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Air emissions from the U.S. pulp and paper sector have been federally regulated since 1978; however, regulations are periodically reviewed and revised to improve efficiency and effectiveness of existing emission standards. The Industrial Sectors Integrated Solutions (ISIS) model for the pulp and paper sector is currently under development at the U.S. Environmental Protection Agency (EPA), and can be utilized to facilitate multi-pollutant, sector-based analyses that are performed in conjunction with regulatory development. The model utilizes a multi-sector, multi-product dynamic linear modeling framework that evaluates the economic impact of emission reduction strategies for multiple air pollutants. The ISIS model considers facility-level economic, environmental, and technical parameters, as well as sector-level market data, to estimate the impacts of environmental regulations on the pulp and paper industry. Specifically, the model can be used to estimate U.S. and global market impacts of new or more stringent air regulations, such as impacts on product price, exports and imports, market demands, capital investment, and mill closures. One major challenge to developing a representative model is the need for an extensive amount of data. This article discusses the collection and processing of data for use in the model, as well as the methods used for building the ISIS pulp and paper database that facilitates the required analyses to support the air quality management of the pulp and paper sector. [ABSTRACT FROM AUTHOR]

Background: Citation data can be used to evaluate the editorial policies and procedures of scientific journals. Here we investigate citation counts for the three different publication tracks of the Proceedings of the National Academy of Sciences of the United States of America (PNAS). This analysis explores the consequences of differences in editor and referee selection, while controlling for the prestige of the journal in which the papers appear. Methodology/Principal Findings: We find that papers authored and ''Contributed'' by NAS members (Track III) are on average cited less often than papers that are ''Communicated'' for others by NAS members (Track I) or submitted directly via the standard peer review process (Track II). However, we also find that the variance in the citation count of Contributed papers, and to a lesser extent Communicated papers, is larger than for direct submissions. Therefore when examining the 10% most-cited papers from each track, Contributed papers receive the most citations, followed by Communicated papers, while Direct submissions receive the least citations. Conclusion/Significance: Our findings suggest that PNAS ''Contributed'' papers, in which NAS-member authors select their own reviewers, balance an overall lower impact with an increased probability of publishing exceptional papers. This analysis demonstrates that different editorial procedures are associated with different levels of impact, even within the same prominent journal, and raises interesting questions about the most appropriate metrics for judging an editorial policy's success. [ABSTRACT FROM AUTHOR]

The soybean cyst nematode (SCN), Heterodera glycines, is the most damaging pathogen of soybeans in the United States. To assess the severity of nematode infestations in the field, SCN egg population densities are determined. Cysts (dead females) of the nematode must be extracted from soil samples and then ground to extract the eggs within. Sucrose centrifugation commonly is used to separate debris from suspensions of extracted nematode eggs. We present a method using OptiPrep as a density gradient medium with improved separation and recovery of extracted eggs compared to the sucrose centrifugation technique. Also, computerized methods were developed to automate the identification and counting of nematode eggs from the processed samples. In one approach, a high-resolution scanner was used to take static images of extracted eggs and debris on filter papers, and a deep learning network was trained to identify and count the eggs among the debris. In the second approach, a lensless imaging setup was developed using off-the-shelf components, and the processed egg samples were passed through a microfluidic flow chip made from double-sided adhesive tape. Holographic videos were recorded of the passing eggs and debris, and the videos were reconstructed and processed by custom software program to obtain egg counts. The performance of the software programs for egg counting was characterized with SCN-infested soil collected from two farms, and the results using these methods were compared with those obtained through manual counting. [ABSTRACT FROM AUTHOR]

Background: Sponsors that seek to commercialize new drugs apply to the Food and Drug Administration (FDA) which independently analyzes the raw data and reports the results on its website., Objectives: This study sought to determine if there are differences between the FDA assessments and journal reports on biologic agents developed for the treatment of rheumatoid arthritis., Methods: Available data on FDA-approved drugs were extracted from the website, and a systematic literature search was conducted to identify matching studies in peer-reviewed medical journals. Outcome measures were the American College of Rheumatology response criteria ACR20 (efficacy) and withdrawal due to adverse events (safety). As effect size odds ratios were estimated for each active trial arm vs. control arm (i.e. for both sources: FDA and journal report), followed by calculation of the ratios of the FDA and journal report odds ratios. A ratio of odds ratios not equal to 1 was categorized as a discrepancy., Results: FDA reports were available for 8 of 9 FDA-approved biologic agents for rheumatoid arthritis; all identified trials (34) except one were published in peer-reviewed journals. Overall, discrepancies were noted for 20 of the 33 evaluated trials. Differences in the apparent benefit reporting were found in 39% (24/61) pairwise comparisons and in 11 cases these were statistically significant; the FDA report showed greater benefit than the journal publication in 15 comparisons and lesser benefit in 9. Differences in the reported harms were found in 51% (28/55) pairwise comparisons and were statistically significant in 5. The "signal" in FDA reports showed a less harmful effect than the journal publication in 17 comparisons whereas a more harmful effect in 11. The differences were attributed to differences in analytic approach, patient inclusion, rounding effect, and counting discrepancies. However, no differences were categorized as critical., Conclusion: There was no empirical evidence to suggest biased estimates between the two sources. Increased and detailed transparency in publications would improve the understanding and credibility of published results. Further, the FDA report was found to be a useful source when data are missing in the published report (i.e. reporting bias).

Skin cancer has a significant impact on the lives of many individuals annually and is recognized as the most prevalent type of cancer. In the United States, an estimated annual incidence of approximately 3.5 million people receiving a diagnosis of skin cancer underscores its widespread prevalence. Furthermore, the prognosis for individuals afflicted with advancing stages of skin cancer experiences a substantial decline in survival rates. This paper is dedicated to aiding healthcare experts in distinguishing between benign and malignant skin cancer cases by employing a range of machine learning and deep learning techniques and different feature extractors and feature selectors to enhance the evaluation metrics. In this paper, different transfer learning models are employed as feature extractors, and to enhance the evaluation metrics, a feature selection layer is designed, which includes diverse techniques such as Univariate, Mutual Information, ANOVA, PCA, XGB, Lasso, Random Forest, and Variance. Among transfer models, DenseNet-201 was selected as the primary feature extractor to identify features from data. Subsequently, the Lasso method was applied for feature selection, utilizing diverse machine learning approaches such as MLP, XGB, RF, and NB. To optimize accuracy and precision, ensemble methods were employed to identify and enhance the best-performing models. The study provides accuracy and sensitivity rates of 87.72% and 92.15%, respectively. [ABSTRACT FROM AUTHOR]

A Kuhnian approach to research assessment requires us to consider that the important scientific breakthroughs that drive scientific progress are infrequent and that the progress of science does not depend on normal research. Consequently, indicators of research performance based on the total number of papers do not accurately measure scientific progress. Similarly, those universities with the best reputations in terms of scientific progress differ widely from other universities in terms of the scale of investments made in research and in the higher concentrations of outstanding scientists present, but less so in terms of the total number of papers or citations. This study argues that indicators for the 1% high-citation tail of the citation distribution reveal the contribution of universities to the progress of science and provide quantifiable justification for the large investments in research made by elite research universities. In this tail, which follows a power low, the number of the less frequent and highly cited important breakthroughs can be predicted from the frequencies of papers in the upper part of the tail. This study quantifies the false impression of excellence produced by multinational papers, and by other types of papers that do not contribute to the progress of science. Many of these papers are concentrated in and dominate lists of highly cited papers, especially in lower-ranked universities. The h-index obscures the differences between higher- and lower-ranked universities because the proportion of h-core papers in the 1% high-citation tail is not proportional to the value of the h-index.

China's export benefits from the significant fiscal stimulus in the United States. This paper analyzes the global spillover effect of the American economy on China's macro-economy using the Markov Chain Monte Carlo (MCMC)-Gibbs sampling approach, with the goal of improving the ability of China's financial system to protect against foreign threats. This paper examines the theories of the consequences of uncertainty on macroeconomics first. Then, using medium-sized economic and financial data, the uncertainty index of the American and Chinese economies is built. In order to complete the test and analysis of the dynamic relationship between American economic uncertainty and China's macro-economy, a Time Varying Parameter-Stochastic Volatility-Vector Autoregression (TVP- VAR) model with random volatility is constructed. The model is estimated using the Gibbs sampling method based on MCMC. For the empirical analysis, samples of China's and the United States' economic data from January 2001 to January 2022 were taken from the WIND database and the FRED database, respectively. The data reveal that there are typically fewer than 5 erroneous components in the most estimated parameters of the MCMC model, which suggests that the model's sampling results are good. China's pricing level reacted to the consequences of the unpredictability of the American economy by steadily declining, reaching its lowest point during the financial crisis in 2009, and then gradually diminishing. After 2012, the greatest probability density range of 68% is extremely wide and contains 0, indicating that the impact of economic uncertainty in the United States on China's pricing level is no longer significant. China should therefore focus on creating a community of destiny by working with nations that have economic cooperation to lower systemic financial risks and guarantee the stability of the capital market. [ABSTRACT FROM AUTHOR]

Background: Dengue virus (DENV) is endemic to many parts of the world and has serious health and socioeconomic effects even in high-income countries, especially with rapid changes in the climate globally. We explored the literature on dengue vector control methods used in high-income, city settings and associations with dengue incidence, dengue prevalence, or mosquito vector densities. Methods: Studies of any design or year were included if they reported effects on human DENV infection or Aedes vector indices of dengue-specific vector control interventions in high-income, city settings. Results: Of 24 eligible sources, most reported research in the United States (n = 8) or Australia (n = 5). Biocontrol (n = 12) and chemical control (n = 13) were the most frequently discussed vector control methods. Only 6 sources reported data on the effectiveness of a given method in reducing human DENV incidence or prevalence, 2 described effects of larval and adult control on Aedes DENV positivity, 20 reported effectiveness in reducing vector density, using insecticide, larvicide, source reduction, auto-dissemination of pyriproxyfen and Wolbachia, and only 1 described effects on human-vector contact. Conclusions: As most studies reported reductions in vector densities, rather than any effects on human DENV incidence or prevalence, we can draw no clear conclusions on which interventions might be most effective in reducing dengue in high-income, city areas. More research is needed linking evidence on the effects of different DENV vector control methods with dengue incidence/prevalence or mosquito vector densities in high-income, city settings as this is likely to differ from low-income settings. This is a significant evidence gap as climate changes increase the global reach of DENV. The importance of community involvement was clear in several studies, although it is impossible to tease out the relative contributions of this from other control methods used. Author summary: Dengue virus is present in many parts of the world and has serious health and socioeconomic effects even in high-income countries, especially with rapid changes in the climate globally. In this study, we explored the literature on dengue vector control methods used in high-income, city settings and associations with number of human dengue infections, and the density of mosquitoes. We found 24 papers with relevant results. Most of these described studies in the United States or Australia. Most were about various forms of biological or chemical control of the mosquitoes. Few papers discussed effects on human dengue infection. We were unable to draw any clear conclusions on how effective mosquito control methods were as we could identify little research on this subject in this specific setting. More research is needed on this topic, particularly as climate change will make more areas of the world vulnerable to dengue infection. [ABSTRACT FROM AUTHOR]

The average age at which U.S. researchers receive their first grant from NIH has increased from 34.3 in 1970, to 41.7 in 2004. These data raise the crucial question of the effects of aging on the scientific productivity and impact of researchers. Drawing on a sizeable sample of 6,388 university professors in Quebec who have published at least one paper between 2000 and 2007, our results identify two turning points in the professors' careers. A first turning point is visible at age 40 years, where researchers start to rely on older literature and where their productivity increases at a slower pace-after having increased sharply since the beginning of their career. A second turning point can be seen around age 50, when researchers are the most productive whereas their average scientific impact is at its lowest. Our results also show that older professors publish fewer first-authored papers and move closer to the end of the list of co-authors. Although average scientific impact per paper decreases linearly until about age 50, the average number of papers in highly cited journals and among highly cited papers rises continuously until retirement. Our results show clearly that productivity and impact are not a simple and declining function of age and that we must take into account the collaborative aspects of scientific research. Science is a collective endeavor and, as our data shows, researchers of all ages play a significant role in its dynamic. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic prompted governments worldwide to implement a range of containment measures, including mass gathering restrictions, social distancing, and school closures. Despite these efforts, vaccines continue to be the safest and most effective means of combating such viruses. Yet, vaccine hesitancy persists, posing a significant public health concern, particularly with the emergence of new COVID-19 variants. To effectively address this issue, timely data is crucial for understanding the various factors contributing to vaccine hesitancy. While previous research has largely relied on traditional surveys for this information, recent sources of data, such as social media, have gained attention. However, the potential of social media data as a reliable proxy for information on population hesitancy, especially when compared with survey data, remains underexplored. This paper aims to bridge this gap. Our approach uses social, demographic, and economic data to predict vaccine hesitancy levels in the ten most populous US metropolitan areas. We employ machine learning algorithms to compare a set of baseline models that contain only these variables with models that incorporate survey data and social media data separately. Our results show that XGBoost algorithm consistently outperforms Random Forest and Linear Regression, with marginal differences between Random Forest and XGBoost. This was especially the case with models that incorporate survey or social media data, thus highlighting the promise of the latter data as a complementary information source. Results also reveal variations in influential variables across the five hesitancy classes, such as age, ethnicity, occupation, and political inclination. Further, the application of models to different MSAs yields mixed results, emphasizing the uniqueness of communities and the need for complementary data approaches. In summary, this study underscores social media data's potential for understanding vaccine hesitancy, emphasizes the importance of tailoring interventions to specific communities, and suggests the value of combining different data sources. [ABSTRACT FROM AUTHOR]

Socio-economic and geographical inequalities in breast cancer mortality have been widely described in European countries and the United States. To investigate the combined effects of geographic access and socio-economic characteristics on breast cancer outcomes, a systematic review was conducted exploring the relationships between: (i) geographic access to healthcare facilities (oncology services, mammography screening), defined as travel time and/or travel distance; (ii) breast cancer-related outcomes (mammography screening, stage of cancer at diagnosis, type of treatment and rate of mortality); (iii) socioeconomic status (SES) at individuals and residential context levels. In total, n = 25 studies (29 relationships tested) were included in our systematic review. The four main results are: The statistical significance of the relationship between geographic access and breast cancer-related outcomes is heterogeneous: 15 were identified as significant and 14 as non-significant. Women with better geographic access to healthcare facilities had a statistically significant fewer mastectomy (n = 4/6) than women with poorer geographic access. The relationship with the stage of the cancer is more balanced (n = 8/17) and the relationship with cancer screening rate is not observed (n = 1/4). The type of measures of geographic access (distance, time or geographical capacity) does not seem to have any influence on the results. For example, studies which compared two different measures (travel distance and travel time) of geographic access obtained similar results. The relationship between SES characteristics and breast cancer-related outcomes is significant for several variables: at individual level, age and health insurance status; at contextual level, poverty rate and deprivation index. Of the 25 papers included in the review, the large majority (n = 24) tested the independent effect of geographic access. Only one study explored the combined effect of geographic access to breast cancer facilities and SES characteristics by developing stratified models. [ABSTRACT FROM AUTHOR]

Traditional sociodemographic disparities in adolescent vaccination initiation for the HPV, Tdap, and MenACWY vaccines have declined in the United States of America. This decline raises the question of whether inequities in access have been successfully addressed. This paper synthesizes research on the resource barriers that inhibit vaccination alongside research on vaccine hesitancy where parents actively refuse vaccination. To do so, I classify the primary reason why teens are unvaccinated in the National Immunization Survey-Teen 2012–2022 into three categories: resource failure, agentic refusal, and other reasons. I use three non-exclusive subsamples of teens who are unvaccinated against the HPV (N = 87,163), MenACWY (N = 54,726), and Tdap (N = 10,947) vaccines to examine the relative importance of resource failure reasons and agentic refusal reasons for non-vaccination across time and teens' sociodemographic characteristics. Results indicate that resource failure reasons continue to explain a substantial portion of the reasons why teens are unvaccinated and disproportionately affect racially/ethnically and economically marginalized teens. Thus, even as sociodemographic inequalities in rates of vaccination have declined, inequities in access remain consequential. [ABSTRACT FROM AUTHOR]

Background: Like many infectious diseases, there is no practical gold standard for diagnosing clinical visceral leishmaniasis (VL). Latent class modeling has been proposed to estimate a latent gold standard for identifying disease. These proposed models for VL have leveraged information from diagnostic tests with dichotomous serological and PCR assays, but have not employed continuous diagnostic test information. Methods/Principal findings: In this paper, we employ Bayesian latent class models to improve the identification of canine visceral leishmaniasis using the dichotomous PCR assay and the Dual Path Platform (DPP) serology test. The DPP test has historically been used as a dichotomous assay, but can also yield numerical information via the DPP reader. Using data collected from a cohort of hunting dogs across the United States, which were identified as having either negative or symptomatic disease, we evaluate the impact of including numerical DPP reader information as a proxy for immune response. We find that inclusion of DPP reader information allows us to illustrate changes in immune response as a function of age. Conclusions/Significance: Utilization of continuous DPP reader information can improve the correct discrimination between individuals that are negative for disease and those with clinical VL. These models provide a promising avenue for diagnostic testing in contexts with multiple, imperfect diagnostic tests. Specifically, they can easily be applied to human visceral leishmaniasis when diagnostic test results are available. Also, appropriate diagnosis of canine visceral leishmaniasis has important consequences for curtailing spread of disease to humans. Author summary: Diagnostic tests often aid in diagnosis of infectious diseases, like the neglected tropical disease visceral leishmaniasis (VL). While such tests provide useful information, they are imperfect, so they can produce false positive or false negative results. Moreover, diagnostic tests for VL are often dichotomous, meaning they only provide positive or negative results. The Dual Path Platform (DPP) serology test has been used as a dichotomous assay, but can also yield numerical information via the DPP reader. Although we can only observe diagnostic test results, rather than the true disease status of an individual, we can use a set of statistical methods called latent class models to estimate the true disease status. We can also incorporate the numerical information from the DPP reader in latent class models. In this paper, we evaluate the impact of including this numerical information in a Bayesian latent class model to provide additional information about immune response. We compare this to a more traditional latent class model, which only incorporates dichotomized assay information. We fit these models to data collected from a cohort of hunting dogs across the United States. Incorporating DPP reader information allows us to illustrate changes in immune response for different ages. [ABSTRACT FROM AUTHOR]

The author reflects on the studies on smoking in the U.S. The author states that the first study uses the internal documents of the tobacco company that provides evidence of the deception strategy use by the tobacco industry and the second study shows that the tobacco industry is distorted by the increased medicalization of smoke stopping. The author also mentions that journals rejected bans on research papers from the authors funded by the tobacco industry.

The coronavirus pandemic led to a severe economic shock in the United States. This paper uses a unique survey data collected early on in the pandemic to measure economic deprivation among individuals. The Federal Reserve Board fielded a Survey of Household Economics and Decision-making (SHED) in April 2020. This survey is used to compile data on four indicators of economic deprivation, namely: i) Overall financial condition, ii) Loss of employment, iii) Reduction in income and iv) Inability to pay bills in full. Data on these indicators is compiled for each individual and is used in a novel way to construct a set of multidimensional deprivation indices. These indices measure the overlap of deprivations experienced by an individual. Results show that almost 25 percent of the respondents faced hardships in at least two of the four indicators. More than 13 percent of adults reported their inability to pay monthly bills and struggled to make ends meet financially. One in four respondents had lower income compared to income from previous month. The economic shock affected Hispanics in a more profound way. More than 37 percent Hispanics reported hardship in two or more indicators and 8 percent reported hardship in all four indicators. Higher proportion of young adults and those without a college degree suffered multiple hardships. The paper highlights the plight of Americans during the early months of the economic crisis set in motion amid the coronavirus pandemic and sheds light on how economic disparities deepened along racial/ethnic lines. [ABSTRACT FROM AUTHOR]

The pathogenic bacteria Neisseria meningitidis, which causes invasive meningococcal disease (IMD), predominantly colonizes humans asymptomatically; however, invasive disease occurs in a small proportion of the population. Here, we explore the seasonality of IMD and develop and validate a suite of models for simulating and forecasting disease outcomes in the United States. We combine the models into multi-model ensembles (MME) based on the past performance of the individual models, as well as a naive equally weighted aggregation, and compare the retrospective forecast performance over a six-month forecast horizon. Deployment of the complete vaccination regimen, introduced in 2011, coincided with a change in the periodicity of IMD, suggesting altered transmission dynamics. We found that a model forced with the period obtained by local power wavelet decomposition best fit and forecast observations. In addition, the MME performed the best across the entire study period. Finally, our study included US-level data until 2022, allowing study of a possible IMD rebound after relaxation of non-pharmaceutical interventions imposed in response to the COVID-19 pandemic; however, no evidence of a rebound was found. Our findings demonstrate the ability of process-based models to retrospectively forecast IMD and provide a first analysis of the seasonality of IMD before and after the complete vaccination regimen. Author summary: This paper presents a time series analysis of Invasive Meningococcal Disease caused by the pathogenic bacteria Neisseria meningitidis as well as the development and validation of a suite of models for simulating and forecasting disease outcomes in the United States. We present the models and their performance and construct a multi-model ensemble (MME) forecast that combines individual models based on past forecast performance, as well as an equally weighted aggregation. Deployment of a complete vaccination regimen, introduced for adolescents in 2011, coincided with a change in the periodicity of IMD, suggesting altered transmission dynamics. We found that a model forced with the period obtained by local power wavelet decomposition best fits and forecasts observations. In addition, the MME forecasts performed the best across the entire study period. Finally, our study included US-level data until 2022, allowing study of a possible IMD rebound after the relaxation of non-pharmaceutical interventions imposed in response to the COVID-19 pandemic; however, no evidence of a rebound was found. Our findings demonstrate the ability of process-based models to retrospectively forecast IMD and provide a first analysis of the seasonality of IMD before and after the complete vaccination regimen. [ABSTRACT FROM AUTHOR]

Opioid use disorder (OUD) represents a public health crisis in the United States. Medication for opioid use disorder (MOUD) with buprenorphine in primary care is a proven OUD treatment strategy. MOUD induction is when patients begin withdrawal and receive the first doses of buprenorphine. Differences between induction methods might influence short-term stabilization, long-term maintenance, and quality of life. This paper describes the protocol for a study designed to: (1) compare short-term stabilization and long-term maintenance treatment engagement in MOUD in patients receiving office, home, or telehealth induction and (2) identify clinically-relevant practice and patient characteristics associated with successful long-term treatment. The study design is a randomized, parallel group, pragmatic comparative effectiveness trial of three care models of MOUD induction in 100 primary care practices in the United States. Eligible patients are at least 16 years old, have been identified by their clinician as having opioid dependence and would benefit from MOUD. Patients will be randomized to one of three induction comparators: office, home, or telehealth induction. Primary outcomes are buprenorphine medication-taking and illicit opioid use at 30, 90, and 270 days post-induction. Secondary outcomes include quality of life and potential mediators of treatment maintenance (intentions, planning, automaticity). Potential moderators include social determinants of health, substance use history and appeal, and executive function. An intent to treat analysis will assess effects of the interventions on long-term treatment, using general/generalized linear mixed models, adjusted for covariates, for the outcomes analysis. Analysis includes practice- and patient-level random effects for hierarchical/longitudinal data. No large-scale, randomized comparative effectiveness research has compared home induction to office or telehealth MOUD induction on long-term outcomes for patients with OUD seen in primary care settings. The results of this study will offer primary care providers evidence and guidance in selecting the most beneficial induction method(s) for specific patients. [ABSTRACT FROM AUTHOR]

Despite the proven efficacy of pre-exposure prophylaxis (PrEP) in preventing HIV acquisition, PrEP is underutilized by sexual and gender minority young adults in the 13 states in the United States "South." In this paper, we describe the process we used to develop a digital campaign to promote awareness and engagement of sexual and gender minority young adults in the Louisiana Department of Health's TelePrEP Program and provide campaign performance metrics. In Phase 1, we conducted formative research that informed campaign development and strategy. In total, 109 sexual and gender minority young adults completed a survey of PrEP constructs (e.g., facilitators, barriers). We also conducted three, sequential focus groups to iteratively generate, revise and refine the digital material. In collaboration with our strategic marketing partner (SMP), we developed 3 different ads and 1 video ad to promote on web and mobile-in app display, as well as Facebook, Instagram, and YouTube. Phase 2 focused on campaign implementation and evaluation (e.g., number of impressions, user activities on LA TelePrEP landing page). In the first few weeks of the campaign, data from tracking pixels indicated minimal activities on the landing page. We paused to revamp the campaign. Our SMP determined that a more young adult-focused landing page could bolster engagement. We created a new landing page and reran the campaign for 33 days. We saw substantially more user activities on the new landing page (n = 382) compared to the LA TelePrEP landing page (n = 185). Overall, we had 730,665 impressions and 475 link clicks. By collaborating with our SMP, we effectively and efficiently translated our community-engaged formative research into relevant and engaging digital content. This pilot study is one of the first to demonstrate the importance of using tracking pixels to monitor real-time user data to optimize performance of a digital PrEP campaign. [ABSTRACT FROM AUTHOR]

Influenza affects millions of people every year. It causes a considerable amount of medical visits and hospitalisations as well as hundreds of thousands of deaths. Forecasting influenza prevalence with good accuracy can significantly help public health agencies to timely react to seasonal or novel strain epidemics. Although significant progress has been made, influenza forecasting remains a challenging modelling task. In this paper, we propose a methodological framework that improves over the state-of-the-art forecasting accuracy of influenza-like illness (ILI) rates in the United States. We achieve this by using Web search activity time series in conjunction with historical ILI rates as observations for training neural network (NN) architectures. The proposed models incorporate Bayesian layers to produce associated uncertainty intervals to their forecast estimates, positioning themselves as legitimate complementary solutions to more conventional approaches. The best performing NN, referred to as the iterative recurrent neural network (IRNN) architecture, reduces mean absolute error by 10.3% and improves skill by 17.1% on average in nowcasting and forecasting tasks across 4 consecutive flu seasons. Author summary: Modelling the prevalence of an infectious disease enables public health organisations to prepare for and minimise its impact. However, traditional disease indicators are often quite restrictive as they provide information with a significant delay. Recent research efforts have provided evidence of the value of alternative information sources such as Web search activity trends. Our work incorporates this information into influenza forecasting models to achieve state-of-the-art accuracy. In addition, the proposed Bayesian neural network architectures also provide associated uncertainty estimates for the forecasts, positioning our methodology as a practical complementary tool for disease surveillance and policy making. [ABSTRACT FROM AUTHOR]

Parkinson's disease which is the second most prevalent neurodegenerative disorder in the United States is a serious and complex disease that may progress to mild cognitive impairment and dementia. The early detection of the mild cognitive impairment and the identification of its biomarkers is crucial to support neurologists in monitoring the progression of the disease and allow an early initiation of effective therapeutic treatments that will improve the quality of life for the patients. In this paper, we propose the first deep-learning based approaches to detect mild cognitive impairment in the sleep Electroencephalography for patients with Parkinson's disease and further identify the discriminative features of the disease. The proposed frameworks start by segmenting the sleep Electroencephalography time series into three sleep stages (i.e., two non-rapid eye movement sleep-stages and one rapid eye movement sleep stage), further transforming the segmented signals in the time-frequency domain using the continuous wavelet transform and the variational mode decomposition and finally applying novel convolutional neural networks on the time-frequency representations. The gradient-weighted class activation mapping was also used to visualize the features based on which the proposed deep-learning approaches reached an accurate prediction of mild cognitive impairment in Parkinson's disease. The proposed variational mode decomposition-based model offered a superior accuracy, sensitivity, specificity, area under curve, and quadratic weighted Kappa score, all above 99% as compared with the continuous wavelet transform-based model (that achieved a performance that is almost above 92%) in differentiating mild cognitive impairment from normal cognition in sleep Electroencephalography for patients with Parkinson's disease. In addition, the features attributed to the mild cognitive impairment in Parkinson's disease were demonstrated by changes in the middle and high frequency variational mode decomposition components across the three sleep-stages. The use of the proposed model on the time-frequency representation of the sleep Electroencephalography signals will provide a promising and precise computer-aided diagnostic tool for detecting mild cognitive impairment and hence, monitoring the progression of Parkinson's disease. [ABSTRACT FROM AUTHOR]

Peer review is the gold standard for scientific communication, but its ability to guarantee the quality of published research remains difficult to verify. Recent modeling studies suggest that peer review is sensitive to reviewer misbehavior, and it has been claimed that referees who sabotage work they perceive as competition may severely undermine the quality of publications. Here we examine which aspects of suboptimal reviewing practices most strongly impact quality, and test different mitigating strategies that editors may employ to counter them. We find that the biggest hazard to the quality of published literature is not selfish rejection of high-quality manuscripts but indifferent acceptance of low-quality ones. Bypassing or blacklisting bad reviewers and consulting additional reviewers to settle disagreements can reduce but not eliminate the impact. The other editorial strategies we tested do not significantly improve quality, but pairing manuscripts to reviewers unlikely to selfishly reject them and allowing revision of rejected manuscripts minimize rejection of above-average manuscripts. In its current form, peer review offers few incentives for impartial reviewing efforts. Editors can help, but structural changes are more likely to have a stronger impact. [ABSTRACT FROM AUTHOR]

Out-of-hospital cardiac arrest (OHCA) affects over 360,000 adults in the United States each year with a 50–80% mortality prior to reaching medical care. Despite aggressive supportive care and targeted temperature management (TTM), half of adults do not live to hospital discharge and nearly one-third of survivors have significant neurologic injury. The current treatment approach following cardiac arrest resuscitation consists primarily of supportive care and possible TTM. While these current treatments are commonly used, mortality remains high, and survivors often develop lasting neurologic and cardiac sequela well after resuscitation. Hence, there is a critical need for further therapeutic development of adjunctive therapies. While select therapeutics have been experimentally investigated, one promising agent that has shown benefit is CO. While CO has traditionally been thought of as a cellular poison, there is both experimental and clinical evidence that demonstrate benefit and safety in ischemia with lower doses related to improved cardiac/neurologic outcomes. While CO is well known for its poisonous effects, CO is a generated physiologically in cells through the breakdown of heme oxygenase (HO) enzymes and has potent antioxidant and anti-inflammatory activities. While CO has been studied in myocardial infarction itself, the role of CO in cardiac arrest and post-arrest care as a therapeutic is less defined. Currently, the standard of care for post-arrest patients consists primarily of supportive care and TTM. Despite current standard of care, the neurological prognosis following cardiac arrest and return of spontaneous circulation (ROSC) remains poor with patients often left with severe disability due to brain injury primarily affecting the cortex and hippocampus. Thus, investigations of novel therapies to mitigate post-arrest injury are clearly warranted. The primary objective of this proposed study is to combine our expertise in swine models of CO and cardiac arrest for future investigations on the cellular protective effects of low dose CO. We will combine our innovative multi-modal diagnostic platform to assess cerebral metabolism and changes in mitochondrial function in swine that undergo cardiac arrest with therapeutic application of CO. [ABSTRACT FROM AUTHOR]

This paper introduces a bibliometric, citation network-based method for assessing the social validation of novel research, and applies this method to the development of high-throughput toxicology research at the US Environmental Protection Agency. Social validation refers to the acceptance of novel research methods by a relevant scientific community; it is formally independent of the technical validation of methods, and is frequently studied in history, philosophy, and social studies of science using qualitative methods. The quantitative methods introduced here find that high-throughput toxicology methods are spread throughout a large and well-connected research community, which suggests high social validation. Further assessment of social validation involving mixed qualitative and quantitative methods are discussed in the conclusion. [ABSTRACT FROM AUTHOR]

Scientists, public servants, and patient advocates alike increasingly question the validity of published scientific results, endangering the public’s acceptance of science. Here, I argue that emerging flaws in the integrity of the peer review system are largely responsible. Distortions in peer review are driven by economic forces and enabled by a lack of accountability of journals, editors, and authors. One approach to restoring trust in the validity of published results may be to establish basic rules that render peer review more transparent, such as publishing the reviews (a practice already embraced by some journals) and monitoring not only the track records of authors but also of editors and journals. [ABSTRACT FROM AUTHOR]

The worldwide outbreak of the coronavirus was first identified in 2019 in Wuhan, China. Since then, the disease has spread worldwide. As it is currently spreading in the United States, policy makers, public health officials and citizens are racing to understand the impact of this virus on the United States healthcare system. They fear a rapid influx of patients overwhelming the healthcare system and leading to unnecessary fatalities. Most countries and states in America have introduced mitigation strategies, such as using social distancing to decrease the rate of newly infected people. This is what is usually meant by flattening the curve. In this paper, we use queueing theoretic methods to analyze the time evolution of the number of people hospitalized due to the coronavirus. Given that the rate of new infections varies over time as the pandemic evolves, we model the number of coronavirus patients as a dynamical system based on the theory of infinite server queues with time inhomogeneous Poisson arrival rates. With this model we are able to quantify how flattening the curve affects the peak demand for hospital resources. This allows us to characterize how aggressive societal policy needs to be to avoid overwhelming the capacity of healthcare system. We also demonstrate how curve flattening impacts the elapsed lag between the times of the peak rate of hospitalizations and the peak demand for the hospital resources. Finally, we present empirical evidence from Italy and the United States that supports the insights from our model analysis. [ABSTRACT FROM AUTHOR]

There are many public health situations within the United States that require fine geographical scale data to effectively inform response and intervention strategies. However, a condition for accessing and analyzing such data, especially when multiple institutions are involved, is being able to preserve a degree of spatial privacy and confidentiality. Hospitals and state health departments, who are generally the custodians of these fine-scale health data, are sometimes understandably hesitant to collaborate with each other due to these concerns. This paper looks at the utility and pitfalls of using Zip4 codes, a data layer often included as it is believed to be "safe", as a source for sharing fine-scale spatial health data that enables privacy preservation while maintaining a suitable precision for spatial analysis. While the Zip4 is widely supplied, researchers seldom utilize it. Nor is its spatial characteristics known by data guardians. To address this gap, we use the context of a near-real time spatial response to an emerging health threat to show how the Zip4 aggregation preserves an underlying spatial structure making it potentially suitable dataset for analysis. Our results suggest that based on the density of urbanization, Zip4 centroids are within 150 meters of the real location almost 99% of the time. Spatial analysis experiments performed on these Zip4 data suggest a far more insightful geographic output than if using more commonly used aggregation units such as street lines and census block groups. However, this improvement in analytical output comes at a spatial privy cost as Zip4 centroids have a higher potential of compromising spatial anonymity with 73% of addresses having a spatial k anonymity value less than 5 when compared to other aggregations. We conclude that while offers an exciting opportunity to share data between organizations, researchers and analysts need to be made aware of the potential for serious confidentiality violations. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has been particularly deadly for residents of nursing homes and other long-term care facilities. This paper analyzes COVID-19 deaths at nursing homes during the first wave of the pandemic in the United States during the spring and early summer 2020. By combining data on facility-level COVID-19 deaths during this period with data on the neighborhoods where nursing home staff reside for a sample of eighteen states, this paper finds that staff neighborhood characteristics were a large and significant predictor of COVID-19 nursing home deaths. Even after controlling for the county where a facility is located, one standard deviation increases in average staff neighborhood (Census tract) population density, public transportation use, and non-white share were associated with 1.3 (p <.001), 1.4 (p <.001), and 0.9 (p <.001) additional deaths per 100 beds, respectively. These effects are larger than all facility management or quality variables, and larger than the effect of the nursing home's own neighborhood characteristics. These results suggest COVID-19 outbreaks in staff communities can have large consequences for the facilities where they work, even in highly-rated facilities, and that disparities in nursing home outbreaks may be related to differences in the types of neighborhoods nursing home staff live in. [ABSTRACT FROM AUTHOR]

Wolf control to reduce cattle depredation is an important issue to ecology and agriculture in the United States. Two recent papers use the same dataset having wolf population characteristics and cattle depredation, but come to opposing conclusions concerning the link between wolf control and cattle depredation. Our paper aims to resolve this issue by using the same dataset and developing a model based on a causal association that would explain the nature of the relationship between wolf control and cattle depredation. We use the data on wolf population, number of cattle, number of wolves killed and number of cattle killed, from the U.S. Fish and Wildlife Services Interagency Annual Wolf Reports over the period of 1987–2012. We find a positive link between wolf control and cattle depredation. However, it would be incorrect to infer that wolf control has a positive effect on the number of cattle depredated. We maintain that this link comes from a growing wolf population, which increases cattle depredation, and in turn, causes an increase in the number of wolves killed. While the wolf population is growing, we see both wolf removal and cattle depredation simultaneously grow. It is not until the wolf population growth nears the steady state, that removal of wolves has a sufficient negative effect to reduce or stabilize the number of cattle depredated. [ABSTRACT FROM AUTHOR]

The goal of this study is to investigate the origin, prevalence, and evolution of the pESI megaplasmid in Salmonella isolated from animals, foods, and humans. We queried 510,097 Salmonella genomes under the National Center for Biotechnology Information (NCBI) Pathogen Detection (PD) database for the presence of potential sequences containing the pESI plasmid in animal, food, and environmental sources. The presence of the pESI megaplasmid was confirmed by using seven plasmid-specific markers (rdA, pilL, SogS, TrbA, ipf, ipr2 and IncFIB(pN55391)). The plasmid and chromosome phylogeny of these isolates was inferred from single nucleotide polymorphisms (SNPs). Our search resolved six Salmonella clusters carrying the pESI plasmid. Four were emergent Salmonella Infantis clusters, and one each belonged to serovar Senftenberg and Alachua. The Infantis cluster with a pESI plasmid carrying blaCTX-M-65 gene was the biggest of the four emergent Infantis clusters, with over 10,000 isolates. This cluster was first detected in South America and has since spread widely in United States. Over time the composition of pESI in United States has changed with the average number of resistance genes showing a decrease from 9 in 2014 to 5 in 2022, resulting from changes in gene content in two integrons present in the plasmid. A recent and emerging cluster of Senftenberg, which carries the blaCTX-M-65 gene and is primarily associated with turkey sources, was the second largest in the United States. SNP analysis showed that this cluster likely originated in North Carolina with the recent acquisition of the pESI plasmid. A single Alachua isolate from turkey was also found to carry the pESI plasmid containing blaCTX-M-65 gene. The study of the pESI plasmid, its evolution and mechanism of spread can help us in developing appropriate strategies for the prevention and further spread of this multi-drug resistant plasmid in Salmonella in poultry and humans. [ABSTRACT FROM AUTHOR]

Using the InCites tool of Thomson Reuters, this study compares normalized citation impact values calculated for China, Japan, France, Germany, United States, and the UK throughout the time period from 1981 to 2010. InCites offers a unique opportunity to study the normalized citation impacts of countries using (i) a long publication window (1981 to 2010), (ii) a differentiation in (broad or more narrow) subject areas, and (iii) allowing for the use of statistical procedures in order to obtain an insightful investigation of national citation trends across the years. Using four broad categories, our results show significantly increasing trends in citation impact values for France, the UK, and especially Germany across the last thirty years in all areas. The citation impact of papers from China is still at a relatively low level (mostly below the world average), but the country follows an increasing trend line. The USA exhibits a stable pattern of high citation impact values across the years. With small impact differences between the publication years, the US trend is increasing in engineering and technology but decreasing in medical and health sciences as well as in agricultural sciences. Similar to the USA, Japan follows increasing as well as decreasing trends in different subject areas, but the variability across the years is small. In most of the years, papers from Japan perform below or approximately at the world average in each subject area. [ABSTRACT FROM AUTHOR]

The article presents the findings of a systematic survey of reporting, experimental design and statistical analysis in published biomedical research which used laboratory animals. Under the survey. Medline and EMBASE were searched for studies reporting research on live rats, mice and non-human primates carried out in publicly funded research establishments in Great Britain and the U.S. Detailed information was collected about the objective or hypothesis of the study, the number, sex, age and/or weight of animals used, and experimental and statistical methods.

Background: Paper currency by its very nature is frequently transferred from one person to another and represents an important medium for human contact with—and potential exchange of—microbes. In this pilot study, we swabbed circulating $1 bills obtained from a New York City bank in February (Winter) and June (Summer) 2013 and used shotgun metagenomic sequencing to profile the communities found on their surface. Using basic culture conditions, we also tested whether viable microbes could be recovered from bills. Results: Shotgun metagenomics identified eukaryotes as the most abundant sequences on money, followed by bacteria, viruses and archaea. Eukaryotic assemblages were dominated by human, other metazoan and fungal taxa. The currency investigated harbored a diverse microbial population that was dominated by human skin and oral commensals, including Propionibacterium acnes, Staphylococcus epidermidis and Micrococcus luteus. Other taxa detected not associated with humans included Lactococcus lactis and Streptococcus thermophilus, microbes typically associated with dairy production and fermentation. Culturing results indicated that viable microbes can be isolated from paper currency. Conclusions: We conducted the first metagenomic characterization of the surface of paper money in the United States, establishing a baseline for microbes found on $1 bills circulating in New York City. Our results suggest that money amalgamates DNA from sources inhabiting the human microbiome, food, and other environmental inputs, some of which can be recovered as viable organisms. These monetary communities may be maintained through contact with human skin, and DNA obtained from money may provide a record of human behavior and health. Understanding these microbial profiles is especially relevant to public health as money could potentially mediate interpersonal transfer of microbes. [ABSTRACT FROM AUTHOR]

Background: Estimating the medical complexity of people aging with HIV can inform clinical programs and policy to meet future healthcare needs. The objective of our study was to forecast the prevalence of comorbidities and multimorbidity among people with HIV (PWH) using antiretroviral therapy (ART) in the United States (US) through 2030. Methods and findings: Using the PEARL model—an agent-based simulation of PWH who have initiated ART in the US—the prevalence of anxiety, depression, stage ≥3 chronic kidney disease (CKD), dyslipidemia, diabetes, hypertension, cancer, end-stage liver disease (ESLD), myocardial infarction (MI), and multimorbidity (≥2 mental or physical comorbidities, other than HIV) were forecasted through 2030. Simulations were informed by the US CDC HIV surveillance data of new HIV diagnosis and the longitudinal North American AIDS Cohort Collaboration on Research and Design (NA-ACCORD) data on risk of comorbidities from 2009 to 2017. The simulated population represented 15 subgroups of PWH including Hispanic, non-Hispanic White (White), and non-Hispanic Black/African American (Black/AA) men who have sex with men (MSM), men and women with history of injection drug use and heterosexual men and women. Simulations were replicated for 200 runs and forecasted outcomes are presented as median values (95% uncertainty ranges are presented in the Supporting information). In 2020, PEARL forecasted a median population of 670,000 individuals receiving ART in the US, of whom 9% men and 4% women with history of injection drug use, 60% MSM, 8% heterosexual men, and 19% heterosexual women. Additionally, 44% were Black/AA, 32% White, and 23% Hispanic. Along with a gradual rise in population size of PWH receiving ART—reaching 908,000 individuals by 2030—PEARL forecasted a surge in prevalence of most comorbidities to 2030. Depression and/or anxiety was high and increased from 60% in 2020 to 64% in 2030. Hypertension decreased while dyslipidemia, diabetes, CKD, and MI increased. There was little change in prevalence of cancer and ESLD. The forecasted multimorbidity among PWH receiving ART increased from 63% in 2020 to 70% in 2030. There was heterogeneity in trends across subgroups. Among Black women with history of injection drug use in 2030 (oldest demographic subgroup with median age of 66 year), dyslipidemia, CKD, hypertension, diabetes, anxiety, and depression were most prevalent, with 92% experiencing multimorbidity. Among Black MSM in 2030 (youngest demographic subgroup with median age of 42 year), depression and CKD were highly prevalent, with 57% experiencing multimorbidity. These results are limited by the assumption that trends in new HIV diagnoses, mortality, and comorbidity risk observed in 2009 to 2017 will persist through 2030; influences occurring outside this period are not accounted for in the forecasts. Conclusions: The PEARL forecasts suggest a continued rise in comorbidity and multimorbidity prevalence to 2030, marked by heterogeneities across race/ethnicity, gender, and HIV acquisition risk subgroups. HIV clinicians must stay current on the ever-changing comorbidities-specific guidelines to provide guideline-recommended care. HIV clinical directors should ensure linkages to subspecialty care within the clinic or by referral. HIV policy decision-makers must allocate resources and support extended clinical capacity to meet the healthcare needs of people aging with HIV. In this modelling study, Keri N Althoff and colleagues employ an agent-based simulation model to forecast the burden of comorbidity and multimorbidity in individuals who have initiated ART in the US. Author summary: Why was this study done?: Individuals with HIV are aging and are experiencing an increased risk of comorbidities and multimorbidity. Anticipating the healthcare needs of an aging population with HIV is crucial for healthcare providers, policymakers, and public health officials to plan for medical and support services tailored to the unique needs of aging people with HIV. In response to the increasing medical complexity among aging adults with HIV, we employed an agent-based simulation model to forecast the potential burden of comorbidity and multimorbidity in individuals who have initiated antiretroviral therapy (ART) in the US. What did the researchers do and find?: PEARL is an agent-based simulation of persons with HIV who have initiated ART in the US, utilizing data from the CDC HIV surveillance system and comorbidity risk functions derived from the North American AIDS Cohort Collaboration on Research and Design (NA-ACCORD), the largest longitudinal cohort of people with HIV who have linked into care in the US. Following a gradual increase in number of people with HIV receiving ART from 2020 to 2030, PEARL forecasted a population of 908,504 in the US in 2030, and an increase in multimorbidity burden from 63% in 2020 to 70% in 2030 among people with HIV. Although mental health conditions had the highest burden, the mix of physical comorbidities of greatest burden was different by demographic and HIV acquisition risk subgroups. What do these findings mean?: HIV clinicians can use these findings to identify the comorbidity-specific screening, diagnoses, and treatment guidelines and tools that will be necessary to care for their panel of patients. HIV care program decision-makers can use these findings to predict the subspecialities that will be in highest demand by their clinical population and make the connections to subspecialists (bringing them into the HIV clinic or by referral) needed meet the healthcare needs of people with HIV. HIV policy decision-makers can use these findings to guide expansion in subspecialty care capacity (particularly for mental health conditions) and the resources needed for expansion. [ABSTRACT FROM AUTHOR]

Introduction: Fourteen million colonoscopies are performed annually in the United States, with the results dependent on bowel preparation before the procedure. However, suitable preparation is neglected in 20–25% of cases, resulting in increased loss of time, failure to diagnose, procedure repetition, and decreased patient satisfaction. Consequently, the current study was performed to evaluate the effect of reinforced education (RE) on the satisfaction of patients undergoing colonoscopy. Methods: This study employed an experimental research method. Participants included 83 patients referred to Ayatollah Taleghani Medical Educational Center for colonoscopy, who were divided into control and intervention groups. The control group received standard education, whereas the intervention group received instruction via media and virtually, and preparation was followed up on the day before the colonoscopy. The research tool was a demographic and satisfaction questionnaire developed by the researcher. Analyses were conducted using IBM SPSS software (v. 20). Results: Eighty-three patients, including 47 men and 36 women with an average age of 49.19 years, participated in the study. Mean (standard deviation) patient satisfaction was 11.78 (4.65) in the intervention group and 9.04 (2.95) in the control group; the independent t-test revealed a significant difference between the two groups (P<0.001). Conclusions: The study suggests that reinforced education using media and virtual means is effective in the satisfaction of patients undergoing colonoscopy. [ABSTRACT FROM AUTHOR]

The United States President's Emergency Plan for AIDS Relief (PEPFAR) has been credited with saving millions lives and helping to change the trajectory of the global human immunodeficiency virus (HIV) epidemic. This study assesses whether PEPFAR has had impacts beyond health by examining changes in five economic and educational outcomes in PEPFAR countries: the gross domestic product (GDP) per capita growth rate; the share of girls and share of boys, respectively, who are out of school; and female and male employment rates. We constructed a panel data set for 157 low- and middle-income countries between 1990 and 2018 to estimate the macroeconomic impacts of PEPFAR. Our PEPFAR group included 90 countries that had received PEPFAR support over the period. Our comparison group included 67 low- and middle-income countries that had not received any PEPFAR support or had received minimal PEPFAR support (<$1M or <$.05 per capita) between 2004 and 2018. We used differences in differences (DID) methods to estimate the program impacts on the five economic and educational outcome measures. This study finds that PEPFAR is associated with increases in the GDP per capita growth rate and educational outcomes. In some models, we find that PEPFAR is associated with reductions in male and female employment. However, these effects appear to be due to trends in the comparison group countries rather than programmatic impacts of PEPFAR. We show that these impacts are most pronounced in COP countries receiving the highest levels of PEPFAR investment. [ABSTRACT FROM AUTHOR]

Over 71 million people are infected with hepatitis C virus (HCV) worldwide, and approximately 400,000 global deaths result from complications of untreated chronic HCV. Pan-genomic direct-acting antivirals (DAAs) have recently become widely available and feature high cure rates in less than 12 weeks of treatment. The rollout of DAAs is reliant on diagnostic tests for HCV RNA to identify eligible patients with viremic HCV infections. Current PCR-based HCV RNA assays are restricted to well-resourced central laboratories, and there remains a prevailing clinical need for expanded access to decentralized HCV RNA testing to provide rapid chronic HCV diagnosis and linkage to DAAs in outpatient clinics. This paper reports a rapid, highly accurate, and minimally instrumented assay for HCV RNA detection using reverse transcription recombinase polymerase amplification (RT-RPA). The assay detects all HCV genotypes with a limit of detection of 25 copies per reaction for genotype 1, the most prevalent in the United States and worldwide. The clinical sensitivity and specificity of the RT-RPA assay were both 100% when evaluated using 78 diverse clinical serum specimens. The accuracy, short runtime, and low heating demands of RT-RPA may enable implementation in a point-of-care HCV test to expand global access to effective treatment via rapid chronic HCV diagnosis. [ABSTRACT FROM AUTHOR]

The paper investigates the factors underlying COVID-19 vaccine and booster hesitancy in the United States, and the efficacy of various incentives or disincentives to expand uptake. We use cross-sectional, national survey data on 3,497 U.S. adults collected online from September 10, 2021 to October 20, 2021 through the Qualtrics platform. Results from a multinomial logistic regression reveal that hesitancy and refusal were greatest among those who expressed a lack of trust either in government or in the vaccine's efficacy (hesitancy relative risk ratio, or RRR: 2.86, 95% CI: 2.13–3.83, p<0.001). Hesitancy and refusal were lowest among those who typically get a flu vaccine (hesitancy RRR: 0.28, 95% CI: 0.21–0.36, p<0.001; refusal RRR: 0.08, 95% CI: 0.05–0.13, p<0.001). Similar results hold for the intention to get a booster shot among the fully vaccinated. Monetary rewards (i.e., lottery ticket and gift cards) fared poorly in moving people toward vaccination. In contrast, the prospect of job loss or increased health insurance premiums was found to significantly increase vaccine uptake, by 8.7 percentage points (p<0.001) and 9.4 percentage points (p<0.001), respectively. We also show that the motivations underlying individuals' hesitancy or refusal to get vaccinated vary, which, in turn, suggests that messaging must be refined and directed accordingly. Also, moving forward, it may be fruitful to more deeply study the intriguing possibility that expanding flu vaccine uptake may also enhance willingness to vaccinate in times of pandemics. Last, disincentives such as work-based vaccination mandates that would result in job loss or higher health insurance premiums for those who refuse vaccination should be strongly considered to improve vaccine uptake in the effort to address the common good. [ABSTRACT FROM AUTHOR]

Measles is one the best-documented and most-mechanistically-studied non-linear infectious disease dynamical systems. However, systematic investigation into the comparative performance of traditional mechanistic models and machine learning approaches in forecasting the transmission dynamics of this pathogen are still rare. Here, we compare one of the most widely used semi-mechanistic models for measles (TSIR) with a commonly used machine learning approach (LASSO), comparing performance and limits in predicting short to long term outbreak trajectories and seasonality for both regular and less regular measles outbreaks in England and Wales (E&W) and the United States. First, our results indicate that the proposed LASSO model can efficiently use data from multiple major cities and achieve similar short-to-medium term forecasting performance to semi-mechanistic models for E&W epidemics. Second, interestingly, the LASSO model also captures annual to biennial bifurcation of measles epidemics in E&W caused by susceptible response to the late 1940s baby boom. LASSO may also outperform TSIR for predicting less-regular dynamics such as those observed in major cities in US between 1932–45. Although both approaches capture short-term forecasts, accuracy suffers for both methods as we attempt longer-term predictions in highly irregular, post-vaccination outbreaks in E&W. Finally, we illustrate that the LASSO model can both qualitatively and quantitatively reconstruct mechanistic assumptions, notably susceptible dynamics, in the TSIR model. Our results characterize the limits of predictability of infectious disease dynamics for strongly immunizing pathogens with both mechanistic and machine learning models, and identify connections between these two approaches. Author summary: Machine learning techniques in infectious disease modeling have grown in popularity in recent years. However, systematic investigation into the comparative performance of these approaches with traditional mechanistic models are still rare. In this paper, we compare one of the most widely used semi-mechanistic models for measles (TSIR) with a commonly used machine learning approach (LASSO), comparing performance and limits in predicting short to long term outbreaks of measles, one of the best-documented and most-mechanistically-studied non-linear infectious disease dynamical systems. Our results show that in general the LASSO outperform TSIR for predicting less-regular dynamics, and it can achieve similar performance in other scenarios when compared to the TSIR. The LASSO also has the advantages of not requiring explicit demographic data in model training. Finally, we identify connections between these two approaches and show that the LASSO model can both qualitatively and quantitatively reconstruct mechanistic assumptions, notably susceptible dynamics, in the TSIR model. [ABSTRACT FROM AUTHOR]

Reported COVID-19 cases and deaths provide a delayed and incomplete picture of SARS-CoV-2 infections in the United States (US). Accurate estimates of both the timing and magnitude of infections are needed to characterize viral transmission dynamics and better understand COVID-19 disease burden. We estimated time trends in SARS-CoV-2 transmission and other COVID-19 outcomes for every county in the US, from the first reported COVID-19 case in January 13, 2020 through January 1, 2021. To do so we employed a Bayesian modeling approach that explicitly accounts for reporting delays and variation in case ascertainment, and generates daily estimates of incident SARS-CoV-2 infections on the basis of reported COVID-19 cases and deaths. The model is freely available as the covidestim R package. Nationally, we estimated there had been 49 million symptomatic COVID-19 cases and 404,214 COVID-19 deaths by the end of 2020, and that 28% of the US population had been infected. There was county-level variability in the timing and magnitude of incidence, with local epidemiological trends differing substantially from state or regional averages, leading to large differences in the estimated proportion of the population infected by the end of 2020. Our estimates of true COVID-19 related deaths are consistent with independent estimates of excess mortality, and our estimated trends in cumulative incidence of SARS-CoV-2 infection are consistent with trends in seroprevalence estimates from available antibody testing studies. Reconstructing the underlying incidence of SARS-CoV-2 infections across US counties allows for a more granular understanding of disease trends and the potential impact of epidemiological drivers. Author summary: Because many COVID-19 infections go undetected, reported numbers of COVID-19 cases and deaths underestimate the true size of the epidemic. To address this problem, we built a model to estimate the number of new SARS-CoV-2 infections over time in each U.S. state and county. In this paper, we present time trends of infections and other disease outcomes from the first reported case in the U.S. until January 1, 2021, for each state and county. The time series of infection estimates suggest that the US epidemic is best described a series of related epidemics, varying in their timing and intensity. We estimate that over a quarter of the US population was infected with SARS-CoV-2 in 2020 and by the end of 2020 0.12% of the population had died from COVID-19. State-level results were consistent with external measures of disease burden, including estimates of SARS-CoV-2 seroprevalence and excess mortality. Our findings help better understand the epidemic in the pre-vaccine era and demonstrate the feasibility of estimating SARS-CoV-2 infections at local levels using routinely reported case and death data. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has presented significant public health and economic challenges worldwide. Various health and non-pharmaceutical policies have been adopted by different countries to control the spread of the virus. To shed light on the impact of vaccination and social mobilization policies during this wide-ranging crisis, this paper applies a system dynamics analysis on the effectiveness of these two types of policies on pandemic containment and the economy in the United States. Based on the simulation of different policy scenarios, the findings are expected to help decisions and mitigation efforts throughout this pandemic and beyond. [ABSTRACT FROM AUTHOR]

Since the 1950s, the number of doctorate recipients has risen dramatically in the United States. In this paper, we investigate whether the longevity of doctorate recipients’ publication careers has changed. This is achieved by matching 1951–2010 doctorate recipients with rare names in astrophysics, chemistry, economics, genetics and psychology in the dissertation database ProQuest to their publications in the publication database Web of Science. Our study shows that pre-PhD publication careers have changed: the median year of first publication has shifted from after the PhD to several years before PhD in most of the studied fields. In contrast, post-PhD publication career spans have not changed much in most fields. The share of doctorate recipients who have published for more than twenty years has remained stable over time; the shares of doctorate recipients publishing for shorter periods also remained almost unchanged. Thus, though there have been changes in pre-PhD publication careers, post-PhD career spans remained quite stable. [ABSTRACT FROM AUTHOR]

Surveys are commonly used to quantify public opinions of climate change and to inform sustainability policies. However, conducting large-scale population-based surveys is often a difficult task due to time and resource constraints. This paper outlines a machine learning framework—grounded in statistical learning theory and natural language processing—to augment climate change opinion surveys with social media data. The proposed framework maps social media discourse to climate opinion surveys, allowing for discerning the regionally distinct topics and themes that contribute to climate opinions. The analysis reveals significant regional variation in the emergent social media topics associated with climate opinions. Furthermore, significant correlation is identified between social media discourse and climate attitude. However, the dependencies between topic discussion and climate opinion are not always intuitive and often require augmenting the analysis with a topic's most frequent n-grams and most representative tweets to effectively interpret the relationship. Finally, the paper concludes with a discussion of how these results can be used in the policy framing process to quickly and effectively understand constituents' opinions on critical issues. [ABSTRACT FROM AUTHOR]

Background: In 2009, the promulgation of US Food and Drug Administration (FDA) tobacco regulation focused attention on cigarette flavor additives. The tobacco industry had prepared for this eventuality by initiating a research program focusing on additive toxicity. The objective of this study was to analyze Philip Morris' Project MIX as a case study of tobacco industry scientific research being positioned strategically to prevent anticipated tobacco control regulations. Methods and Findings: We analyzed previously secret tobacco industry documents to identify internal strategies for research on cigarette additives and reanalyzed tobacco industry peer-reviewed published results of this research. We focused on the key group of studies conducted by Phillip Morris in a coordinated effort known as ''Project MIX.'' Documents showed that Project MIX subsumed the study of various combinations of 333 cigarette additives. In addition to multiple internal reports, this work also led to four peer-reviewed publications (published in 2001). These papers concluded that there was no evidence of substantial toxicity attributable to the cigarette additives studied. Internal documents revealed post hoc changes in analytical protocols after initial statistical findings indicated an additive-associated increase in cigarette toxicity as well as increased total particulate matter (TPM) concentrations in additive-modified cigarette smoke. By expressing the data adjusted by TPM concentration, the published papers obscured this underlying toxicity and particulate increase. The animal toxicology results were based on a small number of rats in each experiment, raising the possibility that the failure to detect statistically significant changes in the end points was due to underpowering the experiments rather than lack of a real effect. Conclusion: The case study of Project MIX shows tobacco industry scientific research on the use of cigarette additives cannot be taken at face value. The results demonstrate that toxins in cigarette smoke increase substantially when additives are put in cigarettes, including the level of TPM. In particular, regulatory authorities, including the FDA and similar agencies elsewhere, could use the Project MIX data to eliminate the use of these 333 additives (including menthol) from cigarettes. [ABSTRACT FROM AUTHOR]

Background: Human knowledge and innovation are recorded in two media: scholarly publication and patents. These records not only document a new scientific insight or new method developed, but they also carefully cite prior work upon which the innovation is built. Methodology: We quantify the impact of information flow across fields using two large citation dataset: one spanning over a century of scholarly work in the natural sciences, social sciences and humanities, and second spanning a quarter century of United States patents. Conclusions: We find that a publication's citing across disciplines is tied to its subsequent impact. In the case of patents and natural science publications, those that are cited at least once are cited slightly more when they draw on research outside of their area. In contrast, in the social sciences, citing within one's own field tends to be positively correlated with impact. [ABSTRACT FROM AUTHOR]

Background: Based on single cross-sectional data, prior research finds evidence of “transportation assimilation” among U.S. immigrants: the length of stay in the U.S. is negatively correlated with public transit use. This paper revisits this question by using repeated cross-sectional data, and examines the trend of transportation assimilation over time. Methods and results: Using 1980, 1990, 2000 1% census and 2010 (1%) American Community Survey, I examine the relationship between the length of stay in the U.S. and public transit ridership among immigrants. I first run regressions separately in four data sets: I regress public transit ridership on the length of stay, controlling for other individual and geographic variables. I then compare the magnitudes of the relationship in four regressions. To study how the rate of transportation assimilation changes over time, I pool the data set and regress public transit ridership on the length of stay and its interactions with year dummies to compare the coefficients across surveys. Results confirm the conclusion of transportation assimilation: as the length of stay in the U.S. increases, an immigrant’s public transit use decreases. However, the repeated cross-section analysis suggests the assimilation rate has been decreasing in the past few decades. Conclusions: This paper finds evidence of transportation assimilation: immigrants become less likely to ride public transit as the length of stay in the U.S. increases. The assimilation rate, however, has been decreasing over time. This paper finds that the rate of public transit ridership among new immigrants upon arrival, the geographic distribution of immigrants, and the changing demographics of the U.S. immigrants play roles in affecting the trend of transportation assimilation. [ABSTRACT FROM AUTHOR]

In an effort to gauge the global pandemic's impact on social thoughts and behavior, it is important to answer the following questions: (1) What kinds of topics are individuals and groups vocalizing in relation to the pandemic? (2) Are there any noticeable topic trends and if so how do these topics change over time and in response to major events? In this paper, through the advanced Sequential Latent Dirichlet Allocation model, we identified twelve of the most popular topics present in a Twitter dataset collected over the period spanning April 3rd to April 13th, 2020 in the United States and discussed their growth and changes over time. These topics were both robust, in that they covered specific domains, not simply events, and dynamic, in that they were able to change over time in response to rising trends in our dataset. They spanned politics, healthcare, community, and the economy, and experienced macro-level growth over time, while also exhibiting micro-level changes in topic composition. Our approach differentiated itself in both scale and scope to study the emerging topics concerning COVID-19 at a scale that few works have been able to achieve. We contributed to the cross-sectional field of urban studies and big data. Whereas we are optimistic towards the future, we also understand that this is an unprecedented time that will have lasting impacts on individuals and society at large, impacting not only the economy or geo-politics, but human behavior and psychology. Therefore, in more ways than one, this research is just beginning to scratch the surface of what will be a concerted research effort into studying the history and repercussions of COVID-19. [ABSTRACT FROM AUTHOR]

Recent years have witnessed wider prevalence of vertebral column pathologies due to lifestyle changes, sedentary behaviors, or injuries. Spondylolisthesis and scoliosis are two of the most common ailments with an incidence of 5% and 3% in the United States population, respectively. Both of these abnormalities can affect children at a young age and, if left untreated, can progress into severe pain. Moreover, severe scoliosis can even lead to lung and heart problems. Thus, early diagnosis can make it easier to apply remedies/interventions and prevent further disease progression. Current diagnosis methods are based on visual inspection by physicians of radiographs and/or calculation of certain angles (e.g., Cobb angle). Traditional artificial intelligence-based diagnosis systems utilized these parameters to perform automated classification, which enabled fast and easy diagnosis supporting tools. However, they still require the specialists to perform error-prone tedious measurements. To this end, automated measurement tools were proposed based on processing techniques of X-ray images. In this paper, we utilize advances in deep transfer learning to diagnose spondylolisthesis and scoliosis from X-ray images without the need for any measurements. We collected raw data from real X-ray images of 338 subjects (i.e., 188 scoliosis, 79 spondylolisthesis, and 71 healthy). Deep transfer learning models were developed to perform three-class classification as well as pair-wise binary classifications among the three classes. The highest mean accuracy and maximum accuracy for three-class classification was 96.73% and 98.02%, respectively. Regarding pair-wise binary classification, high accuracy values were achieved for most of the models (i.e., > 98%). These results and other performance metrics reflect a robust ability to diagnose the subjects' vertebral column disorders from standard X-ray images. The current study provides a supporting tool that can reasonably help the physicians make the correct early diagnosis with less effort and errors, and reduce the need for surgical interventions. [ABSTRACT FROM AUTHOR]

Approximately 300,000 persons in the United States (US) are infected with Trypanosoma cruzi, the protozoan that causes Chagas disease, but less than 1% are estimated to have received antiparasitic treatment. Benznidazole was approved by the US Food and Drug Administration (FDA) for treatment of T. cruzi infection in 2017 and commercialized in May 2018. This paper analyzes factors that affect access to benznidazole following commercialization and suggests directions for future actions to expand access. We applied an access framework to identify barriers, facilitators, and key actors that influence the ability of people with Chagas disease to receive appropriate treatment with benznidazole. Data were collected from the published literature, key informants, and commercial databases. We found that the mean number of persons who obtained benznidazole increased from just under 5 when distributed by the CDC to 13 per month after the commercial launch (from May 2018 to February 2019). Nine key barriers to access were identified: lack of multi-sector coordination, failure of health care providers to use a specific order form, lack of an emergency delivery system, high medical costs for uninsured patients, narrow indications for use of benznidazole, lack of treatment guidelines, limited number of qualified treaters, difficulties for patients to make medical appointments, and inadequate evaluation by providers to determine eligibility for treatment. Our analysis shows that access to benznidazole is still limited after FDA approval. We suggest six areas for strategic action for the pharmaceutical company that markets benznidazole and its allied private foundation to expand access to benznidazole in the US. In addition, we recommend expanding the existing researcher-clinician network by including government agencies, companies and others. This paper's approach could be applied to access programs for benznidazole in other countries or for other health products that target neglected populations throughout the world. Author summary: Less than 1% of persons in the United States with Trypanosoma cruzi infection, the cause of Chagas disease, have received antiparasitic treatment. In this paper, we examine the status of access to benznidazole in the United States following its approval by the FDA for treatment of Chagas disease treatment in 2017 and its commercialization in 2018. We found that the average number of patients who received benznidazole from the manufacturer increased after commercialization from just under 5 (when distributed by the CDC) to 13 patients (when sold in commercial channels) per month. We identified various barriers to access to treatment such as problems in the drug ordering process, low demand from health care providers, inadequate clinical assessment of patient eligibility for treatment, obstacles for patients to make medical appointments or to pay medical costs, and weak coordination among key actors in the health system. To overcome these access barriers, we propose strategic actions that can be taken by the pharmaceutical producer in collaboration with other key actors. Our work contributes to expanding access to Chagas disease treatment in the United States, and our approach could be applied to efforts to expand access to health products for other neglected diseases. [ABSTRACT FROM AUTHOR]

The United States National Institutes of Health (NIH) imposed a public access policy on all publications for which the research was supported by their grants; the policy was drafted in 2004 and took effect in 2008. The policy is now 11 years old, yet no analysis has been presented to assess whether in fact this largest-scale US-based public access policy affected the vitality of the scholarly publishing enterprise, as manifested in changed mortality or natality rates of biomedical journals. We show here that implementation of the NIH policy was associated with slightly elevated mortality rates and mildly depressed natality rates of biomedical journals, but that birth rates so exceeded death rates that numbers of biomedical journals continued to rise, even in the face of the implementation of such a sweeping public access policy. This Perspective article analyzes whether the US National Institutes of Health open access policy, which from 2008 forced open access on thousands of papers, had a detectable effect on the "demography" of biomedical journals. [ABSTRACT FROM AUTHOR]

In light of the outbreak of COVID-19, analyzing and measuring human mobility has become increasingly important. A wide range of studies have explored spatiotemporal trends over time, examined associations with other variables, evaluated non-pharmacologic interventions (NPIs), and predicted or simulated COVID-19 spread using mobility data. Despite the benefits of publicly available mobility data, a key question remains unanswered: are models using mobility data performing equitably across demographic groups? We hypothesize that bias in the mobility data used to train the predictive models might lead to unfairly less accurate predictions for certain demographic groups. To test our hypothesis, we applied two mobility-based COVID infection prediction models at the county level in the United States using SafeGraph data, and correlated model performance with sociodemographic traits. Findings revealed that there is a systematic bias in models' performance toward certain demographic characteristics. Specifically, the models tend to favor large, highly educated, wealthy, young, and urban counties. We hypothesize that the mobility data currently used by many predictive models tends to capture less information about older, poorer, less educated and people from rural regions, which in turn negatively impacts the accuracy of the COVID-19 prediction in these areas. Ultimately, this study points to the need of improved data collection and sampling approaches that allow for an accurate representation of the mobility patterns across demographic groups. [ABSTRACT FROM AUTHOR]