Soybean is a major legume crop originating in temperate regions, and photoperiod responsiveness is a key factor in its latitudinal adaptation. Varieties from temperate regions introduced to lower latitudes mature early and have extremely low grain yields. Introduction of the long-juvenile (LJ) trait extends the vegetative phase and improves yield under short-day conditions, thereby enabling expansion of cultivation in tropical regions. Here we report the cloning and characterization of J, the major classical locus conferring the LJ trait, and identify J as the ortholog of Arabidopsis thaliana EARLY FLOWERING 3 (ELF3). J depends genetically on the legume-specific flowering repressor E1, and J protein physically associates with the E1 promoter to downregulate its transcription, relieving repression of two important FLOWERING LOCUS T (FT) genes and promoting flowering under short days. Our findings identify an important new component in flowering-time control in soybean and provide new insight into soybean adaptation to tropical regions.

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Objective: The 2019 coronavirus disease (COVID-19) epidemic has raised international concern.Mental health is becoming an issue that cannot be ignored in our fight against it. This study aimed to explore the prevalence and factors linked to anxiety and depression in hospitalized patients with COVID-19. Methods:A total of 144 patients diagnosed with COVID-19 were included in this study. We assessed depression and anxiety symptoms using the Hospital Anxiety and Depression Scale (HADS), and social support using the Perceived Social Support Scale (PSSS) among patients at admission. Multivariate linear regression analyses were performed to identify factors associated with symptoms of anxiety and depression.Results: Of the 144 participants, 34.72% and 28.47% patients with COVID-19 had symptoms of anxiety or depression, respectively. The bivariate correlations showed that less social support was correlated with more anxious (r=-0.196, p<0.05) and depressive (r=-0.360，p<0.05) symptoms All rights reserved. No reuse allowed without permission. : medRxiv preprint among patients with COVID-19. The multiple linear regression analysis showed that gender (β=1.446, p=0.034), age (β=0.074, p=0.003), oxygen saturation (β =-2.140, p=0.049), and social support (β =-1.545, p=0.017) were associated with anxiety for COVID-19 patients. Moreover, age (β=0.084, p=0.001), family infection with SARS-CoV-2 (β =1.515, p=0.027) and social support (β =-2.236, p＜0.001) were the factors associated with depression. Conclusion:Hospitalized patients with COVID-19 presented features of anxiety and depression.Mental concern and appropriate intervention are essential parts of clinical care for those who are at risk.

Illumina-based next generation sequencing (NGS) has accelerated biomedical discovery through its ability to generate thousands of gigabases of sequencing output per run at a fraction of the time and cost of conventional technologies. The process typically involves four basic steps: library preparation, cluster generation, sequencing, and data analysis. In 2015, a new chemistry of cluster generation was introduced in the newer Illumina machines (HiSeq 3000/4000/X Ten) called exclusion amplification (ExAmp), which was a fundamental shift from the earlier method of random cluster generation by bridge amplification on a non-patterned flow cell. The ExAmp chemistry, in conjunction with patterned flow cells containing nanowells at fixed locations, increases cluster density on the flow cell, thereby reducing the cost per run. It also increases sequence read quality, especially for longer read lengths (up to 150 base pairs). This advance has been widely adopted for genome sequencing because greater sequencing depth can be achieved for lower cost without compromising the quality of longer reads. We show that this promising chemistry is problematic, however, when multiplexing samples. We discovered that up to 5-10% of sequencing reads (or signals) are incorrectly assigned from a given sample to other samples in a multiplexed pool. We provide evidence that this "spreading-of-signals" arises from low levels of free index primers present in the pool. These index primers can prime pooled library fragments at random via complementary 3' ends, and get extended by DNA polymerase, creating a new library molecule with a new index before binding to the patterned flow cell to generate a cluster for sequencing. This causes the resulting read from that cluster to be assigned to a different sample, causing the spread of signals within multiplexed samples. We show that low levels of free index primers persist after the most common library purification procedure recommended by Illumina, and that the amount of signal spreading among samples is proportional to the level of free index primer present in the library pool. This artifact causes homogenization and misclassification of cells in single cell RNA-seq experiments. Therefore, all data generated in this way must now be carefully re-examined to ensure that "spreading-ofsignals" has not compromised data analysis and conclusions. Re-sequencing samples using an older technology that uses conventional bridge amplification for cluster generation, or improved library cleanup strategies to remove free index primers, can minimize or eliminate this signal spreading artifact.

BackgroundCoronavirus disease 2019 (COVID-19) has produced a significant health burden worldwide, especially in patients with cardiovascular comorbidities. The aim of this systematic review and meta-analysis was to assess the impact of underlying cardiovascular comorbidities and acute cardiac injury on in-hospital mortality risk.MethodsPubMed, Embase and Web of Science were searched for publications that reported the relationship of underlying cardiovascular disease (CVD), hypertension and myocardial injury with in-hospital fatal outcomes in patients with COVID-19. The ORs were extracted and pooled. Subgroup and sensitivity analyses were performed to explore the potential sources of heterogeneity.ResultsA total of 10 studies were enrolled in this meta-analysis, including eight studies for CVD, seven for hypertension and eight for acute cardiac injury. The presence of CVD and hypertension was associated with higher odds of in-hospital mortality (unadjusted OR 4.85, 95% CI 3.07 to 7.70; I2=29%; unadjusted OR 3.67, 95% CI 2.31 to 5.83; I2=57%, respectively). Acute cardiac injury was also associated with a higher unadjusted odds of 21.15 (95% CI 10.19 to 43.94; I2=71%).ConclusionCOVID-19 patients with underlying cardiovascular comorbidities, including CVD and hypertension, may face a greater risk of fatal outcomes. Acute cardiac injury may act as a marker of mortality risk. Given the unadjusted results of our meta-analysis, future research are warranted.