ethicalabs/Research-Intent-Collab huggingface dataset:
PREDICTION OF 3D STRUCTURE OF MITOGEN-ACTIVATED PROTEIN KINASE KINASE 2 USING ALPHAFOLD 2 AND SIMULATION OF ITS INTERACTION WITH MITOGEN-ACTIVATED PROTEIN KINASE 6 IN ARABIDOPSIS THALIANA

Arabidopsis thaliana mitogen-activated protein kinase (MPK or MAPK) signaling network plays significant roles in various cellular processes. The three-dimensional structure of mitogen-activated protein kinase kinase 2 (MKK2), an upstream kinase in the MAP kinase cascade, was predicted using AlphaFold 2, and protein-protein docking simulations were performed between MPK6 and MKK2. The docking analysis identified important residues mediating their interaction. This structural prediction and protein docking analysis provide a further understanding at protein structure level.
"It's mostly a one-way street, to be honest": the subjective relevance of public engagement in the science communication of professional university communicators

This study explores the subjective relevance and challenges of public engagement (PES) in science communication among professional university communicators based on 29 qualitative interviews in one German federal state. Despite recognizing its value, interviewees reveal significant uncertainties in understanding, objectives, and implementation of PES. They cite barriers such as reliance on scientists and control concerns. Surprisingly, social media is rarely considered for PES, with online engagement seen as difficult. This research highlights the complexities and challenges of PES in practice, emphasizing opportunities for optimized digital science communication strategies and clearer role structures between professionals and researchers to enhance PES.
Benchmarking Deep Learning Approaches for Single-Excitation Wideband MRI: Establishing the First SE-Wideband Brain Dataset

Abstract                 Single-Excitation Wideband MRI enables efficient acquisition, but its rapid-switching design to mitigate intrinsic blur leads to incompatibility with GRAPPA and other fast sequences, restricting its clinical adoption. Regarding this challenge, we established the first dedicated SE-Wideband brain dataset, comprising 76 subjects and 8000 paired slices, and developed a systematic framework for benchmarking deep learning–based restoration methods. Nine networks were evaluated across multiple loss configurations, with performance quantified using conventional metrics and human perceptual ratings. Results demonstrate consistent improvements over raw SE-Wideband inputs, improving NRMSE from ~ 8% to ~ 5% and SSIM from 0.62 to 0.80. Human ratings verified the effectiveness of AI-based denoising, and correlation analyses showed that metric-optimized configurations generally aligned better with subjective scores than perceptual-guided ones, suggesting that conventional metrics remain a reliable reference for loss selection. In contrast, SE-Wideband-GRAPPA restoration proved challenging, yielding limited gains across networks. These findings highlight both the promise and limitations of AI-based SE-Wideband image restoration: substantial improvements are achievable for SE-Wideband, but clinical translation will require new strategies tailored to compounded artifacts in SE-GRAPPA. The curated dataset and benchmarking framework presented here provide a valuable reference for future methodological development and the broader integration of Wideband MRI into neuroimaging practice.
Risk-based decision making: estimands for sequential prediction under interventions

Prediction models are used amongst others to inform medical decisions on interventions. Typically, individuals with high risks of adverse outcomes are advised to undergo an intervention while those at low risk are advised to refrain from it. Standard prediction models do not always provide risks that are relevant to inform such decisions: e.g., an individual may be estimated to be at low risk because similar individuals in the past received an intervention which lowered their risk. Therefore, prediction models supporting decisions should target risks belonging to defined intervention strategies. Previous works on prediction under interventions assumed that the prediction model was used only at one time point to make an intervention decision. In clinical practice, intervention decisions are rarely made only once: they might be repeated, deferred and re-evaluated. This requires estimated risks under interventions that can be reconsidered at several potential decision moments. In the current work, we highlight key considerations for formulating estimands in sequential prediction under interventions that can inform such intervention decisions. We illustrate these considerations by giving examples of estimands for a case study about choosing between vaginal delivery and cesarean section for women giving birth. Our formalization of prediction tasks in a sequential, causal, and estimand context provides guidance for future studies to ensure that the right question is answered and appropriate causal estimation approaches are chosen to develop sequential prediction models that can inform intervention decisions.
Wealth, Poverty, and Justice in the Bible: Implications for Business Ethics and Corporate Social Responsibility

This research paper explores the biblical foundations of wealth, poverty, and justice, critically examining how these themes inform contemporary business ethics and corporate social responsibility (CSR). Through comprehensive biblical exegesis of both Old and New Testament scriptures and integration of pertinent scholarly literature, industry reports, and business data, this study demonstrates that ethical business practices are deeply rooted in Judeo-Christian values. The paper presents detailed case studies of Fortune 500 companies over the last five years that have implemented biblical principles to create sustainable economic practices. Insights from at least ten key biblical passages, analyzed in depth, are used to construct a framework that supports ethical decision-making in the business arena. The findings reveal that businesses aligning with these ethical imperatives are better positioned to develop CSR strategies that promote economic justice, reduce poverty, and promote wealth distribution that benefits society. Recommendations for both scholars and business ethics professionals are discussed, providing practical solutions for integrating biblical values within modern corporate practices.
A Systematic Review of Natural Language Processing in Healthcare

The healthcare system is a knowledge driven industry which consists of vast and growing volumes of narrative information obtained from discharge summaries/reports, physicians case notes, pathologists as well as radiologists reports. This information is usually stored in unstructured and non-standardized formats in electronic healthcare systems which make it difficult for the systems to understand the information contents of the narrative information. Thus, the access to valuable and meaningful healthcare information for decision making is a challenge. Nevertheless, Natural Language Processing (NLP) techniques have been used to structure narrative information in healthcare. Thus, NLP techniques have the capability to capture unstructured healthcare information, analyze its grammatical structure, determine the meaning of the information and translate the information so that it can be easily understood by the electronic healthcare systems. Consequently, NLP techniques reduce cost as well as improve the quality of healthcare. It is therefore against this background that this paper reviews the NLP techniques used in healthcare, their applications as well as their limitations.
Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation

Preprint
The Privacy Policy Permission Model: A Unified View of Privacy Policies

Organizations use privacy policies to communicate their data collection practices to their clients. A privacy policy is a set of statements that specifies how an organization gathers, uses, discloses, and maintains a client's data. However, most privacy policies lack a clear, complete explanation of how data providers' information is used. We propose a modeling methodology, called the Privacy Policy Permission Model (PPPM), that provides a uniform, easy-to-understand representation of privacy policies, which can accurately and clearly show how data is used within an organization's practice. Using this methodology, a privacy policy is captured as a diagram. The diagram is capable of highlighting inconsistencies and inaccuracies in the privacy policy. The methodology supports privacy officers in properly and clearly articulating an organization's privacy policy.
RIGHT TO A HEALTHY ENVIRONMENT IN NIGERIA: AN APPRAISAL OF THE NATIONAL ENVIRONMENTAL (MINING AND PROCESSING OF COAL, ORES AND INDUSTRIAL MINERALS) REGULATIONS 2009

This thesis critically examines the right to a healthy environment within the Nigerian legal framework, with particular focus on the National Environmental (Mining and Processing of Coal, Ores and Industrial Minerals) Regulations 2009. It explores the extent to which these Regulations protect environmental rights in the context of Nigeria’s extractive industries, which have historically contributed to environmental degradation, public health risks, and socio-economic disruption.  The study adopts a doctrinal and analytical approach to evaluate the legal, institutional, and enforcement mechanisms embedded in the Regulations. It interrogates their compliance with constitutional provisions, international environmental standards, and emerging principles of environmental justice and sustainable development. Particular attention is given to gaps in implementation, regulatory weaknesses, and the challenges posed by weak enforcement, corruption, and overlapping institutional mandates.  Furthermore, the thesis assesses the role of government agencies, private sector actors, and affected communities in ensuring environmental protection and accountability. Through case analysis and comparative insights, it highlights the disconnect between regulatory intent and practical outcomes in Nigeria’s mining sector.  The research ultimately argues for a strengthened legal and institutional framework that not only recognizes but effectively enforces the right to a healthy environment. It concludes with practical recommendations aimed at improving regulatory compliance, enhancing environmental governance, and safeguarding the health and livelihoods of communities impacted by mining activities.
Study of image reconstruction efficiency in a single-pixel imaging method using generative adversarial networks

Single-pixel imaging is a promising image acquisition method that provides an alternative to traditional imaging methods using multi-pixel matrices. However, algorithmic image reconstruction from measurements of a single-pixel camera is a non-trivial computational task that can be solved by machine learning methods. In this work, we investigate the possibility of image reconstruction in the single-pixel imaging method using generative adversarial neural networks. Using computer simulation of a single-pixel camera, we study the efficiency of image reconstruction using two generative network architectures – a deep convolutional generative adversarial network and a generative least squares adversarial network. We find that the generative least squares adversarial network demonstrates a better image reconstruction quality compared to the deep convolutional generative adversarial network. However, when taking into account optical distortions, the deep convolutional adversarial network is more stable in learning to a higher quality compared to the generative least squares adversarial network. The results obtained in this work may serve as a basis for the development of software for the practical application of a single-pixel camera.
Original Research Health-related quality of life with palbociclib plus endocrine therapy versus capecitabine in postmenopausal patients with hormone receptor-positive metastatic breast cancer: Patient-reported outcomes in the PEARL study

Background: The PEARL study showed that palbociclib plus endocrine therapy (palbociclib/ET) was not superior to capecitabine in improving progression-free survival in postmenopausal patients with metastatic breast cancer resistant to aromatase inhibitors, but was better tolerated. This analysis compared patient-reported outcomes. Patients and methods: The PEARL quality of life (QoL) population comprised 537 patients, 268 randomised to palbociclib/ET (exemestane or fulvestrant) and 269 to capecitabine. Patients completed the European Organisation for Research and Treatment of Cancer QLQC30 and QLQ-BR23 and EQ-5D-3L questionnaires. Changes from the baseline and time to deterioration (TTD) were analysed using linear mixed-effect and stratified Cox regression models, respectively. Results: Questionnaire completion rate was high and similar between treatment arms. Significant differences were observed in the mean change in global health status (GHS)/QoL scores from the baseline to cycle 3 (2.9 for palbociclib/ET vs.-2.1 for capecitabine (95% confidence interval [CI], 1.4-8.6; P = 0.007). The median TTD in GHS/QoL was 8.3 months for palbociclib/ET versus 5.3 months for capecitabine (adjusted hazard ratio, 0.70; 95% CI, 0.55-0.89; P = 0.003). Similar improvements for palbociclib/ET were also seen for other scales as physical, role, cognitive, social functioning, fatigue, nausea/vomiting and appetite loss. No differences were observed between the treatment arms in change from the baseline in any item of the EQ-5D-L3 questionnaire as per the overall index score and visual analogue scale. Conclusion: Patients receiving palbociclib/ET experienced a significant delay in deterioration of GHS/QoL and several functional and symptom scales compared with capecitabine, providing additional evidence that palbociclib/ET is better tolerated. Trial registration number: NCT02028507 (ClinTrials.gov). EudraCT study number: 2013-003170-27. 2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Oral Imaging for Malocclusion Issues Assessments: OMNI Dataset, Deep Learning Baselines and Benchmarking

Malocclusion is a major challenge in orthodontics, and its complex presentation and diverse clinical manifestations make accurate localization and diagnosis particularly important. Currently, one of the major shortcomings facing the field of dental image analysis is the lack of large-scale, accurately labeled datasets dedicated to malocclusion issues, which limits the development of automated diagnostics in the field of dentistry and leads to a lack of diagnostic accuracy and efficiency in clinical practice. Therefore, in this study, we propose the Oral and Maxillofacial Natural Images (OMNI) dataset, a novel and comprehensive dental image dataset aimed at advancing the study of analyzing dental images for issues of malocclusion. Specifically, the dataset contains 4166 multi-view images with 384 participants in data collection and annotated by professional dentists. In addition, we performed a comprehensive validation of the created OMNI dataset, including three CNN-based methods, two Transformer-based methods, and one GNN-based method, and conducted automated diagnostic experiments for malocclusion issues. The experimental results show that the OMNI dataset can facilitate the automated diagnosis research of malocclusion issues and provide a new benchmark for the research in this field. Our OMNI dataset and baseline code are publicly available at https://github.com/RoundFaceJ/OMNI.
Investigation on the Material Failure in a Small Scale Automotive Camera Module via Root Cause Analysis and Experimental Validation

Automotive camera modules are critical components for vision, detection, and recognition in an advanced driver assistance system. They usually consist of housing, a lens, a lens holder, a harness, seals, and printed circuit boards, which are assembled through screws, adhesive, and connectors. Due to a small and complex geometry of the components, the component material failure is a critical issue. In this paper, the lens holder material failure is investigated using the root cause analysis and experimental validation. First, a fishbone diagram model is created to analyze the potential causes and an action checklist is employed to identify the root cause. Second, the structure of the lens holder is optimized according to the finite-element analysis result. The numerical results show that the strength of the optimized lens holder is improved by nearly 35%. Finally, the torque test is carried out to validate the optimized structure experimentally. The presented analysis and validation procedure can be used to solve material failure issues for small-scale automotive components effectively.
A Novel Approach for Protein Structure Prediction

The idea of this project is to study the protein structure and sequence relationship using the hidden markov model and artificial neural network. In this context we have assumed two hidden markov models. In first model we have taken protein secondary structures as hidden and protein sequences as observed. In second model we have taken protein sequences as hidden and protein structures as observed. The efficiencies for both the hidden markov models have been calculated. The results show that the efficiencies of first model is greater that the second one .These efficiencies are cross validated using artificial neural network. This signifies the importance of protein secondary structures as the main hidden controlling factors due to which we observe a particular amino acid sequence. This also signifies that protein secondary structure is more conserved in comparison to amino acid sequence.
An Integrated View of the One-Health Movement: The Oneness of Human HealthReview of: <source>One Health—People, Animals, and the Environment</source>; <person-group> <name name-style="western"><surname>Atlas</surname><given-names>Ronald M.</given-names></name></person-group> and <person-group> <name name-style="western"><surname>Maloy</surname><given-names>Stanley</given-names></name></person-group> (ed.); ( <year>2014</year>). <publisher-name>American Society for Microbiology Press</publisher-name>, <publisher-loc>Washington, DC</publisher-loc>. <fpage>318</fpage> pages.

Review of: One Health—People, Animals, and the Environment; Ronald M. Atlas and Stanley Maloy (ed.); (2014). American Society for Microbiology Press, Washington, DC. 318 pages.
A methodology for implementing risk management and decision support in computerized clinical practice guidelines using evidence-based utility estimation

[EN] Current Clinical Practice Guidelines (CPGs) are mainly supported by evidence-based medicine findings to avoid unjustified practice variations. Nevertheles, they lack the management of risk and uncertainty. Besides, the use of Decision Trees (DTs) in medicine has introduced the management of risk, although, there is not a framework to specify clinical guidelines as DTs and both DTs and CPGs lack personalized recommendations based on the features of the patients. The proposed methodology allows to turn a GPC into a decision problem that can be solved using Bayesian decision theory to manage uncertainty. A proof of concept based on Ductal Carcinoma has been used to validate the methodology.
Collaborative Filtering for Book Recommendation System

Collaborative filtering is one of the most important techniques in the market nowadays. It is prevalent in almost every aspect of the internet, in e-commerce, music, books, social media, advertising, etc., as it greatly grasps the needs of the user and provides a comfortable platform for the user to find what they like without searching. This method has a few drawbacks; one of them being, it is based only on the explicit feedback given by the user in the form of a rating. The real needs of a user are also demonstrated by various implicit indicators such as views, read later lists, etc. This paper proposes and compares various techniques to include implicit feedback into the recommendation system. The paper attempts to assign explicit ratings to users depending on the implicit feedback given by users to specific books using various algorithms and thus, increasing the number of entries available in the table.
Sovereignty, Integration or Bifurcation? Troubled Histories, Contentious Territories and the Political Horizons of the Long Lingering Naga Movement

Taking the upland, tribal Nagas, and the long lingering Indo-Naga conflict, as my case, this article positions divergent contemporary political and territorial claims and counterclaims in the historical longue durée. While the initial assertion for a sovereign, independent Nagalim envisaged a political and administrative reordering of a region cut across by the colonial Indo-Burma border, over time the Naga national movement increasingly conformed to this international boundary—at least in praxis—as political attention shifted towards the integration of Naga lands within India but presently divided across multiple states. More recently, another territorial quandary emerged as the Eastern Naga People’s Organization articulated a demand for the bifurcation of present-day Nagaland, as it was enacted in 1963, through the creation of a new state to be called ‘Frontier Nagaland’. If amidst ongoing strife and uncertainty, these divergent political and territorial aspirations suggest a sinking ever more deeply into a morass of conflicting aspirations, oppositional sentiments and political disorder, this article attempts to historically situate each of these claims, as well as discusses some of their implications, inner intricacies and indeterminacies, in the upshot serving a heady cocktail of colonial legacies, borders that divide, and post-colonial political imaginations, spiked with shots of old tribal antagonisms and new alignments.
A Short Survey on Business Models of Decentralized Finance (DeFi) Protocols

Decentralized Finance (DeFi) services are moving traditional financial operations to the Internet of Value (IOV) by exploiting smart contracts, distributed ledgers, and clever heterogeneous transactions among different protocols. The exponential increase of the Total Value Locked (TVL) in DeFi foreshadows a bright future for automated money transfers in a plethora of services. In this short survey paper, we describe the business model for different DeFi domains - namely, Protocols for Loanable Funds (PLFs), Decentralized Exchanges (DEXs), and Yield Aggregators. We claim that the current state of the literature is still unclear how to value thousands of different competitors (tokens) in DeFi. With this work, we abstract the general business model for different DeFi domains and compare them. Finally, we provide open research challenges that will involve heterogeneous domains such as economics, finance, and computer science.