Graph Neural Processes for Spatio-Temporal Extrapolation

We study the task of spatio-temporal extrapolation that generates data at target locations from surrounding contexts in a graph. This task is crucial as sensors that collect data are sparsely deployed, resulting in a lack of fine-grained information due to high deployment and maintenance costs. Existing methods either use learning-based models like Neural Networks or statistical approaches like Gaussian Processes for this task. However, the former lacks uncertainty estimates and the latter fails to capture complex spatial and temporal correlations effectively. To address these issues, we propose Spatio-Temporal Graph Neural Processes (STGNP), a neural latent variable model which commands these capabilities simultaneously. Specifically, we first learn deterministic spatio-temporal representations by stacking layers of causal convolutions and cross-set graph neural networks. Then, we learn latent variables for target locations through vertical latent state transitions along layers and obtain extrapolations. Importantly during the transitions, we propose Graph Bayesian Aggregation (GBA), a Bayesian graph aggregator that aggregates contexts considering uncertainties in context data and graph structure. Extensive experiments show that STGNP has desirable properties such as uncertainty estimates and strong learning capabilities, and achieves state-of-the-art results by a clear margin.Comment: SIGKDD 202

Rosiglitazone Restores Endothelial Dysfunction in a Rat Model of Metabolic Syndrome through PPARγ- and PPARδ-Dependent Phosphorylation of Akt and eNOS

Vascular endothelial dysfunction has been demonstrated in metabolic syndrome (MS). Chronic administration of rosiglitazone ameliorates endothelial dysfunction through PPARγ-mediated metabolic improvements. Recently, studies suggested that single dose of rosiglitazone also has direct vascular effects, but the mechanisms remain uncertain. Here we established a diet-induced rat model of MS. The impaired vasorelaxation in MS rats was improved by incubating arteries with rosiglitazone for one hour. Importantly, this effect was blocked by either inhibition of PPARγ or PPARδ. In cultured endothelial cells, acute treatment with rosiglitazone increased the phosphorylation of Akt and eNOS and the production of NO. These effects were also abolished by inhibition of PPARγ, PPARδ, or PI3K. In conclusion, rosiglitazone improved endothelial function through both PPARγ- and PPARδ-mediated phosphorylation of Akt and eNOS, which might help to reconsider the complex effects and clinical applications of rosiglitazone

Increased Migration of Monocytes in Essential Hypertension Is Associated with Increased Transient Receptor Potential Channel Canonical Type 3 Channels

Increased transient receptor potential canonical type 3 (TRPC3) channels have been observed in patients with essential hypertension. In the present study we tested the hypothesis that increased monocyte migration is associated with increased TRPC3 expression. Monocyte migration assay was performed in a microchemotaxis chamber using chemoattractants formylated peptide Met-Leu-Phe (fMLP) and tumor necrosis factor-α (TNF-α). Proteins were identified by immunoblotting and quantitative in-cell Western assay. The effects of TRP channel-inhibitor 2–aminoethoxydiphenylborane (2-APB) and small interfering RNA knockdown of TRPC3 were investigated. We observed an increased fMLP-induced migration of monocytes from hypertensive patients compared with normotensive control subjects (246±14% vs 151±10%). The TNF-α-induced migration of monocytes in patients with essential hypertension was also significantly increased compared to normotensive control subjects (221±20% vs 138±18%). In the presence of 2-APB or after siRNA knockdown of TRPC3 the fMLP-induced monocyte migration was significantly blocked. The fMLP-induced changes of cytosolic calcium were significantly increased in monocytes from hypertensive patients compared to normotensive control subjects. The fMLP-induced monocyte migration was significantly reduced in the presence of inhibitors of tyrosine kinase and phosphoinositide 3-kinase. We conclude that increased monocyte migration in patients with essential hypertension is associated with increased TRPC3 channels

Noise Separation from Multiple Copy Images Using the FastICA Algorithm

Abstract. This paper proposes an effective method to separate noise from multiple copy images (MCIs). Suppose that noise and original image are mutually independent in mixed signals, the mixed signals are thus decomposed to an original image independent component and a noise component by using fast independent component analysis (FastICA). The original image independent component is selected to reconstruct the resulting image according to the standard deviation of its time course. By modeling the noise as Gaussian, experimental results show that zero-mean and nonzero-mean Gaussian noises can be separated effectively from multiple copy images by the proposed method, which is effective in the case of stable and unstable noise intensity

Automatic extracting event-related potentials within several trials using Infomax ICA algorithm

468-473This study presents a new method for automatic extracting event-related potentials (ERP) within several trials based on independent component analysis (ICA). After mixed data is decomposed by Infomax ICA, independent component (IC) of ERP is automatically selected according to the standard deviation of fixed temporal pattern of IC, and applied in ERP reconstruction. Visual evoked potential extraction, used to confirm effectiveness of algorithm, can be obtained automatically after 6 trials on experimental data, and result of its Pearson correlation coefficient (PCC) within the average of 205 trials (standard signal) is 0.9106. However, PCC of average result of 6 trials within standard signal is only 0.3066, demonstrating practical applicability of proposed method. This algorithm enhances objectivity of ERP extraction within several trials

Improved U-Net Model to Estimate Cardiac Output Based on Photoplethysmography and Arterial Pressure Waveform

We aimed to estimate cardiac output (CO) from photoplethysmography (PPG) and the arterial pressure waveform (ART) using a deep learning approach, which is minimally invasive, does not require patient demographic information, and is operator-independent, eliminating the need to artificially extract a feature of the waveform by implementing a traditional formula. We aimed to present an alternative to measuring cardiac output with greater accuracy for a wider range of patients. Using a publicly available dataset, we selected 543 eligible patients and divided them into test and training sets after preprocessing. The data consisted of PPG and ART waveforms containing 2048 points with the corresponding CO. We achieved an improvement based on the U-Net modeling framework and built a two-channel deep learning model to automatically extract the waveform features to estimate the CO in the dataset as the reference, acquired using the EV1000, a commercially available instrument. The model demonstrated strong consistency with the reference values on the test dataset. The mean CO was 5.01 ± 1.60 L/min and 4.98 ± 1.59 L/min for the reference value and the predicted value, respectively. The average bias was −0.04 L/min with a −1.025 and 0.944 L/min 95% limit of agreement (LOA). The bias was 0.79% with a 95% LOA between −20.4% and 18.8% when calculating the percentage of the difference from the reference. The normalized root-mean-squared error (RMSNE) was 10.0%. The Pearson correlation coefficient (r) was 0.951. The percentage error (PE) was 19.5%, being below 30%. These results surpassed the performance of traditional formula-based calculation methods, meeting clinical acceptability standards. We propose a dual-channel, improved U-Net deep learning model for estimating cardiac output, demonstrating excellent and consistent results. This method offers a superior reference method for assessing cardiac output in cases where it is unnecessary to employ specialized cardiac output measurement devices or when patients are not suitable for pulmonary-artery-catheter-based measurements, providing a viable alternative solution