Self-adjustment mechanisms and their application for orthosis design

Medical orthoses aim at guiding anatomical joints along their natural trajectories while preventing pathological movements, especially in case of trauma or injuries. The motions that take place between bone surfaces have complex kinematics. These so-called arthrokinematic motions exhibit axes that move both in translation and rotation. Traditionally, orthoses are carefully adjusted and positioned such that their kinematics approximate the arthrokinematic movements as closely as possible in order to protect the joint. Adjustment procedures are typically long and tedious. We suggest in this paper another approach. We propose mechanisms having intrinsic self-aligning properties. They are designed such that their main axis self-adjusts with respect to the joint’s physiological axis during motion. When connected to a limb, their movement becomes homokinetic and they have the property of automatically minimizing internal stresses. The study is performed here in the planar case focusing on the most important component of the arthrokinematic motions of a knee joint

The analysis of European lacquer : optimization of thermochemolysis temperature of natural resins

In order to optimize chromatographic analysis of European lacquer, thermochemolysis temperature was evaluated for the analysis of natural resins. Five main ingredients of lacquer were studied: sandarac, mastic, colophony, Manila copal and Congo copal. For each, five temperature programs were tested: four fixed temperatures (350, 480, 550, 650 degrees C) and one ultrafast thermal desorption (UFD), in which the temperature rises from 350 to 660 degrees C in 1 min. In total, the integrated signals of 27 molecules, partially characterizing the five resins, were monitored to compare the different methods. A compromise between detection of compounds released at low temperatures and compounds formed at high temperatures was searched. 650 degrees C is too high for both groups, 350 degrees C is best for the first, and 550 degrees C for the second. Fixed temperatures of 480 degrees C or UFD proved to be a consensus in order to detect most marker molecules. UFD was slightly better for the molecules released at low temperatures, while 480 degrees C showed best compounds formed at high temperatures

Migraine aura: retracting particle-like waves in weakly susceptible cortex

Cortical spreading depression (SD) has been suggested to underlie migraine aura. Despite a precise match in speed, the spatio-temporal patterns of SD and aura symptoms on the cortical surface ordinarily differ in aspects of size and shape. We show that this mismatch is reconciled by utilizing that both pattern types bifurcate from an instability point of generic reaction-diffusion models. To classify these spatio-temporal pattern we suggest a susceptibility scale having the value [sigma]=1 at the instability point. We predict that human cortex is only weakly susceptible to SD ([sigma]<1), and support this prediction by directly matching visual aura symptoms with anatomical landmarks using fMRI retinotopic mapping. We discuss the increased dynamical repertoire of cortical tissue close to [sigma]=1, in particular, the resulting implications on migraine pharmacology that is hitherto tested in the regime ([sigma]>>1), and potentially silent aura occurring below a second bifurcation point at [sigma]=0 on the susceptible scale

Evaluation of genetic isolation within an island flora reveals unusually widespread local adaptation and supports sympatric speciation

It is now recognized that speciation can proceed even when divergent natural selection is opposed by gene flow. Understanding the extent to which environmental gradients and geographical distance can limit gene flow within species can shed light on the relative roles of selection and dispersal limitation during the early stages of population divergence and speciation. On the remote Lord Howe Island (Australia), ecological speciation with gene flow is thought to have taken place in several plant genera. The aim of this study was to establish the contributions of isolation by environment (IBE) and isolation by community (IBC) to the genetic structure of 19 plant species, from a number of distantly related families, which have been subjected to similar environmental pressures over comparable time scales. We applied an individual-based, multivariate, model averaging approach to quantify IBE and IBC, while controlling for isolation by distance (IBD). Our analyses demonstrated that all species experienced some degree of ecologically driven isolation, whereas only 12 of 19 species were subjected to IBD. The prevalence of IBE within these plant species indicates that divergent selection in plants frequently produces local adaptation and supports hypotheses that ecological divergence can drive speciation in sympatry

Comprehensive annotation of the Parastagonospora nodorum reference genome using next-generation genomics, transcriptomics and proteogenomics

Parastagonospora nodorum, the causal agent of Septoria nodorum blotch (SNB), is an economically important pathogen of wheat (Triticum spp.), and a model for the study of necrotrophic pathology and genome evolution. The reference P. nodorum strain SN15 was the first Dothideomycete with a published genome sequence, and has been used as the basis for comparison within and between species. Here we present an updated reference genome assembly with corrections of SNP and indel errors in the underlying genome assembly from deep resequencing data as well as extensive manual annotation of gene models using transcriptomic and proteomic sources of evidence (https://github.com/robsyme/Parastagonospora_nodorum_SN15). The updated assembly and annotation includes 8,366 genes with modified protein sequence and 866 new genes. This study shows the benefits of using a wide variety of experimental methods allied to expert curation to generate a reliable set of gene models

Seroepidemiology of Bovine Viral Diarrhoea Virus (BVDV) in the Adamawa Region of Cameroon and Use of the SPOT Test to Identify Herds with PI Calves

Bovine viral diarrhoea, caused by the bovine viral diarrhoea virus (BVDV) in the Pestivirus genus of the Flaviviridae, is one of the most important diseases of cattle world wide causing poor reproductive performance in adult cattle and mucosal disease in calves. In addition it causes immunosuppression and increased susceptibility to other infections, the impact of which is uncertain, particularly in sub-Saharan Africa where animals are exposed to a much wider range and higher intensity of infections compared to Europe. There are no previous estimates of the seroprevalence of BVDV in cattle in Cameroon. This paper describes the serological screening for antibodies to BVDV and antigen of BVDV in a cattle population in the Adamawa Region of Cameroon in 2000. The estimates of herd-level and within herd seroprevalences adjusted for test imperfections were 92% and 30% respectively and 16.5% of herds were classed as having a persistently infected calf (PI) in the herd within the last year based on the “spot” test approach. There was evidence of clustering of herds with PI calves across the north and west of the Region which corresponds with the higher cattle density areas and of self-clearance of infection from herds. A multivariable model was developed for the risk of having a PI calf in the herd; proximity to antelope, owning a goat, mixing with 10 other herds at grazing and the catchment area of the veterinary centre the herd was registered at were all significant risk factors. Very little is known about BVDV in sub-Saharan Africa and these high seroprevalences suggest that there is a large problem which may be having both direct impacts on fertility and neonate mortality and morbidity and also indirect effects through immunosuppression and susceptibility to other infections. Understanding and accounting for BVDV should be an important component of epidemiological studies of other diseases in sub-Saharan Africa

Deep learning-based polygenic risk analysis for Alzheimer's disease prediction

BACKGROUND: The polygenic nature of Alzheimer's disease (AD) suggests that multiple variants jointly contribute to disease susceptibility. As an individual's genetic variants are constant throughout life, evaluating the combined effects of multiple disease-associated genetic risks enables reliable AD risk prediction. Because of the complexity of genomic data, current statistical analyses cannot comprehensively capture the polygenic risk of AD, resulting in unsatisfactory disease risk prediction. However, deep learning methods, which capture nonlinearity within high-dimensional genomic data, may enable more accurate disease risk prediction and improve our understanding of AD etiology. Accordingly, we developed deep learning neural network models for modeling AD polygenic risk. METHODS: We constructed neural network models to model AD polygenic risk and compared them with the widely used weighted polygenic risk score and lasso models. We conducted robust linear regression analysis to investigate the relationship between the AD polygenic risk derived from deep learning methods and AD endophenotypes (i.e., plasma biomarkers and individual cognitive performance). We stratified individuals by applying unsupervised clustering to the outputs from the hidden layers of the neural network model. RESULTS: The deep learning models outperform other statistical models for modeling AD risk. Moreover, the polygenic risk derived from the deep learning models enables the identification of disease-associated biological pathways and the stratification of individuals according to distinct pathological mechanisms. CONCLUSION: Our results suggest that deep learning methods are effective for modeling the genetic risks of AD and other diseases, classifying disease risks, and uncovering disease mechanisms

Generation of NSE-MerCreMer Transgenic Mice with Tamoxifen Inducible Cre Activity in Neurons

To establish a genetic tool for conditional deletion or expression of gene in neurons in a temporally controlled manner, we generated a transgenic mouse (NSE-MerCreMer), which expressed a tamoxifen inducible type of Cre recombinase specifically in neurons. The tamoxifen inducible Cre recombinase (MerCreMer) is a fusion protein containing Cre recombinase with two modified estrogen receptor ligand binding domains at both ends, and is driven by the neural-specific rat neural specific enolase (NSE) promoter. A total of two transgenic lines were established, and expression of MerCreMer in neurons of the central and enteric nervous systems was confirmed. Transcript of MerCreMer was detected in several non-neural tissues such as heart, liver, and kidney in these lines. In the background of the Cre reporter mouse strain Rosa26R, Cre recombinase activity was inducible in neurons of adult NSE-MerCreMer mice treated with tamoxifen by intragastric gavage, but not in those fed with corn oil only. We conclude that NSE-MerCreMer lines will be useful for studying gene functions in neurons for the conditions that Cre-mediated recombination resulting in embryonic lethality, which precludes investigation of gene functions in neurons through later stages of development and in adult

Ultra-fast sequence clustering from similarity networks with SiLiX

<p>Abstract</p> <p>Background</p> <p>The number of gene sequences that are available for comparative genomics approaches is increasing extremely quickly. A current challenge is to be able to handle this huge amount of sequences in order to build families of homologous sequences in a reasonable time.</p> <p>Results</p> <p>We present the software package <monospace>SiLiX</monospace> that implements a novel method which reconsiders single linkage clustering with a graph theoretical approach. A parallel version of the algorithms is also presented. As a demonstration of the ability of our software, we clustered more than 3 millions sequences from about 2 billion BLAST hits in 7 minutes, with a high clustering quality, both in terms of sensitivity and specificity.</p> <p>Conclusions</p> <p>Comparing state-of-the-art software, <monospace>SiLiX</monospace> presents the best up-to-date capabilities to face the problem of clustering large collections of sequences. <monospace>SiLiX</monospace> is freely available at <url>http://lbbe.univ-lyon1.fr/SiLiX</url>.</p