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Traditional clustering algorithms are generally only suitable for static data processing，while the real world data are often large and changeable，so static clustering algorithms cannot provide the analysis and learning of evolution rules for dynamic data. On one hand，the clustering of evolutionary data needs to reflect the reasonable cluster partition of data at each snapshot；on the other hand，it needs to make sure the dynamic clustering results are as smooth as possible. This paper proposes an adaptive time-smoothed evolutionary clustering framework，which takes into account of the unknown relationship between the current data and the historical data. By imposing a time window for backtracking，it adaptively finds the most relevant historical snapshot to the current snapshot. Meanwhile，it fuses the static similarity based on the Itakura-Saito distance and the dynamic similarity based on the time series to compute，so as so compute the similarity matrix on each snapshot. Under this framework，this paper further proposes two adaptive time-smoothed evolutionary spectral clustering algorithms，which define the time cost from different aspects，and obtain different evolutionary clustering results. Experiments on real datasets show that the two proposed algorithms can effectively utilize historical data，and achieve better clustering performance as well as better temporal smoothness. [ABSTRACT FROM AUTHOR]

The non‑negative matrix factorization model is a common data dimensionality reduction method. In the existing non‑negative matrix factorization algorithm for clustering research，each category is generally represented by only one or more designated center points. However，this type of representation often fails to accurately describe the characteristics and structure of its category，which affects the clustering performance. In order to solve this problem，we proposed the peaks non‑negative matrix factorization（PNMF）algorithm. The algorithm first finds multiple density peak points for the dataset，constructs a bipartite graph of the density peak points and sample points，then uses the bipartite graph to complete the clustering. In addition， the algorithm introduces a manifold regularization term to make full use of the manifold structure information between the data，and gives the iterative update rules of the algorithm. Sufficient experiments on real‑world datasets demonstrate that the proposed method can effectively utilize the structural information of data and improve the clustering performance. [ABSTRACT FROM AUTHOR]

BACKGROUND: Studies have shown that formononetin possesses potent anti-inflammatory, oxidative stress and immunomodulatory properties. However, it is still unclear whether formononetin can regulate the oxidative stress induced by glucose oxidase in rat hepatic stellate cells. OBJECTIVE: To investigate the possible mechanism and the effect of formononetin on oxidative stress in rat hepatic stellate cells HSC-T6 induced by glucose oxidase. METHODS: The experiment contained control group, model group, low-, medium- and high-dose formononetin groups and curcumin group. The control group was not subjected to any treatment; the model group was treated with glucose oxidase for 2 hours; the formononetin and curcumin groups were treated with different concentrations of formononetin or curcumin for 24 hours, followed by glucose oxidase for 2 hours. CCK8 assay was used to investigate the effect of formononetin on HSC-T6 cell viability. Chemical kits were used to measure the contents of superoxide dismutase and malondialdehyde in each group. DCFH-DA fluorescence staining was performed to determine reactive oxygen species expression in each group. ELISA was performed to measure interleukin 1β and tumor necrosis factor α in each group. Immunofluorescence staining was used to detect the expression of type I collagen in each group. Western blot assay was used to measure the protein level of type I collagen, NOX4 and Nrf2 in each group. RESULTS AND CONCLUSION: (1) Compared with the model group, formononetin could inhibit the activation and proliferation of HSC-T6 cells (P < 0.05). (2) Compared with the control group, superoxide dismutase activity and Nrf2 protein expression of HSC-T6 cells in the model group were significantly downregulated (P < 0.05), while malondialdehyde, reactive oxygen species, interleukin 1β, tumor necrosis factor α, type I collagen and NOX4 protein expression levels were significantly up-regulated (P < 0.05). (3) Compared with the model group, superoxide dismutase activity and Nrf2 protein expression of HSC-T6 cells in formononetin treatment groups and curcumin group were significantly increased (P < 0.05), while malondialdehyde, reactive oxygen species, interleukin 1β, tumor necrosis factor α, type I collagen and NOX4 protein expression levels were markedly decreased (P < 0.05). (4) The results suggested that formononetin could inhibit glucose oxidase-induced activation and proliferation of hepatic stellate cells by regulating the Nrf2/NOX4 signaling pathway. [ABSTRACT FROM AUTHOR]

Abstract：OBJECTIVE: To explore the clinical efficacy and mechanism of Wenpi Huoxue decoction combined with 2HRZE/4HR regimen in the treatment of tuberculous pleurisy. METHODS: Sixty patients with wet tuberculous pleurisy in Hebei Provincial Chest Hospital were extracted, and the trial time of all patients was from Mar. 2020 to Mar. 2021. Patients who met the inclusion criteria were randomly divided into the observation group and control group, with 30 cases in each group. Both groups were treated with 2HRZE/4HR anti-tuberculosis regimen and regular thoracentesis, while the experimental group additionally received Wenpi Huoxue decoction. Both groups completed 6 months of treatment. The traditional Chinese medicine symptoms, peripheral blood CD3+, CD4+, CD8+ and CD4+/CD8+ ratio, pleural thickness, and pulmonary ventilatory function indexes [forced expiratory volume in one second（FEV1）, percentage of forced expiratory volume in first second to forced vital capacity（FVE1/FVC）], serum tumor necrosis factor α（TNF-α）, interleukin 2（IL-10）, interleukin 33（IL-33）, interferon γ（IFN-γ）, interferon γ-inducible protein 10（IP-10）, and monocyte chemotactic protein-1（MCP-1） of two groups were observed before treatment, after treatment of 1 month, after treatment of 2 months and after the end of treatment course. RESULTS: The total effective rate of the observation group was 96.67%（29/30）, significantly higher than 80.00%（24/30） of control group, the difference was statistically significant（P＜0.05）. After treatment of 1 month, after treatment of 2 months and after the end of treatment course, the scores of fever, chest pain, shortness of breath, cough, hemoptysis and total scores of traditional Chinese medicine symptoms in the observation group were significantly lower than those in control group（P＜0.05）; the CD3+, CD4+ and CD4+/CD8+ in the observation group were significantly higher than those in control group（P＜0.05）, while CD8+ was significantly lower than that in control group（P＜0.05）; the pleural thickness of the observation group was significantly lower than that of control group（P＜0.05）; the FEV1, FVE1/FVC in the observation group were significantly higher than those in control group（P＜0.05）; the levels of TNF-α, IL-2, IL-10, IFN-γ and MCP-1 in the observation group were significantly lower than those in control group（P＜0.05）, the differences were statistically significant. CONCLUSIONS: The efficacy of Wenpi Huoxue decoction combined with 2HRZE/4HR regimen in the treatment of tuberculous pleurisy is significant, which can effectively reduce the pleural thickness, reduce the inflammatory response, and improve the immune level. MoreReset [ABSTRACT FROM AUTHOR]

Vegetation plays an important role in regulating the terrestrial carbon balance and the climate system, and also overwhelmingly dominants the provisioning of ecosystem services. However, changes occurred on small temporal scales and the persistency or robustness of the changes was often not fully understood. Documentation of changes in vegetation over the most recent years is limited. These documented changes are especially important for policy development and ecosystem conservation and recovery. In this study, with GIMMS NDVI (1982-2006) and MODIS NDVI (2000-2012) datasets in Xinjiang, the spatio-temporal patterns of changes in monthly NDVI and their linkage with change in temperature, precipitation, evapotranspiration, humidity index and human activity were analyzed from March to November at regional and pixel scales. To detect the trend of NDVI during a given period, a least -squares linear regression was applied. To further explore the climatic factors driving NDVI change, interannual correlations between NDVI and climatic variables were calculated using Pearson correlation analysis. To analyze the temporal patterns and dynamic process, we estimated trends of NDVI and the correlation between NDVI and climatic factors over progressively longer periods of 25 to 31 years since 1982 and calculated the percentage of the area that showed a positive or negative trend in the seven nested time series. The monthly NDVI was significantly increased in all months besides November, but there were two distinct periods with opposite trends in most of these months, especially in June to September, from which it increased before 1998 or 1997 or decreased after 1998 or 1997. The trend of reversal of NDVI change also led to that the rate of NDVI increase was notably slowed or stopped as the NDVI record grew in an incremental length from 1982-2006, 1982-2007, ..., to 1982-2012 for March-October. At pixel scale, the areas with significantly change of NDVI in size highly significantly (P<0.01) increased during seven periods in most months, especially for those with a significant (P<0.05) decrease trend. The rate of increase in size of areas with significant decreasing NDVI was larger than that with significant increasing NDVI in all nine months. Consequently, this caused stall or slow increase of regional scale NDVI. The response of vegetation NDVI to climate change for different months was different. The response of NDVI in March-June and September-November was more sensitive to thermal indicator, such as temperature and evapotranspiration. The correlation between NDVI and precipitation and humidity index was stronger in July-August, and areas with significant (P<0.05) correlation were larger. Moreover, the effects of spring temperature on vegetation growth were more substantial at high elevations, such as Altai Mountains, Tianshan Mountains, than that at low elevation. In addition, the impact of climate on vegetation became more significant over a longer time scale. Meanwhile, change in NDVI was significantly (P<0.05) affected by human activities. The change of planting structure and farming methods were also the driving factors. The promotion of agricultural production such as the rapid increase in the proportion of cotton cultivation and the change from flood to drip irrigation may reduce March-May NDVI in some farmland areas. The difference in study time spanning generates different results. Trend analysis during the multiple nested time series may contribute to a better and thorough understanding of NDVI dynamic. Extending the time series as much as possible and focusing on the course of change are particularly important in studies that monitor vegetation dynamics and its relationship with climate change. [ABSTRACT FROM AUTHOR]