Search

Introduction: This study presents a global research scenario in the broad domain of osteoarthritis (OA) research, using quantitative and qualitative publication and citation indicators. Methods: The study is based on 45,368 global publications, sourced from the Scopus bibliographical database, covering three decades (1994–2023). We studied the performance of the top 12 developed and top 12 developing countries. The key countries, organizations and authors at national and international levels were identified. The broad subject areas and key journals contributing to global OA research were delineated, besides identifying the broad characteristics of highly cited papers in the field. Results: The United States and China were the most productive countries, while the Netherlands and Canada made the largest citation impact. Harvard Medical School and the University of Sydney made the most contribution, while Boston University and Pfizer Inc., USA registered the highest citation impact. Hunter DJ and Guermazi A were the most productive authors, while Lohmander LS, and Hochberg MC registered the highest citation impact. Osteoarthritis and Cartilage (n = 4879) and Annals of the Rheumatic Diseases (n = 786) published the maximum papers, while Arthritis and Rheumatism and Nature Reviews Rheumatology registered the largest citation impact. The highly cited papers with 100 or more citations constituted 6.25% of the total publications. Conclusions: There has been a systematic growth of publications on OA. The research on OA was mainly done in developed countries, with the maximum publications coming from the United States of America, China and Canada. The most impactful publications on OA were from the Netherlands, Canada and the United States of America. [ABSTRACT FROM AUTHOR]

Background: Adolescent subjective well-being is a topic that has gained significant focus over recent years. This focus is reflected in forming measurement tools and interventions to better understand better and improve adolescent mental health. While these steps are necessary, problems still exist regarding adolescent subjective well-being measurement. Notably, current measurement tools are limited in their content, applicability to various populations, and accessibility. Aims: This paper examines the psychometric properties of the Survey on Flourishing (SURF) when used with an adolescent sample from the United States. Method: A sample of 334 participants participated in the present study. We examined the reliability and validity of the SURF by examining its internal consistency, convergent validity, and discriminant validity. We also examined the factor structure of the SURF using a confirmatory factor analysis (CFA). Results: The SURF demonstrated high internal consistency (α = 0.92), a strong positive correlation with convergent measures, and a weak negative correlation with a discriminant measure. A one-factor model best fits the observed data. Conclusion: The SURF demonstrated good psychometric properties and addressed several shortcomings in current measures. Preliminary data suggests the SURF may be a useful and practical measure of adolescent subjective well-being. [ABSTRACT FROM AUTHOR]

Breast cancer is one of the tumors with the highest prevalence rate among women in the world, and its BRCA1/2 gene is a common mutation site. Talazoparib, as a targeted PARP inhibitor, can effectively control the occurrence and development of breast cancer with BRCA1/2 gene mutation, and play a therapeutic role. Based on the findings from the Phase III EMBRACE trial (NCT01945775 clinical trial), our analysis reveals that the talazoparib group demonstrated a significant extension in progression-free survival, along with improved response markers and patient-reported outcomes when compared to conventional therapies. This study aims to assess the cost-effectiveness of talazoparib for treating advanced breast cancer with germline BRCA1/2 mutations and HER2 negativity, considering the perspectives of health services in China and the United States. The results obtained will serve as a valuable reference for promoting rational drug utilization and enhancing medical resource efficiency. To evaluate the cost-effectiveness of Talazoparib more scientifically and provide clinicians with chemotherapy options, this paper developed a Markov model based on the EMBRACA clinical trial (clinical Trails.gov No., NCT01945775) to simulate the survival events of breast cancer patients in the Talazoparib group and the standard treatment group. The state transition probability and clinical data of breast cancer patients during treatment were extracted from the phase III EMBRACA clinical trial. The cost data generated during the treatment process comes from local hospital pricing, other references, and expert consultation. This article uses US dollars to calculate the treatment cost and incremental cost-effectiveness ratio. Health outcomes are expressed in Quality Adjusted Life Years (QALYs). In addition, Outcomes were measured in quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratio, which robustness was evaluated by deterministic and probabilistic sensitivity analyses. This article establishes a Markov model for single-item sensitivity analysis. The results show that the economic benefits of using Talazoparib as a new treatment strategy in both China and the United States are higher than other drugs, and it is cost-effective. Compared to the control group, the incremental cost incurred by the Talazoparib treatment group in China was $2484.48/QALY, with an incremental QALY of 1.5. However, Talazoparib in the United States holds a dominant position, saving costs of $10,223.43 and increasing QALYs by 1.5. The clinical treatment effect of Talazoparib group in BRCA1/2 mutant advanced breast cancer patients is better than that of the standard treatment group, and the progression free survival period is significantly prolonged. From the perspective of medical and health services in China and the United States, the Talazoparib group is more economical than the standard treatment group in treating patients with BRCA1/2 mutant advanced breast cancer. [ABSTRACT FROM AUTHOR]

Background: Diabetes is a prevalent chronic disease. Although self-care is the crucial element in managing diabetes, older Korean immigrants with diabetes face challenges in performing effective self-care related to vulnerability as minority immigrants. Purpose: This study measures sociodemographics, self-efficacy, social support, diabetes knowledge, and diabetes self-care activities among older Korean immigrants in the United States. This study also aims to demonstrate the direct and indirect effects of the related factors on diabetes self-care activities using a path analysis. Methods: This study uses a cross-sectional design. Convenience sampling targeted Korean immigrants aged 55 or older using paper and online surveys. Four instruments were used to measure variables: self-efficacy was measured by the General Self-Efficacy scale, diabetes knowledge by the Simplified Diabetes Knowledge Test, social support by the Lubben Social Network Scale-6, and diabetes self-care by the Summary of Diabetes Self-Care Activities questionnaire. Using path analysis, the effects of related factors on self-care activities were analyzed. Results: 190 older Korean immigrants participated, 53.2% female, and 46.8% male. The mean age was 67.2 (SD = 9.9; range, 58–93). A path model shows that sociodemographics (sex, age, education, and years in the United States), diabetes knowledge, self-efficacy, and family support predict diabetes self-care. Conclusions: The path model demonstrates the effects of sociodemographics, self-efficacy, diabetes knowledge, and social support on diabetes self-care among older Korean immigrants. The findings can help to understand diabetes self-care among the minority ethnic older group and can be used to develop culturally tailored education, counseling, and healthcare services. [ABSTRACT FROM AUTHOR]

This paper investigates the sensitivity of golfers' performance to meteorological conditions at the men's US Masters tournament over the 40-year period 1980–2019. The mean and standard deviation of round scores are related to local temperature, humidity, wind speed and direction, and concurrent and antecedent precipitation. Mean scores are more dependent on weather conditions than the variability of scores in a given round. The best predictor of mean scores is the wet-bulb temperature in rounds one and two, and the zonal wind speed in rounds three and four. Across both sets of rounds (1 and 2, and 3 and 4), the wet-bulb temperature is a better predictor of mean scores than the air temperature, which implies that atmospheric moisture content affects scores. In general, golfers take fewer shots and so perform better in warmer and calmer conditions. The synergestic effect of several weather variables explains over 44% of the variance in mean scores. Mean meteorological conditions during play are a much better predictor of the players' average performance than the standard deviation of the weather variables. The golfers' performance becomes more variable in cooler conditions with a wider range of scores. Precipitation during play and the dampness of the ground (as quantified by rainfall up to ten days before play) do not have a consistent and statistically significant effect on the competitors' performance. In short, this paper demonstrates that golf scores are dependent on weather conditions. [ABSTRACT FROM AUTHOR]

Introduction: The emergence of artificial intelligence (AI) chat programs has opened two distinct paths, one enhancing interaction and another potentially replacing personal understanding. Ethical and legal concerns arise due to the rapid development of these programs. This paper investigates academic discussions on AI in medicine, analyzing the context, frequency, and reasons behind these conversations. Methods: The study collected data from the Web of Science database on articles containing the keyword "ChatGPT" published from January to September 2023, resulting in 786 medically related journal articles. The inclusion criteria were peer-reviewed articles in English related to medicine. Results: The United States led in publications (38.1%), followed by India (15.5%) and China (7.0%). Keywords such as "patient" (16.7%), "research" (12%), and "performance" (10.6%) were prevalent. The Cureus Journal of Medical Science (11.8%) had the most publications, followed by the Annals of Biomedical Engineering (8.3%). August 2023 had the highest number of publications (29.3%), with significant growth between February to March and April to May. Medical General Internal (21.0%) was the most common category, followed by Surgery (15.4%) and Radiology (7.9%). Discussion: The prominence of India in ChatGPT research, despite lower research funding, indicates the platform's popularity and highlights the importance of monitoring its use for potential medical misinformation. China's interest in ChatGPT research suggests a focus on Natural Language Processing (NLP) AI applications, despite public bans on the platform. Cureus' success in publishing ChatGPT articles can be attributed to its open-access, rapid publication model. The study identifies research trends in plastic surgery, radiology, and obstetric gynecology, emphasizing the need for ethical considerations and reliability assessments in the application of ChatGPT in medical practice. Conclusion: ChatGPT's presence in medical literature is growing rapidly across various specialties, but concerns related to safety, privacy, and accuracy persist. More research is needed to assess its suitability for patient care and implications for non-medical use. Skepticism and thorough review of research are essential, as current studies may face retraction as more information emerges. [ABSTRACT FROM AUTHOR]

This paper investigates the homogeneity of United States aircraft reconnaissance data and the impact of these data on the homogeneity of the tropical cyclone (TC) best track data for the seasons 1949–1987 generated by the China Meteorological Administration (CMA). The evaluation of the reconnaissance data shows that the minimum central sea level pressure (MCP) data are relatively homogeneous, whereas the maximum sustained wind (MSW) data show both overestimations and spurious abrupt changes. Statistical comparisons suggest that both the reconnaissance MCP and MSW were well incorporated into the CMA TC best track dataset. Although no spurious abrupt changes were evident in the reconnaissance-related best track MCP data, two spurious changepoints were identified in the remainder of the best-track MCP data. Furthermore, the influence of the reconnaissance MSWs seems to extend to the best track MSWs unrelated to reconnaissance, which might reflect the optimistic confidence in making higher estimates due to the overestimated extreme wind "observations". In addition, the overestimation of either the reconnaissance MSWs or the best track MSWs was greater during the early decades compared to later decades, which reflects the important influence of reconnaissance data on the CMA TC best track dataset. The wind–pressure relationship (WPR) used in the CMA TC best track dataset is also evaluated and is found to overestimate the MSW, which may lead to inhomogeneity within the dataset between the aircraft reconnaissance era and the satellite era. [ABSTRACT FROM AUTHOR]

In most of the United States, insurance companies may use gender to determine car insurance rates. In addition, several studies have shown that women over the age of 25 generally pay more than men for car insurance. Then, we investigate whether the distributions of claims for women and men differ in location, scale and shape by means of the GAMLSS regression framework, using microdata provided by U.S. and Australian insurance companies, to use this evidence to support policy makers' decisions. We also develop a parametric-bootstrap test to investigate the tail behavior of the distributions. When covariates are not considered, the distribution of claims does not appear to differ by gender. When covariates are included, the regressions provide mixed evidence for the location parameter. However, for female claimants, the spread of the distribution is lower. Our research suggests that, at least for the contexts analyzed, there is no clear statistical reason for charging higher rates to women. While providing evidence to support unisex insurance pricing policies, given the limitations represented by the use of country-specific data, this paper aims to promote further research on this topic with different datasets to corroborate our findings and draw more general conclusions. [ABSTRACT FROM AUTHOR]

This present paper aims to examine various epidemiological aspects of the monkeypox viral infection using a fractional-order mathematical model. Initially, the model is formulated using integer-order nonlinear differential equations. The imperfect vaccination is considered for human population in the model formulation. The proposed model is then reformulated using a fractional order derivative with power law to gain a deeper understanding of disease dynamics. The values of the model parameters are determined from the cumulative reported monkeypox cases in the United States during the period from May 10th to October 10th, 2022. Besides this, some of the demographic parameters are evaluated from the population of the literature. We establish sufficient conditions to ensure the existence and uniqueness of the model's solution in the fractional case. Furthermore, the stability of the endemic equilibrium of the fractional monkeypox model is presented. The Lyapunov function approach is used to demonstrate the global stability of the model equilibria. Moreover, the fractional order model is numerically solved using an efficient numerical technique known as the fractional Adams-Bashforth-Moulton method. The numerical simulations are conducted using estimated parameters, considering various values of the fractional order of the Caputo derivative. The finding of this study reveals the impact of various model parameters and fractional order values on the dynamics and control of monkeypox. [ABSTRACT FROM AUTHOR]

An assessment of the likelihood of use and abuse potential for new tobacco products is an important part of tobacco product regulation in the United States and abroad. This paper reports the results of a randomized, open-label, crossover clinical study that assessed factors related to product adoption and abuse liability (AL), comparing two closed electronic nicotine delivery system (ENDS) products to combustible cigarettes and nicotine gum, high- and low-AL comparator products, respectively. During an 11-day confinement period that included multiple product familiarization sessions, healthy adult smokers participated in AL test sessions to evaluate the abuse liability of each product. During these test sessions, changes in subjective measures; speed and amount of nicotine uptake; and maximum changes in physiological effects before, during, and after use of each assigned product were assessed over 4 h. Positive subjective effects measures scores such as product-liking and overall intent to use again were highest for cigarettes, followed by the Vuse ENDS, with nicotine gum consistently having the lowest scores. The PK results (Cmax and Tmax) of the Vuse ENDS products are between UB cigarettes and nicotine gum, which correlates with the subjective effects. All nicotine uptake measures for the Vuse ENDS products were lower than that of usual brand (UB) cigarettes, including peak nicotine uptake and overall nicotine uptake, and were either similar to or lower than nicotine gum. The time course of nicotine uptake after use of the ENDS was more similar to that of combustible cigarettes than nicotine gum. The results indicate that the AL of each ENDS product is lower than that of UB cigarettes and similar to that of nicotine gum. [ABSTRACT FROM AUTHOR]

In the United States, state-funded preschool is a critical component of both K-12 public education and the early childhood education and care system. In 2021, 44 states and the District of Columbia operated 63 school/center-based preschool programs serving over 1.3 million children. The vast majority of state-funded preschool programs require two adults in each classroom: typically, a lead teacher and an assistant teacher, with an expectation for team teaching. This paper examines the trends and variations in state policies that address the qualifications requirements, compensation, and professional development supports for preschool lead and assistant teachers in state-funded preschool programs that provide a foundation for supporting a culture of collective success and potential quality of the classroom. [ABSTRACT FROM AUTHOR]

This present paper aims to examine various epidemiological aspects of the monkeypox viral infection using a fractional-order mathematical model. Initially, the model is formulated using integer-order nonlinear differential equations. The imperfect vaccination is considered for human population in the model formulation. The proposed model is then reformulated using a fractional order derivative with power law to gain a deeper understanding of disease dynamics. The values of the model parameters are determined from the cumulative reported monkeypox cases in the United States during the period from May 10th to October 10th, 2022. Besides this, some of the demographic parameters are evaluated from the population of the literature. We establish sufficient conditions to ensure the existence and uniqueness of the model's solution in the fractional case. Furthermore, the stability of the endemic equilibrium of the fractional monkeypox model is presented. The Lyapunov function approach is used to demonstrate the global stability of the model equilibria. Moreover, the fractional order model is numerically solved using an efficient numerical technique known as the fractional Adams-Bashforth-Moulton method. The numerical simulations are conducted using estimated parameters, considering various values of the fractional order of the Caputo derivative. The finding of this study reveals the impact of various model parameters and fractional order values on the dynamics and control of monkeypox. [ABSTRACT FROM AUTHOR]

Background: Tremendous progress has been made in the field of cartilage repair and regeneration, particularly with tissue-engineering approaches. This study aims to estimate the global status and current trends in the field of cartilage tissue engineering. Methods: Publications from 2011 to 2020 on tissue engineering for cartilage repair and regeneration were retrieved from Web of Science Core Collection database. The source data were statistically evaluated based on the bibliometrics. In terms of visualized analysis, some bibliometric indicators such as bibliographic coupling, co-citation, co-authorship and co-occurrence analysis were performed by VOSviewer software, to investigate the research trends in tissue engineering for cartilage repair and regeneration. Results: In total, 3715 papers were included. Since 2011, the amount of issued papers and relative research interest (RRI) have grown by leaps and bounds globally. The United States was the biggest contributor to the research in this field, due to the greatest citation frequency, the highest H index and the strongest total link strength. Romania had the highest average citation for each. The journal Tissue Engineering Part A published most articles in this field. For institutions, the largest contributors were Shanghai Jiaotong University, University of California System and Sichuan University. Studies could all be grouped into four main clusters: study of biomaterial scaffolds, study of seeding cells and growth factors, experimental animal model and clinical study, and mechanism research. Conclusion: Great efforts should be put into the study of biomaterial scaffolds, seeding cells and growth factors, considered to be the next hot topics in cartilage tissue engineering. This findings provide collaborative insights and research orientation for academic researchers, surgeons and healthcare practitioners to a certain extent. [ABSTRACT FROM AUTHOR]

COVID-19 has dramatically changed people's mobility geste patterns and affected the operations of different functional spots. In the environment of the successful reopening of countries around the world since 2022, it's pivotal to understand whether the reopening of different types of locales poses a threat of wide epidemic transmission. In this paper, by establishing an epidemiological model based on mobile network data, combining the data handed by the Safegraph website, and taking into account the crowd inflow characteristics and the changes of susceptible and latent populations, the trends of the number of crowd visits and the number of epidemic infections at different functional points of interest after the perpetration of continuing strategies were simulated. The model was also validated with daily new cases in ten metropolitan areas in the United States from March to May 2020, and the results showed that the model fitted the evolutionary trend of realistic data more accurately. Further, the points of interest were classified into risk levels, and the corresponding reopening minimum standard prevention and control measures were proposed to be implemented according to different risk levels. The results showed that restaurants and gyms became high-risk points of interest after the perpetration of the continuing strategy, especially the general dine-in restaurants were at higher risk levels. Religious exertion centers were the points of interest with the loftiest average infection rates after the perpetration of the continuing strategy. Points of interest such as convenience stores, large shopping malls, and pharmacies were at a lower risk for outbreak impact after the continuing strategy was enforced. Based on this, continuing forestallment and control strategies for different functional points of interest are proposed to provide decision support for the development of precise forestallment and control measures for different spots. [ABSTRACT FROM AUTHOR]

Access to healthcare continues to be a top priority and prominent challenge in rural communities, with 20% of the total U.S. population living in rural areas while only 10% of physicians practice in rural areas. In response to physician shortages, a variety of programs and incentives have been implemented to recruit and retain physicians in rural areas; however, less is known about the types and structures of incentives that are offered in rural areas and how that compares to physician shortages. The purpose of our study is to conduct a narrative review of the literature to identify and compare current incentives that are offered by rural physician shortage areas to better understand how resources are being allocated to vulnerable areas. We reviewed published peer-reviewed articles from 2015–2022 to identify incentives and programs designed to address physician shortages in rural areas. We augment that review by examining the gray literature, including reports and white papers on the topic. Identified incentive programs were aggregated for comparison and translated into a map that depicts high, medium, and low levels of geographically designated Health Professional Shortage Areas (HPSAs) and the number of incentives offered by state. Surveying current literature regarding different types of incentivization strategies while comparing to primary care HPSAs provides general insights on the potential influence of incentive programs on shortages, allows easy visual review, and may provide greater awareness of available support for potential recruits. Providing a broad overview of the incentives offered in rural areas will help illuminate whether diverse and appealing incentives are offered in the most vulnerable areas and guide future efforts to address these issues. [ABSTRACT FROM AUTHOR]

The prolonged COVID-19 pandemic has tied up significant medical resources, and its management poses a challenge for the public health care decision making. Accurate predictions of the hospitalizations are crucial for the decision makers to make informed decision for the medical resource allocation. This paper proposes a method named County Augmented Transformer (CAT). To generate accurate predictions of four-week-ahead COVID-19 related hospitalizations for every states in the United States. Inspired by the modern deep learning techniques, our method is based on a self-attention model (known as the transformer model) that is actively used in Natural Language Processing. Our transformer based model can capture both short-term and long-term dependencies within the time series while enjoying computational efficiency. Our model is a data based approach that utilizes the publicly available information including the COVID-19 related number of confirmed cases, deaths, hospitalizations data, and the household median income data. Our numerical experiments demonstrate the strength and the usability of our model as a potential tool for assisting the medical resources allocation. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic is a global health concern that has spread around the globe. Machine Learning is promising in the fight against the COVID-19 pandemic. Machine learning and artificial intelligence have been employed by various healthcare providers, scientists, and clinicians in medical industries in the fight against COVID-19 disease. In this paper, we discuss the impact of the Covid-19 pandemic on alcohol consumption habit changes among healthcare workers in the United States during the first wave of the Covid-19 pandemic. We utilize multiple supervised and unsupervised machine learning methods and models such as decision trees, logistic regression, support vector machines, multilayer perceptron, XGBoost, CatBoost, LightGBM, AdaBoost, Chi-Squared Test, mutual information, KModes clustering and the synthetic minority oversampling technique on a mental health survey data obtained from the University of Michigan Inter-University Consortium for Political and Social Research to investigate the links between COVID-19-related deleterious effects and changes in alcohol consumption habits among healthcare workers. Through the interpretation of the supervised and unsupervised methods, we have concluded that healthcare workers whose children stayed home during the first wave in the US consumed more alcohol. We also found that the work schedule changes due to the Covid-19 pandemic led to a change in alcohol use habits. Changes in food consumption, age, gender, geographical characteristics, changes in sleep habits, the amount of news consumption, and screen time are also important predictors of an increase in alcohol use among healthcare workers in the United States. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic is a global health concern that has spread around the globe. Machine Learning is promising in the fight against the COVID-19 pandemic. Machine learning and artificial intelligence have been employed by various healthcare providers, scientists, and clinicians in medical industries in the fight against COVID-19 disease. In this paper, we discuss the impact of the Covid-19 pandemic on alcohol consumption habit changes among healthcare workers in the United States during the first wave of the Covid-19 pandemic. We utilize multiple supervised and unsupervised machine learning methods and models such as decision trees, logistic regression, support vector machines, multilayer perceptron, XGBoost, CatBoost, LightGBM, AdaBoost, Chi-Squared Test, mutual information, KModes clustering and the synthetic minority oversampling technique on a mental health survey data obtained from the University of Michigan Inter-University Consortium for Political and Social Research to investigate the links between COVID-19-related deleterious effects and changes in alcohol consumption habits among healthcare workers. Through the interpretation of the supervised and unsupervised methods, we have concluded that healthcare workers whose children stayed home during the first wave in the US consumed more alcohol. We also found that the work schedule changes due to the Covid-19 pandemic led to a change in alcohol use habits. Changes in food consumption, age, gender, geographical characteristics, changes in sleep habits, the amount of news consumption, and screen time are also important predictors of an increase in alcohol use among healthcare workers in the United States. [ABSTRACT FROM AUTHOR]

In the present paper we generalise the classical newsvendor problem for critical perishable commodities having more severe costs than its linear alternative. Piece wise polynomial cost functions are introduced to accommodate the excess severity. Stochastic demand is assumed to follow a completely unknown probability distribution. Non parametric estimator of the optimal order quantity has been developed from an estimating equation using a random sample. Strong consistency of the estimator is proved for unique optimal order quantity and the result is extended for multiple solutions. Simulation results indicate that non parametric estimator is efficient in terms of mean square error. Real life application of the proposed non-parametric estimator has been demonstrated with Avocado demand in the United States of America and Covid-19 test kit demand during second wave of SARS-COV2 pandemic across 86 countries. [ABSTRACT FROM AUTHOR]

Purpose: The SART CORS database is an informative source of IVF clinic-specific linked data that provides cumulative live birth rates from medically assisted reproduction in the United States (US). These data are used to develop best practice guidelines, for research, quality assurance, and post-market surveillance of assisted reproductive technologies. Here, we sought to investigate the key areas of current research focus (higher-order categories), discover gaps or underserved areas of ART research, and examine the potential application and impact of newer ART adjuvants, future data collection, and analysis needs. Methods: We conducted a systematic review (PRISMA guidelines) to quantify unique output metrics of the SART CORS database. Included were SART member reporting clinics: full-length publications from 2004 to 2021 and conference abstracts from 2015 to 2021, the two key timepoints when the SART CORS database underwent transformative shifts in data collection. Results: We found 206 abstracts presented from 2015 to 2021, 189 full-length peer-reviewed publications since 2004, with 654 unique authors listed on these publications. A total of 19 publications have been highly impactful, garnering over 100 citations at the time of writing. Several higher-order categories, such as endometriosis and tubal infertility, have few publications. The conversion of conference abstracts to full-length papers ranged from 15 to 35% from 2015 to 2021. Conclusions: A substantial body of literature has been generated by analyzing the SART CORS database. Full-length publications have increased year over year. Some topic areas, such as endometriosis and tubal infertility, may be underrepresented. Conversion of conference abstracts to full-length publications has been low, indicating that more organizational support may be needed to ensure that research is methodologically sound and researchers supported to reach full publication status. [ABSTRACT FROM AUTHOR]

In recent decades, data-driven approaches have been developed to analyze demographic and economic surveys on a large scale. Despite advances in multivariate techniques and learning methods, in practice the analysis and interpretations are often focused on a small portion of available data and limited to a single perspective. This paper aims to utilize a selected array of multivariate statistical learning methods in the analysis of income and expenditure patterns of households in the United States using the Public-Use Microdata from the Bureau of Labor Statistics Consumer Expenditure Survey (CE). The objective is to propose an effective data pipeline that provides visualizations and comprehensive interpretations for applications in governmental regulations and economic research, using thirty-five original survey variables covering the categories of demographics, income and expenditure. Details on feature extraction not only showcase CE as a unique publicly-shared big data resource with high potential for in-depth analysis, but also assist interested researchers with pre-processing. Challenges from missing values and categorical variables are treated in the exploratory analysis, while statistical learning methods are comprehensively employed to address multiple economic perspectives. Principal component analysis suggests that after-tax income, wage/salary income, and the quarterly expenditure in food, housing and overall as the five most important of the selected variables, while cluster analysis identifies and visualizes the implicit structure between variables. Based on this, canonical correlation analysis reveals high correlation between two selected groups of variables, one of income and the other of expenditure. [ABSTRACT FROM AUTHOR]

Background: Amyotrophic lateral sclerosis (ALS) is a progressive and fatal neuromuscular disease with no curative therapies. Edaravone (Radicava®) (Mitsubishi Tanabe Pharma Corporation, Tokyo, Japan), approved in the United States (US) for ALS in adults in 2017, was shown in a clinical trial to slow the rate of physical functional decline in ALS and is administered intravenously. The aim of this paper is to summarize the observed safety profile from real-world patient use during the first 3 years of edaravone availability in the US. Methods: Edaravone usage data were collected, and adverse events (AEs) were identified from a postmarketing safety database from August 8, 2017 through August 7, 2020 (cutoff date). Results: As of October 3, 2020, 5207 ALS patients had been treated with edaravone. As of August 7, 2020, the most commonly reported AEs included death (not specified), drug ineffective, disease progression, therapeutic response unexpected, fall, asthenia, fatigue, muscular weakness, gait disturbance, and dyspnea. The most commonly reported serious AEs (SAEs) included death (not specified), pneumonia, disease progression, ALS, fall, dyspnea, respiratory failure, device-related infection, hospitalization, and injection-site infection. There were 687 deaths, with 494 reported as death without specifying the cause. Deaths were most commonly attributed to ALS, disease progression, respiratory failure, or pneumonia. Review for administration-site reactions revealed 95 AEs, including 34 site infections, with 22 SAEs (all non-fatal). Five non-fatal SAEs of anaphylaxis were reported. Conclusion: In the postmarketing reporting to date, no new safety signals were identified beyond those already known from the edaravone clinical trial program. [ABSTRACT FROM AUTHOR]

As the COVID-19 spread over the globe and new variants of COVID-19 keep occurring, reliable real-time forecasts of COVID-19 hospitalizations are critical for public health decisions on medical resources allocations. This paper aims to forecast future 2 weeks national and state-level COVID-19 new hospital admissions in the United States. Our method is inspired by the strong association between public search behavior and hospitalization admissions and is extended from a previously-proposed influenza tracking model, AutoRegression with GOogle search data (ARGO). Our LASSO-penalized linear regression method efficiently combines Google search information and COVID-19 related time series information with dynamic training and rolling window prediction. Compared to other publicly available models collected from COVID-19 forecast hub, our method achieves substantial error reduction in a retrospective out-of-sample evaluation from Jan 4, 2021, to Dec 27, 2021. Overall, we showed that our method is flexible, self-correcting, robust, accurate, and interpretable, making it a potentially powerful tool to assist healthcare officials and decision making for the current and future infectious disease outbreaks. [ABSTRACT FROM AUTHOR]

Though preventable, children with disabilities have a high risk of victimization, contributing to worsening health conditions. Hence, this study examined the exposure of school-age children with mental, emotional, developmental, or behavioral (MEDB) disorder to bully victimization. This study used the 2018 National Survey of Children's Health (NSCH) data of 23,494 children ages 5–17 to estimate multilevel logistic regression with fixed and random effects. Children's health conditions were treated as level one variables, while family poverty level and neighborhood characteristics such as vandalism and presence/absence of recreational centers were treated as level two variables. The paper presents the prevalence of bullying victimization among children with at least one disorder (MDBB = 39.5%), anxiety (20.6%), depression (10.8%), ADD/ADHD (18.3%), behavioral problems (14.9%), learning disability (11.9%), Tourette syndrome (0.5%), developmental delay (10.1%), Autism spectrum disorder (4.6%), speech disorder (10.7), and intellectual disability (1.6%), respectively. Bullying victimization was positively associated with anxiety (AOR = 1.995, 95% CI = 1.634–2.436), depression (AOR = 2.688, 95% CI = 2.031–3.557), developmental delay (AOR = 1.804, 95% CI = 1.422–2.288), but inversely associated with Autism spectrum disorder (AOR = 0.614, 95% CI = 0.399–0.946). Neighborhood disorganization and poverty were also associated with bullying victimization. The NSCH data suggests that children with disabilities in the US had a higher prevalence rate of victimization. Consequently, effective bullying prevention strategies that can protect and improve children's quality of life with special needs should be prioritized. [ABSTRACT FROM AUTHOR]

Synthetic organic colourants are extremely prominent in heritage collections, particularly throughout textiles. They are often generalized as highly light-sensitive; although, a broad distribution of lightfastness exists. This is evident in various fastness ratings published in late nineteenth century literature, the work of Schultz and Julius, and the Colour Index. In heritage conservation, much of the research related to light-sensitivity of dyed textiles has focused on natural colourants. This is likely due to a general interest in the dyes present on older objects, and the overwhelming selection of modern synthetic materials. To address this gap, a shortlist of targeted synthetic dyes was recently developed using census data for dye production and imports in the United States, and information gathered from the Colour Index. Our present work provides a follow up to the prior literature review, where a subset of samples from the published target list was used to evaluate lightfastness. To begin the study, a collection of dyed textiles from the last quarter of the nineteenth century was gathered from trade publications of the period, and those matching the target criteria were reserved for analysis. The lightfastness of more than 100 early synthetic dyes was then investigated using the historic materials. Test specimens were illuminated with a custom-built fadometer containing an LED light source that is characteristic of modern gallery lighting. Four batches of samples were exposed at 20 klx for approximately 6 months each, and diffuse reflectance was periodically measured with a portable spectrophotometer over white and black backgrounds. Measurements were taken at increasing light dose values to reach at least 85 Mlx·h per batch. Visible reflectance spectra were recorded in a dataset to document the colours as a function of light exposure, and foster future colorimetry research. Colour change was also calculated using the CIEDE2000 formula to assess lightfastness, and results were summarized as a resource to further develop risk analysis tools for exhibit lighting. In this paper, a detailed description of the test apparatus is presented with an overview of the lightfastness results and the resulting dataset. Comparisons are given with nineteenth century lightfastness data, and ISO values available in the Colour Index. It is hoped that the associated dataset will provide a foundation to be expanded over time with interest in further materials and test methods. [ABSTRACT FROM AUTHOR]

Thousands of people are reported lost in the wilderness in the United States every year and locating these missing individuals as rapidly as possible depends on coordinated search and rescue (SAR) operations. As time passes, the search area grows, survival rate decreases, and searchers are faced with an increasingly daunting task of searching large areas in a short amount of time. To optimize the search process, mathematical models of lost person behavior with respect to landscape can be used in conjunction with current SAR practices. In this paper, we introduce an agent-based model of lost person behavior which allows agents to move on known landscapes with behavior defined as independent realizations of a random variable. The behavior random variable selects from a distribution of six known lost person reorientation strategies to simulate the agent's trajectory. We systematically simulate a range of possible behavior distributions and find a best-fit behavioral profile for a hiker with the International Search and Rescue Incident Database. We validate these results with a leave-one-out analysis. This work represents the first time-discrete model of lost person dynamics validated with data from real SAR incidents and has the potential to improve current methods for wilderness SAR. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to determine the impact of rare disaster risks, captured by the El Niño-Southern Oscillation (ENSO) cycle, on the volatility of Treasury securities of the United States (US) involving 1- to 360-month maturities. We use a random coefficient panel-data-based heterogeneous autoregressive-realized variance (HAR-RV) model over the monthly period of 1961:06 to 2019:12, with the monthly RV derived from the sum of squared daily changes in yield within a month. Our results show a positive and statistically significant (at the 1% level) impact of the ENSO cycle on RV, with the results being robust to alternative metrics of the ENSO, consideration of lagged values, and decomposition of the ENSO cycle into El Niño and La Niña phases, with the former having a relatively stronger effect. Based on the panel estimation method using heterogeneous slope coefficients, we find that the impact on the entire term-structure is positive yet stronger at the two-ends and the middle-part of the term-structure. Our findings have important implications for investors in US Treasury securities. [ABSTRACT FROM AUTHOR]

Algorithmic vision, the computational process of making meaning from digital images or visual information, has changed the relationship between the image and the human subject. In this paper, I explicate on the role of algorithmic vision as a technique of algorithmic governance, the organization of a population by algorithmic means. With its roots in the United States post-war cybernetic sciences, the ontological status of the computational image undergoes a shift, giving way to the hegemonic use of automated facial recognition technologies towards predatory policing and profiling practices. By way of example, I argue that algorithmic vision reconfigures the philosophical links between vision, image, and truth, paradigmatically changing the way a human subject is represented through imagistic data. With algorithmic vision, the relationship between subject and representation challenges the humanistic discourse around images, calling for a critical displacement of the human subject from the center of an analysis of how computational images make meaning. I will explore the relationship between the operative image, the image that acts but is not seen by human eyes, and what Louise Amoore calls an "emergent subject," a subject that is made visible through algorithmic techniques (2013). Algorithmic vision reveals subjects to power in a mode that requires a new approach towards analyzing the entanglement and invisiblization of the human in automated decision-making systems. [ABSTRACT FROM AUTHOR]

This paper extends the understanding of the relation between ADHD and reading comprehension, through examining how this relation differs depending on the quantile an individual falls in for each. Samples from three twin projects around the United States were used (Florida Twin Project, Colorado component of International Longitudinal Twin Study of Early Reading Development, & Western Reserve Reading and Math Projects). Phenotypic analysis using quantile regression showed relations between ADHD related behaviors and reading comprehension to be stronger in the lower quantiles of reading comprehension in two of three samples. A new method was developed extending this analysis into the bivariate genetic space. Results of this quantile genetic analysis revealed that overlapping common environmental influences accounted for a larger proportion of variance in the lower quantiles of these variables in two of three samples. Finally, in all three samples the phenotypic relation was strongest when shared environmental influences accounted for a larger proportion of the overall variance. [ABSTRACT FROM AUTHOR]

Millions commute to work every day in cities and interact with colleagues, partners, friends, and strangers. Commuting facilitates the mixing of people from distant and diverse neighborhoods, but whether this has an imprint on social inclusion or instead, connections remain assortative is less explored. In this paper, we aim to better understand income sorting in social networks inside cities and investigate how commuting distance conditions the online social ties of Twitter users in the 50 largest metropolitan areas of the United States. An above-median commuting distance in cities is linked to more diverse individual networks, moreover, we find that longer commutes are associated with a nearly uniform, moderate reduction of overall social tie assortativity across all cities. This suggests a universal relation between long-distance commutes and the integration of social networks. Our results inform policy that facilitating access across distant neighborhoods can advance the social inclusion of low-income groups. [ABSTRACT FROM AUTHOR]

The sustainability of large dams has been questioned on several grounds. One aspect that has been less explored is that the development of dams and reservoirs often enables agricultural expansion and urban growth, which in turn increase water consumption. As such, dam development influences, while being influenced by, the spatial and temporal distribution of both supply and demand of water resources. In this paper, we explore the interplay between large dams, patterns of population growth and agricultural expansion in the United States over the past two centuries. Based on a large-scale analysis of spatial and temporal trends, we identify three distinct phases, in which different processes dominated the interplay. Then, we focus on agricultural water use in the Southwest region (Arizona, California and Nevada) and explore chicken-and-egg dynamics where water supply partly meets and partly fuels water demand. Lastly, we show that the legacy of dams in the United States consists of a lock-in condition characterized by high levels of water consumption, especially in the Southwest, which leads to severe water crises and groundwater overexploitation when droughts occur. [ABSTRACT FROM AUTHOR]

Our source code to process and combine these data sources, as well as the resulting combined dataset itself, is part of the Supplementary Information." now reads: "The raw data that support the findings in this paper are openly available at https://usda.library.cornell.edu/concern/publications/rn301137d (United States Department of Agriculture), https://nass.usda.gov/Research%5fand%5fScience/Cropland/Release/index.php (United States Department of Agriculture Cropland Data Layer), and https://www.prism.oregonstate.edu/ (Parameter-elevation Regressions on Independent Slopes Model Climate Group). "The raw data that support the findings in this paper are openly available at https://usda.library.cornell.edu/concern/publications/rn301137d (United States Department of Agriculture), https://nass.usda.gov/Research%5fand%5fScience/Cropland/Release/index.php (United States Department of Agriculture Cropland Data Layer), and https://prism.oregonstate.edu (Parameter-elevation Regressions on Independent Slopes Model Climate Group). [Extracted from the article]