Respiration-introduced tumor location uncertainty is a challenge in lung percutaneous interventions, especially for the respiratory motion estimation of the tumor and surrounding vessel structures. In this work, a local motion modeling method is proposed based on whole-chest computed tomography (CT) and CT-fluoroscopy (CTF) scans. A weighted sparse statistical modeling (WSSM) method that can accurately capture location errors for each landmark point is proposed for lung motion prediction. By varying the sparse weight coefficients of the WSSM method, newly input motion information is approximately represented by a sparse linear combination of the respiratory motion repository and employed to serve as prior knowledge for the following registration process. We have also proposed an adaptive motion prior-based registration method to improve the motion prediction accuracy of the motion model in the region of interest (ROI). This registration method adopts a B-spline scheme to interactively weight the relative influence of the prior knowledge, model surface and image intensity information by locally controlling the deformation in the CTF image region. The proposed method has been evaluated on 15 image pairs between the end-expiratory (EE) and end-inspiratory (EI) phases and 31 four-dimensional CT (4DCT) datasets. The results reveal that the proposed WSSM method achieved a better motion prediction performance than other existing lung statistical motion modeling methods, and the motion prior-based registration method can generate more accurate local motion information in the ROI.Left ventricular stroke work is an important prognostic marker to analyze cardiac function. Standard values for children are, however, missing. For clinicians, standards can help to improve the treatment decision of heart failures. For engineers, they can help to optimize medical devices. In this study, we estimated the left ventricular stroke work for children based on modeled pressure-volume loops. A lumped parameter model was fitted to clinical data of 340 healthy children. Reference curves for standard values were created over age, weight, and height. Left ventricular volume was measured with 3D echocardiography, while maximal ventricular pressure was approximated with a regression model from the literature. For validation of this method, we used 18 measurements acquired by a conductance catheter in 11 patients. The method demonstrated a low absolute mean difference of 0.033 J (SD 0.031 J) for stroke work between measurement and estimation, while the percentage error was 21.66 %. According to the resulting reference curves, left ventricular stroke work of newborns has a median of 0.06 J and increases to 1.15 J at the age of 18 years. Stroke work increases over weight and height in a similar trend. The percentile curves depict the distribution. We demonstrate how reference curves can be used for quantification of differences and comparison in patients. The precise segmentation of kidneys and kidney tumors can help medical specialists to diagnose diseases and improve treatment planning, which is highly required in clinical practice. Manual segmentation of the kidneys is extremely time-consuming and prone to variability between different specialists due to their heterogeneity. Because of this hard work, computational techniques, such as deep convolutional neural networks, have become popular in kidney segmentation tasks to assist in the early diagnosis of kidney tumors. In this study, we propose an automatic method to delimit the kidneys in computed tomography (CT) images using image processing techniques and deep convolutional neural networks (CNNs) to minimize false positives. The proposed method has four main steps (1) acquisition of the KiTS19 dataset, (2) scope reduction using AlexNet, (3) initial segmentation using U-Net 2D, and (4) false positive reduction using image processing to maintain the largest elements (kidneys). The proposed method was evaluated in 210 CTs from the KiTS19 database and obtained the best result with an average Dice coefficient of 96.33%, an average Jaccard index of 93.02%, an average sensitivity of 97.42%, an average specificity of 99.94% and an average accuracy of 99.92%. In the KiTS19 challenge, it presented an average Dice coefficient of 93.03%. In our method, we demonstrated that the kidney segmentation problem in CT can be solved efficiently using deep neural networks to define the scope of the problem and segment the kidneys with high precision and with the use of image processing techniques to reduce false positives.In our method, we demonstrated that the kidney segmentation problem in CT can be solved efficiently using deep neural networks to define the scope of the problem and segment the kidneys with high precision and with the use of image processing techniques to reduce false positives.Protein-protein interactions (PPIs) are involved with most cellular activities at the proteomic level, making the study of PPIs necessary to comprehending any biological process. Machine learning approaches have been explored, leading to more accurate and generalized PPIs predictions. CGS 21680 mouse In this paper, we propose a predictive framework called StackPPI. First, we use pseudo amino acid composition, Moreau-Broto, Moran and Geary autocorrelation descriptor, amino acid composition position-specific scoring matrix, Bi-gram position-specific scoring matrix and composition, transition and distribution to encode biologically relevant features. Secondly, we employ XGBoost to reduce feature noise and perform dimensionality reduction through gradient boosting and average gain. Finally, the optimized features that result are analyzed by StackPPI, a PPIs predictor we have developed from a stacked ensemble classifier consisting of random forest, extremely randomized trees and logistic regression algorithms. Five-fold cross-validation shows StackPPI can successfully predict PPIs with an ACC of 89.27%, MCC of 0.7859, AUC of 0.9561 on Helicobacter pylori, and with an ACC of 94.64%, MCC of 0.8934, AUC of 0.9810 on Saccharomyces cerevisiae. We find StackPPI improves protein interaction prediction accuracy on independent test sets compared to the state-of-the-art models. Finally, we highlight StackPPI's ability to infer biologically significant PPI networks. StackPPI's accurate prediction of functional pathways make it the logical choice for studying the underlying mechanism of PPIs, especially as it applies to drug design. The datasets and source code used to create StackPPI are available here https//github.com/QUST-AIBBDRC/StackPPI/.