Analysis of factors affecting the construction duration of public health emergency medical facilities

ObjectivesThis study explores the factors influencing the construction duration of public health emergency medical facilities and the ways in which they can be enhanced.MethodsCombining 30 relevant emergency medical facility construction cases in different cities in China from 2020 to 2021, seven condition variables and an outcome variable were selected, and necessary and sufficient condition analyses of duration influence factors were conducted using the fsQCA method.ResultsThe consistency of seven condition variables was <0.9, which shows that the construction period of public health emergency medical facilities is not independently affected by a single condition variable but by multiple influencing factors. The solution consistency value of the path configurations was 0.905, indicating that four path configurations were sufficient for the outcome variables. The solution coverage of the four path configurations was 0.637, indicating that they covered ~63.7% of the public health emergency medical facility cases.ConclusionTo reduce the construction duration, the construction of emergency medical facilities should focus on planning and design, the selection of an appropriate form of construction, the reasonable deployment of resources, and the vigorous adoption of information technology

MTGFlow: Unsupervised Multivariate Time Series Anomaly Detection via Dynamic Graph and Entity-aware Normalizing Flow

Multivariate time series anomaly detection has been extensively studied under the semi-supervised setting, where a training dataset with all normal instances is required. However, preparing such a dataset is very laborious since each single data instance should be fully guaranteed to be normal. It is, therefore, desired to explore multivariate time series anomaly detection methods based on the dataset without any label knowledge. In this paper, we propose MTGFlow, an unsupervised anomaly detection approach for Multivariate Time series anomaly detection via dynamic Graph and entity-aware normalizing Flow, leaning only on a widely accepted hypothesis that abnormal instances exhibit sparse densities than the normal. However, the complex interdependencies among entities and the diverse inherent characteristics of each entity pose significant challenges on the density estimation, let alone to detect anomalies based on the estimated possibility distribution. To tackle these problems, we propose to learn the mutual and dynamic relations among entities via a graph structure learning model, which helps to model accurate distribution of multivariate time series. Moreover, taking account of distinct characteristics of the individual entities, an entity-aware normalizing flow is developed to describe each entity into a parameterized normal distribution, thereby producing fine-grained density estimation. Incorporating these two strategies, MTGFlowachieves superior anomaly detection performance. Experiments on the real-world datasets are conducted, demonstrating that MTGFlow outperforms the state-of-the-art (SOTA) by 5.0% and 1.6% AUROC for SWaT and WADI datasets respectively. Also, through the anomaly scores contributed by individual entities, MTGFlow can provide explanation information for the detection results

TurboMGNN : improving concurrent GNN training tasks on GPU with fine-grained kernel fusion

Graph Neural Networks (GNN) have evolved as powerful models for graph representation learning. Many works have been proposed to support GNN training efficiently on GPU. However, these works only focus on a single GNN training task such as operator optimization, task scheduling, and programming model. Concurrent GNN training, which is needed in the applications such as neural network structure search, has not been explored yet. This work aims to improve the training efficiency of the concurrent GNN training tasks on GPU by developing fine-grained methods to fuse the kernels from different tasks. Specifically, we propose a fine-grained Sparse Matrix Multiplication (SpMM) based kernel fusion method to eliminate redundant accesses to graph data. In order to increase the fusion opportunity and reduce the synchronization cost, we further propose a novel technique to enable the fusion of the kernels in forward and backward propagation. Finally, in order to reduce the resource contention caused by the increased number of concurrent, heterogeneous GNN training tasks, we propose an adaptive strategy to group the tasks and match their operators according to resource contention. We have conducted extensive experiments, including kernel- and model-level benchmarks. The results show that the proposed methods can achieve up to 2.6X performance speedup

Angiotensin II upregulates the expression of placental growth factor in human vascular endothelial cells and smooth muscle cells

<p>Abstract</p> <p>Background</p> <p>Atherosclerosis is now recognized as a chronic inflammatory disease. Angiotensin II (Ang II) is a critical factor in inflammatory responses, which promotes the pathogenesis of atherosclerosis. Placental growth factor (PlGF) is a member of the vascular endothelial growth factor (VEGF) family cytokines and is associated with inflammatory progress of atherosclerosis. However, the potential link between PlGF and Ang II has not been investigated. In the current study, whether Ang II could regulate PlGF expression, and the effect of PlGF on cell proliferation, was investigated in human vascular endothelial cells (VECs) and smooth muscle cells (VSMCs).</p> <p>Results</p> <p>In growth-arrested human VECs and VSMCs, Ang II induced PlGF mRNA expression after 4 hour treatment, and peaked at 24 hours. 10^-6mol/L Ang II increased PlGF protein production after 8 hour treatment, and peaked at 24 hours. Stimulation with Ang II also induced mRNA expression of VEGF receptor-1 and -2(VEGFR-1 and -2) in these cells. The Ang II type I receptor (AT₁R) antagonist blocked Ang II-induced PlGF gene expression and protein production. Several intracellular signals elicited by Ang II were involved in PlGF synthesis, including activation of protein kinase C, extracellular signal-regulated kinase 1/2 (ERK1/2) and PI3-kinase. A neutralizing antibody against PlGF partially inhibited the Ang II-induced proliferation of VECs and VSMCs. However, this antibody showed little effect on the basal proliferation in these cells, whereas blocking antibody of VEGF could suppress both basal and Ang II-induced proliferation in VECs and VSMCs.</p> <p>Conclusion</p> <p>Our results showed for the first time that Ang II could induce the gene expression and protein production of PlGF in VECs and VSMCs, which might play an important role in the pathogenesis of vascular inflammation and atherosclerosis.</p

Iterative Reconstruction Based on Latent Diffusion Model for Sparse Data Reconstruction

Reconstructing Computed tomography (CT) images from sparse measurement is a well-known ill-posed inverse problem. The Iterative Reconstruction (IR) algorithm is a solution to inverse problems. However, recent IR methods require paired data and the approximation of the inverse projection matrix. To address those problems, we present Latent Diffusion Iterative Reconstruction (LDIR), a pioneering zero-shot method that extends IR with a pre-trained Latent Diffusion Model (LDM) as a accurate and efficient data prior. By approximating the prior distribution with an unconditional latent diffusion model, LDIR is the first method to successfully integrate iterative reconstruction and LDM in an unsupervised manner. LDIR makes the reconstruction of high-resolution images more efficient. Moreover, LDIR utilizes the gradient from the data-fidelity term to guide the sampling process of the LDM, therefore, LDIR does not need the approximation of the inverse projection matrix and can solve various CT reconstruction tasks with a single model. Additionally, for enhancing the sample consistency of the reconstruction, we introduce a novel approach that uses historical gradient information to guide the gradient. Our experiments on extremely sparse CT data reconstruction tasks show that LDIR outperforms other state-of-the-art unsupervised and even exceeds supervised methods, establishing it as a leading technique in terms of both quantity and quality. Furthermore, LDIR also achieves competitive performance on nature image tasks. It is worth noting that LDIR also exhibits significantly faster execution times and lower memory consumption compared to methods with similar network settings. Our code will be publicly available