Quantifying chlorophyll content, an effective indicator of disease as well as nutritional and environmental stresses on plants, may enable optimal fertilization while managing crops. Hyperspectral remote-sensing is commonly used to estimate chlorophyll content. In this context, the process of variable selection is crucial since it is necessary to identify variables relevant to chlorophyll and eliminate redundant variables. In this study, 14 wavelength selection methods based on partial least squares (PLS; namely, backward variable elimination, backward and forward interval-PLS, competitive adaptive reweighted sampling, genetic algorithm, iterative predictive weighting, loading-weights, PLS with Martens' uncertainty test, regression coefficient, regularized elimination procedure, sparse-PLS, sub-window permutation analysis, uninformative variable elimination and variable importance in projection) were combined with one of five machine learning algorithms (Cubist, deep belief nets, random forests, stochastic gradient boosting and support vector machine) and then evaluated. According to the ratio of performance to deviation (RPD), the best combination of variable selection method and machine learning algorithm was regularized elimination procedure and Cubist achieving an RPD of 1.76 and an RMSE of 2.42 μg cm −2 .

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Background: An atypical B cell subset of age-associated B cells (ABCs) accumulate with age in mice and humans. ABCs acquire the ability to secrete antibodies and inflammatory cytokines in response to TLR7 and TLR9, and present antigens to T cells more efficiently than follicular (FO) B cells, which promotes the development of pathogenic phenotypes in aged individuals. Results:In this study, we demonstrated that actin cytoskeleton remodeling is enhanced in ABCs compared to FO B cells. ABCs showed higher motility across Transwell membranes and 3-dimensional (3D) collagen gels, without chemoattractants. Due to chemokine receptor expression remodeling, ABCs were attracted by CXCL12 and CCL21 rather than CXCL13. Among F-actin remodeling-related factors, expression levels of Fascin1 and Pak1 were increased in ABCs. In the presence of a Pak1 inhibitor, IPA3, migration of ABCs in both Transwell chambers and 3D-collagen gels was significantly attenuated. In contrast, a Fascin1 inhibitor, migrastatin, only reduced ABC migration in the 3D collagen gel. The ability to present antigens to T cells was also enhanced in ABCs, in which the Fascin1 mediated pathway is implicated. Conclusion: Increased expression of Fascin1 and Pak1 enhances actin cytoskeleton remodeling in ABCs, promoting motility and antigen presentation. With these phenotypes, ABCs efficiently uptake and present antigens to T cells, and disperse easily within secondary lymphoid tissues.

Biophysics and Physicobiologyhttps://www.jstage.jst.go.jp/browse/biophysico/

Summary Random gene trapping is the application of insertional mutagenesis techniques that are conventionally used to inactivate protein‐coding genes in mouse embryonic stem (ES) cells. Transcriptionally silent genes are not effectively targeted by conventional random gene trapping techniques, thus we herein developed an unbiased poly (A) trap (UPATrap) method using a Tol2 transposon, which preferentially integrated into active genes rather than silent genes in ES cells. To achieve efficient trapping at transcriptionally silent genes using random insertional mutagenesis in ES cells, we generated a new diphtheria toxin (DT)‐mediated trapping vector, DTrap that removed cells, through the expression of DT that was induced by the promoter activity of the trapped genes, and selected trapped clones using the neomycin‐resistance gene of the vector. We found that a double‐DT, the dDT vector, dominantly induced the disruption of silent genes, but not active genes, and showed more stable integration in ES cells than the UPATrap vector. The dDT vector disrupted differentiated cell lineage genes, which were silent in ES cells, and labeled trapped clone cells by the expression of EGFP upon differentiation. Thus, the dDT vector provides a systematic approach to disrupt silent genes and examine the cellular functions of trapped genes in the differentiation of target cells and development.