Forward and Backward Continuation Ratio Models for Ordinal Response Variables

There are different types of continuation ratio (CR) models for ordinal response variables. The different model equations, corresponding parameterizations, and nonequivalent results are confusing. The purpose of this study is to introduce different types of forward and backward CR models, demonstrate how to implement these models using Stata, and compare the results using data from the Educational Longitudinal Study of 2002 (ELS:2002)

Genus Hyloconis new to China, with descriptions of two new species (Lepidoptera: Gracillariidae, Lithocolletinae)

The genus Hyloconis Kumata is for the first time recorded in China. Hyloconis bicruris sp. n. and H. luminata sp. n. are described. Generic characters are amended based on the Chinese material. Photographs of adults, illustration of genital structures and wing venation are provided, along with a key to all the known specie

A Rough-Set-basedClustering Algorithm for Multi-stream

AbstractThe paper propose a rough-set-based clustering algorithm for multiple data stream, which solve the problem that existing clustering algorithm for multiple data streams can not take into account conflicts between clustering quality and efficiency. Firstly, the algorithm calculates the distance between data stream to determine the initial equivalence relations, and calculates the similarity between the initial equivalence relation to determine the initial cluster. In the second place, the similarity between the initial clusters is used to merge the initial clusters. Finally, k-means clustering algorithm is called to dynamically adjust the clustering results, and then real-time clustering structure is obtained. In conclusion Experimental results demonstrated that the algorithm has higher efficiency and clustering quality

The Effects of Service-Learning Participation on Pre-Internship Educators’ Teachers’ Sense of Efficacy

This study aimed to determine if pre-internship teacher education students’ participation in service-learning activities in K-12 classrooms would significantly affect their teachers’ sense of efficacy (TSE). A secondary focus sought to determine if one type of service-learning activity (e.g., whole-class instruction) would affect teacher efficacy more than another (e.g., small-group tutoring). Findings revealed that pre-internship service-learners in both types of service-learning activities increased significantly in their TSE. However, neither type of service-learning activity was superior to the other as measured by the minimally accepted .05 level. The discussion focuses on factors shared between the two service-learning designs that might mediate a positive mastery experience.L’objectif de cette étude était de déterminer si la participation par des étudiants en pédagogie à des activités de bénévolat dans des classes de la maternelle à la 12e année augmenterait de façon significative leur sentiment d’être efficaces comme enseignants. Un deuxième objectif consistait à déterminer si un type d’activité de bénévolat (par ex. l’enseignement à toute la classe) affecterait l’efficacité d’un enseignant plus qu’une autre (par ex. le tutorat à de petits groupes). Les résultats indiquent que les deux types d’activité ont accru de façon significative le sentiment d’efficacité comme enseignants chez les étudiants bénévoles. Toutefois, les paramètres selon le seuil minimal de 0,05 ont révélé qu’aucune activité n’était supérieure à l’autre. La discussion porte sur des facteurs communs aux deux activités de bénévolat et qui pourraient entrainer une expérience d’apprentissage fructueuse

A CLT for the LSS of large dimensional sample covariance matrices with diverging spikes

In this paper, we establish the central limit theorem (CLT) for linear spectral statistics (LSS) of large-dimensional sample covariance matrix when the population covariance matrices are not uniformly bounded, which is a nontrivial extension of the Bai-Silverstein theorem (BST) (2004). The latter has strongly influenced the development of high-dimensional statistics, especially in applications of random matrix theory to statistics. However, the assumption of uniform boundedness of the population covariance matrices has seriously limited the applications of the BST. The aim of this paper is to remove the barriers for the applications of the BST. The new CLT, allows spiked eigenvalues to exist, which may be bounded or tend to infinity. An important feature of our result is that the roles of either spiked eigenvalues or the bulk eigenvalues predominate in the CLT, depending on which variance is nonnegligible in the summation of the variances. The CLT for LSS is then applied to compare four linear hypothesis tests: The Wilk's likelihood ratio test, the Lawly-Hotelling trace test, the Bartlett-Nanda-Pillai trace test, and Roy's largest root test. We also derive and analyze their power function under particular alternatives.Comment: Comparing with the old manuscript, we modified the title of the paper. arXiv admin note: text overlap with arXiv:2205.07280. arXiv admin note: text overlap with arXiv:2205.0728

Another Look at Resampling: Replenishing Small Samples with Virtual Data through S-SMART

A new resampling method is introduced to generate virtual data through a smoothing technique for replenishing small samples. The replenished analyzable sample retains the statistical properties of the original small sample, has small standard errors and possesses adequate statistical power

Effects Of Web-Based Interactive Modules On Engineering Students’ Learning Motivations

The purpose of this study is to assess the impact of a newly developed modules, Interactive Web-Based Visualization Tools for Gluing Undergraduate Fuel Cell Systems Courses system (IGLU), on learning motivations of engineering students using two samples (n1=144 and n2=135) from senior engineering classes. The multivariate analysis results revealed that the participants had a significant increase in their learning motivation after the treatment with the IGLU modules. This result was cross-validated with the two samples, in which the motivation mean posttest scores are significantly higher than the mean pretest scores, systematically (Sample 1: the mean score is increased by 2.09 [.32, 3.87] points, p = .021; Sample 2: the mean score is increased by 1.38 [.14, 2.61] points, p = .029). With the use of instructional technology prevailing in current university courses, the education initiative of the IGLU system and the assessment of its impact on student learning motivation provide us information to improve the modules to serve a more diverse student body. It will greatly help the development of engineering educational curriculum. With regards to the statistical inference, it is desirable to conduct further studies with a quasi-experiment control group design to assess the program effect focusing on student learning and its associations with student learning motivations and learning styles

Tuberous Sclerosis complex protein 2-independent activation of mTORC1 by human cytomegalovirus pUL38

The mammalian target of rapamycin complex 1 (mTORC1) controls cell growth and anabolic metabolism and is a critical host factor activated by human cytomegalovirus (HCMV) for successful infection. The multifunctional HCMV protein pUL38 previously has been reported to activate mTORC1 by binding to and antagonizing tuberous sclerosis complex protein 2 (TSC2) (J. N. Moorman et al., Cell Host Microbe 3:253–262, 2008, http://dx.doi.org/10.1016/j.chom.2008.03.002). pUL38 also plays a role in blocking endoplasmic reticulum stress-induced cell death during HCMV infection. In this study, we showed that a mutant pUL38 lacking the N-terminal 24 amino acids (pHA-UL38(25–331)) was fully functional in suppressing cell death during infection. Interestingly, pHA-UL38(25–331) lost the ability to interact with TSC2 but retained the ability to activate mTORC1, although to a lesser extent than full-length pHA-UL38. Recombinant virus expressing pHA-UL38(25–331) replicated with ∼10-fold less efficiency than the wild-type virus at a low multiplicity of infection (MOI), but it grew similarly well at a high MOI, suggesting an MOI-dependent importance of pUL38-TSC2 interaction in supporting virus propagation. Site-directed mutational analysis identified a TQ motif at amino acid residues 23 and 24 as critical for pUL38 interaction with TSC2. Importantly, when expressed in isolation, the TQ/AA substitution mutant pHA-UL38 TQ/AA was capable of activating mTORC1 just like pHA-UL38(25–331). We also created TSC2-null U373-MG cell lines by CRISPR genome editing and showed that pUL38 was capable of further increasing mTORC1 activity in TSC2-null cells. Therefore, this study identified the residues important for pUL38-TSC2 interaction and demonstrated that pUL38 can activate mTORC1 in both TSC2-dependent and -independent manners. IMPORTANCE HCMV, like other viruses, depends exclusively on its host cell to propagate. Therefore, it has developed methods to protect against host stress responses and to usurp cellular processes to complete its life cycle. mTORC1 is believed to be important for virus replication, and HCMV maintains high mTORC1 activity despite the stressful cellular environment associated with infection. mTORC1 inhibitors suppressed HCMV replication in vitro and reduced the incidence of HCMV reactivation in transplant recipients. We demonstrated that mTORC1 was activated by HCMV protein pUL38 in both TSC2-dependent and TSC2-independent manners. The pUL38-independent mode of mTORC1 activation also has been reported. These novel findings suggest the evolution of sophisticated approaches whereby HCMV activates mTORC1, indicating its importance in the biology and pathogenesis of HCMV

A pipeline for improved QSAR analysis of peptides: physiochemical property parameter selection via BMSF, near-neighbor sample selection via semivariogram, and weighted SVR regression and prediction

In this paper, we present a pipeline to perform improved QSAR analysis of peptides. The modeling involves a double selection procedure that first performs feature selection and then conducts sample selection before the final regression analysis. Five hundred and thirty-one physicochemical property parameters of amino acids were used as descriptors to characterize the structure of peptides. These high-dimensional descriptors then go through a feature selection process given by the Binary Matrix Shuffling Filter (BMSF) to obtain a set of important low dimensional features. Each descriptor that passed the BMSF filtering also receives a weight defined through its contribution to reduce the estimation error. These selected features were served as the predictors for subsequent sample selection and modeling. Based on the weighted Euclidean distances between samples, a common range was determined with high-dimensional semivariogram and then used as a threshold to select the near-neighbor samples from the training set. For each sample to be predicted, the QSAR model was established using SVR with the weighted, selected features based on the exclusive set of near-neighbor training samples. Prediction was conducted for each test sample accordingly. The performances of this pipeline are tested with the QSAR analysis of angiotensin-converting enzyme (ACE) inhibitors and HLA-A*0201 data sets. Improved prediction accuracy was obtained in both applications. This pipeline can optimize the QSAR modeling from both the feature selection and sample selection perspectives. This leads to improved accuracy over single selection methods. We expect this pipeline to have extensive application prospect in the field of regression prediction