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Microbial endophytes organize symbiotic relationships with the host plant, and their excretions contain diverse plant beneficial matter such as phytohormones and bioactive compounds. In the present investigation, six bacterial and four fungal strains were isolated from the common bean (Phaseolus vulgaris L.) root plant, identified using molecular techniques, and their growth-promoting properties were reviewed. All microbial isolates showed varying activities to produce indole-3-acetic acid (IAA) and different hydrolytic enzymes such as amylase, cellulase, protease, pectinase, and xylanase. Six bacterial endophytic isolates displayed phosphate-solubilizing capacity and ammonia production. We conducted a field experiment to evaluate the promotion activity of the metabolites of the most potent endophytic bacterial (Bacillus thuringiensis PB2 and Brevibacillus agri PB5) and fungal (Alternaria sorghi PF2 and, Penicillium commune PF3) strains in comparison to two exogenously applied hormone, IAA, and benzyl adenine (BA), on the growth and biochemical characteristics of the P. vulgaris L. Interestingly, our investigations showed that bacterial and fungal endophytic metabolites surpassed the exogenously applied hormones in increasing the plant biomass, photosynthetic pigments, carbohydrate and protein contents, antioxidant enzyme activity, endogenous hormones and yield traits. Our findings illustrate that the endophyte Brevibacillus agri (PB5) provides high potential as a stimulator for the growth and productivity of common bean plants.

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Selecting the most discriminative genes/miRNAs has been raised as an important task in bioinformatics to enhance disease classifiers and to mitigate the dimensionality curse problem. Original feature selection methods choose genes/miRNAs based on their individual features regardless of how they perform together. Considering group features instead of individual ones provides a better view for selecting the most informative genes/miRNAs. Recently, deep learning has proven its ability in representing the data in multiple levels of abstraction, allowing for better discrimination between different classes. However, the idea of using deep learning for feature selection is not widely used in the bioinformatics field yet. In this paper, a novel multi-level feature selection approach named MLFS is proposed for selecting genes/miRNAs based on expression profiles. The approach is based on both deep and active learning. Moreover, an extension to use the technique for miRNAs is presented by considering the biological relation between miRNAs and genes. Experimental results show that the approach was able to outperform classical feature selection methods in hepatocellular carcinoma (HCC) by 9%, lung cancer by 6% and breast cancer by around 10% in F1-measure. Results also show the enhancement in F1-measure of our approach over recently related work in [1] and [2].

Abstract. In this paper, we present VSCAN, a novel approach for generating static video summaries. This approach is based on a modified DBSCAN clustering algorithm to summarize the video content utilizing both color and texture features of the video frames. The paper also introduces an enhanced evaluation method that depends on color and texture features. Video Summaries generated by VSCAN are compared with summaries generated by other approaches found in the literature and those created by users. Experimental results indicate that the video summaries generated by VSCAN have a higher quality than those generated by other approaches.