Quartics in E3E^3 which have a triple point and touch the plane at infinity through the absolute conic

This paper gives the classification of the 4th order surfaces in $E^3$ which have a triple point and touch the plane at infinity at the absolute conic. The classification is made according to the type of the tangent cubic cone at a triple point. Three types with sixteen subtypes are obtained. For these surfaces the homogeneous and parametric equations are derived and each type is illustrated with Mathematica graphics

A random regression model in analysis of litter size in pigs

Dispersion parameters for number of piglets born alive (NBA) were estimated using a random regression model (RRM). Two data sets of litter records from the Nemščak farm in Slovenia were used for analyses. The first dataset (DS1) included records from the first to the sixth parity. The second dataset (DS2) was extended to the tenth parity. Four sow genotypes were included: Swedish Landrace (SL), Large White (LW) and their crossbred lines. The fixed part of the model included sow genotype, mating season (as month-year interaction), parity and weaning to conception interval as class effects. The age at farrowing was modelled as a quadratic regression, nested within parity. The previous lactation length was fitted as a linear regression. Random regressions for parity on Legendre polynomials were included for direct additive genetic, permanent environmental and common litter environmental effects. Orthogonal Legendre polynomials from the linear to the cubic power were fitted. Estimates of heritability ranged from 0.09 to 0.14. The ratio of permanent environmental variance to total variance increased along the trajectory from 0.05 to 0.16. Magnitudes of common litter effect were generally small (0.01 to 0.02). The eigenvalues of covariance functions showed that between 10 and 15% of genetic variability was explained by the individual genetic curve of sows in the DS2. This proportion was mainly covered by linear and quadratic coefficients. Results suggest that RRM could be used for genetic analysis of litter size. South African Journal of Animal Science Vol. 34(4) 2004: 241-24

Plasma-assisted deposition of microcapsule containing Aloe vera extract for cosmeto-textiles

There is a growing interest in the application of cosmeto-textiles to incorporate durable fragrances and skin softeners to textile.[1] Microencapsulation technology is a growing area in textile industry.[2, 3] The main disadvantage of using film-forming binders in the application of MCs onto textiles is hindrance of the active substances to be release. To overcome this issue MCs can be covalently linked onto textile substrate by using chemical or physical methods.[4] In recent years plasma technology has assumed a great importance.[5] It is a dry, environmentally- and worker-friendly method to achieve surface alteration without modifies the bulk properties of different materials.[6] It improves the fibre-matrix adhesion by introducing chemically active groups and changing the surface roughness.[7] The dielectric double barrier discharge (DBD) is one of the most effective non-thermal atmospheric plasma to improve the adsorption and adhesion of MCs in textiles [8-10]. The main objective of this study is to investigate the adhesion of MCs containing Aloe vera extract applied by padding and printing methods in a cotton/polyester (50/50) fabric (Co/PES) pre-treated with a DBD plasma discharge in air. Fabrics were analysed by contact angle, SEM and FTIR analysis. The printing and padding methods was compared in term of MCs coating efficiency, plasma dose and washing fastness.This work is supported by CSF - CAPES - Brazil (Bex 18.645-12-7) and FEDER funding on the COMPETE program and by national funds through FCT-Foundation for Science and Technology within the scope of the project POCI-01-0145-FEDER-007136 and UID/CTM/00264.info:eu-repo/semantics/publishedVersio

A method for the allocation of sequencing resources in genotyped livestock populations

International audienceAbstractBackgroundThis paper describes a method, called AlphaSeqOpt, for the allocation of sequencing resources in livestock populations with existing phased genomic data to maximise the ability to phase and impute sequenced haplotypes into the whole population.MethodsWe present two algorithms. The first selects focal individuals that collectively represent the maximum possible portion of the haplotype diversity in the population. The second allocates a fixed sequencing budget among the families of focal individuals to enable phasing of their haplotypes at the sequence level. We tested the performance of the two algorithms in simulated pedigrees. For each pedigree, we evaluated the proportion of population haplotypes that are carried by the focal individuals and compared our results to a variant of the widely-used key ancestors approach and to two haplotype-based approaches. We calculated the expected phasing accuracy of the haplotypes of a focal individual at the sequence level given the proportion of the fixed sequencing budget allocated to its family.ResultsAlphaSeqOpt maximises the ability to capture and phase the most frequent haplotypes in a population in three ways. First, it selects focal individuals that collectively represent a larger portion of the population haplotype diversity than existing methods. Second, it selects focal individuals from across the pedigree whose haplotypes can be easily phased using family-based phasing and imputation algorithms, thus maximises the ability to impute sequence into the rest of the population. Third, it allocates more of the fixed sequencing budget to focal individuals whose haplotypes are more frequent in the population than to focal individuals whose haplotypes are less frequent. Unlike existing methods, we additionally present an algorithm to allocate part of the sequencing budget to the families (i.e. immediate ancestors) of focal individuals to ensure that their haplotypes can be phased at the sequence level, which is essential for enabling and maximising subsequent sequence imputation.ConclusionsWe present a new method for the allocation of a fixed sequencing budget to focal individuals and their families such that the final sequenced haplotypes, when phased at the sequence level, represent the maximum possible portion of the haplotype diversity in the population that can be sequenced and phased at that budget

Excess cardiovascular mortality associated with cold spells in the Czech Republic

<p>Abstract</p> <p>Background</p> <p>The association between cardiovascular mortality and winter cold spells was evaluated in the population of the Czech Republic over 21-yr period 1986–2006. No comprehensive study on cold-related mortality in central Europe has been carried out despite the fact that cold air invasions are more frequent and severe in this region than in western and southern Europe.</p> <p>Methods</p> <p>Cold spells were defined as periods of days on which air temperature does not exceed -3.5°C. Days on which mortality was affected by epidemics of influenza/acute respiratory infections were identified and omitted from the analysis. Excess cardiovascular mortality was determined after the long-term changes and the seasonal cycle in mortality had been removed. Excess mortality during and after cold spells was examined in individual age groups and genders.</p> <p>Results</p> <p>Cold spells were associated with positive mean excess cardiovascular mortality in all age groups (25–59, 60–69, 70–79 and 80+ years) and in both men and women. The relative mortality effects were most pronounced and most direct in middle-aged men (25–59 years), which contrasts with majority of studies on cold-related mortality in other regions. The estimated excess mortality during the severe cold spells in January 1987 (+274 cardiovascular deaths) is comparable to that attributed to the most severe heat wave in this region in 1994.</p> <p>Conclusion</p> <p>The results show that cold stress has a considerable impact on mortality in central Europe, representing a public health threat of an importance similar to heat waves. The elevated mortality risks in men aged 25–59 years may be related to occupational exposure of large numbers of men working outdoors in winter. Early warnings and preventive measures based on weather forecast and targeted on the susceptible parts of the population may help mitigate the effects of cold spells and save lives.</p

Impact of index hopping and bias towards the reference allele on accuracy of genotype calls from low-coverage sequencing

Abstract Background Inherent sources of error and bias that affect the quality of sequence data include index hopping and bias towards the reference allele. The impact of these artefacts is likely greater for low-coverage data than for high-coverage data because low-coverage data has scant information and many standard tools for processing sequence data were designed for high-coverage data. With the proliferation of cost-effective low-coverage sequencing, there is a need to understand the impact of these errors and bias on resulting genotype calls from low-coverage sequencing. Results We used a dataset of 26 pigs sequenced both at 2× with multiplexing and at 30× without multiplexing to show that index hopping and bias towards the reference allele due to alignment had little impact on genotype calls. However, pruning of alternative haplotypes supported by a number of reads below a predefined threshold, which is a default and desired step of some variant callers for removing potential sequencing errors in high-coverage data, introduced an unexpected bias towards the reference allele when applied to low-coverage sequence data. This bias reduced best-guess genotype concordance of low-coverage sequence data by 19.0 absolute percentage points. Conclusions We propose a simple pipeline to correct the preferential bias towards the reference allele that can occur during variant discovery and we recommend that users of low-coverage sequence data be wary of unexpected biases that may be produced by bioinformatic tools that were designed for high-coverage sequence data

Efficient ancestry and mutation simulation with msprime 1.0

Stochastic simulation is a key tool in population genetics, since the models involved are often analytically intractable and simulation is usually the only way of obtaining ground-truth data to evaluate inferences. Because of this, a large number of specialized simulation programs have been developed, each filling a particular niche, but with largely overlapping functionality and a substantial duplication of effort. Here, we introduce msprime version 1.0, which efficiently implements ancestry and mutation simulations based on the succinct tree sequence data structure and the tskit library. We summarize msprime’s many features, and show that its performance is excellent, often many times faster and more memory efficient than specialized alternatives. These high-performance features have been thoroughly tested and validated, and built using a collaborative, open source development model, which reduces duplication of effort and promotes software quality via community engagement

Assessment of the performance of hidden Markov models for imputation in animal breeding

Abstract Background In this paper, we review the performance of various hidden Markov model-based imputation methods in animal breeding populations. Traditionally, pedigree and heuristic-based imputation methods have been used for imputation in large animal populations due to their computational efficiency, scalability, and accuracy. Recent advances in the area of human genetics have increased the ability of probabilistic hidden Markov model methods to perform accurate phasing and imputation in large populations. These advances may enable these methods to be useful for routine use in large animal populations, particularly in populations where pedigree information is not readily available. Methods To test the performance of hidden Markov model-based imputation, we evaluated the accuracy and computational cost of several methods in a series of simulated populations and a real animal population without using a pedigree. First, we tested single-step (diploid) imputation, which performs both phasing and imputation. Second, we tested pre-phasing followed by haploid imputation. Overall, we used four available diploid imputation methods (fastPHASE, Beagle v4.0, IMPUTE2, and MaCH), three phasing methods, (SHAPEIT2, HAPI-UR, and Eagle2), and three haploid imputation methods (IMPUTE2, Beagle v4.1, and Minimac3). Results We found that performing pre-phasing and haploid imputation was faster and more accurate than diploid imputation. In particular, among all the methods tested, pre-phasing with Eagle2 or HAPI-UR and imputing with Minimac3 or IMPUTE2 gave the highest accuracies with both simulated and real data. Conclusions The results of this study suggest that hidden Markov model-based imputation algorithms are an accurate and computationally feasible approach for performing imputation without a pedigree when pre-phasing and haploid imputation are used. Of the algorithms tested, the combination of Eagle2 and Minimac3 gave the highest accuracy across the simulated and real datasets