In-depth studies have been conducted on the risk of exposure to air pollution in urban residents, but most of them are static studies based on the population of residential units. Ignoring the real environmental dynamics during daily activity and mobility of individual residents makes it difficult to accurately estimate the level of air pollution exposure among residents and determine populations at higher risk of exposure. This paper uses the example of the Wuhan metropolitan area, high-precision air pollution, and population spatio-temporal dynamic distribution data, and applies geographically weighted regression models, bivariate LISA analysis, and Gini coefficients. The risk of air pollution exposure in elderly, low-age, and working-age communities in Wuhan was measured and the health equity within vulnerable groups such as the elderly and children was studied. We found that ignoring the spatio-temporal behavioral activities of residents underestimated the actual exposure hazard of PM2.5 to residents. The risk of air pollution exposure was higher for the elderly than for other age groups. Within the aging group, a few elderly people had a higher risk of pollution exposure. The high exposure risk communities of the elderly were mainly located in the central and sub-center areas of the city, with a continuous distribution characteristic. No significant difference was found in the exposure risk of children compared to the other populations, but a few children were particularly exposed to pollution. Children\'s high-exposure communities were mainly located in suburban areas, with a discrete distribution. Compared with the traditional static PM2.5 exposure assessment, the dynamic assessment method proposed in this paper considers the high mobility of the urban population and air pollution. Thus, it can accurately reveal the actual risk of air pollution and identify areas and populations at high risk of air pollution, which in turn provides a scientific basis for proposing planning policies to reduce urban PM2.5 and improve urban spatial equity.

Balancing selection is defined as a class of selective regimes that maintain polymorphism above what is expected under neutrality. Theory predicts that balancing selection reduces population differentiation, as measured by FST. However, balancing selection regimes in which different sets of alleles are maintained in different populations could increase population differentiation. To tackle the connection between balancing selection and population differentiation, we investigated population differentiation at the HLA genes, which constitute the most striking example of balancing selection in humans. We found that population differentiation of single nucleotide polymorphisms (SNPs) at the HLA genes is on average lower than that of SNPs in other genomic regions. We show that these results require using a computation that accounts for the dependence of FST on allele frequencies. However, in pairs of closely related populations, where genome-wide differentiation is low, differentiation at HLA is higher than in other genomic regions. Such increased population differentiation at HLA genes for recently diverged population pairs was reproduced in simulations of overdominant selection, as long as the fitness of the homozygotes differs between the diverging populations. The results give insight into a possible \"divergent overdominance\" mechanism for the nature of balancing selection on HLA genes across human populations.

OBJECTIVE: Project Baseline is a seed bank that offers an unprecedented opportunity to examine spatial and temporal dimensions of microevolution during an era of rapid environmental change. Over the upcoming 50 years, biologists will withdraw genetically representative samples of past populations from this time capsule of seeds and grow them contemporaneously with modern samples to detect any phenotypic and molecular evolution that has occurred during the intervening time.
METHODS: We carefully developed this living genome bank using protocols to enhance its experimental value by collecting from multiple populations and species across a broad geographical range in sites that are likely to be preserved into the future. Seeds are accessioned with site and population data and are stored by maternal line under conditions that maximize seed longevity. This open-access resource will be available to researchers at regular intervals to evaluate contemporary evolution.
RESULTS: To date, the Project Baseline collection includes 100-200 maternal lines of each of 61 species collected from over 831 populations on sites that are likely to be preserved into the future across the United States (∼78,000 maternal lines). Our strategically designed collection circumvents some problems that can cloud the results of \"resurrection\" studies involving naturally preserved or existing seed collections that are available fortuitously.
CONCLUSIONS: The resurrection approach can be coupled with long-established and newer techniques over the next five decades to elucidate genetic change and thereby vastly improve our understanding of temporal and spatial changes in phenotype and the evolutionary processes underlying it.

Landscape features notoriously affect spatial patterns of biodiversity. For instance, in dendritic ecological networks (such as river basins), dendritic connectivity has been proposed to create unique spatial patterns of biodiversity. Here, we compared genetic datasets simulated under a lattice-like, a dendritic and a circular landscape to test the influence of dendritic connectivity on neutral genetic diversity. The circular landscape had a level of connectivity similar to that of the dendritic landscape, so as to isolate the influence of dendricity on genetic diversity. We found that genetic diversity and differentiation varied strikingly among the three landscapes. For instance, the dendritic landscape generated higher total number of alleles and higher global Fst than the lattice-like landscape, and these indices also varied between the dendritic and the circular landscapes, suggesting an effect of dendricity. Furthermore, in the dendritic landscape, allelic richness was higher in highly connected demes (e.g. confluences in rivers) than in low-connected demes (e.g. upstream and downstream populations), which was not the case in the circular landscape, hence confirming the major role of dendricity. This led to bell-shaped distributions of allelic richness along an upstream-downstream gradient. Conversely, genetic differentiation (Fst ) was lower in highly than in low-connected demes (which was not observed in circular landscape), and significant patterns of isolation by distance (IBD) were also observed in the dendritic landscape. We conclude that in dendritic networks, the combined influence of dendricity and connectivity generates unique spatial patterns of neutral genetic diversity, which has implications for population geneticists and conservationists.

Detection of footprints of historical natural selection on quantitative traits in cross-sectional data sets is challenging, especially when the number of populations to be compared is small and the populations are subject to strong random genetic drift. We extend a recent Bayesian multivariate approach to differentiate between selective and neutral causes of population differentiation by the inclusion of habitat information. The extended framework allows one to test for signals of selection in two ways: by comparing the patterns of population differentiation in quantitative traits and in neutral loci, and by comparing the similarity of habitats and phenotypes. We illustrate the framework using data on variation of eight morphological and behavioral traits among four populations of nine-spined sticklebacks (Pungitius pungitius). In spite of the strong signal of genetic drift in the study system (average FST = 0.35 in neutral markers), strong footprints of adaptive population differentiation were uncovered both in morphological and behavioral traits. The results give quantitative support for earlier qualitative assessments, which have attributed the observed differentiation to adaptive divergence in response to differing ecological conditions in pond and marine habitats.