The role of traditional nomadic pastoralism in the spatial and genetic subdivision of the distribution of populations of small mammals in mountain areas and their sanitary and epidemiological significance (on the example of Tuva)

The data on the genetic and spatial subdivision of populations of synanthropic species of small mammals, their dependence on the intensity with the territories of distant-pasture cattle tending are presented, and some patterns of indicators of ectoparasite infestation of their communities are revealed. The factor determining the genetic and spatial differentiation of populations of small mammals can be attributed to the relationship of small mammals with ungulates that has long historical roots (Neogene, Anthropogen). At present, these relations have been preserved with domestic ungulates, which, along with physical and geographical ones, determine the genetic and biotopic differentiation of populations of small mammals. The assumption is made about the deep historical roots of the establishment of such relationships by pasture ungulate animals from the neogene and pleistocene.

Modelling interventions to control COVID-19 outbreaks in a refugee camp

BackgroundIn the absence of effective treatments or vaccines, non-pharmaceutical interventions are the mainstay of control in the COVID-19 pandemic. Refugee populations in displacement camps live under adverse conditions that are likely to favour the spread of disease. To date, only a few cases of COVID-19 have appeared in refugee camps, and whether feasible non-pharmaceutical interventions can prevent the spread of the SARS-CoV-2 virus in such settings remains untested.MethodsWe constructed the first spatially explicit agent-based model of a COVID-19 outbreak in a refugee camp, and applied it to evaluate feasible non-pharmaceutical interventions. We parameterised the model using published data on the transmission rates and progression dynamics of COVID-19, and demographic and spatial data from Europe’s largest refugee camp, the Moria displacement camp on Lesbos, Greece. We simulated COVID-19 epidemics with and without four feasible interventions.ResultsSpatial subdivision of the camp (‘sectoring’) was able to ‘flatten the curve’, reducing peak infection by up to 70% and delaying peak infection by up to several months. The use of face masks coupled with the efficient isolation of infected individuals reduced the overall incidence of infection, and sometimes averted epidemics altogether. These interventions must be implemented quickly in order to be maximally effective. Lockdowns had only small effects on COVID-19 dynamics.ConclusionsAgent-based models are powerful tools for forecasting the spread of disease in spatially structured and heterogeneous populations. Our findings suggest that feasible interventions can slow the spread of COVID-19 in a refugee camp setting, and provide an evidence base for camp managers planning intervention strategies. Our model can be modified to study other closed populations at risk from COVID-19 or future epidemics.

A Method to Use Kriging With Large Sets of Control Points to Morph Finite Element Models of the Human Body

As developing finite element (FE) human body models for automotive impact is a time-consuming process, morphing using interpolation methods such as kriging has often been used to rapidly generate models of different shapes and sizes. Kriging can be computationally expensive when many control points (CPs) are used, i.e., for very detailed target geometry (e.g., shape of bones and skin). It can also lead to element quality issues (up to inverted elements) preventing the use of the morphed models for finite element simulation. This paper presents a workflow combining iterative subsampling and spatial subdivision methodology that effectively reduces the computational costs and allows for the generation of usable models through kriging with hundreds of thousands of control points. As subdivision introduces discontinuities in the interpolation function that can cause distortion of elements on the boundaries of individual subdivision areas, algorithms for smoothing the interpolation over those boundaries are proposed and compared. Those techniques and their combinations were tested and evaluated in a scenario of mass change on the detailed 50th percentile male model of the global human body models consortium (GHBMC): the model, which has body mass index (BMI) 25.34, was morphed toward a statistical surface model of a person with body mass index 20, 22.7 and 35. 234 777 control points were used to successfully morph the model in less than 15 min on an office PC. Open source implementation is provided.

Dispersal alters the nature and scope of sexually antagonistic variation

Intra-locus sexual conflict, or sexual antagonism, occurs when alleles have opposing fitness effects in the two sexes. Previous theory suggests that sexual antagonism is a driver of genetic variation by generating balancing selection. However, these studies assume that populations are well-mixed, neglecting the effects of spatial subdivision. Here we use mathematical modelling to show that limited dispersal can fundamentally change evolution at sexually antagonistic autosomal and X-linked loci due to inbreeding and sex-specific kin competition. We find that if the sexes disperse at different rates, kin competition within the philopatric sex biases intralocus conflict in favour of the more dispersive sex. Furthermore, kin competition diminishes the strength of balancing selection relative to genetic drift, reducing genetic variation in small subdivided populations. Meanwhile, by decreasing heterozygosity, inbreeding reduces the scope for sexually antagonistic polymorphism due to non-additive allelic effects, and this occurs to a greater extent on the X-chromosome than autosomes. Overall, our results demonstrate that spatial structure is an important factor in predicting where to expect sexually antagonistic alleles. We suggest that observed interspecific and intragenomic variation in sexual antagonism may be explained by sex-specific dispersal ecology and demography.

Simulation and Analysis of Road Traffic Noise among Urban Buildings Using Spatial Subdivision-Based Beam Tracing Method

In order to realize the simulation and evaluation of road traffic noise among urban buildings, a spatial subdivision-based beam-tracing method is proposed in this study. First, the road traffic source is divided into sets of point sources and described with the help of vehicle emission model. Next, for each pair of source and receiver, spatial subdivision-based beam-tracing method is used in noise paths generation. At last, noise distribution can be got by noise calculation of all receivers considering the complex transmission among urban buildings. A measurement experiment with a point source is carried out to validate the accuracy of the method; the 0.8 m height and 2.5-m height average errors are about 0.9 dB and 1.2 dB, respectively. Moreover, traffic noise analysis under different building layouts and heights are presented by case applications and conclusions can be reached: (1) Different patterns result in different noise distributions and patterns designed as self-protective can lead to an obvious noise abatement for rear buildings. Noise differences between the front and rear buildings are about 7–12 dB with different patterns. (2) Noise value might not show a linear variation along with the height as shielding of different layers is various in reality.

Using Cartograms for Visualizing extended Floating Car Data (xFCD)

<p><strong>Abstract.</strong> This study assesses the usefulness of cartograms when visualizing extended Floating Car Data (xFCD). Cartograms deform regions in a map proportionally to assigned values. We apply this method for visualizing highresolution extended Floating Car Data (xFCD). Elaborating on this, we perform a case study in Mönchengladbach, Germany using 1.8 Million record points containing information about carbon dioxide (CO2) emissions based on an xFCD dataset. Utilizing a diffusion-based approach, we compute cartograms. Findings indicate a good suitability for identifying areas with a higher (or lower) average emission of CO2. We provide a documented workflow to compute cartograms based on parameters from an extended floating car dataset. The quality and spatial distribution of the basic dataset turns out to be important. Choosing the correct spatial subdivision of the research area as a basis for deforming areas is significant as it strongly influences the visual output.</p>