Market Index Biases and Minimum Risk Indices

Markets, in the real world, are not efficient zero-sum games where hypotheses of the CAPM are fulfilled. Then, it is easy to conclude the market portfolio is not located on Markowitz"s efficient frontier, and passive investments (and indexing) are not optimal but biased. In this paper, we define and analyze biases suffered by passive investors: the sample, construction, efficiency and active biases and tracking error are presented. We propose Minimum Risk Indices (MRI) as an alternative to deal with to market index biases, and to provide investors with portfolios closer to the efficient frontier, that is, more optimal investment possibilities. MRI (using a Parametric Value-at-Risk Minimization approach) are calculated for three stock markets achieving interesting results. Our indices are less risky and more profitable than current Market Indices in the Argentinean and Spanish markets, facing that way the Efficient Market Hypothesis. Two innovations must be outlined: an error dimension has been included in the backtesting and the Sharpe"s Ratio has been used to select the"best" MRI

Interrogating eleven fast-evolving genes for signatures of recent positive selection in worldwide human populations

Different signatures of natural selection persist over varying time scales in our genome, revealing possible episodes of adaptative evolution during human history. Here, we identify genes showing signatures of ancestral positive selection in the human lineage and investigate whether some of those genes have been evolving adaptatively in extant human populations. Specifically, we compared more than 11,000 human genes with their orthologs inchimpanzee, mouse, rat and dog and applied a branch-site likelihood method to test for positive selection on the human lineage. Among the significant cases, a robust set of 11 genes were then further explored for signatures of recent positive selection using SNP data. We genotyped 223 SNPs in 39 worldwide populations from the HGDP Diversity panel and supplemented this information with available genotypes for up to 4,814 SNPs distributed along 2 Mb centered on each gene. After exploring the allele frequency spectrum, population differentiation and the maintainance of long unbroken haplotypes, we found signals of recent adaptative phenomena in only one of the 11 candidate gene regions. However, the signal ofrecent selection in this region may come from a different, neighbouring gene (CD5) ratherthan from the candidate gene itself (VPS37C). For this set of positively-selected genes in thehuman lineage, we find no indication that these genes maintained their rapid evolutionarypace among human populations. Based on these data, it therefore appears that adaptation forhuman-specific and for population-specific traits may have involved different genes.

Distribution of events of positive selection and population differentiation in a metabolic pathway: the case of asparagine N-glycosylation

Asparagine N-Glycosylation is one of the most important forms of protein post-translational modification in eukaryotes. This metabolic pathway can be subdivided into two parts: an upstream sub-pathway required for achieving proper folding for most of the proteins synthesized in the secretory pathway, and a downstream sub-pathway required to give variability to trans-membrane proteins, and involved in adaptation to the environment and innate immunity. Here we analyze the nucleotide variability of the genes of this pathway in human populations, identifying which genes show greater population differentiation and which genes show signatures of recent positive selection. We also compare how these signals are distributed between the upstream and the downstream parts of the pathway, with the aim of exploring how forces of population differentiation and positive selection vary among genes involved in the same metabolic pathway but subject to different functional constraints. Our results show that genes in the downstream part of the pathway are more likely to show a signature of population differentiation, while events of positive selection are equally distributed among the two parts of the pathway. Moreover, events of positive selection are frequent on genes that are known to be at bifurcation points, and that are identified as being in key position by a network-level analysis such as MGAT3 and GCS1. These findings indicate that the upstream part of the Asparagine N-Glycosylation pathway has lower diversity among populations, while the downstream part is freer to tolerate diversity among populations. Moreover, the distribution of signatures of population differentiation and positive selection can change between parts of a pathway, especially between parts that are exposed to different functional constraints. Our results support the hypothesis that genes involved in constitutive processes can be expected to show lower population differentiation, while genes involved in traits related to the environment should show higher variability. Taken together, this work broadens our knowledge on how events of population differentiation and of positive selection are distributed among different parts of a metabolic pathway.

FOSS License Selection and Code Management

With nearly 2,000 free and open source software (FLOSS) licenses, software license proliferation¿ can be a major headache for software development organizations trying to speed development through software component reuse, as well as companies redistributing software packages as components of their products. Scope is one problem: from the Free Beer license to the GPL family of licenses to platform-specific licenses such as Apache and Eclipse, the number and variety of licenses make it difficult for companies to ¿do the right thing¿ with respect to the software components in their products and applications. In addition to the sheer number of licenses, each license carries within it the author¿s specific definition of how the software can be used and re-used. Permissive licenses like BSD and MIT make it easy; software can be redistributed and developers can modify code without the requirement of making changes publicly available. Reciprocal licenses, on the other hand, place varying restrictions on re-use and redistribution. Woe to the developer who snags a bit of code after a simple web search without understanding the ramifications of license restrictions.

Effect of growth selection on morphology in Pirenaica cattle

Twelve variables were identified to define morphology in 109 calves sired by eight Pirenaica bulls widely used in artificial insemination. The effect of selection for weight at 210 days of age (W210) was detected from the regression coefficient between the 12 variables and the selection index for W210 used in the breeding plan. Unbiased estimates of the genetic correlated responses on the morphology were obtained without previously estimating the genetic correlation among traits. In Pirenaica cattle, selection for W210 increased slaughter live weight, chest depth and corporal length. The expected changes on height (withers, back and rump), rump width and thoracic perimeter were lower but positive and statistically significant.

Selection response of growth rate in rabbits for meat production

Genetic and environmental trends in 2 lines of rabbit (B and R) selected on individual weight gain (WG) from weaning (4 wk) to slaughter (11 wk) were estimated using mixed model methodology. Line B was derived from the California breed and line R was a synthetic of stock of different origin. The data were collected from a single herd and comprised 7 718 individuals in line B and 9 391 in line R, the lines having 12 and 9 generations of selection respectively. Realized responses in the 2 lines were 2.7% and 2.2% of the initial mean per year respectively and showed that selection on WG was effective but was less than expected. Selection on slaughter weight (SW) and effects of selection on other economic traits are discussed. It is concluded that selection on either WG or SW is a simple method for improving growth rate in rabbit sire line stocks.

Review. Characterization and selection of hexaploid wheats containing resistance to Heterodera avenae or Mayetiola destructor introgressed from Aegilops

Cereal cyst nematode (CCN, Heterodera avenae) and Hessian fly (HF, Mayetiola destructor) are two major pests affecting wheat crops worldwide including important cereal areas of Spain. Aegilops ventricosa and Ae. triuncialis were used as donors in a strategy to introduce resistance genes (RG) for these pests in hexaploid wheat (Triticum aestivum L.). Two 42 chromosomes introgression lines have been derived from Ae. ventricosa: H-93-8 and H-93-33 carrying genes Cre2 and H27 conferring resistance to CCN and HF, respectively. Line TR-3531 with 42 chromosomes has been derived from Ae. triuncialis and carries RGs conferring resistance for CCN (Cre7) and for HF (H30). Alien material has been incorporated in lines H-93 by chromosomal substitution and recombination, while in line TR-3531 homoeologous recombination affecting small DNA fragments has played a major role. It has been demonstrated that Cre2, Cre7, H27 and H30 are major single dominant genes and not allelic of other previously described RGs. Biochemical and molecular-biology studies of the defense mechanism triggered by Cre2 and Cre7 have revealed specific induction of peroxidase and other antioxidant enzymes. In parallel to these basic studies advanced lines carrying resistance genes for CNN and/or HF have been developed. Selection was done using molecular markers for eventually «pyramiding» resistance genes. Several isozyme and RAPD markers have been described and, currently, new markers based on transposable elements and NBS-LRR sequences are being developed. At present, two advanced lines have already been included at the Spanish Catalogue of Commercial Plant Varieties.

Selection of internal control genes for real-time quantitative PCR in ovary and uterus of sows across pregnancy

Reproductive traits play a key role in pig production in order to reduce costs and increase economic returns. Among others, gene expression analyses represent a useful approach to study genetic mechanisms underlying reproductive traits in pigs. The application of reverse-transcription quantitative PCR requires the selection of appropriate reference genes, whose expression levels should not be affected by the experimental conditions, especially when comparing gene expression across different physiological stages.

Patterns of nucleotide diversity at the regions encompassing the Drosophila insulin-like peptide (dilp) genes: demography vs positive selection in Drosophila melanogaster.

In Drosophila, the insulin-signaling pathway controls some life history traits, such as fertility and lifespan, and it is considered to be the main metabolic pathway involved in establishing adult body size. Several observations concerning variation in body size in the Drosophila genus are suggestive of its adaptive character. Genes encoding proteins in this pathway are, therefore, good candidates to have experienced adaptive changes and to reveal the footprint of positive selection. The Drosophila insulin-like peptides (DILPs) are the ligands that trigger the insulin-signaling cascade. In Drosophila melanogaster, there are several peptides that are structurally similar to the single mammalian insulin peptide. The footprint of recent adaptive changes on nucleotide variation can be unveiled through the analysis of polymorphism and divergence. With this aim, we have surveyed nucleotide sequence variation at the dilp1-7 genes in a natural population of D. melanogaster. The comparison of polymorphism in D. melanogaster and divergence from D. simulans at different functional classes of the dilp genes provided no evidence of adaptive protein evolution after the split of the D. melanogaster and D. simulans lineages. However, our survey of polymorphism at the dilp gene regions of D. melanogaster has provided some evidence for the action of positive selection at or near these genes. The regions encompassing the dilp1-4 genes and the dilp6 gene stand out as likely affected by recent adaptive events.

Incidence, prevalence and clinical significance of abnormal hematological indices in compensated cirrhosis.

Background & Aims: Patients with cirrhosis develop abnormal hematologic indices (HI) from multiple factors, including hypersplenism. We aimed to analyze the sequence of events and determine whether abnormal HI has prog-nostic significance. Methods: We analyzed a database of 213 subjects with compensated cirrhosis without esopha-geal varices. Subjects were followed for approximately 9 years until the development of varices or variceal bleeding or completion of the study; 84 subjects developed varices. Abnormal HI was defined as anemia at baseline (hemoglo-bin,<13.5 g/dL for men and 11.5 g/dL for women), leuko-penia (white blood cell counts,<4000/mm 3 ), or thrombo-cytopenia (platelet counts, < 150,000/mm 3 ). The primary end points were death or transplant surgery. Results: Most subjects had thrombocytopenia at baseline. Kaplan-Meier analysis showed that leukopenia occurred by 30 months (95% confidence interval, 18.5-53.6), and anemia occurred by 39.6 months (95% confidence interval, 24.1-49.9). Baseline thrombocytopenia (P .0191) and leukope-nia (P.0383) were predictors of death or transplant, after adjusting for baseline hepatic venous pressure gradient (HVPG), and Child-Pugh scores. After a median of 5 years,a significant difference in death or transplant, mortality,and clinical decompensation was observed in patients who had leukopenia combined with thrombocytopenia at base- line compared with patients with normal HI (P < .0001). HVPG correlated with hemoglobin and white blood cell count (hemoglobin, r 0.35, P < .0001; white blood cell count, r 0.31, P < .0001). Conclusions: Thrombocy-topenia is the most common and first abnormal HI to occurin patients with cirrhosis, followed by leukopenia and anemia. A combination of leukopenia and thrombocytopenia at baselin predicted increased morbidity and mortality.

Market Index Biases and Minimum Risk Indices

Markets, in the real world, are not efficient zero-sum games where hypotheses of the CAPM are fulfilled. Then, it is easy to conclude the market portfolio is not located on Markowitz"s efficient frontier, and passive investments (and indexing) are not optimal but biased. In this paper, we define and analyze biases suffered by passive investors: the sample, construction, efficiency and active biases and tracking error are presented. We propose Minimum Risk Indices (MRI) as an alternative to deal with to market index biases, and to provide investors with portfolios closer to the efficient frontier, that is, more optimal investment possibilities. MRI (using a Parametric Value-at-Risk Minimization approach) are calculated for three stock markets achieving interesting results. Our indices are less risky and more profitable than current Market Indices in the Argentinean and Spanish markets, facing that way the Efficient Market Hypothesis. Two innovations must be outlined: an error dimension has been included in the backtesting and the Sharpe"s Ratio has been used to select the"best" MRI

Disentangling the formation of contrasting tree line physiognomies combining model selection and Bayesian parameterization for simulation models.

Alpine tree-line ecotones are characterized by marked changes at small spatial scales that may result in a variety of physiognomies. A set of alternative individual-based models was tested with data from four contrasting Pinus uncinata ecotones in the central Spanish Pyrenees to reveal the minimal subset of processes required for tree-line formation. A Bayesian approach combined with Markov chain Monte Carlo methods was employed to obtain the posterior distribution of model parameters, allowing the use of model selection procedures. The main features of real tree lines emerged only in models considering nonlinear responses in individual rates of growth or mortality with respect to the altitudinal gradient. Variation in tree-line physiognomy reflected mainly changes in the relative importance of these nonlinear responses, while other processes, such as dispersal limitation and facilitation, played a secondary role. Different nonlinear responses also determined the presence or absence of krummholz, in agreement with recent findings highlighting a different response of diffuse and abrupt or krummholz tree lines to climate change. The method presented here can be widely applied in individual-based simulation models and will turn model selection and evaluation in this type of models into a more transparent, effective, and efficient exercise.

Feature Selection, Ranking of Each Feature and Classification for the Diagnosis of Community Acquired Legionella Pneumonia

Diagnosis of community acquired legionella pneumonia (CALP) is currently performed by means of laboratory techniques which may delay diagnosis several hours. To determine whether ANN can categorize CALP and non-legionella community-acquired pneumonia (NLCAP) and be standard for use by clinicians, we prospectively studied 203 patients with community-acquired pneumonia (CAP) diagnosed by laboratory tests. Twenty one clinical and analytical variables were recorded to train a neural net with two classes (LCAP or NLCAP class). In this paper we deal with the problem of diagnosis, feature selection, and ranking of the features as a function of their classification importance, and the design of a classifier the criteria of maximizing the ROC (Receiving operating characteristics) area, which gives a good trade-off between true positives and false negatives. In order to guarantee the validity of the statistics; the train-validation-test databases were rotated by the jackknife technique, and a multistarting procedure was done in order to make the system insensitive to local maxima.

Gene filtering with optimal threshold selection

Gene filtering is a useful preprocessing technique often applied to microarray datasets. However, it is no common practice because clear guidelines are lacking and it bears the risk of excluding some potentially relevant genes. In this work, we propose to model microarray data as a mixture of two Gaussian distributions that will allow us to obtain an optimal filter threshold in terms of the gene expression level.