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

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

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

Warming stimulates cellulose decomposition by recruiting phylogenetically diverse but functionally similar microorganisms.

Cellulose is the most abundant component of plant litter, which is critical for terrestrial carbon cycling. Nonetheless, it remains unknown how global warming affects cellulose-decomposing microorganisms. Here, we carried out a 3-year litterbag experiment to examine cellulose decomposition undergoing +3°C warming in a tallgrass prairie. Most cellulose-associated bacteria and fungi in litterbags were also detected in bulk soil, and bacteria in litterbags had higher community-level rrn copy numbers, larger genome sizes, and higher genome guanine-cytosine (GC) contents than those in bulk soil, implying higher growth rates. Warming stimulated soil respiration by 32.3% and accelerated mass loss of cellulose, concurring with the increase in relative abundances of most functional genes associated with carbon decomposition in litterbags. Incorporating cellulose-decomposing genes into an ecosystem model reduced model parameter uncertainty and showed that warming stimulated microbial biomass, activity, and soil carbon decomposition. Collectively, our study supports a trait-centric view since cellulose-decomposing genes or genomic traits are amenable for ecosystem modeling. By characterizing the phylogenetically diverse yet functionally similar cellulose-associated microorganisms and their responses to warming, we take a step toward more precise predictions of soil carbon dynamics under future climate scenarios

Taxonomic and Functional Responses of Soil Microbial Communities to Annual Removal of Aboveground Plant Biomass

Clipping, removal of aboveground plant biomass, is an important issue in grassland ecology. However, few studies have focused on the effect of clipping on belowground microbial communities. Using integrated metagenomic technologies, we examined the taxonomic and functional responses of soil microbial communities to annual clipping (2010-2014) in a grassland ecosystem of the Great Plains of North America. Our results indicated that clipping significantly (P < 0.05) increased root and microbial respiration rates. Annual temporal variation within the microbial communities was much greater than the significant changes introduced by clipping, but cumulative effects of clipping were still observed in the long-term scale. The abundances of some bacterial and fungal lineages including Actinobacteria and Bacteroidetes were significantly (P < 0.05) changed by clipping. Clipping significantly (P < 0.05) increased the abundances of labile carbon (C) degrading genes. More importantly, the abundances of recalcitrant C degrading genes were consistently and significantly (P < 0.05) increased by clipping in the last 2 years, which could accelerate recalcitrant C degradation and weaken long-term soil carbon stability. Furthermore, genes involved in nutrient-cycling processes including nitrogen cycling and phosphorus utilization were also significantly increased by clipping. The shifts of microbial communities were significantly correlated with soil respiration and plant productivity. Intriguingly, clipping effects on microbial function may be highly regulated by precipitation at the interannual scale. Altogether, our results illustrated the potential of soil microbial communities for increased soil organic matter decomposition under clipping land-use practices

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

Belowground cross-trophic networks impact CH4 and CO2 emissions in degraded alpine peatlands

Belowground organisms forming complex cross-trophic ecological networks are essential for maintaining peatland carbon stability and energy flow. However, how peatland degradation affects the biodiversity and cross-trophic ecological networks of soil communities remains poorly understood. Here, we examined the degradation effects on soil prokaryotes (i.e., bacteria, archaea), fungi and nematodes in alpine peatlands on the eastern Tibetan Plateau, characterized by varying water table depths (indicating degradation levels). We found that peatland degradation, accompanied by significant shifts in soil moisture and pH (P < 0.05), reduced the taxonomic richness and phylogenetic diversity of prokaryotes, fungi, and nematodes, particularly in deeper soil layers (20–50 cm). Crucially, peatland degradation weakened potential cross-trophic interactions within bipartite networks of prokaryotes-nematodes and fungi-nematodes, resulting in less than 6.5 %–28.8 % of unchanged modules. Degradation-induced changes in soil moisture and pH were identified as primary drivers of biodiversity loss and network restructuring. Furthermore, such changes of belowground cross-trophic networks (particularly prokaryote-nematode) were significantly correlated with greenhouse gas emissions, such as decreased CO2 emissions, maintained CH4 emissions (leading to a higher CH4/CO2 ratio in deep layers), and reduced temperature sensitivity (Q10) of soil respiration. These findings underscore the critical need to protect soil biodiversity and cross-trophic networks in peatlands, particularly under the threat of climate change, to preserve peatland carbon stocks and maintain ecosystem stability. Our findings highlight that belowground cross-trophic networks are pivotal to decipher soil carbon dynamics of degraded peatlands and project the fate of peatland carbon stocks under future climate change scenarios