High-dimensional Analysis Research Articles

Neural network big data fusion in remote sensing image processing technology

Abstract Remote sensing (RS) image processing has made significant progress in the past few years, but it still faces some problems such as the difficulty in processing large-scale RS image data, difficulty in recognizing complex background, and low accuracy and efficiency of processing. In order to improve the existing problems in RS image processing, this study dealt with ConvNext-convolutional neural network (CNN) and big data (BD) in parallel. Moreover, it combined the existing RS image processing with the high dimensional analysis of data and other technologies. In this process, the parallel processing of large data and high-dimensional data analysis technology improves the difficulty and low efficiency of large-scale RS image data processing in the preprocessing stage. The ConvNext-CNN optimizes the two modules of feature extraction and object detection in RS image processing, which improves the difficult problem of complex background recognition and improves the accuracy of RS image processing. At the same time, the performance of RS image processing technology after neural networks (NNs) and BD fusion and traditional RS image processing technology in many aspects are analyzed by experiments. In this study, traditional RS image processing and RS image processing combined with NN and BD were used to process 2,328 sample datasets. The image processing accuracy and recall rate of traditional RS image processing technology were 81 and 82%, respectively, and the F1 score was about 0.81 (F1 value is the reconciled average of accuracy and recall, a metric that combines accuracy and recall to evaluate the quality of the results, a higher F1 value indicates a better overall performance of the retrieval system). The accuracy rate and recall rate of RS image processing technology, which integrates NN and BD, were 97 and 98%, respectively, and its F1 score was about 0.97. After analyzing the process of these experiments and the final output results, it can be determined that the RS image processing technology combined with NN and BD can improve the problems of large-scale data processing difficulty, recognition difficulty under complex background, low processing accuracy and efficiency. In this study, the RS image processing technology combined with NN and BD has stronger adaptability with the help of NN and BD technology, and can adjust parameters and can be applied in more tasks.

Anti-synthetase syndrome is associated with a higher risk of hospitalization among patients with idiopathic inflammatory myopathy and COVID-19.

Data with fine granularity about COVID-19-related outcomes and risk factors were still limited in the idiopathic inflammatory myopathies (IIMs) population. This study aimed to investigate clinical factors associated with hospitalized and severe COVID-19 in patients with IIMs, particularly those gauged by myositis-specific antibodies. This retrospective cohort study was conducted in the Renji IIM cohort in Shanghai, China, under an upsurge of SARS-CoV-2 omicron variant infections from December 2022 to January 2023. Clinical data were collected and analyzed by multivariable logistic regression to determine risk factors. High-dimensional flow cytometry analysis was performed to outline the immunological features. Among 463 infected patients in the eligible cohort (n=613), 65 (14.0%) were hospitalized, 19 (4.1%) suffered severe COVID-19, and 10 (2.2%) died. Older age (OR=1.59/decade, 95% CI 1.18 to 2.16, p=0.003), requiring family oxygen supplement (2.62, 1.11 to 6.19, 0.028), patients with anti-synthetase syndrome (ASyS) (2.88, 1.12 to 7.34, 0.027, vs. other dermatomyositis), higher IIM disease activity, and prednisone intake >10mg/day (5.59, 2.70 to 11.57, <0.001) were associated with a higher risk of hospitalization. Conversely, 3-dose inactivated vaccination reduced the risk of hospitalization (0.10, 0.02 to 0.40, 0.001, vs. incomplete vaccination). Janus kinase inhibitor (JAKi) pre-exposure significantly reduced the risk of severe COVID-19 in hospitalized patients (0.16, 0.04 to 0.74, 0.019, vs. csDMARDs). ASyS patients with severe COVID-19 had significantly reduced peripheral CD4+ T cells, lower CD4/CD8 ratio, and fewer naive B cells but more class-switched memory B cells compared with controls. ASyS and family oxygen supplement were first identified as risk factors for COVID-19-related hospitalization in patients with IIMs. JAKi pre-exposure might protect IIM patients against severe COVID-19 complications.

Isolation and high-dimensional flow cytometric analysis of tumor-infiltrating leukocytes in a mouse model of colorectal cancer.

Colorectal cancer (CRC) is a complex and heterogeneous disease characterized by dysregulated interactions between tumor cells and the immune system. The tumor microenvironment plays a pivotal role in cancer initiation as well as progression, with myeloid immune cells such as dendritic cell and macrophage subsets playing diverse roles in cancer immunity. On one hand, they exert anti-tumor effects, but they can also contribute to tumor growth. The AOM/DSS colitis-associated cancer mouse model has emerged as a valuable tool to investigate inflammation-driven CRC. To understand the role of different leukocyte populations in tumor development, the preparation of single cell suspensions from tumors has become standard procedure for many types of cancer in recent years. However, in the case of AOM/DSS-induced colorectal tumors, this is still challenging and rarely described. For one, to be able to properly distinguish tumor-associated immune cells, separate processing of cancerous and surrounding colon tissue is essential. In addition, cell yield, due to the low tumor mass, viability, as well as preservation of cell surface epitopes are important for successful flow cytometric profiling of tumor-infiltrating leukocytes. Here we present a fast, simple, and economical step-by-step protocol for isolating colorectal tumor-associated leukocytes from AOM/DSS-treated mice. Furthermore, we demonstrate the feasibility of this protocol for high-dimensional flow cytometric identification of the different tumor-infiltrating leukocyte populations, with a specific focus on myeloid cell subsets.

Single Cell High Dimensional Analysis of Human Peripheral Blood Mononuclear Cells Reveals Unique Intermediate Monocyte Subsets Associated with Sex Differences in Coronary Artery Disease.

Monocytes are associated with human cardiovascular disease progression. Monocytes are segregated into three major subsets: classical (cMo), intermediate (iMo), and nonclassical (nMo). Recent studies have identified heterogeneity within each of these main monocyte classes, yet the extent to which these subsets contribute to heart disease progression is not known. Peripheral blood mononuclear cells (PBMC) were obtained from 61 human subjects within the Coronary Assessment of Virginia (CAVA) Cohort. Coronary atherosclerosis severity was quantified using the Gensini Score (GS). We employed high-dimensional single-cell transcriptome and protein methods to define how human monocytes differ in subjects with low to severe coronary artery disease. We analyzed 487 immune-related genes and 49 surface proteins at the single-cell level using Antibody-Seq (Ab-Seq). We identified six subsets of myeloid cells (cMo, iMo, nMo, plasmacytoid DC, classical DC, and DC3) at the single-cell level based on surface proteins, and we associated these subsets with coronary artery disease (CAD) incidence based on Gensini score (GS) in each subject. Only frequencies of iMo were associated with high CAD (GS > 32), adj.p = 0.024. Spearman correlation analysis with GS from each subject revealed a positive correlation with iMo frequencies (r = 0.314, p = 0.014) and further showed a robust sex-dependent positive correlation in female subjects (r = 0.663, p = 0.004). cMo frequencies did not correlate with CAD severity. Key gene pathways differed in iMo among low and high CAD subjects and between males and females. Further single-cell analysis of iMo revealed three iMo subsets in human PBMC, distinguished by the expression of HLA-DR, CXCR3, and CD206. We found that the frequency of immunoregulatory iMo_HLA-DR+CXCR3+CD206+ was associated with CAD severity (adj.p = 0.006). The immunoregulatory iMo subset positively correlated with GS in both females (r = 0.660, p = 0.004) and males (r = 0.315, p = 0.037). Cell interaction analyses identified strong interactions of iMo with CD4+ effector/memory T cells and Tregs from the same subjects. This study shows the importance of iMo in CAD progression and suggests that iMo may have important functional roles in modulating CAD risk, particularly among females.

High-Dimensional Analyses Reveal IL15 Enhances Activation of Sipuleucel-T Lymphocyte Subsets and Reverses Immunoresistance.

Sipuleucel-T (sip-T) is the only FDA-approved autologous cellular immunotherapy for metastatic castration-resistant prostate cancer (mCRPC). To elucidate parameters of the response profile to this therapy, we report high-dimensional analyses of sip-T using cytometry by time of flight (CyTOF) and show a lymphoid predominance, with CD3+ T cells constituting the highest proportion (median ∼60%) of sip-T, followed by B cells, and natural killer (NK) and NKT cells. We hypothesized that treatment of sip-T with homeostatic cytokines known to activate/expand effector lymphocytes could augment efficacy against prostate tumors. Of the cytokines tested, IL15 was the most effective at enhancing activation and proliferation of effector lymphocytes, as well as augmenting tumor cytotoxicity in vitro. Co-culture of sip-T with IL15 and control or prostate-relevant antigens showed substantial activation and expansion of CD8+ T cells and NKT cells in an antigen-specific manner. Adoptive transfer of IL15-treated sip-T into NSG mice resulted in more potent prostate tumor growth inhibition compared with control sip-T. Evaluation of tumor-infiltrating lymphocytes revealed a 2- to 14-fold higher influx of sip-T and a significant increase in IFNγ producing CD8+ T cells and NKT cells within the tumor microenvironment in the IL15 group. In conclusion, we put forward evidence that IL15 treatment can enhance the functional antitumor immunity of sip-T, providing rationale for combining IL15 or IL15 agonists with sip-T to treat patients with mCRPC.

Contributions of prenatal risk factors and neonatal epigenetics to cognitive outcome in children born very preterm.

Children born less than 30 weeks gestational age (GA) are at high risk for neurodevelopmental delay compared to term peers. Prenatal risk factors and neonatal epigenetics could help identify preterm children at highest risk for poor cognitive outcomes. We aimed to understand the associations among cumulative prenatal risk, neonatal DNA methylation, and child cognitive ability at age 3 years, including whether DNA methylation mediates the association between prenatal risk and cognitive ability. We studied 379 neonates (54% male) born less than 30 weeks GA who had DNA methylation measured at neonatal intensive care unit discharge along with 3-year follow-up data. Cumulative prenatal risk was calculated from 24 risk factors obtained from maternal report and medical record and epigenome-wide neonatal DNA methylation was assayed from buccal swabs. At 3-year follow-up, child cognitive ability was assessed using the Bayley Scales of Infant and Toddler Development (third edition). Cumulative prenatal risk and DNA methylation at two cytosine-phosphate-guanines (CpGs) were uniquely associated with child cognitive ability. Using high-dimensional mediation analysis, we also identified differential methylation of 309 CpGs that mediated the association between cumulative prenatal risk and child cognitive ability. Many of the associated CpGs were located in genes (TNS3, TRAPPC4, MAD1L1, APBB2, DIP2C, TRAPPC9, DRD2) that have previously been associated with prenatal exposures and/or neurodevelopmental phenotypes. Our findings suggest a role for both prenatal risk factors and DNA methylation in explaining outcomes for children born preterm and suggest we should further study DNA methylation as a potential mechanism underlying the association between prenatal risk and child neurodevelopment. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Differential DNA methylation in the brain as potential mediator of the association between traffic-related PM2.5 and neuropathology markers of Alzheimer's disease.

Growing evidence indicates that fine particulate matter (PM2.5) is a risk factor for Alzheimer's disease (AD), but the underlying mechanisms have been insufficiently investigated. We hypothesized differential DNA methylation (DNAm) in brain tissue as a potential mediator of this association. We assessed genome-wide DNAm (Illumina EPIC BeadChips) in prefrontal cortex tissue and three AD-related neuropathological markers (Braak stage, CERAD, ABC score) for 159 donors, and estimated donors' residential traffic-related PM2.5 exposure 1, 3, and 5 years prior to death. We used a combination of the Meet-in-the-Middle approach, high-dimensional mediation analysis, and causal mediation analysis to identify potential mediating CpGs. PM2.5 was significantly associated with differential DNAm at cg25433380 and cg10495669. Twenty-four CpG sites were identified as mediators of the association between PM2.5 exposure and neuropathology markers, several located in genes related to neuroinflammation. Our findings suggest differential DNAm related to neuroinflammation mediates the association between traffic-related PM2.5 and AD. First study to evaluate the potential mediation effect of DNA methylation for the association between PM2.5 exposure and neuropathological changes of Alzheimer's disease. Study was based on brain tissues rarely investigated in previous air pollution research. Cg10495669, assigned to RBCK1 gene playing a role in inflammation, was associated consistently with 1-year, 3-year, and 5-year traffic-related PM2.5 exposures prior to death. Meet-in-the-middle approach and high-dimensional mediation analysis were used simultaneously to increase the potential of identifying the differentially methylated CpGs. Differential DNAm related to neuroinflammation was found to mediate the association between traffic-related PM2.5 and Alzheimer's disease.

System Fragility Analysis of Bridges Based on Response Variables Dimensionality Reduction

Earthquake resistance of bridge structures is an important part of ensuring the functionality of transportation network systems. The structural system of a bridge is composed of multiple components, and the damage of different components may affect the function of the bridge, while the system seismic fragility analysis provides an effective way to evaluate the overall seismic performance of the bridge. In the past, the failure probability of a system is often estimated by the correlation between components, but the complex correlation between components brings certain difficulties to the actual calculation. In this paper, an efficient, accurate and universal method of system fragility analysis is proposed. This method decouples the correlation between components from the perspective of relative damage, adopts the critical demand-to-capacity ratio as the response variable of the system, realizing the dimensionality reduction of the response variables and simplifying the system fragility analysis from high-dimensional fragility analysis to one-dimensional fragility analysis. This methodology offers a viable solution to the challenges posed by the wide estimation of fragility boundaries in the boundaries estimation method and the complexities involved in analyzing correlation of components in the joint probability method. Comparison with traditional methods indicates that the proposed method exhibits high efficiency and convenience, making it suitable for practical engineering applications in system fragility computation. The proposed method holds great potential for future applications in the field of complex bridge structure fragility analysis, large-scale regional seismic damage prediction, rapid assessment, and beyond.

High-Dimensional Analysis of Double Descent for Linear Regression with Random Projections

High-Dimensional Analysis of Double Descent for Linear Regression with Random Projections

High-dimensional quantile mediation analysis with application to a birth cohort study of mother-newborn pairs.

There has been substantial recent interest in developing methodology for high-dimensional mediation analysis. Yet, the majority of mediation statistical methods lean heavily on mean regression, which limits their ability to fully capture the complex mediating effects across the outcome distribution. To bridge this gap, we propose a novel approach for selecting and testing mediators throughout the full range of the outcome distribution spectrum. The proposed high-dimensional quantile mediation model provides a comprehensive insight into how potential mediators impact outcomes via their mediation pathways. This method's efficacy is demonstrated through extensive simulations. The study presents a real-world data application examining the mediating effects of DNA methylation on the relationship between maternal smoking and offspring birthweight. Our method offers a publicly available and user-friendly function qHIMA(), which can be accessed through the R package HIMA at https://CRAN.R-project.org/package=HIMA.

Dyadic and Individual Variation in 24-Hour Heart Rates of Cancer Patients and Their Caregivers.

Twenty-four-hour heart rate (HR) integrates multiple physiological and psychological systems related to health and well-being, and can be continuously monitored in high temporal resolution over several days with wearable HR monitors. Using HR data from two independent datasets of cancer patients and their caregivers, we aimed to identify dyadic and individual patterns of 24 h HR variation and assess their relationship to demographic, environmental, psychological, and clinical variables of interest. a novel regularized approach to high-dimensional canonical correlation analysis (CCA) was used to identify factors reflecting dyadic and individual variation in the 24 h (circadian) HR trajectories of 430 people in 215 dyads, then regression analysis was used to relate these patterns to explanatory variables. Four distinct factors of dyadic covariation in circadian HR were found, contributing approximately 7% to overall circadian HR variation. These factors, along with non-dyadic factors reflecting individual variation exhibited diverse and statistically robust patterns of association with explanatory variables of interest. Both dyadic and individual anomalies are present in the 24 h HR patterns of cancer patients and their caregivers. These patterns are largely synchronous, and their presence robustly associates with multiple explanatory variables. One notable finding is that higher mood scores in cancer patients correspond to an earlier HR nadir in the morning and higher HR during the afternoon.

Abstract C029: Multiple non-redundant immune checkpoints direct therapeutic resistance to chemotherapy and anti-CSF1R in pancreatic ductal adenocarcinoma

Abstract A hallmark of pancreatic ductal adenocarcinoma (PDA) is the formation of a myeloid rich immunosuppressive microenvironment which acts as a barrier to treatment. Therapeutic blockade/depletion of immunosuppressive myeloid cells in combination with chemotherapy or anti-PD-1 checkpoint blockade is under investigation in PDA but has yet to demonstrate broad clinical success. Here, we studied mechanisms of resistance to myeloid-targeted therapy to inform rational combination strategies. Spatial and molecular profiling of the tumor microenvironment in mouse and human PDA revealed vast intra-tumoral myeloid heterogeneity. Depletion of distinct myeloid cell populations triggered T cell infiltration into tumors but also provoked compensatory remodeling of the myeloid compartment. However, concurrent depletion of multiple myeloid subsets using drugs targeting CSF1R, CCR2/5, and CXCR2 was unexpectedly insufficient to overcome treatment resistance and failed to restore productive T cell surveillance in murine models of PDA. Therefore, we next investigated the role of the lymphoid compartment. High-dimensional single cell analyses of T cell infiltrates in PDA revealed an upregulation of multiple checkpoint molecules, including PD-1, LAG3 and CTLA-4, in both mice and humans, suggesting potential resistance mechanisms. In contrast to combinatory myeloid cell depletion, combined blockade of PD-1, LAG3, and CTLA-4 with gemcitabine and anti-CSF1R triggered peripheral CD4+ and CD8+ T cell activation and produced deep and durable tumor responses with a nearly 3-fold increase in median survival and in some cases, tumor cures. Taken together, these studies highlight the contribution of multiple immune checkpoints to cancer immune evasion and reveal a novel role for LAG3 in treatment resistance in PDA. Together, these findings suggest that a multipronged approach to disrupting negative regulatory signals controlling T cell function may be needed to realize the potential of immunotherapy in PDA. Citation Format: Meredith L. Stone, Veronica M. Herrera, Yan Li, Heather Coho, Yuqing Yue, Kathleen Graham, Devora Delman, Shaun O'Brien, Gregory L. Beatty. Multiple non-redundant immune checkpoints direct therapeutic resistance to chemotherapy and anti-CSF1R in pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Pancreatic Cancer; 2023 Sep 27-30; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(2 Suppl):Abstract nr C029.

Understanding Spondyloarthritis Pathogenesis: The Promise of Single-Cell Profiling.

Single-cell profiling, either in suspension or within the tissue context, is a rapidly evolving field. The purpose of this review is to outline recent advancements and emerging trends with a specific focus on studies in spondyloarthritis. The introduction of sequencing-based approaches for the quantification of RNA, protein, or epigenetic modifications at single-cell resolution has provided a major boost to discovery-driven research. Fluorescent flow cytometry, mass cytometry, and image-based cytometry continue to evolve. Spatial transcriptomics and imaging mass cytometry have extended high-dimensional analysis to cells in tissues. Applications in spondyloarthritis include the indexing and functional characterization of cells, discovery of disease-associated cell states, and identification of signatures associated with therapeutic responses. Single-cell TCR-seq has provided evidence for clonal expansion of CD8+ T cells in spondyloarthritis. The use of single-cell profiling approaches in spondyloarthritis research is still in its early stages. Challenges include high cost and limited availability of diseased tissue samples. To harness the full potential of the rapidly expanding technical capabilities, large-scale collaborative efforts are imperative.

Principal and independent genomic components of brain structure and function.

The highly polygenic and pleiotropic nature of behavioural traits, psychiatric disorders and structural and functional brain phenotypes complicate mechanistic interpretation of related genome-wide association study (GWAS) signals, thereby obscuring underlying causal biological processes. We propose genomic principal and independent component analysis (PCA, ICA) to decompose a large set of univariate GWAS statistics of multimodal brain traits into more interpretable latent genomic components. Here we introduce and evaluate this novel methods various analytic parameters and reproducibility across independent samples. Two UK Biobank GWAS summary statistic releases of 2240 imaging-derived phenotypes (IDPs) were retrieved. Genome-wide beta-values and their corresponding standard-error scaled z-values were decomposed using genomic PCA/ICA. We evaluated variance explained at multiple dimensions up to 200. We tested the inter-sample reproducibility of output of dimensions 5, 10, 25 and 50. Reproducibility statistics of the respective univariate GWAS served as benchmarks. Reproducibility of 10-dimensional PCs and ICs showed the best trade-off between model complexity and robustness and variance explained (PCs: |rz - max| = 0.33, |rraw - max| = 0.30; ICs: |rz - max| = 0.23, |rraw - max| = 0.19). Genomic PC and IC reproducibility improved substantially relative to mean univariate GWAS reproducibility up to dimension 10. Genomic components clustered along neuroimaging modalities. Our results indicate that genomic PCA and ICA decompose genetic effects on IDPs from GWAS statistics with high reproducibility by taking advantage of the inherent pleiotropic patterns. These findings encourage further applications of genomic PCA and ICA as fully data-driven methods to effectively reduce the dimensionality, enhance the signal to noise ratio and improve interpretability of high-dimensional multitrait genome-wide analyses.

Convex Optimization Techniques for High-Dimensional Data Clustering Analysis: A Review

Convex Optimization Techniques for High-Dimensional Data Clustering Analysis: A Review

Population-based high-dimensional analyses identify multiple intrinsic characters for cancer vaccines against lung squamous cell carcinoma.

In lung squamous cell carcinoma (LUSC), current cancer vaccines show promising effects, despite a lack of benefit for a large number of patients. We first identified the tumor antigens into shared and private antigens, and determined the population by clustering analysis in public datasets. For vaccine development, The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC) were collected. WGCNA method was furthermore applied to construct a consensus gene co-expression network based on TCGA and CPTAC datasets. The main analyses in bulk sequencing included survival, clinical features, tumor microenvironment (TME), and pathways enrichment. In addition, single-cell RNA (scRNA) analysis of cancer epithelium dissected consensus subtype. We identified the ideal population for cancer vaccines, and candidate neoantigens including AOC1, COL5A2, LGI2, and POSTN. According to subtype analysis, Lung squamous 1 (LSQ1) type exhibited a higher tumor mutational load (TMB) and copy number but no immune infiltration, whereas lung squamous 2 (LSQ2) tumors had a higher global methylation level and more fibroblasts but had less stemness. Meanwhile, trajectory analysis further revealed that the evolution of TME influenced prognosis. We emphasized specific pathways or targets with the potential for combination immunotherapy by consensus network and single-cell RNA analyses. Anti-androgen therapy has been validated in vitro experiments of LUSC as proof of concept. In conclusion,LSQ1 was linked to immune exclusion and might be utilized for vaccination, while LSQ2 was linked to immune dysfunction and could be used for programmed cell death protein 1 (PD1) blocking therapy.

Comprehensive Immunophenotyping by Polychromatic Cytometry.

The human immune system comprises a diverse array of cells involved in innate and adaptive immunity, and these immune cells coordinate immune responses against pathogens through intricate interactions. Multicolor flow cytometry is a powerful technique for qualitatively and quantitatively measuring the characteristics of immune cells, offering advantages, such as high-dimensional analysis, elucidation of cellular heterogeneity, understanding of pathogenesis, development of therapeutic strategies, and platform flexibility. Here, we demonstrate a new immunophenotyping panel that allows simultaneous evaluation of the characteristics of T and B cells. This panel enables tracking of changes in the immune status due to aging, environmental factors, pathogen infections, and vaccine administration. Additionally, it includes co-stimulatory molecules for assessing the activation state of immune cells and inhibitory checkpoint molecules for evaluating exhaustion status, thereby providing valuable insights into the features of human immune responses. These analyses contribute to understanding the pathophysiology of diseases and developing therapeutic strategies while offering crucial information for assessing the correlation of symptoms with infections and evaluating the efficacy of vaccines.

A novel method for high-dimensional reliability analysis based on activity score and adaptive Kriging

A novel method for high-dimensional reliability analysis based on activity score and adaptive Kriging

Consecutive adaptive Kriging method for high-dimensional reliability analysis based on multi-fidelity framework

Consecutive adaptive Kriging method for high-dimensional reliability analysis based on multi-fidelity framework

Harmonized analysis of PBMC by mass cytometry.

Mass cytometry permits the high dimensional analysis of cellular systems at single-cell resolution with high throughput in various areas of biomedical research. Here, we provide a state-of-the-art protocol for the analysis of human peripheral blood mononuclear cells (PBMC) by mass cytometry. We focus on the implementation of measures promoting the harmonization of large and complex studies to aid robustness and reproducibility of immune phenotyping data.