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This paper applies topological methods to study complex high dimensional data sets by extracting shapes (patterns) and obtaining insights about them. Our method combines the best features of existing standard methodologies such as principal component and cluster analyses to provide a geometric representation of complex data sets. Through this hybrid method, we often find subgroups in data sets that traditional methodologies fail to find. Our method also permits the analysis of individual data sets as well as the analysis of relationships between related data sets. We illustrate the use of our method by applying it to three very different kinds of data, namely gene expression from breast tumors, voting data from the United States House of Representatives and player performance data from the NBA, in each case finding stratifications of the data which are more refined than those produced by standard methods.

Housing markets play a crucial role in economies and the collapse of a real-estate bubble usually destabilizes the financial system and causes economic recessions. We investigate the systemic risk and spatiotemporal dynamics of the US housing market (1975-2011) at the state level based on the Random Matrix Theory (RMT).Weidentify richer economic information in the largest eigenvalues deviating from RMT predictions for the housing market than for stock markets and find that the component signs of the eigenvectors contain either geographical information or the extent of differences in house price growth rates or both. By looking at the evolution of different quantities such as eigenvalues and eigenvectors, we find that the US housing market experienced six different regimes, which is consistent with the evolution of state clusters identified by the box clustering algorithm and the consensus clustering algorithm on the partial correlation matrices. We find that dramatic increases in the systemic risk are usually accompanied by regime shifts, which provide a means of early detection of housing bubbles. [ABSTRACT FROM AUTHOR]

Using road GIS (geographical information systems) data and travel demand data for two U.S. urban areas, the dynamical driver sources of each road segment were located. A method to target road clusters closely related to urban traffic congestion was then developed to improve road network efficiency. The targeted road clusters show different spatial distributions at different times of a day, indicating that our method can encapsulate dynamical travel demand information into the road networks. As a proof of concept, when we lowered the speed limit or increased the capacity of road segments in the targeted road clusters, we found that both the number of congested roads and extra travel time were effectively reduced. In addition, the proposed modeling framework provided new insights on the optimization of transport efficiency in any infrastructure network with a specific supply and demand distribution. [ABSTRACT FROM AUTHOR]

Attempts to understand the relationship between diversity, productivity and scale have remained limited due to the scheme-dependent nature of the taxonomies describing complex systems. We analyze the diversity of US metropolitan areas in terms of profession diversity and employment to show how this frequency distribution takes a universal scale-invariant form, common to all cities, in the limit of infinite resolution of occupational taxonomies. We show that this limit is obtained under general conditions that follow from the analysis of the variation of the occupational frequency across taxonomies at different resolutions in a way analogous to finite-size scaling in statistical physical systems. We propose a theoretical framework that derives the form and parameters of the limiting distribution of professions based on the appearance, in urban social networks, of new occupations as the result of specialization and coordination of labor. By deriving classification scheme-independent measures of functional diversity and modeling cities as social networks embedded in infrastructural space, these results show how standard economic arguments of division and coordination of labor can be articulated in detail in cities and provide a microscopic basis for explaining increasing returns to population scale observed at the level of entire metropolitan areas. [ABSTRACT FROM AUTHOR]

The recent availability of data for cities has allowed scientists to exhibit scalings which present themselves in the form of a power-law dependence on population of various socio-economical and structural indicators. We propose here a stochastic theory of urban growth which accounts for some of the observed scalings and we confirm these predictions on US and OECD empirical data. In particular, we show that the dependence on population size of the total number of miles driven daily, the total length of the road network, the total traffic delay, the total consumption of gasoline, the quantity of CO2 emitted and the relation between area and population of cities, are all governed by a single parameter which characterizes the sensitivity to congestion. Our results suggest that diseconomies associated with congestion scale superlinearly with population size, implying that -despite polycentrism- cities whose transportation infrastructure rely heavily on traffic sensitive modes are unsustainable. [ABSTRACT FROM AUTHOR]

Climate change causes species range shifts and potentially alters biological invasions. The invasion of European earthworm species across northern North America has severe impacts on native ecosystems. Given the long and cold winters in that region that to date supposedly have slowed earthworm invasion, future warming is hypothesized to accelerate earthworm invasions into yet non-invaded regions. Alternatively, warming-induced reductions in soil water content (SWC) can also decrease earthworm performance. We tested these hypotheses in a field warming experiment at two sites in Minnesota, USA by sampling earthworms in closed and open canopy in three temperature treatments in 2010 and 2012. Structural equation modeling revealed that detrimental warming effects on earthworm densities and biomass could indeed be partly explained by warming-induced reductions in SWC. The direction of warming effects depended on the current average SWC: warming had neutral to positive effects at high SWC, whereas the opposite was true at low SWC. Our results suggest that warming limits the invasion of earthworms in northern North America by causing less favorable soil abiotic conditions, unless warming is accompanied by increased and temporally even distributions of rainfall sufficient to offset greater water losses from higher evapotranspiration. [ABSTRACT FROM AUTHOR]

Road-salt pollution in streams in the Northeastern United States has become a major concern, but historical data are scarce. Freshwater bivalve shells have the ability to record past environmental information, and may act as archives of road-salt pollution. We sampled Elliptio complanata shells from four streams, as well as specimens collected in 1877. Average [Na/Ca]shell was highest in modern shells from the stream with the highest sodium concentrations, and low in shells collected from this same stream in 1877 as well as in the shells from other streams, suggesting that [Na/Ca]shell serves as a proxy for road-salt pollution. We expected higher [Na/Ca]shell in winter and spring. However, high-resolution [Na/Ca]shellanalyses along the growth axis of one shell did not reveal any clear subannual patterns, which could be the result of shell growth cessation in winter and/or during periods of high stream sodium concentrations. Therefore, bulk [Na/ Ca]shell analysis from multiple shells can be used as a proxy of large changes in stream sodium concentrations, but high-resolution variations in stream sodium concentrations do not seem to be recorded in the shells. [ABSTRACT FROM AUTHOR]

Over the past century forest regrowth in Europe and North America expanded forest carbon (C) sinks and offset C emissions but future C accumulation is uncertain. Policy makers need insights into forest C dynamics as they anticipate emissions futures and goals. We used land use and forest inventory data to estimate how forest C dynamics have changed in the southeastern United States and attribute changes to land use, management, and disturbance causes. From 2007-2012, forests yielded a net sink of C because of net land use change (+6.48 Tg C yr−1) and net biomass accumulation (+75.4 Tg C yr−1). Forests disturbed by weather, insect/disease, and fire show dampened yet positive forest C changes (+1.56, +1.4, +5.48 Tg C yr−1, respectively). Forest cutting caused net decreases in C (−76.7 Tg C yr−1) but was offset by forest growth (+143.77 Tg C yr−1). Forest growth rates depend on age or stage of development and projected C stock changes indicate a gradual slowing of carbon accumulation with anticipated forest aging (a reduction of 9.5% over the next five years). Additionally, small shifts in land use transitions consistent with economic futures resulted in a 40.6% decrease in C accumulation. [ABSTRACT FROM AUTHOR]

Terrestrial carbon sequestration potential is widely considered as a realistic option for mitigating greenhouse gas emissions. However, this potential may be threatened by global changes including climate, land use, and management changes such as increased corn stover harvesting for rising production of cellulosic biofuel. Therefore, it is critical to investigate the dynamics of soil organic carbon (SOC) at regional or global scale. This study simulated the corn production and spatiotemporal changes of SOC in the U.S. Temperate Prairies, which covers over one-third of the U.S. corn acreage, using a biogeochemical model with multiple climate and land-use change projections. The corn production (either grain yield or stover biomass) could reach 88.7-104.7 TgC as of 2050, 70-101% increase when compared to the base year of 2010. A removal of 50% stover at the regional scale could be a reasonable cap in view of maintaining SOC content and soil fertility especially in the beginning years. The projected SOC dynamics indicated that the average carbon sequestration potential across the entire region may vary from 12.7 to 19.6 g C/m2/yr (i.e., 6.6-10.2 g TgC/yr). This study not only helps understand SOC dynamics but also provides decision support for sustainable biofuel development. [ABSTRACT FROM AUTHOR]

Grain legume production in Europe has recently come under scrutiny. Although legume crops are often promoted to provide environmental services, European farmers tend to turn to non-legume crops. It is assumed that high variability in legume yields explains this aversion, but so far this hypothesis has not been tested. Here, we estimate the variability of major grain legume and non-legume yields in Europe and the Americas from yield time series over 1961-2013. Results show that grain legume yields are significantly more variable than non-legume yields in Europe. These differences are smaller in the Americas. Our results are robust at the level of the statistical methods. In all regions, crops with high yield variability are allocated to less than 1% of cultivated areas. Although the expansion of grain legumes in Europe may be hindered by high yield variability, some species display risk levels compatible with the development of specialized supply chains. [ABSTRACT FROM AUTHOR]

Tropical cyclones (TCs) are among the most devastating weather systems affecting the United States and Central America (USCA). Here we show that the Interdecadal Pacific Oscillation (IPO) strongly modulates TC activity over the North Atlantic (NA) and eastern North Pacific (eNP). During positive IPO phases, less (more) TCs were observed over NA (eNP), likely due to the presence of stronger (weaker) vertical wind shear and the resulting changes in genesis potential. Furthermore, TCs over NA tend to keep their tracks more eastward and recurve at lower latitudes during positive IPO phases. Such variations are largely determined by changes in steering flow instead of changes in genesis locations. Over the eNP, smaller track variations are observed at different IPO phases with stable, westward movements of TCs prevailing. These findings have substantial implications for understanding decadal to inter-decadal fluctuations in the risk of TC landfalls along USCA coasts. [ABSTRACT FROM AUTHOR]

Climate change is expected to decrease heating demand and increase cooling demand for buildings and affect outdoor thermal comfort. Here, we project changes in residential heating degree-days (HDD) and cooling degree-days (CDD) for the historical (1981-2010) and future (2080-2099) periods in the United States using median results from the Climate Model Intercomparison Project phase 5 (CMIP5) simulations under the Representation Concentration Pathway 8.5 (RCP8.5) scenario. We project future HDD and CDD values by adding CMIP5 projected changes to values based on historical observations of US climate. The sum HDD + CDD is an indicator of locations that are thermally comfortable, with low heating and cooling demand. By the end of the century, station median HDD + CDD will be reduced in the contiguous US, decreasing in the North and increasing in the South. Under the unmitigated RCP8.5 scenario, by the end of this century, in terms of HDD and CDD values considered separately, future New York, NY, is anticipated to become more like present Oklahoma City, OK; Denver, CO, becomes more like Raleigh, NC, and Seattle, WA, becomes more like San Jose, CA. These results serve as an indicator of projected climate change and can help inform decision-making. [ABSTRACT FROM AUTHOR]

We recently demonstrated that lysosomal protein transmembrane 4 beta (LAPTM4B) is elevated in non-small cell lung cancers (NSCLCs) and in the surrounding premalignant airway field of cancerization. In the present study, we sought to begin to understand the relevance of LAPTM4B expression and signaling to NSCLC pathogenesis. In situ hybridization analysis of LAPTM4B transcript in tissue microarrays comprised of 368 NSCLCs demonstrated that LAPTM4B expression was significantly increased in smoker compared to non-smoker lung adenocarcinoma tumors (P < 0.001) and was significantly associated with poor overall survival (P < 0.05) in adenocarcinoma patients. Knockdown of LAPTM4B expression inhibited cell growth, induced cellular apoptosis and decreased cellular autophagy in serum starved lung cancer cells. Expression profiling coupled with pathways analysis revealed decreased activation of the nuclear factor erythroid 2-like 2 (NRF2) stress response pathway following LAPTM4B knockdown. Further analysis demonstrated that LAPTM4B augmented the expression and nuclear translocation of the NRF2 transcription factor following serum deprivation as well as increased the expression of NRF2 target genes such as heme oxygenase 1/HMOX1). Our study points to the relevance of LAPTM4B expression to NSCLC pathogenesis as well as to the probable role of LAPTM4B/NRF2 signaling in promoting lung cancer cell survival. [ABSTRACT FROM AUTHOR]

Building resilience into today's complex infrastructures is critical to the daily functioning of society and its ability to withstand and recover from natural disasters, epidemics, and cyber-threats. This study proposes quantitative measures that capture and implement the definition of engineering resilience advanced by the National Academy of Sciences. The approach is applicable across physical, information, and social domains. It evaluates the critical functionality, defined as a performance function of time set by the stakeholders. Critical functionality is a source of valuable information, such as the integrated system resilience over a time interval, and its robustness. The paper demonstrates the formulation on two classes of models: 1) multi-level directed acyclic graphs, and 2) interdependent coupled networks. For both models synthetic case studies are used to explore trends. For the first class, the approach is also applied to the Linux operating system. Results indicate that desired resilience and robustness levels are achievable by trading off different design parameters, such as redundancy, node recovery time, and backup supply available. The nonlinear relationship between network parameters and resilience levels confirms the utility of the proposed approach, which is of benefit to analysts and designers of complex systems and networks.

Climate variability and trends affect global crop yields and are characterized as highly dependent on location, crop type, and irrigation. U.S. Great Plains, due to its significance in national food production, evident climate variability, and extensive irrigation is an ideal region of investigation for climate impacts on food production. This paper evaluates climate impacts on maize, sorghum, and soybean yields and effect of irrigation for individual counties in this region by employing extensive crop yield and climate datasets from 1968-2013. Variability in crop yields was a quarter of the regional average yields, with a quarter of this variability explained by climate variability, and temperature and precipitation explained these in singularity or combination at different locations. Observed temperature trend was beneficial for maize yields, but detrimental for sorghum and soybean yields, whereas observed precipitation trend was beneficial for all three crops. Irrigated yields demonstrated increased robustness and an effective mitigation strategy against climate impacts than their non-irrigated counterparts by a considerable fraction. The information, data, and maps provided can serve as an assessment guide for planners, managers, and policy- and decision makers to prioritize agricultural resilience efforts and resource allocation or re-allocation in the regions that exhibit risk from climate variability.

Because influenza is a contagious respiratory illness that seriously threatens public health, accurate real-time prediction of influenza outbreaks may help save lives. In this paper, we use the Twitter data set and the United States Centers for Disease Control's influenza-like illness (ILI) data set to predict a nearly real-time regional unweighted percentage ILI in the United States by use of an artificial neural network optimized by the improved artificial tree algorithm. The results show that the proposed method is an efficient approach to real-time prediction.

In this paper, we consider the yearly influenza epidemic, as reflected in the seasonal surveillance data compiled by the CDC (Center for Disease Control and Prevention, USA) and we explore a new methodology for comparing specific features of these data. In particular, we focus on the ten HHS (Health and Human Services) regions, and how the incidence data evolves in these regions. In order to perform the comparisons, we consider the relative distribution of weekly new cases over one season and replace the crude data with predicted values. These predictions are obtained after fitting a negative binomial regression model that controls for important covariates. The prediction is computed on a 'generic' set of covariate values that takes into account the relative size (population wise) of the regions to be compared. The main results are presented in graphical form, that quickly emphasizes relevant features of the seasonal data and facilitates the comparisons.

Human migration and trade facilitated domesticated livestock movement, gene flow and development of diverse populations upon which agriculture is based. In addition, varying USA ecological conditions has led to a diverse set of livestock populations to utilize. Quantifying genetic diversity of these populations is incomplete. This paper quantifies genetic diversity captured by the National Animal Germplasm Program and explores genetic structure and differences among 19 pig populations (feral populations from Pacific islands, continental US, and Chinese breeds) using 70,231 SNP from 500 animal samples. Among continental US breeds F is was consistently low suggesting genetic variability is sufficiently available for breeders to use. A unique population structure using principal component analysis illustrated clear distinctions between Duroc, Yorkshire, Hampshire, breeds of Chinese origin, and feral Pacific Island populations were identified. Five Y chromosome haplotypes were evaluated and demonstrated migration patterns from European, central Asia, and potentially Polynesian waves of gene flow. Quantifying diversity and potential origin of Pacific populations provides insight for future uses, and the need for preservation. Viewing gene bank holdings in context of diversity measures we found a lack of inbreeding within breeds, suggesting the collection represents a wide sampling of individual breeds.

River drainage networks are important landscape features that have been studied for several decades from a range of geomorphological and hydrological perspectives. However, identifying the most vital (critical) nodes on river networks and analyzing their relationships with geomorphic and climatic properties have not yet been extensively addressed in the literature. In this study, we use an algorithm that determines the set of critical nodes whose removal results in maximum network fragmentation and apply it to various topologies of simulated and natural river networks. Specifically, we consider simulated river networks obtained from optimal channel network (OCN) approach as well as extracted networks from several natural basins across the United States. Our results indicate a power-law relationship between the number of connected node pairs in the remaining network and the number of removed critical nodes. We also investigate the characteristics of sub-basins resulted from the removal of critical nodes and compare them with those of central nodes (in the context of betweenness centrality) for both natural basins and OCNs with varying energy exponent γ to understand vulnerability and resilience of river networks under potential external disruptions. [ABSTRACT FROM AUTHOR]

An association between climatic conditions and asthma mortality has been widely assumed. However, it is unclear whether climatic variations have a fingerprint on asthma dynamics over long time intervals. The aim of this study is to detect a possible correlation between climatic indices, namely the Atlantic Multidecadal Oscillation and Pacific Decadal Oscillation, and asthma mortality rates over the period from 1950 to 2015 in the contiguous US. To this aim, an analysis of non-stationary and non-linear signals was performed on time series of US annual asthma mortality rates, AMO and PDO indices to search for characteristic periodicities. Results revealed that asthma death rates evaluated for four different age groups (5–14 yr; 15–24 yr; 25–34 yr; 35–44 yr) share the same pattern of fluctuation throughout the 1950–2015 time interval, but different trends, i.e. a positive (negative) trend for the two youngest (oldest) categories. Annual asthma death rates turned out to be correlated with the dynamics of the AMO, and also modulated by the PDO, sharing the same averaged ∼44 year-periodicity. The results of the current study suggest that, since climate patterns have proved to influence asthma mortality rates, they could be advisable in future studies aimed at elucidating the complex relationships between climate and asthma mortality. [ABSTRACT FROM AUTHOR]

River networks are typically treated as conduits of fixed discharge conveyance capacity in flood models and engineering design, despite knowledge that alluvial channel networks adjust their geometry, conveyance, planform, extent and drainage density over time in response to shifts in the magnitude and frequency of streamflows and sediment supply. Consistent relationships between modes of climate variability conducive to wetter-/drier-than-average conditions and changes in channel conveyance have never been established, hindering geomorphological prediction over interannual to multidecadal timescales. This paper explores the relationship between river channel conveyance/geometry and three modes of climate variability (the El Niño–Southern Oscillation, Atlantic Multidecadal Oscillation, and Arctic Oscillation) using two-, five- and ten-year medians of channel measurements, streamflow, precipitation and climate indices over seven decades in 67 United States rivers. We find that in two thirds of these rivers, channel capacity undergoes coherent phases of expansion/contraction in response to shifts in catchment precipitation and streamflow, driven by climate modes with different periodicities. Understanding the sensitivity of channel conveyance to climate modes would enable better river management, engineering design, and flood predictability over interannual to multidecadal timescales. [ABSTRACT FROM AUTHOR]

Natural microbial communities consist of a limited number of abundant species and an extraordinarily diverse population of rare species referred to as the rare biosphere. Recent studies have revealed that the rare biosphere is not merely an inactive dormant population but may play substantial functional roles in the ecosystem. However, structure, activity and community assembly processes of the rare biosphere are poorly understood. In this study, we evaluated the present and living microbial community structures including rare populations in an aquifer ecosystem, the Mahomet Aquifer, USA, by both 16S rDNA and rRNA amplicon deep sequencing. The 13 groundwater samples formed three distinct groups based on the "entire" community structure, and the same grouping was obtained when focusing on the "rare" subcommunities (<0.1% of total abundance), while the "abundant" subcommunities (>1.0%) gave a different grouping. In the correlation analyses, the observed grouping pattern is associated with several geochemical factors, and structures of not only the entire community but also the rare subcommunity are correlated with geochemical profiles in the aquifer ecosystem. Our findings first indicate that the living rare biosphere in the aquifer system has the metabolic potential to adapt to local geochemical factors which dictate the community assembly processes. [ABSTRACT FROM AUTHOR]

Genomic instability plays a significant role in lung cancer. Although substantial research has been conducted using both clinical and theoretical studies, it is still a hotly debated issue to whether genomic instability is necessary or whether genomic instability precedes oncogenes activation and tumor suppressor genes inactivation for lung cancer. In response to this issue, we come up with a mathematical model incorporating effects of genomic instability to investigate the genomic instability pathway of human lung cancer. The presented model are applied to match the incidence rate data of lung cancer from the Life Span Study cohort of the atomic bomb survivors in Nagasaki and Hiroshima and the Surveillance Epidemiology and End Results registry in the United States. Model results suggest that genomic instability is necessary in the tumorigenesis of lung cancer, and genomic instability has no significant impact on the net proliferation rate of cells by statistical criteria. By comparing the results of the LSS data to those of the SEER data, we conclude that the genomic instability pathway exhibits a sensitivity to radiation exposure, more intensive in male patients. [ABSTRACT FROM AUTHOR]

It is unclear if additional inoculation with Bradyrhizobia at varying soybean [Glycine max (L.) Merr.] growth stages can impact biological nitrogen fixation (BNF), increase yield and improve seed composition [protein, oil, and amino acid (AA) concentrations]. The objectives of this study were to evaluate the effect of different soybean inoculation strategies (seed coating and additional soil inoculation at V4 or R1) on: (i) seed yield, (ii) seed composition, and (iii) BNF traits [nodule number and relative abundance of ureides (RAU)]. Soybean field trials were conducted in 11 environments (four states of the US) to evaluate four treatments: (i) control without inoculation, (ii) seed inoculation, (iii) seed inoculation + soil inoculation at V4, and (iv) seed inoculation + soil inoculation at R1. Results demonstrated no effect of seed or additional soil inoculation at V4 or R1 on either soybean seed yield or composition. Also, inoculation strategies produced similar values to the non-inoculated control in terms of nodule number and RAU, a reflection of BNF. Therefore, we conclude that in soils with previous history of soybean and under non-severe stress conditions (e.g. high early-season temperature and/or saturated soils), there is no benefit to implementing additional inoculation on soybean yield and seed composition. [ABSTRACT FROM AUTHOR]

Southeast Brazil has experienced two large yellow fever (YF) outbreaks since 2016. While the 2016–2017 outbreak mainly affected the states of Espírito Santo and Minas Gerais, the 2017–2018 YF outbreak primarily involved the states of Minas Gerais, São Paulo, and Rio de Janeiro, the latter two of which are highly populated and popular destinations for international travelers. This analysis quantifies the risk of YF virus (YFV) infected travelers arriving in the United States via air travel from Brazil, including both incoming Brazilian travelers and returning US travelers. We assumed that US travelers were subject to the same daily risk of YF infection as Brazilian residents. During both YF outbreaks in Southeast Brazil, three international airports—Miami, New York-John F. Kennedy, and Orlando—had the highest risk of receiving a traveler infected with YFV. Most of the risk was observed among incoming Brazilian travelers. Overall, we found low risk of YFV introduction into the United States during the 2016–2017 and 2017–2018 outbreaks. Decision makers can use these results to employ the most efficient and least restrictive actions and interventions. [ABSTRACT FROM AUTHOR]

Globally, maternal birth season affects fertility later in life. The purpose of this systematic literature review is to comprehensively investigate the birth season and female fertility relationship. Using PubMed, we identified a set of 282 relevant fertility/birth season papers published between 1972 and 2018. We screened all 282 studies and removed 131 non-mammalian species studies on fertility and 122 studies that were on non-human mammals. Our meta-analysis focused on the remaining 29 human studies, including twelve human datasets from around the world (USA, Europe, Asia). The main outcome was change in female fertility as observed by maternal birth month and whether this change was correlated with either temperature or rainfall. We found that temperature was either strongly correlated or anti-correlated in studies, indicating that another factor closely tied to temperature may be the culprit exposure. We found that rainfall only increases fertility in higher altitude locations (New Zealand, Romania, and Northern Vietnam). This suggests the possibility of a combined or multi-factorial mechanism underlying the female fertility - birth season relationship. We discuss other environmental and sociological factors on the birth season - female fertility relationship. Future research should focus on the role of birth season and female fertility adjusting for additional factors that modulate female fertility as discussed in this comprehensive review.

The underlying reasons behind the unprecedented increase of the mortality rates due to the opioid epidemics in the United States are still not fully uncovered. Most efforts have been focused on targeting opioids, but there is little information about vulnerable populations at high risk of opioid abuse and death. In this study, we used data from the Ohio Department of Health for deaths caused by prescription opioids from 2010–2017 to analyze the spatiotemporal dynamics of the opioid overdose epidemic. Our results showed a rapid increase in prescription opioid death rates among the white male population aged 30–39 but also a considerable increase among the black male population with an exponential growth trend. Our geospatial analysis suggests that the increasing rates of the opioid overdose epidemic in Ohio were driven by the epidemic hotspot areas. Our findings highlight the relevance of prioritizing public health measures targeting specific locations and vulnerable populations to mitigate the current opioids crisis. [ABSTRACT FROM AUTHOR]

Sea-level rise will radically redefine the coastline of the 21st century. For many coastal regions, projections of global sea-level rise by the year 2100 (e.g., 0.5–2 meters) are comparable in magnitude to today's extreme but short-lived increases in water level due to storms. Thus, the 21st century will see significant changes to coastal flooding regimes (where present-day, extreme-but-rare events become common), which poses a major risk to the safety and sustainability of coastal communities worldwide. So far, estimates of future coastal flooding frequency focus on endpoint scenarios, such as the increase in flooding by 2050 or 2100. Here, we investigate the continuous shift in coastal flooding regimes by quantifying continuous rates of increase in the occurrence of extreme water-level events due to sea-level rise. We find that the odds of exceeding critical water-level thresholds increases exponentially with sea-level rise, meaning that fixed amounts of sea-level rise of only ~1–10 cm in areas with a narrow range of present-day extreme water levels can double the odds of flooding. Combining these growth rates with established sea-level rise projections, we find that the odds of extreme flooding double approximately every 5 years into the future. Further, we find that the present-day 50-year extreme water level (i.e., 2% annual chance of exceedance, based on historical records) will be exceeded annually before 2050 for most (i.e., 70%) of the coastal regions in the United States. Looking even farther into the future, the present-day 50-year extreme water level will be exceeded almost every day during peak tide (i.e., daily mean higher high water) before the end of the 21st century for 90% of the U.S. coast. Our findings underscore the need for immediate planning and adaptation to mitigate the societal impacts of future flooding. [ABSTRACT FROM AUTHOR]

We examined whether the co-occurrence of four behavioural risk factors (alcohol consumption, smoking, physical inactivity and obesity) is associated with disability-free and chronic disease-free life expectancy similarly in two longitudinal studies of ageing in England and the United States. Data were from 17,351 individuals aged 50+ from the US Health and Retirement Study (HRS) and, 10,388 from the English Longitudinal Study of Ageing (ELSA), from 2002 to 2013. Disability-free life expectancy was estimated using repeat measures of limitations with instrumental activities and activities of daily living and, chronic disease-free life expectancy was based on chronic health conditions. Multistate life table models were used to estimate sex-specific health expectancy at the ages of 50, 60 and 70. In both countries and at all ages, there was a clear gradient towards shorter health expectancy with increasing number of behavioural risk factors. Compared to people with 2+ behavioural risk factors, in both countries, those with no behavioural risk factors could expect to live up to 11 years longer without disability and, up to 12 years longer without chronic conditions. Individual and co-occurring behavioural risk factors were strongly associated with shorter healthy life expectancy in both countries, attesting to the robustness of the contribution of lifestyle factors on health expectancy. [ABSTRACT FROM AUTHOR]

Cannabis users who are also tobacco smokers are more likely to exhibit cannabis dependence and psychosocial problems. However, there has been minimal research around various cannabis and tobacco mixing (spliff usage) behaviours and likeliness to smoke the first joint within an hour of waking, known colloquially as wake and bake. The time of first joint and spliff usage may be related as they are associated with the intersection of tobacco and cannabis use. Compared to non-morning cannabis users, morning users reported significantly more cannabis-related problems. Through a survey of US cannabis users, we test the association between various cannabis and spliff use behaviours and likeliness to smoke the first joint within an hour of waking. Compared to those who smoked tobacco and used spliffs, the following spliff use behaviour groups were less likely to have their first joint within 60 minutes after waking: those who smoked tobacco and used spliffs (95%CI: 0.605–0.988); those who never smoked tobacco and did not use spliffs (95%CI: 0.489–0.892); those who never smoked tobacco and used spliffs (95%CI:0.022–0.915). We provided possible explanations for our results and suggested further research to better understand findings, important given expanding US cannabis markets. [ABSTRACT FROM AUTHOR]

We tested the immediate and delayed effects of a low-intensity prescribed fire on beetles, ants and termites inhabiting log sections cut from moderately decomposed pine trees in the southeastern United States. We also explored co-occurrence patterns among these insects. Half the logs were placed at a site scheduled for a prescribed fire while the rest were assigned to a neighboring site not scheduled to be burned. We then collected insects emerging from sets of logs collected immediately after the fire as well as after 2, 6, 26 and 52 weeks. The fire had little effect on the number of beetles and ants collected although beetle richness was significantly higher in burned logs two weeks after the fire. Both beetle and ant communities differed between treatments, however, with some species preferring either burned or unburned logs. We found no evidence that subterranean termites (Reticulitermes) were influenced by the fire. Based on co-occurrence analysis, positive associations among insect species were over two times more common than negative associations. This difference was significant overall as well for ant × beetle and beetle × beetle associations. Relatively few significant positive or negative associations were detected between termites and the other insect taxa, however. [ABSTRACT FROM AUTHOR]

Medical folk wisdom (MFW) refers to widely held, but factually inaccurate, beliefs about disease, immunity, pregnancy, and other medically-relevant topics. Examples include the idea that fasting when feverish ("starving a fever") can increase the pace of recovery, or that showering after sex can prevent pregnancy. The pervasiveness of MFW, and whether or not it-like other forms of medically-relevant misinformation-shapes Americans' health behaviors and policy preferences is an important and under-studied question. We begin this research by proposing and validating a novel measure of MFW; including a short-form scale suitable for administration in public opinion surveys. We find that nearly all Americans-irrespective of socio-economic status, political orientation, and educational background-endorse at least some aspects of MFW. Concerningly, and consistent with the idea that folk wisdom challenges scientific expertise, we additionally find that those highest in MFW tend to place less value on medical expertise and the role experts play in shaping health policy. However, this skepticism does not appear to translate to peoples' health actions, as MFW appears to have an inconsistent effect on public participation in healthy behaviors.

The novel coronavirus SARS-CoV-2 and resulting COVID-19 disease have had an unprecedented spread and continue to cause an increasing number of fatalities worldwide. While vaccines are still under development, social distancing, extensive testing, and quarantining of confirmed infected subjects remain the most effective measures to contain the pandemic. These measures carry a significant socioeconomic cost. In this work, we introduce a novel optimization-based decision-making framework for managing the COVID-19 outbreak in the US. This includes modeling the dynamics of affected populations, estimating the model parameters and hidden states from data, and an optimal control strategy for sequencing social distancing and testing events such that the number of infections is minimized. The analysis of our extensive computational efforts reveals that social distancing and quarantining are most effective when implemented early, with quarantining of confirmed infected subjects having a much higher impact. Further, we find that "on-off" policies alternating between strict social distancing and relaxing such restrictions can be effective at "flattening" the curve while likely minimizing social and economic cost. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

We study biological aging of elderly U.S. Americans born 1904-1966. We use thirteen waves of the Health and Retirement Study and construct a frailty index as the number of health deficits present in a person measured relative to the number of potential deficits. We find that, on average, Americans develop 5% more health deficits per year, that men age slightly faster than women, and that, at any age above 50, Caucasians display significantly fewer health deficits than African Americans. We also document a steady time trend of health improvements. For each year of later birth, health deficits decline on average by about 1%. This health trend is about the same across regions and for men and women, but significantly lower for African Americans compared to Caucasians. In non-linear regressions, we find that regional differences in aging follow a particular regularity, akin to the compensation effect of mortality. Health deficits converge for men and women and across American regions and suggest a life span of the American population of about 97 years.

Nine in ten major outages in the US have been caused by hurricanes. Long-term outage risk is a function of climate change-triggered shifts in hurricane frequency and intensity; yet projections of both remain highly uncertain. However, outage risk models do not account for the epistemic uncertainties in physics-based hurricane projections under climate change, largely due to the extreme computational complexity. Instead they use simple probabilistic assumptions to model such uncertainties. Here, we propose a transparent and efficient framework to, for the first time, bridge the physics-based hurricane projections and intricate outage risk models. We find that uncertainty in projections of the frequency of weaker storms explains over 95% of the uncertainty in outage projections; thus, reducing this uncertainty will greatly improve outage risk management. We also show that the expected annual fraction of affected customers exhibits large variances, warranting the adoption of robust resilience investment strategies and climate-informed regulatory frameworks. [ABSTRACT FROM AUTHOR]

To clarify the approval status of molecular targeted antineoplastic drugs in the United States (U.S.), the European Union (E.U.), and Japan (JP), we checked the status of pediatric indications according to the package insert of each drug. A total of 103 drugs were approved for adult patients in at least one of the three regions whereas only 19 drugs were approved for pediatric patients. Sixty-six of 103 drugs (64.1%) had adult indications in the U.S., the E.U., and JP, whereas only three drugs had pediatric indications in all three regions. Abnormalities in six genes (NRAS, ABL1, JAK2, KIT, ALK and BRAF) were common in childhood cancers as well as adult cancers, for which at least one approved drug could be a potentially actionable drug. Although there were 16 candidate drugs that had adult indications for these abnormalities, only three drugs (18.8%) had pediatric indications. We confirmed that there were few molecular targeted antineoplastic drugs with pediatric indications in the U.S., the E.U., and JP compared with the number of approved drugs for adults. Drugs targeting genomic abnormalities which were common in both adult and pediatric cancers were considered to be good candidates for expansion of their indication for pediatric patients. [ABSTRACT FROM AUTHOR]

Scaling techniques such as the well known NOMINATE position political actors in a low dimensional space to represent the similarity or dissimilarity of their political orientation based on roll-call voting patterns. Starting from the same kind of data we propose an alternative, discrete, representation that replaces positions (points and distances) with niches (boxes and overlap). In the one-dimensional case, this corresponds to replacing the left-to-right ordering of points on the real line with an interval order. As it turns out, this seemingly simplistic one-dimensional model is sufficient to represent the similarity of roll-call votes by U.S. senators in recent years. In a historic context, however, low dimensionality represents the exception which stands in contrast to what is suggested by scaling techniques. [ABSTRACT FROM AUTHOR]

We use an individual based model and national level epidemic simulations to estimate the medical costs of keeping the US economy open during COVID-19 pandemic under different counterfactual scenarios. We model an unmitigated scenario and 12 mitigation scenarios which differ in compliance behavior to social distancing strategies and in the duration of the stay-home order. Under each scenario we estimate the number of people who are likely to get infected and require medical attention, hospitalization, and ventilators. Given the per capita medical cost for each of these health states, we compute the total medical costs for each scenario and show the tradeoffs between deaths, costs, infections, compliance and the duration of stay-home order. We also consider the hospital bed capacity of each Hospital Referral Region (HRR) in the US to estimate the deficit in beds each HRR will likely encounter given the demand for hospital beds. We consider a case where HRRs share hospital beds among the neighboring HRRs during a surge in demand beyond the available beds and the impact it has in controlling additional deaths. [ABSTRACT FROM AUTHOR]

According to a recent study, around 99% of hospitals across the US now use electronic health record systems (EHRs). One of the most common types of EHR is the unstructured textual data, and unlocking hidden details from this data is critical for improving current medical practices and research endeavors. However, these textual data contain sensitive information, which could compromise our privacy. Therefore, medical textual data cannot be released publicly without undergoing any privacy-protective measures. De-identification is a process of detecting and removing all sensitive information present in EHRs, and it is a necessary step towards privacy-preserving EHR data sharing. Over the last decade, there have been several proposals to de-identify textual data using manual, rule-based, and machine learning methods. In this article, we propose new methods to de-identify textual data based on the self-attention mechanism and stacked Recurrent Neural Network. To the best of our knowledge, we are the first to employ these techniques. Experimental results on three different datasets show that our model performs better than all state-of-the-art mechanism irrespective of the dataset. Additionally, our proposed method is significantly faster than the existing techniques. Finally, we introduced three utility metrics to judge the quality of the de-identified data. [ABSTRACT FROM AUTHOR]

While many informal factors influence how people interact, modern societies rely upon law as a primary mechanism to formally control human behaviour. How legal rules impact societal development depends on the interplay between two types of actors: the people who create the rules and the people to which the rules potentially apply. We hypothesise that an increasingly diverse and interconnected society might create increasingly diverse and interconnected rules, and assert that legal networks provide a useful lens through which to observe the interaction between law and society. To evaluate these propositions, we present a novel and generalizable model of statutory materials as multidimensional, time-evolving document networks. Applying this model to the federal legislation of the United States and Germany, we find impressive expansion in the size and complexity of laws over the past two and a half decades. We investigate the sources of this development using methods from network science and natural language processing. To allow for cross-country comparisons over time, based on the explicit cross-references between legal rules, we algorithmically reorganise the legislative materials of the United States and Germany into cluster families that reflect legal topics. This reorganisation reveals that the main driver behind the growth of the law in both jurisdictions is the expansion of the welfare state, backed by an expansion of the tax state. Hence, our findings highlight the power of document network analysis for understanding the evolution of law and its relationship with society. [ABSTRACT FROM AUTHOR]

Opioid use disorder and overdose deaths is a public health crisis in the United States, and there is increasing recognition that its etiology is rooted in part by social determinants such as poverty, isolation and social upheaval. Limiting research and policy interventions is the low temporal and spatial resolution of publicly available administrative data such as census data. We explore the use of municipal service requests (also known as "311" requests) as high resolution spatial and temporal indicators of neighborhood social distress and opioid misuse. We analyze the spatial associations between georeferenced opioid overdose event (OOE) data from emergency medical service responders and 311 service request data from the City of Columbus, OH, USA for the time period 2008–2017. We find 10 out of 21 types of 311 requests spatially associate with OOEs and also characterize neighborhoods with lower socio-economic status in the city, both consistently over time. We also demonstrate that the 311 indicators are capable of predicting OOE hotspots at the neighborhood-level: our results show code violation, public health, and street lighting were the top three accurate predictors with predictive accuracy as 0.92, 0.89 and 0.83, respectively. Since 311 requests are publicly available with high spatial and temporal resolution, they can be effective as opioid overdose surveillance indicators for basic research and applied policy. [ABSTRACT FROM AUTHOR]

Nanophthalmos is a rare condition defined by a small, structurally normal eye with resultant high hyperopia. While six genes have been implicated in this hereditary condition (MFRP, PRSS56, MYRF, TMEM98, CRB1,VMD2/BEST1), the relative contribution of these to nanophthalmos or to less severe high hyperopia (≥ + 5.50 spherical equivalent) has not been fully elucidated. We collected probands and families (n = 56) with high hyperopia or nanophthalmos (≤ 21.0 mm axial length). Of 53 families that passed quality control, plausible genetic diagnoses were identified in 10/53 (18.8%) by high-throughput panel or pooled exome sequencing. These include 1 TMEM98 family (1.9%), 5 MFRP families (9.4%), and 4 PRSS56 families (7.5%), with 4 additional families having single allelic hits in MFRP or PRSS56 (7.5%). A novel deleterious TMEM98 variant (NM_015544.3, c.602G>C, p.(Arg201Pro)) segregated with disease in 4 affected members of a family. Multiple novel missense and frameshift variants in MFRP and PRSS56 were identified. PRSS56 families were more likely to have choroidal folds than other solved families, while MFRP families were more likely to have retinal degeneration. Together, this study defines the prevalence of nanophthalmos gene variants in high hyperopia and nanophthalmos and indicates that a large fraction of cases remain outside of single gene coding sequences. [ABSTRACT FROM AUTHOR]

We combine COVID-19 case data with mobility data to estimate a modified susceptible-infected-recovered (SIR) model in the United States. In contrast to a standard SIR model, we find that the incidence of COVID-19 spread is concave in the number of infectious individuals, as would be expected if people have inter-related social networks. This concave shape has a significant impact on forecasted COVID-19 cases. In particular, our model forecasts that the number of COVID-19 cases would only have an exponential growth for a brief period at the beginning of the contagion event or right after a reopening, but would quickly settle into a prolonged period of time with stable, slightly declining levels of disease spread. This pattern is consistent with observed levels of COVID-19 cases in the US, but inconsistent with standard SIR modeling. We forecast rates of new cases for COVID-19 under different social distancing norms and find that if social distancing is eliminated there will be a massive increase in the cases of COVID-19. [ABSTRACT FROM AUTHOR]

During the unprecedented situation that all countries around the globe are facing due to the Coronavirus disease 2019 (COVID-19) pandemic, which has also had severe socioeconomic consequences, it is imperative to explore novel approaches to monitoring and forecasting regional outbreaks as they happen or even before they do so. To that end, in this paper, the role of Google query data in the predictability of COVID-19 in the United States at both national and state level is presented. As a preliminary investigation, Pearson and Kendall rank correlations are examined to explore the relationship between Google Trends data and COVID-19 data on cases and deaths. Next, a COVID-19 predictability analysis is performed, with the employed model being a quantile regression that is bias corrected via bootstrap simulation, i.e., a robust regression analysis that is the appropriate statistical approach to taking against the presence of outliers in the sample while also mitigating small sample estimation bias. The results indicate that there are statistically significant correlations between Google Trends and COVID-19 data, while the estimated models exhibit strong COVID-19 predictability. In line with previous work that has suggested that online real-time data are valuable in the monitoring and forecasting of epidemics and outbreaks, it is evident that such infodemiology approaches can assist public health policy makers in addressing the most crucial issues: flattening the curve, allocating health resources, and increasing the effectiveness and preparedness of their respective health care systems.

Since the first case of the novel coronavirus disease (COVID-19) was confirmed in Wuhan, China, social distancing has been promoted worldwide, including in the United States, as a major community mitigation strategy. However, our understanding remains limited in how people would react to such control measures, as well as how people would resume their normal behaviours when those orders were relaxed. We utilize an integrated dataset of real-time mobile device location data involving 100 million devices in the contiguous United States (plus Alaska and Hawaii) from February 2, 2020 to May 30, 2020. Built upon the common human mobility metrics, we construct a Social Distancing Index (SDI) to evaluate people's mobility pattern changes along with the spread of COVID-19 at different geographic levels. We find that both government orders and local outbreak severity significantly contribute to the strength of social distancing. As people tend to practice less social distancing immediately after they observe a sign of local mitigation, we identify several states and counties with higher risks of continuous community transmission and a second outbreak. Our proposed index could help policymakers and researchers monitor people's real-time mobility behaviours, understand the influence of government orders, and evaluate the risk of local outbreaks. [ABSTRACT FROM AUTHOR]

Most models of the COVID-19 pandemic in the United States do not consider geographic variation and spatial interaction. In this research, we developed a travel-network-based susceptible-exposed-infectious-removed (SEIR) mathematical compartmental model system that characterizes infections by state and incorporates inflows and outflows of interstate travelers. Modeling reveals that curbing interstate travel when the disease is already widespread will make little difference. Meanwhile, increased testing capacity (facilitating early identification of infected people and quick isolation) and strict social-distancing and self-quarantine rules are most effective in abating the outbreak. The modeling has also produced state-specific information. For example, for New York and Michigan, isolation of persons exposed to the virus needs to be imposed within 2 days to prevent a broad outbreak, whereas for other states this period can be 3.6 days. This model could be used to determine resources needed before safely lifting state policies on social distancing. [ABSTRACT FROM AUTHOR]

As of November 12, 2020, the mortality to incidence ratio (MIR) of COVID-19 was 5.8% in the US. A longitudinal model-based clustering system on the disease trajectories over time was used to identify "vulnerable" clusters of counties that would benefit from allocating additional resources by federal, state and county policymakers. County-level COVID-19 cases and deaths, together with a set of potential risk factors were collected for 3050 U.S. counties during the 1st wave of COVID-19 (Mar25–Jun3, 2020), followed by similar data for 1344 counties (in the "sunbelt" region of the country) during the 2nd wave (Jun4–Sep2, 2020), and finally for 1055 counties located broadly in the great plains region of the country during the 3rd wave (Sep3–Nov12, 2020). We used growth mixture models to identify clusters of counties exhibiting similar COVID-19 MIR growth trajectories and risk-factors over time. The analysis identifies "more vulnerable" clusters during the 1st, 2nd and 3rd waves of COVID-19. Further, tuberculosis (OR 1.3–2.1–3.2), drug use disorder (OR 1.1), hepatitis (OR 13.1), HIV/AIDS (OR 2.3), cardiomyopathy and myocarditis (OR 1.3), diabetes (OR 1.2), mesothelioma (OR 9.3) were significantly associated with increased odds of being in a more vulnerable cluster. Heart complications and cancer were the main risk factors increasing the COVID-19 MIR (range 0.08–0.52% MIR↑). We identified "more vulnerable" county-clusters exhibiting the highest COVID-19 MIR trajectories, indicating that enhancing the capacity and access to healthcare resources would be key to successfully manage COVID-19 in these clusters. These findings provide insights for public health policymakers on the groups of people and locations they need to pay particular attention while managing the COVID-19 epidemic. [ABSTRACT FROM AUTHOR]

Avian leukosis caused by avian leukosis virus (ALV) is one of the most severe diseases endangering the poultry industry. When the eradication measures performed in commercial broilers and layers have achieved excellent results, ALV in some local chickens has gradually attracted attention. Since late 2018, following the re-outbreak of ALV-J in white feather broilers in China, AL-like symptoms also suddenly broke out in some local flocks, leading to great economic losses. In this study, a systematic epidemiological survey was carried out in eight local chicken flocks in Jiangxi Province, China, and 71 strains were finally isolated from 560 samples, with the env sequences of them being successfully sequenced. All of those new isolates belong to subgroup J but they have different molecular features and were very different from the strains that emerged in white feature broilers recently, with some strains being highly consistent with those previously isolated from commercial broilers, layers and other flocks or even isolated from USA and Russian, suggesting these local chickens have been acted as reservoirs to accumulate various ALV-J strains for a long time. More seriously, phylogenetic analysis shows that there were also many novel strains emerging and in a separate evolutionary branch, indicating several new mutated ALVs are being bred in local chickens. Besides, ALV-J strains isolated in this study can be further divided into ten groups, while there were more or fewer groups in different chickens, revealing that ALV may cross propagate in those flocks. The above analyses explain the complex background and future evolution trend of ALV-J in Chinese local chickens, providing theoretical support for the establishment of corresponding prevention and control measures. [ABSTRACT FROM AUTHOR]

Contact tracing can play a key role in controlling human-to-human transmission of a highly contagious disease such as COVID-19. We investigate the benefits and costs of contact tracing in the COVID-19 transmission. We estimate two unknown epidemic model parameters (basic reproductive number R 0 and confirmed rate δ 2 ) by using confirmed case data. We model contact tracing in a two-layer network model. The two-layer network is composed by the contact network in the first layer and the tracing network in the second layer. In terms of benefits, simulation results show that increasing the fraction of traced contacts decreases the size of the epidemic. For example, tracing 25 % of the contacts is enough for any reopening scenario to reduce the number of confirmed cases by half. Considering the act of quarantining susceptible households as the contact tracing cost, we have observed an interesting phenomenon. The number of quarantined susceptible people increases with the increase of tracing because each individual confirmed case is mentioning more contacts. However, after reaching a maximum point, the number of quarantined susceptible people starts to decrease with the increase of tracing because the increment of the mentioned contacts is balanced by a reduced number of confirmed cases. The goal of this research is to assess the effectiveness of contact tracing for the containment of COVID-19 spreading in the different movement levels of a rural college town in the USA. Our research model is designed to be flexible and therefore, can be used to other geographic locations. [ABSTRACT FROM AUTHOR]