Septum formation in fungi is equivalent to cytokinesis. It differs mechanistically in filamentous ascomycetes (Pezizomycotina) from that of ascomycete yeasts by the retention of a central septal pore in the former group. However, septum formation in both groups is accomplished by contractile actin ring (CAR) assembly and constriction. The specific components regulating septal pore organization during septum formation are poorly understood. In this study, a novel Pezizomycotina-specific actin regulatory protein GlpA containing gelsolin domains was identified using bioinformatics. A glpA deletion mutant exhibited increased distances between septa, abnormal septum morphology and defective regulation of septal pore closure. In glpA deletion mutant hyphae, overaccumulation of actin filament (F-actin) was observed, and the CAR was abnormal with improper assembly and failure in constriction. In wild-type cells, GlpA was found at the septum formation site similarly to the CAR.The N-terminal 329 residues of GlpA are required for its localization to the septum formation site and essential for proper septum formation, while its C-terminal gelsolin domains are required for the regular CAR dynamics during septum formation.Finally, in this study we elucidated a novel Pezizomycotina-specific actin modulating component, which participates in septum formation by regulating the CAR dynamics.

Considering the recent growth of experimentally determined structures, a broad survey and cumulative analysis of the sum of knowledge as presented in the membrane protein structure databases can be helpful to elucidate structures and functions of membrane proteins. We also aim to provide a framework for future research and classification of membrane proteins.

Multicellular filamentous fungi have septal pores that allow cytoplasmic exchange, and thus connectivity, between neighboring cells in the filament. Hyphal wounding and other stress conditions induce septal pore closure to minimize cytoplasmic loss. However, the composition of the septal pore and the mechanisms underlying its function are not well understood. Here, we set out to identify new septal components by determining the subcellular localization of 776 uncharacterized proteins in a multicellular ascomycete, Aspergillus oryzae. The set of 776 uncharacterized proteins was selected on the basis that their genes were present in the genomes of multicellular, septal pore-bearing ascomycetes (three Aspergillus species, in subdivision Pezizomycotina) and absent/divergent in the genomes of septal pore-lacking ascomycetes (yeasts). Upon determining their subcellular localization, 62 proteins were found to localize to the septum or septal pore. Deletion of the encoding genes revealed that 23 proteins are involved in regulating septal pore plugging upon hyphal wounding. Thus, this study determines the subcellular localization of many uncharacterized proteins in A. oryzae and, in particular, identifies a set of proteins involved in septal pore function.

Multicellular organisms exhibit cytoplasmic exchange using porous structures for cooperation among cells. Fungal multicellular lineages have evolved septal pores for this function. Interconnected hyphal cells possess the risk of wound-related cytoplasmic loss unless the septal pores are plugged. However, the gene evolution of regulatory mechanisms underlying fungal septal pore plugging remains poorly understood. To identify novel septal components, 776 uncharacterized proteins were identified using genomic comparisons between septal pore-bearing and -lacking ascomycete species. We then determined their subcellular localizations, and in total 62 proteins localized to the septum or septal pore. We analyzed the effects of deleting the encoding genes on septal pore plugging upon hyphal wounding. Of the 62 proteins, 23 were involved in regulating septal pore plugging. Here, using orthologous group and phylogenetic analyses, this study suggests that septal pore regulation has evolved either by co-option of preexisting genes or by Pezizomycotina-specific gene acquisition.

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.

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In the "worst-case" selection of hip prosthesis wear, it is necessary to calculate the contact stress of the acetabular liner. However, there are various combinations of acetabular prostheses. If calculated one by one, it will cause a large workload, a repeated and tedious calculation problem. To solve this problem, a machine learning prediction method by combining principal component analysis and support vector regressions (PCA-SVR) was established. First, the finite element method is used to analyze and calculate the contact stress of the acetabular liner in a typical combination to form a basic data set with the key size of the acetabular prosthesis as input and the contact stress as output; then, based on this data set, the PCA reduces the dimension of the input to obtain a new data set. Finally, based on this data set, SVR is used to establish the mapping model, and the optimal value of the model parameter C and c is obtained by combining K-fold cross-validation and grid search method. The maximum absolute error of the prediction on the test data set is only 0.1986, the root mean square error RMSE is only 0.09309, and the R 2 value is 0.9426, which verifies the effectiveness of the prediction model. At the same time, the prediction performance is compared with the Ridge regression and Lasso models, which further verifies the superiority of the proposed method.

Attachment of a myristoyl group to NH2-terminus of a nascent protein among protein post-translational modification (PTM) is called myristoylation. The myristate moiety of proteins plays an important role for their biological functions, such as regulation of membrane binding (HIV-1 Gag) and enzyme activity (AMPK). Several predictors based on protein sequences alone are hitherto proposed. However, they produce a great number of false positive and false negative predictions; or they cannot be used for general purpose (i.e., taxon-specific); or threshold values of the decision rule of predictors need to be selected with cautiousness. Here, we present novel and taxon-free predictors based on protein primary structure. To identify myristoylated proteins accurately, we employ a widely used machinelearning algorithm, support vector machine (SVM). A series of SVM predictors are developed in the present study where various scales representing physicochemical and biological properties of amino acids (from the AAindex database) are used for numerical transformation of protein sequences. Of the predictors, the top ten achieve accuracies of >98% (the average value is 98.34%), and also the area under the ROC curve (AUC) values of >0.98. Compared with those of previous studies, the prediction accuracies are improved by about 3 to 4%.