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Background: This study specifically focused on anatomical MRI characterization of the low shear stress-induced atherosclerotic plaque in mice. We used machine learning algorithms to analyze multiple correlation factors of plaque to generate predictive models and to find the predictive factor for vulnerable plaque., Methods: Branches of the left carotid artery in apoE -/- and C57BL/6J mice were ligated to produce the partial left carotid artery model. Before surgery, and 7, 14, and 28 days after surgery, in vivo serial MRI measurements of carotid artery diameter were obtained. Meanwhile, proximal blood flow was evaluated. After image acquisition and animal sacrifice, carotid arteries were harvested for histological analysis. Support vector machine (SVM) and decision tree (DT) were used to select features and generate predictive models of vulnerable plaque progression., Result: Seven days after surgery, neointima formation was visualized on micro-MRI in both apoE -/- and C57BL/6J mice. Ultrasonography showed that blood flow had significantly decreased compared to that in the contralateral artery. Partial ligation of the carotid artery for 4 weeks in apoE -/- mice induced vulnerable plaque; however, in C57BL/6J mice this same technique performed for 4 weeks induced arterial stenosis. Contralateral carotid artery diameter at 7 days after surgery was the most reliable predictive factor in plaque progression. We achieved over 87.5% accuracy, 80% sensitivity, and 95% specificity with SVM. The accuracy, sensitivity, and specificity for the DT classifier were 90, 90, and 90%, respectively., Conclusions: This study is the first to demonstrate that SVM and DT methods could be suitable models for identifying vulnerable plaque progression in mice. And contralateral artery enlargement can predict the vulnerable plaque in carotid artery at the very early stage. It may be a valuable tool which helps to optimize the clinical work flow process by providing more decision in selecting patients for treatment.

This article used support vector machine (SVM) algorithm to recognize the particles in urine sediment in this paper. After feature extraction, cross-validation method and the contour chart of the accuracy were implemented to select the kernel function and the parameters of SVM, and according to the characteristics of SVM classifier and sample data, Multi-SVMs with two-level-classifier was successfully designed and A classification matrix was eventually obtained. The evaluation by using clinical data and comparative results with the artificial neural network have demonstrated that the proposed algorithm gets better results.

The assessment of closed-circuit television (CCTV) as an infrastructure designed to improve the policing environment has gained widespread attention. The effect of CCTV is influenced not only by the time of installation, however, but also by locations, making it challenging to assess accurately from a single dimension. Existing studies have analyzed the differences in assessment results due to the period of installation, but little has been said about the sensitivity of assessment results to spatial changes. Moreover, existing assessment results rarely consider the varying degrees of influence that CCTV might have in different locations. To address these issues, we use the weighted displacement quotient (WDQ) algorithm to assess the effect of 603 CCTVs installed by the Green Light Project in Detroit from 2016 to 2019. This assessment examines the sensitivity of the WDQ algorithm to spatial changes, particularly when the radius of the target area is altered. We also optimize the matching algorithm and consider the spatial heterogeneity of CCTV's effects in the assessment process. The results show that over 50 percent of the CCTVs installed have a diffusion of benefits on crime reduction (WDQ > 0), and the assessment results obtained using the WDQ algorithm are highly sensitive to spatial changes. These findings provide valuable insights for subsequent assessments of CCTV, and for the optimization of CCTV installation and layout to enhance their effect. [ABSTRACT FROM AUTHOR]

The leaf area index (LAI) is a crucial parameter for understanding the exchanges of mass and energy between terrestrial ecosystems and the atmosphere. In this study, the Data Assimilation Research Testbed (DART) has been successfully coupled to the Community Land Model with explicit carbon and nitrogen components (CLM4CN) by assimilating Global Land Surface Satellite (GLASS) LAI data. Within this framework, four sequential assimilation algorithms, including the kernel filter (KF), the ensemble Kalman filter (EnKF), the ensemble adjust Kalman filter (EAKF), and the particle filter (PF), are thoroughly analyzed and compared. The results show that assimilating GLASS LAI into the CLM4CN is an effective method for improving model performance. In detail, the assimilation accuracies of the EnKF and EAKF algorithms are better than those of the KF and PF algorithm. From the perspective of the average and RMSD, the PF algorithm performs worse than the EAKF and EnKF algorithms because of the gradually reduced acceptance of observations with assimilation steps. In other words, the contribution of the observations to the posterior probability during the assimilation process is reduced. The EAKF algorithm is the best method because the matrix is adjusted at each time step during the assimilation procedure. If all the observations are accepted, the analyzed LAI seem to be better than that when some observations are rejected, especially in low-latitude regions. [ABSTRACT FROM AUTHOR]

Investigates time and space efficiency in scheduling and executing parallel irregular computations on distributed-memory machines. Scheduling techniques proposed; Implementation of the technique in the RAPID programming tool; Directed acyclic dependence graphs (DAGs) scheduling algorithms; Importance of optimizing memory in solving large parallel scientific problems.

Background: This study specifically focused on anatomical MRI characterization of the low shear stress-induced atherosclerotic plaque in mice. We used machine learning algorithms to analyze multiple correlation factors of plaque to generate predictive models and to find the predictive factor for vulnerable plaque.Methods: Branches of the left carotid artery in apoE-/- and C57BL/6J mice were ligated to produce the partial left carotid artery model. Before surgery, and 7, 14, and 28 days after surgery, in vivo serial MRI measurements of carotid artery diameter were obtained. Meanwhile, proximal blood flow was evaluated. After image acquisition and animal sacrifice, carotid arteries were harvested for histological analysis. Support vector machine (SVM) and decision tree (DT) were used to select features and generate predictive models of vulnerable plaque progression.Result: Seven days after surgery, neointima formation was visualized on micro-MRI in both apoE-/- and C57BL/6J mice. Ultrasonography showed that blood flow had significantly decreased compared to that in the contralateral artery. Partial ligation of the carotid artery for 4 weeks in apoE-/- mice induced vulnerable plaque; however, in C57BL/6J mice this same technique performed for 4 weeks induced arterial stenosis. Contralateral carotid artery diameter at 7 days after surgery was the most reliable predictive factor in plaque progression. We achieved over 87.5% accuracy, 80% sensitivity, and 95% specificity with SVM. The accuracy, sensitivity, and specificity for the DT classifier were 90, 90, and 90%, respectively.Conclusions: This study is the first to demonstrate that SVM and DT methods could be suitable models for identifying vulnerable plaque progression in mice. And contralateral artery enlargement can predict the vulnerable plaque in carotid artery at the very early stage. It may be a valuable tool which helps to optimize the clinical work flow process by providing more decision in selecting patients for treatment. [ABSTRACT FROM AUTHOR]