ethicalabs/Research-Intent-Collab huggingface dataset:
Genotypic Characterisation and Antimicrobial Resistance of Extended-Spectrum β-Lactamase-Producing Escherichia coli in Humans, Animals, and the Environment from Lusaka, Zambia: Public Health Implications and One Health Surveillance

Background: Extended-spectrum β-lactamases (ESBL) in Escherichia coli are a serious concern due to their role in developing multidrug resistance (MDR) and difficult-to-treat infections. Objective: This study aimed to identify ESBL-carrying E. coli strains from both clinical and environmental sources in Lusaka District, Zambia. Methods: This cross-sectional study included 58 ESBL-producing E. coli strains from hospital inpatients, outpatients, and non-hospital environments. Antimicrobial susceptibility was assessed using the Kirby–Bauer disk diffusion method and the VITEK® 2 Compact System, while genotypic analyses utilised the Illumina NextSeq 2000 sequencing platform. Results: Among the strains isolated strains, phylogroup B2 was the most common, with resistant MLST sequence types including ST131, ST167, ST156, and ST69. ESBL genes such as blaTEM-1B, blaCTX-M,blaOXA-1, blaNDM-5, and blaCMY were identified, with ST131 and ST410 being the most common. ST131 exhibited a high prevalence of blaCTX-M-15 and resistance to fluoroquinolones. Clinical and environmental isolates carried blaNDM-5 (3.4%), with clinical isolates showing a higher risk of carbapenemase resistance genes and the frequent occurrence of blaCTX-M and blaTEM variants, especially blaCTX-M-15 in ST131. Conclusions: This study underscores the public health risks of blaCTX-M-15- and blaNDM-5-carrying E. coli. The strengthening antimicrobial stewardship programmes and the continuous surveillance of AMR in clinical and environmental settings are recommended to mitigate the spread of resistant pathogens.
Truncated Huber Penalty for Sparse Signal Recovery with Convergence Analysis

Sparse signal recovery from under-determined systems presents significant challenges when using conventional L_0 and L_1 penalties, primarily due to computational complexity and estimation bias. This paper introduces a truncated Huber penalty, a non-convex metric that effectively bridges the gap between unbiased sparse recovery and differentiable optimization. The proposed penalty applies quadratic regularization to small entries while truncating large magnitudes, avoiding non-differentiable points at optima. Theoretical analysis demonstrates that, for an appropriately chosen threshold, any s-sparse solution recoverable via conventional penalties remains a local optimum under the truncated Huber function. This property allows the exact and robust recovery theories developed for other penalty regularization functions to be directly extended to the truncated Huber function. To solve the optimization problem, we develop a block coordinate descent (BCD) algorithm with finite-step convergence guarantees under spark conditions. Numerical experiments are conducted to validate the effectiveness and robustness of the proposed approach. Furthermore, we extend the truncated Huber-penalized model to the gradient domain, illustrating its applicability in signal denoising and image smoothing.
Leveraging Professional Radiologists' Expertise to Enhance LLMs' Evaluation for AI-generated Radiology Reports

In radiology, Artificial Intelligence (AI) has significantly advanced report generation, but automatic evaluation of these AI-produced reports remains challenging. Current metrics, such as Conventional Natural Language Generation (NLG) and Clinical Efficacy (CE), often fall short in capturing the semantic intricacies of clinical contexts or overemphasize clinical details, undermining report clarity. To overcome these issues, our proposed method synergizes the expertise of professional radiologists with Large Language Models (LLMs), like GPT-3.5 and GPT-4. Utilizing In-Context Instruction Learning (ICIL) and Chain of Thought (CoT) reasoning, our approach aligns LLM evaluations with radiologist standards, enabling detailed comparisons between human and AI-generated reports. This is further enhanced by a Regression model that aggregates sentence evaluation scores. Experimental results show that our "Detailed GPT-4 (5-shot)" model achieves a correlation that is 0.48, outperforming the METEOR metric by 0.19, while our "Regressed GPT-4" model shows even greater alignment(0.64) with expert evaluations, exceeding the best existing metric by a 0.35 margin. Moreover, the robustness of our explanations has been validated through a thorough iterative strategy. We plan to publicly release annotations from radiology experts, setting a new standard for accuracy in future assessments. This underscores the potential of our approach in enhancing the quality assessment of AI-driven medical reports.
The Responsibility to Protect Doctrine and Humanitarian Intervention: Too Many Ambiguities for a Working Doctrine

The question about possible remedies, including military intervention, to avoid or to put an end to massive violations of human rights committed by a state towards its own citizens or in situations where state authorities critically lack effectiveness has been extensively debated since the issuance in 2001 of the report of the International Commission on Intervention and State Sovereignty (ICISS) on the responsibility to protect. After a succinct and critical review of the ICISS’ report and the subsequent international instruments dealing with the responsibility to protect, this contribution focuses on the positions adopted by states, especially over the last three years at the General Assembly and at the Security Council of the United Nations on humanitarian intervention as a ‘corollary’ of the responsibility to protect doctrine. It appears that humanitarian intervention aimed at implementing the responsibility to protect is not only feared as imperialistic by several weak states, but it also significantly fails to find an unconditioned support even amongst the most powerful states. Given its extreme and multifaceted ambiguity, which is discussed in the last section of this contribution, the innovative content of the purported ‘emerging norm’ on the responsibility to protect, as well as its prospect to emerge in the future, remain rather unclear.
Deep-Bench: Deep Learning Benchmark Dataset for Code Generation

Deep learning (DL) has revolutionized areas such as computer vision, natural language processing, and more. However, developing DL systems is challenging due to the complexity of DL workflows. Large Language Models (LLMs), such as GPT, Claude, Llama, Mistral, etc., have emerged as promising tools to assist in DL code generation, offering potential solutions to these challenges. Despite this, existing benchmarks such as DS-1000 are limited, as they primarily focus on small DL code snippets related to pre/post-processing tasks and lack a comprehensive coverage of the full DL pipeline, including different DL phases and input data types.  To address this, we introduce DeepBench, a novel benchmark dataset designed for function-level DL code generation. DeepBench categorizes DL problems based on three key aspects: phases such as pre-processing, model construction, and training; tasks, including classification, regression, and recommendation; and input data types such as tabular, image, and text.  GPT-4o -- the state-of-the-art LLM -- achieved 31% accuracy on DeepBench, significantly lower than its 60% on DS-1000. We observed similar difficulty for other LLMs (e.g., 28% vs. 54% for Claude, 21% vs. 41% for LLaMA, and 15% vs. 20% for Mistral). This result underscores DeepBench's greater complexity. We also construct a taxonomy of issues and bugs found in LLM-generated DL code, which highlights the distinct challenges that LLMs face when generating DL code compared to general code.  Furthermore, our analysis also reveals substantial performance variations across categories, with differences of up to 7% among phases and 37% among tasks. These disparities suggest that DeepBench offers valuable insights into the LLMs' performance and areas for potential improvement in the DL domain.
Deep Learning-Based Traffic Accident Prediction: An Investigative Study for Enhanced Road Safety

INTRODUCTION: Traffic accidents cause enormous loss of life as well as property, which is a global concern. Effective accident prediction is essential for raising road safety and reducing the effects of accidents. To increase traffic safety, a deep learning-based technique for predicting accidents was developed in this research study.  OBJECTIVES: It gathers a large amount of data on elements including weather, road features, volume of traffic, and past accident reports. The dataset goes through pre-processing, such as normalization, to ensure that the scales of the input characteristics are uniform. Normalizing the gathered dataset ensures consistent scaling for the input features during the data processing step. This process enables efficient model training and precise forecasting. In order to track and examine the movement patterns of automobiles, people, and other relevant entities, real-time tracking and monitoring technologies, such as the deep sort algorithm, are also employed.  METHODS: The model develops a thorough grasp of the traffic situation by incorporating this tracking data with the dataset.  Convolutional Neural Networks (CNN), in particular, are utilized in this research for feature extraction and prediction. CNNs capture crucial road characteristics by extracting spatial features from images or spatial data. With its insights into improved road safety, this study advances the prediction of traffic accidents.  RESULTS: A safer transport infrastructure could result from the developed deep learning-based strategy, which has the potential to enable pre-emptive interventions, enhance traffic management, and eventually reduce the frequency and severity of traffic accidents.  CONCLUSION: The proposed CNN demonstrates superior accuracy when compared to the existing method.
New advances in High Performance Computing and simulation: parallel and distributed systems, algorithms, and applications

Recent developments in research and technological studies have shown that high performance computing (HPC) will lead to advances in several areas of Science, engineering, and technology, permitting the successful completion of more computationally intensive and data-intensive problems such as those in healthcare, biomedical and biosciences, climate and environmental changes, multimedia processing, design and manufacturing of advanced materials, geology, astronomy, chemistry, physics, and even financial systems. The development of the HPC infrastructure has been accelerated by the advances in silicon technology, which permitted the design of complex systems able to incorporate many hardware and software blocks and cores.
Administrative Justice System in Italy, relazione al convegno International Conference on dispute resolution: A Comparative Perspective, in Londra, 17-18/12/2012, in The Culture of Judicial Independence: Rule of Law and World Peace, Brill-NIJHOFF, Leiden ??? Boston, 2014, pp. 436-441.

Administrative Justice System in Italy
The effectiveness of the Dispute Settlement Body of the World Trade Organization

PurposeThe purpose of this paper is to analyse the performance of the Dispute Settlement Body (DSB) of World Trade Organization (WTO) performance, between 1995 and 2007, as well as to discuss the main proposals for its improvement.Design/methodology/approachFirst, the paper presents the legal predictability of the system; compliance with its procedural stipulations in regard to its proposed timeframes; and the participation of different groups of States within this system. The timeframes are compared with those that really happened. Thus, there is an analysis of the effectiveness of its decisions and the necessity to induce compliance and retaliation. Finally, the main proposals to change the system are discussed.FindingsIt is concluded that most of legal procedures are accomplished as previewed by DSB, but the system itself is highly dependent of the action of States, who need time to negotiate. It is also possible to conclude that there is a high level of effectiveness and States prefer to respect the decisions of DSB and maintain the legitimacy of the system as a whole than keep advantages in specific matters.Originality/valueWhen the WTO was founded, there was an effort to generate a system guided by legal rules. The DSB has made efforts to maintain a high level of legal preciosity. However, it is clear that this system is still very limited by the traditional method of negotiation among States. The implementation of a rule‐oriented system contributes to greater democratization of access to justice and, of course, in a limited way, the principle of sovereign equality of States. Finally, the majority of proposed changes attempt to apply the logic of domestic courts to an international body, assuming a level of organization of the international community as a whole that still does not exist. Others proposals suggest increasing the politicization of the system, which is also not appropriate.
Supplementary Table S7 from Prediction of Stage, Grade, and Survival in Bladder Cancer Using Genome-wide Expression Data: A Validation Study

&lt;p&gt;Supplementary Table S7.&lt;/p&gt;
The land rights of indigenous and traditional peoples in Brazil and Australia

A conservacao da diversidade biologica depende, segundo grande parte da comunidade cientifica, dos conhecimentos desenvolvidos, ao longo de sucessivas geracoes, pelas comunidades tradicionais. A manutencao desses conhecimentos, por sua vez, esta diretamente relacionada a capacidade dessas comunidades de continuar a desenvolver, nas areas por elas tradicionalmente ocupadas, praticas culturais ancestrais que os transmitam e reivifiquem continuamente. Para tanto, Brasil e Australia utilizam diferentes mecanismos.  Desse modo, o objetivo desse artigo consiste em identificar e examinar tais mecanismos, a fim de que cada experiencia possa ser adaptada, aprimorada e utilizada pelo outro. Os principais achados incluem o potencial de se fortalecer direitos territoriais na Australia, por meio do reconhecimento constitucional de direitos ja positivados ou declarados pela Common Law, assim como, no Brasil, a capacidade de se evitar a transferencia compulsoria de grupos tradicionais nao-indigenas quando da criacao de unidades de conservacao de protecao integral, a partir da adocao de instrumentos de gestao compartilhada de areas protegidas. As principais conclusoes sugerem que os dois paises tem muito a contribuir com o desenvolvimento de um modelo adaptativo de direitos territoriais que avance na protecao da socio e da biodiversidade.
FuncPhos-STR: An integrated deep neural network for functional phosphosite prediction based on AlphaFold protein structure and dynamics

Phosphorylation modifications play important regulatory roles in most biological processes. However, the functional assignment for the vast majority of the identified phosphosites remains a major challenge. Here, we provide a deep learning framework named FuncPhos-STR as an online resource, for functional prediction and structural visualization of human proteome-level phosphosites. Based on our reported FuncPhos-SEQ framework, which was built by integrating phosphosite sequence evolution and protein-protein interaction (PPI) information, FuncPhos-STR was developed by further integrating the structural and dynamics information on AlphaFold protein structures. The characterized structural topology and dynamics features underlying functional phosphosites emphasized their molecular mechanism for regulating protein functions. By integrating the structural and dynamics, sequence evolutionary, and PPI network features from protein different dimensions, FuncPhos-STR has advantage over other reported models, with the best AUC value of 0.855. Using FuncPhos-STR, the phosphosites inside the pocket regions are accessible to higher functional scores, theoretically supporting their potential regulatory mechanism. Overall, FuncPhos-STR would accelerate the functional identification of huge unexplored phosphosites, and facilitate the elucidation of their allosteric regulation mechanisms. The web server of FuncPhos-STR is freely available at http://funcptm.jysw.suda.edu.cn/str.
A discrete event simulation for the analytical modeling of M/D/1 queues: Output buffer of an ATM multiplexer

Queueing theory is the study of prediction and evaluation of the system performance. It has been studied and used since long time. It mainly studies the waiting line or queues. A queueing system can be described as customers arriving for service, waiting for service if it is not immediate, and if having waited for service, leaving the system after being served. Queueing theory was developed to provide models to predict the behavior of systems that attempt to provide service for such randomly arising demands. Different queueing models are proposed to measure the system performance. These models differentiate with each other according to the Kendall's notation indicating the arrivals, service, servers, system capacity and the queue discipline. The purpose of our study is to design a discrete event simulation for a commonly used Queueing Model M/D/l. The model represents exponential arrival of customers with a deterministic service rate on a single server system, for example a production company with a single server machine or an output buffer of an ATM multiplexer where packets arrive exponentially and serviced as deterministically. The case study of the output buffer of an ATM multiplexer is included to compare the simulation results with analytical findings of the Model. We have designed the simulation using C/C++ programming language.
503. HEALTH-RELATED QUALITY OF LIFE FOLLOWING CURATIVE INTENT TREATMENT FOR ESOPHAGEAL CANCER: IMPACT OF TREATMENT PATHWAY ON PATIENT-REPORTED OUTCOMES IN SURVIVORSHIP

Abstract                                   Background                   Recent improvements in treatment modalities for esophageal cancer have resulted in improved survival outcomes, and optimisation of health-related quality of life (HR-QL) in survivorship is now an increasing focus. This study aimed to compare HR-QL in survivorship following treatment with definitive chemoradiation (dCRT), neoadjuvant chemoradiation and surgery (nCRT), neoadjuvant chemotherapy and surgery (nCT), and surgery only (S).                                                   Methods                   This was a cross-sectional study of patients who underwent oesophageal cancer treatment between 2010 and 2021. A multicomponent questionnaire was distributed to disease-free patients at least one year post completion of treatment. Scores were calculated according to the European Organisation for Research and Treatment of Cancer manual. A mean difference of ten points or more for symptom and function scales was considered clinically significant. One-way ANOVA was performed to detect statistically significant differences between treatment groups.                                                   Results                   Some 125 patients were included. Global HR-QL was similar between treatment groups (mean±SD, dCRT 67.4±20.9, nCRT 71.5±19.6, nCT 80.0±15.0, S 69.0±20.0, P=0.265). Analysis of functional scales found lower emotional functioning (S 71.1±25.5 vs dCRT 87.1±15.6, nCRT 79.6±19.5, nCT 88.9±26.3, P=0.028) and body image scores (S 71.8±29.2 vs dCRT 87.9±22.5, nCRT 83.3±27.8, nCT 91.1±19.8, P=0.056) among patients treated with surgery only. Dysphagia (P=0.034), trouble with coughing (P=0.014) and choking when swallowing (P=0.018) were increased following dCRT as compared with surgical treatment. Following surgical treatment, patients reported more diarrhoea as compared with dCRT (P=0.020), while dCRT was associated with increased constipation (P=0.004).                                                   Conclusion                   Overall HR-QL was similar between groups, but distinct patterns of symptoms were observed in different cohorts. Symptoms related to swallow function were more common in patients treated with dCRT, while patients who underwent surgery exhibited more diarrhoea, possibly reflective of increased dumping syndrome. Interestingly, patients who received surgery upfront reported lower scores for emotional functioning and body image in the long-term after treatment, as compared with other groups. Further research is needed to determine the differential psychological impact of major resectional upper GI surgery among patients presenting with early stage versus locally advanced disease.
Genetic Screening Technology: Ethical Issues in Access to Tests by Employers and Health Insurance Companies

Whereas the introduction of new technologies previously has raised the ethical question of who ought to have access to a new procedure or device, genetic testing technology raises the new ethical question of to whom access to a new technology ought to be limited. In this article we discuss the implications of employers and private health insurance companies having access to genetic testing technology. Although there may be legitimate business interests in allowing employers and insurers to conduct genetic screening, there are other valid societal interests in regulating or limiting the use of this technology by third parties. Public policy developed in the area of new genetic testing technology must reflect such interests.
Enhancing biodiversity conservation in existing land-use plans with widely available datasets and spatial analysis techniques

SUMMARYIn many developing countries, high rates of deforestation and biodiversity loss make conservation efforts urgent. Improving existing land-use plans can be an option for enhancing biodiversity conservation. We showcase an approach to enhancing an existing forest land-use plan using widely available data and spatial tools, focusing on Argentina's Southern Yungas ecoregion. We mapped the distribution of wilderness areas and species and habitats of conservation concern, assessed their representation in the land-use plan and quantified potential changes in habitat availability and forest connectivity. Wilderness comprised 48% of the study area, and the highest concentrations of elements of conservation concern were in the north. In the current land-use plan, wilderness areas often occur in regions where logging and grazing are allowed, and a large proportion of the forest with the highest conservation value (43%) is under some level of human influence. Furthermore, we found that deforestation being legally allowed in the land-use plan could reduce forest connectivity and habitat availability substantially. We recommend updating the current land-use plan by considering human influence and elements of conservation concern. More broadly, we demonstrate that widely available spatial datasets and straightforward approaches can improve the usefulness of existing land-use plans so that they more fully incorporate conservation goals.
Information theory in systems biology. Part I: Gene regulatory and metabolic networks

"A Mathematical Theory of Communication", was published in 1948 by Claude Shannon to establish a framework that is now known as information theory. In recent decades, information theory has gained much attention in the area of systems biology. The aim of this paper is to provide a systematic review of those contributions that have applied information theory in inferring or understanding of biological systems. Based on the type of system components and the interactions between them, we classify the biological systems into 4 main classes: gene regulatory, metabolic, protein-protein interaction and signaling networks. In the first part of this review, we attempt to introduce most of the existing studies on two types of biological networks, including gene regulatory and metabolic networks, which are founded on the concepts of information theory.