Fully Bayesian Analysis of the Relevance Vector Machine Classification for Imbalanced Data

Relevance Vector Machine (RVM) is a supervised learning algorithm extended from Support Vector Machine (SVM) based on the Bayesian sparsity model. Compared with the regression problem, RVM classification is difficult to be conducted because there is no closed-form solution for the weight parameter posterior. Original RVM classification algorithm used Newton's method in optimization to obtain the mode of weight parameter posterior then approximated it by a Gaussian distribution in Laplace's method. It would work but just applied the frequency methods in a Bayesian framework. This paper proposes a Generic Bayesian approach for the RVM classification. We conjecture that our algorithm achieves convergent estimates of the quantities of interest compared with the nonconvergent estimates of the original RVM classification algorithm. Furthermore, a Fully Bayesian approach with the hierarchical hyperprior structure for RVM classification is proposed, which improves the classification performance, especially in the imbalanced data problem. By the numeric studies, our proposed algorithms obtain high classification accuracy rates. The Fully Bayesian hierarchical hyperprior method outperforms the Generic one for the imbalanced data classification.Comment: 24 Pages, 3 figures, preprint to submit to Electronic Journal of Statistic

The Family Language Policy of Chinese Immigrant Families in a New Gateway State: An Explanatory Sequential Mixed Methods Study on Parents’ Views

Among various groups of immigrant students who face challenges in maintaining their heritage languages (HLs), Asian American students are especially vulnerable to HL loss (Fillmore, 2000; Pew Research Center, 2012). As the loss of HLs has negative effects on children’s academic, psychological, and emotional well-being (Tse, 2000; Li & Wen, 2015), there is an urgent need to support immigrant students’ HL maintenance. Among efforts in countering the HL loss, families often play the most important role by implementing family language policies (FLP; Curdt-Christiansen & Lanza, 2018). To better understand factors influencing immigrant parents’ FLP, this study uses an explanatory sequential mixed methods design (Creswell & Clark, 2018) to analyze parents’ perspectives, practices, and efforts on their children’s HL maintenance, using the data from Chinese immigrant parents in North Carolina. This study collects quantitative data from an online survey on parents’ demographics and general FLP and then collects qualitative data through interviews. In between these two phases, the quantitative results are used to inform the design of the qualitative interview protocols and the sampling of interviewees from the survey participants. The qualitative results are analyzed through an AsianCrit lens (Museus, 2013) and are connected back to the quantitative data to provide further explanation and contextualization. Findings reveal that parents’ immigration history, racialized experiences living in the U.S. and North Carolina, and their children’s schooling experience contribute to their various language ideologies towards their children’s HL maintenance. These language ideologies, together with the larger sociocultural, socioeconomic, sociohistorical, and sociopolitical context, shape parents’ language practices and management strategies that support their children’s bilingual development. Implications of findings for researchers, educators, and policymakers are also discussed.Doctor of Philosoph

Profilometry with volume holographic imaging

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2006.Includes bibliographical references (p. 127-133).High resolution, non-contact object profile measurement (profilometry) at long working distance is important in a number of application areas, such as precise parts manufacturing, optical element grounding and polishing, adversary target identification in military, terrace profiling, etc. The Volume Holographic (VH) lens is a novel optical element which process the incident light field in a 3D fashion. It has been shown with promising applications in object profile acquisition and 3D imaging areas. In this thesis, we propose, design and implemented a number of volume holographic computational imaging systems for profilometry related applications. We show that the rich functionalities of the VH lens can be exploited to process the incident optical field. Some of the unique imaging behavior can not be easily achieved by using conventional optics. We first develop the theoretical framework for investigating the VH lens optical behavior. We concentrate on a simple design: using the VH lens as the spatial spectrum plane filter in a 4F imaging system. We derived the point spread function (PSF), the depth resolution, the diffraction field distribution of the proposed imaging system. Experimental system characterization and profilometry measurements were carried out with our setups.(cont.) We find the resolution of the volume holographic imaging (VHI) profilometry system degrades quadratically with the increase of working distance. We addressed this problem by two approaches: 1. We discuss the effect of objective optics design on the VHI resolution. We proposed and implemented the use of appropriately designed telephoto objective optics to achieve very good resolution at long working distance. 2. We developed a maximum likelihood estimation based post-processing method to improve the depth resolution by more than 5 times. An important issue on VHI profilometry is the "slit-shaped" limited field of view (FoV). This makes measurement over the entire big object is very time consuming because scanning is necessary. Otherwise hundreds or thousands of VH lenses must be multiplexed on a single crystal to concatenate the slit FoV of each VH lens to form a wide exit window. However the multiplexing method suffers the "M/#" penalty on photon efficiency. We solved this problem by utilizing the wavelength degeneracy of the VH lens and designed a rainbow illumination VHI to expand the FoV.(cont.) We also extended the application of VHI to hyper-spectral imaging. The experimental implementation of the hyper-spectral imaging system shows it is capable of not only reconstructing the 3D spatial profile but also restoring the spectral information of the object, both at high resolution. Finally, we conclude with some directions for the future work in this emerging field.by Wenyang Sun.Ph.D

Fully Bayesian Analysis of Relevance Vector Machine Classification With Probit Link Function for Imbalanced Data Problem

The original RVM classification model uses the logistic link function to build the likelihood function making the model hard to be conducted since the posterior of the weight parameter has no closed-form solution. This article proposes the probit link function approach instead of the logistic one for the likelihood function in the RVM classification model, namely PRVM (RVM with the probit link function). We show that the posterior of the weight parameter in PRVM follows the Multivariate Normal distribution and achieves a closed-form solution. A latent variable is needed in our algorithms to simplify the Bayesian computation greatly, and its conditional posterior follows a truncated Normal distribution. Compared with the original RVM classification model, our proposed one is a Fully Bayesian approach, and it has a more efficient computation process. For the prior structure, we first consider the Normal-Gamma independent prior to propose a Generic Bayesian PRVM algorithm. Furthermore, the Fully Bayesian PRVM algorithm with a hierarchical hyperprior structure is proposed, which improves the classification performance, especially in the imbalanced data problem

A semiparametric spatial dynamic model

Stimulated by the Boston house price data, in this paper, we propose a semiparametric spatial dynamic model, which extends the ordinary spatial autoregressive models to accommodate the effects of some covariates associated with the house price. A profile likelihood based estimation procedure is proposed. The asymptotic normality of the proposed estimators are derived. We also investigate how to identify the parametric/nonparametric components in the proposed semiparametric model. We show how many unknown parameters an unknown bivariate function amounts to, and propose an AIC/BIC of nonparametric version for model selection. Simulation studies are conducted to examine the performance of the proposed methods. The simulation results show our methods work very well. We finally apply the proposed methods to analyze the Boston house price data, which leads to some interesting finding

A novel non‑selective atypical PKC agonist could protect neuronal cell line from A β ‑oligomer induced toxicity by suppressing A β generation

Atypical protein kinase C (aPKCs) serve key functions in embryonic development by regulating apical-basal polarity. Previous studies have shed light on their roles during adulthood, especially in the development of Alzheimer\u27s disease (AD). Although the crystal structure of PKCι has been resolved, an agonist of aPKCs remains to be discovered. In the present study, by using the Discovery Studio program and LibDock methodology, a small molecule library (K66-X4436 KINA Set) of compounds were screened for potential binding to PKCι. Subsequently, the computational docking results were validated using affinity selection-mass spectrometry, before in vitro kinase activity was used to determine the function of the hit compounds. A cell-based model assay that can mimic the pathology of AD was then established and used to assess the function of these hit compounds. As a result, the aPKC agonist Z640 was identified, which could bind to PKCι in silico, in vitro and in this cell-based model. Z640 was further confirmed as a non-selective aPKC agonist that can activate the kinase activity of both PKCι and PKCζ. In the cell-based assay, Z640 was found to protect neuronal cell lines from amyloid-β (Aβ) oligomer-induced cell death by reducing reactive oxygen species production and restore mitochondrial function. In addition, Z640 could reduce Aβ40 generation in a dose-dependent manner and shift amyloid precursor protein processing towards the non-amyloid pathway. To conclude, the present study is the first, to the best of the authors\u27 knowledge to identify an aPKC agonist by combining computer-assisted drug discovery and cell-based assays. The present study also revealed that aPKC agonists have therapeutic potential for the treatment of AD

Analyses of long non-coding RNA and mRNA profiling in the spleen of diarrheic piglets caused by Clostridium perfringens type C

Background Clostridium perfringens (C. perfringens) type C is the most common bacteria causing piglet diarrheal disease and it greatly affects the economy of the global pig industry. The spleen is an important immune organ in mammals; it plays an irreplaceable role in resisting and eradicating pathogenic microorganisms. Based on different immune capacity in piglets, individuals display the resistance and susceptibility to diarrhea caused by C. perfringens type C. Recently, long non-coding RNA (lncRNA) and mRNA have been found to be involved in host immune and inflammatory responses to pathogenic infections. However, little is known about spleen transcriptome information in piglet diarrhea caused by C. perfringens type C. Methods Hence, we infected 7-day-old piglets with C. perfringens type C to lead to diarrhea. Then, we investigated lncRNA and mRNA expression profiles in spleens of piglets, including control (SC), susceptible (SS), and resistant (SR) groups. Results As a result, 2,056 novel lncRNAs and 2,417 differentially expressed genes were found. These lncRNAs shared the same characteristics of fewer exons and shorter length. Bioinformatics analysis identified that two lncRNAs (ALDBSSCT0000006918 and ALDBSSCT0000007366) may be involved in five immune/inflammation-related pathways (such as Toll-like receptor signaling pathway, MAPK signaling pathway, and Jak-STAT signaling pathway), which were associated with resistance and susceptibility to C. perfringens type C infection. This study contributes to the understanding of potential mechanisms involved in the immune response of piglets infected with C. perfringens type C

Integrative Analyses of Long Non-coding RNA and mRNA Involved in Piglet Ileum Immune Response to Clostridium perfringens Type C Infection

Long non-coding RNAs (lncRNAs) have been shown to play important roles in regulating host immune and inflammatory responses to bacterial infection. Infection with Clostridium perfringens (C. perfringens), a food-borne zoonotic pathogen, can lead to a series of inflammatory diseases in human and piglet, greatly challenging the healthy development of global pig industry. However, the roles of lncRNAs involved in piglet immune response against C. perfringens type C infection remain unknown. In this study, the regulatory functions of ileum lncRNAs and mRNAs were investigated in piglet immune response to C. perfringens type C infection among resistance (IR), susceptibility (IS) and sham-inoculation (control, IC) groups. A total of 480 lncRNAs and 3,669 mRNAs were significantly differentially expressed, the differentially expressed lncRNAs and mRNAs in the IR and IS groups were enriched in various pathways of ABC transporters, olfactory transduction, PPAR signaling pathway, chemokine signaling pathway and Toll-like receptor signaling pathway, involving in regulating piglet immune responses and resistance during infection. There were 212 lncRNAs and 505 target mRNAs found to have important association with C. perfringens infectious diseases, furthermore, 25 dysregulated lncRNAs corresponding to 13 immune-related target mRNAs were identified to play potential roles in defense against bacterial infection. In conclusion, the results improve our understanding on the characteristics of lncRNAs and mRNAs on regulating host immune response against C. perfringens type C infection, which will provide a reference for future research into exploring C. perfringens-related diseases in human

Peiminine Inhibits Glioblastoma in Vitro and in Vivo Through Cell Cycle Arrest and Autophagic Flux Blocking

Background/Aims: Glioblastoma multiforme (GBM) is the most devastating and widespread primary central nervous system tumour in adults, with poor survival rate and high mortality rates. Existing treatments do not provide substantial benefits to patients; therefore, novel treatment strategies are required. Peiminine, a natural bioactive compound extracted from the traditional Chinese medicine Fritillaria thunbergii, has many pharmacological effects, especially anticancer activities. However, its anticancer effects on GBM and the underlying mechanism have not been demonstrated. This study was conducted to investigate the potential antitumour effects of peiminine in human GBM cells and to explore the related molecular signalling mechanisms in vitro and in vivo Methods: Cell viability and proliferation were detected with MTT and colony formation assays. Morphological changes associated with autophagy were assessed by transmission electron microscopy (TEM). The cell cycle rate was measured by flow cytometry. To detect changes in related genes and signalling pathways in vitro and in vivo, RNA-seq, Western blotting and immunohistochemical analyses were employed. Results: Peiminine significantly inhibited the proliferation and colony formation of GBM cells and resulted in changes in many tumour-related genes and transcriptional products. The potential anti-GBM role of peiminine might involve cell cycle arrest and autophagic flux blocking via changes in expression of the cyclin D1/CDK network, p62 and LC3. Changes in Changes in flow cytometry results and TEM findings were also observed. Molecular alterations included downregulation of the expression of not only phospho-Akt and phospho-GSK3β but also phospho-AMPK and phospho-ULK1. Furthermore, overexpression of AKT and inhibition of AKT reversed and augmented peiminine-induced cell cycle arrest in GBM cells, respectively. The cellular activation of AMPK reversed the changes in the levels of protein markers of autophagic flux. These results demonstrated that peiminine mediates cell cycle arrest by suppressing AktGSk3β signalling and blocks autophagic flux by depressing AMPK-ULK1 signalling in GBM cells. Finally, peiminine inhibited the growth of U251 gliomas in vivo. Conclusion: Peiminine inhibits glioblastoma in vitro and in vivo via arresting the cell cycle and blocking autophagic flux, suggesting new avenues for GBM therapy