Variance-Reduced Stochastic Learning by Networked Agents under Random Reshuffling

A new amortized variance-reduced gradient (AVRG) algorithm was developed in \cite{ying2017convergence}, which has constant storage requirement in comparison to SAGA and balanced gradient computations in comparison to SVRG. One key advantage of the AVRG strategy is its amenability to decentralized implementations. In this work, we show how AVRG can be extended to the network case where multiple learning agents are assumed to be connected by a graph topology. In this scenario, each agent observes data that is spatially distributed and all agents are only allowed to communicate with direct neighbors. Moreover, the amount of data observed by the individual agents may differ drastically. For such situations, the balanced gradient computation property of AVRG becomes a real advantage in reducing idle time caused by unbalanced local data storage requirements, which is characteristic of other reduced-variance gradient algorithms. The resulting diffusion-AVRG algorithm is shown to have linear convergence to the exact solution, and is much more memory efficient than other alternative algorithms. In addition, we propose a mini-batch strategy to balance the communication and computation efficiency for diffusion-AVRG. When a proper batch size is employed, it is observed in simulations that diffusion-AVRG is more computationally efficient than exact diffusion or EXTRA while maintaining almost the same communication efficiency.Comment: 23 pages, 12 figures, submitted for publicatio

A well-balanced lattice Boltzmann model for binary fluids based on the incompressible phase-field theory

Spurious velocities arising from the imperfect offset of the undesired term at the discrete level are frequently observed in numerical simulations of equilibrium multiphase flow systems using the lattice Boltzmann equation (LBE) method. To capture the physical equilibrium state of two-phase fluid systems and eliminate spurious velocities, a well-balanced LBE model based on the incompressible phase-field theory is developed. In this model, the equilibrium distribution function for the Cahn-Hilliard (CH) equation is designed by treating the convection term as a source to avoid the introduction of undesired terms, enabling achievement of possible discrete force balance. Furthermore, this approach allows for the attainment of a divergence-free velocity field, effectively mitigating the impact of artificial compression effects and enhancing numerical stability. Numerical tests, including a flat interface problem, a stationary droplet, and the coalescence of two droplets, demonstrate the well-balanced properties and improvements in the stability of the present model

Angiotensin II mediates the high-glucose-induced endothelial-to-mesenchymal transition in human aortic endothelial cells

<p>Abstract</p> <p>Background</p> <p>Substantial evidence suggests that high glucose (HG) causes endothelial cell damage; however, the potential mechanism therein has yet to be clarified. The aim of this study was to investigate the influence of HG on the endothelial-to-mesenchymal transition (EndMT) and its relevance to the activation of the renin-angiotensin system.</p> <p>Methods</p> <p>Primary human aortic endothelial cells (HAECs) were divided into three groups: a normal glucose (NG) group, HG group, and irbesartan (1 μM)-treated (HG+irbesartan) group. The concentration of angiotensin II in the supernatant was detected by radioimmunoassay. Pathological changes were investigated using fluorescence microscopy and electron microscopy. Immunofluorescence staining was performed to detect the co-expression of CD31 and fibroblast markers, such as fibroblast-specific protein 1 (FSP1). The expressions of FSP1 and α-SMA were detected by RT-PCR and Western blot.</p> <p>Results</p> <p>The treatment of HAECs in the HG group resulted in significant increases in the expressions of FSP1 and angiotensin II in dose-and time-dependent manners. The incubation of HAECs exposure to HG resulted in a fibroblast-like phenotype, wherein increased microfilamentation and a roughened endoplasmic reticulum structure were observed in the cytoplasm. The expressions of FSP1 and α-SMA were significantly increased in the HG group, and these changes were inhibited by irbesartan treatment (<it>P </it>< 0.05). Double staining of the HAECs indicated a co-localization of CD31 and FSP1 and that some cells acquired spindle-shaped morphologies and a loss of CD31 staining; however, treatment with irbesartan attenuated the expression of EndMT (<it>P </it>< 0.05).</p> <p>Conclusions</p> <p>These findings suggest a novel mechanism in HG-induced endothelial damage via the mediation of the EndMT by angiotensin II, which was inhibited by Irbesartan.</p

Numerical Study of the Effects of Topography and Urbanization on the Local Atmospheric Circulations over the Beijing-Tianjin-Hebei, China

The effects of the topography and urbanization on the local atmospheric circulations over the Beijing-Tianjin-Hebei (BTH) region were studied by the weather research and forecasting (WRF) model, as well as the interactions among these local atmospheric circulations. It was found that, in the summer day time, the multiscale thermally induced local atmospheric circulations may exist and interact in the same time over the BTH region; the topography played a role in the strengthening of the sea breeze circulations; after sunset, the inland progress of sea breeze was slowed down by the opposite mountain breeze; when the land breeze circulation dominated the Bohai bay, the mountain breeze circulation can couple with the land breeze circulation to form a large circulation ranging from the coastline to the mountains. And the presence of cities cannot change the general state of the sea-land breeze (SLB) circulation and mountain-valley breeze (MVB) circulation but acted to modify these local circulations slightly. Meanwhile, the development of the urban heat island (UHI) circulation was also strongly influenced by the nearby SLB circulation and MVB circulation

Synergistic growth inhibition by sorafenib and vitamin K2 in human hepatocellular carcinoma cells

OBJECTIVE: Sorafenib is an oral multikinase inhibitor that has been proven effective as a single-agent therapy in hepatocellular carcinoma, and there is a strong rationale for investigating its use in combination with other agents. Vitamin K2 is nearly non-toxic to humans and has been shown to inhibit the growth of hepatocellular carcinoma. In this study, we evaluated the effects of a combination of sorafenib and vitamin K2 on the growth of hepatocellular carcinoma cells. METHODS: Flow cytometry, 3-(4,5-dimethyl-2-thiazolyl-2,5-diphenyl-2H-tetrazolium bromide) and nude mouse xenograft assays were used to examine the effects of sorafenib and vitamin K2 on the growth of hepatocellular carcinoma cells. Western blotting was used to elucidate the possible mechanisms underlying these effects. RESULTS: Assays for 3-(4,5-dimethyl-2-thiazolyl-2,5-diphenyl-2H-tetrazolium bromide) revealed a strong synergistic growth-inhibitory effect between sorafenib and vitamin K2. Flow cytometry showed an increase in cell cycle arrest and apoptosis after treatment with a combination of these two drugs at low concentrations. Sorafenib-mediated inhibition of extracellular signal-regulated kinase phosphorylation was promoted by vitamin K2, and downregulation of Mcl-1, which is required for sorafenib-induced apoptosis, was observed after combined treatment. Vitamin K2 also attenuated the downregulation of p21 expression induced by sorafenib, which may represent the mechanism by which vitamin K2 promotes the inhibitory effects of sorafenib on cell proliferation. Moreover, the combination of sorafenib and vitamin K2 significantly inhibited the growth of hepatocellular carcinoma xenografts in nude mice. CONCLUSIONS: Our results determined that combined treatment with sorafenib and vitamin K2 can work synergistically to inhibit the growth of hepatocellular carcinoma cells. This finding raises the possibility that this combined treatment strategy might be promising as a new therapy against hepatocellular carcinoma, especially for patients with poor liver tolerance

Simulating Flow and Dispersion by Using WRF-CFD Coupled Model in a Built-Up Area of Shenyang, China

Results are presented from a series of numerical studies designed to investigate the atmospheric boundary layer structure, ambient wind, and pollutant source location and their impacts on the wind field and pollutant distribution within the built-up areas of Shenyang, China. Two models, namely, Open Source Field Operation and Manipulation (OpenFOAM) software package and Weather Research and Forecasting (WRF) model, are used in the present study. Then the high resolution computational fluid dynamics (CFD) numerical experiments were performed under the typical simulated atmospheric boundary conditions. It was found that the atmospheric boundary structure played a crucial role in the pollution within the building cluster, which determined the potential turbulent diffusion ability of the atmospheric surface layer; the change of the ambient wind direction can significantly affect the dispersion pattern of pollutants, which was a more sensitive factor than the ambient wind speed; under a given atmospheric state, the location of the pollution sources would dramatically determine the pollution patterns within built-up areas. The WRF-CFD numerical evaluation is a reliable method to understand the complicated flow and dispersion within built-up areas

A novel T-cell exhaustion-related feature can accurately predict the prognosis of OC patients

The phenomenon of T Cell exhaustion (TEX) entails a progressive deterioration in the functionality of T cells within the immune system during prolonged conflicts with chronic infections or tumors. In the context of ovarian cancer immunotherapy, the development, and outcome of treatment are closely linked to T-cell exhaustion. Hence, gaining an in-depth understanding of the features of TEX within the immune microenvironment of ovarian cancer is of paramount importance for the management of OC patients. To this end, we leveraged single-cell RNA data from OC to perform clustering and identify T-cell marker genes utilizing the Unified Modal Approximation and Projection (UMAP) approach. Through GSVA and WGCNA in bulk RNA-seq data, we identified 185 TEX-related genes (TEXRGs). Subsequently, we transformed ten machine learning algorithms into 80 combinations and selected the most optimal one to construct TEX-related prognostic features (TEXRPS) based on the mean C-index of the three OC cohorts. In addition, we explored the disparities in clinicopathological features, mutational status, immune cell infiltration, and immunotherapy efficacy between the high-risk (HR) and low-risk (LR) groups. Upon the integration of clinicopathological features, TEXRPS displayed robust predictive power. Notably, patients in the LR group exhibited a superior prognosis, higher tumor mutational load (TMB), greater immune cell infiltration abundance, and enhanced sensitivity to immunotherapy. Lastly, we verified the differential expression of the model gene CD44 using qRT-PCR. In conclusion, our study offers a valuable tool to guide clinical management and targeted therapy of OC

Crosstalk of disulfidptosis-related subtypes, establishment of a prognostic signature and immune infiltration characteristics in bladder cancer based on a machine learning survival framework

BackgroundBladder cancer (BLCA) is the most common malignancy of the urinary tract. On the other hand, disulfidptosis, a mechanism of disulfide stress-induced cell death, is closely associated with tumorigenesis and progression. Here, we investigated the impact of disulfidptosis-related genes (DRGs) on the prognosis of BLCA, identified various DRG clusters, and developed a risk model to assess patient prognosis, immunological profile, and treatment response.MethodsThe expression and mutational characteristics of four DRGs were first analyzed in bulk RNA-Seq and single-cell RNA sequencing data, IHC staining identified the role of DRGs in BLCA progression, and two DRG clusters were identified by consensus clustering. Using the differentially expressed genes (DEGs) from these two clusters, we transformed ten machine learning algorithms into more than 80 combinations and finally selected the best algorithm to construct a disulfidptosis-related prognostic signature (DRPS). We based this selection on the mean C-index of three BLCA cohorts. Furthermore, we explored the differences in clinical characteristics, mutational landscape, immune cell infiltration, and predicted efficacy of immunotherapy between high and low-risk groups. To visually depict the clinical value of DRPS, we employed nomograms. Additionally, we verified whether DRPS predicts response to immunotherapy in BLCA patients by utilizing the Tumour Immune Dysfunction and Rejection (TIDE) and IMvigor 210 cohorts.ResultsIn the integrated cohort, we identified several DRG clusters and DRG gene clusters that differed significantly in overall survival (OS) and tumor microenvironment. After the integration of clinicopathological features, DRPS showed robust predictive power. Based on the median risk score associated with disulfidptosis, BLCA patients were divided into low-risk (LR) and high-risk (HR) groups, with patients in the LR group having a better prognosis, a higher tumor mutational load and being more sensitive to immunotherapy and chemotherapy.ConclusionOur study, therefore, provides a valuable tool to further guide clinical management and tailor the treatment of BLCA patients, offering new insights into individualized treatment