ethicalabs/Research-Intent-Collab huggingface dataset:
Hydrogeochemistry and Isotope Hydrology of Surface Water and Groundwater in the Mountain Watersheds of Daqing River, North China

Surface water and groundwater interaction variations in time and space are crucial for effective water management, especially in low-precipitation regions. To comprehensively determine the hydrochemical characteristics and interaction processes of surface water and groundwater and to investigate the decreasing causes of water resources in semi-arid mountainous watersheds under changing environments, intensive field surveys were conducted in the Daqing River watershed, a tributary of the Haihe River basin in northern China, during two different times of the year: after the rainy season (September 2018) and before the rainy season (July 2019). Sixty surface water and groundwater samples were collected along the mountainous watershed. Using a combination method of hydrogen and oxygen stable isotope tracing and hydrochemical analysis, the hydrogen and oxygen isotopes and hydrochemical characteristics of surface water and groundwater in the mountainous watershed of the Daqing River were analyzed. Furthermore, the effect of elevation (altitude) on isotopes was discussed, and the correlation between hydrogen and oxygen isotope composition and hydrochemical characteristics was obtained. The results were processed using endmember mixing analysis to determine the amount of contribution of the surface water and groundwater interaction processes. The results show that the hydrochemical characteristics are relatively stable in the mountainous watersheds of the Daqing River, and the surface water and groundwater are mainly of the HCO3-Ca type. The slope of the local meteoric water line is smaller than the slope of the global meteoric water line, and the δD and δ18O in surface water and groundwater show a good linear relationship both before and after the rainy season. There is a decreasing trend of the value of δ18O in surface water samples with decreasing altitude, but a decreasing trend of the value of δ18O in groundwater samples is not obvious. The evaporation intensity of surface water is stronger after the rainy season than before the rainy season, and the connection between the surface water and the groundwater is stronger before the rainy season. Influenced by topographic conditions and other factors, the exchange of surface water and groundwater is frequent, and there is a large difference in the exchange ratio before and after the rainy season. The exchange ratio can be more than 50% after the rainy season. Thus, the reasons for decreasing water resources in the mountains can be implied to be due to the increasing hydraulic gradient between the mountains and the piedmont plains, and the water resources are discharged more in the form of groundwater to the downstream. The conclusions help to enhance the understanding of the water cycle in the mountainous watershed and can provide some theoretical basis for the sustainable development and utilization of water resources in the Haihe River basin and the regional water ecology of the Xiong’an New Area.
Institutional Phase Transitions and Power Concentration. A Statistical Mechanics Model of Economic Networks.

Institutional instability and the concentration of economic power are often analyzed through historical or qualitative frameworks. In this work we propose a quantitative model that interprets institutional collapse as a phase transition in a complex socio-economic system.   We introduce an order parameter $D = MH$ which combines the capital share of income $M$ with a structural concentration index $H$. The micro-level dynamics of power shares are modeled through replicator dynamics on an influence network, while the macroscopic behavior of the system is described by an effective Landau free energy potential.   Within this framework we show that the institutional system exhibits a continuous phase transition between dispersed and concentrated power regimes. The stochastic dynamics of the order parameter are derived using a Langevin formulation, leading to a Fokker–Planck equation whose stationary solution corresponds to a Boltzmann-type distribution over institutional states.   The model predicts the emergence of critical thresholds, hysteresis effects, universal scaling laws, and early warning signals such as variance divergence and critical slowing down. Numerical experiments and empirical illustrations highlight the bifurcation structure and the stochastic evolution of the order parameter across different regimes.  The results suggest that the interaction between macroeconomic resource distribution and network concentration may generate universal patterns of institutional instability analogous to critical phenomena in statistical physics, situating the framework within the mean-field universality class.
Impact of integrated palliative care model on end-of-life (EOL) quality metrics for patients with kidney cancer (RCC) and melanoma (M).

137  Background: Early palliative care (PC) improves quality of life (QOL) and enhances end-of-life (EOL) care, but the optimal timing and most effective model for integrating PC into oncologic care is uncertain. To understand the impact of an integrated model with PC providers embedded with oncologists vs. usual care (UC) with referral at the discretion of the same oncologists, we examined the timing and delivery of PC and Quality Oncology Practice Initiative (QPOI) EOL metrics among patients with RCC and M in a single clinic. We hypothesized that integrated PC would result in more referrals, earlier contact with PC and better QOPI EOL metrics compared with UC. Methods: In a retrospective cohort study of patients with RCC and M in the Beth Israel Deaconess Biologics Clinic who expired between 10/1/12 and 12/31/14, we compared patients seen 2 days/week, when referral to PC was discretionary, with a third day when PC providers shared the clinic for real-time consultations. Patients were identified as meeting PC eligibility if they had recurrent, metastatic disease and were on active treatment or had a symptom severity of 7+ on Edmonton Symptom Assessment Scale (ESAS). Two oncologists saw all patients, regardless of day. Results: Seventy-six patients expired, 19 in the Integrated PC model and 57 with UC. Patients were similar with respect to diagnosis and demographics except for smoking. The integrated model substantially improved timing and location of PC. In the integrated PC model, 85% were seen by PC compared with 45% in UC (P = 0.002). All patients in the integrated model began PC as an outpatient compared with 36% in UC (P &lt; 0.001). The mean number of days from first PC contact to death was 28 (SD = 54) for UC and 118 (SD = 120) with integrated PC (P &lt; 0.001). The location of death did not differ significantly between models, occurring outside the hospital with hospice among 71% of patients in the integrated model and 53% in UC (P = 0.25). Results were similar in relative risk models adjusted for smoking. Conclusions: A practice model that integrated PC with oncologic care was associated with more PC referrals, earlier contact, and a nonsignificant trend toward fewer deaths in hospital and ICU.
AI Governance in Saudi Arabia: Cultural Values and Ethical AI Regulations in Comparative Perspective

Abstract                                        This country survey examines Saudi Arabia’s approach to artificial intelligence (                     AI                     ) governance, focusing on the regulatory and ethical frameworks that shape its                     AI                     ecosystem. The study situates Saudi Arabia’s                     AI                     policies within the broader context of Vision 2030, emphasising the role of the Saudi Data and Artificial Intelligence Authority (                     SDAIA                     ) in developing guidelines for                     AI                     ethics and generative                     AI                     applications. The Kingdom’s                     AI                     strategy is characterised by a balance between cultural values, international                     AI                     ethics standards, and economic development goals. Unlike rigid regulatory models, Saudi Arabia’s                     AI                     governance adopts a flexible, principle-based approach, incorporating voluntary compliance incentives such as motivational badges. The survey also contrasts Saudi Arabia’s                     AI                     governance with other major regulatory models, including those of the European Union, the United States, and China. The findings highlight the Kingdom’s goal to position itself as a global                     AI                     hub while ensuring alignment with national priorities and ethical considerations.
Asset freezing at the International Criminal Court and the United Nations Security Council

The International Criminal Court (ICC) and the United Nations Security Council (UNSC) are both empowered to request States to freeze individuals’ assets. The ICC can do so with respect to persons accused of the crimes under its jurisdiction, while the UNSC can make such requests in order to maintain or restore international peace and security. Regardless of their duration, such measures necessarily infringe upon the targets’ rights. But the longer assets are frozen, the more acute these infringements can become. ICC-requested asset freezes can endure from the issuance of a warrant of arrest until the accused person is acquitted or convicted, whereas UNSC-ordered measures can persist until international peace and security is restored. Asset freezes executed at the behest of the ICC and the UNSC are therefore rarely short in duration. The focus of this thesis rests on the two bodies’ exercise of their asset freezing powers, with a particular emphasis on the legal protections available to those individuals at the receiving end of the measures at the disposal of the ICC and the UNSC, respectively. The thesis concludes that the asset freezing measures implemented by States at the request of the two bodies are largely consistent with the rights of the individuals against whom they are imposed. At the same time, certain problems remain as far as the protracted application of these procedures are concerned. In view of this conclusion, the thesis details two recommendations that the two institutions could consider in order to respond to the issues identified.
Deep Learning versus Classical Regression for Brain Tumor Patient Survival Prediction

Deep learning for regression tasks on medical imaging data has shown promising results. However, compared to other approaches, their power is strongly linked to the dataset size. In this study, we evaluate 3D-convolutional neural networks (CNNs) and classical regression methods with hand-crafted features for survival time regression of patients with high grade brain tumors. The tested CNNs for regression showed promising but unstable results. The best performing deep learning approach reached an accuracy of 51.5% on held-out samples of the training set. All tested deep learning experiments were outperformed by a Support Vector Classifier (SVC) using 30 radiomic features. The investigated features included intensity, shape, location and deep features. The submitted method to the BraTS 2018 survival prediction challenge is an ensemble of SVCs, which reached a cross-validated accuracy of 72.2% on the BraTS 2018 training set, 57.1% on the validation set, and 42.9% on the testing set. The results suggest that more training data is necessary for a stable performance of a CNN model for direct regression from magnetic resonance images, and that non-imaging clinical patient information is crucial along with imaging information.
Inequities in Health Insurance Coverage and Access for Black and Hispanic Adults: The Impact of Medicaid Expansion and the Pandemic

This brief analyzes coverage and access inequities for Black and Hispanic adults in the U.S., focusing on the effects of Medicaid expansion and COVID-19 policies.
Gradual Domain Adaptation for Segmenting Whole Slide Images Showing Pathological Variability

Although there is a strong demand, the utilization of automated segmentation approaches in histopathological imaging is often inhibited by a high degree of variability. To tackle the thereby arising challenges, we propose an unsupervised “gradual” domain adaptation framework which exploits the knowledge that disease progression is a gradual process and that the approximate level-of-progression is known. We extend an existing approach by adding two methods for regularization of the fully-unsupervised adaptation process. Experiments performed on three datasets corresponding to three different renal pathologies showed excellent segmentation accuracies. The framework is not restricted to the considered task, but can also be adapted to other similar (biomedical) application scenarios.
Figure 2 from Blood-Based Genomic Alteration Signature for Predicting Progression-Free Survival in &lt;i&gt;De Novo&lt;/i&gt; Metastatic Hormone-Sensitive Prostate Cancer: A Real-World Study

&lt;p&gt;Genomic landscape and concordance of alterations in tumor tissue and ctDNA. AR, androgen receptor; Doc, docetaxel; N/A, not applicable.&lt;/p&gt;
One Health antimicrobial resistance: stewardship in Australia

Antimicrobial stewardship (AMS) is promoted as a core set of actions that aim to mitigate the emergence of antimicrobial resistance, but to also ensure safety and quality use of antimicrobials. By improving the appropriate use of antimicrobials we can improve patient care, reduce adverse effects and perhaps slow the emergence of antimicrobial resistance. However, changing behaviour of prescribers is challenging, with barriers and facilitators often common across diverse prescribing groups – doctors and veterinarians. Many of the physical challenges facing prescribers are also shared and lessons can be learned about implementation of interventions between groups. In this article we discuss qualitative and quantitative research, and interventions in AMS, in Australia from a One Health perspective.
EXPLAINABLE DEEP LEARNING MODELS FOR MEDICAL IMAGE DIAGNOSISBRIDGING ACCURACY AND INTERPRETABILITY

The growing use of deep learning models in diagnosing medical images has resulted in impressive gains in terms of the accuracy in diagnosis. Still, the lack of transparency in these models tends to increase the resistance of these models in clinical use. This paper will fill the accuracy-interpretability gap by creating explainable deep learning models that do not affect predictive accuracy. An explainability methodology was incorporated into a convolutional neural network (CNN) architecture using the state-of-the-art explainability methods, Gradient-weighted Class Activation Mapping (Grad-CAM), Local Interpretable Model-Agnostic Explanations (LIME), and Integrated Gradients, to visualize model reasoning and locate important diagnostic regions of medical images. Benchmark radiology and histopathology data sets were evaluated experimentally to assess their accuracy and F1-score, interpretability measures, and the level of clinician agreement. The proposed model attained a diagnostic accuracy of 95.8 and an interpretability score of 0.89, which indicates a high similarity of model explanations and expert annotations. Using an explainability tool compared to the control group led to a marked increase in clinical trust and comprehension of model predictions. The results highlight that deep learning systems can be highly diagnostic and, at the same time, produce a transparent decision-making process. The study can be considered significant to developing human-centric artificial intelligence in healthcare as it contributes to accountability, reliability, and interpretability in medical image analysis.
Effect of Medicaid Expansion on Health Insurance Coverage and Access to Care Among Adults With Depression

Multiple studies have detailed the relationship between Medicaid expansion under the Affordable Care Act and various health and financial outcomes. However, fewer studies have examined Medicaid expansion's effects on individuals with psychiatric diagnoses. This study sought to determine the relationship between Medicaid expansion and various health and financial outcomes among low-income adults with depression.This quasi-experimental study used a random-digit-dial survey of U.S. citizens ages 19-64 with incomes below 138% of the federal poverty level. Surveys were conducted in three southern states (two expansion states, Arkansas and Kentucky, and one nonexpansion state, Texas) between 2013 and 2016. The study sample consisted of those with a positive screen for depression-score of ≥2 on the two-item Patient Health Questionnaire (N=4,853). Survey-weighted difference-in-differences regressions were conducted with insurance status, health care access and utilization, and affordability of care as outcomes of interest. Subgroup analyses stratified the sample on the basis of the respondent's residence in a health professional shortage area (HPSA) in mental health and severity of depression.Medicaid expansion was associated with a significant reduction in the proportion of adults with depression who lacked health insurance (-23 percentage points, 95% confidence interval=-32 to -14, p<.001). Medicaid expansion was also associated with significant reductions in delaying care and medications because of cost. These changes were similar regardless of residence in a mental health HPSA and severity of depression.Medicaid expansion was associated with improved access to care and medication among persons with depression, even in areas with relative shortages of mental health professionals.
Uncertainty, imprecision, and the precautionary principle in climate change assessment

Statistical decision theory can provide useful support for climate change decisions made under conditions of uncertainty. However, the probability distributions used to calculate expected costs in decision theory are themselves subject to uncertainty, disagreement, or ambiguity in their specification. This imprecision can be described using sets of probability measures, from which upper and lower bounds on expectations can be calculated. However, many representations, or classes, of probability measures are possible. We describe six of the more useful classes and demonstrate how each may be used to represent climate change uncertainties. When expected costs are specified by bounds, rather than precise values, the conventional decision criterion of minimum expected cost is insufficient to reach a unique decision. Alternative criteria are required, and the criterion of minimum upper expected cost may be desirable because it is consistent with the precautionary principle. Using simple climate and economics models as an example, we determine the carbon dioxide emissions levels that have minimum upper expected cost for each of the selected classes. There can be wide differences in these emissions levels and their associated costs, emphasizing the need for care when selecting an appropriate class.
Gene Editing with CRISPR/Cas Methodology and Thyroid Cancer: Where Are We?

Important advances on the role of genetic alterations in thyroid cancer have been achieved in the last two decades. One key reason is linked to the development of technical approaches that allowed for the mimicking of genetic alterations in vitro and in vivo and, more recently, the gene editing methodology. The CRISPR/Cas methodology has emerged as a tangible tool for editing virtually any DNA sequence in the genome. To induce a double-strand break and programmable gene editing, Cas9 endonuclease is guided by a single-guide RNA (sgRNA) that is complementary to the target sequence in DNA. The gene editing per se occurs as the cells repair the broken DNA and may erroneously change the original DNA sequence. In this review, we explore the principles of the CRISPR/Cas system to facilitate an understanding of the mainstream technique and its applications in gene editing. Furthermore, we explored new applications of CRISPR/Cas for gene modulation without changing the DNA sequence and provided a Dry Lab experience for those who are interested in starting “CRISPRing” any given gene. In the last section, we will discuss the progress in the knowledge of thyroid cancer biology fostered by the CRISPR/Cas gene editing tools.
Hawkes processes for credit indices time series analysis: How random are trades arrival times?

Targeting a better understanding of credit market dynamics, the authors have studied a stochastic model named the Hawkes process. Describing trades arrival times, this kind of model allows for the capture of self-excitement and mutual interactions phenomena. The authors propose here a simple yet conclusive method for fitting multidimensional Hawkes processes with exponential kernels, based on a maximum likelihood non-convex optimization. The method was successfully tested on simulated data, then used on new publicly available real trading data for three European credit indices, thus enabling quantification of self-excitement as well as volume impacts or cross indices influences.
Large Synoptic Survey Telescope: Dark Energy Science Collaboration

This white paper describes the LSST Dark Energy Science Collaboration (DESC), whose goal is the study of dark energy and related topics in fundamental physics with data from the Large Synoptic Survey Telescope (LSST). It provides an overview of dark energy science and describes the current and anticipated state of the field. It makes the case for the DESC by laying out a robust analytical framework for dark energy science that has been defined by its members and the comprehensive three-year work plan they have developed for implementing that framework. The analysis working groups cover five key probes of dark energy: weak lensing, large scale structure, galaxy clusters, Type Ia supernovae, and strong lensing. The computing working groups span cosmological simulations, galaxy catalogs, photon simulations and a systematic software and computational framework for LSST dark energy data analysis. The technical working groups make the connection between dark energy science and the LSST system. The working groups have close linkages, especially through the use of the photon simulations to study the impact of instrument design and survey strategy on analysis methodology and cosmological parameter estimation. The white paper describes several high priority tasks identified by each of the 16 working groups. Over the next three years these tasks will help prepare for LSST analysis, make synergistic connections with ongoing cosmological surveys and provide the dark energy community with state of the art analysis tools. Members of the community are invited to join the LSST DESC, according to the membership policies described in the white paper. Applications to sign up for associate membership may be made by submitting the Web form at http://www.slac.stanford.edu/exp/lsst/desc/signup.html with a short statement of the work they wish to pursue that is relevant to the LSST DESC.
Stochastic Resonance of Accretion Disk and the Persistent Low-Frequency Quasi-Periodic Oscillations in Black Hole X-ray Binaries

In this paper, we use a Langevin type equation with a damping term and stochastic force to describe the stochastic oscillations on the vertical direction of the accretion disk around a black hole, and calculate the luminosity and power spectral density (PSD) for an oscillating disk. Then we discuss the stochastic resonance (SR) phenomenon in PSD curves for different parameter values of viscosity coefficient, accretion rate, mass of black hole and outer radius of the disk. The results show that our simulated PSD curves of luminosity for disk oscillation have the same profile as the observed PSD of black hole X-ray binaries (BHXBs) in the lowhard state, and the SR of accretion disk oscillation may be an alternative interpretation of the persistent low-frequency quasi-periodic oscillations (LFQPOs).