ethicalabs/Research-Intent-Collab huggingface dataset:
CellPhoneDB v5: inferring cell-cell communication from single-cell multiomics data

Cell-cell communication is essential for tissue development, regeneration and function, and its disruption can lead to diseases and developmental abnormalities. The revolution of single-cell genomics technologies offers unprecedented insights into cellular identities, opening new avenues to resolve the intricate cellular interactions present in tissue niches. CellPhoneDB is a bioinformatics toolkit designed to infer cell-cell communication by combining a curated repository of bona fide ligand-receptor interactions with a set of computational and statistical methods to integrate them with single-cell genomics data. Importantly, CellPhoneDB captures the multimeric nature of molecular complexes, thus representing cell-cell communication biology faithfully. Here we present CellPhoneDB v5, an updated version of the tool, which offers several new features. Firstly, the repository has been expanded by one-third with the addition of new interactions. These encompass interactions mediated by non-protein ligands such as endocrine hormones and GPCR ligands. Secondly, it includes a differentially expression-based methodology for more tailored interaction queries. Thirdly, it incorporates novel computational methods to prioritise specific cell-cell interactions, leveraging other single-cell modalities, such as spatial information or TF activities (i.e. CellSign module). Finally, we provide CellPhoneDBViz, a module to interactively visualise and share results amongst users. Altogether, CellPhoneDB v5 elevates the precision of cell-cell communication inference, ushering in new perspectives to comprehend tissue biology in both healthy and pathological states.
Uncertainty analysis onto models and emission scenarios of climate change for projection of the temperature, precipitation, and evapotranspiration in the Neyshabour area

Climate change is not only caused the rise in temperature but also changes in the global precipitation cycle, which leads to variation in spatial and temporal precipitation patterns. In this study, climate change impacts were investigated on temperature, precipitation, and evapotranspiration of Neyshabour plane with the uncertainty of AOGCM models and emissions scenarios in the future period (2080-2099). First, the minimum and maximum temperature and precipitation for ten models of AOGCM under scenarios A2 and B1 in the future period and the baseline period of 1992-2011 were prepared and then downscaled by using of LARS_WG model. The reference evapotranspiration was calculated by FAO-Penman-Monteith method for all models and emission scenarios. The result showed that the value of evapotranspiration and temperature during the warm months were increased and the value of precipitation was decreased during the cold months. Also, monthly uncertainty was investigated for minimum and maximum temperature, precipitation, and evapotranspiration. The temperature and evapotranspiration in the warm months and precipitation in the cold months have less uncertainty than the other months. Also, the results of uncertainty analysis for models and emission scenarios are showed that NCARPCM and HADCM3 models in the estimation of minimum and maximum temperature, CGCM3T47as well as GFDLCM2 models in the estimation of precipitation have the best uncertainty results based on the two scenarios, respectively. The uncertainty analysis of AOGCM models determined that the best of uncertainty in the estimation of reference evapotranspiration for two scenarios is observed in the result of GISS-ER model. Also, the A2 scenario has the better function than the B1 scenario in the estimation of evapotranspiration parameter.
Enhancing Object Detection with Privileged Information: A Model-Agnostic Teacher-Student Approach

This paper investigates the integration of the Learning Using Privileged Information (LUPI) paradigm in object detection to exploit fine-grained, descriptive information available during training but not at inference. We introduce a general, model-agnostic methodology for injecting privileged information-such as bounding box masks, saliency maps, and depth cues-into deep learning-based object detectors through a teacher-student architecture. Experiments are conducted across five state-of-the-art object detection models and multiple public benchmarks, including UAV-based litter detection datasets and Pascal VOC 2012, to assess the impact on accuracy, generalization, and computational efficiency. Our results demonstrate that LUPI-trained students consistently outperform their baseline counterparts, achieving significant boosts in detection accuracy with no increase in inference complexity or model size. Performance improvements are especially marked for medium and large objects, while ablation studies reveal that intermediate weighting of teacher guidance optimally balances learning from privileged and standard inputs. The findings affirm that the LUPI framework provides an effective and practical strategy for advancing object detection systems in both resource-constrained and real-world settings.
The Role of Artificial Intelligence in the Diagnosis and Prognosis of Heart Diseases: A Systematic Review

The integration of artificial intelligence (AI) into the diagnosis and prognosis of heart diseases is transforming cardiovascular and cardiac healthcare, improving predictive accuracy, and personalizing treatment plans. This review presents a novel contribution by providing a comprehensive overview of both diagnosis and prognosis in heart diseases through AI, covering ML and DL models. Following the PRISMA guidelines, a total of 84 recent research articles sourced from significant journals are reported. A bibliometric analysis using the VOSviewer tool was performed to map the impact of AI, enabling a detailed examination of academic connections and contributions. The findings reveal that DL models were employed 63% for diagnosis tasks, while ML models were utilized in 37% of the studies. Key recommendations include the incorporation of essential model evaluation metrics, as clinical validation indicators, integrating explainable artificial intelligence (XAI) to improve the transparency and interpretability of models, and adopting standardized frameworks to enable smooth clinical integration. This review highlights the potential of AI to improve cardiac and cardiovascular diagnosis and prognosis, providing an overview of its strengths, limitations, challenges and the possible application as AI-driven tools in patient monitoring and to support specialists in the decision-making process.
Kovács' conjecture on characterization of projective space and hyperquadrics

We prove Kovács' conjecture that claims that if the $p^{th}$ exterior power of the tangent bundle of a smooth complex projective variety contains the $p^{th}$ exterior power of an ample vector bundle then the variety is either projective space or the $p$-dimensional quadric hypersurface. We also prove a similar characterization involving symmetric powers instead of exterior powers. This provides a common generalization of Mori, Wahl, Cho-Sato, Andreatta-Wiśniewski, Kobayashi-Ochiai, and Araujo-Druel-Kovács type characterizations of such varieties.
Spectators to the Spectacle of Law: The Formation of a ‘Validating Public’ at the International Criminal Tribunal for Rwanda

While the ethnography of contemporary courtrooms has been dominated by a concern with speech, this article considers how a silent, validating public is constructed within court complexes. Drawing on fieldwork at the International Criminal Tribunal for Rwanda (Arusha, Tanzania), I explore how the court’s threshold practices form validating witnesses whose embodied deference contributes to the constitution of the courtroom as a space of privileged speech. I suggest, therefore, that court spectators are not incidental, but are integral to juridical spectacle and the authority of law.
Correction to: AI auditing and impact assessment: according to the UK information commissioner’s office

This article is based on an archived draft version of the Information Commissioner's Office Guidance on the AI auditing Framework released for consultation in February 2020.
Security education as a fundamental pillar of critical infrastructure protection and resilience

The increasing society dependence on the technologies also increases the society dependence on the critical infrastructure as the basis of ensuring and maintaining the society functional continuity. Logical consequence of this fact is the security and safety measures/standards development in relation to their potential to minimize the impacts of identified risk. For the fulfillment of that argument, however, it is necessary to constitute the security education that would provide a basis for security liaison officer education whose skills and knowledge would become fundamental for the operator security plan elaboration. Article therefore defines the challenges and objectives that should be achieved by security education and optimal structure of security education in relation to critical infrastructure protection and resilience. Security education structure will be based on the critical infrastructure resilience evaluation methodology as a security research output. To better understanding of the fundamental relationships between defined parameters it will be the methodology and robustness as its part presented.
The Effect of Human Resource Information System (HRIS) Integration, Conflict Management, and Work Flexibility on Work-Life Balance in Banten Manufacturing Industry

Within the context of the manufacturing sector in Banten, this research explores the impact that the integration of Human Resource Information Systems (HRIS), conflict management, and work flexibility have on the work-life balance of employees operating in that sector. Through the use of a quantitative research methodology, data were gathered from a total of 160 samples by means of a structured questionnaire that utilized a Likert scale that ranged from 1 to 5. Through the utilization of Structural Equation Modeling-Partial Least Squares (SEM-PLS 3), the data were examined in order to determine the links that exist between the variables. According to the findings, high-resolution information system (HRIS) integration considerably improves work-life balance by optimizing human resource operations and increasing employee access to information. Effective conflict management practices are shown to mitigate workplace tensions, further contributing to a better work-life balance. Additionally, work flexibility is found to be a crucial factor in allowing employees to manage their professional and personal responsibilities more effectively. The study underscores the importance of integrating advanced HR systems, adopting robust conflict management strategies, and promoting flexible work arrangements to enhance the work-life balance of employees in the manufacturing sector.
Fermi Large Area Telescope First Source Catalog

We present a catalog of high-energy gamma-ray sources detected by the Large Area Telescope (LAT), the primary science instrument on the Fermi Gamma-ray Space Telescope (Fermi), during the first 11 months of the science phase of the mission, which began on 2008 August 4. The First Fermi-LAT catalog (1FGL) contains 1451 sources detected and characterized in the 100 MeV to 100 GeV range. Source detection was based on the average flux over the 11-month period, and the threshold likelihood Test Statistic is 25, corresponding to a significance of just over 4 sigma. The 1FGL catalog includes source location regions, defined in terms of elliptical fits to the 95% confidence regions and power-law spectral fits as well as flux measurements in 5 energy bands for each source. In addition, monthly light curves are provided. Using a protocol defined before launch we have tested for several populations of gamma-ray sources among the sources in the catalog. For individual LAT-detected sources we provide firm identifications or plausible associations with sources in other astronomical catalogs. Identifications are based on correlated variability with counterparts at other wavelengths, or on spin or orbital periodicity. For the catalogs and association criteria that we have selected, 630 of the sources are unassociated. Care was taken to characterize the sensitivity of the results to the model of interstellar diffuse gamma-ray emission used to model the bright foreground, with the result that 161 sources at low Galactic latitudes and toward bright local interstellar clouds are flagged as having properties that are strongly dependent on the model or as potentially being due to incorrectly modeled structure in the Galactic diffuse emission.
Trace elements and stable isotopic geochemistry of an Early Cambrian chert-phosphorite unit from the lower Yurtus Formation of the Sugetbrak section in the Tarim Basin

A chert-phosphorite unit from the Sugetbrak section in the Tarim Basin was analyzed for rare earth elements (Ce, Eu), redox sensitive proxies, and carbon isotopic compositions (δ13Ccarb and δ                 13Corg) in the lower Yurtus Formation of the Early Cambrian period. Redox sensitive element ratios (Th/U, V/Cr, Ni/Co, and V/Sc) were employed to determine the palaeoenvironmental conditions during this period. The ratios indicated that the depositional environment of the chert-phosphorite-black shale unit ranged from suboxic to anoxic. Negative Ce and positive Eu anomalies in the chert-phosphorite assemblages of the studied Yurtus Formation indicated the existence of a redox-stratified ocean, similar to that of South China. Overturn or upward expansion of the deep water-mass probably reached the shallow marine zone after the formation of the Yurtus phosphorites. The characteristics of the negative Ce anomaly may be due to phosphoritic inheritance from the Ce-depleted signature of the overlying water column. Subsequent hydrothermal inputs and reduced detrital supplies of the deep water caused by the upwelling affected certain redox sensitive elements in the sedimentary basin. δ                 13Ccarb and δ13Corg negative excursions in the Yurtus chert-phosphorite unit may be related to a transgression phase when episodic basinal upwelling moved 12C- and P-rich waters from the pelagic basin floor to the continental shelf. Although carbon isotopic compositions in the Yurtus chert-phosphorite assemblages may have suffered from diagenetic alteration, they can be used to probe diagenetic conditions. Multi-proxy geochemical studies indicated that the δ                 13Ccarb values of the Yurtus chert-phosphorite assemblages might be considered reflections of a predominantly suboxic environment that was subsequently affected by hydrothermal inputs due to the upwelling.
Cognitive artificial intelligence: philosophy digital law and ethics

The textbook complements the lecture material with topical issues of phi-losophy and methodology cognitive artificial intelligence. This applied material is important for specialists in management, marketing, logistics and engineering and economic specialties.
Research on Risk Early Warning Model of Oil and Gas Pipeline Graph Neural Network Based on Knowledge Graph

With the rapid expansion of oil and gas pipeline networks, their operational safety faces increasingly complex risk threats. Conventional accident risk assessment methods mainly rely on manually defined rules and static indicators, making it difficult to uncover the deeper causal logic and structural patterns of accidents. To address this challenge, a risk early-warning model for oil and gas pipelines is proposed, integrating knowledge graph techniques with graph neural networks. Specifically, pipeline accident reports from Pipeline and Hazardous Materials Safety Administration (PHMSA) are consolidated to construct a comprehensive knowledge graph of pipeline risks. Based on this graph, a relation-aware graph neural network node classification approach is designed, which incorporates both structural features and numerical attributes to enable risk prediction. Within this framework, the Composition-based Multi-Relational Graph Convolutional Networks (CompMRGCN) model is further developed, extending the relation-aware graph convolutional network by embedding a Markov random field-based dependency mechanism to capture correlations among node labels during prediction. Experimental results demonstrate that the proposed early-warning model and CompMRGCN method achieve 96.2% accuracy, 95.8% F1-score, and 97.1% mAP, representing improvements of 6.7%, 5.6%, and 5.4% over the best existing baselines, respectively. Comparative analysis indicates that this approach substantially outperforms competing models in terms of accuracy, generalization, and interpretability, offering an effective and practical technical support for intelligent accident early warning and safety management of oil and gas pipelines.
Equitable representation in councils: theory and an application to the United Nations Security Council\ud \ud

We analyze democratic equity in council voting games (CVGs). In a\ud CVG, a voting body containing all members delegates decisionmaking\ud to a (time-varying) subset of its members, as describes, e.g.,\ud the relationship between the United Nations General Assembly and\ud the United Nations Security Council (UNSC). We develop a\ud theoretical framework for analyzing democratic equitability in CVGs\ud at both the country and region levels, and for different assumptions\ud regarding preference correlation. We apply the framework to evaluate\ud the equitability of the UNSC, and the claims of those who seek to\ud reform it. We find that the individual permanent members are overrepresented\ud by between 21.3 times (United Kingdom) and 3.8 times\ud (China) from a country-level perspective, while from a region\ud perspective Eastern Europe is the most heavily overrepresented region\ud with more than twice its equitable representation, and Africa the most\ud heavily under-represented. Our equity measures do not preclude some\ud UNSC members from exercising veto rights, however.
AI-Driven Radiology Report Generation for Traumatic Brain Injuries

Traumatic brain injuries present significant diagnostic challenges in emergency medicine, where the timely interpretation of medical images is crucial for patient outcomes. In this paper, we propose a novel AI-based approach for automatic radiology report generation tailored to cranial trauma cases. Our model integrates an AC-BiFPN with a Transformer architecture to capture and process complex medical imaging data such as CT and MRI scans. The AC-BiFPN extracts multi-scale features, enabling the detection of intricate anomalies like intracranial hemorrhages, while the Transformer generates coherent, contextually relevant diagnostic reports by modeling long-range dependencies. We evaluate the performance of our model on the RSNA Intracranial Hemorrhage Detection dataset, where it outperforms traditional CNN-based models in both diagnostic accuracy and report generation. This solution not only supports radiologists in high-pressure environments but also provides a powerful educational tool for trainee physicians, offering real-time feedback and enhancing their learning experience. Our findings demonstrate the potential of combining advanced feature extraction with transformer-based text generation to improve clinical decision-making in the diagnosis of traumatic brain injuries.
Effects of Different Integrated Pest Management Programs on Biological Control of Mites on Apple by Predatory Mites (Acari) in Nova Scotia

For 3 yr (1989–1991) we examined effects of different integrated pest management (IPM) programs on populations of mites in an apple orchard in Nova Scotia, Canada. The stage 1 IPM program involved pest monitoring and application of insecticides (phosmet, pirimicarb, Bacillus thuringiensis var. kurstaki Berliner + one-tenth the recommended rate of cypermethrin) known to be harmless or slightly harmful to the phytoseiid predator Typhlodmmus pyri Scheuten. With the stage 2 program, most insecticide applications were replaced by alternatives such as insecticidal soap, insect pathogens, and insect growth regulators. In stage 1 and stage 2 plots, apple scab, Venturia inaequalis (Cke.) Wint., was controlled either with captan, a fungicide that is harmless to phytophagous mites and T. pyri , or with metiram, a fungicide that is slightly harmful to phytophagous mites and moderately harmful to T. pyri . In June and July 1989, densities of European red mite, Panonychus ulmi (Koch), and apple rust mite, Aculus schlechtendali (Nalepa), were too high (maximum 7.6 to > 100 per leaf) to be controlled by the low numbers of T. pyri (<0.1 per leaf) present in the orchard. An early August application of the miticide propargite in stage 1 plots or hexythiazox in stage 2 plots caused populations of P ulmi and A. schlechtendali to decline to 0 per leaf without reducing T. pyri populations. In 1990 and 1991, mite counts in stage 2 plots did not differ with fungicide regime. In stage 1 plots, however, counts of P. ulmi and A. schlechtendali were higher and counts of T. pyri , but not the stigmaeid predator Zetzellia mali (Ewing), were lower with metiram than with captan. Biological control was effective in stage 2 plots and all captan plots: maximum counts of P. ulmi and A. schlechtendali were < 1.5 per leaf and 6 per leaf, respectively. Maxima were higher, but still subeconomic, in the stage 1 metiram plots: 5 per leaf for P. ulmi and 9 per leaf for A. schlechtendali . Additive toxic effects of insecticides and fungicides on T. pyri , the possible contribution of Z. mali to biological control despite competition with T. pyri , and recommendations for integrated mite control are discussed.
Development and comparison of natural language processing models for abdominal aortic aneurysm repair identification and classification using unstructured electronic health records

Abstract                                    Background                   Patient identification for national registries often relies upon clinician recognition of cases or retrospective searches using potentially inaccurate clinical codes, potentially leading to incomplete data capture and inefficiencies. Natural Language Processing (NLP) offers a promising solution by automating analysis of electronic health records (EHRs). This study aimed to develop NLP models for identifying and classifying abdominal aortic aneurysm (AAA) repairs from unstructured EHRs, demonstrating proof-of-concept for automated patient identification in registries like the National Vascular Registry.                                                     Method                   Using the MIMIC-IV-Note dataset, a multi-tiered approach was developed to identify vascular patients (Task 1), AAA repairs (Task 2), and classify repairs as primary or revision (Task 3). Four NLP models were trained and evaluated using 4,870 annotated records: scispaCy, BERT-base, Bio-clinicalBERT, and a scispaCy/Bio-clinicalBERT ensemble. Models were compared using accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve.                                                     Results                   The scispaCy model demonstrated the fastest training (2 mins/epoch) and inference times (2.87 samples/sec). For Task 1, scispaCy and ensemble models achieved the highest accuracy (0.97). In Task 2, all models performed exceptionally well, with ensemble, scispaCy, and Bio-clinicalBERT models achieving 0.99 accuracy and 1.00 AUC. For Task 3, Bio-clinicalBERT and the ensemble model achieved an AUC of 1.00, with Bio-clinicalBERT displaying the best overall accuracy (0.98).                                                     Conclusion                   This study demonstrates that NLP models can accurately identify and classify AAA repair cases from unstructured EHRs, suggesting significant potential for automating patient identification in vascular surgery and other medical registries, reducing administrative burden and improving data capture for audit and research.