MemBrain: Improving the Accuracy of Predicting Transmembrane Helices

Prediction of transmembrane helices (TMH) in α helical membrane proteins provides valuable information about the protein topology when the high resolution structures are not available. Many predictors have been developed based on either amino acid hydrophobicity scale or pure statistical approaches. While these predictors perform reasonably well in identifying the number of TMHs in a protein, they are generally inaccurate in predicting the ends of TMHs, or TMHs of unusual length. To improve the accuracy of TMH detection, we developed a machine-learning based predictor, MemBrain, which integrates a number of modern bioinformatics approaches including sequence representation by multiple sequence alignment matrix, the optimized evidence-theoretic K-nearest neighbor prediction algorithm, fusion of multiple prediction window sizes, and classification by dynamic threshold. MemBrain demonstrates an overall improvement of about 20% in prediction accuracy, particularly, in predicting the ends of TMHs and TMHs that are shorter than 15 residues. It also has the capability to detect N-terminal signal peptides. The MemBrain predictor is a useful sequence-based analysis tool for functional and structural characterization of helical membrane proteins; it is freely available at http://chou.med.harvard.edu/bioinf/MemBrain/

Impact of Environmental Parameters on Marathon Running Performance

PURPOSE: The objectives of this study were to describe the distribution of all runners' performances in the largest marathons worldwide and to determine which environmental parameters have the maximal impact. METHODS: We analysed the results of six European (Paris, London, Berlin) and American (Boston, Chicago, New York) marathon races from 2001 to 2010 through 1,791,972 participants' performances (all finishers per year and race). Four environmental factors were gathered for each of the 60 races: temperature (°C), humidity (%), dew point (°C), and the atmospheric pressure at sea level (hPA); as well as the concentrations of four atmospheric pollutants: NO(2)-SO(2)-O(3) and PM(10) (μg x m(-3)). RESULTS: All performances per year and race are normally distributed with distribution parameters (mean and standard deviation) that differ according to environmental factors. Air temperature and performance are significantly correlated through a quadratic model. The optimal temperatures for maximal mean speed of all runners vary depending on the performance level. When temperature increases above these optima, running speed decreases and withdrawal rates increase. Ozone also impacts performance but its effect might be linked to temperature. The other environmental parameters do not have any significant impact. CONCLUSIONS: The large amount of data analyzed and the model developed in this study highlight the major influence of air temperature above all other climatic parameter on human running capacity and adaptation to race conditions

An Evidential k-nearest Neighbors Combination Rule for Tree Species Recognition

International audienceThe task of tree species recognition is to recognize the tree species using photos of their leaves and barks. In this paper, we propose an evidential k-nearest neighbors (k-NN) combination rule. The proposed rule is adapted to classication problems where we have a large number of classes with an intra-class variability and an inter-class similarity like the problem of tree species recognition. Finally, we compare the performance of the proposed solution to the evidential k-NN

Bagging Improves Uncertainty Representation In Evidential Pattern Classification

Uncertainty representation is a major issue in pattern recognition when the outputs of a classi er do not lead directly to a nal decision, but are used in combination with other systems, or as input to an interactive decision process. In such contexts, it may be advantageous to resort to rich and exible formalisms for representing and manipulating uncertain information, such as the Dempster-Shafer theory of Evidence. In this paper, it is shown that the quality and reliability of the outputs from an evidence-theoretic classi er may be improved using an adaptation from a resample-and-combine approach introduced by Breiman and known as "bagging". This approach is explained and studied experimentally using simulated data. In particular, results show that bagging improves classi cation accuracy and limits the inuence of outliers and ambiguous training patterns

Evidential classification of incomplete data via imprecise relabelling : Application to plastic sorting

International audienceBesides ecological issues, the recycling of plastics involves economic incentives that encourage industrial firms to invest in the field. Some of them have focused on the waste sorting phase by designing optical devices able to discriminate on-line between plastic categories. To achieve both ecological and economic objectives, sorting errors must be minimized to avoid serious recycling problems and significant quality degradation of the final recycled product. Even with the most recent acquisition technologies based on spectral imaging, plastic recognition remains a tough task due to the presence of imprecision and uncertainty, e.g. variability in measurement due to atmospheric disturbances, ageing of plastics, black or dark-coloured materials etc. The enhancement of recent sorting techniques based on classification algorithms has led to quite good performance results, however the remaining errors have serious consequences for such applications. In this article, we propose an imprecise classification algorithm to minimize the sorting errors of standard classifiers when dealing with incomplete data, by both integrating the processing of classification doubt and hesitation in the decision process and improving the classification performances. To this end, we propose a relabelling procedure that enables better representation of the imprecision of the learning data, and we introduce the belief functions framework to represent the posterior probability provided by a classifier. Finally, the performances of our approach compared to existing imprecise classifiers is illustrated on the sorting problem of four plastic categories from mid-wavelength infra-red spectra acquired in an industrial context

Low Energy Availability in Athletes: A Review of Prevalence, Dietary Patterns, Physiological Health, and Sports Performance

In a high-performance sports environment, athletes can present with low energy availability (LEA) for a variety of reasons, ranging from not consuming enough food for their specific energy requirements to disordered eating behaviors. Both male and female high-performance athletes are at risk of LEA. Longstanding LEA can cause unfavorable physiological and psychological outcomes which have the potential to impair an athlete’s health and sports performance. This narrative review summarizes the prevalence of LEA and its associations with athlete health and sports performance. It is evident in the published scientific literature that the methods used to determine LEA and its associated health outcomes vary. This contributes to poor recognition of the condition and its sequelae. This review also identifies interventions designed to improve health outcomes in athletes with LEA and indicates areas which warrant further investigation. While return-to-play guidelines have been developed for healthcare professionals to manage LEA in athletes, behavioral interventions to prevent the condition and manage its associated negative health and performance outcomes are required.Irish Research CouncilSport Irelan