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Summary: Given the escalating magnitude and intricacy of software systems, software measurement data often contains irrelevant and redundant features, resulting in significant resource and storage requirements for software defect prediction (SDP). Feature selection (FS) has a vital impact on the initial data preparation phase of SDP. Nonetheless, existing FS methods suffer from issues such as insignificant dimensionality reduction, low accuracy in classifying chosen optimal feature sets, and neglect of complex interactions and dependencies between defect data and features as well as between features and classes. To tackle the aforementioned problems, this paper proposes a many‐objective SDPFS (MOSDPFS) model and the binary many‐objective PSO algorithm with adaptive enhanced selection strategy (BMaOPSO‐AR2) is proposed within this paper. MOSDPFS selects F1 score, the number of features within subsets, and correlation and redundancy measures based on mutual information (MI) as optimization objectives. BMaOPSO‐AR2 constructs a binary version of MaOPSO using transfer functions specifically for binary classification. Adaptive update formulas and the introduction of the R2 indicator are employed to augment the variety and convergence of algorithm. Additionally, performance of MOSDPFS and BMaOPSO‐AR2 are tested on the NASA‐MDP and PROMISE datasets. Numerical results prove that a proposed model and algorithm effectively reduces feature count while enhancing predictive accuracy and minimizing model complexity. [ABSTRACT FROM AUTHOR]

Global climate change‐caused drought stress, high temperatures and other extreme weather profoundly impact plant growth and development, restricting sustainable crop production. To cope with various environmental stimuli, plants can optimize the opening and closing of stomata to balance CO2 uptake for photosynthesis and water loss from leaves. Guard cells perceive and integrate various signals to adjust stomatal pores through turgor pressure regulation. Molecular mechanisms and signaling networks underlying the stomatal movements in response to environmental stresses have been extensively studied and elucidated. This review focuses on the molecular mechanisms of stomatal movements mediated by abscisic acid, light, CO2, reactive oxygen species, pathogens, temperature, and other phytohormones. We discussed the significance of elucidating the integrative mechanisms that regulate stomatal movements in helping design smart crops with enhanced water use efficiency and resilience in a climate‐changing world. [ABSTRACT FROM AUTHOR]

Background: The overwhelming majority of subjects in the current silicosis mRNA and microRNA (miRNA) expression profile are of human blood, lung cells or a rat model, which puts limits on the understanding of silicosis pathogenesis and therapy. To address the limitations, our investigation was focused on differentially expressed mRNA and miRNA profiles in lung tissue from silicosis patients to explore potential biomarker for early detection of silicosis. Methods: A transcriptome study was conducted based on lung tissue from 15 silicosis patients and eight normal people, and blood samples from 404 silicosis patients and 177 normal people. Three early stage silicosis, five advanced silicosis and four normal lung tissues were randomly selected for microarray processing and analyze. The differentially expressed mRNAs were further used to conduct Gene Ontology and pathway analyses. Series test of cluster was performed to explore possible changes in differentially expressed mRNA and miRNA expression patterns during the process of silicosis. The blood samples and remaining lung tissues were used in a quantitative real‐time PCR (RT‐qPCR) (RT‐qPCR). Results: In total, 1417 and 241 differentially expressed mRNAs and miRNAs were identified between lung tissue from silicosis patients and normal people (p < 0.05). However, there was no significant difference in most mRNA or miRNA expression between early stage and advanced stage silicosis lung tissues. RT‐qPCR validation results in lung tissues showed expression of four mRNAs (HIF1A, SOCS3, GNAI3 and PTEN) and seven miRNAs was significantly down‐regulated compared to those of control group. Nevertheless, PTEN and GNAI3 expression was significantly up‐regulated (p < 0.001) in blood samples. The bisulfite sequencing PCR demonstrated that PTEN had significantly decreased the methylation rate in blood samples of silicosis patients. Conclusions: PTEN might be a potential biomarker for silicosis as a result of low methylation in the blood. [ABSTRACT FROM AUTHOR]

Premise: The ~140 species of Lonicera are characterized by variously fused leaves, bracteoles, and ovaries, making it a model system for studying the evolution and development of organ fusion. However, previous phylogenetic analyses, based mainly on chloroplast DNA markers, have yielded uncertain and conflicting results. A well‐supported phylogeny of Lonicera will allow us to trace the evolutionary history of organ fusion. Methods: We inferred the phylogeny of Lonicera using restriction site–associated DNA sequencing (RADSeq), sampling all major clades and 18 of the 23 subsections. This provided the basis for inferring the evolution of five fusion‐related traits. Results: RADSeq data yielded a well‐resolved and well‐supported phylogeny. The two traditionally recognized subgenera (Periclymenum and Chamaecerasus), three of the four sections (Isoxylosteum, Coeloxylosteum, and Nintooa), and half of the subsections sampled were recovered as monophyletic. However, the large and heterogeneous section Isika was strongly supported as paraphyletic. Nintooa, a clade of ~22 mostly vine‐forming species, including L. japonica, was recovered in a novel position, raising the possibility of cytonuclear discordance. We document the parallel evolution of fused leaves, bracteoles, and ovaries, with rare reversals. Most strikingly, complete cupules, in which four fused bracteoles completely enclose two unfused ovaries, arose at least three times. Surprisingly, these appear to have evolved directly from ancestors with free bracteoles instead of partial cupules. Conclusions: We provide the most comprehensive and well‐supported phylogeny of Lonicera to date. Our inference of multiple evolutionary shifts in organ fusion provides a solid foundation for in depth developmental and functional analyses. [ABSTRACT FROM AUTHOR]

Summary: Drought is a major environmental stress that threatens crop production. Therefore, identification of genes involved in drought stress response is of vital importance to decipher the molecular mechanism of stress signal transduction and breed drought tolerance crops, especially for maize.Clade A PP2C phosphatases are core abscisic acid (ABA) signaling components, regulating ABA signal transduction and drought response. However, the roles of other clade PP2Cs in drought resistance remain largely unknown. Here, we discovered a clade F PP2C, ZmPP84, that negatively regulates drought tolerance by screening a transgenic overexpression maize library.Quantitative RT‐PCR indicates that the transcription of ZmPP84 is suppressed by drought stress. We identified that ZmMEK1, a member of the MAPKK family, interacts with ZmPP84 by immunoprecipitation and mass spectrometry analysis. Additionally, we found that ZmPP84 can dephosphorylate ZmMEK1 and repress its kinase activity on the downstream substrate kinase ZmSIMK1, while ZmSIMK1 is able to phosphorylate S‐type anion channel ZmSLAC1 at S146 and T520 in vitro. Mutations of S146 and T520 to phosphomimetic aspartate could activate ZmSLAC1 currents in Xenopus oocytes.Taken together, our study suggests that ZmPP84 is a negative regulator of drought stress response that inhibits stomatal closure through dephosphorylating ZmMEK1, thereby repressing ZmMEK1–ZmSIMK1 signaling pathway. [ABSTRACT FROM AUTHOR]

Summary: The information society has higher and higher requirements for the collection, transmission and storage of digital signals, and signal utilization efficiency has become an increasingly important part of the digital signal processing process. The traditional digital signal processing needs to satisfy the Nyquist sampling theorem to ensure the restoration of the signal, while the digital signal processing method based on compressed sensing can sample and reconstruct the signal under the conditions that much lower than the Nyquist sampling theorem. Undoubtedly, the utilization efficiency of digital signals has been greatly accelerated. In this article, taking hyperspectral images and videos as examples, we review the basic theory, reconstruction model, and reconstruction algorithm of 3D data compressed sensing. We analyze, summarize, and discuss the existing literature. Finally, the research status of compressive sensing reconstruction methods for hyperspectral images and videos is compared, and several important research directions for compressive sensing reconstruction of 3D data in the future are proposed. [ABSTRACT FROM AUTHOR]

Premise: The inflorescences of Solanaceae are unique and complex, which has led to long‐standing disputes over floral symmetry mainly due to different interpretations of the cyme‐like inflorescence structure. The main disagreements have been over how the phyllomes associated with the flower were arranged relative to the inflorescence axis especially during early flower initiation. Methods: Here we investigated the evolution of inflorescences in Solanaceae by analyzing inflorescence structure in the context of phylogeny using ancestral state reconstruction (ASR) to determine the evolutionary transitions between loosely arranged and tightly clustered inflorescences and between monochasial‐like and dichasial‐like cymes. We also reconstructed two‐ and three‐dimensional models for 12 solanaceous species that represent both inflorescence and phylogenetic diversity in the family. Results: Our results indicate that the most recent common ancestor of Solanaceae had a loosely arranged and monochasial‐like cyme, while tightly clustered inflorescences and dichasial‐like cymes were derived. Compared to the known process of scorpioid cyme evolution, Solanaceae achieved their scorpioid cyme‐like inflorescences through a previously undescribed way. Along the pedicel, the two flower‐preceding prophylls are not in the typical transverse position of dicotyledonous plants; they frequently have axillary buds, and the main inflorescence axis continues in a sympodial fashion. As a result, the plane of symmetry of the flower is 36° from the median, and the inflorescence axis and the two flower‐preceding prophylls are symmetrically located along that plane. Conclusions: A better understanding of the morphological evolution of solanaceous inflorescence structure helped clarify the floral symmetry of Solanaceae. [ABSTRACT FROM AUTHOR]

Forests and grasslands play a significant role in global nitrous oxide (N2O) emission, while a sophisticated model is missing to interpret the microbial processes involved in N2O production and consumption in these ecosystems. A new N2O emission model, MicN model, was developed for natural forest and grassland ecosystems by integrating with Integrated BIosphere Simulator (IBIS). This new model includes four major N2O correlated processes: autotrophic nitrification, heterotrophic nitrification, nitrifier denitrification and denitrifier denitrification. Thirty‐nine observation sites, including various forest and grassland ecosystem types, were selected to calibrate and validate IBIS‐MicN model. The IBIS‐MicN model explained 89% and 85% of observed daily variability in N2O emission for all calibration and validation sites, respectively. In addition, the model successfully predicted the concentrations of two important intermediates (i.e., ammonium and nitrate) for N2O production at nine validation sites. Compared with Forest‐DNDC model, IBIS‐MicN model performed better in simulating N2O emission, soil NH4+ and NO3−concentrations. At site scale, the simulated N2O emissions by IBIS‐MicN model positively correlated with annual mean soil temperature and annual precipitation, but negatively correlated with soil pH. This study provides an alternative model to estimate global N2O emission from natural forest and grassland ecosystems and to investigate corresponding environmental regulations. Plain Language Summary: A new process‐based model (IBIS‐MicN model) was developed to estimate N2O emission from forest and grassland ecosystems. Four microbial processes, autotrophic nitrification, heterotrophic nitrification, nitrifier denitrification and denitrifier denitrification, were considered in the new model. In addition, IBIS‐MicN model depicted the related microbial dynamics and simulated intermediate products (i.e., ammonium and nitrate) of N2O production. The model reproduced the magnitude and seasonality of ammonium, nitrate and N2O emission at 39 global observation sites very well, which implying the robust model structure and performance for reproducing soil N2O emission. We then analyzed the response of N2O emissions to meteorological and environmental variables, including air temperature, precipitation and soil pH. Key Points: The new model simulated four major N2O related processes and the microbial dynamics related to N2O production and consumptionThe IBIS‐MicN model explained 89% and 85% of observed daily variability in N2O emission at calibration and validation sitesThe new model performed better than Forest‐DNDC model for reproducing the observed daily N2O emission, and concentration of NH4+ and NO3− at site‐scale [ABSTRACT FROM AUTHOR]