Generalized gravity model for human migration

The gravity model (GM) analogous to Newton's law of universal gravitation has successfully described the flow between different spatial regions, such as human migration, traffic flows, international economic trades, etc. This simple but powerful approach relies only on the 'mass' factor represented by the scale of the regions and the 'geometrical' factor represented by the geographical distance. However, when the population has a subpopulation structure distinguished by different attributes, the estimation of the flow solely from the coarse-grained geographical factors in the GM causes the loss of differential geographical information for each attribute. To exploit the full information contained in the geographical information of subpopulation structure, we generalize the GM for population flow by explicitly harnessing the subpopulation properties characterized by both attributes and geography. As a concrete example, we examine the marriage patterns between the bride and the groom clans of Korea in the past. By exploiting more refined geographical and clan information, our generalized GM properly describes the real data, a part of which could not be explained by the conventional GM. Therefore, we would like to emphasize the necessity of using our generalized version of the GM, when the information on such nongeographical subpopulation structures is available.Comment: 14 pages, 6 figures, 2 table

Synthetic prions generated in vitro are similar to a newly identified subpopulation of PrPSc from sporadic Creutzfeldt-Jakob disease

In recent studies, the amyloid form of recombinant prion protein (PrP) encompassing residues 89–230 (rPrP 89-230) produced in vitro induced transmissible prion disease in mice. These studies showed that unlike “classical” PrPSc produced in vivo, the amyloid fibrils generated in vitro were more proteinase-K sensitive. Here we demonstrate that the amyloid form contains a proteinase K-resistant core composed only of residues 152/153–230 and 162–230. The PK-resistant fragments of the amyloid form are similar to those observed upon PK digestion of a minor subpopulation of PrPSc recently identified in patients with sporadic Creutzfeldt-Jakob disease (CJD). Remarkably, this core is sufficient for self-propagating activity in vitro and preserves a β-sheet-rich fibrillar structure. Full-length recombinant PrP 23-230, however, generates two subpopulations of amyloid in vitro: One is similar to the minor subpopulation of PrPSc, and the other to classical PrPSc. Since no cellular factors or templates were used for generation of the amyloid fibrils in vitro, we speculate that formation of the subpopulation of PrPSc with a short PK-resistant C-terminal region reflects an intrinsic property of PrP rather than the influence of cellular environments and/or cofactors. Our work significantly increases our understanding of the biochemical nature of prion infectious agents and provides a fundamental insight into the mechanisms of prions biogenesis

Discriminant analysis of principal components and pedigree assessment of genetic diversity and population structure in a tetraploid potato panel using SNPs

The reported narrow genetic base of cultivated potato (Solanum tuberosum) can be expanded by the introgression of many related species with large genetic diversity. The analysis of the genetic structure of a potato population is important to broaden the genetic base of breeding programs by the identification of different genetic pools. A panel composed by 231 diverse genotypes was characterized using single nucleotide polymorphism (SNP) markers of the Illumina Infinium Potato SNP Array V2 to identify population structure and assess genetic diversity using discriminant analysis of principal components (DAPC) and pedigree analysis. Results revealed the presence of five clusters within the populations differentiated principally by ploidy, taxonomy, origin and breeding program. The information obtained in this work could be readily used as a guide for parental introduction in new breeding programs that want to maximize variability by combination of contrasting variability sources such as those presented here.Fil: Deperi, Sofía Irene. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata; ArgentinaFil: Tagliotti, Martin Enrique. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata; ArgentinaFil: Bedogni, María Cecilia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires Sur. Estación Experimental Agropecuaria Balcarce; ArgentinaFil: Manrique Carpintero, Norma C.. Michigan State University; Estados UnidosFil: Coombs, Joseph. Michigan State University; Estados UnidosFil: Zhang, Ruofang. Inner Mongolia University; ChinaFil: Douches, David. Michigan State University; Estados UnidosFil: Huarte, Marcelo Atilio. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires Sur. Estación Experimental Agropecuaria Balcarce; Argentin

Genome wide association mapping of grain arsenic, copper, molybdenum and zinc in rice (Oryza sativa L.) grown at four international field sites

Peer reviewedPublisher PD

Optimal Tests of Treatment Effects for the Overall Population and Two Subpopulations in Randomized Trials, using Sparse Linear Programming

We propose new, optimal methods for analyzing randomized trials, when it is suspected that treatment effects may differ in two predefined subpopulations. Such sub-populations could be defined by a biomarker or risk factor measured at baseline. The goal is to simultaneously learn which subpopulations benefit from an experimental treatment, while providing strong control of the familywise Type I error rate. We formalize this as a multiple testing problem and show it is computationally infeasible to solve using existing techniques. Our solution involves a novel approach, in which we first transform the original multiple testing problem into a large, sparse linear program. We then solve this problem using advanced optimization techniques. This general method can solve a variety of multiple testing problems and decision theory problems related to optimal trial design, for which no solution was previously available. In particular, we construct new multiple testing procedures that satisfy minimax and Bayes optimality criteria. For a given optimality criterion, our new approach yields the optimal tradeoff? between power to detect an effect in the overall population versus power to detect effects in subpopulations. We demonstrate our approach in examples motivated by two randomized trials of new treatments for HIV

Identifying spatial invasion of pandemics on metapopulation networks via anatomizing arrival history

Spatial spread of infectious diseases among populations via the mobility of humans is highly stochastic and heterogeneous. Accurate forecast/mining of the spread process is often hard to be achieved by using statistical or mechanical models. Here we propose a new reverse problem, which aims to identify the stochastically spatial spread process itself from observable information regarding the arrival history of infectious cases in each subpopulation. We solved the problem by developing an efficient optimization algorithm based on dynamical programming, which comprises three procedures: i, anatomizing the whole spread process among all subpopulations into disjoint componential patches; ii, inferring the most probable invasion pathways underlying each patch via maximum likelihood estimation; iii, recovering the whole process by assembling the invasion pathways in each patch iteratively, without burdens in parameter calibrations and computer simulations. Based on the entropy theory, we introduced an identifiability measure to assess the difficulty level that an invasion pathway can be identified. Results on both artificial and empirical metapopulation networks show the robust performance in identifying actual invasion pathways driving pandemic spread.Comment: 14pages, 8 figures; Accepted by IEEE Transactions on Cybernetic

Admixture in the Hispanics of the San Luis Valley, Colorado, and its implications for complex trait gene mapping.

Hispanic populations are a valuable resource that can and should facilitate the identification of complex trait genes by means of admixture mapping (AM). In this paper we focus on a particular Hispanic population living in the San Luis Valley (SLV) in Southern Colorado. We used a set of 22 Ancestry Informative Markers (AIMs) to describe the admixture process and dynamics in this population. AIMs are defined as genetic markers that exhibit allele frequency differences between parental populations >or=30%, and are more informative for studying admixed populations than random markers. The ancestral proportions of the SLV Hispanic population are estimated as 62.7 +/- 2.1% European, 34.1 +/- 1.9% Native American and 3.2 +/- 1.5% West African. We also estimated the ancestral proportions of individuals using these AIMs. Population structure was demonstrated by the excess association of unlinked markers, the correlation between estimates of admixture based on unlinked marker sets, and by a highly significant correlation between individual Native American ancestry and skin pigmentation (R2= 0.082, p < 0.001). We discuss the implications of these findings in disease gene mapping efforts

Multiscale mobility networks and the large scale spreading of infectious diseases

Among the realistic ingredients to be considered in the computational modeling of infectious diseases, human mobility represents a crucial challenge both on the theoretical side and in view of the limited availability of empirical data. In order to study the interplay between small-scale commuting flows and long-range airline traffic in shaping the spatio-temporal pattern of a global epidemic we i) analyze mobility data from 29 countries around the world and find a gravity model able to provide a global description of commuting patterns up to 300 kms; ii) integrate in a worldwide structured metapopulation epidemic model a time-scale separation technique for evaluating the force of infection due to multiscale mobility processes in the disease dynamics. Commuting flows are found, on average, to be one order of magnitude larger than airline flows. However, their introduction into the worldwide model shows that the large scale pattern of the simulated epidemic exhibits only small variations with respect to the baseline case where only airline traffic is considered. The presence of short range mobility increases however the synchronization of subpopulations in close proximity and affects the epidemic behavior at the periphery of the airline transportation infrastructure. The present approach outlines the possibility for the definition of layered computational approaches where different modeling assumptions and granularities can be used consistently in a unifying multi-scale framework.Comment: 10 pages, 4 figures, 1 tabl