In vivo antidiabetic activity of qwueous extract of Artemisia argyi (Chinese mugwort) in alloxan-induced diabetic rats

Purpose: To determine the antidiabetic, antioxidant and anti-hyperlipidemic effects of aqueous leaf extract of Artemisia argyi (Asteraceae) in alloxan (ALX)-induced diabetic rats. Experimental: Soxhlet apparatus was packed with grinded leaves of A. Argyi and subjected to extraction by double distillation using water as  running solvent for 4 – 5 h. Male albino Wistar rats weighing 150 ± 10 g were used in this study. Diabetes was induced in overnight-fasted rats via intraperitoneal administration of freshly prepared 10 % alloxan solution at a dose of 186.9 mg/kg. Serum glucose (Glc), high-density lipoprotein cholesterol (HDL-c), triglycerides (TGs) and total cholesterol (TC) were evaluated using Randox assay kits. Serum reduced glutathione (GSH) was assayed using a slight modification of a previously reproted procedure, while histological examination was carried out microscopically after hematoxylin and eosin staining. Results: Oral administration of aqueous extract of Artemisia argyi significantly reduced ALX-induced increases in glycosylated hemoglobin and blood glucose, but significantly increased total protein, hemoglobin, insulin, and C-peptide levels (p < 0.05). Administration of the extract also led to a significant upsurge in non-enzymic antioxidants i.e. ceruloplasmin, GSH, vitamin E and vitamin C. The extract produced a hypolipidemic effect by significantly reducing total cholesterol (TC) and serum TGs. The hypoglycemic and hypolipidemic effects of the extract were dose-dependent (p < 0.05). Histological examination of the pancreas revealed that the extract protected the integrity of beta cells in ALXinduced diabetic rats. Conclusion: These results indicate the beneficial effects of Artemisia argyi against diabetes mellitus. Thus, Artemisia argyi may be useful in the management of diabetes mellitus. Keywords: Artemisia argyi, Antidiabetic, Glutathione, Histopathology, Antioxidan

Pharmacological Study of Phenolic Components in Parkinson's Disease

In this study, cell experiments were conducted to investigate the effects of extracts on cell viability and apoptosis of Parkinson model in vitro, as well as the expression of cysteine protease-3 (Caspase-3) and B lymphocytoma-2-associated X protein (BAX). The results showed that extract of phenols could improve the loss of cell viability and apoptosis induced by MPP+, and inhibit the enhanced expression of Bax and Caspase-3 by MPP+. The potential targets and signaling pathways of phenols in the treatment of Parkinson's disease were predicted by network pharmacology

Efficient Multi-Scale Attention Module with Cross-Spatial Learning

Remarkable effectiveness of the channel or spatial attention mechanisms for producing more discernible feature representation are illustrated in various computer vision tasks. However, modeling the cross-channel relationships with channel dimensionality reduction may bring side effect in extracting deep visual representations. In this paper, a novel efficient multi-scale attention (EMA) module is proposed. Focusing on retaining the information on per channel and decreasing the computational overhead, we reshape the partly channels into the batch dimensions and group the channel dimensions into multiple sub-features which make the spatial semantic features well-distributed inside each feature group. Specifically, apart from encoding the global information to re-calibrate the channel-wise weight in each parallel branch, the output features of the two parallel branches are further aggregated by a cross-dimension interaction for capturing pixel-level pairwise relationship. We conduct extensive ablation studies and experiments on image classification and object detection tasks with popular benchmarks (e.g., CIFAR-100, ImageNet-1k, MS COCO and VisDrone2019) for evaluating its performance.Comment: Accepted to ICASSP202

ReST meets ReAct: Self-Improvement for Multi-Step Reasoning LLM Agent

Answering complex natural language questions often necessitates multi-step reasoning and integrating external information. Several systems have combined knowledge retrieval with a large language model (LLM) to answer such questions. These systems, however, suffer from various failure cases, and we cannot directly train them end-to-end to fix such failures, as interaction with external knowledge is non-differentiable. To address these deficiencies, we define a ReAct-style LLM agent with the ability to reason and act upon external knowledge. We further refine the agent through a ReST-like method that iteratively trains on previous trajectories, employing growing-batch reinforcement learning with AI feedback for continuous self-improvement and self-distillation. Starting from a prompted large model and after just two iterations of the algorithm, we can produce a fine-tuned small model that achieves comparable performance on challenging compositional question-answering benchmarks with two orders of magnitude fewer parameters.Comment: 19 pages, 4 figures, 4 tables, 8 listing

Climate warming accelerates temporal scaling of grassland soil microbial biodiversity.

Determining the temporal scaling of biodiversity, typically described as species-time relationships (STRs), in the face of global climate change is a central issue in ecology because it is fundamental to biodiversity preservation and ecosystem management. However, whether and how climate change affects microbial STRs remains unclear, mainly due to the scarcity of long-term experimental data. Here, we examine the STRs and phylogenetic-time relationships (PTRs) of soil bacteria and fungi in a long-term multifactorial global change experiment with warming (+3 °C), half precipitation (-50%), double precipitation (+100%) and clipping (annual plant biomass removal). Soil bacteria and fungi all exhibited strong STRs and PTRs across the 12 experimental conditions. Strikingly, warming accelerated the bacterial and fungal STR and PTR exponents (that is, the w values), yielding significantly (P < 0.001) higher temporal scaling rates. While the STRs and PTRs were significantly shifted by altered precipitation, clipping and their combinations, warming played the predominant role. In addition, comparison with the previous literature revealed that soil bacteria and fungi had considerably higher overall temporal scaling rates (w = 0.39-0.64) than those of plants and animals (w = 0.21-0.38). Our results on warming-enhanced temporal scaling of microbial biodiversity suggest that the strategies of soil biodiversity preservation and ecosystem management may need to be adjusted in a warmer world

Warming-induced permafrost thaw exacerbates tundra soil carbon decomposition mediated by microbial community.

BACKGROUND:It is well-known that global warming has effects on high-latitude tundra underlain with permafrost. This leads to a severe concern that decomposition of soil organic carbon (SOC) previously stored in this region, which accounts for about 50% of the world's SOC storage, will cause positive feedback that accelerates climate warming. We have previously shown that short-term warming (1.5 years) stimulates rapid, microbe-mediated decomposition of tundra soil carbon without affecting the composition of the soil microbial community (based on the depth of 42684 sequence reads of 16S rRNA gene amplicons per 3 g of soil sample). RESULTS:We show that longer-term (5 years) experimental winter warming at the same site altered microbial communities (p < 0.040). Thaw depth correlated the strongest with community assembly and interaction networks, implying that warming-accelerated tundra thaw fundamentally restructured the microbial communities. Both carbon decomposition and methanogenesis genes increased in relative abundance under warming, and their functional structures strongly correlated (R2 > 0.725, p < 0.001) with ecosystem respiration or CH4 flux. CONCLUSIONS:Our results demonstrate that microbial responses associated with carbon cycling could lead to positive feedbacks that accelerate SOC decomposition in tundra regions, which is alarming because SOC loss is unlikely to subside owing to changes in microbial community composition. Video Abstract

Targeted synthesis of an electroactive organic framework

A new strategy for targeted design and synthesis of an electroactive microporous organic molecular sieve (JUC-Z2) is described. Experiment demonstrated that such a targeted synthesis approach to achieve phenyl-phenyl coupling was a controllable process and predominately generated two-dimensional polymer sheets, significantly different from the traditional chemical or electrochemical oxidation methods to prepare conducting polymers. Successive self-assembly leads to a lamellar organic framework comprised of stacked polymer sheets with an hcb topology. JUC-Z2 was found to have a well-defined uniform micropore distribution (similar to 1.2 nm), a large surface area (BET = 2081 m(2) g(-1)) and high physicochemical stability (> 440 degrees C). After doping with I(2), JUC-Z2 exhibits typical p-type semiconductive properties. As the first example of an electroactive organic framework, JUC-Z2 possesses a unique ability of electrochemical ion recognition, arising from the synergistic function of the uniform micropores and the N-atom redox site.State Basic Research Project[2011CB808703]; NSFC[91022030, 20771041, 20773101, 20833005]; "111'' project[B07016]; Ministry of Science and Technology[2006DFA41190]; Jilin Science and Technology Department[20106021

Research on Intelligent Recognition and Classification Algorithm of Music Emotion in Complex System of Music Performance

In the complex system of music performance, there are differences in the expression of music emotions by listeners, so it is of great significance to study the classification of different emotions under different audio signals. In this paper, the research of human emotional intelligence recognition and classification algorithm in the complex system of music performance is proposed. Through the recognition of SVM, KNN, ANN, and ID3 classifiers, the accuracy of a single classifier is compared, and then the four classifiers are combined to compare the classification accuracy of audio signals before and after preprocessing. The results show that the accuracy of SVM and ANN fusion is the highest. Finally, recall and F1 are comprehensively compared in the fusion algorithm, and the fusion classification effect of SVM and ANN is better than that of the algorithm model

Effect of Microwave Treatment of Graphite on the Electrical Conductivity and Electrochemical Properties of Polyaniline/Graphene Oxide Composites

Polyaniline (PANI)/graphene oxide (GO) composites were synthesized via in situ polymerization of aniline in the presence of GO. The effect of microwave treatment of graphite on the electrical conductivity and electrochemical properties of PANI/GO composites was highlighted, and the morphology and microstructure were subsequently characterized using transmission electron microscopy, scanning electron microscopy, Fourier-transformed infrared spectroscopy, X-ray diffraction, and thermogravimetric analysis. The results demonstrated that microwave treatment of graphite imparted a well-dispersed, highly ordered layered structure to the as-prepared GO, and in turn facilitated strong bonding between the GO and PANI nanosheets, which may be responsible for the improved electrical conductivity and electrochemical properties of the resulting PANI/GO composites. The desired PANI/GO composites possessed an electrical conductivity of 508 S/m, an areal capacitance of 172.8 mF/cm2, and a retained capacitance of 87.4% after cycling, representing percentage increases of 102, 232, and 112, respectively, as a result of the microwave treatment of graphite. The resulting composites are promising electrode materials for high-performance and ecofriendly electrical energy storage devices