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Bioinformatics is the new branch of science which deals with the acquisition, storage, analysis and dissemination of biological data with the help of computer science and information technology. It has the enormous ability to analyze a vast quantity of biological data quickly and cost-effectively. In the past decades, enormous sequence information has been generated due to the advances in DNA and protein sequencing techniques. Estimating similarities between biological sequences is becoming necessary to obtain hidden information present within the sequence and to trace evolutionary relationship exist within the sequences. This sequence comparison can be achieved by basic local alignment search tool (BLAST). So BLAST has become a fundamental tools of life science research. Hence it is essential to know how to do sequence comparison using BLAST and how to accurately interpret the BLAST output data. The present article aims to familiarize the biologists and researchers with different BLAST programs and their use in research program. [ABSTRACT FROM AUTHOR]

Cercospora leaf spot (CLS) has a substantial negative impact on mung bean production across the world. It is crucial to identify and incorporate alleles that might be helpful in combating CLS in order to maintain mung bean health, and productivity. Therefore, 90 mung bean genotypes, including four check varieties, and 66 polymorphic SSR markers were included in the present study during pre-rabi 2018 and pre-rabi 2019. Morpho-genetic analysis revealed a higher value of GCV for yield, cluster and pod numbers per plant, and plant height. All traits had excellent genetic advance (GA), with the exception of the number of seeds per pod, which was medium. PCA was used to separate up to 85.656 % of the variation in the characteristics between the two primary PCs. Molecular dissection revealed, the markers CEDG118 and CEDG074 to yield more genetic diversity, followed by CEDG073, CEDG030 and CEDG147. Estimation of allele frequencies revealed some alleles of the markers found linked to CLS resistance, i.e., CEDG001 (111 bp and 135 bp), CEDG006 (135 bp), CEDG153 (117 bp), CEDG210 (193 bp), CEDG117 (112 bp), CEDG020 (143 bp), CEDG067 (93 bp), CEDG146 (111 bp), and CEDG071 (231 bp and 267 bp). The highest private allelic frequency was employed by the markers CEDG153 and CEDG146. The dendrogram divided the panel population into two major clusters. However, two distinct genetic structure were found within the population. According to these findings, the mung bean germplasm panel used in the present study may have the ability to improve the local gene pool and aid in the development of CLS resistance breeding strategies using genomic tools and techniques. • 90 mung bean genotypes, including 4 check varieties, and 66 polymorphic SSR markers were included in the present study and morpho-genetic analysis revealed a higher value of GCV for yield, cluster and pod numbers per plant, and plant height. PCA was used to separate up to 85.656 % of the variation in the characteristics. • Molecular dissection revealed, the markers CEDG118 and CEDG074 to yield more genetic diversity. Estimation of allele frequencies revealed some alleles of the markers found linked to CLS stress resistance. • The dendrogram divided the panel population into two major clusters, followed by sub-clusters and sub-sub-clusters. However, two distinct genetic population structure were found within the population. • The mung bean germplasm panel used in the present study may have the ability to improve the local gene pool and aid in the development of CLS resistance breeding strategies using genomic techniques. [ABSTRACT FROM AUTHOR]

Few-shot learning (FSL) has emerged as an effective learning method and shows great potential. Despite the recent creative works in tackling FSL tasks, learning valid information rapidly from just a few or even zero samples remains a serious challenge. In this context, we extensively investigated 200+ FSL papers published in top journals and conferences in the past three years, aiming to present a timely and comprehensive overview of the most recent advances in FSL with a fresh perspective and to provide an impartial comparison of the strengths and weaknesses of existing work. To avoid conceptual confusion, we first elaborate and contrast a set of relevant concepts including few-shot learning, transfer learning, and meta-learning. Then, we inventively extract prior knowledge related to few-shot learning in the form of a pyramid, which summarizes and classifies previous work in detail from the perspective of challenges. Furthermore, to enrich this survey, we present in-depth analysis and insightful discussions of recent advances in each subsection. What is more, taking computer vision as an example, we highlight the important application of FSL, covering various research hotspots. Finally, we conclude the survey with unique insights into technology trends and potential future research opportunities to guide FSL follow-up research. [ABSTRACT FROM AUTHOR]

Although increased use of modern breeding techniques and technology has resulted in long-term genetic gain, the pace of genetic gain must be sped up to satisfy global agricultural demand. However, marker-assisted selection has proven its potential for improving qualitative traits with large effects regulated by one to few genes. Its contribution to the improvement of the quantitative traits regulated by a number of small-effect genes is modest. In this context, genomic selection (GS) has been regarded as the most promising method for genetically enhancing complicated features that are regulated by several genes, each of which has minor effects. By examining a population's phenotypes and high-density marker scores, genomic selection can forecast the breeding potential of individual lines. The fact that GS uses all marker data in the prediction model prevents skewed marker effect estimations and maximizes the amount of variation caused by small-effect QTL. It has the ability to speed up the breeding cycle and as a consequence of which superior genotypes are selected rapidly. Developing the best GS models while taking into account non-additive effects, genotype-by-environment interaction, and cost-effectiveness will enable the widespread implementation of GS in plants. These steps will also increase heritability estimation and prediction accuracy. This review focuses on the shift from conventional selection methods to GS, underlying statistical tools and methodologies, the state of GS research in agricultural plants, and prospects for its effective use in the creation of climate-resilient crops. [ABSTRACT FROM AUTHOR]

As an emulous alternative to deep neural networks, Deep Forest emerges with features like low complexity, fewer hyper-parameters, and good robustness, which are predominantly desired in distributed computing applications and ecosystems. Recently, an efficient distributed Deep Forest system, named ForestLayer, was proposed, designing a fine-grained sub-Forest-based task-parallel algorithm to improve the parallel computing efficiency of Deep Forest. However, the sub-Forest splitting of ForestLayer is static and one-off without adaptability to the computing environment, nevertheless, the size of splitting granularity has a significant impact on the system performance. To further improve the computing efficiency and scalability of the distributed Deep Forest, in this paper, we propose a novel distributed Deep Forest algorithm, named BLB-gcForest (Bag of Little Bootstraps-gcForest), which augments the gcForest (multi-Grained Cascade Forest) approach for constructing Deep Forest. BLB-gcForest carries out parallel computation for each tree in sub-Forests at a finer parallel granularity and integrates with the Bag of Little Bootstraps (BLB) mechanism to reduce massive transmitted feature instances for Cascade Forest Layers, utterly improving both computation efficiency and communication efficiency. Moreover, to solve the problem of the forest splitting granularity, we further design an adaptive sub-Forest splitting algorithm to ensure the maximum resource utilization for parallel computation of each sub-Forest. Experimental results on four well-known large-scale datasets, namely YEAST, LETTER, MNIST, CIFAR10, show that the training efficiency of BLB-gcForest achieves up to 20.3x and 1.64x speedups compared with the state-of-the-art gcForest and ForestLayer, respectively while guaranteeing higher accuracy and better robustness [ABSTRACT FROM AUTHOR]