ethicalabs/Research-Intent-Collab huggingface dataset:
Epigenetic Regulation and Chromatin Remodeling in Malaria Parasites

Plasmodium falciparum, the human malaria parasite, infects two hosts and various cell types, inducing distinct morphological and physiological changes in the parasite in response to different environmental conditions. These variations required the parasite to adapt and develop elaborate molecular mechanisms to ensure its spread and transmission. Recent findings have significantly improved our understanding of the regulation of gene expression in P. falciparum. Here, we provide an up-to-date overview of technologies used to highlight the transcriptomic adjustments occurring in the parasite throughout its life cycle. We also emphasize the complementary and complex epigenetic mechanisms regulating gene expression in malaria parasites. This review concludes with an outlook on the chromatin architecture, the remodeling systems, and how this 3D genome organization is critical in various biological processes.
The responsibility to protect: A new cloth for humanitarian intervention

Humanitarian interventions in the 1990s provoked great discussions about their efficiency since they call into question the primacy of the State's territorial integrity, as well as its sovereignty (GRIMM; COOPER, 2015).
<Note>The Child Custody Issues at the time of Divorce: from the point of view of the Japanese Family Law including International Marriage Breakdown

Departmental Bulletin Paper application/pdf
Electrochemistry of (CH)<sup>x</sup>: Lightweight rechargeable batteries using (CH)<sup>x</sup> as the Cathode- and anode-active materials

Abstract Polyacetylene, (CH)x, can be controllably doped p-type (oxidized) or n-type (reduced) by simple electrochemical procedures. Cyclic voltammetry studies on freestanding films of cis-(CH)x show that (CH)x can be reversibly oxidized at ~+3.6V vs. Li and reversibly reduced at ~+1.4V vs. Li. The spontaneous electrochemical “undoping” of p- and/or n-doped (CH)x, which occurs when appropriate combinations and configurations of (CH)x are employed, permits (CH)x to be used in the fabrication of lightweight rechargeable batteries having large energy and large power densities. Cis-(CH)x can be readily isomerized to the trans-isomer by electrochemical p-doping followed by electrochemical reduction to (CH)x.
Self-Supervised Learning of Iterative Solvers for Constrained Optimization

The real-time solution of parametric optimization problems is critical for applications that demand high accuracy under tight real-time constraints, such as model predictive control. To this end, this work presents a learning-based iterative solver for constrained optimization, comprising a neural network predictor that generates initial primal-dual solution estimates, followed by a learned iterative solver that refines these estimates to reach high accuracy. We introduce a novel loss function based on Karush-Kuhn-Tucker (KKT) optimality conditions, enabling fully self-supervised training without pre-solved optimizer solutions. Theoretical guarantees ensure that the training loss function attains minima exclusively at KKT points. A convexification procedure enables application to nonconvex problems while preserving these guarantees. Experiments on two nonconvex case studies demonstrate speedups of up to one order of magnitude compared to state-of-the-art solvers such as IPOPT, while achieving orders of magnitude higher accuracy than competing learning-based approaches.
Physics of Binary Star Evolution -- from Stars to X-ray Binaries and Gravitational Wave Sources

The majority of all stars are members of a binary system. The evolution of such binary stars and their subsequent production of pairs of compact objects in tight orbits, such as double neutron stars and double black holes, play a central role in modern astrophysics, Binary evolution leads to the formation of different types of violent cosmic events such as novae, supernova explosions, gamma-ray bursts, mass transfer and accretion processes in X-ray binaries, and the formation of exotic radio millisecond pulsars. In some cases, the binary systems terminate as spectacular collisions between neutron stars and/or black holes. These collisions lead to powerful emission of gravitational waves, as detected by LIGO since 2015. The coming decade is expected to reveal a large number of discoveries of binary compact systems, as well as their progenitors and merger remnants, from major instruments such as the radio Square-Kilometre Array; the gravitational wave observatories LIGO-Virgo-KAGRA-IndIGO and LISA; the astrometric space observatory Gaia; the James Webb Space Telescope; and the X-ray space observatories eXTP, STROBE-X, and Athena. In this light, it is important to have a modern textbook on the physics of binary stars evolution, from ordinary stars to X-ray binaries and gravitational wave sources. The scope of this book is that the reader (student or educated expert) will learn the physics of binary interactions, from stellar birth to compact objects, and relate this knowledge to the latest observations. The reader will learn about stellar structure and evolution, and detailed binary interactions covering a broad range of phenomena, including mass transfer and orbital evolution, formation and accretion onto compact objects (white dwarfs, neutron stars and black holes), and their observational properties. Exercises are provided throughout the book.
Patients seeking stem cell therapies—a prospective qualitative analysis from a Regenerative Medicine Consult Service

AbstractDespite patient demand for stem cell therapies (SCTs) for musculoskeletal conditions, there remains limited research on why patients seek SCTs or their sources of information. We employ three questions into a consult intake form: (1) Why are you interested in stem cell treatment for your condition? (2) How did you find out about stem cell treatment for your condition? (3) Have you contacted a stem cell clinic? Responses analyzed, using a qualitative content analysis approach to identify themes reveal many patients seek SCTs to treat pain or delay surgery which may align with some current clinical evidence while other patients express motivations as expected outcomes (e.g., SCTs are better than standard of care or can regenerate tissue) which are not supported by current medical evidence. These differences suggests that patient-centered counseling may help patients by addressing misconceptions and increasing health literacy about expected outcomes of SCTs for treating musculoskeletal conditions.
Sustainable agriculture and food security in Africa: An overview

The development of the agricultural sector and the improvement of the food security situation are seen as essential components to sustainable development in Africa. However, continuing population growth, impacts of climate change and environmental degradation add to an unprecedented combination of pressures that threaten existing efforts and solutions. This article discusses the challenges of meeting the food security needs in a sustainable way. Due to its involvement of all three dimensions of sustainable development, economic, social and ecological, we argue that organic farming could be one possible approach to create a more sustainable agricultural system.
Personalized AI Tutor: An Intelligent Adaptive Learning System for Early Education

Technologies and Tools Used:• Python• JavaScript• Machine Learning Algorithms• Artificial Intelligence Models• Adaptive Recommendation Systems• React Frontend Framework• Learning Analytics Modules• Intelligent Tutoring Mechanisms  System Modules:• Student Performance Analysis Module• Adaptive Assessment System• AI Recommendation Engine• Personalized Learning Path Generator• Progress Tracking Dashboard• Intelligent Content Recommendation Module• Learning Analytics and Reporting System  Core Functionalities:• Personalized Topic Recommendations• Dynamic Learning Path Generation• Student Performance Monitoring• Adaptive Assessments• AI-Based Educational Assistance• Learning Behavior Analysis• Intelligent Study Material Recommendation• Performance Visualization and Analytics  Research Objectives:• To develop an intelligent adaptive learning platform for personalized education.• To improve student engagement using AI-driven methodologies.• To dynamically recommend educational content based on student understanding levels.• To support scalable digital education systems using intelligent tutoring technologies.
Sampling Trade-Offs in Duty-Cycled Systems for Air Quality Low-Cost Sensors

The use of low-cost sensors in conjunction with high-precision instrumentation for air pollution monitoring has shown promising results in recent years. One of the main challenges for these sensors has been the quality of their data, which is why the main efforts have focused on calibrating the sensors using machine learning techniques to improve the data quality. However, there is one aspect that has been overlooked, that is, these sensors are mounted on nodes that may have energy consumption restrictions if they are battery-powered. In this paper, we show the usual sensor data gathering process and we study the existing trade-offs between the sampling of such sensors, the quality of the sensor calibration, and the power consumption involved. To this end, we conduct experiments on prototype nodes measuring tropospheric ozone, nitrogen dioxide, and nitrogen monoxide at high frequency. The results show that the sensor sampling strategy directly affects the quality of the air pollution estimation and that each type of sensor may require different sampling strategies. In addition, duty cycles of 0.1 can be achieved when the sensors have response times in the order of two minutes, and duty cycles between 0.01 and 0.02 can be achieved when the sensor response times are negligible, calibrating with hourly reference values and maintaining a quality of calibrated data similar to when the node is connected to an uninterruptible power supply.
Nurses' Perceptions of Subspecialization in Pediatric Cardiac Intensive Care Unit

This study assessed the perceptions of nurses about the creation and staffing of a dedicated cardiac intensive care unit. Nurses perceived a clinical benefit to cohorting cardiac surgery patients; however, they reported more knowledge deficits in cardiac patient care than other intensive care unit disease categories. More than 25% of nurses reported a patient assignment in which they identified suboptimal skills to provide safe patient care. Years of clinical experience did not reduce concerns for quality of care or safe practice.
DCIL: Deep Contextual Internal Learning for Image Restoration and Image Retargeting

Recently, there is a vast interest in developing methods which are independent of the training samples such as deep image prior, zero-shot learning, and internal learning. The methods above are based on the common goal of maximizing image features learning from a single image despite inherent technical diversity. In this work, we bridge the gap between the various unsupervised approaches above and propose a general framework for image restoration and image retargeting. We use contextual feature learning and internal learning to improvise the structure similarity between the source and the target images. We perform image resize application in the following setups: classical image resize using super-resolution, a challenging image resize where the low-resolution image contains noise, and content-aware image resize using image retargeting. We also provide comparisons to the relevant state-of-the-art methods.
Predictive Learning, Knowledge Discovery and Philosophy of Science

Various disciplines, such as machine learning, statistics, data mining and artificial neural networks, are concerned with estimation of data-analytic models. A common theme among all these methodologies is estimation of predictive models from data. In our digital age, an abundance of data and cheap computing power offers hope of knowledge discovery via application of statistical and machine learning algorithms to empirical data. This data-analytic knowledge has similarities and differences with classical scientific knowledge. For example, any scientific theory can be viewed as an inductive theory because it generalizes over a finite number of observations (or experiments). The philosophical aspects of induction and knowledge discovery have been thoroughly explored in Western philosophy of science. This philosophical analysis dates back to Kant and Hume. Any knowledge involves a combination of hypotheses/ideas and empirical data. In the modern digital age, the balance between ideas (mental constructs) and observed data (facts) has completely shifted. Classical scientific knowledge was produced mainly by a stroke of genius (e.g., Newton, Maxwell, and Einstein). In contrast, much of modern knowledge in life sciences and social sciences is derived via data-analytic modeling. We argue that such data-driven knowledge can be properly described following the methodology of predictive learning originally developed in VC-theory. This paper presents a brief survey of the philosophical concepts related to inductive inference, and then extends these ideas to predictive data-analytic knowledge discovery. We contrast the differences between classical first-principle knowledge, data-analytic knowledge and beliefs. Several application examples are used to illustrate the differences between classical statistical and predictive learning approaches to data-analytic modeling. Finally, we discuss interpretation of data-analytic models under predictive learning framework.
AlphaFolding: 4D Diffusion for Dynamic Protein Structure Prediction with Reference and Motion Guidance

Protein structure prediction is pivotal for understanding the structure-function relationship of proteins, advancing biological research, and facilitating pharmaceutical development and experimental design. While deep learning methods and the expanded availability of experimental 3D protein structures have accelerated structure prediction, the dynamic nature of protein structures has received limited attention. This study introduces an innovative 4D diffusion model incorporating molecular dynamics (MD) simulation data to learn dynamic protein structures. Our approach is distinguished by the following components: (1) a unified diffusion model capable of generating dynamic protein structures, including both the backbone and side chains, utilizing atomic grouping and side-chain dihedral angle predictions; (2) a reference network that enhances structural consistency by integrating the latent embeddings of the initial 3D protein structures; and (3) a motion alignment module aimed at improving temporal structural coherence across multiple time steps. To our knowledge, this is the first diffusion-based model aimed at predicting protein trajectories across multiple time steps simultaneously. Validation on benchmark datasets demonstrates that our model exhibits high accuracy in predicting dynamic 3D structures of proteins containing up to 256 amino acids over 32 time steps, effectively capturing both local flexibility in stable states and significant conformational changes. URL: https://fudan-generative-vision.github.io/AlphaFolding/#/
The role of business intelligence and knowledge management systems in marketing information services in academic libraries under the Coved 19 pandemic: an analytical study

The value of open solutions and the need for them has become critical to the Coved 19 pandemic. As the free use of scientific information and open source data is guaranteed to ensure that the information reaches the beneficiaries, and from this stand libraries and information institutions take upon themselves a major role through their “open source curricula” by providing a set of services that address the goals, processes, types, and restrictions that exist in the context of open scientific communication. This study aims to identify the role of innovative solutions such as, business intelligence systems, knowledge management, and the opportunities and capabilities it offers for libraries to market their various services to beneficiaries. The researcher used the descriptive analytical approach to determine the impact of the importance of these solutions in improving the possibility and increasing the content sharing with the beneficiaries in light of this crisis.
Interoperability in AI Safety Governance: Ethics, Regulations, and Standards

122 pages
META-pipe - Pipeline Annotation, Analysis and Visualization of Marine Metagenomic Sequence Data

The marine environment is one of the most important sources for microbial biodiversity on the planet. These microbes are drivers for many biogeochemical processes, and their enormous genetic potential is still not fully explored or exploited. Marine metagenomics (DNA shotgun sequencing), not only offers opportunities for studying structure and function of microbial communities, but also identification of novel biocatalysts and bioactive compounds. However, data analysis, management, storage, processing and interpretation are significant challenges in marine metagenomics due to the high diversity in samples and the size of the marine flagship projects. We provide a new pipeline, META-pipe, for marine metagenomics analysis. It offers pre- processing, assembly, taxonomic classification and functional analysis. To reduce the effort to develop and deploy it, we have integrated existing biological analysis frameworks, and compute and storage infrastructure resources. Our current META-pipe web service provides integration with identity provider services, distributed storage, computation on a Supercomputer, Galaxy workflows, and interactive data visualizations. We have evaluated the scalability and performance of the analysis pipeline. Our results demonstrate how to develop and deploy a pipeline on distributed compute and storage resources, and discusses important challenges related to this process.
Corporate Social Responsibility : En studie av hur fem svenska företag följer Global Reporting Initiatives riktlinje

Den ökade globaliseringen och den stigande medvetenheten om hur vi påverkas av vår omvärld har gjort att fler företag insett vikten av att arbeta med hållbar utvecklig, inte bara ekonomiskt och inom företaget utan också för samhället där de verkar. 1994 myntades uttrycket ”The Tripple Bottom Line” av John Elkington. The Tripple Bottom Line innebär att företags prestationer ska mätas utifrån ekonomiska, miljömässiga och sociala grunder. Trenden är att rapportera dessa perspektiv i en hållbarhetsredovisning. Många företag väljer att göra hållbarhetsredovisningen i enlighet med Global Reporting Initiatives riktlinjer. Syftet med denna uppsats är att beskriva och förklara hur väl fem svenska företag följer GRI:s riktlinjer i deras hållbarhetsredovisning. För att uppfylla syftet har vi genomfört en fallstudie, vi har vidare gjort en kvalitativ innehållsanalys på de utvalda företagens hållbarhetsredovisningar. Samtliga företag i urvalet redovisar utvalda delar av GRI:s riktlinjer. Vi har i vår studie sett tendenser på att hållbarhetsredovisningar för företag i vissa branscher är mer omfattande än andra. Vidare är alla rekommendationer inte applicerbara för alla företag.
Cyber Defence Strategies in the Healthcare Sector: Deterrence, Resilience, and Critical Infrastructure Protection

The integration of robust cybersecurity tools and technologies within healthcare institutions is a critical strategy for strengthening national security and enhancing deterrence, particularly within NATO framework. This article highlights the strategic importance in mitigating targeted cyber threats. Critical tools for real-time threat detection, establishing secure network perimeters, and comprehensive network security are discussed for their efficacy in pre-empting and responding to sophisticated cyber-attacks. Additionally, the strategic outsourcing of cybersecurity services to specialized third-party providers is explored, demonstrating the enhanced defence mechanisms achieved through leveraging external expertise and advanced technologies. The article underscores the necessity of a multi-faceted cybersecurity approach, analysing the strategy and policy applied to the unique demands of the healthcare sector. By enhancing the cybersecurity posture of healthcare institutions, this research posits a significant increase in national preparedness against targeted cyber threats.
Additional file 1 of Comparing quality indicator rates for home care clients receiving palliative and end-of-life care before and during the Covid-19 pandemic

Supplementary Material 1
The 22-Year Hale Cycle in Cosmic Ray Flux – Evidence for Direct Heliospheric Modulation

16 pages, 7 figures
Conserving biodiversity in a changing world: land use change and species richness in northern Tanzania

Even though human induced habitat changes are a major driver of biodiversity loss worldwide, our understanding of the impact of land use change on ecological communities remains poor. Yet without such information it is difficult to develop management strategies for maintaining biodiversity in the face of anthropogenic change. To address this gap, we explored how land use practices impacted species richness in a mammalian community in northern Tanzania. Using camera traps, we estimated the number of mammalian species inhabiting three land use types subjected to increasing levels of anthropogenic pressure: (1) Tarangire National Park, (2) pastoral grazing areas; and (3) cultivated areas outside the park. Results showed that land use practice is correlated with different levels of species richness. Interestingly, mammal species richness was highest in the grazing areas and lowest in cultivated areas. When we focused our analyses on carnivores, we found little significant difference in species richness between the park and pastoral grazing areas, however, carnivore richness were significantly lower in the cultivated areas. We found no significant link between species body weight and presence in the three areas considered. Altogether, our results show that biodiversity conservation can be achieved outside national parks, with pastoral grazing areas holding a significant proportion of mammal communities; however increasing cultivation of pastoral rangelands may represent a major threat to mammalian communities.
Quality seed production using cryobanking assisted carp broodstock management for sustainable fisheries and aquaculture enhancement

Quality seed is the most important input in the commercial freshwater aquaculture sector of the country and it is mostly supplied by hatcheries of the private and public sectors. With decades of hatchery-based carp culture and seed production intensification, a serious concern has emerged about seed quality, especially with the risks from inbreeding. Sperm cryobanking as a conservation tool, can not only provide support to captive breeding technology in the propagation-assisted rehabilitation of the target species in its natural habitat but can also assist carp broodstock management for quality seed production and sustainable fisheries & aquaculture enhancement. This report presents efforts and experiences of ICAR-NBFGR, Lucknow in customization of the sperm cryopreservation technique, primarily developed for ex-situ conservation, for upscaled fertilization through field validation, demonstrations, and capacity development of stakeholders in eleven states of the country.
Corrigendum to "Gains from the upgrade of the cold neutron triple-axis spectrometer FLEXX at the BER-II reactor" [Nuclear Instruments and Methods in Physics Research A 729 (2013) 220–226]

a Helmholtz-Zentrum Berlin fur Materialen und Energie, Hahn-Meitner-Platz 1, D-14109 Berlin, Germany b Department of Physics and Astronomy, Seoul National University, Seoul 151-747, South Korea c Center for Correlated Electron Systems, Institute for Basic Science, Seoul 151-747, South Korea d Laboratory for Neutron Scattering, Paul Scherrer Institut, CH-5232 Villigen PSI, Switzerland e The Bragg Institute, ANSTO, Kirrawee DC NSW 2234, Australia