ethicalabs/Research-Intent-Collab huggingface dataset:
The surveillance of antibiotic resistance in clinical isolates to inform antimicrobial stewardship in Tanzania: a systematic review and meta-analysis

Abstract                                    Background Antimicrobial resistance (AMR), particularly antibiotic resistance (AR), is a major global public health concern, with the impact being especially severe in low-resource settings. In Tanzania, the availability of national-level data on AR has remained limited, particularly following COVID-19 pandemic. To address this gap, this study synthesized recent evidence on AR patterns among clinical bacterial isolates from Tanzanian patients to inform antimicrobial stewardship (AMS) and guide national policymaking. Methods This systematic review and meta-analysis were conducted following PRISMA 2020 guidelines and registered in PROSPERO (CRD420251043755). PubMed, Scopus, and Embase were searched for studies published from inception to May 2025. Eligible studies reported antibiotic susceptibility testing (AST) results from clinical specimens collected in Tanzania between 2021 and 2025. Pooled resistance proportions with 95% confidence intervals (CI), heterogeneity (                   I                   ²), and p-values were calculated using a random-effects model. All statistics were conducted using R software. Results Twelve studies were included; 11 qualified for meta-analysis.                   Staphylococcus epidermidis                   (43.37%) and                   Escherichia coli                   (38.23%) were the most frequently isolated pathogens.                   Streptococcus agalactiae                   had the highest resistance rates among gram-positive bacteria 69% (95% CI: 38–89%,                   I                   2                    = 96.9%, p-value = &lt; 0.0001) and                   Klebsiella pneumoniae                   among gram-negatives 57% (95% CI: 42–71%,                   I                   2                    = 74.1%, p-value = &lt; 0.0001). Penicillin showed the highest pooled resistance at 91% (95% CI: 78% − 97%,                   I²                   = 76.5%; p = 0.0052). The lowest resistance was observed for vancomycin (3%; 95% CI: 0–20%;                   I²                   = 0%; p = 0.9071), nitrofurantoin (6%; 95% CI: 3–11%;                   I²                   = 1.8%; p = &lt; 0.0001), and meropenem (5%; 95% CI: 2–12%;                   I²                   = 52.3%; p = 0.0094). Neonates exhibited high resistance to ampicillin (83%; 95% CI: 47–97%;                   I²                   = 91%; p = &lt; 0.0001) and gentamicin (53%; 95% CI: 44–62%;                   I²                   = 19.8%; p = 0.2889). Conclusion This study demonstrates a high burden of antibiotic resistance in Tanzania, with resistance proportions exceeding 50% in nearly 40% of the antibiotics analysed. The widespread resistance in community-acquired pathogens and first-line treatments, especially among neonates, reflects a critical threat to clinical effectiveness. These findings highlight the urgent need for localized surveillance and data-driven AMS initiatives.
Chandra Observations of Six Peter Pan Disks: Diversity of X-ray-driven Internal Photoevaporation Rates Doesn't Explain Their Rare Longevity

We present Chandra X-ray observations of 6 previously-identified Peter Pan objects, rare 40 Myr systems with evidence of primordial disk retention. We observe X-ray luminosities (0.8-3.0 keV) ranging from log Lx 27.7-29.1. We find that our Peter Pan sample exhibits X-ray properties similar to that of weak-lined T-Tauri stars and do not exhibit evidence of stellar accretion induced X-ray suppression. Our observed Peter Pan X-ray luminosities are consistent with that measured for field dM stars of similar spectral type and age, implying their long primordial disk lifetimes are likely not a consequence of unusually faint X-ray host stars. Our derived X-ray photoevaporative mass loss rates predict our systems have passed the point of rapid gas dispersal and call into question the impact of this internal mechanism for primordial disk dispersal around dM stars. Our qualitative assessment of the surrounding Peter Pan environments also does not predict unusually low levels of external photoevaporation relative to other respective moving group members. Overall, our results suggest Peter Pan disks may be a consequence of the low FUV flux incident on the disk in low-mass DM stars given their relatively lower levels of accretion over the course of their pre-main-sequence evolution.
Ensemble Learning For Mega Man Level Generation

Procedural content generation via machine learning (PCGML) is the process of procedurally generating game content using models trained on existing game content. PCGML methods can struggle to capture the true variance present in underlying data with a single model. In this paper, we investigated the use of ensembles of Markov chains for procedurally generating \emph{Mega Man} levels. We conduct an initial investigation of our approach and evaluate it on measures of playability and stylistic similarity in comparison to a non-ensemble, existing Markov chain approach.
Terraform vs Ansible: When and how to use infrastructure tools as code

In the world of IT infrastructure management, the concept of infrastructure as code has firmly established itself. Two popular tools for implementing this approach – Terraform and Ansible – are widely used by DevOps professionals to automate and manage cloud and on-premises resources. Although both tools perform analogous tasks, they have distinct principles of operation, architecture, and application scenarios. The purpose of this study was to provide the key differences between Terraform and Ansible, their strengths and weaknesses, and use cases for each of these tools. Recommendations for choosing a tool depending on specific tasks were offered. Terraform, as a declarative style tool, enables users to describe the end state of the infrastructure, after which it automatically brings it to that state. Ansible, on the other hand, supports both declarative and imperative approaches, making it flexible for managing server configurations and performing orchestration. The study presented practical examples of using both tools. The first example demonstrated how Terraform can be used to automatically deploy cloud infrastructure in Hetzner Cloud. In this example, actions with a cloud service took place using declarative configuration files. The second example described how Ansible can be used to configure servers and how to automate server tasks. The study included scientific aspects related to the evaluation of IaC effectiveness, specifically formulas for calculating the time of application of changes in the infrastructure. The use of formulas helped to quantify the time and overall efficiency of work in the infrastructure to increase overall transparency and control over management processes. Thus, understanding the scenarios in which each tool is most effective will help engineers properly organise infrastructure management processes.
Enhancing Anti-Corruption, Accountability and Transparency Laws in the Malaysia's Health Sector

Good governance, alongside the principles of accountability and transparency, are potential strategies for combating corruption in the healthcare sector. This study aims to examine the role of law to curb the corrupt practices, and it employs a qualitative research methodology through a content analysis approach in which all relevant literature on corruption and law are reviewed. The major finding has significant implications for lawmakers to propound legislative framework for the risks of corruption in the healthcare sector towards promoting accountability and transparency. This paper concludes that the law is an effective tool for instigating change to the healthy anti-corruption environment. Keywords: Corruption; risks; laws; healthcare eISSN: 2398-4287 © 2022. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BYNC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning &amp; Surveying, Universiti Teknologi MARA, Malaysia. DOI: https://doi.org/10.21834/ebpj.v7iSI7%20(Special%20Issue).3816
Quantum chemistry with the Douglas-Kroll-Hess approach to relativistic density functional theory: Efficient methods for molecules and materials

We review the Douglas-Kroll-Hess (DKH) approach to relativistic density functional calculations for molecular systems, also in comparison with other two-component approaches and four-component relativistic quantum chemistry methods. The scalar relativistic variant of the DKH method of solving the Dirac-Kohn-Sham problem is an efficient procedure for treating compounds of heavy elements including such complex systems as transition metal clusters, adsorption complexes, and solvated actinide compounds. This method allows routine all-electron density functional calculations on heavy-element compounds and provides a reliable alternative to the popular approximate strategy based on relativistic effective core potentials. We discuss recent method development aimed at an efficient treatment of spin-orbit interaction in the DKH approach as well as calculations of g tensors. Comparison with results of four-component methods for small molecules reveals that, for many application problems, a two-component treatment of spin-orbit interaction can be competitive with these more precise procedures.
The Emergence of a New Superpower Rivalry and the Vulnerabilities of the United Nations Security Council

Throughout the past decade, the People's Republic of China has experienced rapid economic and military growth, increasing its influence in Asia. With the abandonment of its ‘biding time’ approach in its foreign policy, China has transformed into a competitive challenger against the American hegemony, engendering a new geopolitical rivalry. This newly emerging rivalry threatens the United Nations’ effectiveness as it exposes numerous structural flaws of the United Nations. A binary atmosphere is slowly encroaching the United Nations Security Council, causing the council to be further polarised. Certain vulnerabilities of the Security Council often cause the council to be effectively paralysed, forcing the UN to be unable to respond to global conflicts. This paper evaluated three different proposed structural flaws of the council through case studies and data analyses and offered recommendations accordingly. The United Nations must amend the structure of the Security Council in order to continue to effectively maintain international peace and avoid repeating the failure of its predecessor, the League of Nations
The Right to Clean Water and Sanitation and its Influence on Gender-based Violence in the Dadaab Refugee Camp (Kenya)

El objetivo de esta investigación es analizar el derecho al agua limpia y saneamiento y su influencia en la violencia de género en el campo de refugiados de Dadaab (Kenia). Para ello se exploran los parámetros recogidos en la Observación General n.º 15 del Consejo Económico y Social de Naciones Unidas: disponibilidad, calidad y accesibilidad del agua y el saneamiento. Además, se estudian varias formas de violencia de género presentes en el campo que puedan estar relacionadas con el incumplimiento del Objetivo de Desarrollo Sostenible 6, relativo al agua limpia y saneamiento.
Updated Living Systematic Review and Meta-analysis of Controlled Trials of Mesenchymal Stromal Cells to Treat COVID-19: A Framework for Accelerated Synthesis of Trial Evidence for Rapid Approval—FASTER Approval

AbstractBackgroundMesenchymal stromal cells (MSCs) may reduce mortality in patients with COVID-19; however, early evidence is based on few studies with marked interstudy heterogeneity. The second iteration of our living systematic review and meta-analysis evaluates a framework needed for synthesizing evidence from high-quality studies to accelerate consideration for approval.MethodsA systematic search of the literature was conducted on November 15, 2021, to identify all English-language, full-text, and controlled clinical studies examining MSCs to treat COVID-19 (PROSPERO: CRD42021225431).FindingsEleven studies were identified (403 patients with severe and/or critical COVID-19, including 207 given MSCs and 196 controls). All 11 studies reported mortality and were pooled through random-effects meta-analysis. MSCs decreased relative risk of death at study endpoint (RR: 0.50 [95% CI, 0.34-0.75]) and RR of death at 28 days after treatment (0.19 [95% CI], 0.05-0.78) compared to controls. MSCs also decreased length of hospital stay (mean difference (MD: −3.97 days [95% CI, −6.09 to −1.85], n = 5 studies) and increased oxygenation levels at study endpoint compared to controls (MD: 105.62 mmHg O2 [95% CI, 73.9-137.3,], n = 3 studies). Only 2 of 11 studies reported on all International Society for Cellular Therapy (ISCT) criteria for MSC characterization. Included randomized controlled trials were found to have some concerns (n = 2) to low (n = 4) risk of bias (RoB), while all non-randomized studies were found to have moderate (n = 5) RoB.InterpretationOur updated living systematic review concludes that MSCs can likely reduce mortality in patients with severe or critical COVID-19. A master protocol based on our Faster Approval framework appears necessary to facilitate the more accelerated accumulation of high-quality evidence that would reduce RoB, improve consistency in product characterization, and standardize outcome reporting.
Anti-corruption barriers, financial transparency and accounting quality in political parties: Evidence from Spain

The aim of this paper is to study the transparency and quality of the Spanish political parties’ accounting information, as well as the factors that determine it, in a temporal context marked by the introduction of the institutional anti-corruption barriers, which harden the obligations in terms of financial transparency. The sample is composed by all the political organizations audited in the Auditors Court (AC) during the period analysed. The econometric model is estimated through the statistical methodologies of the linear Panel Data fixed effects and the ordered logistic regression, both with different types of accounting information. One formed with original accounting data, and another corrected and improved using the AC reports. The results show that the quality of the accounting information of the political parties is deficient and so is the level of the financial transparency. Likewise, the explanatory power of the models estimated with corrected accounting information is more significant, being the ratio of the private and public income, age, debt and the saving rate, variables that strongly affect the quality of the accounting information. Finally, the institutional anti-corruption barriers imposed in terms of financial transparency have not been enough. In fact, last year was the worst in the time series, since there is no party that discloses the quality financial information.                   Este trabajo estudia la transparencia y la calidad de la información contable de los partidos políticos españoles, así como los factores que condicionan la misma, en un contexto temporal marcado por la implementación una serie de barreras institucionales anticorrupción, que endurecen las obligaciones en materia de transparencia financiera. Se utiliza una muestra exhaustiva compuesta por todas las organizaciones políticas fiscalizadas por el Tribunal de Cuentas (TC) en el período analizado. Se implementan modelos lineales de Datos de Panel con efectos fijos y una regresión logística ordenada, ambos con diferentes tipos de información contable, una información original y otra corregida y mejorada utilizando los Informes del TC. Los resultados muestran que, la calidad de la información contable de los partidos es deficiente y, por lo tanto, también el nivel de transparencia financiera. Asimismo, el poder explicativo de los modelos que se estiman con información contable corregida es mayor, siendo la relación ingresos privados y públicos, la antigüedad, el endeudamiento y la tasa de ahorro, variables que afectan de forma significativa a la calidad de la información contable. Se concluye que las barreras institucionales anticorrupción impuestas en materia de transparencia financiera no han sido suficientes, siendo el último ejercicio evaluado, el peor de la serie estudiada, ya que no existe ningún partido que revele información financiera de calidad.
Modelling changes in species’ abundance in response to projected climate change

AbstractAim Existing climate envelope models give an indication of broad scale shifts in distribution, but do not specifically provide information on likely future population changes useful for conservation prioritization and planning. We demonstrate how these techniques can be developed to model likely future changes in absolute density and population size as a result of climate change.Location Great Britain.Methods Generalized linear models were used to model breeding densities of two northerly‐ and two southerly‐distributed bird species as a function of climate and land use. Models were built using count data from extensive national bird monitoring data and incorporated detectability to estimate absolute abundance. Projections of likely future changes in the distribution and abundance of these species were made by applying these models to projections of future climate change under two emissions scenarios.Results Models described current spatial variation in abundance for three of the four species and produced modelled current estimates of national populations that were similar to previously published estimates for all species. Climate change was projected to result in national population declines in the two northerly‐distributed species, with declines for Eurasian curlewNumenius arquataprojected to be particularly severe. Conversely, the abundances of the two southerly distributed species were projected to increase nationally. Projected maps of future abundance may be used to identify priority areas for the future conservation of each species.Main conclusions The analytical methods provide a framework to make projections of impacts of climate change on species abundance, rather than simply projected range changes. Outputs may be summarized at any spatial scale, providing information to inform future conservation planning at national, regional and local scales. Results suggest that as a consequence of climate change, northerly distributed bird species in Great Britain are likely to become an increasingly high conservation priority within the UK.
Optical implementation of neural network models and photon-echo effect

Properties of the photon-echo effect are theoretically investigated for implementation of neural network models in optics. Optical schemes of the HopfieXd's network, the second-order neural network as well as the neural network with retarded response are proposed on this basis. The importance of an additional time coordinate inherent in the photon-echo effect is shown.
Composable Rate-Independent Computation in Continuous Chemical Reaction Networks

Biological regulatory networks depend upon chemical interactions to process information. Engineering such molecular computing systems is a major challenge for synthetic biology and related fields. The chemical reaction network (CRN) model idealizes chemical interactions, allowing rigorous reasoning about the computational power of chemical kinetics. Here we focus on function computation with CRNs, where we think of the initial concentrations of some species as the input and the equilibrium concentration of another species as the output. Specifically, we are concerned with CRNs that are rate-independent (the computation must be correct independent of the reaction rate law) and composable ($f\circ g$ can be computed by concatenating the CRNs computing $f$ and $g$). Rate independence and composability are important engineering desiderata, permitting implementations that violate mass-action kinetics, or even "well-mixedness", and allowing the systematic construction of complex computation via modular design. We show that to construct composable rate-independent CRNs, it is necessary and sufficient to ensure that the output species of a module is not a reactant in any reaction within the module. We then exactly characterize the functions computable by such CRNs as superadditive, positive-continuous, and piecewise rational linear. Thus composability severely limits rate-independent computation unless more sophisticated input/output encodings are used.
Auditing Artificial Intelligence in Public Management: A Framework for Accountability, Transparency, and Ethical Governance

The integration of Artificial Intelligence (AI) in public management has transformed governmental operations, decision-making processes, and service delivery mechanisms. However, this technological adoption introduces unprecedented challenges in accountability, transparency, bias mitigation, and ethical governance. This article proposes a comprehensive framework for auditing AI systems in public sector contexts, addressing both technical and governance dimensions. Through systematic literature review and analysis of emerging regulatory frameworks, we identify critical audit domains including algorithmic transparency, data governance, decision explicability, bias detection, and compliance verification. The study examines case implementations in public procurement, social services, and administrative decision-making, drawing insights from international experiences and regulatory developments. Key findings reveal that effective AI auditing requires multi-dimensional approaches combining technical validation, ethical assessment, legal compliance verification, and stakeholder engagement. The proposed framework establishes methodological guidelines for internal auditors, external oversight bodies, and governance structures, emphasizing risk-based auditing strategies aligned with public sector accountability principles. This research contributes to the emerging discourse on AI governance in public administration by providing practical audit methodologies adaptable to diverse governmental contexts while maintaining democratic values and citizen protection standards.
Does Training on a Virtual Reality Robotic Simulator Improve Performance on the da Vinci                     <sup>®</sup>                     Surgical System?

The primary objective of this study is to determine if training on the Mimic dV-Trainer (MdVT) simulator results in improved ability on the da Vinci surgical system (dVSS) using exercises with inanimate objects.Twelve trainees (MdVT group) and 10 residents and one fellow (dVSS group) were recruited for the study. Each participant in the MdVT group completed one session of five exercises on the dVSS that were scored for timing and accuracy, followed by four training sessions on the MdVT, and concluded with a final session on the dVSS in which the initial exercises were repeated. Improvement on the dVSS exercises was compared with dVSS group who completed four to six training sessions using the same exercises on the dVSS without any simulator training.Both groups had similar significant improvements in the Letter Board and String Running exercises for both timing and accuracy. The MdVT group demonstrated significant improvement in the Pattern Cutting and Peg Board times. Only the dVSS group significantly improved in the Knot Tying time and the Peg Board accuracy.Training with the MdVT provided similar improvement on five exercises performed on the dVSS when compared with training on the dVSS alone. The use of this simulator in resident and student training may help bridge the gap between the safe acquisition of surgical skills and effective performance during live robot-assisted surgery.