ethicalabs/Research-Intent-Collab huggingface dataset:
35522 Implementing and Disseminating Translational Science Virtually, Successfully and Saving a Whole Lot of Money

ABSTRACT IMPACT: We hosted the Indiana Clinical and Translational Sciences Annual meeting virtually this year which resulted in positive feedback survey scores over 90% and an estimated 87% cost savings OBJECTIVES/GOALS: COVID-19 has forced many in-person meetings to become virtual, not unlike our 2020 Indiana Clinical and Translational Sciences Institute Annual Meeting. However, where anecdotal feedback has shown dissatisfaction with some on-line meetings, we were able to exceed our goals of engaging our audience, securing positive feedback and even saving money. METHODS/STUDY POPULATION: More than 500 people attended the virtual 2020 Indiana Clinical and Translational Sciences Institute (CTSI) Annual Meeting on September 11. The event had two plenary speakers and was completely online, utilizing both Zoom and Microsoft Teams to connect participants with the presenters. Brian Druker, MD, director of the Knight Cancer Institute at Oregon Health &amp; Science University, was the winner of this year’s August M. Watanabe Prize in Translational Research. He gave the first plenary presentation titled, ‘Imatinib as a Paradigm of Targeted Cancer Therapies.’ Consuelo Wilkins, MD, Vice President for Health Equity at Vanderbilt University Medical Center, gave the second plenary titled, ‘Confronting Racial Inequities through Research.' Concurrent online breakout rooms hosted the live poster session. RESULTS/ANTICIPATED RESULTS: Despite being conducted online, the virtual Indiana CTSI annual meeting registered more participants than in years past and secured high feedback scores of 90%, all while experiencing 87% cost savings over last year’s in-person meeting. By utilizing Microsoft Teams as a technology for attendees to the meeting to 'chat’ and 'network’ with one another during the poster presentations and virtual lunch break we were able to demonstrate the implementation of translational science through online plenary and general session presentations as well as the poster presentations. Mailing certificates to the poster winners in advance, allowed them to share their accolades with the audience by holding up their certificates once their winning posters were announced. An e-annual report also supported the success of the meeting. DISCUSSION/SIGNIFICANCE OF FINDINGS: The cost savings and traditionally high feedback scores received through this year’s Indiana CTSI annual meeting, mean virtual meetings are a viable way to disseminate and implement translational science. In addition the 2020 Indiana CTSI annual report received a Gold MarComm award, providing third party recognition of its impact.
One country's journey to interoperability: Tanzania's experience developing a national health information exchange

Abstract         Background and purpose: Robust, flexible, and integrated health information (HIS) systems are essential to achieving national and international goals in health and development. Such systems are still uncommon in most low and middle income countries. This article describes a first-phase activity in Tanzania to integrate the country’s vertical health management information system with the help of an interoperability layer that enables cross-program data exchange. Methods: From 2014 to 2019, the Tanzanian government and partners implemented a five-step procedure based on the “Mind the GAPS” (governance, architecture, program management, and standards) framework and using both proprietary and open-source tools. In collaboration with multiple stakeholders, the team developed the system to address major data challenges via four fully documented “use case scenarios” addressing data exchange among hospitals, between services and the supply chain, across digital data systems, and within the supply chain reporting system. This work included developing the architecture for health system data exchange, putting a middleware interoperability layer in place to facilitate the exchange, and training to support use of the system and the data it generates. Results: Tanzania successfully completed the five-step procedure for all four use cases. Data exchange is currently enabled among 15 separate information systems, and has resulted in improved data availability and significant time savings. The government has adopted the health information exchange within the national strategy for health care information, and the system is being operated and managed by Tanzanian officials. Conclusion: Developing an integrated HIS requires a significant time investment; but ultimately benefit both programs and patients. Tanzania’s experience may interest countries that are developing their HIS programs.
Optimizing Healthcare Economics: Unveiling the Economic Impact of Nursing Interventions on Patient Safety and Quality of Care

This article explores the economic outcomes of nursing interventions and their impact on enhancing patient safety and the quality of care. Through a comprehensive examination of empirical data, the study assesses the financial implications of nursing interventions, shedding light on the cost-effectiveness, return on investment, and broader economic benefits associated with proactive nursing practices.
ASSESSMENT OF THE ROLE OF INDIGINOUS CONFLICT RESOLUTION MECHANISMS FOR PEACE BUILDING: THE CASE OF GETTA WOREDA, GURAGHE ZONE, IN SNNP REGIONAL STATE.

IN PARTIAL FULFILLMENT OF REQUIREMENTS FOR THE DEGREE OF MASTER OF ARTS IN CIVICS AND ETHICAL STUDIES
The Implications of Climate Change Litigation for International Environmental Law-Making

Climate advocates are increasingly raising specific climate change concerns before domestic courts, human rights tribunals, international commissions and other national and international decisionmaking bodies. Win or lose, these litigation strategies are significantly changing and enhancing the public dialogue around climate change. This article discusses the awareness-building impacts of climate litigation as well as related impacts such strategies may have on the development of climate law and policy. The article argues that litigation's focus on specific victims facing immediate threats from climate change has increased the political will to address climate change both internationally and nationally. It has also shifted the debate towards questions of compensation and adaptation, and has brought new and democratic voices to the climate policy debate. As a result, climate litigation is leaving an important imprint on climate policy regardless of whether a tort action in the United States or the Inuit human rights claims, for example, ultimately prevail - and as demonstrated by the recent US Supreme Court decision in Massachusetts v. EPA, some climate claims will prevail, setting important precedents for the future direction of climate law and policy.
L-FAME: Longitudinal Focused Attention Meditation EEG Dataset and Benchmark

We introduce a novel Longitudinal Focused Attention Meditation Electroencephalography (L-FAME) dataset and an accompanying benchmark, designed to foster research into the neural effects of various meditation practices and the evolution of these effects over a six-week training period. The dataset contains EEG recordings and psychological assessments from 74 healthy college participants, collected at two distinct time points: pre-intervention and post-intervention. Participants were randomly assigned to one of three distinct meditation groups: two mantra-based techniques (SA-TA-NA-MA and Hare Krishna) and one Breath Focus practice. Leveraging this unique longitudinal and comparative dataset, we propose a benchmark suite comprising three distinct classification tasks: (1) cognitive state decoding to distinguish between resting and meditation states, (2) fine-grained classification of the specific meditation techniques, and (3) cross-session adaptation to evaluate model generalization across the longitudinal time gap. We provide comprehensive baseline results for these tasks utilizing a range of classical machine learning algorithms and deep learning architectures. The complete dataset, preprocessing pipelines, and benchmark evaluation code will be publicly released, offering a valuable resource and a standardized framework for the development and comparison of new analytical methods in computational meditation research and EEG-based machine learning. The dataset is available at https://huggingface.co/datasets/L-FAME-Dataset-Benchmark/L-FAME
Before and After<i>Silent Spring</i>: From Chemical Pesticides to Biological Control and Integrated Pest Management — Britain, 1945–1980

The use of chemical pesticides increased considerably after World War II, and ecological damage was noticeable by the late 1940s. This paper outlines some ecological problems experienced during the post-war period in the UK, and in parts of what is now Malaysia. Also discussed is the government's response. Although Rachel Carson's book, Silent Spring (1962), was important in bringing the problems to a wider public, she was not alone in sounding the alarm. Pressure from the public and from British scientists led, among other things, to the founding of the Natural Environment Research Council in 1965. By the 1970s, environmentalism was an important movement, and funding for ecological and environmental research was forthcoming even during the economic recession. Some of the recipients were ecologists working at Imperial College London. Moved by the political climate, and by the evidence of ecological damage, they carried out research on the biological control of insect pests.
Mind the gap: Epigenetic regulation of chromatin accessibility in plants

Abstract                Chromatin plays a crucial role in genome compaction and is fundamental for regulating multiple nuclear processes. Nucleosomes, the basic building blocks of chromatin, are central in regulating these processes, determining chromatin accessibility by limiting access to DNA for various proteins and acting as important signaling hubs. The association of histones with DNA in nucleosomes and the folding of chromatin into higher-order structures are strongly influenced by a variety of epigenetic marks, including DNA methylation, histone variants, and histone post-translational modifications. Additionally, a wide array of chaperones and ATP-dependent remodelers regulate various aspects of nucleosome biology, including assembly, deposition, and positioning. This review provides an overview of recent advances in our mechanistic understanding of how nucleosomes and chromatin organization are regulated by epigenetic marks and remodelers in plants. Furthermore, we present current technologies for profiling chromatin accessibility and organization.
The Palliative Care Needs, Quality Of Life and Coping Strategies among Oncology Patients And End Stage Organ Disease

Palliative care is the active total care of patients whose disease is incurable and where the control of physical, psychological, social, and spiritual problems is paramount. A comparative Cohort study to assess the Palliative care Needs, , Anxiety , depression, Quality of Life, Coping Strategies and Satisfaction with Care among Patients with Cancer and End stage Organ diseases was undertaken among 30 patients with cancer and end stage diseases totaling 30 with 6 patients categorized under each : COPD, ESRD, DCLD, HF and CVA at a selected Hospital, Chennai. End stage organ diseases patients had increased level of Anxiety , depression, Palliative care needs and lower quality of life compared to patients with Cancers especially in the social and Overall (global) . Both group used expressed lack of social support especially with regard to information and communication and used similar strategies for Coping.
RGBT Salient Object Detection: A Large-scale Dataset and Benchmark

Salient object detection in complex scenes and environments is a challenging research topic. Most works focus on RGB-based salient object detection, which limits its performance of real-life applications when confronted with adverse conditions such as dark environments and complex backgrounds. Taking advantage of RGB and thermal infrared images becomes a new research direction for detecting salient object in complex scenes recently, as thermal infrared spectrum imaging provides the complementary information and has been applied to many computer vision tasks. However, current research for RGBT salient object detection is limited by the lack of a large-scale dataset and comprehensive benchmark. This work contributes such a RGBT image dataset named VT5000, including 5000 spatially aligned RGBT image pairs with ground truth annotations. VT5000 has 11 challenges collected in different scenes and environments for exploring the robustness of algorithms. With this dataset, we propose a powerful baseline approach, which extracts multi-level features within each modality and aggregates these features of all modalities with the attention mechanism, for accurate RGBT salient object detection. Extensive experiments show that the proposed baseline approach outperforms the state-of-the-art methods on VT5000 dataset and other two public datasets. In addition, we carry out a comprehensive analysis of different algorithms of RGBT salient object detection on VT5000 dataset, and then make several valuable conclusions and provide some potential research directions for RGBT salient object detection.
International Criminal Law And Victor's Justice:acase Study Of The Tribunal For Rwanda

The background of the study focuses on Rwanda in the situation of selective justice by the victors after the civil war between the Rwanda Patriotic Front and the Rwandan government in 1994 and subsequent atrocities the victors committed.  The purpose of the study is to critically examine the credibility and the way the International Criminal Tribunal for Rwanda selectively discharged its mandate by only prosecuting the defeated individual perpetrators whilst protecting victors’ perpetrators who also committed the same atrocities. The selectivity done by the ICTR was contrary to the Tribunal’s competence enshrined in the Statute that the Tribunal shall prosecute individual perpetrators regardless of their affiliations. Ignoring the selective prosecution discharged by the Tribunal would promote the continued violation of International Humanitarian Law and genocide in Rwanda and in other countries worldwide. The methodology applied under this study is based on analytical review of the literature relating to the topic, because it deeply explains the application of the selective prosecution of the perpetrators.  The research also analyzed international instruments like ICTR’s Statute and Geneva Conventions. The analytical method is also complemented by the methodology of case study of the Tribunal for Rwanda. The data collected was subsequently analyzed qualitatively through narrative data analysis approach showing individual prosecution of perpetrators. The findings of the study show that the ICTR discharged its obligations selectively due to the lack of cooperation from the Rwandan State and support from the United Nations Security Council. Had the RPF led government cooperated with the United Nations Security Council, could lead the ICTR to prosecute both individual perpetrators of armed conflict, end impunity, redress successfully, and contributed to the process of national reconciliation and maintenance of peace. The study recommends to the United Nations Security Council to re- evaluate its mandate and call upon the RPF led government to comply with its obligation.
Bayesian deep-learning structured illumination microscopy enables reliable super-resolution imaging with uncertainty quantification

The objective of optical super-resolution imaging is to acquire reliable sub-diffraction information on bioprocesses to facilitate scientific discovery. Structured illumination microscopy (SIM) is acknowledged as the optimal modality for live-cell super-resolution imaging. Although recent deep learning techniques have substantially advanced SIM, their transparency and reliability remain uncertain and under-explored, often resulting in unreliable results and biological misinterpretation. Here, we develop Bayesian deep learning (BayesDL) for SIM, which enhances the reconstruction of densely labeled structures while enabling the quantification of super-resolution uncertainty. With the uncertainty, BayesDL-SIM achieves high-fidelity distribution-informed SIM imaging, allowing for the communication of credibility estimates to users regarding the model outcomes. We also demonstrate that BayesDL-SIM boosts SIM reliability by identifying and preventing erroneous generalizations in various model misuse scenarios. Moreover, the BayesDL uncertainty shows versatile utilities for daily super-resolution imaging, such as error estimation, data acquisition evaluation, etc. Furthermore, we demonstrate the effectiveness and superiority of BayesDL-SIM in live-cell imaging, which reliably reveals F-actin dynamics and the reorganization of the cell cytoskeleton. This work lays the foundation for the reliable implementation of deep learning-based SIM methods in practical applications.
A benchmark deep learning dataset for the classification of supraglacial lake drainage mechanism across the central-west Greenland Ice Sheet

Supraglacial lakes on the Greenland Ice Sheet drain through physically distinct pathways: hydrofracture, moulins, lateral stream routing, and crevasse-fields. Each drainage mechanism carries unique implications for ice sheet dynamics. Existing automated classifications reduce each lake's drainage behavior to a time-series of scalar values representing the observed water surface-area and classify each lake based on drainage rate (e.g., rapid vs. slow). This scalar reduction conflates physically different drainage mechanisms, which can only be determined through consideration of full spatio-temporal tracking. Here we introduce a human-benchmarked, machine learning-ready benchmark dataset that pairs full Sentinel-2 multispectral satellite imagery time series with human-expert-labels assigned for N=1679 supraglacial lakes in the central-west basin of the Greenland Ice Sheet during the 2018 (n=679) and 2019 (n=1000) melt seasons. The dataset is formatted as per-lake CF-1.8 NetCDF files each containing: six Sentinel-2 reflectance bands at 10 meter spatial resolution and daily cadence over the 153 day melt season (1 May to 30 September); a per-pixel binary cloud mask; co-registered lake water masks (both static and dynamic); and the human-assigned drainage classification labels. We accompany the dataset with a baseline deep learning classifier, demonstrating the utility of the dataset both in deep learning workflows and in extending lake drainage classification from rate-based to mechanism-based. The dataset is released through the Stanford Digital Repository under a CC BY 4.0 license, and the accompanying open-source sat-tile-stack preprocessing software under an MIT license.
Heliospheric modulation of cosmic ray protons: Role of enhanced perpendicular diffusion during periods of minimum solar modulation

The effect of enhanced perpendicular diffusion in the polar regions of a simulated heliosphere seems required in drift models for the modulation of galactic cosmic rays in order to find a latitude dependence of cosmic ray protons in the inner heliosphere that is compatible with Ulysses observations. This effect was studied in detail to establish how important it is and to understand the role it plays in global cosmic ray modulation, particularly during minimum modulation periods. After searching through a large parameter space, it was found that enhanced perpendicular diffusion was indeed necessary to obtain compatibility with observations in the inner heliosphere, especially during A &gt; O magnetic field polarity epochs (e.g., from 1990 to ∼2000). This approach, however, also increased the radial dependence during this epoch. Switching to A &lt; O epochs (e.g., from ∼1981 to 1989), the same set of parameters did not fit the observed 1987 radial dependence for 200 MeV cosmic ray protons, so a completely different set of diffusion coefficients had to be used, including less enhanced perpendicular diffusion. Apart from a polarity difference, minimum modulation conditions and, by implication, the diffusion tensor seem to be different during A &gt; O and A &lt; O solar minimum modulation periods.
Secondary use under the European Health Data Space: setting the scene and towards a research agenda on privacy-enhancing technologies

The Regulation for European Health Data Space (EHDS) aims to address the fragmented health data landscape across Europe by promoting ethical and responsible reuse of data, seeking to balance the opportunities for data reuse with the risks it entails. However, the techno-legal aspects of navigating this balance remain poorly understood. This study adopts a qualitative and inductive approach, using semi-structured interviews to explore the risks, challenges, and gaps in the implementation of privacy-enhancing technologies (PETs) within EHDS, particularly in the context of its governance structure and data permits for secondary data use. The findings identify five distinct categories of concerns, based on fourteen risks, and highlight seven governance and technological solutions, illustrating how these solutions address multiple, often correlated risks. The interdependence between concerns and solutions emphasises the need for a strategic and integrated approach to both governance and technology. This mapping between the risks and solutions also highlights the central role of certain solutions, such as public engagement and awareness, in addressing multiple risks. Furthermore, it introduces a new dimension to the concerns by focusing on the structural imbalances in access to the health data economy. We conclude by proposing a research agenda to advance the integration of PETs into the EHDS framework, ensuring that data permits can effectively facilitate secure, ethical, and innovative health data use.
The First Response to Generative Artificial Intelligence Tools in Chinese Higher Education: A Content Analysis of Universities' Posts on Social Media

This study investigates the initial response of Chinese higher education institutions to generative artificial intelligence (AI) tools by analyzing social media posts from top universities. As generative AI, such as ChatGPT, transforms education and knowledge production, universities face the challenge of integrating these technologies while addressing concerns about academic integrity and pedagogical impact. Using a qualitative content analysis of social media posts from 30 leading Chinese universities, this paper identifies the primary themes, opportunities, and challenges discussed. The findings highlight three major themes: awareness and guidance, ethical considerations, and academic innovation. Universities emphasize the need to educate stakeholders about AI tools, ensure ethical use, and leverage AI to innovate teaching and research. This study provides valuable insights into how higher education in China is adapting to generative AI, offering practical implications for policymakers, educators, and researchers navigating this evolving landscape.
Affine Isoperimetric Inequalities for Higher-Order Projection and Centroid Bodies

In 1970, Schneider introduced the $m$th order difference body of a convex body, and also established the $m$th-order Rogers-Shephard inequality. In this paper, we extend this idea to the projection body, centroid body, and radial mean bodies, as well as prove the associated inequalities (analogues of Zhang's projection inequality, Petty's projection inequality, the Busemann-Petty centroid inequality and Busemann's random simplex inequality). We also establish a new proof of Schneider's $m$th-order Rogers-Shephard inequality. As an application, a $m$th-order affine Sobolev inequality for functions of bounded variation is provided.