A Bayesian nonparametric approach for handling item and examinee heterogeneity in assessment data.

We propose a novel nonparametric Bayesian item response theory model that estimates clusters at the question level, while simultaneously allowing for heterogeneity at the examinee level under each question cluster, characterized by a mixture of binomial distributions. The main contribution of this work is threefold. First, we present our new model and demonstrate that it is identifiable under a set of conditions. Second, we show that our model can correctly identify question-level clusters asymptotically, and the parameters of interest that measure the proficiency of examinees in solving certain questions can be estimated at a n rate (up to a log term). Third, we present a tractable sampling algorithm to obtain valid posterior samples from our proposed model. Compared to the existing methods, our model manages to reveal the multi-dimensionality of the examinees' proficiency level in handling different types of questions parsimoniously by imposing a nested clustering structure. The proposed model is evaluated via a series of simulations as well as apply it to an English proficiency assessment data set. This data analysis example nicely illustrates how our model can be used by test makers to distinguish different types of students and aid in the design of future tests.

Changes in beat-to-beat blood pressure and pulse rate variability following stroke.

Associations between cerebrovascular disease and impaired autonomic function and cerebrovascular reactivity have led to increased interest in variability of heart rate (HRV) and blood pressure (BPV) following stroke. In this study, beat-to-beat pulse rate variability (PRV) and BPV were measured in clinically stable stroke patients (6 ischemic, 2 hemorrhagic) at least one year after their last cerebrovascular event. Beat-to-beat blood pressure (BP) measurements were collected from subjects while resting in the sitting position for one hour. Compared with healthy controls, stroke patients exhibited significantly greater time-domain (standard deviation, coefficient of variation, average real variability) and normalized high-frequency BPV (all p < 0.05). Stroke patients also exhibited lower LF:HF ratios than control subjects (p = 0.003). No significant differences were observed in PRV between the two groups, suggesting that BPV may be a more sensitive biomarker of cerebrovascular function in long-term post-stroke patients. Given a paucity of existing literature investigating beat-to-beat BPV in clinically stable post-stroke patients long (> 1 year) after their cerebrovascular events, this pilot study can help inform future studies investigating the mechanisms and effects of BPV in stroke. Elucidating this physiology may facilitate long-term patient monitoring and pharmacological management to mitigate the risk for recurrent stroke.

Genomic Alterations and Tumor Mutation Burden in Merkel Cell Carcinoma.

Importance: Merkel cell carcinoma (MCC) is a rare and highly aggressive cutaneous neuroendocrine carcinoma with increasing incidence. Cytotoxic chemotherapy and checkpoint inhibitors provide treatment options in the metastatic setting; however, there are no approved or standard of care targeted therapy treatment options. Objective: To identify actionable alterations annotated by the OncoKB database therapeutic evidence level in association with tumor mutation burden (TMB). Design, Setting, and Participants: This is a retrospective, cross-sectional study using data from the American Association for Cancer Research Genomics Evidence Neoplasia Information Exchange, a multicenter international cancer consortium database. Patients with MCC were enrolled in participating institutions between 2017 and 2022. Data from version 11.0 of the database were released in January 2022 and analyzed from April to June 2022. Main Outcomes and Measures: The main outcome was the percentage of patients with high TMB and OncoKB level 3B and 4 alterations. Results: A total of 324 tumor samples from 313 patients with MCC (107 women [34.2%]; 287 White patients [91.7%]; 7 Black patients [2.2%]) were cataloged in the database. The median (range) number of alterations was 4.0 (0.0-178.0), with a mean (SD) of 13.6 (21.2) alterations. Oncogenic alterations represented 20.2% of all alterations (862 of 4259 alterations). Tissue originated from primary tumor in 55.0% of patients (172 patients) vs metastasis in 39.6% (124 patients). TMB-high (≥10 mutations per megabase) was present in 26.2% of cases (82 patients). Next-generation sequencing identified 55 patients (17.6%) with a level 3B variation for a Food and Drug Administration-approved drug for use in a biomarker-approved indication or approved drug in another indication. An additional 8.6% of patients (27 patients) had a level 4 variation. Actionable alterations were more common among high TMB cases, with 37 of 82 patients (45.1%) harboring level 3 alterations compared with only 18 of 231 patients (7.8%) with low TMB. The most common level 3B gene variants included PIK3CA (12 patients [3.8%]), BRCA1/2 (13 patients [4.2%]), ATM (7 patients [2.2%]), HRAS (5 patients [1.6%]), and TSC1/2 (6 patients [1.9%]). The most common level 4 variants include PTEN (13 patients [4.1%]), ARID1A (9 patients [2.9%]), NF1 (7 patients [2.2%]), and CDKN2A (7 patients [2.2%]). Copy number alterations and fusions were infrequent. In 61.0% of cases (191 cases), a PanCancer pathway was altered, and 39.9% (125 cases) had alterations in multiple pathways. Commonly altered pathways were RTK-RAS (119 patients [38.0%]), TP53 (103 patients [32.9%]), cell cycle (104 patients [33.2%]), PI3K (99 patients [31.6%]), and NOTCH (93 patients [29.7%]). In addition, oncogenic DNA mismatch repair gene alterations were present in 8.0% of cases (25 patients). Conclusions and Relevance: In this cross-sectional retrospective study of alterations and TMB in MCC, a minority of patients had potentially actionable alterations. These findings support the investigation of targeted therapies as single agent or in combination with immunotherapy or cytotoxic chemotherapy in selected MCC populations.

Maternal Western-style diet remodels the transcriptional landscape of fetal hematopoietic stem and progenitor cells in rhesus macaques.

Maternal obesity adversely impacts the in utero metabolic environment, but its effect on fetal hematopoiesis remains incompletely understood. During late development, the fetal bone marrow (FBM) becomes the major site where macrophages and B lymphocytes are produced via differentiation of hematopoietic stem and progenitor cells (HSPCs). Here, we analyzed the transcriptional landscape of FBM HSPCs at single-cell resolution in fetal macaques exposed to a maternal high-fat Western-style diet (WSD) or a low-fat control diet. We demonstrate that maternal WSD induces a proinflammatory response in FBM HSPCs and fetal macrophages. In addition, maternal WSD consumption suppresses the expression of B cell development genes and decreases the frequency of FBM B cells. Finally, maternal WSD leads to poor engraftment of fetal HSPCs in nonlethally irradiated immunodeficient NOD/SCID/IL2rγ-/- mice. Collectively, these data demonstrate for the first time that maternal WSD impairs fetal HSPC differentiation and function in a translationally relevant nonhuman primate model.

Ethanol Consumption Induces Nonspecific Inflammation and Functional Defects in Alveolar Macrophages.

Chronic alcohol drinking is associated with increased susceptibility to viral and bacterial respiratory pathogens. In this study, we use a rhesus macaque model of voluntary ethanol self-administration to study the effects of long-term alcohol drinking on the immunological landscape of the lung. We report a heightened inflammatory state in alveolar macrophages (AMs) obtained from ethanol (EtOH)-drinking animals that is accompanied by increased chromatin accessibility in intergenic regions that regulate inflammatory genes and contain binding motifs for transcription factors AP-1, IRF8, and NFKB p-65. In line with these transcriptional and epigenetic changes at the basal state, AMs from EtOH-drinking animals generate elevated inflammatory mediator responses to lipopolysaccharides and respiratory syncytial virus. However, the transcriptional analysis revealed an inefficient induction of interferon-stimulated genes with EtOH in response to the respiratory syncytial virus, suggesting disruption of antimicrobial defenses. Correspondingly, AMs from EtOH-drinking animals exhibited transcriptional shifts indicative of increased oxidative stress and oxidative phosphorylation, which was coupled with higher cytosolic reactive oxygen species and mitochondrial potential. This heightened oxidative stress state was accompanied by decreased ability to phagocytose bacteria. Bulk RNA and assay for transposase-accessible chromatin sequencing data further revealed reduced expression and chromatin accessibility of loci associated with tissue repair and maintenance with chronic EtOH drinking. Similarly, analysis of single-cell RNA sequencing data revealed shifts in cell states from tissue maintenance to inflammatory responses with EtOH. Collectively, these data provide novel insight into mechanisms by which chronic EtOH drinking increases susceptibility to infection in patients with alcohol use disorders.

Addressing patient heterogeneity in disease predictive model development.

This paper addresses patient heterogeneity associated with prediction problems in biomedical applications. We propose a systematic hypothesis testing approach to determine the existence of patient subgroup structure and the number of subgroups in patient population if subgroups exist. A mixture of generalized linear models is considered to model the relationship between the disease outcome and patient characteristics and clinical factors, including targeted biomarker profiles. We construct a test statistic based on expectation maximization (EM) algorithm and derive its asymptotic distribution under the null hypothesis. An important computational advantage of the test is that the involved parameter estimates under the complex alternative hypothesis can be obtained through a small number of EM iterations, rather than optimizing the objective function. We demonstrate the finite sample performance of the proposed test in terms of type-I error rate and power, using extensive simulation studies. The applicability of the proposed method is illustrated through an application to a multicenter prostate cancer study.

Profiling of extracellular vesicle-bound miRNA to identify candidate biomarkers of chronic alcohol drinking in nonhuman primates.

BACKGROUND: Long-term alcohol drinking is associated with numerous health complications including susceptibility to infection, cancer, and organ damage. However, due to the complex nature of human drinking behavior, it has been challenging to identify reliable biomarkers of alcohol drinking behavior prior to signs of overt organ damage. Recently, extracellular vesicle-bound microRNAs (EV-miRNAs) have been found to be consistent biomarkers of conditions that include cancer and liver disease. METHODS: In this study, we profiled the plasma EV-miRNA content by miRNA-Seq from 80 nonhuman primates after 12 months of voluntary alcohol drinking. RESULTS: We identified a list of up- and downregulated EV-miRNA candidate biomarkers of heavy drinking and those positively correlated with ethanol dose. We overexpressed these candidate miRNAs in control primary peripheral immune cells to assess their potential functional mechanisms. We found that overexpression of miR-155, miR-154, miR-34c, miR-450a, and miR-204 led to increased production of the inflammatory cytokines TNFα or IL-6 in peripheral blood mononuclear cells after stimulation. CONCLUSION: This exploratory study identified several EV-miRNAs that could serve as biomarkers of long-term alcohol drinking and provide a mechanism to explain alcohol-induced peripheral inflammation.

Obesity Correlates With Pronounced Aberrant Innate Immune Responses in Hospitalized Aged COVID-19 Patients.

Both age and obesity are leading risk factors for severe coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Specifically, although most infections occur in individuals under the age of 55 years, 95% of hospitalizations, admissions to the intensive care unit, and deaths occur in those over the age of 55 years. Moreover, hospitalized COVID-19 patients have a higher prevalence of obesity. It is generally believed that chronic low-grade inflammation and dysregulated innate and adaptive immune responses that are associated with aging and obesity are responsible for this elevated risk of severe disease. However, the impact of advanced age and obesity on the host response to SARS-CoV-2 infection remains poorly defined. In this study, we assessed changes in the concentration of soluble immune mediators, IgG antibody titers, frequency of circulating immune cells, and cytokine responses to mitogen stimulation as a function of BMI and age. We detected significant negative correlations between BMI and myeloid immune cell subsets that were more pronounced in aged patients. Similarly, inflammatory cytokine production by monocytes was also negatively correlated with BMI in aged patients. These data suggest that the BMI-dependent impact on host response to SARS-CoV-2 is more pronounced on innate responses of aged patients.

Bayesian phase II clinical trial design with noncompliance.

Noncompliance issue is common in early phase clinical trials; and may lead to biased estimation of the intent-to-treat effect and incorrect conclusions for the clinical trial. In this work, we propose a Bayesian approach for sequentially monitoring the phase II randomized clinical trials that takes account for the noncompliance information. We adopt the principal stratification framework and propose to use Bayesian additive regression trees for selecting useful baseline covariates and estimating the complier average causal effect (CACE) for both efficacy and toxicity outcomes. The decision of early termination or not is then made adaptively based on the estimated CACE from the accumulated data. Simulation studies have confirmed the excellent performance of the proposed design in the presence of noncompliance.

Land use change simulation and spatial analysis of ecosystem service value in Shijiazhuang under multi-scenarios.

Simulating changes in the value of ecosystem services caused by land use changes in large cities under multiple scenarios is of great significance for cities to formulate land use policies and improve ecosystem services. Take Shijiazhuang, which is in the process of rapid urbanization, as an example. Based on the remote sensing image data and statistical yearbook of 1988, 1998, 2008, and 2018 as the basic data to analyze and estimate the 30 years of land use and ecosystem service value changes in Shijiazhuang. According to this, the CA-Markov model was used to simulate the land use change in Shijiazhuang under three scenarios in 2030 and estimate the value of ecosystem services under each scenario, using grid tools to visually express the spatial distribution of ecosystem service values and the degree of agglomeration under three scenarios. The results indicate that the most obvious feature of land use change in Shijiazhuang from 1988 to 2018 was that the farmland area decreased year by year, the built-up expanded rapidly, the farmland area decreased by 86,874.75 hm2 in 30 years, and the built-up increased by 154,711.90 hm2. In 1988, 1998, 2008, and 2018, the ecosystem service value of Shijiazhuang was 32.578 billion yuan, 32.799 billion yuan, 29.944 billion yuan, and 31.251 billion yuan respectively. In 2030, under three scenarios of natural development, farmland protection, and ecological protection, the value of ecosystem services is 331.111 billion yuan, 33.670 billion yuan, and 33.891 billion yuan in order. The hot spots are mainly concentrated in the northwest and southwest of Shijiazhuang, and cold spots are concentrated in the eastern cities, counties, and districts. Based on changes in land use brought about by urban expansion, simulating the value of ecosystem services under multiple scenarios in the future, providing scientific guidance for building urban ecological networks, and realizing sustainable urban ecological development.

Nonparametric matrix response regression with application to brain imaging data analysis.

With the rapid growth of neuroimaging technologies, a great effort has been dedicated recently to investigate the dynamic changes in brain activity. Examples include time course calcium imaging and dynamic brain functional connectivity. In this paper, we propose a novel nonparametric matrix response regression model to characterize the nonlinear association between 2D image outcomes and predictors such as time and patient information. Our estimation procedure can be formulated as a nuclear norm regularization problem, which can capture the underlying low-rank structure of the dynamic 2D images. We present a computationally efficient algorithm, derive the asymptotic theory, and show that the method outperforms other existing approaches in simulations. We then apply the proposed method to a calcium imaging study for estimating the change of fluorescent intensities of neurons, and an electroencephalography study for a comparison in the dynamic connectivity covariance matrices between alcoholic and control individuals. For both studies, the method leads to a substantial improvement in prediction error.

Differential dynamics of peripheral immune responses to acute SARS-CoV-2 infection in older adults.

In this study, peripheral blood mononuclear cells from young and old patients with COVID-19 were examined phenotypically, transcriptionally and functionally to reveal age-, time- and severity-specific adaptations. Gene signatures within memory B cells and plasmablasts correlated with reduced frequency of antigen-specific B cells and neutralizing antibodies in older patients with severe COVID-19. Moreover, these patients exhibited exacerbated T cell lymphopenia, which correlated with lower plasma interleukin-2, and diminished antigen-specific T cell responses. Single-cell RNA sequencing revealed augmented signatures of activation, exhaustion, cytotoxicity and type I interferon signaling in memory T and natural killer cells with age. Although cytokine storm was evident in both age groups, older individuals exhibited elevated levels of myeloid cell recruiting factors. Furthermore, we observed redistribution of monocyte and dendritic cell subsets and emergence of a suppressive phenotype with severe disease, which was reversed only in young patients over time. This analysis provides new insights into the impact of aging on COVID-19.

Regularized matrix data clustering and its application to image analysis.

We propose a novel regularized mixture model for clustering matrix-valued data. The proposed method assumes a separable covariance structure for each cluster and imposes a sparsity structure (eg, low rankness, spatial sparsity) for the mean signal of each cluster. We formulate the problem as a finite mixture model of matrix-normal distributions with regularization terms, and then develop an expectation maximization type of algorithm for efficient computation. In theory, we show that the proposed estimators are strongly consistent for various choices of penalty functions. Simulation and two applications on brain signal studies confirm the excellent performance of the proposed method including a better prediction accuracy than the competitors and the scientific interpretability of the solution.

Pollution and Ecological Risk Evaluation of Heavy Metals in the Soil and Sediment around the HTM Tailings Pond, Northeastern China.

Tailings ponds are a main heavy metal pollution source in mining areas. In this study, the geo-accumulation index (Igeo) and the potential ecological risk index (RI) are used to evaluate the environmental impact of Hongtou Mountain (HTM) tailings pond on the surrounding area. Farmland soil, surface water, and sediment samples in the Hun River around the HTM tailings pond were collected. Heavy metal contents in the samples were analyzed by Inductively Coupled Plasma Mass Spectrometry (ICP-MS). Results show that Cu, Zn, and Cd content in the farmland soil and sediment around the lower reaches of the Hun River (HTM tailings pond section) are obviously higher than the upper reaches. The Igeo values show that the farmland soil near the outlet of the tailings pond is the most polluted area. Cu was classified as moderate-strongly pollution, Zn was moderately pollution, and Cd was strongly pollution. Cd is the major pollutant in farmland soil, the monomial ecological risk (Eri) for Cd is a very high potential ecological risk. The potential ecological risk of sediment in the dry season is more serious than in the raining season. In the dry season, the Igeo index shows strong pollution for Cu and Cd at the confluence of the Hun River and the tributary from the HTM tailings pond, and a moderate-strongly pollution for Zn. Whereas, the Eri index shows that the monomial ecological risk for Zn at H3 is low, and Cu is moderate. The potential ecological risk at H3 is high, and Cd is the main source of the ecological risk around the HTM tailings pond.

Evolutionary State-Space Model and Its Application to Time-Frequency Analysis of Local Field Potentials.

We propose an evolutionary state space model (E-SSM) for analyzing high dimensional brain signals whose statistical properties evolve over the course of a non-spatial memory experiment. Under E-SSM, brain signals are modeled as mixtures of components (e.g., AR(2) process) with oscillatory activity at pre-defined frequency bands. To account for the potential non-stationarity of these components (since the brain responses could vary throughout the entire experiment), the parameters are allowed to vary over epochs. Compared with classical approaches such as independent component analysis and filtering, the proposed method accounts for the entire temporal correlation of the components and accommodates non-stationarity. For inference purpose, we propose a novel computational algorithm based upon using Kalman smoother, maximum likelihood and blocked resampling. The E-SSM model is applied to simulation studies and an application to a multi-epoch local field potentials (LFP) signal data collected from a non-spatial (olfactory) sequence memory task study. The results confirm that our method captures the evolution of the power for different components across different phases in the experiment and identifies clusters of electrodes that behave similarly with respect to the decomposition of different sources. These findings suggest that the activity of different electrodes does change over the course of an experiment in practice; treating these epoch recordings as realizations of an identical process could lead to misleading results. In summary, the proposed method underscores the importance of capturing the evolution in brain responses over the study period.

Matrix Linear Discriminant Analysis.

We propose a novel linear discriminant analysis (LDA) approach for the classification of high-dimensional matrix-valued data that commonly arises from imaging studies. Motivated by the equivalence of the conventional LDA and the ordinary least squares, we consider an efficient nuclear norm penalized regression that encourages a low-rank structure. Theoretical properties including a nonasymptotic risk bound and a rank consistency result are established. Simulation studies and an application to electroencephalography data show the superior performance of the proposed method over the existing approaches.

Single-Cell Transcriptomic Analyses of Cell Fate Transitions during Human Cardiac Reprogramming.

Direct cellular reprogramming provides a powerful platform to study cell plasticity and dissect mechanisms underlying cell fate determination. Here, we report a single-cell transcriptomic study of human cardiac (hiCM) reprogramming that utilizes an analysis pipeline incorporating current data normalization methods, multiple trajectory prediction algorithms, and a cell fate index calculation we developed to measure reprogramming progression. These analyses revealed hiCM reprogramming-specific features and a decision point at which cells either embark on reprogramming or regress toward their original fibroblast state. In combination with functional screening, we found that immune-response-associated DNA methylation is required for hiCM induction and validated several downstream targets of reprogramming factors as necessary for productive hiCM reprograming. Collectively, this single-cell transcriptomics study provides detailed datasets that reveal molecular features underlying hiCM determination and rigorous analytical pipelines for predicting cell fate conversion.

Lin28a Regulates Pathological Cardiac Hypertrophic Growth Through Pck2-Mediated Enhancement of Anabolic Synthesis.

BACKGROUND: Hypertrophic response to pathological stimuli is a complex biological process that involves transcriptional and epigenetic regulation of the cardiac transcriptome. Although previous studies have implicated transcriptional factors and signaling molecules in pathological hypertrophy, the role of RNA-binding protein in this process has received little attention. METHODS: Here we used transverse aortic constriction and in vitro cardiac hypertrophy models to characterize the role of an evolutionary conserved RNA-binding protein Lin28a in pathological cardiac hypertrophy. Next-generation sequencing, RNA immunoprecipitation, and gene expression analyses were applied to identify the downstream targets of Lin28a. Epistatic analysis, metabolic assays, and flux analysis were further used to characterize the effects of Lin28a and its downstream mediator in cardiomyocyte hypertrophic growth and metabolic remodeling. RESULTS: Cardiac-specific deletion of Lin28a attenuated pressure overload-induced hypertrophic growth, cardiac dysfunction, and alterations in cardiac transcriptome. Mechanistically, Lin28a directly bound to mitochondrial phosphoenolpyruvate carboxykinase 2 ( Pck2) mRNA and increased its transcript level. Increasing Pck2 was sufficient to promote hypertrophic growth similar to that caused by increasing Lin28a, whereas knocking down Pck2 attenuated norepinephrine-induced cardiac hypertrophy. Epistatic analysis demonstrated that Pck2 mediated, at least in part, the role of Lin28a in cardiac hypertrophic growth. Furthermore, metabolomic analyses highlighted the role for Lin28a and Pck2 in promoting cardiac biosynthesis required for cell growth. CONCLUSIONS: Our study demonstrates that Lin28a promotes pathological cardiac hypertrophy and glycolytic reprograming, at least in part, by binding to and stabilizing Pck2 mRNA.

Regression analysis of longitudinal data with outcome-dependent sampling and informative censoring.

We consider regression analysis of longitudinal data in the presence of outcome-dependent observation times and informative censoring. Existing approaches commonly require correct specification of the joint distribution of the longitudinal measurements, observation time process and informative censoring time under the joint modeling framework, and can be computationally cumbersome due to the complex form of the likelihood function. In view of these issues, we propose a semi-parametric joint regression model and construct a composite likelihood function based on a conditional order statistics argument. As a major feature of our proposed methods, the aforementioned joint distribution is not required to be specified and the random effect in the proposed joint model is treated as a nuisance parameter. Consequently, the derived composite likelihood bypasses the need to integrate over the random effect and offers the advantage of easy computation. We show that the resulting estimators are consistent and asymptotically normal. We use simulation studies to evaluate the finite-sample performance of the proposed method, and apply it to a study of weight loss data that motivated our investigation.

Model-free scoring system for risk prediction with application to hepatocellular carcinoma study.

There is an increasing need to construct a risk-prediction scoring system for survival data and identify important risk factors (e.g., biomarkers) for patient screening and treatment recommendation. However, most existing methodologies either rely on strong model assumptions (e.g., proportional hazards) or only handle binary outcomes. In this article, we propose a flexible method that simultaneously selects important risk factors and identifies the optimal linear combination of risk factors by maximizing a pseudo-likelihood function based on the time-dependent area under the receiver operating characteristic curve. Our method is particularly useful for risk evaluation and recommendation of optimal subsequent treatments. We show that the proposed method has desirable theoretical properties, including asymptotic normality and the oracle property after variable selection. Numerical performance is evaluated on several simulation data sets and an application to hepatocellular carcinoma data.