A New Process Control Chart for Monitoring Short-Range Serially Correlated Data

Abstract–Statistical process control (SPC) charts are critically important for quality control and management in manufacturing industries, environmental monitoring, disease surveillance, and many other applications. Conventional SPC charts are designed for cases when process observations are independent at different observation times. In practice, however, serial data correlation almost always exists in sequential data. It has been well demonstrated in the literature that control charts designed for independent data are unstable for monitoring serially correlated data. Thus, it is important to develop control charts specifically for monitoring serially correlated data. To this end, there is some existing discussion in the SPC literature. Most existing methods are based on parametric time series modeling and residual monitoring, where the data are often assumed to be normally distributed. In applications, however, the assumed parametric time series model with a given order and the normality assumption are often invalid, resulting in unstable process monitoring. Although there is some nice discussion on robust design of such residual monitoring control charts, the suggested designs can only handle certain special cases well. In this article, we try to make another effort by proposing a novel control chart that makes use of the restarting mechanism of a CUSUM chart and the related spring length concept. Our proposed chart uses observations within the spring length of the current time point and ignores all history data that are beyond the spring length. It does not require any parametric time series model and/or a parametric process distribution. It only requires the assumption that process observation at a given time point is associated with nearby observations and independent of observations that are far away in observation times, which should be reasonable for many applications. Numerical studies show that it performs well in different cases.</p

A Diagnostic Procedure for High-Dimensional Data Streams via Missed Discovery Rate Control

Monitoring complex systems involving high-dimensional data streams (HDS) provides quick real-time detection of abnormal changes of system performance, but accurate and efficient diagnosis of the streams responsible has also become increasingly important in many data-rich statistical process control applications. Existing diagnostic procedures, designed for low/moderate dimensional multivariate process, may miss too much important information in the out-of-control streams with a high signal-to-noise ratio (SNR) or waste too many resources finding useless in-control streams with a low SNR. In addition, these procedures do not differentiate between streams according to their severity. In this article, we formulate the diagnosis problem of HDS as a multiple testing problem and provide a computationally fast diagnostic procedure to control the weighted missed discovery rate (wMDR) at some satisfactory level. The proposed procedure overcomes the limitations of conventional diagnostic procedures by controlling the wMDR and minimizing the expected number of false positives as well. We show theoretically that the proposed procedure is asymptotically valid and optimal in a certain sense. Simulation studies and a real data analysis from a semiconductor manufacturing process show that the proposed procedure works very well in practice.</p

Signal Classification in Large-Scale Multi-Sequence Integrative Analysis Under the HMM Dependence

The integrative analysis of multiple sequences of multiple tests has enjoyed increasing popularity in many applications, especially in large-scale genomics. In the context of large-scale multiple testing, the concept of signal classification has been developed recently for cases when the same features are involved in several independent studies, with the goal of classifying each feature into one of several classes. This paper considers the problem of such signal classification in a generalized compound decision-making framework, where the observed data are assumed to be generated from an underlying four-state Cartesian hidden Markov model. Two oracle procedures are proposed for the total and set-specific control of misclassification rates, respectively, while the number of correct classifications is maximized. Optimal data-driven procedures are also proposed, with their asymptotic properties derived. It is shown that signal-classification could be improved significantly by taking into account the dependence structure among features, and the proposed procedures could have a better performance than their competitors that ignore the dependence structure. The proposed methods are applied to a psychiatric genetics study for detecting genetic variants that affect either or both of bipolar disorder and schizophrenia.</p

Reliable Post-Signal Fault Diagnosis for Correlated High-Dimensional Data Streams

Rapid advance of sensor technology is facilitating the collection of high-dimensional data streams (HDS). Apart from real-time detection of potential out-of-control (OC) patterns, post-signal fault diagnosis of HDS is becoming increasingly important in the filed of statistical process control to isolate abnormal data streams. The major limitations of the existing methods on that topic include (i) they cannot achieve reliable diagnostic results in the sense that their performance is highly variable, and (ii) the informative correlation among different streams is often neglected by them. This article elaborates the problem of reliable fault diagnosis for monitoring correlated HDS using the large-scale multiple testing. Under the framework of hidden Markov model dependence, new diagnostic procedures are proposed, which can control the missed discovery exceedance (MDX) at a desired level. Extensive numerical studies along with some theoretical results show that the proposed procedures can control MDX properly, leading to diagnostics with high reliability and efficiency. Also, their diagnostic performance can be improved significantly by exploiting the dependence among different data streams, which is especially appealing in practice for identifying clustered OC streams.</p

Visible-Light-Induced Three-Component 1,2-Alkylpyridylation of Alkenes via a Halogen-Atom Transfer Process

Visible-light-induced three-component 1,2-alkylpyridylation of alkenes with unactivated alkyl iodides and aryl cyanides is reported via a photocatalytic halogen-atom transfer (XAT) strategy. This metal-free protocol utilizes readily available tertiary alkylamine as the terminal reductant to smoothly convert alkyl iodides into the corresponding carbon radical species. The reaction features a broad substrate scope, excellent functional group tolerance, high efficiency, and mild reaction conditions. The practicability of this methodology is further demonstrated in the late-stage difunctionalization of bioactive molecules

Additional file 1 of Prognostic models for outcome prediction in patients with advanced hepatocellular carcinoma treated by systemic therapy: a systematic review and critical appraisal

Additional file 1: Table S1. Key items for framing aim, search strategy, and study inclusion and exclusion criteria for systematic review, following PICOTS guidance

Optimizing Ghost Imaging via Analysis and Design of Speckle Patterns

We study the influence rules of the speckle size of light source on ghost imaging, and propose a new type of speckle patterns to improve the quality of ghost imaging. The results show that the image quality will first increase and then decrease with the increase of the speckle size, and there is an optimal speckle size for a specific object. Moreover, by using the random distribution of speckle positions, a new type of displacement speckle patterns is designed, and the imaging quality is better than that of the random speckle patterns. These results are of great significances for finding the best speckle patterns suitable for detecting targets, which further promotes the practical applications of ghost imaging

Additional file 2 of Transcription factor AP2 enhances malignancy of non-small cell lung cancer through upregulation of USP22 gene expression

Additional file 1. Supplementary figures and tables

DataSheet_1_Sintilimab Combined with Lenvatinib for Advanced Intrahepatic Cholangiocarcinoma in Second-Line Setting—A Multi-Center Observational Study.pdf

BackgroundPatients with advanced intrahepatic cholangiocarcinoma (iCCA) have a poor prognosis and a substantial unmet clinical need. The study was aimed to investigate the efficacy and safety of sintilimab combined with lenvatinib for advanced iCCA in second-line setting.MethodsThe patients at multiple centers, who progressed after the first-line chemotherapy or could not tolerate chemotherapy, were treated with the combination of sintilimab plus lenvatinib. The primary endpoint was time to progression (TTP), and the secondary endpoints included tumor objective response rate (ORR), disease control rate (DCR), overall survival (OS), and toxicity. Prognostic factors were analyzed using Cox regression analysis.ResultsA total of 41 patients with advanced iCCA were enrolled for this multi-center observational study. Under a median follow-up of 12.1 months, the median age was 59 years (range, 33–75 years). Sixteen patients died of disease progression, with a median TTP of 6.6 months (95% CI, 4.9–8.3). ORR and DCR were 46.3% and 70.3%, respectively. The patients with PD-L1 TPS ≥10% reported a significantly higher ORR compared to those with PD-L1 TPS ConclusionThe combination of sintilimab plus lenvatinib is effective and well tolerated for advanced iCCA in the second-line setting. PD-L1 TPS expression may predict the efficacy of the combination therapy. Further investigation is warranted to investigate this combination regimen in advanced iCCA.</p

MOESM1 of Upregulation of miR-483-3p contributes to endothelial progenitor cells dysfunction in deep vein thrombosis patients via SRF

Additional file 1: Figure S1. Identification of differentially expressed miRNAs in EPCs from healthy control and patients with DVT by microarray. The heat map diagram showed the results of the two-way hierarchical clustering of miRNAs and samples (Arraystar, human miRNA 18.0 chip). The color scale shown at the top illustrated the relative expression level of a miRNA in the certain slide: red color represented upregulated miRNAs while green color represented downregulated miRNAs