Bayesian pathway analysis over brain network mediators for survival data

Technological advancements in noninvasive imaging facilitate the construction of whole brain interconnected networks, known as brain connectivity. Existing approaches to analyze brain connectivity frequently disaggregate the entire network into a vector of unique edges or summary measures, leading to a substantial loss of information. Motivated by the need to explore the effect mechanism among genetic exposure, brain connectivity and time to disease onset, we propose an integrative Bayesian framework to model the effect pathway between each of these components while quantifying the mediating role of brain networks. To accommodate the biological architectures of brain connectivity constructed along white matter fiber tracts, we develop a structural modeling framework that includes a symmetric matrix-variate accelerated failure time model and a symmetric matrix response regression to characterize the effect paths. We further impose within-graph sparsity and between-graph shrinkage to identify informative network configurations and eliminate the interference of noisy components. Extensive simulations confirm the superiority of our method compared with existing alternatives. By applying the proposed method to the landmark Alzheimer's Disease Neuroimaging Initiative study, we obtain neurobiologically plausible insights that may inform future intervention strategies

Inference-based statistical network analysis uncovers star-like brain functional architectures for internalizing psychopathology in children

To improve the statistical power for imaging biomarker detection, we propose a latent variable-based statistical network analysis (LatentSNA) that combines brain functional connectivity with internalizing psychopathology, implementing network science in a generative statistical process to preserve the neurologically meaningful network topology in the adolescents and children population. The developed inference-focused generative Bayesian framework (1) addresses the lack of power and inflated Type II errors in current analytic approaches when detecting imaging biomarkers, (2) allows unbiased estimation of biomarkers' influence on behavior variants, (3) quantifies the uncertainty and evaluates the likelihood of the estimated biomarker effects against chance and (4) ultimately improves brain-behavior prediction in novel samples and the clinical utilities of neuroimaging findings. We collectively model multi-state functional networks with multivariate internalizing profiles for 5,000 to 7,000 children in the Adolescent Brain Cognitive Development (ABCD) study with sufficiently accurate prediction of both children internalizing traits and functional connectivity, and substantially improved our ability to explain the individual internalizing differences compared with current approaches. We successfully uncover large, coherent star-like brain functional architectures associated with children's internalizing psychopathology across multiple functional systems and establish them as unique fingerprints for childhood internalization

Case Report: A Novel GJB2 Missense Variant Inherited From the Low-Level Mosaic Mother in a Chinese Female With Palmoplantar Keratoderma With Deafness

Dominant variants in the gap junction beta-2 (GJB2) gene may lead to various degrees of syndromic hearing loss (SHL) which is manifest as sensorineural hearing impairment and hyperproliferative epidermal disorders, including palmoplantar keratoderma with deafness (PPKDFN). So far, only a few GJB2 dominant variants causing PPKDFN have been discovered. Through the whole-exome sequencing (WES), a Chinese female patient with severe palmoplantar hyperkeratosis and delayed-onset hearing loss has been identified. She had a novel heterozygous variant, c.224G>C (p.R75P), in the GJB2 gene, which was unreported previously. The proband’s mother who had a mild phenotype was suggested the possibility of mosaicism by WES (∼120×), and the ultra-deep targeted sequencing (∼20,000×) was used for detecting low-level mosaic variants which provided accurate recurrence-risk estimates and genetic counseling. In addition, the analysis of protein structure indicated that the structural stability and permeability of the connexin 26 (Cx26) gap junction channel may be disrupted by the p.R75P variant. Through retrospective analysis, it is detected that the junction of extracellular region-1 (EC1) and transmembrane region-2 (TM2) is a variant hotspot for PPKDFN, such as p.R75. Our report reflects the important and effective diagnostic role of WES in PPKDFN and low-level mosaicism, expands the spectrum of the GJB2 variant, and furthermore provides strong proof about the relevance between the p.R75P variant in GJB2 and PPKDFN

Noncanonical amino acid mutagenesis in response to recoding signal-enhanced quadruplet codons

While amber suppression is the most common approach to introduce noncanonical amino acids into proteins in live cells, quadruplet codon decoding has potential to enable a greatly expanded genetic code with up to 256 new codons for protein biosynthesis. Since triplet codons are the predominant form of genetic code in nature, quadruplet codon decoding often displays limited efficiency. In this work, we exploited a new approach to significantly improve quadruplet UAGN and AGGN (N = A, U, G, C) codon decoding efficiency by using recoding signals imbedded in mRNA. With representative recoding signals, the expression level of mutant proteins containing UAGN and AGGN codons reached 48% and 98% of that of the wild-type protein, respectively. Furthermore, this strategy mitigates a common concern of reading-through endogenous stop codons with amber suppression-based system. Since synthetic recoding signals are rarely found near the endogenous UAGN and AGGN sequences, a low level of undesirable suppression is expected. Our strategy will greatly enhance the utility of noncanonical amino acid mutagenesis in live-cell studies

InterFormer: Interactive Local and Global Features Fusion for Automatic Speech Recognition

The local and global features are both essential for automatic speech recognition (ASR). Many recent methods have verified that simply combining local and global features can further promote ASR performance. However, these methods pay less attention to the interaction of local and global features, and their series architectures are rigid to reflect local and global relationships. To address these issues, this paper proposes InterFormer for interactive local and global features fusion to learn a better representation for ASR. Specifically, we combine the convolution block with the transformer block in a parallel design. Besides, we propose a bidirectional feature interaction module (BFIM) and a selective fusion module (SFM) to implement the interaction and fusion of local and global features, respectively. Extensive experiments on public ASR datasets demonstrate the effectiveness of our proposed InterFormer and its superior performance over the other Transformer and Conformer models.Comment: Accepted by Interspeech 202

Deciphering the age-dependent changes of pulmonary fibroblasts in mice by single-cell transcriptomics

Background and objectives: The heterogeneity of pulmonary fibroblasts, a critical aspect of both murine and human models under physiological and pathological conditions, is well-documented. Yet, consensus remains elusive on the subtypes, lineage, biological attributes, signal transduction pathways, and plasticity of these fibroblasts. This ambiguity significantly impedes our understanding of the fibrotic processes that transpire in lung tissue during aging. This study aims to elucidate the transcriptional profiles, differentiation pathways, and potential roles of fibroblasts within aging pulmonary tissue.Methods: We employed single-cell transcriptomic sequencing via the 10x Genomics platform. The downstream data were processed and analyzed using R packages, including Seurat. Trajectory and stemness of differentiation analyses were conducted using the Monocle2 and CytoTRACE R packages, respectively. Cell interactions were deciphered using the CellChat R package, and the formation of collagen and muscle fibers was identified through Masson and Van Geison staining techniques.Results: Our analysis captured a total of 22,826 cells, leading to the identification of fibroblasts and various immune cells. We observed a shift in fibroblasts from lipogenic and immune-competent to fibrotic and myofibroblast-like phenotype during the aging process. In the aged stage, fibroblasts exhibited a diminished capacity to express chemokines for immune cells. Experimental validation confirmed an increase of collagen and muscle fiber in the aged compared to young lung tissues. Furthermore, we showed that TGFβ treatment induced a fibrotic, immunodeficient and lipodystrophic transcriptional phenotype in young pulmonary fibroblasts.Conclusion: We present a comprehensive single-cell transcriptomic landscape of lung tissue from aging mice at various stages, revealing the differentiation trajectory of fibroblasts during aging. Our findings underscore the pivotal role of fibroblasts in the regulation of immune cells, and provide insights into why age increases the risk of pulmonary fibrosis

An Integration Method of Bursting Strain Energy and Seismic Velocity Tomography for Coal Burst Hazard Assessment

AbstractThe occurrence of coal burst in underground coal mines is complex, abrupt, and diverse, and the evaluation and prediction of coal burst hazard is the premise of effective prevention and control of coal burst. In this study, a coal burst carrier system model under the synergistic action of roof, coal seams, and floor was established, and the evolution of coal burst in underground coal mines was discussed based on the stress-vibration-energy coupling principle. On this basis, an integration method of bursting strain energy and seismic velocity tomography for coal burst assessment was proposed. With the deep and complex panel in a mine as the research object, the coal burst risk of the panel during excavation was evaluated in time and space domains, respectively. Results showed that the bursting strain energy and the active seismic velocity tomography technology can accurately identify both the positive anomalies and the negative anomalies of stress field and energy field in the mining period. Moreover, the method can not only evaluate the coal burst risk of the panel in the temporal domain but also predict the area with potential strong seismic events in the spatial domain. The research conclusions can accurately illustrate the whole complex evolution process of coal burst in underground coal mines

The role of m6A demethylases in lung cancer: diagnostic and therapeutic implications

m6A is the most prevalent internal modification of eukaryotic mRNA, and plays a crucial role in tumorigenesis and various other biological processes. Lung cancer is a common primary malignant tumor of the lungs, which involves multiple factors in its occurrence and progression. Currently, only the demethylases FTO and ALKBH5 have been identified as associated with m6A modification. These demethylases play a crucial role in regulating the growth and invasion of lung cancer cells by removing methyl groups, thereby influencing stability and translation efficiency of mRNA. Furthermore, they participate in essential biological signaling pathways, making them potential targets for intervention in lung cancer treatment. Here we provides an overview of the involvement of m6A demethylase in lung cancer, as well as their potential application in the diagnosis, prognosis and treatment of the disease

Dosiomics: Extracting 3D Spatial Features From Dose Distribution to Predict Incidence of Radiation Pneumonitis

Radiation pneumonitis (RP) is one of the major toxicities of thoracic radiation therapy. RP incidence has been proven to be closely associated with the dosimetric factors and normal tissue control possibility (NTCP) factors. However, because these factors only utilize limited information of the dose distribution, the prediction abilities of these factors are modest. We adopted the dosiomics method for RP prediction. The dosiomics method first extracts spatial features of the dose distribution within ipsilateral, contralateral, and total lungs, and then uses these extracted features to construct prediction model via univariate and multivariate logistic regression (LR). The dosiomics method is validated using 70 non-small cell lung cancer (NSCLC) patients treated with volumetric modulated arc therapy (VMAT) radiotherapy. Dosimetric and NTCP factors based prediction models are also constructed to compare with the dosiomics features based prediction model. For the dosimetric, NTCP and dosiomics factors/features, the most significant single factors/features are the mean dose, parallel/serial (PS) NTCP and gray level co-occurrence matrix (GLCM) contrast of ipsilateral lung, respectively. And the area under curve (AUC) of univariate LR is 0.665, 0.710 and 0.709, respectively. The second significant factors are V5 of contralateral lung, equivalent uniform dose (EUD) derived from PS NTCP of contralateral lung and the low gray level run emphasis of gray level run length matrix (GLRLM) of total lungs. The AUC of multivariate LR is improved to 0.676, 0.744, and 0.782, respectively. The results demonstrate that the univariate LR of dosiomics features has approximate predictive ability with NTCP factors, and the multivariate LR outperforms both the dosimetric and NTCP factors. In conclusion, the spatial features of dose distribution extracted by the dosiomics method effectively improves the prediction ability