The cold responsive mechanism of the paper mulberry: decreased photosynthesis capacity and increased starch accumulation

Representative gel images of proteins from the control and treatment. 2-DE was performed using 800 μg of total protein and 11 cm immobilized dry strips with linear pH gradients from 4 to 7. Gels were stained with CBB R-250. Arrow indicates proteins significantly changing in abundance in comparison with control (ANOVA, p < 0.05). Circle indicates proteins appeared after treatment. (TIFF 4732 kb

Ribosylation Rapidly Induces α-Synuclein to Form Highly Cytotoxic Molten Globules of Advanced Glycation End Products

BACKGROUND: Alpha synuclein (alpha-Syn) is the main component of Lewy bodies which are associated with several neurodegenerative diseases such as Parkinson's disease. While the glycation with D-glucose that results in alpha-Syn misfold and aggregation has been studied, the effects of glycation with D-ribose on alpha-Syn have not been investigated. METHODOLOGY/PRINCIPAL FINDINGS: Here, we show that ribosylation induces alpha-Syn misfolding and generates advanced glycation end products (AGEs) which form protein molten globules with high cytotoxcity. Results from native- and SDS-PAGE showed that D-ribose reacted rapidly with alpha-Syn, leading to dimerization and polymerization. Trypsin digestion and sequencing analysis revealed that during ribosylation the lysinyl residues (K(58), K(60), K(80), K(96), K(97) and K(102)) in the C-terminal region reacted more quickly with D-ribose than those of the N-terminal region. Using Western blotting, AGEs resulting from the glycation of alpha-Syn were observed within 24 h in the presence of D-ribose, but were not observed in the presence of D-glucose. Changes in fluorescence at 410 nm demonstrated again that AGEs were formed during early ribosylation. Changes in the secondary structure of ribosylated alpha-Syn were not clearly detected by CD spectrometry in studies on protein conformation. However, intrinsic fluorescence at 310 nm decreased markedly in the presence of D-ribose. Observations with atomic force microscopy showed that the surface morphology of glycated alpha-Syn looked like globular aggregates. thioflavin T (ThT) fluorescence increased during alpha-Syn incubation regardless of ribosylation. As incubation time increased, ribosylation of alpha-Syn resulted in a blue-shift (approximately 100 nm) in the fluorescence of ANS. The light scattering intensity of ribosylated alpha-Syn was not markedly different from native alpha-Syn, suggesting that ribosylated alpha-Syn is present as molten protein globules. Ribosylated products had a high cytotoxicity to SH-SY5Y cells, leading to LDH release and increase in the levels of reactive oxygen species (ROS). CONCLUSIONS/SIGNIFICANCE: alpha-Syn is rapidly glycated in the presence of D-ribose generating molten globule-like aggregations which cause cell oxidative stress and result in high cytotoxicity

Adaptive Resource Management for Edge Network Slicing using Incremental Multi-Agent Deep Reinforcement Learning

Multi-access edge computing provides local resources in mobile networks as the essential means for meeting the demands of emerging ultra-reliable low-latency communications. At the edge, dynamic computing requests require advanced resource management for adaptive network slicing, including resource allocations, function scaling and load balancing to utilize only the necessary resources in resource-constraint networks. Recent solutions are designed for a static number of slices. Therefore, the painful process of optimization is required again with any update on the number of slices. In addition, these solutions intend to maximize instant rewards, neglecting long-term resource scheduling. Unlike these efforts, we propose an algorithmic approach based on multi-agent deep deterministic policy gradient (MADDPG) for optimizing resource management for edge network slicing. Our objective is two-fold: (i) maximizing long-term network slicing benefits in terms of delay and energy consumption, and (ii) adapting to slice number changes. Through simulations, we demonstrate that MADDPG outperforms benchmark solutions including a static slicing-based one from the literature, achieving stable and high long-term performance. Additionally, we leverage incremental learning to facilitate a dynamic number of edge slices, with enhanced performance compared to pre-trained base models. Remarkably, this approach yields superior reward performance while saving approximately 90% of training time costs