An educational tool to assist the design process of switched reluctance machines

The design of electric machines is a hot topic in the syllabuses of several undergraduate and graduate courses. With the development of hybrid and electrical vehicles, this subject is gaining more popularity, especially in electrical engineering courses. This paper presents a computeraided educational tool to guide engineering students in the design process of a switched reluctance machine (SRM). A step-by-step design procedure is detailed and a user guide interface (GUI) programmed in the Matlab® environment developed for this purpose is shown. This GUI has been proved a useful tool to help the students to validate the results obtained in their lecture assignments, while aiding to achieve a better understanding of the design process of electric machines. A validation of the educational tool is done by means of finite element method (FEM) simulations.Postprint (author's final draft

Aerobic capacity and telomere length in human skeletal muscle and leukocytes across the lifespan

A reduction in aerobic capacity and the shortening of telomeres are hallmarks of the ageing process. We examined whether a lower aerobic capacity is associated with shorter TL in skeletal muscle and/or leukocytes, across a wide age range of individuals. We also tested whether TL in human skeletal muscle (MTL) correlates with TL in leukocytes (LTL). Eighty-two recreationally active, healthy men from the Gene SMART cohort (31.4±8.2 years; body mass index (BMI)=25.3±3.3kg/m2), and 11 community dwelling older men (74.2±7.5years-old; BMI=28.7±2.8kg/m2) participated in the study. Leukocytes and skeletal muscle samples were collected at rest. Relative telomere length (T/S ratio) was measured by RT-PCR. Associations between TL, aerobic capacity (VO2 peak and peak power) and age were assessed with robust linear models. Older age was associated with shorter LTL (45% variance explained, P<0.001), but not MTL (P= 0.7). Aerobic capacity was not associated with MTL (P=0.5), nor LTL (P=0.3). MTL and LTL were correlated across the lifespan (rs=0.26, P=0.03). In healthy individuals, age explain most of the variability of LTL and this appears to be independent of individual aerobic capacity. Individuals with longer LTL also have a longer MTL, suggesting that there might be a shared molecular mechanism regulating telomere length

The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

Background The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. Results Here, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory. Conclusion We conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.Peer reviewe

The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

BackgroundThe Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function.ResultsHere, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory.ConclusionWe conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.</p