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BackgroundHot spots are interface residues that contribute most binding affinity to protein-protein interaction. A compact and relevant feature subset is important for building machine learning methods to predict hot spots on protein-protein interfaces. Although different methods have been used to detect the relevant feature subset from a variety of features related to interface residues, it is still a challenge to detect the optimal feature subset for building the final model.ResultsIn this study, three different feature selection methods were compared to propose a new hybrid feature selection strategy. This new strategy was proved to effectively reduce the feature space when we were building the prediction models for identifying hotspot residues. It was tested on eighty-two features, both conventional and newly proposed. According to the strategy, combining the feature subsets selected by decision tree and mRMR (maximum Relevance Minimum Redundancy) individually, we were able to build a model with 6 features by using a PSFS (Pseudo Sequential Forward Selection) process. Compared with other state-of-art methods for the independent test set, our model had shown better or comparable predictive performances (with F-measure 0.622 and recall 0.821). Analysis of the 6 features confirmed that our newly proposed feature CNSV_REL1 was important for our model. The analysis also showed that the complementarity between features should be considered as an important aspect when conducting the feature selection.ConclusionIn this study, most important of all, a new strategy for feature selection was proposed and proved to be effective in selecting the optimal feature subset for building prediction models, which can be used to predict hot spot residues on protein-protein interfaces. Moreover, two aspects, the generalization of the single feature and the complementarity between features, were proved to be of great importance and should be considered in feature selection methods. Finally, our newly proposed feature CNSV_REL1 had been proved an alternative and effective feature in predicting hot spots by our study. Our model is available for users through a webserver: http://zhulab.ahu.edu.cn/iPPHOT/.Electronic supplementary materialThe online version of this article (10.1186/s12859-018-2009-5) contains supplementary material, which is available to authorized users.

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Drought stress induced pollen sterility is a detrimental factor reducing grain number in wheat. Exploring the mechanisms underlying pollen fertility under drought conditions could assist breeding high-yielding wheat cultivars with stress tolerance. Here, by using two Chinese wheat cultivars subjected to different levels of polyethylene glycol (PEG)-induced drought stress, possible links between pollen fertility and stress tolerance were analyzed under different levels of drought stress at the young microspore stage. In both cultivars, higher grain number reduction was observed under condition of lower water availability. Overall, the drought tolerant cultivar (Jinmai47) exhibited less grain number reduction than the drought sensitive cultivar (Shiluan02-1) under all stress conditions. Compared with Shiluan02-1, Jinmai47 exhibited superior physiological performance in terms of leaf photosynthetic rate, ear carbohydrate accumulation, pollen sink strength, pollen development and fertility under stress. Moreover, Jinmai47 showed a lower increase in endogenous abscisic acid in ears than Shiluan02-1. Furthermore, higher levels of superoxide dismutase (SOD) and peroxidase (POD) activities were also found in the drought tolerant cultivar Jinmai47 under PEG stress, compared with the drought sensitive cultivar Shiluan02-1. Changes in these physiological traits could contribute to better pollen development and male fertility, ultimately leading to the maintenance of grain number under drought stress.

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Speech communication consists of three steps: production, transmission, and hearing. Every step inevitably involves acoustic distortions due to gender differences, age, microphone-and room-related factors, and so on. In spite of these variations, listeners can extract linguistic information from speech as easily as if the communications had not been affected by variations at all. One may hypothesize that listeners modify their internal acoustic models whenever extralinguistic factors change. Another possibility is that the linguistic information in speech can be represented separately from the extralinguistic factors. In this study, being inspired by studies of humans and animals, a novel solution to the problem of intrinsic variations is proposed. Speech structures invariant to these variations are derived as transform-invariant features and their linguistic validity is discussed. Their high robustness is demonstrated by applying the speech structures to automatic speech recognition and pronunciation proficiency estimation. This paper also describes the immaturity of the current implementation and application of speech structures.

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