Miracle-dz/UniFAR-FTUs huggingface dataset:
The effect of political turnover on corporate ESG performance: Evidence from China

This paper aims to investigate the effect of political turnover on corporate ESG performance in China. By analyzing data from Chinese A-share-listed companies between 2010 and 2020, we have discovered that changes in the municipal party committee secretary or the mayor of the prefecture-level city where a firm is located have a detrimental effect on corporate ESG performance. Compared with the change of the party committee, the change of mayor has a more pronounced negative impact on ESG performance. The reason behind this negative effect is primarily attributed to policy uncertainty, which leads to a decrease in governmental subsidies and an increase in ineffective under-investment by companies, consequently resulting in decreased corporate ESG performance. Furthermore, we have also observed that the adverse influence of political turnover on corporate ESG performance is relatively mitigated in SOEs, politically connected firms, and tertiary industries. These findings contribute to a deeper understanding of the relationship between political uncertainty and corporate behavior, particularly in emerging markets.
Limited-memory trust-region methods for sparse relaxation

In this paper, we solve the ℓ2-ℓ1 sparse recovery problem by transforming the objective function of this problem into an unconstrained differentiable function and applying a limited-memory trust-region method. Unlike gradient projection-type methods, which uses only the current gradient, our approach uses gradients from previous iterations to obtain a more accurate Hessian approximation. Numerical experiments show that our proposed approach eliminates spurious solutions more effectively while improving computational time.
The anti-inflammatory and haemodynamic effects of levosimendan on advanced heart failure patients: a meta-analysis of published studies

Objective Increasing evidence from randomized controlled trials shows the anti-inflammatory and haemodynamic effects of levosimendan in advanced heart failure (AdHF), however, conflicting results have been reported in some studies. The aim of this study was to estimate the anti-inflammatory and haemodynamic effects of levosimendan on AdHF (registration number: INPLASY202250097). Methods The MEDLINE, PubMed, ClinicalTrials.com and Cochrane Library databases were systematically searched for studies published in English up to April 2019. Data were extracted from applicable articles. Meta-analyses were performed to assess interleukin (IL)-6, cardiac index, pulmonary artery pressure (PAP) and New York Heart Association (NYHA) functional class efficacy outcomes, following PRISMA 2020 guidelines. Results A total of 11 studies were included (211 patients who received levosimendan and 193 controls). Meta-analyses showed that the levosimendan group displayed significantly reduced IL-6 (standardized mean difference [SMD] −1.05; 95% confidence interval [CI] −1.44, −0.66; I 2 = 50.9%), improved cardiac index (SMD 0.59; 95% CI 0.29, 0.88; I2 = 0.0%); reduced PAP (SMD −1.22; 95% CI −1.91, −0.53; I2 = 89.7%) and improved NYHA functional class (SMD −1.66; 95% CI −2.27, −1.04; I2 = 74.6%) versus controls. Conclusions Levosimendan infusion was beneficial in patients with AdHF, displayed by anti-inflammatory and improved haemodynamic effects, and improved NYHA functional class.
A resource for introducing students to the integral concept

ABSTRACT Many students do not have a deep understanding of the integral concept. This article defines what a deep understanding of the integral is in respect to integration involving one independent variable; briefly discusses factors which may inhibit such an understanding; and then describes the design of a mathematical resource for introducing students to the integral concept. The resource addresses a number of challenges when introducing the integral: (1) choosing an appropriate, intuitive context which gives meaning to the symbols in the integral expression; (2) aiding the transfer of the integral expression to different contexts via using the Riemann sum in an informal way so that students can see and interpret the rectangles which are inherent in this sum; and (3) the gradual formalizing of the Riemann sum and its linkage with the Fundamental Theorem of Calculus. The resource has been used over a number of years at this university amongst first-year undergraduate science and engineering students. Anecdotal evidence would suggest that the resource is beneficial.
A high‐order flux reconstruction adaptive mesh refinement method for magnetohydrodynamics on unstructured grids

We report our recent development of the high‐order flux reconstruction adaptive mesh refinement (AMR) method for magnetohydrodynamics (MHD). The resulted framework features a shock‐capturing duo of AMR and artificial resistivity (AR), which can robustly capture shocks and rotational and contact discontinuities with a fraction of the cell counts that are usually required. In our previous paper, we have presented a shock‐capturing framework on hydrodynamic problems with artificial diffusivity and AMR. Our AMR approach features a tree‐free, direct‐addressing approach in retrieving data across multiple levels of refinement. In this article, we report an extension to MHD systems that retains the flexibility of using unstructured grids. The challenges due to complex shock structures and divergence‐free constraint of magnetic field are more difficult to deal with than those of hydrodynamic systems. The accuracy of our solver hinges on 2 properties to achieve high‐order accuracy on MHD systems: removing the divergence error thoroughly and resolving discontinuities accurately. A hyperbolic divergence cleaning method with multiple subiterations is used for the first task. This method drives away the divergence error and preserves conservative forms of the governing equations. The subiteration can be accelerated by absorbing a pseudo time step into the wave speed coefficient, therefore enjoys a relaxed CFL condition. The AMR method rallies multiple levels of refined cells around various shock discontinuities, and it coordinates with the AR method to obtain sharp shock profiles. The physically consistent AR method localizes discontinuities and damps the spurious oscillation arising in the curl of the magnetic field. The effectiveness of the AMR and AR combination is demonstrated to be much more powerful than simply adding AR on finer and finer mesh, since the AMR steeply reduces the required amount of AR and confines the added artificial diffusivity and resistivity to a narrower and narrower region. We are able to verify the designed high‐order accuracy in space by using smooth flow test problems on unstructured grids. The efficiency and robustness of this framework are fully demonstrated through a number of two‐dimensional nonsmooth ideal MHD tests. We also successfully demonstrate that the AMR method can help significantly save computational cost for the Orszag‐Tang vortex problem.
Machine Learning Classification of Psychiatric Data Associated with Compensation Claims for Patient Injuries

Abstract Background Adverse events are common in health care. In psychiatric treatment, compensation claims for patient injuries appear to be less common than in other medical specialties. The most common types of patient injury claims in psychiatry include diagnostic flaws, unprevented suicide, or coercive treatment deemed as unnecessary or harmful. Objectives The objective was to study whether it is possible to form different categories of patient injury types associated with the psychiatric evaluations of compensation claims and to base machine learning classification on these categories. Further, the binary classification of positive and negative decisions for compensation claims was the other objective. Methods Finnish psychiatric specialist evaluations for the compensation claims of patient injuries were classified into six different categories called classes applying the machine learning methods of artificial intelligence. In addition, another classification of the same data into two classes was performed to test whether it was possible to classify data cases according to their known decisions, either accepted or declined compensation claim. Results The former classification task produced relatively good classification results subject to separating between different classes. Instead, the latter was more complex. However, classification accuracies of both tasks could be improved by using the generation of artificial data cases in the preprocessing phase before classifications. This preprocessing improved the classification accuracy of six classes up to 88% when the method of random forests was used for classification and that of the binary classification to 89%. Conclusion The results show that the objectives defined were possible to solve reasonably.
An artificial intelligence algorithm for the classification of sphenoid sinus pneumatisation on sinus computed tomography scans

Classifying sphenoid pneumatisation is an important but often overlooked task in reporting sinus CT scans. Artificial intelligence (AI) and one of its key methods, convolutional neural networks (CNNs), can create algorithms that can learn from data without being programmed with explicit rules and have shown utility in radiological image classification.
Mortality in Wild Turkeys (Meleagris gallopavo) Associated with Natural Infection with H5N1 Highly Pathogenic Avian Influenza Virus (HPAIV) Subclade 2.3.4.4

Abstract: A Eurasian strain of H5N1 highly pathogenic avian influenza virus (HPAIV) was first detected in North America in December 2021 and has since been confirmed in numerous wild and domestic avian species. In April 2022, 41 Wild Turkeys (Meleagris gallopavo) were found dead in Johnson County, Wyoming, USA adjacent to a property with confirmed HPAIV in a backyard poultry flock. Oropharyngeal swabs were collected from 11 of the 41 turkeys and necropsy was performed on seven. Avian influenza virus RNA was detected in all 11 turkeys by real-time reverse-transcription PCR. Acute, multiorgan necrosis was observed grossly and identified in all seven turkeys evaluated by histopathology, most consistently in the lung, spleen, liver, gastrointestinal tract, and gonads. Lesions indicate high virulence of subclade 2.3. 4.4b H5N1 HPAIV in Wild Turkeys, with infections presenting as clusters of acute mortality. Although documented cases of HPAIV in Wild Turkeys are rare, these findings signify a risk of spillback from domestic poultry, which may be heightened by the recent rise in backyard poultry ownership and the use of peridomestic habitat by wild birds. Additional research is needed to better understand the risk of disease transmission at the interface of Wild Turkeys and backyard poultry and the potential conservation and management implications of HPAIV in wild gallinaceous birds.
Examining the validity and reliability of the Persian version of the MiND‐B questionnaire in ALS patients

In addition to affecting the nerves and muscles, amyotrophic lateral sclerosis (ALS) disease also affects the behavior and cognition of patients. In this study, we examine the validity and reliability of the Persian version of Motor Neuron Disease Behavioral instrument (MiND‐B) questionnaire to investigate behavioral changes in Persian‐speaking ALS patients.
The Devil Is in the Detail: Clinical Practice Guideline for the Optimal Timing of Elective Hip or Knee Arthroplasty

What is the optimal timing for hip or knee total joint arthroplasty (TJA) in people with symptomatic and moderate-to-severe radiographic osteoarthritis (OA) or osteonecrosis (ON) who have not responded to nonoperative treatments? The new clinical practice guidelines by the American College of Rheumatology (ACR) and American Academy of Hip and Knee Surgeons (AAHKS) (1), published in this issue of Arthritis & Rheumatology , sought to provide evidence-based recommendations to address this question. This is an important topic. With rising prevalence of symptomatic knee OA, including at younger ages, the rates and associated costs of TJA are skyrocketing (2). Policies that restrict access to TJA have emerged to combat rising TJA costs, ideally while also ensuring access and quality of care. The new recommendations aim to inform shared decision-making (SDM) between patients and clinicians regarding TJA while also challenging health insurance coverage policies that restrict timely access to TJA. The conditional recommendations are based on low or very low-quality evidence. Thus, they provide little substance to guide TJA shared decision-making, other than under-scoring the importance of SDM in the context of TJA. However, they do serve to highlight the tremendous burden on patients imposed by advanced, symptomatic hip and knee arthritis and the injustice of imposing undue delays to receipt of TJA to candidates deemed “ appropriate ” for surgery. In essence, the
Accounting for Medicaid expansion and regional policy and programs to advance equity in cancer prevention in the United States

Many studies compare state‐level outcomes to estimate changes attributable to Medicaid expansion. However, it is imperative to conduct more granular, demographic‐level analyses to inform current efforts on cancer prevention among low‐income adults. Therefore, the authors compared the volume of patients with cancer and disease stage at diagnosis in Ohio, which expanded its Medicaid coverage in 2014, with those in Georgia, a nonexpansion state, by cancer site and health insurance status.
Discussion of “Optimal test procedures for multiple hypotheses controlling the familywise expected loss” by Willi Maurer, Frank Bretz, and Xiaolei Xun

We provide commentary on the paper by Willi Maurer, Frank Bretz, and Xiaolei Xun entitled, “Optimal test procedures for multiple hypotheses controlling for the familywise expected loss. ” The authors provide an excellent discussion of the multiplicity problem in clinical trials and propose a novel approach based on a decision‐theoretic framework that incorporates loss functions that can vary across multiple hypotheses in a family. We provide some considerations for the practical use of the authors' proposed methods as well as some alternative methods that may also be of interest in this setting.
Classifying Free Texts Into Predefined Sections Using AI in Regulatory Documents: A Case Study with Drug Labeling Documents

The US Food and Drug Administration (FDA) regulatory process often involves several reviewers who focus on sets of information related to their respective areas of review. Accordingly, manufacturers that provide submission packages to regulatory agencies are instructed to organize the contents using a structure that enables the information to be easily allocated, retrieved, and reviewed. However, this practice is not always followed correctly; as such, some documents are not well structured, with similar information spreading across different sections, hindering the efficient access and review of all of the relevant data as a whole. To improve this common situation, we evaluated an artificial intelligence (AI)-based natural language processing (NLP) methodology, called Bidirectional Encoder Representations from Transformers (BERT), to automatically classify free-text information into standardized sections, supporting a holistic review of drug safety and efficacy. Specifically, FDA labeling documents were used in this study as a proof of concept, where the labeling section structure defined by the Physician Label Rule (PLR) was used to classify labels in the development of the model. The model was subsequently evaluated on texts from both well-structured labeling documents (i.e., PLR-based labeling) and less- or differently structured documents (i.e., non-PLR and Summary of Product Characteristic [SmPC] labeling.) In the training process, the model yielded 96% and 88% accuracy for binary and multiclass tasks, respectively. The testing accuracies observed for the PLR, non-PLR, and SmPC testing data sets for the binary model were 95%, 88%, and 88%, and for the multiclass model were 82%, 73%, and 68%, respectively. Our study demonstrated that automatically classifying free texts into standardized sections with AI language models could be an advanced regulatory science approach for supporting the review process by effectively processing unformatted documents.
Enhancing stochastic kriging for queueing simulation with stylized models

ABSTRACT Stochastic kriging is a popular metamodeling technique to approximate computationally expensive simulation models. However, it typically treats the simulation model as a black box in practice and often fails to capture the highly nonlinear response surfaces that arise from queueing simulations. We propose a simple, effective approach to improve the performance of stochastic kriging by incorporating stylized queueing models that contain useful information about the shape of the response surface. We provide several statistical tools to measure the usefulness of the incorporated stylized models. We show that even a relatively crude stylized model can substantially improve the prediction accuracy of stochastic kriging.
Noncanonical binding of transcription factors: time to revisit specificity?

Transcription factors (TFs) are one of the most studied classes of DNA-binding proteins that have a direct functional impact on gene transcription and thus, on human physiology and disease. The mechanisms that TFs use for recognizing target DNA binding sites have been studied for nearly five decades, yet they remain poorly understood. It is classically assumed that a TF recognizes a specific sequence pattern, or motif, as its binding sites. However, recent studies are consistently finding examples of noncanonical binding, that is, TFs binding at sites that do not resemble their sequence motifs. Here we review the current literature on four major types of noncanonical TF binding, namely binding based on DNA shape readout, at Guanine-quadruplex structures, at repeat sequences, and bispecific binding. These examples point to a critical need for studies to unify our current observations, many of which are at odds with the “one TF, one motif” view, into a more comprehensive definition of the DNA-binding specificity of TFs.
Photorespiration: regulation and new insights on the potential role of persulfidation

Abstract Photorespiration has been considered a ‘futile’ cycle in C3 plants, necessary to detoxify and recycle the metabolites generated by the oxygenating activity of Rubisco. However, several reports indicate that this metabolic route plays a fundamental role in plant metabolism and constitutes a very interesting research topic. Many open questions still remain with regard to photorespiration. One of these questions is how the photorespiratory process is regulated in plants and what factors contribute to this regulation. In this review, we summarize recent advances in the regulation of the photorespiratory pathway with a special focus on the transcriptional and post-translational regulation of photorespiration and the interconnections of this process with nitrogen and sulfur metabolism. Recent findings on sulfide signaling and protein persulfidation are also described.
RABIES VIRUS SEROSURVEY OF THE SMALL INDIAN MONGOOSE (URVA AUROPUNCTATA) ACROSS MULTIPLE HABITATS IN PUERTO RICO, 2014–21

Abstract: The small Indian mongoose (Urva auropuncata) is a rabies reservoir in Puerto Rico and accounts for over 70% of reported animal rabies cases annually. The presence of rabies virus–neutralizing antibodies (RVNA) is often used as a tool to measure exposure to rabies virus in wildlife populations. We conducted a serosurvey of mongooses at 11 sites representing six habitat types across Puerto Rico. We collected a serum sample from 464 individual mongooses during 2014–21. Overall, 80/464 (17.0%; 95% confidence interval, 14.1–20.9%; 55 male, 23 female, and two sexes not recorded) of individual mongooses sampled across all habitats were RVNA positive. The geometric mean (SD) RVNA titer for 80 unique seropositive animals was 0.58 (2.92) IU/mL. Our models indicated that the probability of mongooses being RVNA seropositive mostly varied by habitat, with some influence of sex in the individual-level analyses. Population-level RVNA seroprevalence is dynamic in mongoose populations, but these data may shed light on rabies virus transmission across regions to help inform rabies management activities in Puerto Rico.
An Exploratory Study of Nutrition Knowledge and Challenges Faced by Informal Carers of Community-Dwelling People with Dementia: Online Survey and Thematic Analysis

Nutrition knowledge is a primary factor influencing food choices and the ability to identify nutritional risk for carers of people with dementia. Acquiring nutrition knowledge helps carers monitor changes in food intake and micronutrient intake, and whether a healthy and balanced diet is being consumed. This study aimed to assess the nutrition knowledge of carers in the Australian community and their experiences with nutrition education. Using a mixed-methods approach, the nutrition knowledge of informal carers was assessed using the revised General Nutrition Knowledge Questionnaire (AUS-R-NKQ), and interviews of informal carers were used to explore the perspectives in a sub-sample. A total of 57 carers (44 females; mean age of 63.0 ± 13.1) completed the survey, and 11 carers participated in follow-up interviews. The total sample scored 22.9 (±4.57) out of 38 on the AUS-R-NKQ, suggesting basic nutrition knowledge. The interviewed carers acknowledged the importance of healthy nutrition but viewed the provision of a healthy diet for a person with dementia as challenging. In both the survey and interviews, carers reported limited use and availability of dementia-specific nutrition resources. Carers were unsatisfied with the advice and number of referrals provided to improve the nutrition of the person with dementia and desired less confusing nutrition education materials adapted to their level of knowledge. The present study highlights the need for informal carers to be supported to acquire adequate nutrition knowledge.
The effect of COVID-19 on utilization of chronic diseases services

Objectives The aim of the present study is to evaluate the impact of Covid-19 on utilization of chronic diseases services. Methods Interrupted time-series design was used to examine the utilization of chronic diseases services before and during the Covid-19 pandemic among hospitals in Iran. Chronic obstructive pulmonary disease (COPD), asthma, type 2 diabetes, heart failure, and chemotherapy were selected as a proxy to indicate the impact of Covid-19 on utilization of chronic diseases services. Data were collected in 24 sites from 12 months before the onset of Covid-19 (from March 2019 to February 2020) to 12 months during the Covid-19 pandemic (February 2020 to March 2021). Results A total of 7,039,378 services were provided, of which 51.92% were provided for women and 62.73% for >65 age group. A sudden decrease was observed in monthly utilization of services during the Covid-19 pandemic; ranging from 13.91 (95% CI = −21.73, 6.10, P = 0.001) for chemotherapy to 606.39 (95% CI = −1040.72, 172.06, P = 0.009) for heart failure services per 100 thousand population. A decrease was observed in COPD services; 15.28 services compared with the period before Covid-19. Subsequently, the monthly utilization trends of asthma, type 2 diabetes, and chemotherapy services increased significantly (P < 0.05). Discussion Although chronic diseases are a factor in more severe form of Covid-19, their failure to seek diagnostic, prevention and treatment services has somewhat complicated the issue.