University of Georgia

About: University of Georgia is a education organization based out in Athens, Georgia, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 41934 authors who have published 93622 publications receiving 3713212 citations. The organization is also known as: UGA & Franklin College.

Mitochondrial DNA and the evolutionary genetics of higher animals.

Abstract: Mitochondrial DNA (mtDNA) in higher animals is rapidly becoming a well characterized genetic system at the molecular level. In this paper, I shift the focus to consider questions in organismal evolution that can be addressed by mtDNA assay. For the first time, it is possible to estimate empirically matriarchal phylogeny; to determine directionality in crosses producing hybrids; and to study the population genetic consequences of varying female demographies and life histories. The data obtainable from mtDNA may be especially well suited for studies of population genetic structure, dispersal, and historical zoogeography. The female-mediated, clonal transmission of mtDNA is also stimulating new ways of thinking about times to common ancestry of asexual lineages within otherwise sexually reproducing populations; about the possible relevance of mtDNA-nuclear DNA interactions to reproductive isolation; and about the very meaning of the phylogenetic status of related species with respect to particular kinds of genetic characters. These and other topics are reviewed.

Human gut Bacteroidetes can utilize yeast mannan through a selfish mechanism

Abstract: Yeasts, which have been a component of the human diet for at least 7,000 years, possess an elaborate cell wall α-mannan The influence of yeast mannan on the ecology of the human microbiota is unknown Here we show that yeast α-mannan is a viable food source for the Gram-negative bacterium Bacteroides thetaiotaomicron, a dominant member of the microbiota Detailed biochemical analysis and targeted gene disruption studies support a model whereby limited cleavage of α-mannan on the surface generates large oligosaccharides that are subsequently depolymerized to mannose by the action of periplasmic enzymes Co-culturing studies showed that metabolism of yeast mannan by B thetaiotaomicron presents a 'selfish' model for the catabolism of this difficult to breakdown polysaccharide Genomic comparison with B thetaiotaomicron in conjunction with cell culture studies show that a cohort of highly successful members of the microbiota has evolved to consume sterically-restricted yeast glycans, an adaptation that may reflect the incorporation of eukaryotic microorganisms into the human diet

Genome-wide association mapping including phenotypes from relatives without genotypes.

Abstract: A common problem for genome-wide association analysis (GWAS) is lack of power for detection of quantitative trait loci (QTLs) and precision for fine mapping. Here, we present a statistical method, termed single-step GBLUP (ssGBLUP), which increases both power and precision without increasing genotyping costs by taking advantage of phenotypes from other related and unrelated subjects. The procedure achieves these goals by blending traditional pedigree relationships with those derived from genetic markers, and by conversion of estimated breeding values (EBVs) to marker effects and weights. Additionally, the application of mixed model approaches allow for both simple and complex analyses that involve multiple traits and confounding factors, such as environmental, epigenetic or maternal environmental effects. Efficiency of the method was examined using simulations with 15 800 subjects, of which 1500 were genotyped. Thirty QTLs were simulated across genome and assumed heritability was 0·5. Comparisons included ssGBLUP applied directly to phenotypes, BayesB and classical GWAS (CGWAS) with deregressed proofs. An average accuracy of prediction 0·89 was obtained by ssGBLUP after one iteration, which was 0·01 higher than by BayesB. Power and precision for GWAS applications were evaluated by the correlation between true QTL effects and the sum of m adjacent single nucleotide polymorphism (SNP) effects. The highest correlations were 0·82 and 0·74 for ssGBLUP and CGWAS with m=8, and 0·83 for BayesB with m=16. Standard deviations of the correlations across replicates were several times higher in BayesB than in ssGBLUP. The ssGBLUP method with marker weights is faster, more accurate and easier to implement for GWAS applications without computing pseudo-data.

The 2013 Release of Cloudy

Abstract: This is a summary of the 2013 release of the plasma simulation code Cloudy. Cloudy models the ionization, chemical, and thermal state of material that may be exposed to an external radiation field or other source of heating, and predicts observables such as emission and absorption spectra. It works in terms of elementary processes, so is not limited to any particular temperature or density regime. This paper summarizes advances made since the last major review in 1998. Much of the recent development has emphasized dusty molecular environments, improvements to the ionization / chemistry solvers, and how atomic and molecular data are used. We present two types of simulations to demonstrate the capability of the code. We consider a molecular cloud irradiated by an X-ray source such as an Active Nucleus and show how treating EUV recombination lines and the full SED affects the observed spectrum. A second example illustrates the very wide range of particle and radiation density that can be considered.

Parallel Multi-Block ADMM with o(1 / k) Convergence

Abstract: This paper introduces a parallel and distributed algorithm for solving the following minimization problem with linear constraints: $$\begin{aligned} \text {minimize} ~~&f_1(\mathbf{x}_1) + \cdots + f_N(\mathbf{x}_N)\\ \text {subject to}~~&A_1 \mathbf{x}_1 ~+ \cdots + A_N\mathbf{x}_N =c,\\&\mathbf{x}_1\in {\mathcal {X}}_1,~\ldots , ~\mathbf{x}_N\in {\mathcal {X}}_N, \end{aligned}$$minimizef1(x1)+ź+fN(xN)subject toA1x1+ź+ANxN=c,x1źX1,ź,xNźXN,where $$N \ge 2$$Nź2, $$f_i$$fi are convex functions, $$A_i$$Ai are matrices, and $${\mathcal {X}}_i$$Xi are feasible sets for variable $$\mathbf{x}_i$$xi. Our algorithm extends the alternating direction method of multipliers (ADMM) and decomposes the original problem into N smaller subproblems and solves them in parallel at each iteration. This paper shows that the classic ADMM can be extended to the N-block Jacobi fashion and preserve convergence in the following two cases: (i) matrices $$A_i$$Ai are mutually near-orthogonal and have full column-rank, or (ii) proximal terms are added to the N subproblems (but without any assumption on matrices $$A_i$$Ai). In the latter case, certain proximal terms can let the subproblem be solved in more flexible and efficient ways. We show that $$\Vert {\mathbf {x}}^{k+1} - {\mathbf {x}}^k\Vert _M^2$$źxk+1-xkźM2 converges at a rate of o(1 / k) where M is a symmetric positive semi-definte matrix. Since the parameters used in the convergence analysis are conservative, we introduce a strategy for automatically tuning the parameters to substantially accelerate our algorithm in practice. We implemented our algorithm (for the case ii above) on Amazon EC2 and tested it on basis pursuit problems with >300 GB of distributed data. This is the first time that successfully solving a compressive sensing problem of such a large scale is reported.