A Simple Framework for Multi-mode Spatial-Temporal Data Modeling

Spatial-temporal data modeling aims to mine the underlying spatial relationships and temporal dependencies of objects in a system. However, most existing methods focus on the modeling of spatial-temporal data in a single mode, lacking the understanding of multiple modes. Though very few methods have been presented to learn the multi-mode relationships recently, they are built on complicated components with higher model complexities. In this paper, we propose a simple framework for multi-mode spatial-temporal data modeling to bring both effectiveness and efficiency together. Specifically, we design a general cross-mode spatial relationships learning component to adaptively establish connections between multiple modes and propagate information along the learned connections. Moreover, we employ multi-layer perceptrons to capture the temporal dependencies and channel correlations, which are conceptually and technically succinct. Experiments on three real-world datasets show that our model can consistently outperform the baselines with lower space and time complexity, opening up a promising direction for modeling spatial-temporal data. The generalizability of the cross-mode spatial relationships learning module is also validated

Continuous-Time Graph Learning for Cascade Popularity Prediction

Information propagation on social networks could be modeled as cascades, and many efforts have been made to predict the future popularity of cascades. However, most of the existing research treats a cascade as an individual sequence. Actually, the cascades might be correlated with each other due to the shared users or similar topics. Moreover, the preferences of users and semantics of a cascade are usually continuously evolving over time. In this paper, we propose a continuous-time graph learning method for cascade popularity prediction, which first connects different cascades via a universal sequence of user-cascade and user-user interactions and then chronologically learns on the sequence by maintaining the dynamic states of users and cascades. Specifically, for each interaction, we present an evolution learning module to continuously update the dynamic states of the related users and cascade based on their currently encoded messages and previous dynamic states. We also devise a cascade representation learning component to embed the temporal information and structural information carried by the cascade. Experiments on real-world datasets demonstrate the superiority and rationality of our approach.Comment: 9 pages, 5 figures, IJCAI 202

Glomerular Endothelial Cells Are the Coordinator in the Development of Diabetic Nephropathy

The prevalence of diabetes is consistently rising worldwide. Diabetic nephropathy is a leading cause of chronic renal failure. The present study aimed to explore the crosstalk among the different cell types inside diabetic glomeruli, including glomerular endothelial cells, mesangial cells, podocytes, and immune cells, by analyzing an online single-cell RNA profile (GSE131882) of patients with diabetic nephropathy. Differentially expressed genes in the glomeruli were processed by gene enrichment and protein-protein interactions analysis. Glomerular endothelial cells, as well as podocytes, play a critical role in diabetic nephropathy. A subgroup of glomerular endothelial cells possesses characteristic angiogenesis genes, indicating that angiogenesis takes place in the progress of diabetic nephropathy. Immune cells such as macrophages, T lymphocytes, B lymphocytes, and plasma cells also contribute to the disease progression. By using iTALK, the present study reports complicated cellular crosstalk inside glomeruli. Dysfunction of glomerular endothelial cells and immature angiogenesis result from the activation of both paracrine and autocrine signals. The present study reinforces the importance of glomerular endothelial cells in the development of diabetic nephropathy. The exploration of the signaling pathways involved in aberrant angiogenesis reported in the present study shed light on potential therapeutic target(s) for diabetic nephropathy

Identifying the most influential roads based on traffic correlation networks

Prediction of traffic congestion is one of the core issues in the realization of smart traffic. Accurate prediction depends on understanding of interactions and correlations between different city locations. While many methods merely consider the spatio-temporal correlation between two locations, here we propose a new approach of capturing the correlation network in a city based on realtime traffic data. We use the weighted degree and the impact distance as the two major measures to identify the most influential locations. A road segment with larger weighted degree or larger impact distance suggests that its traffic flow can strongly influence neighboring road sections driven by the congestion propagation. Using these indices, we find that the statistical properties of the identified correlation network is stable in different time periods during a day, including morning rush hours, evening rush hours, and the afternoon normal time respectively. Our work provides a new framework for assessing interactions between different local traffic flows. The captured correlation network between different locations might facilitate future studies on predicting and controlling the traffic flows. © 2019, The Author(s)

COVID-19 infection: the China and Italy perspectives.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the COVID-19 pandemic. Since its first report in December 2019, despite great efforts made in almost every country worldwide, this disease continues to spread globally, especially in most parts of Europe, Iran, and the United States. Here, we update the recent understanding in clinical characteristics, diagnosis strategies, as well as clinical management of COVID-19 in China as compared to Italy, with the purpose to integrate the China experience with the global efforts to outline references for prevention, basic research, treatment as well as final control of the disease. Being the first two countries we feel appropriate to evaluate the evolution of the disease as well as the early result of the treatment, in order to offer a different baseline to other countries. It is also interesting to compare two countries, with a very significant difference in population, where the morbidity and mortality has been so different, and unrelated to the size of the country

Long-Term Protection of CHBP Against Combinational Renal Injury Induced by Both Ischemia-Reperfusion and Cyclosporine A in Mice.

Renal ischemia-reperfusion (IR) injury and cyclosporine A (CsA) nephrotoxicity affect allograft function and survival. The prolonged effects and underlying mechanisms of erythropoietin derived cyclic helix B peptide (CHBP) and/or caspase-3 small interfering RNA (CASP-3siRNA) were investigated in mouse kidneys, as well as kidney epithelial cells (TCMK-1), subjected to transplant-related injuries. Bilateral renal pedicles were clamped for 30 min followed by reperfusion for 2 and 8 weeks, with/without 35 mg/kg CsA gavage daily and/or 24 nmol/kg CHBP intraperitoneal injection every 3 days. The ratio of urinary albumin to creatinine was raised by IR injury, further increased by CsA and lowered by CHBP at 2, 4, 6 and 8 weeks, whereas the level of SCr was not significantly affected. Similar change trends were revealed in tubulointerstitial damage and fibrosis, HMGB1 and active CASP-3 protein. Increased apoptotic cells in IR kidneys were decreased by CsA and CHBP at 2 and/or 8 weeks. p70 S6 kinase and mTOR were reduced by CsA with/without CHBP at 2 weeks, so were S6 ribosomal protein and GSK-3β at 8 weeks, with reduced CASP-3 at both time points. CASP-3 was further decreased by CHBP in IR or IR + CsA kidneys at 2 or 8 weeks. Furthermore, in TCMK-1 cells CsA induced apoptosis was decreased by CHBP and/or CASP-3siRNA treatment. Taken together, CHBP predominantly protects kidneys against IR injury at 2 weeks and/or CsA nephrotoxicity at 8 weeks, with different underlying mechanisms. Urinary albumin/creatinine is a good biomarker in monitoring the progression of transplant-related injuries. CsA divergently affects apoptosis in kidneys and cultured kidney epithelial cells, in which CHBP and/or CASP-3siRNA reduces inflammation and apoptosis