Effect of Spatial-Temporal Light Competition on Cotton Yield and Yield Distribution

The photosynthetically active radiation (PAR) of crop canopy is highly related to yield formation, but how it relates to yield and yield distribution is not well understood. The focus of this study was to explore the relationship between light competition under different densities and yield distributions of cotton. The experiment was conducted in 2019 and 2020 at the Cotton Research Institute of the Chinese Academy of Agricultural Sciences in Anyang city, Henan Province, China. A randomized block design was employed, with a total of three repeats. Each repeat had six density treatments: D1: 15,000;D2: 33,000;D3: 51,000;D4: 69,000;D5: 87,000;and D6: 105,000 plants·ha−1. As predicted, the results showed that the canopy light interception, leaf area index, plant height, and biomass of high-density cotton were higher than those of low-density cotton. The aboveground biomass produced by D6 was the highest, and was 12.9, 19.5, 25.4, 46.3, and 69.2% higher in 2019 and 14.3, 19.9, 32.5, 53.7, and 109.9% higher in 2020 than D5, D4, D3, D2, and D1, respectively. Leaf area, plant height, biomass, boll number, and boll weight were significantly correlated with the light interception rate. D5 (87,000 plants·ha−1) had a higher light interception rate and the highest yield. The highest lint yields produced by D5 were 1673.5 and 1375.4 kg·ha−1 in two years, and was 3.2, 4.3, 5.6, 9.7, and 24.7% higher in 2019, and 6.8, 10.6, 13.5, 21.5, and 34.4% higher in 2020 than D6, D4, D3, D2, and D1, respectively. The boll retention of the lower fruit branch under D5 reached 0.51 and 0.57 in two years, respectively. The shedding rate of the upper fruit branch decreased with the increase in cotton density in two years. The boll retention rate and shedding rate in the lower part of cotton plants were most closely related to light interception, with R2 values of 0.91 and 0.96, respectively. Our study shows cotton yield could be improved through higher light interception by optimizing planting density and canopy structure. [ FROM AUTHOR] Copyright of Agronomy is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Anxiety and associated factors among medical college students during COVID-19 epidemic

Objective: To analyze anxiety and its influencing factors of medical college students during the epidemic of COVID-19, so as to provide theoretical basis for targeted mental health promotion.

Calibrating multi-dimensional complex ODE from noisy data via deep neural networks (preprint)

Ordinary differential equations (ODEs) are widely used to model complex dynamics that arises in biology, chemistry, engineering, finance, physics, etc. Calibration of a complicated ODE system using noisy data is generally very difficult. In this work, we propose a two-stage nonparametric approach to address this problem. We first extract the de-noised data and their higher order derivatives using boundary kernel method, and then feed them into a sparsely connected deep neural network with ReLU activation function. Our method is able to recover the ODE system without being subject to the curse of dimensionality and complicated ODE structure. When the ODE possesses a general modular structure, with each modular component involving only a few input variables, and the network architecture is properly chosen, our method is proven to be consistent. Theoretical properties are corroborated by an extensive simulation study that demonstrates the validity and effectiveness of the proposed method. Finally, we use our method to simultaneously characterize the growth rate of Covid-19 infection cases from 50 states of the USA.

Conformational dynamics of SARS-CoV-2 trimeric spike glycoprotein in complex with receptor ACE2 revealed by cryo-EM (preprint)

The recent outbreaks of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its rapid international spread pose a global health emergency. The trimeric spike (S) glycoprotein interacts with its receptor human ACE2 to mediate viral entry into host-cells. Here we present cryo-EM structures of an uncharacterized tightly closed SARS-CoV-2 S-trimer and the ACE2-bound-S-trimer at 2.7-Å and 3.8-Å-resolution, respectively. The tightly closed S-trimer with inactivated fusion peptide may represent the ground prefusion state. ACE2 binding to the up receptor-binding domain (RBD) within S-trimer triggers continuous swing-motions of ACE2-RBD, resulting in conformational dynamics of S1 subunits. Noteworthy, SARS-CoV-2 S-trimer appears much more sensitive to ACE2-receptor than SARS-CoV S-trimer in terms of receptor-triggered transformation from the closed prefusion state to the fusion-prone open state, potentially contributing to the superior infectivity of SARS-CoV-2. We defined the RBD T470-T478 loop and residue Y505 as viral determinants for specific recognition of SARS-CoV-2 RBD by ACE2, and provided structural basis of the spike D614G-mutation induced enhanced infectivity. Our findings offer a thorough picture on the mechanism of ACE2-induced conformational transitions of S-trimer from ground prefusion state towards postfusion state, thereby providing important information for development of vaccines and therapeutics aimed to block receptor binding.Competing Interest StatementThe authors have declared no competing interest.View Full Text