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"Wu, Si-yao"
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Upregulation of interferon-γ activation in patients with anti-interferon-γ autoantibodies immunodeficiency syndrome: insights from single-cell analysis
Anti-interferon-γ autoantibodies (AIGAs) immunodeficiency syndrome is an emerging adult-onset immunodeficiency causing opportunistic infections. However, its comprehensive immune landscape remains elusive. This study presents the first single-cell RNA sequencing (scRNA-seq) analysis of AIGAs immunodeficiency syndrome, aiming to delineate its pathogenic mechanisms.
We performed scRNA-seq on peripheral blood mononuclear cells (PBMCs) from 8 AIGAs immunodeficiency syndrome patients (4 infective, 4 stable phase) and 3 healthy controls. Findings were validated by flow cytometry in an expanded cohort (15 patients vs. 10 controls).
Single-cell RNA sequencing of PBMCs from patients with AIGAs immunodeficiency syndrome identified a comprehensive immune subset profile, including effector memory CD4
T cells, naive CD4
T cells, regulatory T cells, GNLY
CD8
Tem, GZMK
CD8
Tem, naive CD8
T cells, naive B cells, memory B cells, plasma cells, ISG
atypical B cells, monocytes, and NKT cells. ScRNA-seq analysis revealed a significantly higher proportion of Th1 cells (16.62% vs. 6.94% in controls) and ISG
B cells (2.95% vs. 0.53%), alongside a lower proportion of plasma cells (9.30% vs. 17.79%) and memory B cells (9.54% vs. 27.35%). Flow cytometry consistently confirmed the increase in Th1 cells (21.84% [14.87-27.57] vs. 11.96% [7.19-15.74]) and decreases in marginal zone B cells (2.87% [1.71-4.45] vs. 8.60% [6.77-15.65]), memory B cells (13.85% [5.72-20.23] vs. 22.96% [16.39-33.83]), and class-switched B cells (6.11% [2.39-9.10] vs. 10.18% [5.35-15.77]). Transcriptome analysis demonstrated upregulated expression of interferon-response and HLA genes (e.g., HLA-DQB1, HLA-DQA1, HLA-DRB1), whereas IRF1 was downregulated across all subsets; functional enrichment analyses further highlighted significant activation in IFN signaling and B cell activation pathways. CellChat and pseudotime analyses indicated that CD4
Tem and CD14
monocytes drive sustained Th1 inflammation and monocyte hyperactivation through enhanced pro-inflammatory and antigen-presenting interactions, with T-cell differentiation skewed toward terminal effectors and B-cell development disrupted by ISG
B cell emergence, premature plasma cell formation, and IGLC3-biased class switching, collectively delineating the interferon-mediated immunopathology of AIGAs immunodeficiency syndrome.
In summary, this first single-cell atlas maps AIGAs immunodeficiency syndrome as a Th1-skewed, IFN-γ-driven disorder sustained by CD4
Tem-CD14
monocyte crosstalk. It combines T-cell activation, expanded Th1 and ISG
B cells, and loss of memory/plasma B cells to drive autoantibody generation. Skewed T- and B-cell trajectories and polygenic up-regulation of interferon/HLA genes provide a clear mechanistic rationale for targeted therapy.
Journal Article
Identification of Sudden Stiffness Change in the Acceleration Response of a Nonlinear Hysteretic Structure
The integration of discrete wavelet transform and independent component analysis (DWT-ICA) method can directly identify time-varying changes in linear structures. However, better metrics of structural seismic damage and future performance after an event are related to structural permanent and total plastic deformations. This study proposes a two-stage technique based on DWT-FastICA and improved multiparticle swarm coevolution optimization (IMPSCO) using a baseline nonlinear Bouc–Wen structural model to directly identify changes in stiffness caused by damage as well as plastic or permanent deflections. In the first stage, the measured structural dynamic responses are preprocessed firstly by DWT, and then the Fast ICA is used to extract the feature components that contain the damage information for the purpose of initially locating damage. In the second stage, the structural responses are divided at the identified damage instant into segments that are used to identify the time-varying physical parameters by using the IMPSCO, and the location and extent of damage can accordingly be identified accurately. The efficiency of the proposed method in identifying stiffness changes is assessed under different ground motions using a suite of two different ground acceleration records. Meanwhile, the effect of noise level and damage extent on the proposed method is also analyzed. The results show that in a realistic scenario with fixed filter tuning parameters, the proposed approach identifies stiffness changes within 1.25% of true stiffness within 8.96 s; therefore, it can work in real time. Parameters are identified within 14% of the actual as-modeled value using noisy simulation-derived structural responses. This indicates that, in accordance with different demands, the proposed method can not only locate and quantify damage within a short time with a high precision but also has excellent noise tolerance, robustness, and practicality.
Journal Article
A Time-Domain Structural Damage Detection Method Based on Improved Multiparticle Swarm Coevolution Optimization Algorithm
Optimization techniques have been applied to structural health monitoring and damage detection of civil infrastructures for two decades. The standard particle swarm optimization (PSO) is easy to fall into the local optimum and such deficiency also exists in the multiparticle swarm coevolution optimization (MPSCO). This paper presents an improved MPSCO algorithm (IMPSCO) firstly and then integrates it with Newmark’s algorithm to localize and quantify the structural damage by using the damage threshold proposed. To validate the proposed method, a numerical simulation and an experimental study of a seven-story steel frame were employed finally, and a comparison was made between the proposed method and the genetic algorithm (GA). The results show threefold: (1) the proposed method not only is capable of localization and quantification of damage, but also has good noise-tolerance; (2) the damage location can be accurately detected using the damage threshold proposed in this paper; and (3) compared with the GA, the IMPSCO algorithm is more efficient and accurate for damage detection problems in general. This implies that the proposed method is applicable and effective in the community of damage detection and structural health monitoring.
Journal Article
Structural Reliability Assessment by Integrating Sensitivity Analysis and Support Vector Machine
To reduce the runtime and ensure enough computation accuracy, this paper proposes a structural reliability assessment method by the use of sensitivity analysis (SA) and support vector machine (SVM). The sensitivity analysis is firstly applied to assess the effect of random variables on the values of performance function, while the small-influence variables are rejected as input vectors of SVM. Then, the trained SVM is used to classify the input vectors, which are produced by sampling the residual variables based on their distributions. Finally, the reliability assessment is implemented with the aid of reliability theory. A 10-bar planar truss is used to validate the feasibility and efficiency of the proposed method, and a performance comparison is made with other existing methods. The results show that the proposed method can largely save the runtime with less reduction of the accuracy; furthermore, the accuracy using the proposed method is the highest among the methods employed.
Journal Article
A Time-Domain Structural Damage Detection Method Based on Improved Multiparticle Swarm Coevolution Optimization Algorithm
Optimization techniques have been applied to structural health monitoring and damage detection of civil infrastructures for two decades. The standard particle swarm optimization (PSO) is easy to fall into the local optimum and such deficiency also exists in the multiparticle swarm coevolution optimization (MPSCO). This paper presents an improved MPSCO algorithm (IMPSCO) firstly and then integrates it with Newmark’s algorithm to localize and quantify the structural damage by using the damage threshold proposed. To validate the proposed method, a numerical simulation and an experimental study of a seven-story steel frame were employed finally, and a comparison was made between the proposed method and the genetic algorithm (GA). The results show threefold: (1) the proposed method not only is capable of localization and quantification of damage, but also has good noise-tolerance; (2) the damage location can be accurately detected using the damage threshold proposed in this paper; and (3) compared with the GA, the IMPSCO algorithm is more efficient and accurate for damage detection problems in general. This implies that the proposed method is applicable and effective in the community of damage detection and structural health monitoring.
Journal Article
Structural Reliability Assessment by Integrating Sensitivity Analysis and Support Vector Machine
To reduce the runtime and ensure enough computation accuracy, this paper proposes a structural reliability assessment method by the use of sensitivity analysis (SA) and support vector machine (SVM). The sensitivity analysis is firstly applied to assess the effect of random variables on the values of performance function, while the small-influence variables are rejected as input vectors of SVM. Then, the trained SVM is used to classify the input vectors, which are produced by sampling the residual variables based on their distributions. Finally, the reliability assessment is implemented with the aid of reliability theory. A 10-bar planar truss is used to validate the feasibility and efficiency of the proposed method, and a performance comparison is made with other existing methods. The results show that the proposed method can largely save the runtime with less reduction of the accuracy; furthermore, the accuracy using the proposed method is the highest among the methods employed.
Journal Article
Mapping cis-regulatory chromatin contacts in neural cells links neuropsychiatric disorder risk variants to target genes
Mutations in gene regulatory elements have been associated with a wide range of complex neuropsychiatric disorders. However, due to their cell-type specificity and difficulties in characterizing their regulatory targets, the ability to identify causal genetic variants has remained limited. To address these constraints, we perform an integrative analysis of chromatin interactions, open chromatin regions and transcriptomes using promoter capture Hi-C, assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) and RNA sequencing, respectively, in four functionally distinct neural cell types: induced pluripotent stem cell (iPSC)-induced excitatory neurons and lower motor neurons, iPSC-derived hippocampal dentate gyrus-like neurons and primary astrocytes. We identify hundreds of thousands of long-range
cis-
interactions between promoters and distal promoter-interacting regions, enabling us to link regulatory elements to their target genes and reveal putative processes that are dysregulated in disease. Finally, we validate several promoter-interacting regions by using clustered regularly interspaced short palindromic repeats (CRISPR) techniques in human excitatory neurons, demonstrating that
CDK5RAP3
,
STRAP
and
DRD2
are transcriptionally regulated by physically linked enhancers.
An integrative three-dimensional genomic and transcriptional profiling of four human neural cell types links regulatory elements to their target genes and elucidates the function of noncoding variants in neuropsychiatric disorders.
Journal Article