No abstract

Many control loops in process plants perform poorly because of valve stiction as one of the most common equipment problems. Valve stiction may cause oscillation in control loops, which increases variability in product quality, accelerates equipment wear, or leads to control system instability and other issues that potentially disrupt the operation. In this work, data-driven valve stiction models are first reviewed and a simplified model is presented. Next, a stiction detection method is proposed based on curve fitting of the output signal of the first integrating component after the valve, i.e., the controller output for self-regulating processes or the process output for integrating processes. A metric that is called the stiction index (SI) is introduced, based on the proposed method to facilitate the automatic detection of valve stiction. The effectiveness of the proposed method is demonstrated using both simulated data sets based on the proposed valve stiction model and real industrial data sets.

Multivariate statistical methods such as principal component analysis (PCA) and partial least squares (PLS) have been widely applied to the statistical process monitoring (SPM) of chemical processes and their effectiveness for fault detection is well recognized. These methods make use of normal process data to define a tight normal operation region for monitoring. In practice, however, historical process data are often corrupted with faulty data. In this paper, a new process monitoring method is proposed that is composed of three parts: (1) a preanalysis step that first roughly identifies various clusters in a historical data set and then precisely isolates normal and abnormal data clusters by the k-means clustering method; (2) a fault visualization step that visualizes high-dimensional data in 2-D space by performing global Fisher discriminant analysis (FDA), and (3) a new fault diagnosis method based on fault directions in

Kaiso, a p120 catenin-binding protein, is expressed in the cytoplasmic and nuclear compartments of cells; however, the biological consequences and clinical implications of a shift between these compartments have yet to be established. Herein, we report an enrichment of nuclear Kaiso expression in cells of primary and metastatic prostate tumors relative to the normal prostate epithelium. Nuclear expression of Kaiso correlates with Gleason score (P < 0.001) and tumor grade (P < 0.001). There is higher nuclear expression of Kaiso in primary tumor/normal matched samples and in primary tumors from African American men (P < 0.0001). We further found that epidermal growth factor (EGF) receptor up-regulates Kaiso at the RNA and protein levels in prostate cancer cell lines, but more interestingly causes a shift of cytoplasmic Kaiso to the nucleus that is reversed by the EGF receptor-specific kinase inhibitor, PD153035. In both DU-145 and PC-3 prostate cancer cell lines, Kaiso inhibition (short hairpin RNA-Kaiso) decreased cell migration and invasion even in the presence of EGF. Further, Kaiso directly binds to the E-cadherin promoter, and inhibition of Kaiso in PC-3 cells results in increased E-cadherin expression, as well as re-establishment of cell-cell contacts. In addition, Kaiso-depleted cells show more epithelial morphology and a reversal of the mesenchymal markers N-cadherin and fibronectin. Our findings establish a defined oncogenic role of Kaiso in promoting the progression of prostate cancer.

In this work, a new multivariate method to monitor continuous processes is developed based on the statistics pattern analysis (SPA) framework. The SPA framework was proposed recently to address some challenges associated with batch process monitoring, such as unsynchronized batch trajectories and multimodal distribution. The major difference between the principal component analysis (PCA) based and SPA-based fault detection methods is that PCA monitors process variables while SPA monitors the statistics of process variables. In other words, PCA examines the variance-covariance of the process variables to perform fault detection while SPA examines the variance-covariance of the process variable statistics (e.g., mean, variance, autocorrelation, cross-correlation, etc.) to perform fault detection. In this paper, a window-based SPA method is proposed to address the challenges associated with continuous processes such as nonlinear process dynamics. First, the details of the window-based SPA method are presented; then the basic properties of the SPA method for fault detection are discussed and illustrated using a simple nonlinear example. Finally, the potential of the windowbased SPA method in monitoring continuous processes is explored using two case studies (a 2 × 2 linear dynamic process and the challenging Tennessee Eastman process). The performance of the window-based SPA method is compared with the benchmark PCA and DPCA methods. The monitoring results clearly demonstrate the superiority of the proposed method.

In the semiconductor industry, process monitoring has been recognized as a critical component of the manufacturing system. Multivariate statistical process monitoring (SPM) techniques, such as multiway principal component analysis and multiway partial least squares, have been extend to monitor semiconductor processes. These SPM methods require extensive, often off-line data preprocessing such as data unfolding, trajectory mean shift, and trajectory alignment. This requirement is probably not an issue for the traditional chemical batch processes but it poses a significant challenge for semiconductor batch processes. This is because data preprocessing makes model building and maintenance extremely labor intensive due to the large number of models in a typical semiconductor fab. In addition, semiconductor process data often show more severe nonnormality compared to those of the traditional chemical process under closed-loop control, which results in suboptimal performance in many applications. To address these challenges, several pattern classification based monitoring (PCM) methods have been developed recently, but some limitations remain and trajectory alignment is still required. In this article, we analyze the fundamental reasons for the limitations of the SPM and PCM methods when applied to monitor semiconductor processes. In addition, we propose a new statistics pattern analysis (SPA) framework to address the challenges associated with semiconductor processes. By monitoring batch statistics, the proposed SPA framework not only eliminates all data preprocessing steps but also provides superior fault detection performance. Finally, we use an industrial example to demonstrate the advantages of the proposed SPA framework, and examine the fundamental reasons for the improved performance from SPA.

There is increasing evidence that Androgen Receptor (AR) expression has prognostic usefulness in Triple negative breast cancer (TNBC), where tumors that lack AR expression are considered “Quadruple negative” Breast Cancers (“QNBC”). However, a comprehensive analysis of AR expression within all breast cancer subtypes or stratified by race has not been reported. We assessed AR mRNA expression in 925 tumors from The Cancer Genome Atlas (TCGA), and 136 tumors in 2 confirmation sets. AR protein expression was determined by immunohistochemistry in 197 tumors from a multi-institutional cohort, for a total of 1258 patients analyzed. Cox hazard ratios were used to determine correlations to PAM50 breast cancer subtypes, and TNBC subtypes. Overall, AR-negative patients are diagnosed at a younger age compared to AR-positive patients, with the average age of AA AR-negative patients being, 49. AA breast tumors express AR at lower rates compared to Whites, independent of ER and PR expression (p<0.0001). AR-negative patients have a (66.60; 95% CI, 32–146) odds ratio of being basal-like compared to other PAM50 subtypes, and this is associated with an increased time to progression and decreased overall survival. AA “QNBC” patients predominately demonstrated BL1, BL2 and IM subtypes, with differential expression of E2F1, NFKBIL2, CCL2, TGFB3, CEBPB, PDK1, IL12RB2, IL2RA, and SOS1 genes compared to white patients. Immune checkpoint inhibitors PD-1, PD-L1, and CTLA-4 were significantly upregulated in both overall “QNBC” and AA “QNBC” patients as well. Thus, AR could be used as a prognostic marker for breast cancer, particularly in AA “QNBC” patients.

Adoptive T cell therapy has achieved dramatic success in a clinic, and the Food and Drug Administration approved two chimeric antigen receptor-engineered T cell (CAR-T) therapies that target hematological cancers in 2018. A significant issue faced by CART therapies is the lack of tumor-specific biomarkers on the surfaces of solid tumor cells, which hampers the application of CART therapies to solid tumors. Intracellular tumor-related antigens can be presented as peptides in the major histocompatibility complex (MHC) on the cell surface, which interact with the T cell receptors (TCR) on antigen-specific T cells to stimulate an anti-tumor response. Multiple immunotherapy strategies have been developed to eradicate tumor cells through targeting the TCR-peptide/MHC interactions. Here, we summarize the current status of TCR-based immunotherapy strategies, with particular focus on the TCR structure, activated signaling pathways, the effects and toxicity associated with TCR-based therapies in clinical trials, preclinical studies examining immune-mobilizing monoclonal TCRs against cancer (ImmTACs), and TCR-fusion molecules. We propose several TCR-based therapeutic strategies to achieve optimal clinical responses without the induction of autoimmune diseases.