Monitoring plant diseases is essential for farmers to secure crop quantity and quality. Deep learning has recently been applied to plant disease recognition to help farmers take prompt and proper actions to prevent reductions in crop quantity and quality. Generally, deep learning requires a large‐scale dataset with supervised information annotated often by specialists. However, because collecting plant disease images in natural environments is difficult and obtaining proper annotations from specialists is costly, deep learning is infeasible for plant disease recognition tasks. Few‐shot learning (FSL) is an alternative for plant disease recognition using prior knowledge. Although FSL has attracted considerable attention, comprehensive reports on the application of FSL methods for plant disease recognition are required. Here, we introduce FSL with its applications in plant disease recognition. We begin with an overview of computer vision tasks using machine learning and FSL. We provide practical examples of FSL applications. Utilizing these practical examples, we describe different approaches for data augmentation and FSL methods of embedding, multitask learning, transfer learning, and meta‐learning. Further, we summarize how models are optimized for performance with reference to existing studies. Finally, the advantages and disadvantages are discussed, along with potential challenges for FSL applications in plant disease recognition.
Comparing a family structure to a company, one can often think of parents as leaders and adolescents as employees. Stressful family environments and anxiety levels, depression levels, personality disorders, emotional regulation difficulties, and childhood trauma may all contribute to non-suicidal self-injury (NSSI) behaviors. We presented a support vector machine (SVM) based method for discovering the key factors among mazy candidates that affected NSSI in adolescents. Using SVM as the base learner, and the binary dragonfly algorithm was used to find the feature combination that minimized the objective function, which took into account both the prediction error and the number of selected variables. Unlike univariate model analysis, we used a multivariate model to explore the risk factors, which better revealed the interactions between factors. Our research showed that adolescent education level, anxiety and depression level, borderline and avoidant personality traits, as well as emotional abuse and physical neglect in childhood, were associated with mood disorders in adolescents. Furthermore, gender, adolescent education level, physical abuse in childhood, non-acceptance of emotional responses, as well as paranoid, borderline, and histrionic personality traits, were associated with an increased risk of NSSI. These findings can help us make better use of artificial intelligence technology to extract potential factors leading to NSSI in adolescents from massive data, and provide theoretical support for the prevention and intervention of NSSI in adolescents.
RNA sequencing (RNA-seq) technology has now become one of the standard tools for studying biological mechanisms at the transcriptome level. Advances in RNA-seq technology have led to the emergence of a large number of publicly available tools for RNA-seq data analysis. Most of them target linear genome sequences although it is necessary to study organisms with circular genome sequences. For example, by studying the infection mechanisms of viroids which comprise 246–401 nucleotides circular RNAs and target plants, tremendous economic and agricultural damage may be prevented. Unfortunately, using the available tools to construct workflows for the analysis of circular genome sequences is difficult, especially for non-bioinformaticians. To overcome this limitation, we present CircSeqAlignTk, an easy-to-use and richly documented R package. CircSeqAlignTk performs end-to-end RNA-seq data analysis, from alignment to the visualization of circular genome sequences, through a series of functions. Additionally, it implements a function to generate synthetic sequencing data that mimics real RNA-seq data obtained from biological experiments. CircSeqAlignTk not only provides an easy-to-use analysis interface for novice users but also allows developers to evaluate the performance of alignment tools and new workflows.
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