A new framework for exploratory network mediator analysis in omics data.

Omics methods are widely used in basic biology and translational medicine research. More and more omics data are collected to explain the impact of certain risk factors on clinical outcomes. To explain the mechanism of the risk factors, a core question is how to find the genes/proteins/metabolites that mediate their effects on the clinical outcome. Mediation analysis is a modeling framework to study the relationship between risk factors and pathological outcomes, via mediator variables. However, high-dimensional omics data are far more challenging than traditional data: (1) From tens of thousands of genes, can we overcome the curse of dimensionality to reliably select a set of mediators? (2) How do we ensure that the selected mediators are functionally consistent? (3) Many biological mechanisms contain nonlinear effects. How do we include nonlinear effects in the high-dimensional mediation analysis? (4) How do we consider multiple risk factors at the same time? To meet these challenges, we propose a new exploratory mediation analysis framework, medNet, which focuses on finding mediators through predictive modeling. We propose new definitions for predictive exposure, predictive mediator, and predictive network mediator, using a statistical hypothesis testing framework to identify predictive exposures and mediators. Additionally, two heuristic search algorithms are proposed to identify network mediators, essentially subnetworks in the genome-scale biological network that mediate the effects of single or multiple exposures. We applied medNet on a breast cancer data set and a metabolomics data set combined with food intake questionnaire data. It identified functionally consistent network mediators for the exposures' impact on the outcome, facilitating data interpretation.

Bayesian functional analysis for untargeted metabolomics data with matching uncertainty and small sample sizes.

Untargeted metabolomics based on liquid chromatography-mass spectrometry technology is quickly gaining widespread application, given its ability to depict the global metabolic pattern in biological samples. However, the data are noisy and plagued by the lack of clear identity of data features measured from samples. Multiple potential matchings exist between data features and known metabolites, while the truth can only be one-to-one matches. Some existing methods attempt to reduce the matching uncertainty, but are far from being able to remove the uncertainty for most features. The existence of the uncertainty causes major difficulty in downstream functional analysis. To address these issues, we develop a novel approach for Bayesian Analysis of Untargeted Metabolomics data (BAUM) to integrate previously separate tasks into a single framework, including matching uncertainty inference, metabolite selection and functional analysis. By incorporating the knowledge graph between variables and using relatively simple assumptions, BAUM can analyze datasets with small sample sizes. By allowing different confidence levels of feature-metabolite matching, the method is applicable to datasets in which feature identities are partially known. Simulation studies demonstrate that, compared with other existing methods, BAUM achieves better accuracy in selecting important metabolites that tend to be functionally consistent and assigning confidence scores to feature-metabolite matches. We analyze a COVID-19 metabolomics dataset and a mouse brain metabolomics dataset using BAUM. Even with a very small sample size of 16 mice per group, BAUM is robust and stable. It finds pathways that conform to existing knowledge, as well as novel pathways that are biologically plausible.

Chromosome structure modeling tools and their evaluation in bacteria.

The three-dimensional (3D) structure of bacterial chromosomes is crucial for understanding chromosome function. With the growing availability of high-throughput chromosome conformation capture (3C/Hi-C) data, the 3D structure reconstruction algorithms have become powerful tools to study bacterial chromosome structure and function. It is highly desired to have a recommendation on the chromosome structure reconstruction tools to facilitate the prokaryotic 3D genomics. In this work, we review existing chromosome 3D structure reconstruction algorithms and classify them based on their underlying computational models into two categories: constraint-based modeling and thermodynamics-based modeling. We briefly compare these algorithms utilizing 3C/Hi-C datasets and fluorescence microscopy data obtained from Escherichia coli and Caulobacter crescentus, as well as simulated datasets. We discuss current challenges in the 3D reconstruction algorithms for bacterial chromosomes, primarily focusing on software usability. Finally, we briefly prospect future research directions for bacterial chromosome structure reconstruction algorithms.

Role of the GLP2-Wnt1 axis in silicon-rich alkaline mineral water maintaining intestinal epithelium regeneration in piglets under early-life stress.

Stress-induced intestinal epithelial injury (IEI) and a delay in repair in infancy are predisposing factors for refractory gut diseases in adulthood, such as irritable bowel syndrome (IBS). Hence, it is necessary to develop appropriate mitigation methods for mammals when experiencing early-life stress (ELS). Weaning, as we all know, is a vital procedure that all mammalian newborns, including humans, must go through. Maternal separation (MS) stress in infancy (regarded as weaning stress in animal science) is a commonly used ELS paradigm. Drinking silicon-rich alkaline mineral water (AMW) has a therapeutic effect on enteric disease, but the specific mechanisms involved have not been reported. Herein, we discover the molecular mechanism by which silicon-rich AMW repairs ELS-induced IEI by maintaining intestinal stem cell (ISC) proliferation and differentiation through the glucagon-like peptide (GLP)2-Wnt1 axis. Mechanistic study showed that silicon-rich AMW activates GLP2-dependent Wnt1/ß-catenin pathway, and drives ISC proliferation and differentiation by stimulating Lgr5+ ISC cell cycle passage through the G1-S-phase checkpoint, thereby maintaining intestinal epithelial regeneration and IEI repair. Using GLP2 antagonists (GLP23-33) and small interfering RNA (SiWnt1) in vitro, we found that the GLP2-Wnt1 axis is the target of silicon-rich AMW to promote intestinal epithelium regeneration. Therefore, silicon-rich AMW maintains intestinal epithelium regeneration through the GLP2-Wnt1 axis in piglets under ELS. Our research contributes to understanding the mechanism of silicon-rich AMW promoting gut epithelial regeneration and provides a new strategy for the alleviation of ELS-induced IEI.

Impact of Using Pre- and Post-Bronchodilator Spirometry Reference Values in a Chinese Population.

RATIONALE: Spirometry reference equations that are derived from a large, nationally representative, general population are warranted in China and the impact of using pre- and post-BD spirometry reference values has yet to be assessed in Chinese populations. OBJECTIVES: To present both the pre-BD and post-BD spirometry reference values for Chinese adults using the China Pulmonary Health (CPH) study. METHODS: A reference population of 17969 healthy, non-smoking participants in the CPH study was used to calculate the pre- and post-BD reference values for the forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC) and FEV1/FVC. Both pre- and post-BD reference values were applied to the entire CPH population (50991 individuals) to illustrate the divergence between the use of references in determining the disease prevalence and severity grading. MEASUREMENTS AND MAIN RESULTS: The prevalence of airflow limitation was 5.36% using pre-BD reference and 8.02% using the post-BD reference. Individuals who had post-BD FEV1/FVC below post-BD but higher than pre-BD reference values were found to have significantly higher rates of self-reported respiratory symptoms, and significantly lower values in spirometry indicators than those above post-BD reference values. An additional 3.51% of participants were identified as grade II-IV COPD using the post-BD FEV1 predicted values. CONCLUSION: This study generated and applied pre- and post-bronchodilator spirometry reference values in a nationally representative Chinese adult population. Post-BD reference values may serve as an additional criterion in identifying individuals at risk for obstructive pulmonary diseases, its diagnostic and prognostic values should be further investigated.

Effects of organic fertilizers on plant growth and the rhizosphere microbiome.

Application of organic fertilizers is an important strategy for sustainable agriculture. The biological source of organic fertilizers determines their specific functional characteristics, but few studies have systematically examined these functions or assessed their health risk to soil ecology. To fill this gap, we analyzed 16S rRNA gene amplicon sequencing data from 637 soil samples amended with plant- and animal-derived organic fertilizers (hereafter plant fertilizers and animal fertilizers). Results showed that animal fertilizers increased the diversity of soil microbiome, while plant fertilizers maintained the stability of soil microbial community. Microcosm experiments verified that plant fertilizers were beneficial to plant root development and increased carbon cycle pathways, while animal fertilizers enriched nitrogen cycle pathways. Compared with animal fertilizers, plant fertilizers harbored a lower abundance of risk factors such as antibiotic resistance genes and viruses. Consequently, plant fertilizers might be more suitable for long-term application in agriculture. This work provides a guide for organic fertilizer selection from the perspective of soil microecology and promotes sustainable development of organic agriculture.IMPORTANCEThis study provides valuable guidance for use of organic fertilizers in agricultural production from the perspective of the microbiome and ecological risk.

High luminous efficiency and excellent thermal performance in rod-shaped YAG:Ce phosphor ceramics for laser lighting.

High power and high brightness laser lighting puts forward new requirements for phosphor converters such as high luminous efficiency, high thermal conductivity and high saturation threshold due to the severe thermal effect. The structure design of phosphor converters is proposed as what we believe to be a novel strategy for less heat production and more heat conduction. In this work, the rod-shaped YAG:Ce phosphor ceramics (PCs) and disc-shaped YAG:Ce PCs as control group were fabricated by the gel casting and vacuum sintering, to comparatively study the luminescence performance for LD lighting, on the premise that the total number of transverse Ce3+ ions and the volume of samples from two comparison groups were same. All rod YAG:Ce PCs with low Ce3+ concentration exhibited the high luminous efficiency and better thermal stability than YAG:Ce discs with high Ce3+ concentration. Under the laser power density of 47.8 W/mm2, the luminous saturation was never observed in all rod-shaped YAG:Ce PCs. The high luminous efficacy of 245∼274 lm/W, CRI of 56.3∼59.5 and CCT of 4509∼4478 K were achieved. More importantly, due to the extremely low Ce3+ doping concentration (0.01 at%), rod-shaped ceramics based LDs devices showed the excellent thermal performance and their surface temperatures were even below 30.5 °C surprisingly under the laser power density of 20.3 W·mm-2 (2 W). These results indicate that the rod shape of phosphor converter is a promising structure engineering for high power laser lighting.

High luminous efficiency and high saturation threshold in highly transparent LuAG:Ce phosphor ceramics for laser diodes lighting.

Lu3Al5O12:Ce (LuAG:Ce) phosphor ceramics (PCs) with the excellent thermal stability and high saturation threshold are considered as the best green-fluorescent converters for high-power laser diodes (LDs) lighting. In this study, the effects of sintering additives and sintering processes on the transmittance and microstructure of LuAG:Ce PCs were systematically studied, and the luminescence performance of ceramics with different transmittance was compared. LuAG:Ce PCs with the transmittance of 80% (@800 nm, 1.5 mm) were obtained by using 0.1 wt.% MgO and 0.5 wt.% TEOS as sintering additives, combined with optimized vacuum pre-sintering and hot isostatic pressing. Compared to the non-HIP samples, the transmittance had increased by 11%. The microstructure of ceramics indicated that high transparency was closely related to the decrease in intergranular pores. Notably, the luminous efficiency of 253 lm/W and its saturation thresholds of > 46 W/mm2 were obtained simultaneously in green-emitting LDs devices. Moreover, under 3W laser irradiation, highly transparent ceramics had the low surface temperature of 66.4 °C, indicating the good heat dissipation performance. The observed high luminous efficiency and high saturation threshold of LuAG:Ce PCs were attributed to fewer pores and oxygen vacancies. Therefore, this work proves that highly transparent LuAG:Ce PCs are promising green-fluorescent converters for high-power LDs lighting.

High-dimensional multisubject time series transition matrix inference with application to brain connectivity analysis.

Brain-effective connectivity analysis quantifies directed influence of one neural element or region over another, and it is of great scientific interest to understand how effective connectivity pattern is affected by variations of subject conditions. Vector autoregression (VAR) is a useful tool for this type of problems. However, there is a paucity of solutions when there is measurement error, when there are multiple subjects, and when the focus is the inference of the transition matrix. In this article, we study the problem of transition matrix inference under the high-dimensional VAR model with measurement error and multiple subjects. We propose a simultaneous testing procedure, with three key components: a modified expectation-maximization (EM) algorithm, a test statistic based on the tensor regression of a bias-corrected estimator of the lagged auto-covariance given the covariates, and a properly thresholded simultaneous test. We establish the uniform consistency for the estimators of our modified EM, and show that the subsequent test achieves both a consistent false discovery control, and its power approaches one asymptotically. We demonstrate the efficacy of our method through both simulations and a brain connectivity study of task-evoked functional magnetic resonance imaging.

Penalized deep partially linear cox models with application to CT scans of lung cancer patients.

Lung cancer is a leading cause of cancer mortality globally, highlighting the importance of understanding its mortality risks to design effective patient-centered therapies. The National Lung Screening Trial (NLST) employed computed tomography texture analysis, which provides objective measurements of texture patterns on CT scans, to quantify the mortality risks of lung cancer patients. Partially linear Cox models have gained popularity for survival analysis by dissecting the hazard function into parametric and nonparametric components, allowing for the effective incorporation of both well-established risk factors (such as age and clinical variables) and emerging risk factors (eg, image features) within a unified framework. However, when the dimension of parametric components exceeds the sample size, the task of model fitting becomes formidable, while nonparametric modeling grapples with the curse of dimensionality. We propose a novel Penalized Deep Partially Linear Cox Model (Penalized DPLC), which incorporates the smoothly clipped absolute deviation (SCAD) penalty to select important texture features and employs a deep neural network to estimate the nonparametric component of the model. We prove the convergence and asymptotic properties of the estimator and compare it to other methods through extensive simulation studies, evaluating its performance in risk prediction and feature selection. The proposed method is applied to the NLST study dataset to uncover the effects of key clinical and imaging risk factors on patients' survival. Our findings provide valuable insights into the relationship between these factors and survival outcomes.

Characterizations of gut bacteriome, mycobiome, and virome of healthy individuals living in sea-level and high-altitude areas.

BACKGROUND: The contribution of gut microbiota to human high-altitude adaptation remains inadequately understood. METHODS: Here a comparative analysis of gut microbiota was conducted between healthy individuals living at sea level and high altitude using deep whole-metagenome shotgun sequencing, to investigate the adaptive mechanisms of gut microbiota in plateau inhabitants. RESULTS: The results showed the gut bacteriomes in high-altitude individuals exhibited greater within-sample diversity and significant alterations in both bacterial compositional and functional profiles when compared to those of sea-level individuals, indicating the potential selection of unique bacteria associated with high-altitude environments. The strain-level investigation revealed enrichment of Collinsella aerofaciens and Akkermansia muciniphila in high-altitude populations. The characteristics of gut virome and gut mycobiome were also investigated. Compared to sea-level subjects, high-altitude subjects exhibited a greater diversity in their gut virome, with an increased number of viral operational taxonomic units (vOTUs) and unique annotated genes. Finally, correlation analyses revealed 819 significant correlations between 42 bacterial species and 375 vOTUs, while no significant correlations were observed between bacteria and fungi or between fungi and viruses. CONCLUSION: The findings have significantly contributed to an enhanced comprehension of the mechanisms underlying the high-altitude geographic adaptation of the human gut microbiota.

COVID-19 and the risk of acute cardiovascular diseases: a two-sample Mendelian randomization study.

BACKGROUND: Evidence suggests that coronavirus disease 2019 (COVID-19) is associated with the risk of cardiovascular diseases (CVDs). However, the results are inconsistent, and the causality remains to be established. We aimed to investigate the potential causal relationship between COVID-19 and CVDs by using two-sample Mendelian randomization (MR) analysis. METHODS: Summary-level data for COVID-19 and CVDs including myocarditis, heart failure (HF), acute myocardial infarction (AMI), arrhythmia and venous thromboembolism (VTE) were obtained from the IEU OpenGWAS project, a public genome-wide association study (GWAS). Single nucleotide polymorphisms (SNPs) were used as instrumental variables. Five complementary MR methods were performed, including inverse variance weighted (IVW), MR-Egger, weighted median, weighted mode and simple mode methods. IVW method was considered as the primary approach. Besides, sensitivity analyses, including Cochran's Q test, MR-Egger intercept test, and leave-one-out analysis, were performed to evaluate the robustness of the results. RESULTS: According to the IVW results, our MR study indicated that genetically predicted COVID-19 was not causally connected with the risk of CVDs [myocarditis: odds ratio (OR) = 1.407, 95% confidence interval (CI) = 0.761-2.602, p-value = 0.277; HF: OR = 1.180, 95% CI = 0.980-1.420, p-value = 0.080; AMI: OR = 1.002, 95% CI = 0.998-1.005, p-value = 0.241; arrhythmia: OR = 0.865, 95% CI = 0.717-1.044, p-value = 0.132; VTE: OR = 1.013, 95% CI = 0.997-1.028, p-value = 0.115]. The supplementary MR methods showed similar results. Sensitivity analyses suggested that the causal estimates were robust. CONCLUSION: This two-sample MR analysis did not provide sufficient evidence for a causal relationship between COVID-19 and the risk of acute CVDs, which may provide new insights into the prevention of acute CVDs in COVID-19 patients.

Ultrasonic AccV: a potential indicator of peripheral arteriosclerosis in patients with chronic obstructive pulmonary disease.

OBJECTIVE: This study aimed to investigate the risk factors for peripheral arteriosclerosis (PAS) and peripheral artery disease (PAD) in chronic obstructive pulmonary disease (COPD) patients and potential ultrasound indicators that could be used to improve detection. METHOD: Outpatients seeking care between January 1, 2017, and December 31, 2020, in The First Affiliated Hospital of China Medical University were prospectively recruited. Subjects were divided into COPD and non-COPD (control) groups, and the COPD group was further divided into PAD and non-PAD subgroup, at the same time, PAS and non-PAS subgroup. Indicators of PAD -ankle-brachial index (ABI), indicators of PAS- pulse wave velocity (PWV), and ultrasound indices -peak systolic blood flow velocity (PSV) and blood flow acceleration velocity (AccV) were compared. RESULT: Sixty-nine (61.6%) of 112 enrolled subjects had COPD. COPD patients had higher age, and blood pressure (BP)lower than controls. Seventeen (24.6%) COPD patients had PAD, the prevalence of PAD increases with the decrease of lung function, and seven (16.3%) non-COPD patients had PAD, however, there was no significant statistical difference between COPD and non-COPD groups. Fifty (72.5%) COPD patients had PAS, and thirty-four (79.1%) non-COPD patients had PAS, however, there was also no significant difference. The PAS subgroup had higher age, body mass index(BMI), body fat percentage(BFP), lower FEV1 and FEV1/FVC, as well as higher levels of right brachial artery and left dorsalis pedis artery AccV. Factors that correlated with ABI were 6MWD, post-bronchodilator FEV1, FEV1/ FVC, and maximal middle expiratory flow between 75% and 25% of FVC. Age, BP, and 6MWD, but not pulmonary function, were associated with brachial-ankle PWV (baPWV). There was a positive correlation between baPWV and radial artery AccV bilaterally. CONCLUSION: Radial artery AccV correlated well with baPWV, which suggests that ultrasound could be used to assess both morphological and functional changes in vessels, may serving as a better method to identify PAS in high-risk COPD patients.

Comparison between perceived and felt emotions in the soundscape evaluation of urban open spacesa).

In the current study on soundscape, the distinction between felt emotion and perceived emotion in soundscape measurement has not been addressed as much as that in music studies. This research was conducted to investigate perceived and felt emotions associated with soundscape evaluation in urban open spaces through a laboratory audio-visual experiment using photographs and binaural recordings of 16 urban open locations across Harbin, China. In total, 46 participants were required to assess both the "perceived emotion" and "felt emotion" of the soundscapes using a questionnaire (in Chinese). First, five felt emotions and seven perceived emotions associated with the soundscape were identified, among which the dominant factors were enjoyment and excitement for felt emotions and comfortable and festive for perceived emotions. Second, when comparing perceived and felt emotions, the holistic soundscape descriptor "preference" is more suitable for predicting through felt emotion, while the holistic soundscape descriptor "appropriateness" is more suitable for predicting through perceived emotion. Third, preference is a more stringent soundscape descriptor than appropriateness, indicating a higher level of requirement in its definition. Meanwhile, preference is a more emotional soundscape descriptor than appropriateness. It may be inferred that for evaluating soundscapes, the more emotional the descriptor, the greater its stringency.

Exploring the problems and coping strategies of pharmacy internship in large general hospitals in China: from the perspective of preceptors.

OBJECTIVE: The role of the Hospital Pharmacy Preceptor (HPP) is pivotal in upholding the excellence of experiential training and fostering the professional growth of pharmacy interns. However, there is a lack of studies that provide an overview of pharmacy internships from the perspective of HPP. This study explores the experience and expectations of HPPs regarding existing problems and possible coping strategies in intern teaching. METHODS: This is a qualitative study that was conducted through individual interviews and focus group discussions. HPPs were invited as participants from large-scale tertiary hospitals in representative provinces of mainland China. Interview and focus group discussion data were analyzed using thematic analysis to see emerging themes from the data. Nvivo 12 was utilized for data management and processing. RESULTS: Eight individual interviews and two focus group discussions were conducted, involving 14 HPPs as participants. Upon the examination of the interviews and focus group data, four themes were summarized regarding HPPs' perceptions: 1) current presenting problems; 2) possible coping strategies; 3) something HPPs should do; 4) something interns should do. CONCLUSION: This study found that from the HPPs' perspective, the hospital-based pharmacy internship still has some problems from policy to practice, which need to be addressed by the joint efforts of the state, schools, internship bases, pharmacy preceptors, and students.

Shrinkage priors for isotonic probability vectors and binary data modeling, with applications to dose-response modeling.

Motivated by the need to model dose-response or dose-toxicity curves in clinical trials, we develop a new horseshoe-based prior for Bayesian isotonic regression modeling a binary outcome against an ordered categorical predictor, where the probability of the outcome is assumed to be monotonically non-decreasing with the predictor. The set of differences between outcome probabilities in consecutive categories of the predictor is equipped with a multivariate prior having support over simplex. The Dirichlet distribution, which can be derived from a normalized sum of independent gamma-distributed random variables, is a natural choice of prior, but using mathematical and simulation-based arguments, we show that the resulting posterior is prone to underflow and other numerical instabilities, even under simple data configurations. We propose an alternative prior based on horseshoe-type shrinkage that is numerically more stable. We show that this horseshoe-based prior is not subject to the numerical instability seen in the Dirichlet/gamma-based prior and that the horseshoe-based posterior can estimate the underlying true curve more efficiently than the Dirichlet-based one. We demonstrate the use of this prior in a model predicting the occurrence of radiation-induced lung toxicity in lung cancer patients as a function of dose delivered to normal lung tissue. Our methodology is implemented in the R package isotonicBayes and therefore suitable for use in the design of dose-finding studies or other dose-response modeling contexts.

Seven oral traditional Chinese medicine combined with chemotherapy for the treatment of non-small cell lung cancer: a network meta-analysis.

CONTEXT: Traditional Chinese medicines (TCMs) have emerged as potential adjuvant therapies to treat non-small cell lung cancer. More direct comparative studies must be conducted among various oral TCMs. OBJECTIVE: This network meta-analysis evaluates the efficacy and safety of seven oral TCMs combined with chemotherapy in treating NSCLC. METHODS: The analysis included Zilongjin, Banmao, Hongdoushan, Huachansu, Kanglaite, Xihuang, and Pingxiao TCMs. Randomized-controlled trials (RCTs) were identified from the following databases: China National Infrastructure, Wanfang, PubMed, Embase, and the Cochrane Library up to April 2023. Two researchers independently extracted data. RESULTS: Sixty-eight RCTs (5,099 patients) were included. Compared to chemotherapy, Banmao capsules [odds ratio (OR) = 2.69, 95% confidence interval (CI) 1.96-3.69)] and Huachansu tablets [OR = 2.35, 95%CI (1.81, 3.05)] ranked in the top two in terms of increasing disease control rate. The two main TCMs to improve the objective response rate were Banmao capsules [OR = 3.49, 95%CI (2.17, 5.60)] and Zilongjin tablets [OR = 2.62, 95%CI (1.92, 3.57)]. Zilongjin tablets [OR = 3.47, 95%CI (2.14, 5.63)] and Huachansu tablets [OR = 3.30, 95%CI (1.65, 6.60)] were ranked as the top two in improving Karnofsky performance status. Hongdoushan capsules (SUCRA = 18.8%) and Banmao capsules (SUCRA = 19.8%) were the top two in reducing gastrointestinal toxicity. Zilongjin tablets (SUCRA = 18.9%) and Banmao capsules (SUCRA = 26.6%) were the top two to reduce liver and kidney toxicity. Hongdoushan capsules (SUCRA = 15.7%) and Huachansu tablets (SUCRA = 16.8%) ranked the top two in reducing thrombocytopenia. Banmao capsules (SUCRA = 14.3%) and Zilongjin tablets (SUCRA = 26.3%) were the top two decreasing leukopenia. CONCLUSIONS: Combining oral TCMs with platinum-based chemotherapy has shown superior efficacy compared to platinum-based chemotherapy alone in treating NSCLC.

Image response regression via deep neural networks.

Delineating associations between images and covariates is a central aim of imaging studies. To tackle this problem, we propose a novel non-parametric approach in the framework of spatially varying coefficient models, where the spatially varying functions are estimated through deep neural networks. Our method incorporates spatial smoothness, handles subject heterogeneity, and provides straightforward interpretations. It is also highly flexible and accurate, making it ideal for capturing complex association patterns. We establish estimation and selection consistency and derive asymptotic error bounds. We demonstrate the method's advantages through intensive simulations and analyses of two functional magnetic resonance imaging data sets.

Bayesian Spatial Blind Source Separation via the Thresholded Gaussian Process.

Blind source separation (BSS) aims to separate latent source signals from their mixtures. For spatially dependent signals in high dimensional and large-scale data, such as neuroimaging, most existing BSS methods do not take into account the spatial dependence and the sparsity of the latent source signals. To address these major limitations, we propose a Bayesian spatial blind source separation (BSP-BSS) approach for neuroimaging data analysis. We assume the expectation of the observed images as a linear mixture of multiple sparse and piece-wise smooth latent source signals, for which we construct a new class of Bayesian nonparametric prior models by thresholding Gaussian processes. We assign the vMF priors to mixing coefficients in the model. Under some regularity conditions, we show that the proposed method has several desirable theoretical properties including the large support for the priors, the consistency of joint posterior distribution of the latent source intensity functions and the mixing coefficients, and the selection consistency on the number of latent sources. We use extensive simulation studies and an analysis of the resting-state fMRI data in the Autism Brain Imaging Data Exchange (ABIDE) study to demonstrate that BSP-BSS outperforms the existing method for separating latent brain networks and detecting activated brain activation in the latent sources.

Identifying barriers to engage with soundscape standards: Insights from national standards bodies and expertsa).

This study explores the engagement of national standards bodies and practitioners with the ISO 12913 series on soundscape. It reveals critical challenges in stakeholder engagement, communication, competence, and practical application. A strategic roadmap, aligned with the normalization process theory, is proposed, comprising meaningful stakeholder engagement, building workability and integration, and community building and reflective monitoring. Results underscore the influence of national priorities, communication gaps, limited resources, and the need for practical guidance. Future efforts should focus on promoting cross-disciplinary collaboration and developing tools to quantify the societal and economic impact of soundscape interventions, addressing the multifaceted barriers identified.