987 resultados para sperm selection


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The origin of eusociality in haplo-diploid organisms such as Hymenoptera has been mostly explained by kin selection. However, several studies have uncovered decreased relatedness values within colonies, resulting primarily from multiple queen matings (polyandry) and/or from the presence of more than one functional queen (polygyny). Here, we report on the use of microsatellite data for the investigation of sociogenetic parameters, such as relatedness, and levels of polygyny and polyandry, in the ant Pheidole pallidula. We demonstrate, through analysis of mother-offspring combinations and the use of direct sperm typing, that each queen is inseminated by a single male. The inbreeding coefficient within colonies and the levels of relatedness between the queens and their mate are not significantly different from zero, indicating that matings occur between unrelated individuals. Analyses of worker genotypes demonstrate that 38% of the colonies are polygynous with 2-4 functional queens, and suggest the existence of reproductive skew, i.e. unequal respective contribution of queens to reproduction. Finally, our analyses indicate that colonies are genetically differentiated and form a population exhibiting significant isolation-by-distance, suggesting that some colonies originate through budding.

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Comment

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Timing-related defects are major contributors to test escapes and in-field reliability problems for very-deep submicrometer integrated circuits. Small delay variations induced by crosstalk, process variations, power-supply noise, as well as resistive opens and shorts can potentially cause timing failures in a design, thereby leading to quality and reliability concerns. We present a test-grading technique that uses the method of output deviations for screening small-delay defects (SDDs). A new gate-delay defect probability measure is defined to model delay variations for nanometer technologies. The proposed technique intelligently selects the best set of patterns for SDD detection from an n-detect pattern set generated using timing-unaware automatic test-pattern generation (ATPG). It offers significantly lower computational complexity and excites a larger number of long paths compared to a current generation commercial timing-aware ATPG tool. Our results also show that, for the same pattern count, the selected patterns provide more effective coverage ramp-up than timing-aware ATPG and a recent pattern-selection method for random SDDs potentially caused by resistive shorts, resistive opens, and process variations. © 2010 IEEE.

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We consider the problem of variable selection in regression modeling in high-dimensional spaces where there is known structure among the covariates. This is an unconventional variable selection problem for two reasons: (1) The dimension of the covariate space is comparable, and often much larger, than the number of subjects in the study, and (2) the covariate space is highly structured, and in some cases it is desirable to incorporate this structural information in to the model building process. We approach this problem through the Bayesian variable selection framework, where we assume that the covariates lie on an undirected graph and formulate an Ising prior on the model space for incorporating structural information. Certain computational and statistical problems arise that are unique to such high-dimensional, structured settings, the most interesting being the phenomenon of phase transitions. We propose theoretical and computational schemes to mitigate these problems. We illustrate our methods on two different graph structures: the linear chain and the regular graph of degree k. Finally, we use our methods to study a specific application in genomics: the modeling of transcription factor binding sites in DNA sequences. © 2010 American Statistical Association.

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This paper studies the multiplicity-correction effect of standard Bayesian variable-selection priors in linear regression. Our first goal is to clarify when, and how, multiplicity correction happens automatically in Bayesian analysis, and to distinguish this correction from the Bayesian Ockham's-razor effect. Our second goal is to contrast empirical-Bayes and fully Bayesian approaches to variable selection through examples, theoretical results and simulations. Considerable differences between the two approaches are found. In particular, we prove a theorem that characterizes a surprising aymptotic discrepancy between fully Bayes and empirical Bayes. This discrepancy arises from a different source than the failure to account for hyperparameter uncertainty in the empirical-Bayes estimate. Indeed, even at the extreme, when the empirical-Bayes estimate converges asymptotically to the true variable-inclusion probability, the potential for a serious difference remains. © Institute of Mathematical Statistics, 2010.

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Males of many insect species feed their partner during courtship and mating. Studies of male nutrient donation in various systems have established that nuptial feeding has evolved mostly through sexual selection. Although there is extensive diversity in form, the function of nuptial gifts is typically limited to either facilitating copulation or increasing ejaculate transfer, depending on the time at which the gift is consumed by females. Unlike other insects, the Hawaiian swordtail cricket Laupala (Gryllidae: Trigonidiinae) exhibits serial transfer of nuptial gifts. Males transfer multiple spermless 'micro' spermatophores over several hours before mating at the end of the day (i.e. before the transfer of a single sperm-containing 'macro' spermatophore). By experimental manipulation of male microspermatophore donation, I tested several hypotheses pertaining to the adaptive significance of nuptial gifts in this system. I found that microspermatophore transfer improves insemination, by causing the female reproductive tract to take in more sperm. This result reveals a previously undocumented function for premating nuptial gift donation among insects. Enhanced sperm transfer due to microspermatophore donation may represent male manipulation or an internal mechanism of post-copulatory choice by females. I also performed experimental manipulation of male photoperiod to investigate how time and gender influence nuptial gift production and mating behavior. I found that the timing of mating is limited in males but not females and that the time of pair formation has consequences for the degree of nuptial gift donation, which suggests that both mating timing and microspermatophore number is important for male reproductive success. Finally, I observed the mating behavior of several trigonidiine taxa for a comparative analysis of sexual behavior and found that other genera also utilize spermless microspermatophores, which suggests that microspermatophore donation may be a common nuptial gift strategy among swordtail crickets. The elaborate nuptial feeding behavior of Hawaiian swordtail crickets prior to mating represents a newly discovered strategy to increase male insemination success rather than mating success. Based on this unexpected result, it is worth exploring whether courtship behaviors in other cricket or insect mating systems have also evolved to increase sperm uptake.

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Social and ecological factors are important in shaping sexual dimorphism in Anthropoidea, but there is also a tendency for body-size dimorphism and canine dimorphism to increase with increased body size (Rensch's rule) (Rensch: Evolution Above the Species Level. London: Methuen, 1959.) Most ecologist interpret Rensch's rule to be a consequence of social and ecological selective factors that covary with body size, but recent claims have been advanced that dimorphism is principally a consequence of selection for increased body size alone. Here we assess the effects of body size, body-size dimorphism, and social structure on canine dimorphism among platyrrhine monkeys. Platyrrhine species examined are classified into four behavioral groups reflecting the intensity of intermale competition for access to females or to limiting resources. As canine dimorphism increases, so does the level of intermale competition. Those species with monogamous and polyandrous social structures have the lowest canine dimorphism, while those with dominance rank hierarchies of males have the most canine dimorphism. Species with fission-fusion social structures and transitory intermale breeding-season competition fall between these extremes. Among platyrrhines there is a significant positive correlation between body size and canine dimorphism However, within levels of competition, no significant correlation was found between the two. Also, with increased body size, body-size dimorphism tends to increase, and this correlation holds in some cases within competition levels. In an analysis of covariance, once the level of intermale competition is controlled for, neither molar size nor molar-size dimorphism accounts for a significant part of the variance in canine dimorphism. A similar analysis using body weight as a measure of size and dimorphism yields a less clear-cut picture: body weight contributes significantly to the model when the effects of the other factors are controlled. Finally, in a model using head and body length as a measure of size and dimorphism, all factors and the interactions between them are significant. We conclude that intermale competition among platyrrhine species is the most important factor explaining variations in canine dimorphism. The significant effects of size and size dimorphism in some models may be evidence that natural (as opposed to sexual) selection also plays a role in the evolution of increased canine dimorphism.

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Gemstone Team FISH

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BACKGROUND: Few educational resources have been developed to inform patients' renal replacement therapy (RRT) selection decisions. Patients progressing toward end stage renal disease (ESRD) must decide among multiple treatment options with varying characteristics. Complex information about treatments must be adequately conveyed to patients with different educational backgrounds and informational needs. Decisions about treatment options also require family input, as families often participate in patients' treatment and support patients' decisions. We describe the development, design, and preliminary evaluation of an informational, evidence-based, and patient-and family-centered decision aid for patients with ESRD and varying levels of health literacy, health numeracy, and cognitive function. METHODS: We designed a decision aid comprising a complementary video and informational handbook. We based our development process on data previously obtained from qualitative focus groups and systematic literature reviews. We simultaneously developed the video and handbook in "stages." For the video, stages included (1) directed interviews with culturally appropriate patients and families and preliminary script development, (2) video production, and (3) screening the video with patients and their families. For the handbook, stages comprised (1) preliminary content design, (2) a mixed-methods pilot study among diverse patients to assess comprehension of handbook material, and (3) screening the handbook with patients and their families. RESULTS: The video and handbook both addressed potential benefits and trade-offs of treatment selections. The 50-minute video consisted of demographically diverse patients and their families describing their positive and negative experiences with selecting a treatment option. The video also incorporated health professionals' testimonials regarding various considerations that might influence patients' and families' treatment selections. The handbook was comprised of written words, pictures of patients and health care providers, and diagrams describing the findings and quality of scientific studies comparing treatments. The handbook text was written at a 4th to 6th grade reading level. Pilot study results demonstrated that a majority of patients could understand information presented in the handbook. Patient and families screening the nearly completed video and handbook reviewed the materials favorably. CONCLUSIONS: This rigorously designed decision aid may help patients and families make informed decisions about their treatment options for RRT that are well aligned with their values.

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Although many feature selection methods for classification have been developed, there is a need to identify genes in high-dimensional data with censored survival outcomes. Traditional methods for gene selection in classification problems have several drawbacks. First, the majority of the gene selection approaches for classification are single-gene based. Second, many of the gene selection procedures are not embedded within the algorithm itself. The technique of random forests has been found to perform well in high-dimensional data settings with survival outcomes. It also has an embedded feature to identify variables of importance. Therefore, it is an ideal candidate for gene selection in high-dimensional data with survival outcomes. In this paper, we develop a novel method based on the random forests to identify a set of prognostic genes. We compare our method with several machine learning methods and various node split criteria using several real data sets. Our method performed well in both simulations and real data analysis.Additionally, we have shown the advantages of our approach over single-gene-based approaches. Our method incorporates multivariate correlations in microarray data for survival outcomes. The described method allows us to better utilize the information available from microarray data with survival outcomes.

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Individuals with genetic defects in CD40 ligand (CD40L) or B-cell antigen receptor coreceptor molecules CD19 and CD81 suffer from an antibody deficiency. Still, these patients carry low levels of memory B cells and serum antibodies.

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We consider a deterministic system with two conserved quantities and infinity many invariant measures. However the systems possess a unique invariant measure when enough stochastic forcing and balancing dissipation are added. We then show that as the forcing and dissipation are removed a unique limit of the deterministic system is selected. The exact structure of the limiting measure depends on the specifics of the stochastic forcing.

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The dynamics of a population undergoing selection is a central topic in evolutionary biology. This question is particularly intriguing in the case where selective forces act in opposing directions at two population scales. For example, a fast-replicating virus strain outcompetes slower-replicating strains at the within-host scale. However, if the fast-replicating strain causes host morbidity and is less frequently transmitted, it can be outcompeted by slower-replicating strains at the between-host scale. Here we consider a stochastic ball-and-urn process which models this type of phenomenon. We prove the weak convergence of this process under two natural scalings. The first scaling leads to a deterministic nonlinear integro-partial differential equation on the interval $[0,1]$ with dependence on a single parameter, $\lambda$. We show that the fixed points of this differential equation are Beta distributions and that their stability depends on $\lambda$ and the behavior of the initial data around $1$. The second scaling leads to a measure-valued Fleming-Viot process, an infinite dimensional stochastic process that is frequently associated with a population genetics.

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© 2014, The International Biometric Society.A potential venue to improve healthcare efficiency is to effectively tailor individualized treatment strategies by incorporating patient level predictor information such as environmental exposure, biological, and genetic marker measurements. Many useful statistical methods for deriving individualized treatment rules (ITR) have become available in recent years. Prior to adopting any ITR in clinical practice, it is crucial to evaluate its value in improving patient outcomes. Existing methods for quantifying such values mainly consider either a single marker or semi-parametric methods that are subject to bias under model misspecification. In this article, we consider a general setting with multiple markers and propose a two-step robust method to derive ITRs and evaluate their values. We also propose procedures for comparing different ITRs, which can be used to quantify the incremental value of new markers in improving treatment selection. While working models are used in step I to approximate optimal ITRs, we add a layer of calibration to guard against model misspecification and further assess the value of the ITR non-parametrically, which ensures the validity of the inference. To account for the sampling variability of the estimated rules and their corresponding values, we propose a resampling procedure to provide valid confidence intervals for the value functions as well as for the incremental value of new markers for treatment selection. Our proposals are examined through extensive simulation studies and illustrated with the data from a clinical trial that studies the effects of two drug combinations on HIV-1 infected patients.