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Abstract: This paper presents the conceptual design and application of a new land-change modelling framework that represents geographical, sociological, economic, and ecological aspects of a land system. The framework provides an overarching design that can be extended into specific model implementations to evaluate how policy, land-management preferences, and land-market dynamics affect (and are affected by) land-use and land-cover change patterns and subsequent carbon storage and flux. To demonstrate the framework, we implement a simple integration of a new agent-based model of exurban residential development and land-management decisions with the ecosystem process model BIOME-BGC. Using a stylized scenario, we evaluate the influence of different exurban residential-land-management strategies on carbon storage at the parcel level over a 48-year period from 1958 to 2005, simulating stocks of carbon in soil, litter, vegetation, and net primary productivity. Results show 1) residential parcels with management practices that only provided additions in the form of fertilizer and irrigation to turfgrass stored slightly more carbon than parcels that did not include management practices, 2) conducting no land-management strategy stored more carbon than implementing a strategy that included removals in the form of removing coarse woody debris from dense tree cover and litter from turfgrass, and 3) the removal practices modelled had a larger impact on total parcel carbon storage than our modelled additions. The degree of variation within the evaluated land-management practices was approximately 42,104 kg C storage on a 1.62 ha plot after 48 years, demonstrating the substantial effect that residential land-management practices can have on carbon storage. [Copyright &y& Elsevier]

Abstract: Preterm birth is one of the leading causes of perinatal mortality and is associated with long-term adverse health consequences for surviving infants. Preterm birth rates are rising worldwide, and no effective means for prevention currently exists. Air pollution exposure may be a significant cause of prematurity, but many published studies lack the individual, clinical data needed to elucidate possible biological mechanisms mediating these epidemiological associations. This paper presents the design of a prospective study now underway to evaluate those mechanisms in a cohort of pregnant women residing in Mexico City. We address how air quality may act together with other factors to induce systemic inflammation and influence the duration of pregnancy. Data collection includes: biomarkers relevant to inflammation in cervico-vaginal exudate and peripheral blood, along with full clinical information, pro-inflammatory cytokine gene polymorphisms and air pollution data to evaluate spatial and temporal variability in air pollution exposure. Samples are collected on a monthly basis and participants are followed for the duration of pregnancy. The data will be used to evaluate whether ambient air pollution is associated with preterm birth, controlling for other risk factors. We will evaluate which time windows during pregnancy are most influential in the air pollution and preterm birth association. In addition, the epidemiological study will be complemented with a parallel toxicology invitro study, in which monocytic cells will be exposed to air particle samples to evaluate the expression of biomarkers of inflammation. [Copyright &y& Elsevier]

On an annual basis, heat is the chief cause of weather-related deaths in the United States. Therefore, understanding the temperature structure where people live is important for reducing the health burden imposed by hot weather. This study focused on the air temperatures in the Detroit, Michigan, metropolitan region during the summer of 2009. An observational network was established that included 1) monitors sited in the backyards of residential participants, 2) National Weather Service standard observations, and 3) a network of monitors operated by the State of Michigan. Daily high and low temperatures were analyzed for spatial pattern, magnitude of spatial variability, and relationships with weather conditions. The existence of spatial variability was confirmed specifically during weather that was considered to be dangerous to public health. The relationships between temperature observations and distance to water, distance to city center, and local percent of impervious surface were investigated. The spatial variability during the daily low was typically stronger in magnitude and the spatial pattern was more consistent than were those during the daily high. The largest correlation with land-cover and location attributes was between values of percent of impervious surface and daily low temperatures. Daily high temperatures were most correlated with distance to water. Consistent with previous studies on spatial variability in urban environments, the results suggest a need for sensitivity to the spatially variable nature of exposure to heat events in both public health and urban planning. For example, these results showed that the downtown area experienced elevated temperatures during nights and that the eastern portions of Detroit experienced decreased temperatures during afternoons. [ABSTRACT FROM AUTHOR]

Exurban residential settings are relevant for understanding how societal desires for open space impact landscapes and their ecosystem services. In a 2005 image-based web survey of 468 exurban homeowners in southeast Michigan, we investigated how exurban home-buying choices may be related to having open space nearby, and we measured homeowners’ relative preferences for seven different types of open spaces characterized by different ecosystem services. Our study confirms that nearby open space is important to exurban homeowners, but it suggests that homeowners are somewhat heterogeneous in their preferences for different types of open spaces. They generally prefer forests, lakes, and streams, but their preferences for wetlands, prairies, playgrounds, and golf courses vary somewhat with education, age, or having children. In addition, homeowners for whom open space was less important when they bought their home had somewhat different preferences. We discuss the implications for protecting ecosystem services in planning, design, and development. [ABSTRACT FROM AUTHOR]

Abstract: We use an exploratory agent-based model of adults'' walking behavior within a city to examine the possible impact of interventions on socioeconomic differences in walking. Simulated results show that for persons of low socioeconomic status, increases in walking resulting from increases in their positive attitude towards walking may diminish over time if other features of the environment are not conducive to walking. Similarly, improving the safety level for the lower SES neighborhoods may be effective in increasing walking, however, the magnitude of its effectiveness varies by levels of land use mix, such that effects of safety are greatest when persons live in areas with a large mix of uses. [Copyright &y& Elsevier]

Background: Climate change and associated increases in climate variability will likely further exacerbate global health disparities. More research is needed, particularly in developing countries, to accurately predict the anticipated impacts and inform effective interventions. oBjectives: Building on the information presented at the 2009 Joint Indo-U.S. Workshop on Climate Change and Health in Goa, India, we reviewed relevant literature and data, addressed gaps in knowledge, and identified priorities and strategies for future research in India. discussion: The scope of the problem in India is enormous, based on the potential for climate change and variability to exacerbate endemic malaria, dengue, yellow fever, cholera, and chikungunya, as well as chronic diseases, particularly among the millions of people who already experience poor sanitation, pollution, malnutrition, and a shortage of drinking water. Ongoing efforts to study these risks were discussed but remain scant. A universal theme of the recommendations developed was the importance of improving the surveillance, monitoring, and integration of meteorological, environmental, geospatial, and health data while working in parallel to implement adaptation strategies. conclusions: It will be critical for India to invest in improvements in information infrastructure that are innovative and that promote interdisciplinary collaborations while embarking on adapta­tion strategies. This will require unprecedented levels of collaboration across diverse institutions in India and abroad. The data can be used in research on the likely impacts of climate change on health that reflect India's diverse climates and populations. Local human and technical capacities for risk communication and promoting adaptive behavior must also be enhanced. [ABSTRACT FROM AUTHOR]

Abstract: Environmental effects on walking behavior have received attention in recent years because of the potential for policy interventions to increase population levels of walking. Most epidemiologic studies describe associations of walking behavior with environmental features. These analyses ignore the dynamic processes that shape walking behaviors. A spatial agent-based model (ABM) was developed to simulate people''s walking behaviors within a city. Each individual was assigned properties such as age, SES, walking ability, attitude toward walking and a home location. Individuals perform different activities on a regular basis such as traveling for work, for basic needs, and for leisure. Whether an individual walks and the amount she or he walks is a function of distance to different activities and her/his walking ability and attitude toward walking. An individual''s attitude toward walking evolves over time as a function of past experiences, walking of others along the walking route, limits on distances walked per day, and attitudes toward walking of the other individuals within her/his social network. The model was calibrated and used to examine the contributions of land use and safety to socioeconomic differences in walking. With further refinement and validation, ABMs may help to better understand the determinants of walking and identify the most promising interventions to increase walking. [Copyright &y& Elsevier]

Abstract: Few studies have investigated the spatial clustering of multiple health-related resources. We constructed 0.5 mile kernel densities of resources for census areas in New York City, NY (n=819 block groups), Baltimore, MD (n=737), and Winston-Salem, NC (n=169). Three of the four resource densities (supermarkets/produce stores, retail areas, and recreational facilities) tended to be correlated with each other, whereas park density was less consistently and sometimes negatively correlated with others. Blacks were more likely to live in block groups with multiple low resource densities. Spatial regression models showed that block groups with higher proportions of black residents tended to have lower supermarket/produce, retail, and recreational facility densities, although these associations did not always achieve statistical significance. A measure that combined local and neighboring block group racial composition was often a stronger predictor of resources than the local measure alone. Overall, our results from three diverse U.S. cities show that health-related resources are not randomly distributed across space and that disadvantage in multiple domains often clusters with residential racial patterning. [ABSTRACT FROM AUTHOR]

An important debate in the literature on exurban sprawl is whether low-density development results from residential demand, as operationalized by developers, or from exclusionary zoning policies. Central to this debate is the purpose of zoning, which could alternatively be a mechanism to increase the utility of residents by separating land uses and reducing spillover effects of development, or an obstacle to market mechanisms that would otherwise allow the realization of residential preferences. To shed light on this debate, we developed an agent-based model of land-use change to study how the combined effects of zoning-enforcement levels, density preferences, preference heterogeneity, and negative externalities from development affect exurban development and the utility of residents. Our computational experiments show that sprawl is not inevitable, even when most of the population prefers low densities. The presence of negative externalities consistently encourage sprawl while decreasing average utility and flattening the utility distribution. Zoning can reduce sprawl by concentrating development in specific areas, but in doing so decreases average utility and increases inequality. Zoning does not internalize externalities; instead, it contains externalities in areas of different development density so that residents bear the burden of the external effects of the density they prefer. Effects vary with residential preference distributions and levels of zoning enforcement. These initial investigations can help inform policy makers about the conditions under which zoning enforcement is preferable to free-market development and vice versa. Future work will focus on the environmental impacts of different settlement patterns and the role land-use and market-based policies play in this relationship. [ABSTRACT FROM AUTHOR]

BACKGROUND: The evidence that heat waves can result in both increased deaths and illness is substantial, and concern over this issue is rising because of climate change. Adverse health impacts from heat waves can be avoided, and epidemiologic studies have identified specific population and community characteristics that mark vulnerability to heat waves. OBJECTIVES: We situated vulnerability to heat in geographic space and identified potential areas for intervention and further research. METHODS: We mapped and analyzed 10 vulnerability factors for heat-related morbidity/mortality in the United States: six demographic characteristics and two household air conditioning variables from the U.S. Census Bureau, vegetation cover from satellite images, and diabetes prevalence from a national survey. We performed a factor analysis of these 10 variables and assigned values of increasing vulnerability for the four resulting factors to each of 39,794 census tracts. We added the four factor scores to obtain a cumulative heat vulnerability index value. RESULTS: Four factors explained > 75% of the total variance in the original 10 vulnerability variables: a) social/environmental vulnerability (combined education/poverty/race/green space), b) social isolation, c) air conditioning prevalence, and d) proportion elderly/diabetes. We found substantial spatial variability of heat vulnerability nationally, with generally higher vulnerability in the Northeast and Pacific Coast and the lowest in the Southeast. In urban areas, inner cities showed the highest vulnerability to heat. CONCLUSIONS: These methods provide a template for making local and regional heat vulnerability maps. After validation using health outcome data, interventions can be targeted at the most vulnerable populations. [ABSTRACT FROM AUTHOR]

Objectives. Using data from a large cohort of adults aged 45 to 84 years-old, we investigated whether availability of recreational resources is related to physical activity levels. Methods. Data from a multiethnic sample of 2723 adult residents of New York City, NY; Baltimore, Md; and Forsyth County, NC, were linked to data on locations of recreational resources. We measured the availability (density) of resources within 0.5 (0.8 km), 1, 2, and 5 miles of each participant's residence and used binomial regression to investigate associations of density with physical activity. Results. After adjustment for potential confounders, individuals in the tertile of participants residing in areas with the highest density of resources were more likely to report physical activity during a typical week than were individuals in the lowest tertile. Associations between availability of recreational resources and physical activity levels were not present for the smallest area assessed (0.5 miles) but were present for areas ranging from 1 to 5 miles. These associations were slightly stronger among minority and low-income residents. Conclusions. Availability of resources may be 1 of several environmental factors that influence individuals' physical activity behaviors. [ABSTRACT FROM AUTHOR]

Solifluction patterns on Lee Ridge, Glacier National Park, Montana, USA, were characterized through a combination of field transects, enhanced, digital aircraft data, and geostatistics. The objectives were to (i) assess the role of high spatial resolution digital imagery for mapping the areal extent and spatial pattern of solifluction steps and risers through digital enhancements [i.e., principal components analysis (PCA) and wavelet transforms (WTFs)] of ADAR-5500 aircraft imagery; (ii) describe the pattern of solifluction steps and risers through the placement of twelve 50-m field transects oriented normal to the steps and risers and organized along an elevational gradient extending from highest to lowest positions along the ridge; (iii) define scale dependent relationships of the solifluction steps and risers mapped along one-dimensional field transects and two-dimensional remote sensing images using indicator semivariograms to define the structure of the spatial data across a range of sampled scales; and (iv) relate the solifluction patterns observed through the transects and the remote sensing interpretations to topographic trends associated with distance from the dominant peak, a proxy for elevation change along Lee Ridge. Findings suggest that the semivariance pattern of the transects changes with elevation, that is the semivariograms of the lower transects show smaller amplitudes and shorter wavelengths than those at higher elevations, and the pattern of steps and risers from the enhanced ADAR-5500 images indicate less variation in the semivariance with elevation than represented in the field transects. [Copyright &y& Elsevier]

Knowledge of land cover and land use nationally is a prerequisite of many studies on drivers of land change, impacts on climate, carbon storage and other ecosystem services, and allows for sufficient planning and management. Despite this, many regions globally do not have accurate and consistent coverage at the national scale. This is certainly true for Ethiopia. Large-area land-cover characterization (LALCC), at a national scale is thus an essential first step in many studies of land-cover change, and yet is itself problematic. Such LALCC based on remote-sensing image classification is associated with a spectrum of technical challenges such as data availability, radiometric inconsistencies within/between images, and big data processing. Radiometric inconsistencies could be exacerbated for areas, such as Ethiopia, with a high frequency of cloud cover, diverse ecosystem and climate patterns, and large variations in elevation and topography. Obtaining explanatory variables that are more robust can improve classification accuracy. To create a base map for the future study of large-scale agricultural land transactions, we produced a recent land-cover map of Ethiopia. Of key importance was the creation of a methodology that was accurate and repeatable and, as such, could be used to create earlier, comparable land-cover classifications in the future for the same region. We examined the effects of band normalization and different time-series image compositing methods on classification accuracy. Both top of atmosphere and surface reflectance products from the Landsat 8 Operational Land Imager (OLI) were tested for single-time classification independently, where the latter resulted in 1.1% greater classification overall accuracy. Substitution of the original spectral bands with normalized difference spectral indices resulted in an additional improvement of 1.0% in overall accuracy. Three approaches for multi-temporal image compositing, using Landsat 8 OLI and Moderate Resolution Imaging Spectroradiometer (MODIS) data, were tested including sequential compositing, i.e., per-pixel summary measures based on predefined periods, probability density function compositing, i.e., per-pixel characterization of distribution of spectral values, and per-pixel sinusoidal models. Multi-temporal composites improved classification overall accuracy up to 4.1%, with respect to single-time classification with an advantage of the Landsat OLI-driven composites over MODIS-driven composites. Additionally, night-time light and elevation data were used to improve the classification. The elevation data and its derivatives improved classification accuracy by 1.7%. The night-time light data improve producer's accuracy of the Urban/Built class with the cost of decreasing its user's accuracy. Results from this research can aid map producers with decisions related to operational large-area land-cover mapping, especially with selecting input explanatory variables and multi-temporal image compositing, to allow for the creation of accurate and repeatable national-level land-cover products in a timely fashion. [ABSTRACT FROM AUTHOR]

Research tool patents that cover technology used to discover drugs and drug candidates, are an important source of revenue for many biotechnology companies and academic research institutions in the U.S. Biologic research tools are products and processes used in experimental research, such as drug discovery. Examples of research tool patents include patents directed to receptors or other peptides used in screens to identify ligand-binding activity; patents directed to enzymes and patents directed to nondiagnostic and nontherapeutic antibodies used in screening assays. Thus, advancement in many fields is centered on research tools, and the use of biologic research tools is typically a first step in the chain of modern drug discovery. There are two competing policy interests in patent law: an inventor's interest to have exclusive rights to his or her invention, and the interest of the public to benefit from the progress of knowledge and technology. The U.S. Congress enacted the Hatch-Waxman Act of 1984 to balance these competing interests in the pharmaceutical area. Three principal components of the Act are patent term extension, Abbreviated New Drug Application (ANDA) filing and a research exemption.

Global drivers and carbon emissions associated with large-scale land transactions have been poorly investigated. Here we examine major factors behind such transactions (income, agricultural productivity, availability of arable land and water scarcity) and estimate potential carbon emissions under different levels of deforestation. We find that clearing lands transacted between 2000 and 2016 (36.7 Mha) could have emitted ~2.26 GtC, but constraining land clearing to historical deforestation rates would reduce emissions related to large-scale land transactions to ~0.81 GtC., (© 2021. The Author(s), under exclusive licence to Springer Nature Limited.)

We describe the use of the Fréchet mean and variance in the Billera-Holmes-Vogtmann (BHV) treespace to summarize and explore the diversity of a set of phylogenetic trees. We show that the Fréchet mean is comparable to other summary methods, and, despite its stickiness property, is more likely to be binary than the majority-rule consensus tree. We show that the Fréchet variance is faster and more precise than commonly used variance measures. The Fréchet mean and variance are more theoretically justified, and more robust, than previous estimates of this type and can be estimated reasonably efficiently, providing a foundation for building more advanced statistical methods and leading to applications such as mean hypothesis testing and outlier detection., (© The Author(s) 2019. Published by Oxford University Press, on behalf of the Society of Systematic Biologists. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Exurban residential land (one housing unit per 0.2-16.2 ha) is growing in importance as a human-dominated land use. Carbon storage in the soils and vegetation of exurban land is poorly known, as are the effects on C storage of choices made by developers and residents. We studied C storage in exurban yards in southeastern Michigan, USA, across a range of parcel sizes and different types of neighborhoods. We divided each residential parcel into ecological zones (EZ) characterized by vegetation, soil, and human behavior such as mowing, irrigation, and raking. We found a heterogeneous mixture of trees and shrubs, turfgrasses, mulched gardens, old-field vegetation, and impervious surfaces. The most extensive zone type was turfgrass with sparse woody vegetation (mean 26% of parcel area), followed by dense woody vegetation (mean 21% of parcel area). Areas of turfgrass with sparse woody vegetation had trees in larger size classes (> 50 cm dbh) than did areas of dense woody vegetation. Using aerial photointerpretation, we scaled up C storage to neighborhoods. Varying C storage by neighborhood type resulted from differences in impervious area (8-26% of parcel area) and area of dense woody vegetation (11-28%). Averaged and multiplied across areas in differing neighborhood types, exurban residential land contained 5240 ± 865 g C/m 2 in vegetation, highly sensitive to large trees, and 13 800 ± 1290 g C/m 2 in soils (based on a combined sampling and modeling approach). These contents are greater than for agricultural land in the region, but lower than for mature forest stands. Compared with mature forests, exurban land contained more shrubs and less downed woody debris and it had similar tree size-class distributions up to 40 cm dbh but far fewer trees in larger size classes. If the trees continue to grow, exurban residential land could sequester additional C for decades. Patterns and processes of C storage in exurban residential land were driven by land management practices that affect soil and vegetation, reflecting the choices of designers, developers, and residents. This study provides an example of human-mediated C storage in a coupled human-natural system., (© 2016 by the Ecological Society of America.)

Land-use change in the U.S. Great Plains since agricultural settlement in the second half of the nineteenth century has been well documented. While aggregate historical trends are easily tracked, the decision-making of individual farmers is difficult to reconstruct. We use an agent-based model to tell the history of the settlement of the West by simulating farm-level agricultural decision making based on historical data about prices, yields, farming costs, and environmental conditions. The empirical setting for the model is the period between 1875 and 1940 in two townships in Kansas, one in the shortgrass region and the other in the mixed grass region. Annual historical data on yields and prices determine profitability of various land uses and thereby inform decision-making, in conjunction with the farmer's previous experience and randomly assigned levels of risk aversion. Results illustrating the level of agreement between model output and unique and detailed household-level records of historical land use and farm size suggest that economic behavior and natural endowments account for land change processes to some degree, but are incomplete. Discrepancies are examined to identify missing processes through model experiments, in which we adjust input and output prices, crop yields, agent memory, and risk aversion. These analyses demonstrate how agent-based modeling can be a useful laboratory for thinking about social and economic behavior in the past.

Background: Second-generation sequencers generate millions of relatively short, but error-prone, reads. These errors make sequence assembly and other downstream projects more challenging. Correcting these errors improves the quality of assemblies and projects which benefit from error-free reads., Results: We have developed a general-purpose error corrector that corrects errors introduced by Illumina, Ion Torrent, and Roche 454 sequencing technologies and can be applied to single- or mixed-genome data. In addition to correcting substitution errors, we locate and correct insertion, deletion, and homopolymer errors while remaining sensitive to low coverage areas of sequencing projects. Using published data sets, we correct 94% of Illumina MiSeq errors, 88% of Ion Torrent PGM errors, 85% of Roche 454 GS Junior errors. Introduced errors are 20 to 70 times more rare than successfully corrected errors. Furthermore, we show that the quality of assemblies improves when reads are corrected by our software., Conclusions: Pollux is highly effective at correcting errors across platforms, and is consistently able to perform as well or better than currently available error correction software. Pollux provides general-purpose error correction and may be used in applications with or without assembly.

Local changes in land use result from the decisions and actions of land-users within land systems, which are structured by local and global environmental, economic, political, and cultural contexts. Such cross-scale causation presents a major challenge for developing a general understanding of how local decision-making shapes land-use changes at the global scale. This paper implements a generalized agent-based model (ABM) as a virtual laboratory to explore how global and local processes influence the land-use and livelihood decisions of local land-users, operationalized as settlement-level agents, across the landscapes of six real-world test sites. Test sites were chosen in USA, Laos, and China to capture globally-significant variation in population density, market influence, and environmental conditions, with land systems ranging from swidden to commercial agriculture. Publicly available global data were integrated into the ABM to model cross-scale effects of economic globalization on local land-use decisions. A suite of statistics was developed to assess the accuracy of model-predicted land-use outcomes relative to observed and random (i.e. null model) landscapes. At four of six sites, where environmental and demographic forces were important constraints on land-use choices, modeled land-use outcomes were more similar to those observed across sites than the null model. At the two sites in which market forces significantly influenced land-use and livelihood decisions, the model was a poorer predictor of land-use outcomes than the null model. Model successes and failures in simulating real-world land-use patterns enabled the testing of hypotheses on land-use decision-making and yielded insights on the importance of missing mechanisms. The virtual laboratory approach provides a practical framework for systematic improvement of both theory and predictive skill in land change science based on a continual process of experimentation and model enhancement.

Users of phylogenetic methods require rooted trees, because the direction of time depends on the placement of the root. While phylogenetic trees are typically rooted by using an out-group, this mechanism is inappropriate when the addition of an out-group changes the in-group topology. We perform a formal analysis of phylogenetic algorithms under the inclusion of distant out-groups. It turns out that linkage-based algorithms (including UPGMA) and a class of bisecting methods do not modify the topology of the in-group when an out-group is included. By contrast, the popular neighbour joining algorithm fails this property in a strong sense: every data set can have its structure destroyed by some arbitrarily distant outlier. Furthermore, including multiple outliers can lead to an arbitrary topology on the in-group. The standard rooting approach that uses out-groups may be fundamentally unsuited for neighbour joining.

Rural populations are undergoing rapid changes in both their livelihoods and land uses, with associated impacts on ecosystems, global biogeochemistry, and climate change. A primary challenge is, thus, to explain these shifts in terms of the actors and processes operating within a variety of land systems in order to understand how land users might respond locally to future changes in broader-scale environmental and economic conditions. Using 'induced intensification' theory as a benchmark, we develop a generalized agent-based model to investigate mechanistic explanations of relationships between agricultural intensity and population density, environmental suitability, and market influence. Land-use and livelihood decisions modeled from basic micro-economic theories generated spatial and temporal patterns of agricultural intensification consistent with predictions of induced intensification theory. Further, agent actions in response to conditions beyond those described by induced intensification theory were explored, revealing that interactions among environmental constraints, population pressure, and market influence may produce transitions to multiple livelihood regimes of varying market integration. The result is new hypotheses that could modify and enrich understanding of the classic relationship between agricultural intensity and population density. The strength of this agent-based model and the experimental results is the generalized form of the decision-making processes underlying land-use and livelihood transitions, creating the prospect of a virtual laboratory for systematically generating hypotheses of how agent decisions and interactions relate to observed land-use and livelihood patterns across diverse land systems.

Background: Kinship inference is the task of identifying genealogically related individuals. Kinship information is important for determining mating structures, notably in endangered populations. Although many solutions exist for reconstructing full sibling relationships, few exist for half-siblings., Results: We consider the problem of determining whether a proposed half-sibling population reconstruction is valid under Mendelian inheritance assumptions. We show that this problem is NP-complete and provide a 0/1 integer program that identifies the minimum number of individuals that must be removed from a population in order for the reconstruction to become valid. We also present SibJoin, a heuristic-based clustering approach based on Mendelian genetics, which is strikingly fast. The software is available at http://github.com/ddexter/SibJoin.git+., Conclusions: Our SibJoin algorithm is reasonably accurate and thousands of times faster than existing algorithms. The heuristic is used to infer a half-sibling structure for a population which was, until recently, too large to evaluate.

This paper examines drivers of land-cover change in the U.S. Great Plains in the last half of the twentieth century. Its central aim is to evaluate the dynamics of grassland preservation and conversion, across the region, and to identify areas of grassland that were never plowed during the period. The research compares land-cover data from 400 sample areas, selected from and nested within 50 counties, to aggregate data from the agricultural and population censuses. The spatially explicit land-cover data were interpreted from aerial photographs taken at three time points (1950s, 1970s and 2000s). Sample areas were chosen using a stratified random design based on the Public Land Survey grid with in the target counties, in several clusters across the region. We modeled the sequences and magnitudes of changes in the interpreted air photo data in a multi-level panel model that included soil quality and slope of sample areas and agricultural activities and employment reported in the U.S. Censuses of Agriculture and Population. We conclude that land retirement programs and production subsidies have worked at cross purposes, destabilizing micro-level patterns of land use in recent decades, increasing levels of switching between cropland and grassland and reducing the size of remaining areas of native grassland in the U.S. Great Plains.

Preterm birth is one of the leading causes of perinatal mortality and is associated with long-term adverse health consequences for surviving infants. Preterm birth rates are rising worldwide, and no effective means for prevention currently exists. Air pollution exposure may be a significant cause of prematurity, but many published studies lack the individual, clinical data needed to elucidate possible biological mechanisms mediating these epidemiological associations. This paper presents the design of a prospective study now underway to evaluate those mechanisms in a cohort of pregnant women residing in Mexico City. We address how air quality may act together with other factors to induce systemic inflammation and influence the duration of pregnancy. Data collection includes: biomarkers relevant to inflammation in cervico-vaginal exudate and peripheral blood, along with full clinical information, pro-inflammatory cytokine gene polymorphisms and air pollution data to evaluate spatial and temporal variability in air pollution exposure. Samples are collected on a monthly basis and participants are followed for the duration of pregnancy. The data will be used to evaluate whether ambient air pollution is associated with preterm birth, controlling for other risk factors. We will evaluate which time windows during pregnancy are most influential in the air pollution and preterm birth association. In addition, the epidemiological study will be complemented with a parallel toxicology invitro study, in which monocytic cells will be exposed to air particle samples to evaluate the expression of biomarkers of inflammation., (Copyright © 2012 Elsevier B.V. All rights reserved.)

Climate changes on the Mongolian Plateau are creating new challenges for the households and communities of the region. Much of the existing research on household choices in response to climate variability and change focuses on environmental risks and stresses. In contrast, our analysis highlights the importance of taking into account environmental and economic opportunities in explaining household adaptation choices. We surveyed over 750 households arrayed along an ecological gradient and matched across the national border in Mongolia and the Inner Mongolia Autonomous Region, China, asking what changes in livelihoods strategies households made over the last ten years, and analyzed these choices in two broad categories of options: diversification and livestock management. We combined these data with remotely sensed information about vegetation growth and self-reported exposure to price fluctuations. Our statistical results showed that households experiencing lower ecological and economic variability, higher average levels of vegetation growth, and with greater levels of material wealth, were often those that undertook more actions to improve their conditions in the face of variability. The findings have implications both for how interventions aimed at supporting ongoing choices might be targeted and for theory construction related to social adaptation.

We consider the problem of phylogenetic placement, in which large numbers of sequences (often next-generation sequencing reads) are placed onto an existing phylogenetic tree. We adapt our recent work on phylogenetic tree inference, which uses ancestral sequence reconstruction and locality-sensitive hashing, to this domain. With these ideas, new sequences can be placed onto trees with high fidelity in strikingly fast runtimes. Our results are two orders of magnitude faster than existing programs for this domain, and show a modest accuracy tradeoff. Our results offer the possibility of analyzing many more reads in a next-generation sequencing project than is currently possible.

Background: Illumina paired-end reads are used to analyse microbial communities by targeting amplicons of the 16S rRNA gene. Publicly available tools are needed to assemble overlapping paired-end reads while correcting mismatches and uncalled bases; many errors could be corrected to obtain higher sequence yields using quality information., Results: PANDAseq assembles paired-end reads rapidly and with the correction of most errors. Uncertain error corrections come from reads with many low-quality bases identified by upstream processing. Benchmarks were done using real error masks on simulated data, a pure source template, and a pooled template of genomic DNA from known organisms. PANDAseq assembled reads more rapidly and with reduced error incorporation compared to alternative methods., Conclusions: PANDAseq rapidly assembles sequences and scales to billions of paired-end reads. Assembly of control libraries showed a 4-50% increase in the number of assembled sequences over naïve assembly with negligible loss of "good" sequence.

Background: Identifying recombinations in HIV is important for studying the epidemiology of the virus and aids in the design of potential vaccines and treatments. The previous widely-used tool for this task uses the Viterbi algorithm in a hidden Markov model to model recombinant sequences., Results: We apply a new decoding algorithm for this HMM that improves prediction accuracy. Exactly locating breakpoints is usually impossible, since different subtypes are highly conserved in some sequence regions. Our algorithm identifies these sites up to a certain error tolerance. Our new algorithm is more accurate in predicting the location of recombination breakpoints. Our implementation of the algorithm is available at http://www.cs.uwaterloo.ca/~jmtruszk/jphmm_balls.tar.gz., Conclusions: By explicitly accounting for uncertainty in breakpoint positions, our algorithm offers more reliable predictions of recombination breakpoints in HIV-1. We also document a new domain of use for our new decoding approach in HMMs.

Background: Existing hidden Markov model decoding algorithms do not focus on approximately identifying the sequence feature boundaries., Results: We give a set of algorithms to compute the conditional probability of all labellings "near" a reference labelling lambda for a sequence y for a variety of definitions of "near". In addition, we give optimization algorithms to find the best labelling for a sequence in the robust sense of having all of its feature boundaries nearly correct. Natural problems in this domain are NP-hard to optimize. For membrane proteins, our algorithms find the approximate topology of such proteins with comparable success to existing programs, while being substantially more accurate in estimating the positions of transmembrane helix boundaries., Conclusion: More robust HMM decoding may allow for better analysis of sequence features, in reasonable runtimes.

Background: Traditional algorithms for hidden Markov model decoding seek to maximize either the probability of a state path or the number of positions of a sequence assigned to the correct state. These algorithms provide only a single answer and in practice do not produce good results., Results: We explore an alternative approach, where we efficiently compute the k paths of highest probability to explain a sequence and then either use those paths to explore alternative explanations for a sequence or to combine them into a single explanation. Our procedure uses an online pruning technique to reduce usage of primary memory., Conclusion: Out algorithm uses much less memory than naive approach. For membrane proteins, even simple path combination algorithms give good explanations, and if we look at the paths we are combining, we can give a sense of confidence in the explanation as well. For proteins with two topologies, the k best paths can give insight into both correct explanations of a sequence, a feature lacking from traditional algorithms in this domain.

In this tutorial, we discuss two main algorithms for Hidden Markov Models or HMMs: the Viterbi algorithm and the expectation phase of the Baum-Welch algorithm, and we describe ways to improve their naïve implementations. For the Baum-Welch algorithm we first present an implementation of the expectation computations using constant space. We then discuss the classical implementation of this calculation and describe ways to reduce its space usage to logarithmic and O(square root n), with their respective CPU costs. We also note where each respective algorithm can be parallelized. For the Viterbi algorithm, we describe O(square root n) and logarithmic space algorithms which increase CPU use by a factor of two and by a logarithmic factor respectively. We also present two recent heuristics for decreasing space use, which in practice lead to logarithmic space use. Classical version of Viterbi cannot be parallelized by splitting sequence in several subsequences, but we show a parallelization that works if we are willing to pay a significant extra CPU cost. Finally we show a very simple parallelization trick which enables full usage of multiple CPUs/cores under the condition that they share memory.

Objective: : Little is known about the influence of the built environment, and in particular neighborhood resources, on health. We hypothesized that neighborhood resources for physical activity and healthy foods are associated with insulin resistance., Methods: : Person-level data (n = 2026) came from 3 sites of The Multi-Ethnic Study of Atherosclerosis, a study of adults aged 45-84 years. Area-level data were derived from a population-based residential survey. The homeostasis model assessment index was used as an insulin resistance measure among persons not treated for diabetes. We used linear regression to estimate associations between area features and insulin resistance., Results: : Greater neighborhood physical activity resources consistently were associated with lower insulin resistance. Adjusted for age, sex, family history of diabetes, race/ethnicity, income and education, insulin resistance was reduced by 17% (95% confidence interval = -31% to -1%) for an increase from the 10th to 90th percentiles of resources. Greater healthy food resources were also inversely related to insulin resistance, although the association was not robust to adjustment for race/ethnicity. Analyses including diet, physical activity, and body mass index suggested that these variables partly mediated observed associations. Results were similar when impaired fasting glucose/diabetes was considered as the outcome variable., Conclusion: : Diabetes prevention efforts may need to consider features of residential environment.

The measurement of area-level attributes remains a major challenge in studies of neighborhood health effects. Even when neighborhood survey data are collected, they necessarily have incomplete spatial coverage. We investigated whether interpolation of neighborhood survey data was aided by information on spatial dependencies and supplementary data. Neighborhood "availability of healthy foods" was measured in a population-based survey of 5186 persons in Baltimore, New York, and Forsyth County (North Carolina). The following supplementary data were compiled from Census 2000 and InfoUSA, Inc.: distance to supermarkets, density of supermarkets and fruit and vegetable stores, housing density, distance to a high-income area, and percent of households that do not own a vehicle. We compared 4 interpolation models (ordinary least squares, residual kriging, spatial error regression, and thin-plate splines) using error statistics and Pearson correlation coefficients (r) from repeated replications of cross-validations. There was positive spatial autocorrelation in neighborhood availability of healthy foods (by site, Moran coefficient range = 0.10-0.28; all P<0.0001). Prediction performances were generally similar for the evaluated models (r approximately 0.35 for Baltimore and Forsyth; r approximately 0.54 for New York). Supplementary data accounted for much of the spatial autocorrelation and, thus, spatial modeling was only advantageous when spatial correlation was at least moderate. A variety of interpolation techniques will likely need to be utilized in order to increase the data available for examining health effects of residential environments. The most appropriate method will vary depending on the construct of interest, availability of relevant supplementary data, and types of observed spatial patterns.

Objectives: Using data from a large cohort of adults aged 45 to 84 years-old, we investigated whether availability of recreational resources is related to physical activity levels., Methods: Data from a multiethnic sample of 2723 adult residents of New York City, NY; Baltimore, Md; and Forsyth County, NC, were linked to data on locations of recreational resources. We measured the availability (density) of resources within 0.5 (0.8 km), 1, 2, and 5 miles of each participant's residence and used binomial regression to investigate associations of density with physical activity., Results: After adjustment for potential confounders, individuals in the tertile of participants residing in areas with the highest density of resources were more likely to report physical activity during a typical week than were individuals in the lowest tertile. Associations between availability of recreational resources and physical activity levels were not present for the smallest area assessed (0.5 miles) but were present for areas ranging from 1 to 5 miles. These associations were slightly stronger among minority and low-income residents., Conclusions: Availability of resources may be 1 of several environmental factors that influence individuals' physical activity behaviors.

Little is known about environmental determinants of type 2 diabetes. The authors hypothesized that insulin resistance is positively related to distance to a wealthy area and to local neighborhood poverty. Data were derived from The Multi-Ethnic Study of Atherosclerosis, a study of adults aged 45-84 years in six US locales, and the 2000 US Census. The homeostasis model assessment (HOMA) index was used to measure insulin resistance. Linear regression was used to estimate associations between area characteristics and insulin resistance after adjustment for age, sex, income, education, and race/ethnicity and for the potential mediators diet, physical activity, and body mass index (n = 4,821). Among persons not treated for diabetes, distance to a wealthy area was associated with HOMA independent of local poverty and person-level covariates: per 4.4-km change, the relative increase in HOMA was 13% (95% confidence interval: 7%, 19%), similar to the effect of a body mass index increase of 1.7 kg/m(2) on HOMA. This association was reduced after adjustment for physical activity, diet, and body mass index (relative increase = 9%, 95% confidence interval: 3%, 15%). Local neighborhood poverty was also positively, but more weakly associated with insulin resistance, with no association after adjustment for race/ethnicity. This study shows that proximity to resources in high-income areas is related to insulin resistance.

In 2003, Gusfield introduced the Haplotype Inference by Pure Parsimony (HIPP) problem and presented an integer program (IP) that quickly solved many simulated instances of the problem. Although it solved well on small instances, Gusfield's IP can be of exponential size in the worst case. Several authors have presented polynomial-sized IPs for the problem. In this paper, we further the work on IP approaches to HIPP. We extend the existing polynomial-sized IPs by introducing several classes of valid cuts for the IP. We also present a new polynomial-sized IP formulation that is a hybrid between two existing IP formulations and inherits many of the strengths of both. Many problems that are too complex for the exponential-sized formulations can still be solved in our new formulation in a reasonable amount of time. We provide a detailed empirical comparison of these IP formulations on both simulated and real genotype sequences. Our formulation can also be extended in a variety of ways to allow errors in the input or model the structure of the population under consideration.

Haplotype inference by pure parsimony (HIPP) is known to be NP-Hard. Despite this, many algorithms successfully solve HIPP instances on simulated and real data. In this paper, we explore the connection between algebraic rank and the HIPP problem, to help identify easy and hard instances of the problem. The rank of the input matrix is known to be a lower bound on the size an optimal HIPP solution. We show that this bound is almost surely tight for data generated by randomly pairing p haplotypes derived from a perfect phylogeny when the number of distinct population members is more than (1 + epsilon/2)p log p (for some positive epsilon). Moreover, with only a constant multiple more population members, and a common mutation, we can almost surely recover an optimal set of haplotypes in polynomial time. We examine the algebraic effect of allowing recombination, and bound the effect recombination has on rank. In the process, we prove a stronger version of the standard haplotype lower bound. We also give a complete classification of the rank of a haplotype matrix derived from a galled tree. This classification identifies a set of problem instances with recombination when the rank lower bound is also tight for the HIPP problem.

Background: Multiple genome alignment is an important problem in bioinformatics. An important subproblem used by many multiple alignment approaches is that of aligning two multiple alignments. Many popular alignment algorithms for DNA use the sum-of-pairs heuristic, where the score of a multiple alignment is the sum of its induced pairwise alignment scores. However, the biological meaning of the sum-of-pairs of pairs heuristic is not obvious. Additionally, many algorithms based on the sum-of-pairs heuristic are complicated and slow, compared to pairwise alignment algorithms. An alternative approach to aligning alignments is to first infer ancestral sequences for each alignment, and then align the two ancestral sequences. In addition to being fast, this method has a clear biological basis that takes into account the evolution implied by an underlying phylogenetic tree. In this study we explore the accuracy of aligning alignments by ancestral sequence alignment. We examine the use of both maximum likelihood and parsimony to infer ancestral sequences. Additionally, we investigate the effect on accuracy of allowing ambiguity in our ancestral sequences., Results: We use synthetic sequence data that we generate by simulating evolution on a phylogenetic tree. We use two different types of phylogenetic trees: trees with a period of rapid growth followed by a period of slow growth, and trees with a period of slow growth followed by a period of rapid growth. We examine the alignment accuracy of four ancestral sequence reconstruction and alignment methods: parsimony, maximum likelihood, ambiguous parsimony, and ambiguous maximum likelihood. Additionally, we compare against the alignment accuracy of two sum-of-pairs algorithms: ClustalW and the heuristic of Ma, Zhang, and Wang., Conclusion: We find that allowing ambiguity in ancestral sequences does not lead to better multiple alignments. Regardless of whether we use parsimony or maximum likelihood, the success of aligning ancestral sequences containing ambiguity is very sensitive to the choice of gap open cost. Surprisingly, we find that using maximum likelihood to infer ancestral sequences results in less accurate alignments than when using parsimony to infer ancestral sequences. Finally, we find that the sum-of-pairs methods produce better alignments than all of the ancestral alignment methods.

We present a framework for improving local protein alignment algorithms. Specifically, we discuss how to extend local protein aligners to use a collection of vector seeds or ungapped alignment seeds to reduce noise hits. We model picking a set of seed models as an integer programming problem and give algorithms to choose such a set of seeds. While the problem is NP-hard, and Quasi-NP-hard to approximate to within a logarithmic factor, it can be solved easily in practice. A good set of seeds we have chosen allows four to five times fewer false positive hits, while preserving essentially identical sensitivity as BLASTP.

We review recent results on local alignment. We begin with a review of classical methods and early heuristic methods, and then focus on more recent work on the seeding of local alignment. We show that these techniques give a vast improvement in both sensitivity and specificity over previous methods, and can achieve sensitivity at the level of classical algorithms while requiring orders of magnitude less runtime.

Optimal spaced seeds were developed as a method to increase sensitivity of local alignment programs similar to BLASTN. Such seeds have been used before in the program PatternHunter, and have given improved sensitivity and running time relative to BLASTN in genome-genome comparison. We study the problem of computing optimal spaced seeds for detecting homologous coding regions in unannotated genomic sequences. By using well-chosen seeds, we are able to improve the sensitivity of coding sequence alignment over that of TBLASTX, while keeping runtime comparable to BLASTN. We identify good seeds by first giving effective hidden Markov models of conservation in alignments of homologous coding regions. We give an efficient algorithm to compute the optimal spaced seed when conservation patterns are generated by these models. Our results offer the hope of improved gene finding due to fewer missed exons in DNA/DNA comparison, and more effective homology search in general, and may have applications outside of bioinformatics.

The sequence of the mouse genome is a key informational tool for understanding the contents of the human genome and a key experimental tool for biomedical research. Here, we report the results of an international collaboration to produce a high-quality draft sequence of the mouse genome. We also present an initial comparative analysis of the mouse and human genomes, describing some of the insights that can be gleaned from the two sequences. We discuss topics including the analysis of the evolutionary forces shaping the size, structure and sequence of the genomes; the conservation of large-scale synteny across most of the genomes; the much lower extent of sequence orthology covering less than half of the genomes; the proportions of the genomes under selection; the number of protein-coding genes; the expansion of gene families related to reproduction and immunity; the evolution of proteins; and the identification of intraspecies polymorphism.