Bract suppression regulated by the miR156/529-SPLs-NL1-PLA1 module is required for the transition from vegetative to reproductive branching in rice

Reproductive transition of grasses is characterized by switching the pattern of lateral branches,featuring the suppression of outgrowth of the subtending leaves(bracts)and rapid formation of higher-order branches in the inflorescence(panicle).However,the molecular mechanisms underlying such changes remain largely unknown.Here,we show that bract suppression is required for the reproductive branching in rice.We identified a pathway involving the intrinsic time ruler microRNAI56/529,their targets SQUAMOSA PROMOTER BINDING PROTEIN LIKE(SPL)genes,NECK LEAF1(NL1),and PLASTOCHRON1(PLA1),which regulates the bract outgrowth and thus affects the pattern switch between vegetative and reproductive branching.Suppression of the bract results in global reprogramming of transcriptome and chromatin accessibility following the reproductive transition,while these processes are largely dysregu-lated in the mutants of these genes.These discoveries contribute to our understanding of the dynamic plant architecture and provide novel insights for improving crop yields.

Cuprizone-induced demyelination in mice: age-related vulner-ability and exploratory behavior deficit

Schizophrenia is a mental disease that mainly affects young individuals （15 to 35 years old） but its etiology remains largely undefined. Recently, accumulating evidence indicated that demyelination and/or dysfunction of oligodendrocytes is an important feature of its pathogenesis. We hypothesized that the vulnerability of young individuals to demyelination may contribute to the onset of schizophrenia. In the present study, three different age cohorts of mice, i.e. juvenile （3 weeks）, young-adult （6 weeks） and middle-aged （8 months）, were subjected to a 6-week diet containing 0.2% cuprizone （CPZ） to create an animal model of acute demyelination. Then, age-related vulnerability to CPZ-induced demyelination, behavioral outcomes, and myelination-related molecular biological changes were assessed. We demonstrated： （1） CPZ treatment led to more severe demyelination in juvenile and young-adult mice than in middle-aged mice in the corpus callosum, a region closely associated with the pathophysiology of schizophrenia; （2） the higher levels of demyelination in juvenile and young-adult mice were correlated with a greater reduction of myelin basic protein, more loss of CC-1- positive mature oligodendrocytes, and higher levels of astrocyte activation; and （3） CPZ treatment resulted in a more prominent exploratory behavior deficit in juvenile and young-adult mice than in middle-aged mice. Together, our data demonstrate an age-relatedvulnerability to demyelination with a concurrent behavioral deficit, providing supporting evidence for better understanding the susceptibility of the young to the onset of schizophrenia.

Bacterial-fungal interactions under agricultural settings:from physical to chemical interactions

Bacteria and fungi are dominant members of environmental microbiomes.Various bacterial-fungal interactions(BFIs)and their mutual regulation are important factors for ecosystem functioning and health.Such interactions can be highly dynamic,and often require spatiotemporally resolved assessments to understand the interplay which ranges from antagonism to mutualism.Many of these interactions are still poorly understood,especially in terms of the underlying chemical and molecular interplay,which is crucial for inter-kingdom communication and interference.BFIs are highly relevant under agricultural settings;they can be determinative for crop health.Advancing our knowledge related to mechanisms underpinning the interactions between bacteria and fungi will provide an extended basis for biological control of pests and pathogens in agriculture.Moreover,it will facilitate a better understanding of complex microbial community networks that commonly occur in nature.This will allow us to determine factors that are crucial for community assembly under different environmental conditions and pave the way for constructing synthetic communities for various biotechnological applications.Here,we summarize the current advances in the field of BFIs with an emphasis on agriculture.

Anomaly Detection of Multivariate Time Series Based on Metric Learning

Most of the current methods for anomaly detection in time series are unsupervised.However,unsupervised learning assumes the distribution of the data and cannot obtain satisfactory results in some scenarios.In this paper,we design a semisupervised time series anomaly detection algorithm based on metric learning.The algorithm model mines the features in the time series from the perspectives of the time domain and frequency domain.Furthermore,we design a loss function for anomaly detection.Different from the two-class loss function,in the scenario of the loss function we designed,the normal data will be clustered and distributed in the embedding space,and the abnormal data will be far from the normal data distribution.Furthermore,we extend our designed metric learning model to a semisupervised learning model,extending the labeled dataset with the unlabeled dataset by setting different confidence levels.We conduct experiments on different public datasets and compare them with commonly used time series anomaly detection algorithms.The results show that our model has a good effect.At the same time the semisupervised setting does improve the accuracy of model detection.