Background In late December, 2019, patients presenting with viral pneumonia due to an unidentified microbial agent were reported in Wuhan, China. A novel coronavirus was subsequently identified as the causative pathogen, provisionally named 2019 novel coronavirus (2019-nCoV). As of Jan 26, 2020, more than 2000 cases of 2019-nCoV infection have been confirmed, most of which involved people living in or visiting Wuhan, and human-to-human transmission has been confirmed. MethodsWe did next-generation sequencing of samples from bronchoalveolar lavage fluid and cultured isolates from nine inpatients, eight of whom had visited the Huanan seafood market in Wuhan. Complete and partial 2019-nCoV genome sequences were obtained from these individuals. Viral contigs were connected using Sanger sequencing to obtain the full-length genomes, with the terminal regions determined by rapid amplification of cDNA ends. Phylogenetic analysis of these 2019-nCoV genomes and those of other coronaviruses was used to determine the evolutionary history of the virus and help infer its likely origin. Homology modelling was done to explore the likely receptor-binding properties of the virus.Findings The ten genome sequences of 2019-nCoV obtained from the nine patients were extremely similar, exhibiting more than 99·98% sequence identity. Notably, 2019-nCoV was closely related (with 88% identity) to two bat-derived severe acute respiratory syndrome (SARS)-like coronaviruses, bat-SL-CoVZC45 and bat-SL-CoVZXC21, collected in 2018 in Zhoushan, eastern China, but were more distant from SARS-CoV (about 79%) and MERS-CoV (about 50%). Phylogenetic analysis revealed that 2019-nCoV fell within the subgenus Sarbecovirus of the genus Betacoronavirus, with a relatively long branch length to its closest relatives bat-SL-CoVZC45 and bat-SL-CoVZXC21, and was genetically distinct from SARS-CoV. Notably, homology modelling revealed that 2019-nCoV had a similar receptor-binding domain structure to that of SARS-CoV, despite amino acid variation at some key residues.Interpretation 2019-nCoV is sufficiently divergent from SARS-CoV to be considered a new human-infecting betacoronavirus. Although our phylogenetic analysis suggests that bats might be the original host of this virus, an animal sold at the seafood market in Wuhan might represent an intermediate host facilitating the emergence of the virus in humans. Importantly, structural analysis suggests that 2019-nCoV might be able to bind to the angiotensinconverting enzyme 2 receptor in humans. The future evolution, adaptation, and spread of this virus warrant urgent investigation.

BackgroundAn outbreak of dengue fever (DF) occurred in Guangdong Province, China in 2013 with the highest number of cases observed within the preceding ten years. DF cases were clustered in the Pearl River Delta economic zone (PRD) in Guangdong Province, which accounted for 99.6% of all cases in Guangdong province in 2013. The main vector in PRD was Aedes albopictus. We investigated the socioeconomic and environmental factors at the township level and explored how the independent variables jointly affect the DF epidemic in the PRD.Methodology/Principal FindingsSix factors associated with the incidence of DF were identified in this project, representing the urbanization, poverty, accessibility and vegetation, and were considered to be core contributors to the occurrence of DF from the perspective of the social economy and the environment. Analyses were performed with Generalized Additive Models (GAM) to fit parametric and non-parametric functions to the relationships between the response and predictors. We used a spline-smooth technique and plotted the predicted against the observed co-variable value. The distribution of DF cases was over-dispersed and fit the negative binomial function better. The effects of all six socioeconomic and environmental variables were found to be significant at the 0.001 level and the model explained 45.1% of the deviance by DF incidence. There was a higher risk of DF infection among people living at the prefectural boundary or in the urban areas than among those living in other areas in the PRD. The relative risk of living at the prefectural boundary was higher than that of living in the urban areas. The associations between the DF cases and population density, GDP per capita, road density, and NDVI were nonlinear. In general, higher “road density” or lower “GDP per capita” were considered to be consistent risk factors. Moreover, higher or lower values of “population density” and “NDVI” could result in an increase in DF cases.ConclusionIn this study, we presented an effect analysis of socioeconomic and environmental factors on DF occurrence at the smallest administrative unit (township level) for the first time in China. GAM was used to effectively detect the nonlinear impact of the predictors on the outcome. The results showed that the relative importance of different risk factors may vary across the PRD. This work improves our understanding of the differences and effects of socioeconomic and environmental factors on DF and supports effectively targeted prevention and control measures.

Stalk lodging in maize causes annual yield losses between 5 and 20% worldwide. Many studies have indicated that maize stalk strength significantly negatively correlates with lodging observed in the field. Rind penetrometer resistance (RPR) measurements can be used to effectively evaluate maize stalk strength, but little is known about the genetic basis of this parameter. The objective of this study was to explore a genetic model and detect quantitative trait loci (QTL) of RPR and determine relationships between RPR and other stalk traits, especially cell wall chemical components. RPR is quantitative trait in nature, and both additive and non-additive effects may be important to consider for the improvement of RPR. Nine additive-effect QTLs covering nine chromosomes, except chromosome 5, and one pair of epistatic QTLs were detected for RPR. CeSA11 involved in cellulose synthesis and colorless2 involved in lignin synthesis were identified as possible candidate genes for RPR. Internode diameter (InD), fresh weight of internode (FreW), dry weight of internode (DryW), fresh weight and dry weight as well as cell wall components per unit volume significantly positively correlated with RPR. The internode water content (InW) significantly negatively correlated with RPR. Notably, these traits significantly correlated with RPR, and the QTLs of these traits co-localized with those of RPR. The corresponding results obtained from correlation analysis and QTL mapping suggested the presence of pleitropism or linkage between genes and indicated that these different approaches may be used for cross authentication of relationships between different traits.

Background. During the outbreak of coronavirus disease 2019 (COVID-19), people are under the dual pressure of interpersonal isolation and concerns about infection. An evaluation of people’s psychological status and risk factors is needed to conduct target interventions. Methods. This was a nationwide, multicenter, cross-sectional study using quota and snowball sampling methods during the COVID-19 epidemic in China. Participants’ characteristics and experiences were obtained by an online questionnaire and telephone review. Psychological distress and sleep problems were measured by the Generalized Anxiety Disorder-7, the Patient Health Questionnaire-9, and the Insomnia Severity Index. Results. A total of 23,500 participants were recruited, and 19,372 valid questionnaires were received from 11 centers. Overall, 11.0–13.3% of the participants had anxiety, depression, or insomnia symptoms, and 1.9–2.7% had severe symptoms. The prevalence of psychological and sleep problems has increased. Working as frontline medical staff (Odds Ratio OR = 3.406), living in Hubei Province (OR = 2.237), close contacts with COVID-19 (OR = 1.808), and age 35–49 years (OR = 1.310) were risk factors for anxiety symptoms; no outside activity for 2 weeks (OR = 2.167) and age 35–49 years (OR = 1.198) were risk factors for depression symptoms; and living in Hubei Province (OR = 2.376), no outside activity for 2 weeks (OR = 1.927), and age 35–49 years (OR = 1.262) were risk factors for insomnia symptoms. Only 1.9% of participants received counseling during the epidemic. Conclusions. Psychological and sleep problems increased during interpersonal isolation due to COVID-19. Current psychological interventions are far from sufficient.

Background Microbial acquisition and development of the gut microbiota impact the establishment of a healthy host-microbes symbiosis. Compared with other animals, the eusocial bumblebees and honeybees possess a simple, recurring, and similar set of gut microbiota. However, all bee gut phylotypes have high strain-level diversity. Gut communities of different bee species are composed of host-specific groups of strains. The variable genomic regions among strains of the same species often confer critical functional differences, such as carbon source utilization, essential for the natural selection of specific strains. The annual bumblebee colony founded by solitary queens enables tracking the transmission routes of gut bacteria during development stages. Results Here, we first showed the changes in the microbiome of individual bumblebees across their holometabolous life cycle. Some core gut bacteria persist throughout different stages of development. Gut microbiota of newly emerged workers always resembles those of their queens, suggesting a vertical transmission of strains from queens to the newborn workers. We then follow the dynamic changes in the gut community by comparing strain-level metagenomic profiles of queen-worker pairs longitudinally collected across different stages of the nest development. Species composition of both queen and worker shifts with the colony’s growth, and the queen-to-worker vertical inheritance of specific strains was identified. Finally, comparative metagenome analysis showed clear host-specificity for microbes across different bee hosts. Species from honeybees often possess a higher level of strain variation, and they also exhibited more complex gene repertoires linked to polysaccharide digestion. Our results demonstrate bacterial transmission events in bumblebee, highlighting the role of social interactions in driving the microbiota composition. Conclusions By the community-wide metagenomic analysis based on the custom genomic database of bee gut bacteria, we reveal strain transmission events at high resolution and the dynamic changes in community structure along with the colony development. The social annual life cycle of bumblebees is key for the acquisition and development of the gut microbiota. Further studies using the bumblebee model will advance our understanding of the microbiome transmission and the underlying mechanisms, such as strain competition and niche selection.