Vine Copula-Based Dependence Description for Multivariate Multimode Process Monitoring

A novel vine copula-based dependence description (VCDD) process monitoring approach is proposed. The main contribution is to extract the complex dependence among process variables rather than perform dimensionality reduction or other decoupling processes. For a multimode chemical process, the C-vine copula model of each mode is initially created, in which a multivariate optimization problem is simplified as coping with a series of bivariate copulas listed in a sparse matrix. To measure the distance of the process data from each non-Gaussian mode, a generalized local probability (GLP) index is defined. Consequently, the generalized Bayesian inference-based probability (GBIP) index under a given control limit can be further calculated in real time via searching the density quantile table created offline. The validity and effectiveness of the proposed approach are illustrated using a numerical example and the Tennessee Eastman benchmark process. The results show that the proposed VCDD approach achieves good performance in both monitoring results and computation load

Fault Detection and Diagnosis for Nonlinear and Non-Gaussian Processes Based on Copula Subspace Division

A novel copula subspace division strategy is proposed for fault detection and diagnosis. High-dimensional industrial data are analyzed in two elemental subspaces: margin distribution subspace (MDS) modeled by joint margin distribution, and dependence structure subspace (DSS) modeled by copula. The highest density regions of two submodels are introduced and quantified using probability indices. To improve the robustness of the monitoring index, a hyperrectangular control boundary in MDS is designed, and the equivalent univariate control limits are estimated. Two associated contribution indices are also constructed for fault diagnosis. The interactive relationships among the root-cause variables are investigated via a proposed state chart. The effectiveness and superiority of the proposed approaches (double-subspace and multisubspace) are validated using a numerical example and the Tennessee Eastman chemical process. Better monitoring performance is achieved compared with some conventional approaches such as principal component analysis, independent component analysis, kernel principal component analysis and vine copula-based dependence description. The proposed multisubspace approach fully utilizes univariate-based alarm data with a dependence restriction modulus, which is promising for industrial application

Fault Detection and Diagnosis for Nonlinear and Non-Gaussian Processes Based on Copula Subspace Division

A novel copula subspace division strategy is proposed for fault detection and diagnosis. High-dimensional industrial data are analyzed in two elemental subspaces: margin distribution subspace (MDS) modeled by joint margin distribution, and dependence structure subspace (DSS) modeled by copula. The highest density regions of two submodels are introduced and quantified using probability indices. To improve the robustness of the monitoring index, a hyperrectangular control boundary in MDS is designed, and the equivalent univariate control limits are estimated. Two associated contribution indices are also constructed for fault diagnosis. The interactive relationships among the root-cause variables are investigated via a proposed state chart. The effectiveness and superiority of the proposed approaches (double-subspace and multisubspace) are validated using a numerical example and the Tennessee Eastman chemical process. Better monitoring performance is achieved compared with some conventional approaches such as principal component analysis, independent component analysis, kernel principal component analysis and vine copula-based dependence description. The proposed multisubspace approach fully utilizes univariate-based alarm data with a dependence restriction modulus, which is promising for industrial application

Sequential Dependence Modeling Using Bayesian Theory and D‑Vine Copula and Its Application on Chemical Process Risk Prediction

An emerging kind of prediction model for sequential data with multiple time series is proposed. Because D-vine copula provides more flexibility in dependence modeling, accounting for conditional dependence, asymmetries, and tail dependence, it is employed to describe sequential dependence between variables in the sample data. A D-vine model with the form of a time window is created to fit the correlation of variables well. To describe the randomness dynamically, Bayesian theory is also applied. As an application, a detailed modeling of prediction of abnormal events in a chemical process is given. Statistics (e.g., mean, variance, skewness, kurtosis, confidence interval, etc.) of the posterior predictive distribution are obtained by Markov chain Monte Carlo simulation. It is shown that the model created in this paper achieves a prediction performance better than that of some other system identification methods, e.g., autoregressive moving average model and back propagation neural network

Nanobody-Based Apolipoprotein E Immunosensor for Point-of-Care Testing

Alzheimer’s disease (AD) biomarkers can reflect the neurochemical indicators used to estimate the risk in clinical nephrology. Apolipoprotein E (ApoE) is an early biomarker for AD in clinical diagnosis. In this research, through bactrian camel immunization, lymphocyte isolation, RNA extraction, and library construction, ApoE-specific Nbs with high affinity were successfully separated from an immune phage display nanobody library. Herein, a colorimetric immunosensor was developed for the point-of-care testing of ApoE by layer-by-layer nanoassembly techniques and novel nanobodies (Nbs). Using highly oriented Nbs as the capture and detection antibodies, an on-site immunosensor was developed by detecting the mean gray value of fade color due to the glutaraldehyde@3-aminopropyltrimethoxysilane oxidation by H₂O₂. The detection limit of AopE is 0.42 pg/mL, and the clinical analysis achieves a good performance. The novel easily operated immunosensor may have potential application in the clinical diagnosis and real-time monitoring for AD

Single-Cell Mechanical Characteristics Analyzed by Multiconstriction Microfluidic Channels

A microfluidic device composed of variable numbers of multiconstriction channels is reported in this paper to differentiate a human breast cancer cell line, MDA-MB-231, and a nontumorigenic human breast cell line, MCF-10A. Differences between their mechanical properties were assessed by comparing the effect of single or multiple relaxations on their velocity profiles which is a novel measure of their deformation ability. Videos of the cells were recorded via a microscope using a smartphone, and imported to a tracking software to gain the position information on the cells. Our results indicated that a multiconstriction channel design with five deformation (50 μm in length, 10 μm in width, and 8 μm in height) separated by four relaxation (50 μm in length, 40 μm in width, and 30 μm in height) regions was superior to a single deformation design in differentiating MDA-MB-231 and MCF-10A cells. Velocity profile criteria can achieve a differentiation accuracy around 95% for both MDA-MB-231 and MCF-10A cells

Full Water Splitting Electrocatalyzed by NiWO₄ Nanowire Array

It is attractive to develop an effective bifunctional electrocatalyst for full water splitting. In this Letter, we report that a NiWO₄ nanowire array on a Ti mesh (NiWO₄/TM) is a high-performance and stable water-splitting electrocatalyst at alkaline pH. As a 3D electrocatalyst, such a NiWO₄/TM attains 20 mA cm^–2 under overpotentials of 101 mV for cathodic water reduction and 322 mV for anodic water oxidation. We also demonstrate the use of NiWO₄/TM to make a two-electrode electrolyzer capable of driving 20 mA cm^–2 at a cell voltage of 1.65 V

Nanoporous CoP₃ Nanowire Array: Acid Etching Preparation and Application as a Highly Active Electrocatalyst for the Hydrogen Evolution Reaction in Alkaline Solution

Transition-metal phosphides have been intensively and extensively studied as earth-abundant catalysts for effective hydrogen evolution electrocatalysis, but it is highly desired to explore a new strategy to improve the catalytic activity. In this work, a nanoporous CoP₃ nanowire array on Ti mesh (np-CoP₃/TM) was derived from MnO₂–CoP₃/TM by acid etching of MnO₂ that acts as a pore-forming agent. As a non-noble-metal catalyst for the hydrogen evolution reaction, the resulting np-CoP₃/TM demonstrates enhanced performance with the need of an overpotential of 76 mV (<i>j</i> = 10 mV cm^–2), 45 mV less than that needed by MnO₂–CoP₃/TM. Moreover, it also shows a good durability for at least 60 h

Image1_EZH2-mediated H3K27me3 is a predictive biomarker and therapeutic target in uveal melanoma.PNG

Although gene mutations and aberrant chromosomes are associated with the pathogenesis and prognosis of uveal melanoma (UM), potential therapeutic targets still need to be explored. We aim to determine the predictive value and potential therapeutic target of EZH2 in uveal melanoma. Eighty-five uveal melanoma samples were recruited in our study, including 19 metastatic and 66 nonmetastatic samples. qRT-PCR, immunohistochemistry staining, and western blotting were applied to detect the expression of EZH2 and H3K27me3. We found that EZH2 (41/85, 48.24%) and H3K27me3 (49/85, 57.65%) were overexpressed in uveal melanoma. The expression of EZH2 was not significantly associated with metastasis. High H3K27me3 expression was correlated with poor patient prognosis. UNC 1999, an EZH2 inhibitor, can downregulate H3K27me3 expression and has the most potency to inhibit OMM1 cell growth by the cell cycle and ferroptosis pathway. These results indicate that H3K27me3 can be a biomarker predicting a poor prognosis of UM. EZH2 is the potential therapeutic target for UM.</p

Photoelectrochemical Sensor with a Z‑Scheme Fe₂O₃/CdS Heterostructure for Sensitive Detection of Mercury Ions

Mercury (Hg2+) is a highly toxic element and can seriously affect human health. This work proposed a photoelectrochemical (PEC) sensor with a Z-scheme Fe2O3/CdS heterostructure and two thymine-rich DNA strands (DNA-1 and Au@DNA-2) for sensitive detection of Hg2+. The light excitation of the Fe2O3/CdS composite accelerated the electron transfer among Fe2O3, CdS, and the electrode to produce a stable photocurrent response. Upon the recognition of Hg2+ to thymine bases (T) in two DNA strands to form a stable T-Hg2+-T biomimetic structure, the photocurrent response increased with the increasing concentration of Hg2+ due to the opening of electronic transmission channels from Au nanoparticles to Fe2O3/CdS nanocomposite. Under the optimal conditions screened by the Box–Behnken experiments, the proposed PEC sensor showed excellent analytical performance for Hg2+ detection with high sensitivity, a detection limit of 0.20 pM at a signal-to-noise ratio of 3, high selectivity, a detectable concentration range of 1 pM–100 nM, and acceptable stability. The good recovery and low relative standard deviation for the analysis of Hg2+ in lake and tap water samples demonstrated the potential application of the designed Z-scheme Fe2O3/CdS heterostructure in the PEC detection of heavy metal ions