Integrated network analysis and metabolomics reveal the molecular mechanism of Yinchen Sini decoction in CCl4-induced acute liver injury

Objective: Yinchen Sini decoction (YCSND), a traditional Chinese medicine (TCM) formula, plays a crucial role in the treatment of liver disease. However, the bioactive constituents and pharmacological mechanisms of action remain unclear. The present study aimed to reveal the molecular mechanism of YCSND in the treatment of acute liver injury (ALI) using integrated network analysis and metabolomics.Methods: Ultra-high-performance liquid chromatography coupled with Q-Exactive focus mass spectrum (UHPLC-QE-MS) was utilized to identify metabolites in YCSND, and high-performance liquid chromatography (HPLC) was applied to evaluate the quality of four botanical drugs in YCSND. Cell damage and ALI models in mice were established using CCl4. 1H-NMR metabolomics approach, along with histopathological observation using hematoxylin and eosin (H&E), biochemical measurements, and reverse transcription quantitative real-time PCR (RT-qPCR), was applied to evaluate the effect of YCSND on CCl4- induced ALI. Network analysis was conducted to predict the potential targets of YCSND in ALI.Result: Our results showed that 89 metabolites in YCSND were identified using UHPLC-QE-MS. YCSND protected against ALI by reducing the levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST), and malondialdehyde (MDA) contents and increasing those of superoxide dismutase (SOD), and glutathione (GSH) both in vivo and in vitro. The 1H-NMRmetabolic pattern revealed that YCSND reversed CCl4-induced metabolic abnormalities in the liver. Additionally, the Kyoto Encyclopedia of Genes and Genome (KEGG) pathway enrichment analysis identified five pathways related to liver injury, including the PI3K-AKT, MAPK, HIF-1, apoptosis, and TNF signaling pathways. Moreover, RT-qPCR showed YCSND regulated the inflammatory response (Tlr4, Il6, Tnfα, Nfκb1, Ptgs2, and Mmp9) and apoptosis (Bcl2, Caspase3, Bax, and Mapk3), and inhibited PI3K-AKT signaling pathway (Pi3k and Akt1). Combined network analysis and metabolomics showed a link between the key targets (Tlr4, Ptgs2, and Mmp9) and vital metabolites (choline, xanthine, lactate, and 3-hydroxybutyric acid) of YCSND in ALI.Conclusion: Overall, the results contribute to the understanding of the therapeutic effects of YCSND on ALI, and indicate that the integrated network analysis and metabolomics could be a powerful strategy to reveal the pharmacological effects of TCM

GradAuto:Energy-oriented Attack on Dynamic Neural Networks

Dynamic neural networks could adapt their structures or parameters based on different inputs. By reducing the computation redundancy for certain samples, it can greatly improve the computational efficiency without compromising the accuracy. In this paper, we investigate the robustness of dynamic neural networks against energy-oriented attacks. We present a novel algorithm, named GradAuto, to attack both dynamic depth and dynamic width models, where dynamic depth networks reduce redundant computation by skipping some intermediate layers while dynamic width networks adaptively activate a subset of neurons in each layer. Our GradAuto carefully adjusts the direction and the magnitude of the gradients to efficiently find an almost imperceptible perturbation for each input, which will activate more computation units during inference. In this way, GradAuto effectively boosts the computational cost of models with dynamic architectures. Compared to previous energy-oriented attack techniques, GradAuto obtains the state-of-the-art result and recovers 100% dynamic network reduced FLOPs on average for both dynamic depth and dynamic width models. Furthermore, we demonstrate that GradAuto offers us great control over the attacking process and could serve as one of the keys to unlock the potential of the energy-oriented attack. Please visit https://github.com/JianhongPan/GradAuto for code

Effects of sea ice melt water input on phytoplankton biomass and community structure in the eastern Amundsen Sea

Sea ice melt water and circumpolar deep water (CDW) intrusion have important impacts on the ecosystem of the Amundsen Sea. In this study, samples of nutrients and phytoplankton pigments from nine stations in the eastern Amundsen Sea were collected during the austral summer. Based on in-situ hydrological observations, sea ice density data from satellite remote sensing, and chemical taxonomy calculations, the relationships between environmental factors and phytoplankton biomass and community structure were studied. The results showed that with increasing latitude, the contribution of sea ice melt water (MW%) and the stability of the water body increased, and the depth of the mixed layer (MLD) decreased. The integrated concentration of chlorophyll a (Chl-a) ranged from 21.4 mg·m−2 to 148.4 mg·m−2 (the average value was 35.7±53.4 mg·m−2). Diatoms (diatoms-A [Fragilariopsis spp., Chaetoceros spp., and Proboscia spp.] and diatoms-B [Pseudonitzschia spp.]) and Phaeocystis antarctica were the two most widely distributed phytoplankton groups and contributed 32%±16% and 28%±11%, respectively, of the total biomass. The contributions of Dinoflagellates, Chlorophytes, Cryptophytes, the high-iron group of P. antarctica, and Diatom group A were approximately 17%±8%, 15%±13%, 9%±6%, 5%±9%, and 3%±7%, respectively. The area with the highest phytoplankton biomass was located near the ice-edge region, with a short time lag (Tlag) between sampling and complete sea ice melt and a high MW%, while the area with the second-highest Chl-a concentration was located in the area affected by the upwelling of CDW, with thorough water mixing. Vertically, in the area with a short Tlag and a shallow MLD, the phytoplankton biomass and proportion of diatoms decreased rapidly with increasing water depth. In contrast, in the region with a long Tlag and limited CDW upwelling, the phytoplankton community was dominated by a relatively constant and high proportion of micro phytoplankton, and the phytoplankton biomass was low and relatively stable vertically. Generally, the phytoplankton community structure and biomass in the study area showed high spatial variation and were sensitive to environmental changes

Progressive Channel-Shrinking Network

Currently, salience-based channel pruning makes continuous breakthroughs in network compression. In the realization, the salience mechanism is used as a metric of channel salience to guide pruning. Therefore, salience-based channel pruning can dynamically adjust the channel width at run-time, which provides a flexible pruning scheme. However, there are two problems emerging: a gating function is often needed to truncate the specific salience entries to zero, which destabilizes the forward propagation; dynamic architecture brings more cost for indexing in inference which bottlenecks the inference speed. In this paper, we propose a Progressive Channel-Shrinking (PCS) method to compress the selected salience entries at run-time instead of roughly approximating them to zero. We also propose a Running Shrinking Policy to provide a testing-static pruning scheme that can reduce the memory access cost for filter indexing. We evaluate our method on ImageNet and CIFAR10 datasets over two prevalent networks: ResNet and VGG, and demonstrate that our PCS outperforms all baselines and achieves state-of-the-art in terms of compression-performance tradeoff. Moreover, we observe a significant and practical acceleration of inference. The code will be released upon acceptance.Ministry of Education (MOE)National Research Foundation (NRF)This work is supported by MOE AcRF Tier 2 (Proposal ID: T2EP20222-0035), National Research Foundation Singapore under its AI Singapore Programme (AISG-100E-2020-065), and SUTD SKI Project (SKI 2021 02 06). This work is also supported by TAILOR, a project funded by EU Horizon 2020 research and innovation programme under GA No 952215

Partitioning the contributions of glacier melt and precipitation to the 1971–2010 runoff increases in a headwater basin of the Tarim River

Glacier retreat and runoff increases in the last few decades characterize conditions in the Kumalak River Basin, which is a headwater basin of the Tarim River with a catchment area of 12,800 km2. To address the scientific question of whether, and to what extent, the observed runoff increase can be attributed to enhanced glacier melt and/or increased precipitation, a glacier evolution scheme and precipitation-runoff model are developed. Using the glacio-hydrological model, we find that both glacier cover area and glacier mass in the study area have decreased from 1971 to 2010. On average, the contribution to total runoff from rainfall, glacier melt and snowmelt are 60.6%, 28.2% and 11.2%, respectively. Despite covering only 21.3% of the basin area, glacier areas contributed 43.3% (including rainfall, snowmelt and glacier melt) to the total runoff from our model estimates. Furthermore, as primary causes of increased runoff in response to the warmer and wetter climate over the period 1971–2010, contribution from increases in rainfall and glacier melt are 56.7% and 50.6%, respectively. In comparison to rainfall and glacier melt, snowmelt has a minor influence on runoff increase, accounting for −7.3%. The research has important implications for water resources development in this arid region and for some similar river basins in which glacial melt forms an important part of the hydrological cycle

Water budget record from variable infiltration capacity (VIC) model

No abstract available

Editorial for the Special Issue “Remote Sensing of the Terrestrial Hydrologic Cycle”

To address global water security issues, it is important to understand the evolving global water system and its natural and anthropogenic influencing factors [...