Mechanistically, we found that the R140H mutation causes intracellular accumulation of Ile-introns derived from isoleucine pre-tRNAs and 5' tRNA fragments derived from tyrosine pre-tRNAs, suggesting that these two types of RNA fragments were cooperatively or independently involved in the onset and progression of the disease. Taken together, the CLP1-R140H mouse model provided new insights into the pathogenesis of neurodegenerative diseases, such as PCH10, caused by genetic mutations in tRNA metabolism-related molecules.We have developed a new real-time neutron detector, which is able to measure a direct neutron beam of boron neutron capture therapy. The detector consists of both a 40-μm-thick pn diode and around 0.09-μm-thick LiF neutron converter. Experimental results indicate that this neutron detector can measure neutron flux up to 1 × 109 (cm-2 s-1), separately from gamma rays around 500 mGy/h. The measured depth distribution of neutron flux in an acrylic block is in agreement with the activation results of gold.An improved semi automatic technique for counting the tracks formed on LR-115 films with the advantages of simplicity and speed is reported. In this technique, a microscope with a Dino-Eye eyepiece camera is coupled to a PC equipped with a python compiler. After etching of the LR-115 film, 16 track images were taken to find the track density. The images generated were binarized before application of a Python algorithm. This process does not disfigure the original track and increase the spatial resolution. learn more The batch process option in Jasc Paint Shop Pro was used to binarize the 16 images simultanously. The Python program automatically counts the total number of tracks formed on the 16 track images. This method was compared with manual counting and counting with the software program-Scion image to verify it. The results showed that the proposed method is reasonably good at counting the tracks. It is a faster and less time-consuming method, and will facilitate measurements of etched tracks in a variety of applications.Least squares twin support vector machine (LSTSVM) is an effective and efficient learning algorithm for pattern classification. However, the distance in LSTSVM is measured by squared L2-norm metric that may magnify the influence of outliers. In this paper, a novel robust least squares twin support vector machine framework is proposed for binary classification, termed as CL2,p-LSTSVM, which utilizes capped L2,p-norm distance metric to reduce the influence of noise and outliers. The goal of CL2,p-LSTSVM is to minimize the capped L2,p-norm intra-class distance dispersion, and eliminate the influence of outliers during training process, where the value of the metric is controlled by the capped parameter, which can ensure better robustness. The proposed metric includes and extends the traditional metrics by setting appropriate values of p and capped parameter. This strategy not only retains the advantages of LSTSVM, but also improves the robustness in solving a binary classification problem with outliers. However, the nonconvexity of metric makes it difficult to optimize. We design an effective iterative algorithm to solve the CL2,p-LSTSVM. In each iteration, two systems of linear equations are solved. Simultaneously, we present some insightful analyses on the computational complexity and convergence of algorithm. Moreover, we extend the CL2,p-LSTSVM to nonlinear classifier and semi-supervised classification. Experiments are conducted on artificial datasets, UCI benchmark datasets, and image datasets to evaluate our method. Under different noise settings and different evaluation criteria, the experiment results show that the CL2,p-LSTSVM has better robustness than state-of-the-art approaches in most cases, which demonstrates the feasibility and effectiveness of the proposed method.Concept drift is an important issue in the field of streaming data mining. However, how to maintain real-time model convergence in a dynamic environment is an important and difficult problem. In addition, the current methods have limited ability to deal with the problem of streaming data classification for complex nonlinear problems. To solve these problems, a selective ensemble-based online adaptive deep neural network (SEOA) is proposed to address concept drift. First, the adaptive depth unit is constructed by combining shallow features with deep features and adaptively controls the information flow in the neural network according to changes in streaming data at adjacent moments, which improves the convergence of the online deep learning model. Then, the adaptive depth units of different layers are regarded as base classifiers for ensemble and weighted dynamically according to the loss of each classifier. In addition, a dynamic selection of base classifiers is adopted according to the fluctuation of the streaming data to achieve a balance between stability and adaptability. The experimental results show that the SEOA can effectively contend with different types of concept drift and has good robustness and generalization.Dissolved oxygen (DO) is an important indicator of river health for environmental engineers and ecological scientists to understand the state of river health. This study aims to evaluate the reliability of four feature selector algorithms i.e., Boruta, genetic algorithm (GA), multivariate adaptive regression splines (MARS), and extreme gradient boosting (XGBoost) to select the best suited predictor of the applied water quality (WQ) parameters; and compare four tree-based predictive models, namely, random forest (RF), conditional random forests (cForest), RANdom forest GEneRator (Ranger), and XGBoost to predict the changes of dissolved oxygen (DO) in the Klang River, Malaysia. The total features including 15 WQ parameters from monitoring site data and 7 hydrological components from remote sensing data. All predictive models performed well as per the features selected by the algorithms XGBoost and MARS in terms applied statistical evaluators. Besides, the best performance noted in case of XGBoost predictive model among all applied predictive models when the feature selected by MARS and XGBoost algorithms, with the coefficient of determination (R2) values of 0.84 and 0.85, respectively, nonetheless the marginal performance came up by Boruta-XGBoost model on in this scenario.