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Abstract DANCE is the first standard, generic, and extensible benchmark platform for accessing and evaluating computational methods across the spectrum of benchmark datasets for numerous single-cell analysis tasks. Currently, DANCE supports 3 modules and 8 popular tasks with 32 state-of-art methods on 21 benchmark datasets. People can easily reproduce the results of supported algorithms across major benchmark datasets via minimal efforts, such as using only one command line. In addition, DANCE provides an ecosystem of deep learning architectures and tools for researchers to facilitate their own model development. DANCE is an open-source Python package that welcomes all kinds of contributions.

IntroductionCitrus is one of the most important fruit crops worldwide, and the root-associated microbiota can have a profound impact on tree health and growth.MethodsIn a collaborative effort, the International Citrus Microbiome Consortium investigated the global citrus root microbiota with samples collected from nine citrus-producing countries across six continents. We analyzed 16S rDNA and ITS2 amplicon sequencing data to identify predominant prokaryotic and fungal taxa in citrus root samples. Comparative analyses were conducted between root-associated microbial communities and those from the corresponding rhizosphere and bulk soil samples. Additionally, genotype-based group-wise comparisons were performed to assess the impact of citrus genotype on root microbiota composition.ResultsTen predominant prokaryotic phyla, containing nine bacterial phyla including Proteobacteria, Actinobacteria, Acidobacteria, and Bacteroidetes and one archaeal phylum (Thaumarchaeota), and multiple fungal phyla including Ascomycota and Basidiomycota were identified in the citrus root samples. Compared with the microbial communities from the corresponding rhizosphere and bulk soil samples from the same trees, the prokaryotic and fungal communities in the roots exhibited lower diversity and complexity but greater modularity compared to those in the rhizosphere. In total, 30 root-enriched and 150 root-depleted genera in bacterial community were identified, whereas 21 fungal genera were enriched, and 147 fungal genera were depleted in the root niche compared with the rhizosphere. The citrus genotype significantly affected the root prokaryotic and fungal communities. In addition, we have identified the core root prokaryotic genera comprising Acidibacter, Allorhizobium, Bradyrhizobium, Chitinophaga, Cupriavidus, Devosia, Dongia, Niastella, Pseudomonas, Sphingobium, Steroidobacter and Streptomyces, and the core fungal genera including Acrocalymma, Cladosporium, Fusarium, Gibberella, Mortierella, Neocosmospora and Volutella. The potential functions of these core genera of root microbiota were predicted.ConclusionOverall, this study provides new insights into the assembly of microbial communities and identifies core members of citrus root microbiota across a wide geographic range. The findings offer valuable information for manipulating root microbiota to enhance plant growth and health.

ObjectiveThe psychological problems among Chinese parents of special children (mental retardation, limb disorder, hearing impairment, autism, cerebral palsy and other types) should be paid more attention. The aim of this study was to investigate the association between anxiety, social support, coping style and sleep quality among Chinese parents of special children during the early COVID-19 epidemic, so as to provide more help for the mental health of parents of special children scientifically and effectively.MethodA total of 305 Chinese parents of special children were invited to accomplish four questionnaires. Anxiety was measured by the Self-Rating Anxiety Scale, social support was evaluated by the Perceived Social Support Scale, sleep quality was assessed by the Pittsburgh Sleep Quality Index, and coping style was measured by the Simplified Coping Style Questionnaire.ResultsThis study revealed that anxiety was positively correlated with sleep quality (p 0.05). Social support was positively correlated with coping style (p

Abstract Background Xanthomonas oryzae (Xo) is one of the important pathogenic bacterial groups affecting rice production. Its pathovars Xanthomonas oryzae pv. oryzae (Xoo) and Xanthomonas oryzae pv. oryzicola (Xoc) cause bacterial blight and bacterial leaf streak in rice, respectively. Xo infects host plants by relying mainly on its transcription activator-like effectors (TALEs) that bind to host DNA targets, named effector binding elements (EBEs), and induce the expression of downstream major susceptibility genes. Blocking TALE binding to EBE could increase rice resistance to the corresponding Xo. Findings We used CRISPR/Cas9 to edit the EBEs of three major susceptibility genes (OsSWEET11, OsSWEET14 and OsSULTR3;6) in the rice varieties Guihong 1 and Zhonghua 11. Both varieties have a natural one-base mutation in the EBE of another major susceptibility gene (OsSWEET13) which is not induced by the corresponding TALE. Two rice lines GT0105 (from Guihong 1) and ZT0918 (from Zhonghua 11) with target mutations and transgene-free were obtained and showed significantly enhanced resistance to the tested strains of Xoo and Xoc. Furthermore, under simulated field conditions, the morphology and other agronomic traits of GT0105 and ZT0918 were basically the same as those of the wild types. Conclusions In this study, we first reported that the engineering rice lines obtained by editing the promoters of susceptibility genes are resistant to Xoo and Xoc, and their original agronomic traits are not affected.

Research on plant root-associated microbial communities may help develop more efficient or sustainable crop production methods. Here the authors analyse the citrus rhizosphere microbiome, using both amplicon and deep shotgun metagenomic sequencing of samples collected across six continents.

The fast development of cloud technology has brought about a new trend in the field of information service: more and more information is being transferred to the cloud as requested. However, the data, such as texts, images, sounds, and videos, before being moved to the cloud, in most cases, has to be encrypted so that intelligible information will not be obtained from unauthorized accesses. While having done a nice work in protecting the data privacy of its owners, this encrypting process, has produced a great challenge for retrieval of the document stored via traditional IR model based on document, query and relevance. In order to retrieve encrypted information from cloud, an alternative retrieval system is needed. To satisfy such a need, we have: 1) build a cloud information retrieval framework characterized by its retrieval risk formula, which, enables, for the very first time to the best of our knowledge, an effective retrieval of keywords from encrypted cloud data without undermining key word privacy and retrieval performance; and 2) upgraded the existing searchable encryption scheme that can only support simple equality queries on encrypted data and has been proved to perform slightly better than random selection, so that it can now support the state-of-art information retrieval methods, such as vector space, probabilistic, and language model. To evaluate the effect of the system proposed above, we've conducted a wide range of experiments on benchmark data sets, of which the results shows that solution can fulfill its purposes quite well in various settings.