Effect of deformation on components of internal stress tensor in grains of FCC-polycristal

Study of contributions of internal stress tensor components in deformed of austenitic steel was carriedout. The tensor components of internal stresses were determined with using bending extinction contours observing on electron microscope images of the steel

Genetic Classification of Populations using Supervised Learning

There are many instances in genetics in which we wish to determine whether two candidate populations are distinguishable on the basis of their genetic structure. Examples include populations which are geographically separated, case--control studies and quality control (when participants in a study have been genotyped at different laboratories). This latter application is of particular importance in the era of large scale genome wide association studies, when collections of individuals genotyped at different locations are being merged to provide increased power. The traditional method for detecting structure within a population is some form of exploratory technique such as principal components analysis. Such methods, which do not utilise our prior knowledge of the membership of the candidate populations. are termed \emph{unsupervised}. Supervised methods, on the other hand are able to utilise this prior knowledge when it is available. In this paper we demonstrate that in such cases modern supervised approaches are a more appropriate tool for detecting genetic differences between populations. We apply two such methods, (neural networks and support vector machines) to the classification of three populations (two from Scotland and one from Bulgaria). The sensitivity exhibited by both these methods is considerably higher than that attained by principal components analysis and in fact comfortably exceeds a recently conjectured theoretical limit on the sensitivity of unsupervised methods. In particular, our methods can distinguish between the two Scottish populations, where principal components analysis cannot. We suggest, on the basis of our results that a supervised learning approach should be the method of choice when classifying individuals into pre-defined populations, particularly in quality control for large scale genome wide association studies.Comment: Accepted PLOS On

Spirometry reference equations for central European populations from school age to old age.

Spirometry reference values are important for the interpretation of spirometry results. Reference values should be updated regularly, derived from a population as similar to the population for which they are to be used and span across all ages. Such spirometry reference equations are currently lacking for central European populations. To develop spirometry reference equations for central European populations between 8 and 90 years of age. We used data collected between January 1993 and December 2010 from a central European population. The data was modelled using "Generalized Additive Models for Location, Scale and Shape" (GAMLSS). The spirometry reference equations were derived from 118'891 individuals consisting of 60'624 (51%) females and 58'267 (49%) males. Altogether, there were 18'211 (15.3%) children under the age of 18 years. We developed spirometry reference equations for a central European population between 8 and 90 years of age that can be implemented in a wide range of clinical settings

Greenhouse gas calculator for electricity and heat from biomass

Industrial Ecolog

Genetic spectrum of hereditary neuropathies with onset in the first year of life

Early onset hereditary motor and sensory neuropathies are rare disorders encompassing congenital hypomyelinating neuropathy with disease onset in the direct post-natal period and Dejerine–Sottas neuropathy starting in infancy. The clinical spectrum, however, reaches beyond the boundaries of these two historically defined disease entities. De novo dominant mutations in PMP22, MPZ and EGR2 are known to be a typical cause of very early onset hereditary neuropathies. In addition, mutations in several other dominant and recessive genes for Charcot–Marie–Tooth disease may lead to similar phenotypes. To estimate mutation frequencies and to gain detailed insights into the genetic and phenotypic heterogeneity of early onset hereditary neuropathies, we selected a heterogeneous cohort of 77 unrelated patients who presented with symptoms of peripheral neuropathy within the first year of life. The majority of these patients were isolated in their family. We performed systematic mutation screening by means of direct sequencing of the coding regions of 11 genes: MFN2, PMP22, MPZ, EGR2, GDAP1, NEFL, FGD4, MTMR2, PRX, SBF2 and SH3TC2. In addition, screening for the Charcot–Marie–Tooth type 1A duplication on chromosome 17p11.2-12 was performed. In 35 patients (45%), mutations were identified. Mutations in MPZ, PMP22 and EGR2 were found most frequently in patients presenting with early hypotonia and breathing difficulties. The recessive genes FGD4, PRX, MTMR2, SBF2, SH3TC2 and GDAP1 were mutated in patients presenting with early foot deformities and variable delay in motor milestones after an uneventful neonatal period. Several patients displaying congenital foot deformities but an otherwise normal early development carried the Charcot–Marie–Tooth type 1A duplication. This study clearly illustrates the genetic heterogeneity underlying hereditary neuropathies with infantile onset

Neandertal introgression partitions the genetic landscape of neuropsychiatric disorders and associated behavioral phenotypes

Despite advances in identifying the genetic basis of psychiatric and neurological disorders, fundamental questions about their evolutionary origins remain elusive. Here, introgressed variants from archaic humans such as Neandertals can serve as an intriguing research paradigm. We compared the number of associations for Neandertal variants to the number of associations of frequency-matched non-archaic variants with regard to human CNS disorders (neurological and psychiatric), nervous system drug prescriptions (as a proxy for disease), and related, non-disease phenotypes in the UK biobank (UKBB). While no enrichment for Neandertal genetic variants were observed in the UKBB for psychiatric or neurological disease categories, we found significant associations with certain behavioral phenotypes including pain, chronotype/sleep, smoking and alcohol consumption. In some instances, the enrichment signal was driven by Neandertal variants that represented the strongest association genome-wide. SNPs within a Neandertal haplotype that was associated with smoking in the UKBB could be replicated in four independent genomics datasets