Psychological Stress Alters Ultrastructure and Energy Metabolism of Masticatory Muscle in Rats

To investigate the effects of psychological stress on the masticatory muscles of rats, a communication box was applied to induce the psychological stress (PS) in rats. The successful establishment of psychological stimulation was confirmed by elevated serum levels of adrenocorticotropic hormone (ACTH) and changed behaviors in the elevated plusmaze apparatus. The energy metabolism of the bilateral masseter muscles was tested via chemocolorimetric analysis, whereas muscle ultrastructure was assessed by electron microscopy. In comparison to the control group, the PS group showed evidence of swollen mitochondria with cristae loss and reduced matrix density in the masticatory muscles after three weeks of stimulation; after five weeks of stimulation, severe vacuolar changes to the mitochondria were observed. Increased vascular permeability of the masticatory muscle capillaries was found in the five-week PS rats. In addition, there was decreased activity of Na+-K+ATPase and Ca2+-ATPase and a simultaneous increase in the activity of lactate dehydrogenase and lactic acid in the masticatory muscles of PS rats. Together, these results indicate that psychological stress induces alterations in the ultrastructure and energy metabolism of masticatory muscles in rats

Release of Danger Signals during Ischemic Storage of the Liver: A Potential Marker of Organ Damage?

Liver grafts suffer from unavoidable injury due to ischemia and manipulation before implantation. Danger signals such as high-mobility group box -1(HMGB1) and macrophage migration inhibitory factor (MIF) play a pivotal role in the immune response. We characterized the kinetics of their release into the effluent during cold/warm ischemia and additional manipulation-induced mechanical damage. Furthermore, we evaluated the relationship between HMGB1/MIF release and ischemic/mechanical damage. Liver enzymes and protein in the effluent increased with increasing ischemia time. HMGB1/MIF- release correlated with the extent of hepatocellular injury. With increasing ischemia time and damage, HMGB1 was translocated from the nucleus to the cytoplasma as indicated by weak nuclear and strong cytoplasmic staining. Enhancement of liver injury by mechanical damage was indicated by an earlier HMGB1 translocation into the cytoplasm and earlier release of danger signals into the effluent. Our results suggest that determination of HMGB1 and MIF reflects the extent of ischemic injury. Furthermore, HMGB1and MIF are more sensitive than liver enzymes to detect the additional mechanical damage inflicted on the organ graft during surgical manipulation

Fingerprint and multi-component quantitative analyses for quality evaluation of Rhizoma coptidis steamed with rice wine

Purpose: To establish a method for the simultaneous determination of multi-components of Rhizoma coptidis steamed with rice wine (RCRW), and to provide a reference for assessing its standard of quality. Method: Chromatographic separation was performed on a high performance liquid chromatography (HPLC) system to determine the characteristic fingerprint of RCRW. The mobile phase consisted of acetonitrile (A) and 0.1 % trifluoroacetic acid (B), with gradients of B as follows: 15 - 20 % from 0 – 30 min; 20 - 25 % from 30 - 50 min; 25 - 35 % for 50 - 60 min, and 35 % for 60 - 70 min. Results: In the multiple reaction monitoring mode, eight components of RCRW were isolated by HPLCphoto-diode array (PDA) method. A fingerprint of the RCRW was established and 8 peaks were calibrated. The method was further validated in terms of linearity (R2 > 0.9993), precision (relative standard deviation, RSD < 1.51 %); repeatability (RSD < 2.98 %) and stability (RSD < 1.93 %). Mean recovery rate ranged from 96.2 to 103.8 %, while RSD values ranged from 0.92 to 2.88 %. Conclusion: These results show that HPLC-PDA method is accurate and feasible, and that they provide a reference for further comprehensive and effective quality control of RCRW

Metatranscriptomics Reveals the Functions and Enzyme Profiles of the Microbial Community in Chinese Nong-Flavor Liquor Starter

Chinese liquor is one of the world's best-known distilled spirits and is the largest spirit category by sales. The unique and traditional solid-state fermentation technology used to produce Chinese liquor has been in continuous use for several thousand years. The diverse and dynamic microbial community in a liquor starter is the main contributor to liquor brewing. However, little is known about the ecological distribution and functional importance of these community members. In this study, metatranscriptomics was used to comprehensively explore the active microbial community members and key transcripts with significant functions in the liquor starter production process. Fungi were found to be the most abundant and active community members. A total of 932 carbohydrate-active enzymes, including highly expressed auxiliary activity family 9 and 10 proteins, were identified at 62°C under aerobic conditions. Some potential thermostable enzymes were identified at 50, 62, and 25°C (mature stage). Increased content and overexpressed key enzymes involved in glycolysis and starch, pyruvate and ethanol metabolism were detected at 50 and 62°C. The key enzymes of the citrate cycle were up-regulated at 62°C, and their abundant derivatives are crucial for flavor generation. Here, the metabolism and functional enzymes of the active microbial communities in NF liquor starter were studied, which could pave the way to initiate improvements in liquor quality and to discover microbes that produce novel enzymes or high-value added products

RACE: An Efficient Redundancy-aware Accelerator for Dynamic Graph Neural Network

Dynamic Graph Neural Network (DGNN) has recently attracted a significant amount of research attention from various domains, because most real-world graphs are inherently dynamic. Despite many research efforts, for DGNN, existing hardware/software solutions still suffer significantly from redundant computation and memory access overhead, because they need to irregularly access and recompute all graph data of each graph snapshot. To address these issues, we propose an efficient redundancy-aware accelerator, RACE, which enables energy-efficient execution of DGNN models. Specifically, we propose a redundancy-aware incremental execution approach into the accelerator design for DGNN to instantly achieve the output features of the latest graph snapshot by correctly and incrementally refining the output features of the previous graph snapshot and also enable regular accesses of vertices\u27 input features. Through traversing the graph on the fly, RACE identifies the vertices that are not affected by graph updates between successive snapshots to reuse these vertices\u27 states (i.e., their output features) of the previous snapshot for the processing of the latest snapshot. The vertices affected by graph updates are also tracked to incrementally recompute their new states using their neighbors\u27 input features of the latest snapshot for correctness. In this way, the processing and accessing of many graph data that are not affected by graph updates can be correctly eliminated, enabling smaller redundant computation and memory access overhead. Besides, the input features, which are accessed more frequently, are dynamically identified according to graph topology and are preferentially resident in the on-chip memory for less off-chip communications. Experimental results show that RACE achieves on average 1139× and 84.7× speedups for DGNN inference, with average 2242× and 234.2× energy savings, in comparison with the state-of-the-art software DGNN running on Intel Xeon CPU and NVIDIA A100 GPU, respectively. Moreover, for DGNN inference, RACE obtains on average 13.1×, 11.7×, 10.4×, and 7.9× speedup and 14.8×, 12.9×, 11.5×, and 8.9× energy savings over the state-of-the-art Graph Neural Network accelerators, i.e., AWB-GCN, GCNAX, ReGNN, and I-GCN, respectively