Liver disease is a global health problem that affects the well-being of tens of thousands of people. Dihydroquercetin (DHQ) is a flavonoid compound derived from various plants. Furthermore, DHQ has ...shown excellent activity in the prevention and treatment of liver injury, such as the inhibition of hepatocellular carcinoma cell proliferation after administration, the normalization of oxidative indices (like SOD, GSH) in this tissue, and the down-regulation of pro-inflammatory molecules (such as IL-6 and TNF-α). DHQ also exerts its therapeutic effects by affecting molecular pathways such as NF-κB and Nrf2. This paper discusses the latest research progress of DHQ in the treatment of various liver diseases (including viral liver injury, drug liver injury, alcoholic liver injury, non-alcoholic liver injury, fatty liver injury, and immune liver injury). It explores how to optimize the application of DHQ to improve its effectiveness in treating liver diseases, which is valuable for preparing potential therapeutic drugs for human liver diseases in conjunction with DHQ.
The Source Region of the Yellow River Basin (SRYRB), China
To improve daily runoff prediction accuracy in data-scarce areas, this study focuses on incorporating multiple grid-based data ...(precipitation, EVI, soil moisture (SM)) to drive the CNN-LSTM hybrid model. The spatial features of precipitation and underlying surface of the basin can be extracted by CNN, while the temporal features of the input data series can be captured by the LSTM. The hybrid model is compared with the single models (CNN, LSTM), and hybrid model performances under different driven data are also investigated.
Driven by the in-situ precipitation, grid-based precipitation (GPM) and SM data, the CNN-LSTM hybrid model achieved the best prediction result with NSE of 0.834, outperforming the single LSTM model (NSE=0.510) and the CNN model (NSE=0.612). It indicates that the hybrid model captures the spatiotemporal change features of precipitation and underlying surface of the basin. When using only GPM and SM data as input, the hybrid model achieved comparable result with NSE of 0.827. It implies that GPM could serve as a good alternative of in-situ precipitation and SM could provide additional value to improve prediction. This study highlights the value of using multiple grid-based data to drive the hybrid model, which provides new insights into runoff prediction in data-scarce regions.
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•Propose a novel CNN-LSTM hybrid model for daily runoff prediction.•The model performed best driven by rain gauge data, grid-based GPM and SM.•The model achieved comparable result driven only by grid-based GPM and SM.•GPM serves as good alternative to rain gauge data for runoff prediction.•Reanalysis-based soil moisture data enhanced prediction.
•A climate-adaptive transfer learning framework for soil moisture estimation is proposed.•The framework mainly uses ERA5-Land data, ISMN data, and global Köppen climate classification data.•The ...framework is designed for data-scarce region and performed well on the Qinghai-Tibet Plateau.•The framework can contribute to historical soil moisture data reconstruction.•A long-term (1960–2019) soil moisture dataset with accuracy improvement is produced.
Soil moisture (SM) plays essential roles in revealing complex interaction mechanisms among air–soil-water-plant processes. In the Qinghai-Tibet Plateau (QTP), the in-situ SM data is sparse and limited, satellite-based SM data has short period, while reanalysis SM data has advantages on long-term and high spatiotemporal resolution but has relatively high error. In this study, to improve soil moisture estimation in the QTP, we aim to propose a Climate-Adaptive Transfer Learning (CATL) framework for data scarce region based on reanalysis data (ERA5-LAND dataset) and the in-situ data (International Soil Moisture Network (ISMN) data). Specifically, regarding the QTP as the target region, selecting the areas with similar climate types with QTP as the source region, we train the CNN-LSTM fusion model in the source region and then transfer it to the target region via fine-tuning strategy. Results indicate that the produced soil moisture data based on CATL framework achieves CC of 0.755 and ubRMSE of 0.042, which has better quality than SMAPL3 during 2015–2019. Additionally, the CATL framework also produced the historical SM data reconstruction during 1960–2010, with CC increased by 11.3 % and ubRMSE reduced by 1.5 % compared with the original ERA5-Land reanalysis data. Furthermore, compared to the direct fine-tuning strategy (without climate adaptive), the CATL framework showed an increase of CC with 2.6 %, and decreases in RMSE, MAE, and ubRMSE of 5.3 %, 4.2 %, and 7.5 %, respectively. Finally, an improved soil moisture dataset (daily, 0.05°) ranging from 1960 to 2019 is produced for the QTP. This study provides a new tool for soil moisture estimation improvement in data-scarce region which will also benefit basin hydrology and water resources management.
Findings from graphical perception can guide visualization recommendation algorithms in identifying effective visualization designs. However, existing algorithms use knowledge from, at best, a few ...studies, limiting our understanding of how complementary (or contradictory) graphical perception results influence generated recommendations. In this paper, we present a pipeline of applying a large body of graphical perception results to develop new visualization recommendation algorithms and conduct an exploratory study to investigate how results from graphical perception can alter the behavior of downstream algorithms. Specifically, we model graphical perception results from 30 papers in Draco-a framework to model visualization knowledge-to develop new recommendation algorithms. By analyzing Draco-generated algorithms, we showcase the feasibility of our method to <xref ref-type="disp-formula" rid="deqn1">(1) identify gaps in existing graphical perception literature informing recommendation algorithms, <xref ref-type="disp-formula" rid="deqn2">(2) cluster papers by their preferred design rules and constraints, and <xref ref-type="disp-formula" rid="deqn3">(3) investigate why certain studies can dominate Draco's recommendations, whereas others may have little influence. Given our findings, we discuss the potential for mutually reinforcing advancements in graphical perception and visualization recommendation research.
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•The nanofiber membrane possesses a controllable drug release system.•The nanofiber membrane can act as a rapid hemostatic agent.•The nanofiber membrane can promote wound healing in ...T2D.
The healing of diabetic wounds has long been a significant challenge in the field of medicine. The elevated sugar levels surrounding diabetic wounds create a conducive environment for harmful bacterial growth, resulting in purulent infections that impede the healing process. Thus, the development of a biomaterial that can enhance the healing of diabetic wounds holds great importance. This study developed electrospun dressings for wound healing by combining traditional Chinese medicine and clay. The study utilized electrospinning technology to prepare polyvinyl alcohol (PVA) nanofiber membranes containing ASB and HNTs. These ASB@HNTs-PVA nanofiber membranes demonstrated rapid hemostasis, along with antibacterial and anti-inflammatory properties, facilitating the recovery of type 2 diabetic (T2D) wounds. Various analyses were conducted to assess the performance of the composite nanofiber membrane, including investigations into its biocompatibility and hemostatic abilities through antibacterial experiments, cell experiments, and mouse liver tail bleeding experiments. Western blot analysis confirmed that the composite nanofiber membrane could decrease the levels of inflammatory factors IL-1β and TNF-α. A type 2 diabetic mouse model was utilized, with wounds artificially induced on the backs of mice. Application of the nanofiber membrane to the wounds further confirmed its anti-inflammatory effects and ability to enhance wound healing in vivo.
•Local cooling is created outdoors by membrane-assisted radiant cooling panels.•Asymmetric radiant cooling helps offset thermal sensation to neutral zone.•The highest coolness appears in the body ...parts directly facing cooling surface.•Overall and local thermal sensation compared with UC Berkeley thermal comfort model.
The global warming and urban heat island effect call for mitigation strategies for improving thermal environments in open urban spaces. Radiant cooling can remove heat from the human body via direct thermal radiation, thereby creating the possibility to provide active cooling for people in outdoor environments. While using a transparent membrane, convection energy lost to the ambient airflow can be minimized. However, current research on the thermal comfort of radiant cooling systems is restricted to indoor applications, while outdoor applications remain unclear. To address this need, the study investigated the effect of membrane-assisted asymmetric radiant cooling on people's thermal perception in outdoor environments. A radiant cooling facility in an outdoor setting was built, and its cooling effects were assessed by using 248 human subjects across hot and transitional seasons. It is found that the radiant cooling facility can lower the mean thermal sensation vote (MTSV) by 0.6 to 1.5 units. The degree of cooling depends both on the panel temperature and environmental conditions. At the panel surface temperature of 14.3°C, MTSV was at the neutral zone (-0.5 ≤ MTSV ≤ 0.5) with the environmental UTCI as high as 38.1°C, whereas the thermal sensation of the group without radiant cooling was rated warm to hot. Under this condition, the ambient UTCI scope of no heat stress can be extended by 10.1°C higher. The strongest local cooling sensation appeared in the back, with an average decrease of 1.33 units in MTSV, contributing to lowering the overall thermal sensation of the body. For the first time, it is demonstrated that membrane-assisted radiant cooling panels can effectively improve thermal comfort in open outdoor settings.
Supporting the interactive exploration of large datasets is a popular and challenging use case for data management systems. Traditionally, the interface and the back-end system are built and ...optimized separately, and interface design and system optimization require different skill sets that are difficult for one person to master. To enable analysts to focus on visualization design, we contribute VegaPlus, a system that automatically optimizes interactive dashboards to support large datasets. To achieve this, VegaPlus leverages two core ideas. First, we introduce an optimizer that can reason about execution plans in Vega, a back-end DBMS, or a mix of both environments. The optimizer also considers how user interactions may alter execution plan performance, and can partially or fully rewrite the plans when needed. Through a series of benchmark experiments on seven different dashboard designs, our results show that VegaPlus provides superior performance and versatility compared to standard dashboard optimization techniques.
While many visualization specification languages are user-friendly, they tend to have one critical drawback: they are designed for small data on the client-side and, as a result, perform poorly at ...scale. We propose a system that takes declarative visualization specifications as input and automatically optimizes the resulting visualization execution plans by offloading computational-intensive operations to a separate database management system (DBMS). Our demo emphasizes live programming of visualizations over big data, enabling users to write or import Vega specifications, view the optimized plans from our system, and even modify these plans and compare their performance via a dedicated performance dashboard.
