein–Carbohydrate Complexes

In this paper, we present the results of molecular dynamics simulations aimed at critical comparison of classical, biomolecular force fields (FFs) in the context of their capabilities to describe the structural and thermodynamic features of carbohydrate-protein interactions. We have considered the three main families of FFs (CHARMM, GROMOS, and GLYCAM/AMBER) by applying them to investigate the seven different carbohydrate-protein complexes. The results indicate that although the qualitative pattern of several structural descriptors (intermolecular hydrogen bonding, ligand dynamic location, etc.) is conserved among the compared FFs, there also exists a number of significant divergences (mainly the patterns of contacts between particular amino acid residues and bound carbohydrate). The carbohydrate-protein unbinding free energies also vary from one FF to another, displaying diversified trends in deviations from the experimental data. The magnitude of those deviations is not negligible and indicates the need for refinement in the currently existing combinations of carbohydrate- and protein-dedicated biomolecular force fields. In spite of the lack of explicit functional terms responsible for the corresponding intermolecular forces, all tested FFs are capable of adequately reproducing the CH-π interactions, crucial for carbohydrate-protein binding.
Natural language processing for urban research: A systematic review

Natural language processing (NLP) has shown potential as a promising tool to exploit under-utilized urban data sources. This paper presents a systematic review of urban studies published in peer-reviewed journals and conference proceedings that adopted NLP. The review suggests that the application of NLP in studying cities is still in its infancy. Current applications fell into five areas: urban governance and management, public health, land use and functional zones, mobility, and urban design. NLP demonstrates the advantages of improving the usability of urban big data sources, expanding study scales, and reducing research costs. On the other hand, to take advantage of NLP, urban researchers face challenges of raising good research questions, overcoming data incompleteness, inaccessibility, and non-representativeness, immature NLP techniques, and computational skill requirements. This review is among the first efforts intended to provide an overview of existing applications and challenges for advancing urban research through the adoption of NLP.
How negative feedback from filamentous actin affects cell shapes and motility

The crawling motility of many eukaryotic cells is driven by filamentous actin (F-actin), and regulated by a network of signaling proteins and lipids (including small GTPases). The tangle of positive and negative feedback loops gives rise to various experimentally observed dynamic patterns (``actin waves''). Here we consider a recent prototypical model for actin waves in which F-actin exerts negative feedback onto a GTPase. Guided by recent numerical PDE bifurcation analysis in Hughes (2025) and Hughes et al (2026), we explore cell shapes and motility associated with polar, oscillatory, and traveling waves solutions of a mass-conserved partial differential equation (PDE) model. We use Morpheus (cellular Potts) simulations to investigate the implications of such regimes of behavior on the shapes and motion of cells, and on transitions between modes of behavior. The model demonstrates various cell states, including resting (spatially uniform GTPase), polar cells (static ``zones'' of GTPase), and traveling waves along the cell edge. In some parameter regimes, such states can coexist, so that cells can transition from one behavior to another in response to noisy stimuli.
Conjectures on Convergence and Scalar Curvature

Here we survey the compactness and geometric stability conjectures formulated by the participants at the 2018 IAS Emerging Topics Workshop on {\em Scalar Curvature and Convergence}. We have tried to survey all the progress towards these conjectures as well as related examples, although it is impossible to cover everything. We focus primarily on sequences of compact Riemannian manifolds with nonnegative scalar curvature and their limit spaces. Christina Sormani is grateful to have had the opportunity to write up our ideas and has done her best to credit everyone involved within the paper even though she is the only author listed above. In truth we are a team of over thirty people working together and apart on these deep questions and we welcome everyone who is interested in these conjectures to join us.
Topological Data Analysis Of Hindi Literary Styles: A Persistent Homology Framework For Author Identification

We introduce a completely new methodology: Topological Data Analysis TDA for Hindi literary stylometry. Unlike traditional frequency‑based or neural approaches, TDA captures the shape of a text&rsquo;s stylistic features across multiple scales. By encoding each Hindi sentence as a point in a high‑dimensional feature space and constructing Vietoris&ndash;Rips complexes, we compute persistent homology barcodes that serve as unique topological signatures of an author&rsquo;s style. This paper presents the first‑ever application of persistent homology to Hindi prose or poetry. The framework is fully implementable using standard TDA libraries and requires no prior training data. We demonstrate the concept on synthetic Hindi text samples. All definitions, algorithms, and the evaluation metric are original
Judicial Interpretation and Indigenous Peoples’ Rights to Lands, Participation and Consultation. The Inter-American Court of Human Rights’ Approach

This article proposes a critical legal analysis of the jurisprudence of the Inter-American Court of Human Rights regarding indigenous peoples’ rights to lands, participation, and consultation. It focuses on the role that cultural diversity as a legal standard has played in the recognition of the indigenous peoples’ right to consultation and participation in all matters that directly affect them, as a guarantee for the protection of their right to communal property and natural resources traditionally used, and for safeguarding their cultural identity. In analysing the jurisprudence of the Inter-American Court, special attention is paid to the interpretative methods applied by the regional tribunal, and to the manner in which a non-restrictive and ‘culturally friendly’ interpretation of conventionally protected human rights has contributed to the enlargement of their scope of protection, and to their enjoyment by one of the most marginalized and excluded sectors of Latin-American societies.
Electrochemistry of Electrode Materials Containing S−Se Bonds for Rechargeable Batteries

AbstractSulfur (S) and selenium (Se) have been considered as promising high capacity cathode materials for rechargeable batteries. They have differences in their physical properties (e.g., electronic conductivity) but the same number of electrons in their outermost shells, which leads to similarity in their electrochemical behavior in batteries. In recent years, some efforts have been taken to combine them in electrodes in the hope of improved battery performance. The S−Se bonds of these electrode materials lead to unusual properties and intriguing electrochemical behavior, which have attracted increasing interest. In this Minireview, electrode materials containing S−Se bonds are summarized, including inorganic SxSey solid solutions, organic compounds, and organic–inorganic hybrid materials. Our understanding in these materials is still premature, but they have shown unique properties to be electrode materials. We hope this Minireview could provide a new insight into the design, synthesis, and understanding of these materials, which could enable high energy density rechargeable batteries.
Orchestrating the flow of information for decision making: new dynamics between management accountants and operational managers leveraging business intelligence &amp; analytics systems

Abstract                   This paper investigates whether and how the use of business intelligence &amp; analytics (BI&amp;A) systems influences the way management accountants (MAs) support the decision-making processes of operational managers within organisations. Drawing on a cross-sectional field study of five Italian companies and using the concepts of technological properties, affordances and constraints, findings show that MAs’ degree of involvement in decision-making processes depends on how MAs and operational managers leverage BI&amp;A properties to perform tasks during the production, transmission and reception of information. Although BI&amp;A systems have reduced MAs’ traditional tasks and responsibilities during the informational flow, by leveraging specific BI&amp;A properties, MAs can recognise themselves as orchestrators of information flows because they establish the entire information framework available to managers for decision making. In intra-functional decision making, MAs’ orchestration is largely invisible within the BI&amp;A infrastructure because operational managers gain greater autonomy in managing information. However, this autonomy is enabled by the tailored information perimeter previously configured by MAs, making their diminished presence more apparent than real. Conversely, in inter-functional decision making, MAs’ orchestration becomes overt as operational managers actively seek their involvement. Moreover, MAs leverage their enterprise-wide access to BI&amp;A information and business knowledge to safeguard local decisions that reflect their broader organisational implications.
Reduced-order modeling of neutron transport separated in energy by Proper Generalized Decomposition with applications to nuclear reactor physics

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Influence diffusion model based on affinity of dynamic social network

Recently,influence maximization model is a hot issue in the field of social network influence,while the traditional independent cascade model is generally based on static network with a fixed value of activation probability.DDIC model,which was a dynamic network influence diffusion model with attenuation factor was proposed.It calculated the activation probability between nodes via affinity propagation,and according with dynamic segmentation of social network time slice,calculation of influence on proliferation of next time slice with the current time slice of activation probability performance decay.The experimental results show that the nodes in the DDIC model have more chances to active the neighbor and the average probability of activing of the DDIC model is higher.Further experiments show that influence value via computing with affinity propagation can reflect the process of the spread model more accurately.
Local structure and electrochemistry of LiNi y Mn y Co1 − 2y O2 electrode materials for Li-ion batteries

A series of LiNi                   x                 Mn                   y                 Co                   z                 O2 (x = y, z = 1 − 2y) oxides have been synthesized by “chimie douce” and investigated as positive electrodes in rechargeable lithium batteries. Layered LiNi                   y                 Mn                   y                 Co1 − 2y                         O2 materials with high homogeneity and crystallinity were synthesized using the wet-chemical method assisted by carboxylic acid as the polymeric agent. The long range and local structural properties are investigated with experiments including X-ray diffraction, Fourier transform infrared spectroscopy, and electron paramagnetic resonance spectroscopy. The evolution of the structure is discussed as a function of the cobalt content that confers layer-like behavior on the framework. Electrochemical performance of LiNi                   y                 Mn                   y                 Co1 − 2y                         O2 oxides is tested in cells using nonaqueous 1 M LiPF6 dissolved in ethylene carbonate–diethyl carbonate. Charge–discharge profiles are investigated as a function of the rate capability and the voltage window. A relation is found between the gravimetric capacity and the cation disorder of the positive electrode as indicated by structural analysis. Fast lithium extraction attributed to the larger interslab space has been observed in the cobalt-rich oxides.
The right to privacy in the age of surveillance to counter terrorism in Ethiopia

Despite being a useful tool to prevent and prosecute terrorism, surveillance is increasingly being utilised as an excuse for infringements on privacy in several jurisdictions. Laws governing counter-terrorism surveillance either are not in compliance with human rights standards governing privacy or inappropriately implemented. Research on the relevant law and practice in Ethiopia is limited. This article identifies legal and practical problems causing violations of the right to privacy while countering terrorism in Ethiopia. In doing so, it evaluates the human rights compatibility of the relevant law and practice mainly based on article 17 of the ICCPR. The analysis is substantiated by an empirical study involving key informant interviews and case file reviews from Ethiopian courts. Legitimate counter-terrorism surveillance should respect the right to privacy. A state may implement counter-terrorism surveillance only when it is properly defined by law and reasonably applied. Prior judicial authorisation of covert interceptions is usually regarded as a safeguard to prevent the misuse of counter-terrorism surveillance. Ethiopia does not have a law regulating mass surveillance in the absence of a specific terrorism threat. However, reports suggest that the Ethiopian government has unrestricted access to private communications since it monopolises telecommunications and postal services. Counter-terrorism related surveillance, however, is legally regulated in Ethiopia. Ethiopian law requires that law enforcement agencies may conduct the interception of communications on terrorist suspects upon getting a court warrant. Nonetheless, evidence suggests that, in practice, there is widespread use of warrantless interceptions which are used as prosecution evidence in defiance of the law. This article may serve to assess and improve counter-terrorism surveillance in Ethiopia so that the law and its implementation reflect the values of a democratic society based on the rule of law and respect for the right to privacy.  Key words: Ethiopia; Anti-Terrorism Proclamation; counter-terrorism surveillance; mass surveillance; targeted surveillance
Depth-First Event Ordering in BDD-Based Fault Tree Analysis

In BDD-based fault tree analysis, the size of BDD encoding fault trees heavily depends on the chosen ordering. From a theoretical point of view, finding the best ordering is an intractable task. So, heuristics are used to get good orderings. The most simple, and often one of the best heuristics is depth first left most (DFLM) heuristic. Although having been used widely, the performance of DFLM heuristic is still only vaguely understood, and not much formal work has been done. This paper starts from two different research objects: fault tree without repeated events (NRFT) and fault tree with repeated events (RFT). For NRFT, the BDD generated according to DFLM ordering is proved to be the smallest BDD with the size equal to the total number of events. For RFT, a randomized algorithm is firstly proposed to create reliable benchmarks including large number of random fault trees with different specificities. Then, these benchmarks are used to perform two types of experiments to study the performance of DFLM heuristic. For RFT with small number of repeated events, it is found that the sizes of the BDD built over DFLM orderings are only slightly larger than the sizes of the RFT with different specificities. However, with the increase of the number of repeated events, we encounter the size explosion problem, and the change of repeated event distribution patterns will have a significant impact on the sizes of the BDD built over DFLM orderings. We also find that the number of repeated events is the more important measure than some other specificities (shape, logical type of top gate and OR/AND gate distribution) to estimate the level of the difficulty in BDD-based fault tree analysis.
Corporate Criminal Liability under International Law: The New TV S.A.L. and Akhbar Beirut S.A.L. Cases at the Special Tribunal for Lebanon

Relying on the Tribunal’s inherent powers, the Appeals Panel of the Special Tribunal for Lebanon decided in two cases, New TV S.A.L. and Akhbar Beirut S.A.L., that the Tribunal has jurisdiction over corporations for the offence of contempt. They decided so despite the absence of a clear provision explicitly granting such jurisdiction. This is the first time an international criminal tribunal asserts jurisdiction over legal persons. The article critically presents the Appeals Panel’s findings and places them in their historical and international context. Although limited in scope, the decisions are of great significance as business and human rights developments at the international level have emphasized the need for enhanced corporate accountability.