ethicalabs/Research-Intent-Collab huggingface dataset:
GLOBAL CLIMATE MODEL FOR PROJECTING FUTURE CLIMATE CHANGES OVER UPPER INDUS RIVER BASIN

Evaluation of scenarios has become a critical component of climate change research. The performance of various GCMs in simulating observed climate at several stations in the Upper Indus River Basin was evaluated using different statistical indicators. The HadCM3, SRNIES, ECHAM4, and NCAR models simulated the spatial variability of mean temperature well, but the precipitation simulation was found to be inadequate.  The results indicate that all the models substantially underestimated the magnitude of temperature on a monthly basis. The projected precipitation, however, showed much higher inter-model variability compared to mean temperature at all stations. Overall, a considerable inter-model variability in the simulation of observed climate was found at all stations.  Owing to its ability to simulate the observed climate well, the HadCM3 model was selected for projection of future climate in the Upper Indus River Basin under several emission scenarios. Using the HadCM3 model, the temperature and precipitation changes across the UIRB were determined for two time periods; 2021-2050 (F1), and 2061-2090 (F2), relative to 1961-1990. The projected mean annual temperature across the study area varied from 1.7 to 4.3°C (for F1 and F2). The HadCM3 model predicted higher increase in mean winter temperature (from 2.1 to 4.5°C) compared to other seasons, whereas the increase in precipitation was observed in all seasons except winter. The increase in annual precipitation of 16 to 28% was projected for the two future periods considered in the present study.
The evolution of surgical skills simulation education: Robotic skills

Robotic surgery is now widely used after the introduction and increase in popularity of the da Vinci Xi Surgical System 25 years ago. Starting with urology, it has expanded into most major surgical specialties, with millions of robotic operations performed each year. With this growth comes the need to educate and train surgeons on its use. Robotic surgery introduces unique cognitive, psychomotor, and team-based skills not used in open and laparoscopic techniques. These demand creation of curricula on the basis of comprehensive and validated educational frameworks, similar to the Fundamentals of Endoscopic Surgery and Fundamentals of Laparoscopic Surgery programs, to train surgeons. Although programs like the Fundamentals of Robotic Surgery have been developed, they focus most on basic skills. Beyond curricula for these and more advanced skills, how performance is assessed is equally critical. Beginning to fill this gap are the Global Evaluative Assessment of Robotic Skills, Robotic Objective Structured Assessment of Technical Skills, and Robotic Ottawa Surgical Competency Operating Room Evaluation assessment tools. Continued development and improvement in curricula and assessment must persist, particularly as new technologies develop rapidly. Sophisticated features of newer robotic systems including objective performance indicators, augmented reality, virtual reality, artificial intelligence, and machine learning are gaining pace and are positioned to expand robotic surgery, and robotic surgical simulation education, even further over the next 25 years.
Quantum computation with universal error mitigation on a superconducting quantum processor

Probabilistic error cancellation is experimentally demonstrated on a superconducting quantum processor.
Investigation of the causes of maternal mortality using root cause analysis in Isfahan, Iran in 2013-2014

Many maternal deaths caused are due to preventable causes during pregnancy and childbirth. Therefore, the detailed analysis of the root causes provides developing a plan and appropriate interventions to prevent these deaths occurring in the health system. This study aims to determine the causes of maternal mortality using root cause analysis (RCA) method.This research is a descriptive explorative study. The data were collected from the files in the maternal health center and the interviews conducted with relevant personnel. The causes of maternal mortality and related reasons were determined by experts' team opinions and through a standard checklist of RCA. Causes consisted of the factors related to health services (human factors and structural factors), maternal family and social status, and maternal disease status. For each of these factors, analysis was performed to determine the root. In the end, interventional suggestions were developed to prevent the recurrence of similar deaths.Causes were classified into human factors, and structural factors in the area of planning and management and social status of mothers. The results showed that human factors were composed of lack of knowledge and skills in the medical team, unfamiliarity with their duties, lack of health care-based on protocols, etc. Structural factors included lack of follow-up after discharge and inadequate supervision of inspectors on academic qualified doctors. Maternal social and family status factor included lack of referral the mothers' to the health care center.Based on the RCA process, the most fundamental factor in creating these deaths was management errors at the level of universities and the Ministry of Health. These errors included inadequate supervision of medical education, failure to identify and introduce the instructions and guidelines related to the care of pregnant mothers by the health workers and experts, and lack of collective strategies to inform the public about the type and model of self-care in health centers. Based on the obtained results, the solutions proposed for elimination of root causes of maternal death are organizing sequential training courses tailored for the staff taking care of pregnant women, sending guidelines related to maternal health care to all private and public institutions, and informing the community to receive health care services by health centers and mass media.
Genomic diversity and                   <i>BCL9L</i>                   mutational status in CTC pools predict overall survival in metastatic colorectal cancer

Abstract                                    The genomic diversity of circulating tumor cells (CTCs) and its clinical implications remain poorly understood. In this study, we characterized the mutational landscape of CTC pools stemming from 29 metastatic colorectal cancer (mCRC) patients and examined its relationship with disease progression. Our analysis revealed substantial variation in mutational burden among patients, with all CTC pools harboring non-silent mutations in key CRC driver genes. Importantly, higher genomic diversity in CTC pools was significantly associated with reduced overall survival. Furthermore, non-silent mutations in                   BCL9L                   emerged as a strong predictor of patient survival. Taken together, these findings underscore the potential of CTC genomic profiling as a promising prognostic tool in mCRC and highlight the need for further research into its clinical applications.
Leadership Management, Organizational Behavior, and Organizational Success

This research explores the intricate relationship between leadership management, organizational behavior, and organizational success. Leadership plays a pivotal role in shaping organizational behavior, which in turn impacts overall success. The study critically analyzes various leadership styles, their influence on employee motivation and performance, and how organizational culture and behavior affect long-term sustainability. A combination of qualitative and quantitative methodologies is employed to assess leadership effectiveness in different organizational contexts. The findings suggest that transformational leadership significantly enhances employee engagement and innovation, contributing to organizational success. Statistical data indicates that organizations with strong leadership development programs report a 15% higher employee retention rate and 20% increased productivity. Future research directions include exploring the impact of digital leadership in remote work environments and the role of artificial intelligence in leadership decision-making
The Lean &amp; Six Sigma model as the basis of Quality Management in the automotive industry

The automotive industry is an extremely turbulent area where changes happen very often and quickly, and it is necessary to monitor them as well as adapt to them. The Lean &amp; Six Sigma concept is one of the most important Quality Management models, enabling local automotive manufacturers and suppliers to become more efficient and competitive, and in particular to increase the ability of their processes, improve technology and innovation, quality and performance, and satisfaction. customers. By applying different methods and techniques of this model, organizations in the field of automotive industry can both implement various improvements and achieve certain savings. The first part of the paper will present the Lean &amp; Six Sigma model in the field of automotive industry as well as the benefits that are realized by applying this model. After that, an overview of the most common tools, methods and techniques used within this model in the automotive industry will be presented. The final part of the paper will present the results of research conducted in Serbia, with the aim of determining how much the automotive industry company applies this model and what improvements this model brings with it. Therefore, the findings of this research paper will relate to quality improvements resulting from the application of the Lean &amp; Six Sigma model in the automotive industry.
Building a Cheap, Scalable, Error Corrected, Room Temperature, Topological Quantum Computer with Current Technology Using Only Social Networks of People

Quantum computing offers great promise in a variety of fields, leveraging superadditive advantages over classical algorithms at solving many intractable problems in cryptography and economics. While progress has been made in developing error corrected scalable quantum computing, architectures currently rely on expensive hardware and are years away from widespread commercialization with significant barriers to entry. To address this, we propose a radical new model for quantum computing that uses human social networks as its core hardware. Drawing from Penrose's Orch-Or theory and Neural Spinfoam Networks (NSNs), it may be possible to encode useful quantum operations on agents-where each agent functions as an error-corrected topological qubit in a trophic social network. When agents synchronize their brain activity during social interaction-a phenomenon known as interbrain synchrony-they become correlated, where it may be possible to build a "Social Quantum Computer" (SQC). Whether or not interbrain synchrony is a result of entanglements is inconsequential towards whether or not this mechanism can be used towards scalability. This collective system can be programmed using semiotic linguistic strategies and metanarratives (acting as software operators) to solve otherwise intractable problems by gradually evolving and collapsing Hamiltonians towards phase transitions and tipping points of spontaneous group consensus which encodes resolutions to intractable problems by the spectral action principle. The model posits that this biological, room-temperature approach could offer a more scalable and energy-efficient path to quantum computation than current technological efforts, while also providing a new framework for understanding collective human intelligence.
Large Language Models for Time Series: A Survey

Large Language Models (LLMs) have seen significant use in domains such as natural language processing and computer vision. Going beyond text, image and graphics, LLMs present a significant potential for analysis of time series data, benefiting domains such as climate, IoT, healthcare, traffic, audio and finance. This survey paper provides an in-depth exploration and a detailed taxonomy of the various methodologies employed to harness the power of LLMs for time series analysis. We address the inherent challenge of bridging the gap between LLMs' original text data training and the numerical nature of time series data, and explore strategies for transferring and distilling knowledge from LLMs to numerical time series analysis. We detail various methodologies, including (1) direct prompting of LLMs, (2) time series quantization, (3) aligning techniques, (4) utilization of the vision modality as a bridging mechanism, and (5) the combination of LLMs with tools. Additionally, this survey offers a comprehensive overview of the existing multimodal time series and text datasets and delves into the challenges and future opportunities of this emerging field. We maintain an up-to-date Github repository which includes all the papers and datasets discussed in the survey.
Towards genotypes adapted to climate change via combination of phenotyping and modelling: The projects DROPS and Phenome

The period of time from now to 2050 is required for designing varieties able to cope with climate changes. Phenotyping is the main limiting step, now that genotyping has become an almost routine activity. A strategy with four pillars is used in the projects UE-DROPS and IA-Phenome-FPPN, involving public and private sectors. (1) Investigating the genetic variability of plant traits as a response to well-defined environmental conditions. Phenotyping platforms, developed in Phenome, allow the identification of genomic regions associated with traits of interest and the estimation of parameters of crop models for 100s genotypes. DROPS has identified QTLs, usable in breeding, for sensitivity of growth to water stress, for root architecture and for water use efficiency. (2) Investigating the performance and yield of hundreds of genotypes under high C02, high temperatures or low rainfall in equipped fields, via detailed imaging using sensors carried by ground vectors developed in Phenome. (3) Testing a large number of genotypes in a wide range of conditions. DROPS analyses yield components in a network of 30 field situations over Europe for 250 genotypes. This allows investigating the genetic variability of the sensitivities of genotypes to high temperatures and drought in the field. (4) Modelling, for testing combinations of alleles in a variety of climatic scenarios and management practices. Climatic conditions sensed by plants have been analyzed in 50 European nodes over 50 years, and in predicted European climates for 2050. Clustering individual scenarios reveals that 4 scenarios capture most of the variability of current climates, but also of predicted climates in 2050. Hence, it is possible to analyze today the performances of genotypes in future climates involving drought and high temperature, by using the current climatic variability between sites and years. We are currently simulating the interests of promising combinations of alleles in different European scenarios.
The principle of constant care, prolonged drone surveillance and the right to privacy of non-combatants in armed conflicts

This chapter critically examines the impact of sustained drone surveillance on non-combatants in war zones and argues that legal constraints should be placed on this practice. It identifies a lacuna in the international humanitarian law (IHL) framework with respect to privacy and data protection rights and demonstrates that IHL and international human rights law (IHRL) apply concurrently in armed conflicts. Further, it contends that the IHRL rules on mass surveillance of communications pertain to this method of intelligence collection. The rationale for their application is the constant care principle set out in Article 57(1) of Additional Protocol I to the Geneva Conventions 1949, as it places state parties under a continuous duty of care of civilian populations in regard to the whole spectrum of military operations, which must necessarily include intelligence gathering.
Impact of Palliative Care on Quality of End-of-Life Care Among Brazilian Patients With Advanced Cancers

Many patients with advanced cancer experience aggressive care during the end of life (EOL). Several studies have evaluated the benefits of palliative care (PC) on the reduction of aggressive measures; however, limited data are available about their benefit in Brazilian patients.To evaluate the impact of PC on the reduction of aggressive measures at the EOL.Longitudinal study analyzed retrospectively medical records of patients who died of advanced cancer from 2010 to 2014. Data were obtained on PC referral and five quality-of-care indicators at the EOL; that is, emergency department visits, hospital admission, intensive care unit admission, use of systemic antineoplastic therapy within the last 30 days of life, and place of death in hospital as well as the use of a composite score for aggressiveness of care.Of the 1284 patients, 832 (65%) received some aggressive measures in EOL care. Over the years, there was a reduction in the aggressiveness of care (score = 0: 33.2% vs. 47.1%; P < 0.001). Patients not seen by PC received greater aggressive care compared with patients consulted by PC (score ≥1: 87.4% vs. 52.8%; P < 0.001). Early PC was associated with less chemotherapy (P = 0.001) and fewer emergency department visits (P = 0.004) in the last 30 days of life, when compared with late PC. However, there were no demonstrated benefits to significantly reduce the composite score at EOL care aggressiveness.Patients with an advanced cancer consultation by PC staff received less aggressive care at the EOL when compared with patients without PC.
Evidence-based mapping of design heterogeneity prior to meta-analysis: a systematic review and evidence synthesis

Assessment of design heterogeneity conducted prior to meta-analysis is infrequently reported; it is often presented post hoc to explain statistical heterogeneity. However, design heterogeneity determines the mix of included studies and how they are analyzed in a meta-analysis, which in turn can importantly influence the results. The goal of this work is to introduce ways to improve the assessment and reporting of design heterogeneity prior to statistical summarization of epidemiologic studies.In this paper, we use an assessment of sugar-sweetened beverages (SSB) and type 2 diabetes (T2D) as an example to show how a technique called 'evidence mapping' can be used to organize studies and evaluate design heterogeneity prior to meta-analysis.. Employing a systematic and reproducible approach, we evaluated the following elements across 11 selected cohort studies: variation in definitions of SSB, T2D, and co-variables, design features and population characteristics associated with specific definitions of SSB, and diversity in modeling strategies.Evidence mapping strategies effectively organized complex data and clearly depicted design heterogeneity. For example, across 11 studies of SSB and T2D, 7 measured diet only once (with 7 to 16 years of disease follow-up), 5 included primarily low SSB consumers, and 3 defined the study variable (SSB) as consumption of either sugar or artificially-sweetened beverages. This exercise also identified diversity in analysis strategies, such as adjustment for 11 to 17 co-variables and a large degree of fluctuation in SSB-T2D risk estimates depending on variables selected for multivariable models (2 to 95% change in the risk estimate from the age-adjusted model).Meta-analysis seeks to understand heterogeneity in addition to computing a summary risk estimate. This strategy effectively documents design heterogeneity, thus improving the practice of meta-analysis by aiding in: 1) protocol and analysis planning, 2) transparent reporting of differences in study designs, and 3) interpretation of pooled estimates. We recommend expanding the practice of meta-analysis reporting to include a table that summarizes design heterogeneity. This would provide readers with more evidence to interpret the summary risk estimates.
Impact of antimicrobial stewardship programs on antibiotic consumption and antimicrobial resistance in four Colombian healthcare institutions

Background: Antimicrobial stewardship programs (ASPs) have become a fundamental pillar in optimizing antimicro‑ bial usage, improving patient care, and reducing antimicrobial resistance (AMR). Herein we evaluated the impact of an ASP on antimicrobial consumption and AMR in Colombia. Methods: We designed a retrospective observational study and measured trends in antibiotic consumption and AMR before and after the implementation of an ASP using interrupted time series analysis over a 4-year period (24 months before and 24 months after ASP implementation). Results: ASPs were implemented according to the available resources in each of the institutions. Before ASP imple‑ mentation, there was a trend toward an increase in the antibiotic consumption of all measured antimicrobials selected. Afterward, an overall decrease in antibiotic consumption was observed. The use of ertapenem and mero‑ penem decreased in hospital wards, while a decrease in the use of ceftriaxone, cefepime, piperacillin/tazobactam, meropenem, and vancomycin was observed in intensive care units. After ASP implementation, the trend toward an increase of oxacillin-resistant Staphylococcus aureus, ceftriaxone-resistant Escherichia coli, and meropenem-resistant Pseudomonas aeruginosa was reversed. Conclusions: In our study, we showed that ASPs are a key strategy in tackling the emerging threat of AMR and have a positive impact on antibiotic consumption and resistance.
Approximation of transition densities of stochastic differential equations by saddlepoint methods applied to small-time Ito–Taylor sample-path expansions

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Metagenomics for clinical diagnostics: technologies and informatics

The human-associated microbiome is closely tied to human health and is of substantial clinical interest. Metagenomics-based tools are emerging for clinical diagnostics, tracking the spread of diseases, and surveillance of potential pathogens. In some cases, these tools are overcoming limitations of traditional clinical approaches. Metagenomics has limitations barring the tools from clinical validation. Once these hurdles are overcome, clinical metagenomics will inform doctors of the best, targeted treatment for their patients and provide early detection of disease. Here we present an overview of metagenomics methods with a discussion of computational challenges and limitations.