Research of influence and mechanism of combining exercise with diet control on a model of lipid metabolism rat induced by high fat diet

OBJECTIVE: To investigate the influence and mechanism of combining exercise with diet control on a model of lipid metabolism rat induced by high fat diet. METHODS: Twenty-four male Wistar rats were randomly divided into 3 groups of 8: normal, model and intervention. The model group and intervention group were fed with high fat diet, while the normal group received basal feed. From day 1, the intervention group was randomly given interventions such as swimming exercise and dietary restriction. The interventions duration were 28 days. At the end of the experiment, the levels of rats’ body weight and liver weight were detected, the serum levels of total cholesterol (TC), high density lipoprotein cholesterol (HDL-C), low density lipoprotein cholesterol (LDL-C) and hepatic triglyceride content (TG) were detected by using biochemical assay, serum level of gastrin (GAS), motilin (MTL) were assayed by the enzyme linked immunosorbent assay (ELISA). RESULTS: Compared with the level of body weight and liver weight in the normal rats, body weight and liver weight in the rat of the model group were significantly increase (P<0.05 or P<0.01). Plasma concentrations of TC, LDL-C and hepatic TG in the model group were significantly increased compared with those in the normal group (P<0.05 or P<0.01). The contents of GAS, MTL, HDL-C in the model rats’plasma were significantly reduced compared with those of the normal group (P<0.05 or P<0.01). Compared with those in the model group, rats’ body weight, liver weight, serum TC, LDL-C, and TG content of liver in the intervention group decreased significantly (P<0.05 or P<0.01). Meanwhile, serum content of GAS, MTL, HDL-C were significantly improved in the intervention rats compared to the model group. CONCLUSION: The action of combining exercise with diet control for lipid metabolism disorder might be related to regulation of GAS, MTL and other gastrointestinal hormones

MD-Manifold: A Medical Distance Based Manifold Learning Approach for Heart Failure Readmission Prediction

Dimension reduction is considered as a necessary technique in Electronic Healthcare Records (EHR) data processing. However, no existing work addresses both of the two points: 1) generating low-dimensional representations for each patient visit; and 2) taking advantage of the well-organized medical concept structure as the domain knowledge. Hence, we propose a new framework to generate low-dimensional representations for medical data records by combining the concept-structure based distance with manifold learning. To demonstrate the efficacy, we generated low-dimensional representations for hospital visits of heart failure patients, which was further used for a 30-day readmission prediction. The experiments showed a great potential of the proposed representations (AUC = 60.7%) that has comparative predictive power of the state-of-the-art methods, including one hot encoding representations (AUC = 60.1%) and PCA representations (AUC = 58.3%), with much less training time (improved by 99%). The proposed framework can also be generalized to various healthcare-related prediction tasks, such as mortality prediction

ICU Outcome Predictions Using Real-Time Signals with Wavelet-Transform-based Convolutional Neural Network

Intensive care units (ICUs) serve patients with life-threatening conditions. The limited ICU resources cause severe economic and healthcare burdens worldwide. It is critical to conduct ICU outcome predictions at an early stage and promote efficient use of ICU resources. However, all the current prediction methods have limitations such as unsatisfactory accuracy and depending on resource-demanding laboratory tests or expert domain knowledge. In this research, we design a wavelet-transformed-based convolutional neural network, WTCNN, which only requires patients’ vital sign series and information at ICU admission for real-time ICU outcome predictions. The model is evaluated using a large real-world ICU database and outperforms state-of-art baselines on both ICU mortality and length-of-stay prediction tasks. We conduct LIME for model interpretation and prescriptive analysis. Our work provides an efficient tool for ICU outcome predictions, allowing healthcare providers to take action promptly on patients at risk and reduce the negative impacts on patient outcomes

ICU Outcome Predictions Using Real-Time Signals with Wavelet-Transform-based Convolutional Neural Network

Intensive care units (ICUs) serve patients with life-threatening conditions. The limited ICU resources cause severe economic and healthcare burdens worldwide. It is critical to conduct ICU outcome predictions at an early stage and promote efficient use of ICU resources. However, all the current prediction methods have limitations such as unsatisfactory accuracy and depending on resource-demanding laboratory tests or expert domain knowledge. In this research, we design a wavelet-transformed-based convolutional neural network, WTCNN, which only requires patients’ vital sign series and information at ICU admission for real-time ICU outcome predictions. The model is evaluated using a large real-world ICU database and outperforms state-of-art baselines on both ICU mortality and length-of-stay prediction tasks. We conduct LIME for model interpretation and prescriptive analysis. Our work provides an efficient tool for ICU outcome predictions, allowing healthcare providers to take action promptly on patients at risk and reduce the negative impacts on patient outcomes

Multi-views Fusion CNN for Left Ventricular Volumes Estimation on Cardiac MR Images

Left ventricular (LV) volumes estimation is a critical procedure for cardiac disease diagnosis. The objective of this paper is to address direct LV volumes prediction task. Methods: In this paper, we propose a direct volumes prediction method based on the end-to-end deep convolutional neural networks (CNN). We study the end-to-end LV volumes prediction method in items of the data preprocessing, networks structure, and multi-views fusion strategy. The main contributions of this paper are the following aspects. First, we propose a new data preprocessing method on cardiac magnetic resonance (CMR). Second, we propose a new networks structure for end-to-end LV volumes estimation. Third, we explore the representational capacity of different slices, and propose a fusion strategy to improve the prediction accuracy. Results: The evaluation results show that the proposed method outperforms other state-of-the-art LV volumes estimation methods on the open accessible benchmark datasets. The clinical indexes derived from the predicted volumes agree well with the ground truth (EDV: R2=0.974, RMSE=9.6ml; ESV: R2=0.976, RMSE=7.1ml; EF: R2=0.828, RMSE =4.71%). Conclusion: Experimental results prove that the proposed method may be useful for LV volumes prediction task. Significance: The proposed method not only has application potential for cardiac diseases screening for large-scale CMR data, but also can be extended to other medical image research fieldsComment: to appear on Transactions on Biomedical Engineerin

Sora Generates Videos with Stunning Geometrical Consistency

The recently developed Sora model [1] has exhibited remarkable capabilities in video generation, sparking intense discussions regarding its ability to simulate real-world phenomena. Despite its growing popularity, there is a lack of established metrics to evaluate its fidelity to real-world physics quantitatively. In this paper, we introduce a new benchmark that assesses the quality of the generated videos based on their adherence to real-world physics principles. We employ a method that transforms the generated videos into 3D models, leveraging the premise that the accuracy of 3D reconstruction is heavily contingent on the video quality. From the perspective of 3D reconstruction, we use the fidelity of the geometric constraints satisfied by the constructed 3D models as a proxy to gauge the extent to which the generated videos conform to real-world physics rules. Project page: https://sora-geometrical-consistency.github.io/Comment: 5 pages, 3 figure

A Quasi-27-Day Oscillation Activity From the Troposphere to the Mesosphere And Lower Thermosphere at Low Latitudes

Using meteor radar, radiosonde observations and MERRA-2 reanalysis data from 12 August to 31 October 2006, we report a dynamical coupling from the tropical lower atmosphere to the mesosphere and lower thermosphere through a quasi-27-day intraseasonal oscillation (ISO). It is interesting that the quasi-27-day ISO is observed in the troposphere, stratopause and mesopause regions, exhibiting a three-layer structure. In the MLT, the amplitude in the zonal wind increases from about 4 ms−1 at 90 km to 15 ms−1 at 100 km, which is diferent from previous observations that ISOs occurs generally in winter with an amplitude peak at about 80–90 km, and then are rapidly weakened with increasing height. Outgoing longwave radiation (OLR) and specifc humidity demonstrate that there is a quasi-27-day periodicity in convective activity in the tropics, which causes the ISO of the zonal wind and gravity wave (GW) activity in the troposphere. The upward propagating GWs are further modulated by the oscillation in the troposphere and upper stratosphere. As the GWs propagate to the MLT, the quasi-27-day oscillation in the wind feld is induced with a clear phase opposite to that in the lower atmosphere through instability and dissipation of these modulated GWs. Wavelet analysis shows that the quasi-27-day variability in the MLT appears as a case event rather than a persistent phenomenon, and has not a clear corresponding relation with the solar rotation efect within 1 year of observations

MD-Manifold: A Medical Distance Based Manifold Learning Approach for Heart Failure Readmission Prediction

Dimension reduction is considered as a necessary technique in Electronic Healthcare Records (EHR) data processing. However, no existing work addresses both of the two points: 1) generating low-dimensional representations for each patient visit; and 2) taking advantage of the well-organized medical concept structure as the domain knowledge. Hence, we propose a new framework to generate low-dimensional representations for medical data records by combining the concept-structure based distance with manifold learning. To demonstrate the efficacy, we generated low-dimensional representations for hospital visits of heart failure patients, which was further used for a 30-day readmission prediction. The experiments showed a great potential of the proposed representations (AUC = 60.7%) that has comparative predictive power of the state-of-the-art methods, including one hot encoding representations (AUC = 60.1%) and PCA representations (AUC = 58.3%), with much less training time (improved by 99%). The proposed framework can also be generalized to various healthcare-related prediction tasks, such as mortality prediction.This proceeding is published as Wang, Shaodong, Qing Li, and Wenli Zhang. "MD-Manifold: A Medical Distance Based Manifold Learning Approach for Heart Failure Readmission Prediction." (2021). Proceedings of the 54th Hawaii International Conference on System Sciences 2021. http://hdl.handle.net/10125/71209