Cyclic helix B peptide inhibits ischemia reperfusion-induced renal fibrosis via the PI3K/Akt/FoxO3a pathway

Renal fibrosis is a main cause of end-stage renal disease. Clinically, there is no beneficial treatment that can effectively reverse the progressive loss of renal function. We recently synthesized a novel proteolysis-resistant cyclic helix B peptide (CHBP) that exhibits promising renoprotective effects. In this study, we evaluated the effect of CHBP on renal fibrosis in an in vivo ischemia reperfusion injury (IRI) model and in vitro TGF-β-stimulated tubular epithelial cells (TCMK-1 and HK-2) model. In the IRI in vivo model, mice were randomly divided into sham (sham operation), IR and IR + CHBP groups (n = 6). CHBP (8 nmol/kg) was administered intraperitoneally at the onset of reperfusion, and renal fibrosis was evaluated at 12 weeks post-reperfusion. Our results showed that CHBP markedly attenuated the IRI-induced deposition of collagen I and vimentin. In the in vitro model, CHBP reversed the TGF-β-induced down-regulation of E-cadherin and up-regulation of α-SMA and vimentin. Furthermore, CHBP inhibited the phosphorylation of Akt and Forkhead box O 3a (FoxO3a), whose anti-fibrotic effect could be reversed by the 3-phosphoinositide-dependent kinase-1 (PI3K) inhibitor wortmannin as well as FoxO3a siRNA. These findings demonstrate that CHBP attenuates renal fibrosis and the epithelial-mesenchymal transition of tubular cells, possibly through suppression of the PI3K/Akt pathway and thereby the inhibition FoxO3a activity. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12967-015-0699-2) contains supplementary material, which is available to authorized users

Analysis of the Decoupling Level and Water-Saving Targets: Economic Growth and Water Consumption in the Production-Living-Ecological Systems

Understanding the relationship between economic growth and water consumption aids in evaluating economic health and managing water resources. This study analyzes the decoupling level between economic growth and water consumption within the Production-Living-Ecological (PLE) systems, focusing on Guangdong Province, China. Using the Tapio decoupling model, this study identified various decoupling states and calculated target water-saving values for achieving decoupling. Results show Guangdong Province generally has an ideal level of decoupling, with production water consumption effectively controlled alongside economic growth. However, living and ecological water consumption remain closely linked to economic growth. The difficulty of achieving strong and weak decoupling is not significant, with required water savings less than 1%. Despite this, some cities have yet to achieve decoupling, indicating a need for targeted water-saving strategies. This study provides valuable insights for sustainable water resource management in Guangdong and similar regions

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

The scaling of human mobility by taxis is exponential

As a significant factor in urban planning, traffic forecasting and prediction of epidemics, modeling patterns of human mobility draws intensive attention from researchers for decades. Power-law distribution and its variations are observed from quite a few real-world human mobility datasets such as the movements of banking notes, trackings of cell phone users' locations and trajectories of vehicles. In this paper, we build models for 20 million trajectories with fine granularity collected from more than 10 thousand taxis in Beijing. In contrast to most models observed in human mobility data, the taxis' traveling displacements in urban areas tend to follow an exponential distribution instead of a power-law. Similarly, the elapsed time can also be well approximated by an exponential distribution. Worth mentioning, analysis of the interevent time indicates the bursty nature of human mobility, similar to many other human activities.Comment: 20 pages, 7 figure

MemMap: An Adaptive and Latent Memory Structure for Dynamic Graph Learning

Dynamic graph learning has attracted much attention in recent years due to the fact that most of the real-world graphs are dynamic and evolutionary. As a result, many dynamic learning methods have been proposed to cope with the changes of node states over time. Among these studies, a critical issue is how to update the representations of nodes when new temporal events are observed. In this paper, we provide a novel memory structure - Memory Map (MemMap) for this problem. MemMap is an adaptive and evolutionary latent memory space, where each cell corresponds to an evolving topic of the dynamic graph. Moreover, the representation of a node is generated from its semantically correlated memory cells, rather than linked neighbors of the node. We have conducted experiments on real-world datasets and compared our method with the SOTA ones. It can be concluded that: 1) By constructing an adaptive and evolving memory structure during the dynamic learning process, our method can capture the dynamic graph changes, and the learned MemMap is actually a compact evolving structure organized according to the latent topics of the graph nodes. 2) Our research suggests that it is a more effective and efficient way to generate node representations from a latent semantic space (like MemMap in our method) than from directly connected neighbors (like most of the previous graph learning methods). The reason is that the number of memory cells in latent space could be much smaller than the number of nodes in a real-world graph, and the representation learning process could well balance the global and local message passing by leveraging the semantic similarity of graph nodes via the correlated memory cells

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)