Linear Data Models Research Articles

Data-Driven Event-Triggered Platoon Control under Denial-of-Service Attacks

This paper proposes an event-triggered model-free adaptive platoon control (MFAPC) solution for non-linear vehicle systems under denial-of-service (DoS) attacks. First, the non-linear vehicle system is transformed into an equivalent linear data model using the dynamic linearization technique. Second, to save limited communication resources and reduce the influence of cyber attacks, a novel event-triggered mechanism and attack compensation method are designed. Then, based on the equivalent linear data model, a new resilient event-triggered MFAPC algorithm is developed to achieve the vehicle platoon control objective under DoS attacks. Finally, the effectiveness of the proposed control scheme is verified using an example.

Joint clinical and socio-demographic determinants of CD4 cell count and body weight in HIV/TB co-infected adult patients on HAART

BackgroundTuberculosis is one of the most lethal communicable diseases in the world. In tuberculosis patients, HIV speeds up the course of the infection from latent to active illness. This study aimed to identify joint clinical and socio demographic determinant factors of CD4+ cell count and body weight in HIV/TB co-infected adult patients. MethodsFrom January 2013 to December 2019, 374 HIV/TB co-infected individuals were treated and had follow-ups. This study used descriptive data analysis, a linear mixed effects model for longitudinal data, and joint modeling of the two longitudinal outcome variables. ResultsIn this study, 51.1% of the 374 HIV-TB co-infected patients were women, 47.1% were rural dwellers, and 71.9% developed extra pulmonary TB. Females made up 54.3% of all patients who died. To detect the presence of correlation among repeated measured data, the AR (1) structure was chosen. The joint model was more appropriate for analysing and interpreting current data. ConclusionsBaseline age, follow-up time, baseline weight, place of residence, adherence level, educational background, functional status, WHO clinical stage, smoking status and type of TB developed were identified as significant joint factors in CD4 cell count and weight of HIV/TB co-infected patients from potential joint predictor variables. Therefore, the concerned bodies, specifically the FMOH and regional health staffs, should enhance public awareness about the factors that lead to a drop in CD4 count and weight gain, as well as an increased risk of death from HIV/TB co-infection. Furthermore, special attention should be paid to women and patients with poor adherence. Medication and treatment must be affordable and accessible to all co-infected patients in order to maximize their CD4 count and proportional body weight to their height, and to lengthen the lives of individuals who are co-infected.

Joint Models for Incomplete Longitudinal Data and Time-to-Event Data

Clinical studies often collect longitudinal and time-to-event data for each subject. Joint modeling is a powerful methodology for evaluating the association between these data. The existing models, however, have not sufficiently addressed the problem of missing data, which are commonly encountered in longitudinal studies. In this paper, we introduce a novel joint model with shared random effects for incomplete longitudinal data and time-to-event data. Our proposed joint model consists of three submodels: a linear mixed model for the longitudinal data, a Cox proportional hazard model for the time-to-event data, and a Cox proportional hazard model for the time-to-dropout from the study. By simultaneously estimating the parameters included in these submodels, the biases of estimators are expected to decrease under two missing scenarios. We estimated the proposed model by Bayesian approach, and the performance of our method was evaluated through Monte Carlo simulation studies.

Model-free adaptive iterative learning containment control for unknown heterogeneous nonlinear MASs with disturbances

A novel model-free adaptive iterative learning control scheme is proposed for the containment control problem of unknown heterogeneous nonlinear multi-agent systems (MASs) with bounded measurable and unmeasurable disturbances. In details, the proposed scheme includes following parts. First, the agent dynamics are transformed into a partial form dynamic linearization data model along the iteration axis by using the novel concept of the pseudo gradient. Second, the distributed containment control scheme is designed for MASs under fixed topology based on the obtained data model at each working point. Then, the scheme is extended to the case of iteration-switching topologies. Finally, the convergences have been proved by rigorous mathematical analysis. The whole containment control process is characterized with data-driven nature by only using the input and output data. Simulation results illustrate the effectiveness of the schemes.

Mining of Potential Biomarkers and Pathway in Valvular Atrial Fibrillation (VAF) via Systematic Screening of Gene Coexpression Network.

Purpose We apply the bioinformatics method to excavate the potential genes and therapeutic targets associated with valvular atrial fibrillation (VAF). Methods The downloaded gene expression files from the gene expression omnibus (GEO) included patients with primary severe mitral regurgitation complicated with sinus or atrial fibrillation rhythm. Subsequently, the differential gene expression in left and right atrium was analyzed by R software. Additionally, weighted correlation network analysis (WGCNA), principal component analysis (PCA), and linear model for microarray data (LIMMA) algorithm were used to determine hub genes. Then, Metascape database, DAVID database, and STRING database were used to annotate and visualize the gene ontology (GO) analysis, KEGG pathway enrichment analysis, and PPI network analysis of differentially expressed genes (DEGs). Finally, the TFs and miRNAs were predicted by using online tools, such as PASTAA and miRDB. Results 20,484 differentially expressed genes related to atrial fibrillation were obtained through the analysis of left and right atrial tissue samples of GSE115574 gene chip, and 1,009 were with statistical significance, including 45 upregulated genes and 964 downregulated genes. And the hub genes implicated in AF of NPC2, ODC1, SNAP29, LAPTM5, ST8SIA5, and FCGR3B were screened. Finally, the main regulators of targeted candidate biomarkers and microRNAs, EIF5A2, HIF1A, ZIC2, ELF1, and STAT2, were found in this study. Conclusion These hub genes, NPC2, ODC1, SNAP29, LAPTM5, ST8SIA5, and FCGR3B, are important for the development of VAF, and their enrichment pathways and TFs elucidate the involved molecular mechanisms and assist in the validation of drug targets.

Forecasting in FOREX the spot price interval of tomorrow with the same information of today. An analysis of the seven majors using a linear regression model based on interval arithmetic

Traders and investors in FOREX use past values to forecast future values where the use of soft computing algorithms is essential. In this path, new linear regression models for interval-valued data (IVD) adapted to interval-valued time series (ITS), or candlestick charts, such as the alpha-[B] model, can help. This is an interval linear regression model based on interval arithmetic with dependent and independent interval-valued variables (IVV) and two parameters, a real number α and an interval [B]. It has been developed for two generic IVV but an open issue is to adapt this model to an autoregressive dynamic (ARD) framework for two consecutive periods. This paper addresses this issue with daily IVD of the seven majors (AUD/USD, EUR/USD, GBP/USD, NZD/USD, USD/CAD, USD/CHF, USD/JPY) in 2010–2021 and some accuracy metrics. An analysis of the alpha estimates is included when using a sliding window of ten until 240 days. Consequently, several rankings for the majors are shown. As a new key research result in FOREX, the alpha-[B]-ARD model is strongly recommended for trading the USD/CHF, USD/CAD, and EUR/USD. Hence, the use of this model provides a key competitive advantage for banks, multinational companies, and organizations. Further research issues are suggested, including extensions of this methodology to stock markets and econophysics.

Plasma protein biomarkers for primary graft dysfunction after lung transplantation: a single-center cohort analysis

The clinical use of circulating biomarkers for primary graft dysfunction (PGD) after lung transplantation has been limited. In a prospective single-center cohort, we examined the use of plasma protein biomarkers as indicators of PGD severity and duration after lung transplantation. The study comprised 40 consecutive lung transplant patients who consented to blood sample collection immediately pretransplant and at 6, 24, 48, and 72 h after lung transplant. An expert grader determined the severity and duration of PGD and scored PGD at T0 (6 h after reperfusion), T24, T48, and T72 h post-reperfusion using the 2016 ISHLT consensus guidelines. A bead-based multiplex assay was used to measure 27 plasma proteins including cytokines, growth factors, and chemokines. Enzyme-linked immunoassay was used to measure cell injury markers including M30, M65, soluble receptor of advanced glycation end-products (sRAGE), and plasminogen activator inhibitor-1 (PAI-1). A pairwise comparisons analysis was used to assess differences in protein levels between PGD severity scores (1, 2, and 3) at T0, T24, T48, and T72 h. Sensitivity and temporal analyses were used to explore the association of protein expression patterns and PGD3 at T48–72 h (the most severe, persistent form of PGD). We used the Benjamini–Hochberg method to adjust for multiple testing. Of the 40 patients, 22 (55%) had PGD3 at some point post-transplant from T0 to T72 h; 12 (30%) had PGD3 at T48–72 h. In the pairwise comparison, we identified a robust plasma protein expression signature for PGD severity. In the sensitivity analysis, using a linear model for microarray data, we found that differential perioperative expression of IP-10, MIP1B, RANTES, IL-8, IL-1Ra, G-CSF, and PDGF-BB correlated with PGD3 development at T48–72 h (FDR < 0.1 and p < 0.05). In the temporal analysis, using linear mixed modeling with overlap weighting, we identified unique protein patterns in patients who did or did not develop PGD3 at T48–72 h. Our findings suggest that unique inflammatory protein expression patterns may be informative of PGD severity and duration. PGD biomarker panels may improve early detection of PGD, predict its clinical course, and help monitor treatment efficacy in the current era of lung transplantation.

Hybrid physics-data-driven online modelling: Framework, methodology and application to electric vehicles

This paper proposes a novel hybrid physics-data-driven framework for system modelling by integrating a physical model and an online learning data model to improve model accuracy, interpretability, and generalization. Taking an in-wheel Motor Driven Vehicle (IMDV) as an example, two hybrid representations, i.e. the Dynamic Linearization Data Model (DLDM) and Recurrent High-Order Neural Network (RHONN) are introduced for the planar dynamics modelling of the electric vehicle. However, it is difficult to obtain the statistical information of the operation process and measurement noise when the weight vectors of the data-driven model is updated online. To address this issue, a H∞-based learning algorithm is adopted. The stability and convergence rate are elaborated and compared with an existing Extended Kalman Filter (EKF)-based method. Finally, we compare four methods, including the physics-based, data-based and two hybrid models, to evaluate their performances of modelling the IMDV’s dynamics. The feasibility test and comparison studies are conducted in simulations and on a Hardware-in-the-Loop (HiL) test rig. The results demonstrated that the proposed H∞-based hybrid method, which does not make any assumption on measurement noise, has better generalization ability and robustness in practical implementations, compared to other baseline methods.

Self-gated, dynamic contrast-enhanced magnetic resonance imaging with compressed-sensing reconstruction for evaluating endothelial permeability in the aortic root of atherosclerotic mice.

High-risk atherosclerotic plaques are characterized by active inflammation and abundant leaky microvessels. We present a self-gated, dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) acquisition with compressed sensing reconstruction and apply it to assess longitudinal changes in endothelial permeability in the aortic root of Apoe-/- atherosclerotic mice during natural disease progression. Twenty-four, 8-week-old, female Apoe-/- mice were divided into four groups (n = 6 each) and imaged with self-gated DCE-MRI at 4, 8, 12, and 16 weeks after high-fatdiet initiation, and then euthanized for CD68 immunohistochemistry for macrophages. Eight additional mice were kept on a high-fat diet and imaged longitudinally at the same time points. Aortic-root pseudo-concentration curves were analyzed using a validated piecewise linear model. Contrast agent wash-in and washout slopes (b1 and b2 ) were measured as surrogates of aortic root endothelial permeability and compared with macrophage density by immunohistochemistry. b2 , indicating contrast agent washout, was significantly higher in mice kept on an high-fat diet for longer periods of time (p= 0.03). Group comparison revealed significant differences between mice on a high-fat diet for 4 versus 16 weeks (p= 0.03). Macrophage density also significantly increased with diet duration (p= 0.009). Spearman correlation between b2 from DCE-MRI and macrophage density indicated a weak relationship between the two parameters (r = 0.28, p= 0.20). Validated piecewise linear modeling of the DCE-MRI data showed that the aortic root contrast agent washout rate is significantly different during disease progression. Further development of this technique from a single-slice to a 3D acquisition may enable better investigation of the relationship between in vivo imaging of endothelial permeability and atherosclerotic plaques' genetic, molecular, and cellular makeup in this important model of disease.

Large-Scale Generalized Linear Models for Longitudinal Data with Grouped Patterns of Unobserved Heterogeneity

ABSTRACT This article provides methods for flexibly capturing unobservable heterogeneity from longitudinal data in the context of an exponential family of distributions. The group memberships of individual units are left unspecified, and their heterogeneity is influenced by group-specific unobservable factor structures. The model includes, as special cases, probit, logit, and Poisson regressions with interactive fixed effects along with unknown group membership. We discuss a computationally efficient estimation method and derive the corresponding asymptotic theory. Uniform consistency of the estimated group membership is established. To test heterogeneous regression coefficients within groups, we propose a Swamy-type test that allows for unobserved heterogeneity. We apply the proposed method to the study of market structure of the taxi industry in New York City. Our method unveils interesting and important insights from large-scale longitudinal data that consist of over 450 million data points.

Data-Driven Event-Triggered Cooperative Control for Multiple Subway Trains With Switching Topologies

In this paper, a data-driven event-triggered cooperative control (DD-ETCC) scheme is proposed for the multiple subway trains to realize speed tracking and dynamic headway adjustment under switching topologies. Firstly, a nonlinear subway train system is transformed into a linearization data model, and the complex nonlinear terms caused by mechanical and aerodynamic resistance and operating environment conditions are estimated by projection algorithm. Then, the DD-ETCC scheme based on the linearization data model and the asynchronous event-triggered condition for multiple subway trains with switching topologies is designed. Theoretical analysis shows that the speed tracking errors of multiple subway trains are bounded, and the headway distances of consecutive trains are stabilized in a safe range with the proposed scheme. Finally, the effectiveness of the proposed DD-ETCC scheme of multiple trains is illustrated by subway train simulations.

Gene Correlation Network Analysis to Identify Biomarkers of Peri-Implantitis

Background and Objectives: The histopathological and clinical conditions for transforming peri-implant mucositis into peri-implantitis (PI) are not fully clarified. We aim to uncover molecular mechanisms and new potential biomarkers of PI. Materials and Methods: Raw GSE33774 and GSE57631 datasets were obtained from the Gene Expression Omnibus (GEO) database. The linear models for microarray data (LIMMA) package in R software completes differentially expressed genes (DEGs). We conducted a weighted gene co-expression network analysis (WGCNA) on the top 25% of altered genes and identified the key modules associated with the clinical features of PI. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed using the R software. We constructed a protein–protein interaction (PPI) network through the STRING database. After that we used Cytohubba plug-ins of Cytoscape to screen out the potential hub genes, which were subsequently verified via receiver operating characteristic (ROC) curves in another dataset, GSE178351, and revalidation of genes through the DisGeNET database. Results: We discovered 632 DEGs (570 upregulated genes and 62 downregulated genes). A total of eight modules were screened by WGCNA, among which the turquoise module was most correlated with PI. The Cytohubba plug-ins were used for filtering hub genes, which are highly linked with PI development, from the candidate genes in the protein–protein interaction (PPI) network. Conclusions: We found five key genes from PI using WGCNA. Among them, ICAM1, CXCL1, and JUN are worthy of further study of new target genes, providing the theoretical basis for further exploration of the occurrence and development mechanism of PI.

Cooperative MFAILC for Multiple Subway Trains With Actuator Faults and Actuator Saturation

In this paper, a cooperative model free adaptive iterative learning control (CMFAILC) scheme is proposed for multiple subway trains subjected to actuator faults and actuator saturation. The controller design includes: the dynamic linearization technique is adopted to transform the subway train system into an equivalent dynamic linearization data model along the iteration axis, the CMFAILC controller is designed by optimizing the cost function, the projection algorithm is applied to deal with the unknown nonlinear terms, the RBFNN is used to compensate the effects of unknown actuator faults, and the anti-windup compensator is introduced to tackle actuator saturation. Theoretical analysis shows that the speed tracking errors of multiple subway trains are bounded, and the distance of adjacent subway trains is stabilized in a safe range. Finally, the validity of the proposed CMFAILC scheme is verified through multiple subway train simulations.

Longitudinal Changes in Vision and Retinal Morphology in Wolfram Syndrome

To report long-term ophthalmic findings in Wolfram syndrome, including rates of visual decline, macular thinning, retinal nerve fiber layer (RNFL) thinning, and outer plexiform layer (OPL) lamination. Single-center, cohort study. A total of 38 participants were studied, who underwent a complete ophthalmic examination as well as optical coherence tomography imaging of the macula and nerve on an annual basis. Linear mixed-effects models for longitudinal data were used to examine both fixed and random effects related to visual acuity and optic nerve quadrants of RNFL and macula thickness. Participants completed a mean of 6.44 years of follow-up (range 2-10 years). Visual acuity declined over time in all participants, with a mean slope of 0.059 logMAR/y (95% CI=0.07-0.05 logMAR/y), although nearly 25% of participants experienced more rapid visual decline. RNFL thickness decreased in superior, inferior, and nasal quadrants (β=-0.5 µm/y, -0.98 µm/y, -0.28 µm/y, respectively). OPL lamination was noted in 3 study participants, 2 of whom had autosomal dominant mutations. Our study describes the longest and largest natural history study of visual acuity decline and retinal morphometry in Wolfram syndrome to date. Results suggest that there are slower and faster progressing subgroups and that OPL lamination is present in some individuals with this disease.

Metrics of color-difference formula improvement.

Metrics of color-difference formula improvement (i.e., standardized residual sum of squares and Pearson product moment correlation) are shown to convey the same information. Furthermore, each metric has two computational forms that assume different linear data models, specifically, with or without an ordinate intercept. It is essential to choose a computational form that matches the data model. We recommend explicitly declaring whether or not the data have been centered, i.e., by subtracting the mean value from each datum, to match the intercept-free data model. Statistical testing of the metrics assumes independent, normally distributed randomness of residuals from the data model, and homogeneous variance. Procedures consistent with these assumptions include robust statistical tests, homogenizing data transformations, and meta-analysis.

Robust variable selection in semiparametric mixed effects longitudinal data models

In this paper, we present a robust joint variable selection procedure for fixed and random effects in semiparametric linear mixed effects model for longitudinal data. Our simultaneous selection method overcomes the defects of typical approaches which select separately each of the two effects components. Meanwhile, the proposed procedure performs better than nonrobust method when there are outliers in the data. This method is based on a robustified penalized joint likelihood of the reparameterized linear mixed effects model through B-splines approximation and Cholesky decomposition. It is further shown that the robust variable selection method enjoys the Oracle property. We demonstrate the performance of the method based on a simulation study in the end.

A varying-coefficient partially linear transformation model for length-biased data with an application to HIV vaccine studies.

Prevalent cohort studies in medical research often give rise to length-biased survival data that require special treatments. The recently proposed varying-coefficient partially linear transformation (VCPLT) model has the virtue of providing a more dynamic content of the effects of the covariates on survival times than the well-known partially linear transformation (PLT) model by allowing flexible interactions between the covariates. However, no existing analysis of the VCPLT model has considered length-biased sampling. In this paper, we consider the VCPLT model when the data are length-biased and right censored, thereby extending the reach of this flexible and powerful tool. We develop a martingale estimating function-based approach to the estimation of this model, provide theoretical underpinnings, evaluate finite sample performance via simulations, and showcase its practical appeal via an empirical application using data from two HIV vaccine clinical trials conducted by the U.S. National Institute of Allergy and Infectious Diseases.

Variable Selection with Scalable Bootstrapping in Generalized Linear Model for Massive Data

Bootstrapping is commonly used as a tool for non-parametric statistical inference to assess the quality of estimators in variable selection models. However, for a massive dataset, the computational requirement when using bootstrapping in variable selection models (BootVS) can be crucial. In this study, we propose a novel framework using a bag of little bootstraps variable selection (BLBVS) method with a ridge hybrid procedure to assess the quality of estimators in generalized linear models with a regularized term, such as lasso and group lasso penalties. The proposed method can be easily and naturally implemented with distributed computing, and thus has significant computational advantages for massive datasets. The simulation results show that our novel BLBVS method performs excellently in both accuracy and efficiency when compared with BootVS. Real data analyses including regression on a bike sharing dataset and classification of a lending club dataset are presented to illustrate the computational superiority of BLBVS in large-scale datasets.

Data-Based Security Fault Tolerant Iterative Learning Control under Denial-of-Service Attacks

This paper mainly studies the data-based security fault tolerant iterative learning control (SFTILC) problem of nonlinear networked control systems (NCSs) under sensor failures and denial-of-service (DoS) attacks. Firstly, the radial basis function neural network (RBFNN) is used to approximate the sensor failure function and a DoS attack compensation mechanism is proposed in the iterative domain to lessen the impact of DoS attacks. Then, using the dynamic linearization technology, the nonlinear system considering failures and network attacks is transformed into a linear data model. Further, based on the designed linearization model, a new data-based SFTILC algorithm is designed to ensure the satisfactory tracking performance of the system. This process only uses the input and output data of the system, and the stability of the system is proved by using the compression mapping principle. Finally, a digital simulation is used to demonstrate the effectiveness of the proposed SFTILC algorithm.

Identification of Hub Genes and Immune-Related Pathways for Membranous Nephropathy by Bioinformatics Analysis.

OBJECTIVE: We aim to explore the detailed molecular mechanisms of membrane nephropathy (MN) related genes by bioinformatics analysis. METHODS: Two microarray datasets (GSE108109 and GSE104948) with glomerular gene expression data from 65 MN patients and 9 healthy donors were obtained from the Gene Expression Omnibus (GEO) database. After processing the raw data, DEGs screening was conducted using the LIMMA (linear model for microarray data) package and Gene set enrichment analysis (GSEA) was performed with GSEA software (v. 3.0), followed by gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. The protein-protein interaction (PPI) network analysis was carried out to determine the hub genes, by applying the maximal clique centrality (MCC) method, which was visualized by Cytoscape. Finally, utilizing the Nephroseq v5 online platform, we analyzed subgroups associated with hub genes. The findings were further validated by immunohistochemistry (IHC) staining in renal tissues from MN or control patients. RESULTS: A sum of 370 DEGs (188 up-regulated genes, 182 down-regulated genes) and 20 hub genes were ascertained. GO and KEGG enrichment analysis demonstrated that DEGs of MN were preponderantly associated with cell damage and complement cascade-related immune responses. Combined with literature data and hub gene-related MN subset analysis, CTSS, ITGB2, and HCK may play important roles in the pathological process of MN. CONCLUSION: This study identified novel hub genes in MN using bioinformatics. We found that some hub genes such as CTSS, ITGB2, and HCK might contribute to MN immunopathological process, providing new insights for further study of the molecular mechanisms underlying glomerular injury of MN.