PoSnf1 affects cellulose utilization through interaction with cellobiose transporter in Pleurotus ostreatus.

Pleurotus ostreatus is one of the most cultivated edible fungi worldwide, but its lignocellulose utilization efficiency is relatively low (<50 %), which eventually affects the biological efficiency of P. ostreatus. Improving cellulase production and activity will contribute to enhancing the lignocellulose-degrading capacity of P. ostreatus. AMP-activated/Snf1 protein kinase plays important roles in regulating carbon and energy metabolism. The Snf1 homolog (PoSnf1) in P. ostreatus was obtained and analyzed using bioinformatics. The cellulose response of PoSnf1, the effect of the phosphorylation level of PoSnf1 on the expression of cellulose degradation-related genes, the putative proteins that interact with the phosphorylated PoSnf1 (P-PoSnf1), the cellobiose transport function of two sugar transporters (STP1 and STP2), and the interactions between PoSnf1 and STP1/STP2 were studied in this research. We found that cellulose treatment improved the phosphorylation level of PoSnf1, which further affected cellulase activity and the expression of most cellulose degradation-related genes. A total of 1, 024 proteins putatively interacting with P-PoSnf1 were identified, and they were enriched mainly in the substances transport and metabolism. Most of the putative cellulose degradation-related protein-coding genes could respond to cellulose. Among the P-PoSnf1-interacting proteins, the functions of two sugar transporters (STP1 and STP2) were further studied, and the results showed that both could transport cellobiose and were indirectly regulated by P-PoSnf1, and that STP2 could directly interact with PoSnf1. The results of this study indicated that PoSnf1 plays an important role in regulating the expression of cellulose degradation genes possibly by affecting cellobiose transport.

Deep Point Set Resampling via Gradient Fields.

3D point clouds acquired by scanning real-world objects or scenes have found a wide range of applications including immersive telepresence, autonomous driving, surveillance, etc. They are often perturbed by noise or suffer from low density, which obstructs downstream tasks such as surface reconstruction and understanding. In this paper, we propose a novel paradigm of point set resampling for restoration, which learns continuous gradient fields of point clouds that converge points towards the underlying surface. In particular, we represent a point cloud via its gradient field-the gradient of the log-probability density function, and enforce the gradient field to be continuous, thus guaranteeing the continuity of the model for solvable optimization. Based on the continuous gradient fields estimated via a proposed neural network, resampling a point cloud amounts to performing gradient-based Markov Chain Monte Carlo (MCMC) on the input noisy or sparse point cloud. Further, we propose to introduce regularization into the gradient-based MCMC during point cloud restoration, which essentially refines the intermediate resampled point cloud iteratively and accommodates various priors in the resampling process. Extensive experimental results demonstrate that the proposed point set resampling achieves the state-of-the-art performance in representative restoration tasks including point cloud denoising and upsampling.

Simultaneously genetic selection of wheat yield and grain protein quality in rice-wheat and soybean-wheat cropping systems through critical nitrogen efficiency-related traits.

Analyzing the contribution of nitrogen (N) uptake and its utilization in grain yield and protein quality-related traits in rice-wheat (RW) and soybean-wheat (SW) cropping systems is essential for simultaneous improvements in the two target traits. A field experiment with nine wheat genotypes was conducted in 2018-19 and 2019-20 cropping years to investigate N uptake and utilization-related traits associated with high wheat yield and good protein quality. Results showed that N uptake efficiency (NUpE) in the RW cropping system and N utilization efficiency (NUtE) in the SW cropping system explained 77.6 and 65.2% of yield variation, respectively, due to the contribution of fertile spikes and grain number per spike to grain yield varied depending on soil water and N availability in the two rotation systems. Lower grain protein content in the RW cropping system in comparison to the SW cropping system was mainly related to lower individual N accumulation at maturity, resulting from higher fertile spikes, rather than N harvest index (NHI). However, NHI in the SW cropping system accounted for greater variation in grain protein content. Both gluten index and post-anthesis N uptake were mainly affected by genotype, and low gluten index caused by high post-anthesis N uptake may be related to the simultaneous increase in kernel weight. N remobilization process associated with gluten quality was driven by increased sink N demand resulting from high grain number per unit area in the RW cropping system; confinement of low sink N demand and source capability resulted in low grain number per spike and water deficit limiting photosynthesis of flag leaf in the SW cropping system. CY-25 obtained high yield and wet gluten content at the expense of gluten index in the two wheat cropping systems, due to low plant height and high post-anthesis N uptake and kernel weight. From these results, we concluded that plant height, kernel weight, and post-anthesis N uptake were the critically agronomic and NUE-related traits for simultaneous selection of grain yield and protein quality. Our research results provided useful guidelines for improving both grain yield and protein quality by identifying desirable N-efficient genotypes in the two rotation systems.