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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: 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]

Background: While the high prevalence of preterm births and its impact on infant mortality in the US have been widely acknowledged, recent data suggest that even full-term births in the US face substantially higher mortality risks compared to European countries with low infant mortality rates. In this paper, we use the most recent birth records in the US to more closely analyze the primary causes underlying mortality rates among full-term births.Methods and Findings: Linked birth and death records for the period 2010-2012 were used to identify the state- and cause-specific burden of infant mortality among full-term infants (born at 37-42 weeks of gestation). Multivariable logistic models were used to assess the extent to which state-level differences in full-term infant mortality (FTIM) were attributable to observed differences in maternal and birth characteristics. Random effects models were used to assess the relative contribution of state-level variation to FTIM. Hypothetical mortality outcomes were computed under the assumption that all states could achieve the survival rates of the best-performing states. A total of 10,175,481 infants born full-term in the US between January 1, 2010, and December 31, 2012, were analyzed. FTIM rate (FTIMR) was 2.2 per 1,000 live births overall, and ranged between 1.29 (Connecticut, 95% CI 1.08, 1.53) and 3.77 (Mississippi, 95% CI 3.39, 4.19) at the state level. Zero states reached the rates reported in the 6 low-mortality European countries analyzed (FTIMR < 1.25), and 13 states had FTIMR > 2.75. Sudden unexpected death in infancy (SUDI) accounted for 43% of FTIM; congenital malformations and perinatal conditions accounted for 31% and 11.3% of FTIM, respectively. The largest mortality differentials between states with good and states with poor FTIMR were found for SUDI, with particularly large risk differentials for deaths due to sudden infant death syndrome (SIDS) (odds ratio [OR] 2.52, 95% CI 1.86, 3.42) and suffocation (OR 4.40, 95% CI 3.71, 5.21). Even though these mortality differences were partially explained by state-level differences in maternal education, race, and maternal health, substantial state-level variation in infant mortality remained in fully adjusted models (SIDS OR 1.45, suffocation OR 2.92). The extent to which these state differentials are due to differential antenatal care standards as well as differential access to health services could not be determined due to data limitations. Overall, our estimates suggest that infant mortality could be reduced by 4,003 deaths (95% CI 2,284, 5,587) annually if all states were to achieve the mortality levels of the best-performing state in each cause-of-death category. Key limitations of the analysis are that information on termination rates at the state level was not available, and that causes of deaths may have been coded differentially across states.Conclusions: More than 7,000 full-term infants die in the US each year. The results presented in this paper suggest that a substantial share of these deaths may be preventable. Potential improvements seem particularly large for SUDI, where very low rates have been achieved in a few states while average mortality rates remain high in most other areas. Given the high mortality burden due to SIDS and suffocation, policy efforts to promote compliance with recommended sleeping arrangements could be an effective first step in this direction. [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]

While road safety in the United States (U.S.) has been continually improving since the 1970's, there are indications that these improvements are becoming increasingly difficult to sustain. Strategic Highway Safety Plans (SHSPs) are prepared by States to guide road safety management, however assessing the appropriateness of these plans remains a significant challenge, especially for the future in which they are to be applied. This study developed a new methodology to assess SHSPs from the perspectives of comprehensive system-based safety management and relevant future issues that can be applied before SHSPs are implemented, thereby avoiding long periods after implementation before assessing the appropriateness of the plans. A rating scale was developed and applied to assess 48 U.S. SHSPs against two key criteria: 1. a comprehensive framework for road safety, and 2. the anticipated changing, difficult and unpredictable nature of future transport and its context. The analysis concluded that current SHSPs have good national oversight with several strengths but were weak in some areas of content and did not address future challenges. Improvements are suggested to strengthen the plans’ thoroughness by being consistent with systems theory and practice, as well as ways that these SHSPs can be more resilient to future circumstances. Implementing the recommendations in this paper provides the opportunity to adopt a system-based safety management practice that has been successful in other hazardous industries. Doing so is expected to most efficiently and effectively continue the recent improvements to road safety, which is likely to be increasingly difficult otherwise. [ABSTRACT FROM AUTHOR]

Background: There have been growing recommendations to include education in multi-disciplinary interventions targeting chronic pain management. However, effects of this strategy on short- and long-term self-management of chronic pain, remain largely unexplored. Objectives: 1. To provide an updated overview of studies that report on the impact of patient education in multi-disciplinary interventions, on self-management of chronic pain; 2. To explore associations between education and chronic pain self-management techniques; and 3. To identify the format and duration of suitable chronic pain interventions targeted at patient self-management. Methods: Design: Narrative systematic literature review of randomised or controlled study designs. Data Sources: PubMed, CINAHL, EMBASE, PsycINFO. Participants: Adult patients with chronic pain of any aetiology participating in multi-disciplinary programs that included education. Main outcome measures: Assessments of level of pain, function, quality of life, self-efficacy, self-management, and any other relevant assessments. Study Appraisal and Synthesis Methods: PRISMA guidelines, Cochrane Risk of Bias tool, and TIDieR model. Results: Database searching identified 485 potential papers. After removal of duplicates, and irrelevant articles by title and abstract, 120 full-text articles were reviewed and 27 studies were included in this systematic review. Studies were predominantly from the United States (n = 8; 29.6%). Over one hundred outcome measures were identified across all studies, with significant variation also observed in terms of how chronic pain duration was defined, and how education was delivered to participants. Overall, positive benefits of education were reported. Conclusions: Education, as part of multi-disciplinary programs, is likely to improve self-management and self-efficacy in people with chronic pain of any aetiology. Heterogeneity in terms of: chronic pain duration; educational resources; healthcare professionals; and outcome measures, were identified as limitations. Further research, in the form of Randomised Controlled Trials addressing these limitations, is recommended. [ABSTRACT FROM AUTHOR]

Prophylactic interventions such as vaccine allocation are some of the most effective public health policy planning tools. The supply of vaccines, however, is limited and an important challenge is to optimally allocate the vaccines to minimize epidemic impact. This resource allocation question (which we refer to as VID) has multiple dimensions: when, where, to whom, etc. Most of the existing literature in this topic deals with the latter (to whom), proposing policies that prioritize individuals by age and disease risk. However, since seasonal influenza spread has a typical spatial trend, and due to the temporal constraints enforced by the availability schedule, the when and where problems become equally, if not more, relevant. In this paper, we study the VID problem in the context of seasonal influenza spread in the United States. We develop a national scale metapopulation model for influenza that integrates both short and long distance human mobility, along with realistic data on vaccine uptake. We also design GA, a greedy algorithm for allocating the vaccine supply at the state level under temporal constraints and show that such a strategy improves over the current baseline of pro-rata allocation, and the improvement is more pronounced for higher vaccine efficacy and moderate flu season intensity. Further, the resulting strategy resembles a ring vaccination applied spatiallyacross the US. [ABSTRACT FROM AUTHOR]

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]

Many US biomedical PhD programs receive more applications for admissions than they can accept each year, necessitating a selective admissions process. Typical selection criteria include standardized test scores, undergraduate grade point average, letters of recommendation, a resume and/or personal statement highlighting relevant research or professional experience, and feedback from interviews with training faculty. Admissions decisions are often founded on assumptions that these application components correlate with research success in graduate school, but these assumptions have not been rigorously tested. We sought to determine if any application components were predictive of student productivity measured by first-author student publications and time to degree completion. We collected productivity metrics for graduate students who entered the umbrella first-year biomedical PhD program at the University of North Carolina at Chapel Hill from 2008–2010 and analyzed components of their admissions applications. We found no correlations of test scores, grades, amount of previous research experience, or faculty interview ratings with high or low productivity among those applicants who were admitted and chose to matriculate at UNC. In contrast, ratings from recommendation letter writers were significantly stronger for students who published multiple first-author papers in graduate school than for those who published no first-author papers during the same timeframe. We conclude that the most commonly used standardized test (the general GRE) is a particularly ineffective predictive tool, but that qualitative assessments by previous mentors are more likely to identify students who will succeed in biomedical graduate research. Based on these results, we conclude that admissions committees should avoid over-reliance on any single component of the application and de-emphasize metrics that are minimally predictive of student productivity. We recommend continual tracking of desired training outcomes combined with retrospective analysis of admissions practices to guide both application requirements and holistic application review. [ABSTRACT FROM AUTHOR]

While basic access to clean water is critical, another important issue is the affordability of water access for people around the globe. Prior international work has highlighted that a large proportion of consumers could not afford water if priced at full cost recovery levels. Given growing concern about affordability issues due to rising water rates, and a comparative lack of work on affordability in the developed world, as compared to the developing world, more work is needed in developed countries to understand the extent of this issue in terms of the number of households and persons impacted. To address this need, this paper assesses potential affordability issues for households in the United States using the U.S. EPA’s 4.5% affordability criteria for combined water and wastewater services. Analytical results from this paper highlight high-risk and at-risk households for water poverty or unaffordable water services. Many of these households are clustered in pockets of water poverty within counties, which is a concern for individual utility providers servicing a large proportion of customers with a financial inability to pay for water services. Results also highlight that while water rates remain comparatively affordable for many U.S. households, this trend will not continue in the future. If water rates rise at projected amounts over the next five years, conservative projections estimate that the percentage of U.S. households who will find water bills unaffordable could triple from 11.9% to 35.6%. This is a concern due to the cascading economic impacts associated with widespread affordability issues; these issues mean that utility providers could have fewer customers over which to spread the large fixed costs of water service. Unaffordable water bills also impact customers for whom water services are affordable via higher water rates to recover the costs of services that go unpaid by lower income households. [ABSTRACT FROM AUTHOR]

Lung cancer, primarily non-small-cell lung cancer (NSCLC), is the leading cause of cancer deaths in the United States and worldwide. While early detection significantly improves five-year survival, there are no reliable diagnostic tools for early detection. Several exosomal microRNAs (miRs) are overexpressed in NSCLC, and have been suggested as potential biomarkers for early detection. The present paper develops a mathematical model for early stage of NSCLC with emphasis on the role of the three highest overexpressed miRs, namely miR-21, miR-205 and miR-155. Simulations of the model provide quantitative relationships between the tumor volume and the total mass of each of the above miRs in the tumor. Because of the positive correlation between these miRs in the tumor tissue and in the blood, the results of the paper may be viewed as a first step toward establishing a combination of miRs 21, 205, 155 and possibly other miRs as serum biomarkers for early detection of NSCLC. [ABSTRACT FROM AUTHOR]

BACKGROUND: The assessment of bone age and skeletal maturity and its comparison to chronological age is an important task in the medical environment for the diagnosis of pediatric endocrinology, orthodontics and orthopedic disorders, and legal environment in what concerns if an individual is a minor or not when there is a lack of documents. Being a time-consuming activity that can be prone to inter- and intra-rater variability, the use of methods which can automate it, like Machine Learning techniques, is of value. OBJECTIVE: The goal of this paper is to present the state of the art evidence, trends and gaps in the research related to bone age assessment studies that make use of Machine Learning techniques. METHOD: A systematic literature review was carried out, starting with the writing of the protocol, followed by searches on three databases: Pubmed, Scopus and Web of Science to identify the relevant evidence related to bone age assessment using Machine Learning techniques. One round of backward snowballing was performed to find additional studies. A quality assessment was performed on the selected studies to check for bias and low quality studies, which were removed. Data was extracted from the included studies to build summary tables. Lastly, a meta-analysis was performed on the performances of the selected studies. RESULTS: 26 studies constituted the final set of included studies. Most of them proposed automatic systems for bone age assessment and investigated methods for bone age assessment based on hand and wrist radiographs. The samples used in the studies were mostly comprehensive or bordered the age of 18, and the data origin was in most of cases from United States and West Europe. Few studies explored ethnic differences. CONCLUSIONS: There is a clear focus of the research on bone age assessment methods based on radiographs whilst other types of medical imaging without radiation exposure (e.g. magnetic resonance imaging) are not much explored in the literature. Also, socioeconomic and other aspects that could influence in bone age were not addressed in the literature. Finally, studies that make use of more than one region of interest for bone age assessment are scarce. [ABSTRACT FROM AUTHOR]

Addressing critical global health issues, such as antimicrobial resistance, infectious disease outbreaks, and natural disasters, requires strong coordination and management across sectors. The One Health approach is the integrative effort of multiple sectors working to attain optimal health for people, animals, and the environment, and is increasingly recognized by experts as a means to address complex challenges. However, practical application of the One Health approach has been challenging. The One Health Systems Mapping and Analysis Resource Toolkit (OH-SMART) introduced in this paper was designed using a multistage prototyping process to support systematic improvement in multi-sectoral coordination and collaboration to better address complex health concerns through an operational, stepwise, and practical One Health approach. To date, OH-SMART has been used to strengthen One Health systems in 17 countries and has been deployed to revise emergency response frameworks, improve antimicrobial resistance national action plans and create multi agency infectious disease collaboration protocols. OH-SMART has proven to be user friendly, robust, and capable of fostering multi-sectoral collaboration and complex system-wide problem solving. [ABSTRACT FROM AUTHOR]

Objective: Lung cancer is the most common type of cancer around the world, and it represents the main cause of death in the USA. Surgical treatment is the optimal therapeutic strategy for resectable non-small cell lung cancer. The principal factor for long-term survival after complete resection is the anatomic extension of the neoplasm. However, other factors also have adverse effects on operative mortality, and influence long-term outcome. In this paper we propose an algorithmic solution for the estimation of 5-years survival rate in lung cancer patients undertaking pulmonary resection. Materials and methods: We address the issue of survival analysis through decision-making techniques based on fuzzy and soft set theories. We develop an expert system based on clinical and functional data of lung cancer resections in patients with cancer that can be used to predict the survival of patients. Results: The evaluation of surgical risk in patients undertaking pulmonary resection is a primary target for thoracic surgeons. Lung cancer survival is influenced by many factors. The computational performance of our algorithm is critically analyzed by an experimental study. The correct survival classification is achieved with an accuracy of 79.0%. Our novel soft-set based criterion is an effective and precise diagnosis application for the determination of the survival rate. [ABSTRACT FROM AUTHOR]

Visual design, learning sciences, and nanotechnology may be strange bedfellows; yet, as this paper highlights, peer interaction between a designer and a scientist is an effective method for helping scientists acquire visual design skills. We describe our findings from observing twelve sessions at the Design Help Desk, a tutoring center at the University of Washington. At each session, a scientist (who is expert in his own domain but a novice in design) consulted a designer (who is expert in design but a novice in science) in order to receive advice and guidance on how to improve a scientific visualization. At the Design Help Desk, this pairing consistently produced a momentary disequilibrium in the scientist’s thought process: a disequilibrium that led to agency (where the scientist gained ownership of his/her own learning) and conceptual change in the scientist’s understanding of visual design. Scientists who visited the Design Help Desk were satisfied with their experience, and their published work demonstrated an improved ability to visually communicate research findings—a skill critical to the advancement of science. To our knowledge, the Design Help Desk is a unique effort to educate scientists in visual design; we are not aware of any other design-advice/tutoring centers at public or private universities in the United States or abroad. [ABSTRACT FROM AUTHOR]

Since the late 1990s, particularly since the global financial crisis, the core inflation of main developed economies’ has been persistently below target. The factors hindering the achievement of inflation targets are nothing more than commodity price, oil supply, weakness of aggregate demand, and various other factors. In addition, technology and globalization have also played a significant role. This paper uses an extended hybrid New Keynesian Phillips Curve (NKPC) model to quantify the contribution of technology and globalization variables to inflation in the United States (U.S.). The analysis suggests that technology and globalization well explain the low inflation dynamics in the U.S., as the impact of globalization on domestic inflation has been weakening over the past 20 years or so, while the impact of technology on inflation has been increasing. At present, technology exerts a greater role than globalization on low-inflation in the U.S.. This raises a different perspective for understanding the phenomenon of low inflation in the U.S. and other regions. [ABSTRACT FROM AUTHOR]

Are public officials more responsive to requests from affluent or poor constituents? A growing body of evidence suggests that lawmakers are more responsive to the rich when they craft policy. However, some scholars theorize that officials also exhibit a corresponding bias in favor of the poor when they handle casework, essentially giving policy to the rich and services to the poor. In this paper, we test this casework prediction using four experiments in which confederates sent simple requests to state or local officials. In each, our confederates’ reported social classes were randomly assigned and signaled with a brief introductory statement mentioning the sender’s occupation or economic situation. Across our samples, we find precisely-estimated null effects of social class biases: the officials we studied were equally likely to respond regardless of the constituent’s class. These findings raise doubts about whether casework is really a class-biased process. [ABSTRACT FROM AUTHOR]

The ability to produce timely and accurate flu forecasts in the United States can significantly impact public health. Augmenting forecasts with internet data has shown promise for improving forecast accuracy and timeliness in controlled settings, but results in practice are less convincing, as models augmented with internet data have not consistently outperformed models without internet data. In this paper, we perform a controlled experiment, taking into account data backfill, to improve clarity on the benefits and limitations of augmenting an already good flu forecasting model with internet-based nowcasts. Our results show that a good flu forecasting model can benefit from the augmentation of internet-based nowcasts in practice for all considered public health-relevant forecasting targets. The degree of forecast improvement due to nowcasting, however, is uneven across forecasting targets, with short-term forecasting targets seeing the largest improvements and seasonal targets such as the peak timing and intensity seeing relatively marginal improvements. The uneven forecasting improvements across targets hold even when “perfect” nowcasts are used. These findings suggest that further improvements to flu forecasting, particularly seasonal targets, will need to derive from other, non-nowcasting approaches. [ABSTRACT FROM AUTHOR]

Although substantial attention has focused on efforts by the new Administration to block environmental policies, climate politics have been contentious in the US since well before the election of Donald Trump. In this paper, we extend previous work on empirical examinations of echo chambers in US climate politics using new data collected on the federal climate policy network in summer 2016. We test for the similarity and differences at two points in time in homophily and echo chambers using Exponential Random Graph Models (ERGM) to compare new findings from 2016 to previous work on data from 2010. We show that echo chambers continue to play a significant role in the network of information exchange among policy elites working on the issue of climate change. In contrast to previous findings where echo chambers centered on a binding international commitment to emission reductions, we find that the pre-existing echo chambers have almost completely disappeared and new structures have formed around one of the main components of the Obama Administration’s national climate policy: the Clean Power Plan. These results provide empirical evidence that science communication and policymaking at the elite level shift in relation to the policy instruments under consideration. [ABSTRACT FROM AUTHOR]

Drawing on recent contributions inferring financial interconnectedness from market data, our paper provides new insights on the evolution of the US financial industry over a long period of time by using several tools coming from network science. Relying on a Time-Varying Parameter Vector AutoRegressive (TVP-VAR) approach on stock market returns to retrieve unobserved directed links among financial institutions, we reconstruct a fully dynamic network in the sense that connections are let to evolve through time. The financial system analysed consists of a large set of 155 financial institutions that are all the banks, broker-dealers, insurance and real estate companies listed in the Standard & Poors’ 500 index over the 1993–2014 period. Looking alternatively at the individual, then sector-, community- and system-wide levels, we show that network sciences’ tools are able to support well-known features of the financial markets such as the dramatic fall of connectivity following Lehman Brothers’ collapse. More importantly, by means of less traditional metrics, such as sectoral interface or measurements based on contagion processes, our results document the co-existence of both fragmentation and integration phases between firms independently from the sectors they belong to, and doing so, question the relevance of existing macroprudential surveillance frameworks which have been mostly developed on a sectoral basis. Overall, our results improve our understanding of the US financial landscape and may have important implications for risk monitoring as well as macroprudential policy design. [ABSTRACT FROM AUTHOR]

This paper calculates the distribution of an adjusted measure of income that deducts damages due to exposure to air pollution from reported market income in the United States from 2011 to 2014. The Gini coefficient for this measure of adjusted income is 0.682 in 2011, as compared to 0.482 for market income. By 2014, we estimate that the Gini for adjusted income fell to 0.646, while the market income Gini did not appreciably change. The inclusion of air pollution damage acts like a regressive tax: with air pollution, the bottom 20% of households lose roughly 10% of the share of income, while the top 20% of households gain 10%. We find that, unlike the case for market income, New England is not the most unequal division with respect to adjusted income. Further, the difference between adjusted income for white and Hispanics is smaller than expected. However, the gap in augmented income between whites and African-Americans is widening. [ABSTRACT FROM AUTHOR]

Introduction: Concerns about reproducibility and impact of research urge improvement initiatives. Current university ranking systems evaluate and compare universities on measures of academic and research performance. Although often useful for marketing purposes, the value of ranking systems when examining quality and outcomes is unclear. The purpose of this study was to evaluate usefulness of ranking systems and identify opportunities to support research quality and performance improvement. Methods: A systematic review of university ranking systems was conducted to investigate research performance and academic quality measures. Eligibility requirements included: inclusion of at least 100 doctoral granting institutions, be currently produced on an ongoing basis and include both global and US universities, publish rank calculation methodology in English and independently calculate ranks. Ranking systems must also include some measures of research outcomes. Indicators were abstracted and contrasted with basic quality improvement requirements. Exploration of aggregation methods, validity of research and academic quality indicators, and suitability for quality improvement within ranking systems were also conducted. Results: A total of 24 ranking systems were identified and 13 eligible ranking systems were evaluated. Six of the 13 rankings are 100% focused on research performance. For those reporting weighting, 76% of the total ranks are attributed to research indicators, with 24% attributed to academic or teaching quality. Seven systems rely on reputation surveys and/or faculty and alumni awards. Rankings influence academic choice yet research performance measures are the most weighted indicators. There are no generally accepted academic quality indicators in ranking systems. Discussion: No single ranking system provides a comprehensive evaluation of research and academic quality. Utilizing a combined approach of the Leiden, Thomson Reuters Most Innovative Universities, and the SCImago ranking systems may provide institutions with a more effective feedback for research improvement. Rankings which extensively rely on subjective reputation and “luxury” indicators, such as award winning faculty or alumni who are high ranking executives, are not well suited for academic or research performance improvement initiatives. Future efforts should better explore measurement of the university research performance through comprehensive and standardized indicators. This paper could serve as a general literature citation when one or more of university ranking systems are used in efforts to improve academic prominence and research performance. [ABSTRACT FROM AUTHOR]

Understanding trends in the diverse resources provided by large rivers will help balance tradeoffs among stakeholders and inform strategies to mitigate the effects of landscape scale stressors such as climate change and invasive species. Absent a cohesive coordinated effort to assess trends in important large river resources, a logical starting point is to assess our ability to draw inferences from existing efforts. In this paper, we use a common analytical framework to analyze data from five disparate fish monitoring programs to better understand the nature of spatial and temporal trends in large river fish assemblages. We evaluated data from programs that monitor fishes in the Colorado, Columbia, Illinois, Mississippi, and Tallapoosa rivers using non-metric dimensional scaling ordinations and associated tests to evaluate trends in fish assemblage structure and native fish biodiversity. Our results indicate that fish assemblages exhibited significant spatial and temporal trends in all five of the rivers. We also document native species diversity trends that were variable within and between rivers and generally more evident in rivers with higher species richness and programs of longer duration. We discuss shared and basin-specific landscape level stressors. Having a basic understanding of the nature and extent of trends in fish assemblages is a necessary first step towards understanding factors affecting biodiversity and fisheries in large rivers. [ABSTRACT FROM AUTHOR]

Objectives: As gentrification continues in New York City as well as other urban areas, residents of lower socioeconomic status maybe at higher risk for residential displacement. Yet, there have been few quantitative assessments of the health impacts of displacement. The objective of this paper is to assess the association between displacement and healthcare access and mental health among the original residents of gentrifying neighborhoods in New York City. Methods: We used 2 data sources: 1) 2005–2014 American Community Surveys to identify gentrifying neighborhoods in New York City, and 2) 2006–2014 Statewide Planning and Research Cooperative System. Our cohort included 12,882 residents of gentrifying neighborhoods in 2006 who had records of emergency department visits or hospitalization at least once every 2 years in 2006–2014. Rates of emergency department visits and hospitalizations post-baseline were compared between residents who were displaced and those who remained. Results: During 2006–2014, 23% were displaced. Compared with those who remained, displaced residents were more likely to make emergency department visits and experience hospitalizations, mainly due to mental health (Rate Ratio = 1.8, 95% confidence interval = 1.5, 2.2), after controlling for baseline demographics, health status, healthcare utilization, residential movement, and the neighborhood of residence in 2006. Conclusions: These findings suggest negative impacts of displacement on healthcare access and mental health, particularly among adults living in urban areas and with a history of frequent emergency department visits or hospitalizations. [ABSTRACT FROM AUTHOR]

Fish consumption in Mexico is considered low (around 12 kg per person per year) and non-homogeneously distributed across the country. One of the reasons for this situation is the scarcity of wholesale selling sites. In this context, the Mercado del Mar (MM), located in Guadalajara city, Jalisco, is the second biggest wholesale fish market in Mexico, with a distribution of about 500 tons per day and a variety of about 350 different species of fish. In this paper, we argue that MM has accumulated social capital, which is formed from two main resources: buyer and seller relationships, and reputation. Specifically, the MM manages a broad and intensive interaction among business actors and the already achieved reputation allows the MM to adapt to market changes. To validate our hypotheses, an empirical study was conducted in 2015 by means of interviews to fish wholesalers in the MM and a sample of their suppliers and buyers. For simplicity we have only considered fresh water fish. We have followed snow-ball sampling as the survey strategy. Results show that the MM has responded to fish market dynamics organizing a complex network of buyers and suppliers whose relationships can be explained in the form of strong and weak ties. At the same time, reputation has been the central resource to build this social capital and also gives place to market transactions. Additionally, the strategic position of Guadalajara city and the well-connected routes have facilitated fish bulking and distribution in the region. [ABSTRACT FROM AUTHOR]

After decades-old efforts to nudge consumers towards healthier lifestyles through dietary guidelines, diet-related diseases are on the rise. In addition, a growing share of U.S. consumers proactively chooses nutritional supplements as an alternative preventative way of maintaining good health, a $25.5 billion industry in the United States. This paper investigates possible linkages between the economics of consumer supplement choices and the relationship to important dietary and health outcomes. We use National Health and Nutrition Examination Survey (NHANES) data to estimate the impact of nutritional supplements intake on respondent’s body weight outcomes, controlling for diet quality.: The focus of this article is to determine whether nutritional supplements takers differ from non-takers with regard to their health outcomes when controlling for differences in diet quality, based on individual Healthy Eating Index (HEI-2010) score. The analysis applies treatment effects estimators that account for the selection bias and endogeneity of self-reported behavior and diet-health outcomes. The analysis demonstrates a negative association between supplement intake and BMI but no significant effect on an individual’s diet quality. Our findings suggest that individuals proactively invest into their health by taking nutritional supplements instead of improving diet quality through more nutritious food choices. Our results provide important contributions to the literature on a key food policy issue. Knowledge of the determinants of supplement demand in the context of strong diet-health trends should also be helpful to stakeholders in the U.S. produce sector in their competition over consumer market share. [ABSTRACT FROM AUTHOR]

Encouraging knowledge flow between mutually relevant disciplines is a worthy aim of research policy makers. Yet, it is less clear what types of research promote cross-disciplinary knowledge flow and whether such research generates particularly influential knowledge. Empirical questions remain as to how to identify knowledge-flow mediating research and how to provide support for this research. This study contributes to addressing these gaps by proposing a new way to identify knowledge-flow mediating research at the individual research article level, instead of at more aggregated levels. We identify journal articles that link two mutually relevant disciplines in three ways—aggregating, bridging, and diffusing. We then examine the likelihood that these papers receive subsequent citations or have funding acknowledgments. Our case study of cognitive science and educational research knowledge flow suggests that articles that aggregate knowledge from multiple disciplines are cited significantly more often than are those whose references are drawn primarily from a single discipline. Interestingly, the articles that meet the criteria for being considered knowledge-flow mediators are less likely to reflect funding, based on reported acknowledgements, than were those that did not meet these criteria. Based on these findings, we draw implications for research policymakers. [ABSTRACT FROM AUTHOR]

The social functions of urbanized areas are highly dependent on and supported by the convenient access to public transportation systems, particularly for the less privileged populations who have restrained auto ownership. To accurately evaluate the public transit accessibility, it is critical to capture the spatiotemporal variation of transit services. This can be achieved by measuring the shortest paths or minimum travel time between origin-destination (OD) pairs at each time-of-day (e.g. every minute). In recent years, General Transit Feed Specification (GTFS) data has been gaining popularity for between-station travel time estimation due to its interoperability in spatiotemporal analytics. Many software packages, such as ArcGIS, have developed toolbox to enable the travel time estimation with GTFS. They perform reasonably well in calculating travel time between OD pairs for a specific time-of-day (e.g. 8:00 AM), yet can become computational inefficient and unpractical with the increase of data dimensions (e.g. all times-of-day and large network). In this paper, we introduce a new algorithm that is computationally elegant and mathematically efficient to address this issue. An open-source toolbox written in C++ is developed to implement the algorithm. We implemented the algorithm on City of St. George’s transit network to showcase the accessibility analysis enabled by the toolbox. The experimental evidence shows significant reduction on computational time. The proposed algorithm and toolbox presented is easily transferable to other transit networks to allow transit agencies and researchers perform high resolution transit performance analysis. [ABSTRACT FROM AUTHOR]

Background: Zika virus transmission dynamics in urban environments follow a complex spatiotemporal pattern that appears unpredictable and barely related to high mosquito density areas. In this context, human activity patterns likely have a major role in Zika transmission dynamics. This paper examines the effect of host variability in the amount of time spent outdoors on Zika epidemiology in an urban environment. Methodology/Principal findings: First, we performed a survey on time spent outdoors by residents of Miami-Dade County, Florida. Second, we analyzed both the survey and previously published national data on outdoors time in the U.S. to provide estimates of the distribution of the time spent outdoors. Third, we performed a computational modeling evaluation of Zika transmission dynamics, based on the time spent outdoors by each person. Our analysis reveals a strong heterogeneity of the host population in terms of time spent outdoors–data are well captured by skewed gamma distributions. Our model-based evaluation shows that in a heterogeneous population, Zika would cause a lower number of infections than in a more homogenous host population (up to 4-fold differences), but, at the same time, the epidemic would spread much faster. We estimated that in highly heterogeneous host populations the timing of the implementation of vector control measures is the major factor for limiting the number of Zika infections. Conclusions/Significance: Our findings highlight the need of considering host variability in exposure time for managing mosquito-borne infections and call for the revision of the triggers for vector control strategies, which should integrate mosquito density data and human outdoor activity patterns in specific areas. [ABSTRACT FROM AUTHOR]

Constraints arise naturally in many scientific experiments/studies such as in, epidemiology, biology, toxicology, etc. and often researchers ignore such information when analyzing their data and use standard methods such as the analysis of variance (ANOVA). Such methods may not only result in a loss of power and efficiency in costs of experimentation but also may result poor interpretation of the data. In this paper we discuss constrained statistical inference in the context of linear mixed effects models that arise naturally in many applications, such as in repeated measurements designs, familial studies and others. We introduce a novel methodology that is broadly applicable for a variety of constraints on the parameters. Since in many applications sample sizes are small and/or the data are not necessarily normally distributed and furthermore error variances need not be homoscedastic (i.e. heterogeneity in the data) we use an empirical best linear unbiased predictor (EBLUP) type residual based bootstrap methodology for deriving critical values of the proposed test. Our simulation studies suggest that the proposed procedure maintains the desired nominal Type I error while competing well with other tests in terms of power. We illustrate the proposed methodology by re-analyzing a clinical trial data on blood mercury level. The methodology introduced in this paper can be easily extended to other settings such as nonlinear and generalized regression models. [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]

In the rapidly changing circumstances of our increasingly digital world, reading is also becoming an increasingly digital experience: electronic books (e-books) are now outselling print books in the United States and the United Kingdom. Nevertheless, many readers still view e-books as less readable than print books. The present study thus used combined EEG and eyetracking measures in order to test whether reading from digital media requires higher cognitive effort than reading conventional books. Young and elderly adults read short texts on three different reading devices: a paper page, an e-reader and a tablet computer and answered comprehension questions about them while their eye movements and EEG were recorded. The results of a debriefing questionnaire replicated previous findings in that participants overwhelmingly chose the paper page over the two electronic devices as their preferred reading medium. Online measures, by contrast, showed shorter mean fixation durations and lower EEG theta band voltage density – known to covary with memory encoding and retrieval – for the older adults when reading from a tablet computer in comparison to the other two devices. Young adults showed comparable fixation durations and theta activity for all three devices. Comprehension accuracy did not differ across the three media for either group. We argue that these results can be explained in terms of the better text discriminability (higher contrast) produced by the backlit display of the tablet computer. Contrast sensitivity decreases with age and degraded contrast conditions lead to longer reading times, thus supporting the conclusion that older readers may benefit particularly from the enhanced contrast of the tablet. Our findings thus indicate that people's subjective evaluation of digital reading media must be dissociated from the cognitive and neural effort expended in online information processing while reading from such devices. [ABSTRACT FROM AUTHOR]

This paper forecasts the 2016 canine Anaplasma spp. seroprevalence in the United States from eight climate, geographic and societal factors. The forecast’s construction and an assessment of its performance are described. The forecast is based on a spatial-temporal conditional autoregressive model fitted to over 11 million Anaplasma spp. seroprevalence test results for dogs conducted in the 48 contiguous United States during 2011–2015. The forecast uses county-level data on eight predictive factors, including annual temperature, precipitation, relative humidity, county elevation, forestation coverage, surface water coverage, population density and median household income. Non-static factors are extrapolated into the forthcoming year with various statistical methods. The fitted model and factor extrapolations are used to estimate next year’s regional prevalence. The correlation between the observed and model-estimated county-by-county Anaplasma spp. seroprevalence for the five-year period 2011–2015 is 0.902, demonstrating reasonable model accuracy. The weighted correlation (accounting for different sample sizes) between 2015 observed and forecasted county-by-county Anaplasma spp. seroprevalence is 0.987, exhibiting that the proposed approach can be used to accurately forecast Anaplasma spp. seroprevalence. The forecast presented herein can a priori alert veterinarians to areas expected to see Anaplasma spp. seroprevalence beyond the accepted endemic range. The proposed methods may prove useful for forecasting other diseases. [ABSTRACT FROM AUTHOR]

Objective: Heroin use in the United States has reached epidemic proportions. The objective of this paper is to estimate the annual societal cost of heroin use disorder in the United States in 2015 US dollars. Methods: An analytic model was created that included incarceration and crime; treatment for heroin use disorder; chronic infectious diseases (HIV, Hepatitis B, Hepatitis C, and Tuberculosis) and their treatments; treatment of neonatal abstinence syndrome; lost productivity; and death by heroin overdose. Results: Using literature-based estimates to populate the model, the cost of heroin use disorder was estimated to be $51.2 billion in 2015 US dollars ($50,799 per heroin user). One-way sensitivity analyses showed that overall cost estimates were sensitive to the number of heroin users, cost of HCV treatment, and cost of incarcerating heroin users. Conclusion: The annual cost of heroin use disorder to society in the United States emphasizes the need for sustained investment in healthcare and non-healthcare related strategies that reduce the likelihood of abuse and provide care and support for users to overcome the disorder. [ABSTRACT FROM AUTHOR]

In this paper, we extend some usual techniques of classification resulting from a large-scale data-mining and network approach. This new technology, which in particular is designed to be suitable to big data, is used to construct an open consolidated database from raw data on 4 million patents taken from the US patent office from 1976 onward. To build the pattern network, not only do we look at each patent title, but we also examine their full abstract and extract the relevant keywords accordingly. We refer to this classification as semantic approach in contrast with the more common technological approach which consists in taking the topology when considering US Patent office technological classes. Moreover, we document that both approaches have highly different topological measures and strong statistical evidence that they feature a different model. This suggests that our method is a useful tool to extract endogenous information. [ABSTRACT FROM AUTHOR]

Roadway elevation data is critical for a variety of transportation analyses. However, it has been challenging to obtain such data and most roadway GIS databases do not have them. This paper intends to address this need by proposing a method to extract roadway elevation data from Google Earth (GE) for transportation applications. A comprehensive accuracy assessment of the GE-extracted elevation data is conducted for the area of conterminous USA. The GE elevation data was compared with the ground truth data from nationwide GPS benchmarks and roadway monuments from six states in the conterminous USA. This study also compares the GE elevation data with the elevation raster data from the U.S. Geological Survey National Elevation Dataset (USGS NED), which is a widely used data source for extracting roadway elevation. Mean absolute error (MAE) and root mean squared error (RMSE) are used to assess the accuracy and the test results show MAE, RMSE and standard deviation of GE roadway elevation error are 1.32 meters, 2.27 meters and 2.27 meters, respectively. Finally, the proposed extraction method was implemented and validated for the following three scenarios: (1) extracting roadway elevation differentiating by directions, (2) multi-layered roadway recognition in freeway segment and (3) slope segmentation and grade calculation in freeway segment. The methodology validation results indicate that the proposed extraction method can locate the extracting route accurately, recognize multi-layered roadway section, and segment the extracted route by grade automatically. Overall, it is found that the high accuracy elevation data available from GE provide a reliable data source for various transportation applications. [ABSTRACT FROM AUTHOR]

Background: Economic inequality in the United States is extreme, but little is known about the national origin of affluent households. Households in the top one percent by total wealth own vastly disproportionate quantities of household assets and have correspondingly high levels of economic, social, and political influence. The overrepresentation of white natives (i.e., those born in the U.S.) among high-wealth households is well-documented, but changing migration dynamics suggest that a growing portion of top households may be immigrants. Methods: Because no single survey dataset contains top wealth holders and data about country of origin, this paper uses two publicly-available data sets: the Survey of Consumer Finances (SCF) and the Survey of Income and Program Participation (SIPP). Multiple imputation is used to impute country of birth from the SIPP into the SCF. Descriptive statistics are used to demonstrate reliability of the method, to estimate the prevalence of immigrants among top wealth holders, and to document patterns of asset ownership among affluent immigrants. Results: Significant numbers of top wealth holders who are usually classified as white natives may be immigrants. Many top wealth holders appear to be European and Canadian immigrants, and increasing numbers of top wealth holders are likely from Asia and Latin America as well. Results suggest that of those in the top one percent of wealth holders, approximately 3% are European and Canadian immigrants, .5% are from Mexico or Cuban, and 1.7% are from Asia (especially Hong Kong, Taiwan, Mainland China, and India). Ownership of key assets varies considerably across affluent immigrant groups. Conclusion: Although the percentage of top wealth holders who are immigrants is relatively small, these percentages represent large numbers of households with considerable resources and corresponding social and political influence. Evidence that the propensity to allocate wealth to real and financial assets varies across immigrant groups suggests that wealth ownership is more global than previous research suggests and that immigrant groups are likely to become more prevalent in top wealth positions in the U.S. As the representation of immigrants in top wealth positions grows, their economic, social, and political influence is likely to increase as well. [ABSTRACT FROM AUTHOR]

Ancestral state reconstruction in Bayesian phylogeography of virus pandemics have been improved by utilizing a Bayesian stochastic search variable selection (BSSVS) framework. Recently, this framework has been extended to model the transition rate matrix between discrete states as a generalized linear model (GLM) of genetic, geographic, demographic, and environmental predictors of interest to the virus and incorporating BSSVS to estimate the posterior inclusion probabilities of each predictor. Although the latter appears to enhance the biological validity of ancestral state reconstruction, there has yet to be a comparison of phylogenies created by the two methods. In this paper, we compare these two methods, while also using a primitive method without BSSVS, and highlight the differences in phylogenies created by each. We test six coalescent priors and six random sequence samples of H3N2 influenza during the 2014–15 flu season in the U.S. We show that the GLMs yield significantly greater root state posterior probabilities than the two alternative methods under five of the six priors, and significantly greater Kullback-Leibler divergence values than the two alternative methods under all priors. Furthermore, the GLMs strongly implicate temperature and precipitation as driving forces of this flu season and nearly unanimously identified a single root state, which exhibits the most tropical climate during a typical flu season in the U.S. The GLM, however, appears to be highly susceptible to sampling bias compared with the other methods, which casts doubt on whether its reconstructions should be favored over those created by alternate methods. We report that a BSSVS approach with a Poisson prior demonstrates less bias toward sample size under certain conditions than the GLMs or primitive models, and believe that the connection between reconstruction method and sampling bias warrants further investigation. [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]

How did post-socialist transition and a parallel shift in international labor division restructure regional innovation systems in Central and Eastern Europe? This question is increasingly important, because current EU innovation policy is combined with regional development in Smart Specialization Strategies; however, spatial trends of innovation in Central and Eastern Europe are not fully understood which might lead to less than perfectly efficient policy. In this paper we describe the spatial dynamics of inventor activity in the Czech Republic, Hungary, Poland and Slovakia between 1981 and 2010 –a period that covers both the late socialist era and the post-socialist transition. Cleaning and analyzing the publicly available data from the United States Patent and Trademark Office we illustrate that Central and Eastern European patents made in international co-operations with partners outside the region receive more citations than those Central and Eastern European patents that lack international co-operation. Furthermore, the technological portfolio of the former patents has become increasingly independent from the technological portfolio of the latter class. A town-level analysis of the applicant-inventor ties reveals that inventors have started to work for foreign assignees in those towns where no innovation activity had been recorded before. However, the positive effect does not last long and patenting seems to be only periodic in the majority of these towns. Therefore, innovation policy in Central and Eastern European countries, as well as in other less developed regions, shall foster synergies between international and domestic collaborations in order to decrease regional disparities in patenting. [ABSTRACT FROM AUTHOR]

Background: Policies designed to restrict marketing, access to, and public use of electronic cigarettes (e-cigarettes) are increasingly under debate in various jurisdictions in the US. Little is known about public perceptions of these policies and factors that predict their support or opposition. Methods: Using a sample of US adults from Amazon Mechanical Turk in May 2015, this paper identifies beliefs about the benefits and costs of regulating e-cigarettes and identifies which of these beliefs predict support for e-cigarette restricting policies. Results: A higher proportion of respondents agreed with 8 different reasons to regulate e-cigarettes (48.5% to 83.3% agreement) versus 7 reasons not to regulate e-cigarettes (11.5% to 18.9%). The majority of participants agreed with 7 out of 8 reasons for regulation. When all reasons to regulate or not were included in a final multivariable model, beliefs about protecting people from secondhand vapor and protecting youth from trying e-cigarettes significantly predicted stronger support for e-cigarette restricting policies, whereas concern about government intrusion into individual choices was associated with reduced support. Discussion: This research identifies key beliefs that may underlie public support or opposition to policies designed to regulate the marketing and use of e-cigarettes. Advocates on both sides of the issue may find this research valuable in developing strategic campaigns related to the issue. Implications: Specific beliefs of potential benefits and costs of e-cigarette regulation (protecting youth, preventing exposure to secondhand vapor, and government intrusion into individual choices) may be effectively deployed by policy makers or health advocates in communicating with the public. [ABSTRACT FROM AUTHOR]

The goal of this paper is to estimate peer influence in video gaming time among adolescents. Using a nationally representative sample of the U.S. school-aged adolescents in 2009–2010, we estimate a structural model that accounts for the potential biases in the estimate of the peer effect. Our peer group is exogenously assigned and includes one year older adolescents in the same school grade as the respondent. The peer measure is based on peers’ own reports of video gaming time. We find that an additional one hour of playing video games per week by older grade-mates results in .47 hours increase in video gaming time by male responders. We do not find significant peer effect among female responders. Effective policies aimed at influencing the time that adolescents spend video gaming should take these findings into account. [ABSTRACT FROM AUTHOR]

Based on coupled networks, taking node load, capacity and load redistribution between two networks into consideration, we propose functionally identical coupled networks, which consist of two networks connected by interlinks. Functionally identical coupled networks are derived from the power grid of the United States, which consists of many independent grids. Many power transmission lines are planned to interconnect those grids and, therefore, the study of the robustness of functionally identical coupled networks becomes important. In this paper, we find that functionally identical coupled networks are more robust than single networks under random attack. By studying the effect of the broadness and average degree of the degree distribution on the robustness of the network, we find that a broader degree distribution and a higher average degree increase the robustness of functionally identical coupled networks under random failure. And SF-SF (two coupled scale-free networks) is more robust than ER-ER (two coupled random networks) or SF-ER (coupled random network and scale-free network). This research is useful to construct robust functionally identical coupled networks such as two cooperative power grids. [ABSTRACT FROM AUTHOR]

The spatial distribution of income shapes the structure and organisation of cities and its understanding has broad societal implications. Despite an abundant literature, many issues remain unclear. In particular, all definitions of segregation are implicitely tied to a single indicator, usually rely on an ambiguous definition of income classes, without any consensus on how to define neighbourhoods and to deal with the polycentric organization of large cities. In this paper, we address all these questions within a unique conceptual framework. We avoid the challenge of providing a direct definition of segregation and instead start from a definition of what segregation is not. This naturally leads to the measure of representation that is able to identify locations where categories are over- or underrepresented. From there, we provide a new measure of exposure that discriminates between situations where categories co-locate or repel one another. We then use this feature to provide an unambiguous, parameter-free method to find meaningful breaks in the income distribution, thus defining classes. Applied to the 2014 American Community Survey, we find 3 emerging classes—low, middle and higher income—out of the original 16 income categories. The higher-income households are proportionally more present in larger cities, while lower-income households are not, invalidating the idea of an increased social polarisation. Finally, using the density—and not the distance to a center which is meaningless in polycentric cities—we find that the richer class is overrepresented in high density zones, especially for larger cities. This suggests that density is a relevant factor for understanding the income structure of cities and might explain some of the differences observed between US and European cities. [ABSTRACT FROM AUTHOR]

A common policy approach to reducing childhood obesity aims to shape the environment in which children spend most of their time: neighborhoods and schools. This paper uses richly detailed data on the body mass index (BMI) of all New York City public school students in grades K-8 to assess the potential for place-based approaches to reduce child obesity. We document variation in the prevalence of obesity across NYC public schools and census tracts, and then estimate the extent to which this variation can be explained by differences in individual-level predictors (such as race and household income). Both unadjusted and adjusted variability across neighborhoods and schools suggest place-based policies have the potential to meaningfully reduce child obesity, but under most realistic scenarios the improvement would be modest. [ABSTRACT FROM AUTHOR]

Background: Reductions in smoking in Arizona and California have been shown to be associated with reduced per capita healthcare expenditures in these states compared to control populations in the rest of the US. This paper extends that analysis to all states and estimates changes in healthcare expenditure attributable to changes in aggregate measures of smoking behavior in all states.Methods and Findings: State per capita healthcare expenditure is modeled as a function of current smoking prevalence, mean cigarette consumption per smoker, other demographic and economic factors, and cross-sectional time trends using a fixed effects panel data regression on annual time series data for each the 50 states and the District of Columbia for the years 1992 through 2009. We found that 1% relative reductions in current smoking prevalence and mean packs smoked per current smoker are associated with 0.118% (standard error [SE] 0.0259%, p < 0.001) and 0.108% (SE 0.0253%, p < 0.001) reductions in per capita healthcare expenditure (elasticities). The results of this study are subject to the limitations of analysis of aggregate observational data, particularly that a study of this nature that uses aggregate data and a relatively small sample size cannot, by itself, establish a causal connection between smoking behavior and healthcare costs. Historical regional variations in smoking behavior (including those due to the effects of state tobacco control programs, smoking restrictions, and differences in taxation) are associated with substantial differences in per capita healthcare expenditures across the United States. Those regions (and the states in them) that have lower smoking have substantially lower medical costs. Likewise, those that have higher smoking have higher medical costs. Sensitivity analysis confirmed that these results are robust.Conclusions: Changes in healthcare expenditure appear quickly after changes in smoking behavior. A 10% relative drop in smoking in every state is predicted to be followed by an expected $63 billion reduction (in 2012 US dollars) in healthcare expenditure the next year. State and national policies that reduce smoking should be part of short term healthcare cost containment. [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]

The “fetal origins” hypothesis suggests that fetal conditions not only affect birth characteristics such as birth weight and gestational age, but also have lifelong health implications. Despite widespread interest in this hypothesis, few methodological advances have been proposed to improve the measurement and modeling of fetal conditions. A Statistics in Medicine paper by Bollen, Noble, and Adair examined favorable fetal growth conditions (FFGC) as a latent variable. Their study of Filipino children from Cebu provided evidence consistent with treating FFGC as a latent variable that largely mediates the effects of mother’s characteristics on birth weight, birth length, and gestational age. This innovative method may have widespread utility, but only if the model applies equally well across diverse settings. Our study assesses whether the FFGC model of Cebu replicates and generalizes to a very different population of children from North Carolina (N = 705) and Pennsylvania (N = 494). Using a series of structural equation models, we find that key features of the Cebu analysis replicate and generalize while we also highlight differences between these studies. Our results support treating fetal conditions as a latent variable when researchers test the fetal origins hypothesis. In addition to contributing to the substantive literature on measuring fetal conditions, we also discuss the meaning and challenges involved in replicating prior research. [ABSTRACT FROM AUTHOR]

There are currently over 2.5 million breast cancer survivors in the United States and, according to the American Cancer Society, 10 to 20 percent of these women will develop recurrent breast cancer. Early detection of recurrence can avoid unnecessary radical treatment. However, self-examination or mammography screening may not discover a recurring cancer if the number of surviving cancer cells is small, while biopsy is too invasive and cannot be frequently repeated. It is therefore important to identify non-invasive biomarkers that can detect early recurrence. The present paper develops a mathematical model of cancer recurrence. The model, based on a system of partial differential equations, focuses on tissue biomarkers that include the plasminogen system. Among them, only uPAR is known to have significant correlation to its concentration in serum and could therefore be a good candidate for serum biomarker. The model includes uPAR and other associated cytokines and cells. It is assumed that the residual cancer cells that survived primary cancer therapy are concentrated in the same location within a region with a very small diameter. Model simulations establish a quantitative relation between the diameter of the growing cancer and the total uPAR mass in the cancer. This relation is used to identify uPAR as a potential serum biomarker for breast cancer recurrence. [ABSTRACT FROM AUTHOR]

HIV incidence estimates are used to monitor HIV-1 infection in the United States. Use of laboratory biomarkers that distinguish recent from longstanding infection to quantify HIV incidence rely on having accurate knowledge of the average time that individuals spend in a transient state of recent infection between seroconversion and reaching a specified biomarker cutoff value. This paper describes five estimation procedures from two general statistical approaches, a survival time approach and an approach that fits binomial models of the probability of being classified as recently infected, as a function of time since seroconversion. We compare these procedures for estimating the mean duration of recent infection (MDRI) for two biomarkers used by the U.S. National HIV Surveillance System for determination of HIV incidence, the Aware BED EIA HIV-1 incidence test (BED) and the avidity-based, modified Bio-Rad HIV-1/HIV-2 plus O ELISA (BRAI) assay. Collectively, 953 specimens from 220 HIV-1 subtype B seroconverters, taken from 5 cohorts, were tested with a biomarker assay. Estimates of MDRI using the non-parametric survival approach were 198.4 days (SD 13.0) for BED and 239.6 days (SD 13.9) for BRAI using cutoff values of 0.8 normalized optical density and 30%, respectively. The probability of remaining in the recent state as a function of time since seroconversion, based upon this revised statistical approach, can be applied in the calculation of annual incidence in the United States. [ABSTRACT FROM AUTHOR]