Clustering computer mouse tracking data with informed hierarchical shrinkage partition priors.

Mouse-tracking data, which record computer mouse trajectories while participants perform an experimental task, provide valuable insights into subjects' underlying cognitive processes. Neuroscientists are interested in clustering the subjects' responses during computer mouse-tracking tasks to reveal patterns of individual decision-making behaviors and identify population subgroups with similar neurobehavioral responses. These data can be combined with neuroimaging data to provide additional information for personalized interventions. In this article, we develop a novel hierarchical shrinkage partition (HSP) prior for clustering summary statistics derived from the trajectories of mouse-tracking data. The HSP model defines a subjects' cluster as a set of subjects that gives rise to more similar (rather than identical) nested partitions of the conditions. The proposed model can incorporate prior information about the partitioning of either subjects or conditions to facilitate clustering, and it allows for deviations of the nested partitions within each subject group. These features distinguish the HSP model from other bi-clustering methods that typically create identical nested partitions of conditions within a subject group. Furthermore, it differs from existing nested clustering methods, which define clusters based on common parameters in the sampling model and identify subject groups by different distributions. We illustrate the unique features of the HSP model on a mouse tracking dataset from a pilot study and in simulation studies. Our results show the ability and effectiveness of the proposed exploratory framework in clustering and revealing possible different behavioral patterns across subject groups.

Modeling intra-individual inter-trial EEG response variability in autism.

Autism spectrum disorder (autism) is a prevalent neurodevelopmental condition characterized by early emerging impairments in social behavior and communication. EEG represents a powerful and non-invasive tool for examining functional brain differences in autism. Recent EEG evidence suggests that greater intra-individual trial-to-trial variability across EEG responses in stimulus-related tasks may characterize brain differences in autism. Traditional analysis of EEG data largely focuses on mean trends of the trial-averaged data, where trial-level analysis is rarely performed due to low neural signal to noise ratio. We propose to use nonlinear (shape-invariant) mixed effects (NLME) models to study intra-individual inter-trial EEG response variability using trial-level EEG data. By providing more precise metrics of response variability, this approach could enrich our understanding of neural disparities in autism and potentially aid the identification of objective markers. The proposed multilevel NLME models quantify variability in the signal's interpretable and widely recognized features (e.g., latency and amplitude) while also regularizing estimation based on noisy trial-level data. Even though NLME models have been studied for more than three decades, existing methods cannot scale up to large data sets. We propose computationally feasible estimation and inference methods via the use of a novel minorization-maximization (MM) algorithm. Extensive simulations are conducted to show the efficacy of the proposed procedures. Applications to data from a large national consortium find that children with autism have larger intra-individual inter-trial variability in P1 latency in a visual evoked potential (VEP) task, compared to their neurotypical peers.

A predictor-informed multi-subject bayesian approach for dynamic functional connectivity.

Dynamic functional connectivity investigates how the interactions among brain regions vary over the course of an fMRI experiment. Such transitions between different individual connectivity states can be modulated by changes in underlying physiological mechanisms that drive functional network dynamics, e.g., changes in attention or cognitive effort. In this paper, we develop a multi-subject Bayesian framework where the estimation of dynamic functional networks is informed by time-varying exogenous physiological covariates that are simultaneously recorded in each subject during the fMRI experiment. More specifically, we consider a dynamic Gaussian graphical model approach where a non-homogeneous hidden Markov model is employed to classify the fMRI time series into latent neurological states. We assume the state-transition probabilities to vary over time and across subjects as a function of the underlying covariates, allowing for the estimation of recurrent connectivity patterns and the sharing of networks among the subjects. We further assume sparsity in the network structures via shrinkage priors, and achieve edge selection in the estimated graph structures by introducing a multi-comparison procedure for shrinkage-based inferences with Bayesian false discovery rate control. We evaluate the performances of our method vs alternative approaches on synthetic data. We apply our modeling framework on a resting-state experiment where fMRI data have been collected concurrently with pupillometry measurements, as a proxy of cognitive processing, and assess the heterogeneity of the effects of changes in pupil dilation on the subjects' propensity to change connectivity states. The heterogeneity of state occupancy across subjects provides an understanding of the relationship between increased pupil dilation and transitions toward different cognitive states.

Precision Delivery of Human Bone Marrow-Derived Mesenchymal Stem Cells Into the Pancreas Via Intra-arterial Injection Prevents the Onset of Diabetes.

Mesenchymal stem cells (MSCs) are a promising therapy to potentially treat diabetes given their potent anti-inflammatory and immune-modulatory properties. While these regenerative cells have shown considerable promise in cell culture, their clinical translation has been challenging. In part, this can be attributed to these cells not reaching the pancreas to exert their regenerative effects following conventional intravenous (IV) injection, with the majority of cells being trapped in the lungs in the pulmonary first-pass effect. In the present study, we will therefore examine whether direct delivery of MSCs to the pancreas via an intra-arterial (IA) injection can improve their therapeutic efficacy. Using a mouse model, in which repetitive low doses of STZ induced a gentle, but progressive, hyperglycemia, we tested bone marrow-derived MSCs (BM-MSCs) which we have shown are enriched with pro-angiogenic and immunomodulatory factors. In cell culture studies, BM-MSCs were shown to preserve islet viability and function following exposure to proinflammatory cytokines (IFN-γ, IL-1ß, and TNF-α) through an increase in pAkt. When tested in our animal model, mice receiving IV BM-MSCs were not able to mitigate the effects of STZ, however those which received the same dose and batch of cells via IA injection were able to maintain basal and dynamic glycemic control, to similar levels as seen in healthy control animals, over 10 days. This study shows the importance of considering precision delivery approaches to ensure cell-based therapies reach their intended targets to enable them to exert their therapeutic effects.

Proteome trait regulation of marine Synechococcus elemental stoichiometry under global change.

Recent studies have demonstrated regional differences in marine ecosystem C:N:P with implications for carbon and nutrient cycles. Due to strong co-variance, temperature and nutrient stress explain variability in C:N:P equally well. A reductionistic approach can link changes in individual environmental drivers with changes in biochemical traits and cell C:N:P. Thus, we quantified effects of temperature and nutrient stress on Synechococcus chemistry using laboratory chemostats, chemical analyses, and data-independent acquisition mass spectrometry proteomics. Nutrient supply accounted for most C:N:Pcell variability and induced tradeoffs between nutrient acquisition and ribosomal proteins. High temperature prompted heat-shock, whereas thermal effects via the "translation-compensation hypothesis" were only seen under P-stress. A Nonparametric Bayesian Local Clustering algorithm suggested that changes in lipopolysaccharides, peptidoglycans, and C-rich compatible solutes may also contribute to C:N:P regulation. Physiological responses match field-based trends in ecosystem stoichiometry and suggest a hierarchical environmental regulation of current and future ocean C:N:P.

A Common Atoms Model for the Bayesian Nonparametric Analysis of Nested Data.

The use of large datasets for targeted therapeutic interventions requires new ways to characterize the heterogeneity observed across subgroups of a specific population. In particular, models for partially exchangeable data are needed for inference on nested datasets, where the observations are assumed to be organized in different units and some sharing of information is required to learn distinctive features of the units. In this manuscript, we propose a nested common atoms model (CAM) that is particularly suited for the analysis of nested datasets where the distributions of the units are expected to differ only over a small fraction of the observations sampled from each unit. The proposed CAM allows a two-layered clustering at the distributional and observational level and is amenable to scalable posterior inference through the use of a computationally efficient nested slice sampler algorithm. We further discuss how to extend the proposed modeling framework to handle discrete measurements, and we conduct posterior inference on a real microbiome dataset from a diet swap study to investigate how the alterations in intestinal microbiota composition are associated with different eating habits. We further investigate the performance of our model in capturing true distributional structures in the population by means of a simulation study.

AI-Based Automated Lipomatous Tumor Segmentation in MR Images: Ensemble Solution to Heterogeneous Data.

Deep learning (DL) has been proposed to automate image segmentation and provide accuracy, consistency, and efficiency. Accurate segmentation of lipomatous tumors (LTs) is critical for correct tumor radiomics analysis and localization. The major challenge of this task is data heterogeneity, including tumor morphological characteristics and multicenter scanning protocols. To mitigate the issue, we aimed to develop a DL-based Super Learner (SL) ensemble framework with different data correction and normalization methods. Pathologically proven LTs on pre-operative T1-weighted/proton-density MR images of 185 patients were manually segmented. The LTs were categorized by tumor locations as distal upper limb (DUL), distal lower limb (DLL), proximal upper limb (PUL), proximal lower limb (PLL), or Trunk (T) and grouped by 80%/9%/11% for training, validation and testing. Six configurations of correction/normalization were applied to data for fivefold-cross-validation trainings, resulting in 30 base learners (BLs). A SL was obtained from the BLs by optimizing SL weights. The performance was evaluated by dice-similarity-coefficient (DSC), sensitivity, specificity, and Hausdorff distance (HD95). For predictions of the BLs, the average DSC, sensitivity, and specificity from the testing data were 0.72 [Formula: see text] 0.16, 0.73 [Formula: see text] 0.168, and 0.99 [Formula: see text] 0.012, respectively, while for SL predictions were 0.80 [Formula: see text] 0.184, 0.78 [Formula: see text] 0.193, and 1.00 [Formula: see text] 0.010. The average HD95 of the BLs were 11.5 (DUL), 23.2 (DLL), 25.9 (PUL), 32.1 (PLL), and 47.9 (T) mm, whereas of SL were 1.7, 8.4, 15.9, 2.2, and 36.6 mm, respectively. The proposed method could improve the segmentation accuracy and mitigate the performance instability and data heterogeneity aiding the differential diagnosis of LTs in real clinical situations.

Multiple hypothesis screening using mixtures of non-local distributions with applications to genomic studies.

The analysis of large-scale datasets, especially in biomedical contexts, frequently involves a principled screening of multiple hypotheses. The celebrated two-group model jointly models the distribution of the test statistics with mixtures of two competing densities, the null and the alternative distributions. We investigate the use of weighted densities and, in particular, non-local densities as working alternative distributions, to enforce separation from the null and thus refine the screening procedure. We show how these weighted alternatives improve various operating characteristics, such as the Bayesian false discovery rate, of the resulting tests for a fixed mixture proportion with respect to a local, unweighted likelihood approach. Parametric and nonparametric model specifications are proposed, along with efficient samplers for posterior inference. By means of a simulation study, we exhibit how our model compares with both well-established and state-of-the-art alternatives in terms of various operating characteristics. Finally, to illustrate the versatility of our method, we conduct three differential expression analyses with publicly-available datasets from genomic studies of heterogeneous nature.

Bayesian nonparametric analysis for the detection of spikes in noisy calcium imaging data.

Recent advancements in miniaturized fluorescence microscopy have made it possible to investigate neuronal responses to external stimuli in awake behaving animals through the analysis of intracellular calcium signals. An ongoing challenge is deconvolving the temporal signals to extract the spike trains from the noisy calcium signals' time series. In this article, we propose a nested Bayesian finite mixture specification that allows the estimation of spiking activity and, simultaneously, reconstructing the distributions of the calcium transient spikes' amplitudes under different experimental conditions. The proposed model leverages two nested layers of random discrete mixture priors to borrow information between experiments and discover similarities in the distributional patterns of neuronal responses to different stimuli. Furthermore, the spikes' intensity values are also clustered within and between experimental conditions to determine the existence of common (recurring) response amplitudes. Simulation studies and the analysis of a dataset from the Allen Brain Observatory show the effectiveness of the method in clustering and detecting neuronal activities.

Beyond t test and ANOVA: applications of mixed-effects models for more rigorous statistical analysis in neuroscience research.

In basic neuroscience research, data are often clustered or collected with repeated measures, hence correlated. The most widely used methods such as t test and ANOVA do not take data dependence into account and thus are often misused. This Primer introduces linear and generalized mixed-effects models that consider data dependence and provides clear instruction on how to recognize when they are needed and how to apply them. The appropriate use of mixed-effects models will help researchers improve their experimental design and will lead to data analyses with greater validity and higher reproducibility of the experimental findings.

A nonparametric Bayesian model for estimating spectral densities of resting-state EEG twin data.

Electroencephalography (EEG) is a noninvasive neuroimaging modality that captures electrical brain activity many times per second. We seek to estimate power spectra from EEG data that ware gathered for 557 adolescent twin pairs through the Minnesota Twin Family Study (MTFS). Typically, spectral analysis methods treat time series from each subject separately, and independent spectral densities are fit to each time series. Since the EEG data were collected on twins, it is reasonable to assume that the time series have similar underlying characteristics, so borrowing information across subjects can significantly improve estimation. We propose a Nested Bernstein Dirichlet prior model to estimate the power spectrum of the EEG signal for each subject by smoothing periodograms within and across subjects while requiring minimal user input to tuning parameters. Furthermore, we leverage the MTFS twin study design to estimate the heritability of EEG power spectra with the hopes of establishing new endophenotypes. Through simulation studies designed to mimic the MTFS, we show our method out-performs a set of other popular methods.

Artificial hyperventilation normalizes haemodynamics and arterial oxygen content in hypoxic rats.

INTRODUCTION: Although humans are capable of enduring critically low levels of oxygen, many hypoxaemic patients die despite aggressive therapies. Mimicking the physiological hyperventilation necessary to survive extreme hypoxic conditions could minimize the derangements caused by acute hypoxic-hypoxia. The objective of this study was to measure the haemodynamic-biochemical response to artificially induced hyperventilation in hypoxic rats. MATERIAL AND METHODS: Twenty-four deeply anaesthetized and mechanically ventilated rats were allocated to 3 groups: control (n = 5, FiO2 = 1); hypoxic spontaneously hyperventilating (n = 10, FiO2 = 0.08); and hypoxic artificially induced hyperventilation (n = 9, targeting PaCO2 = 10 mm Hg, FiO2 = 0.08). We compared the spontaneously and artificially hyperventilating groups. P-values < 0.01 were considered statistically significant. Mean arterial pressure (MAP) and serum chemistry were measured for 180 minutes. RESULTS: The control group remained stable throughout the experiment. The hypoxic groups developed profound hypotension after the decrease in FiO2. However, the artificially induced hyperventilated rats recovered their MAP to levels higher than the spontaneously hyperventilating group (117.1 ± 17.2 vs. 68.1 ± 16.0, P = 0.0048). In regard to the biochemical derangements, even though the serum lactate and PaO2 were not different among the hypoxic groups, the artificially hyperventilated group achieved significantly higher SaO2 (94.3 ± 3.6 vs. 58.6 ± 9.6, P = 0.005), pH (7.87 ± 0.04 vs. 7.50 ± 0.13, P = 0.005), and CaO2 (17.7 ± 2.6 vs. 10.2 ± 1.3, P = 0.005) at 180 minutes. CONCLUSIONS: Artificially induced hyperventilation led to the correction of arterial oxygen content, severe serum chemistry, and haemodynamic derangements. These findings may represent a novel rescue manoeuvre and serve as a bridge to a permanent form of support, but should be further studied before being translated to the clinical setting.

A Blood Bank Standardized Production of Human Platelet Lysate for Mesenchymal Stromal Cell Expansion: Proteomic Characterization and Biological Effects.

Human platelet lysate (hPL) is considered a valid substitute to fetal bovine serum (FBS) in the expansion of mesenchymal stromal cells (MSC), and it is commonly produced starting from intermediate side products of whole blood donations. Through freeze-thaw cycles, hPL is highly enriched in chemokines, growth factors, and adhesion and immunologic molecules. Cell therapy protocols, using hPL instead of FBS for the expansion of cells, are approved by regulatory authorities without concerns, and its administration in patients is considered safe. However, published data are fairly difficult to compare, since the production of hPL is highly variable. This study proposes to optimize and standardize the hPL productive process by using instruments, technologies, and quality/safety standards required for blood bank activities and products. The quality and improved selection of the starting material (i.e., the whole blood), together with the improvement of the production process, guarantee a product characterized by higher content and quality of growth factors as well as a reduction in batch-to-batch variability. By increasing the number of freeze/thaw cycles from one (hPL1c) to four (hPL4c), we obtained a favorable effect on the release of growth factors from platelet α granules. Those changes have directly translated into biological effects leading to a decreasing doubling time (DT) of MSC expansion at 7 days (49.41 ± 2.62 vs. 40.61 ± 1.11 h, p < 0.001). Furthermore, mass spectrometry (MS)-based evaluation has shown that the proliferative effects of hPL4c are also combined with a lower batch-to-batch variability (10-15 vs. 21-31%) at the proteomic level. In conclusion, we have considered lot-to-lot hPL variability, and by the strict application of blood bank standards, we have obtained a standardized, reproducible, safe, cheap, and ready-to-use product.

Two-group Poisson-Dirichlet mixtures for multiple testing.

The simultaneous testing of multiple hypotheses is common to the analysis of high-dimensional data sets. The two-group model, first proposed by Efron, identifies significant comparisons by allocating observations to a mixture of an empirical null and an alternative distribution. In the Bayesian nonparametrics literature, many approaches have suggested using mixtures of Dirichlet Processes in the two-group model framework. Here, we investigate employing mixtures of two-parameter Poisson-Dirichlet Processes instead, and show how they provide a more flexible and effective tool for large-scale hypothesis testing. Our model further employs nonlocal prior densities to allow separation between the two mixture components. We obtain a closed-form expression for the exchangeable partition probability function of the two-group model, which leads to a straightforward Markov Chain Monte Carlo implementation. We compare the performance of our method for large-scale inference in a simulation study and illustrate its use on both a prostate cancer data set and a case-control microbiome study of the gastrointestinal tracts in children from underdeveloped countries who have been recently diagnosed with moderate-to-severe diarrhea.

A Hierarchical Bayesian Model for Differential Connectivity in Multi-trial Brain Signals.

There is a strong interest in the neuroscience community to measure brain connectivity and develop methods that can differentiate connectivity across patient groups and across different experimental stimuli. The development of such statistical tools is critical to understand the dynamics of functional relationships among brain structures supporting memory encoding and retrieval. However, the challenge comes from the need to incorporate within-condition similarity with between-conditions heterogeneity in modeling connectivity, as well as how to provide a natural way to conduct trial- and condition-level inference on effective connectivity. A Bayesian hierarchical vector autoregressive (BH-VAR) model is proposed to characterize brain connectivity and infer differences in connectivity across conditions. Within-condition connectivity similarity and between-conditions connectivity heterogeneity are accounted for by the priors on trial-specific models. In addition to the fully Bayesian framework, an alternative two-stage computation approach is also proposed which still allows straightforward uncertainty quantification of between-trial conditions via MCMC posterior sampling, but provides a fast approximate procedure for the estimation of trial-specific VAR parameters. A novel aspect of the approach is the use of a frequency-specific measure, partial directed coherence (PDC), to characterize effective connectivity under the Bayesian framework. More specifically, PDC allows inferring directionality and explaining the extent to which the present oscillatory activity at a certain frequency in a sender channel influences the future oscillatory activity in a specific receiver channel relative to all possible receivers in the brain network. The proposed model is applied to a large electrophysiological dataset collected as rats performed a complex sequence memory task. This unique dataset includes local field potentials (LFPs) activity recorded from an array of electrodes across hippocampal region CA1 while animals were presented with multiple trials from two main conditions. The proposed modeling approach provided novel insights into hippocampal connectivity during memory performance. Specifically, it separated CA1 into two functional units, a lateral and a medial segment, each showing stronger functional connectivity to itself than to the other. This approach also revealed that information primarily flowed in a lateral-to-medial direction across trials (within-condition), and suggested this effect was stronger on one trial condition than the other (between-conditions effect). Collectively, these results indicate that the proposed model is a promising approach to quantify the variation of functional connectivity, both within- and between-conditions, and thus should have broad applications in neuroscience research.

Impedance-Based Monitoring of Mesenchymal Stromal Cell Three-Dimensional Proliferation Using Aerosol Jet Printed Sensors: A Tissue Engineering Application.

One of the main hurdles to improving scaffolds for regenerative medicine is the development of non-invasive methods to monitor cell proliferation within three-dimensional environments. Recently, an electrical impedance-based approach has been identified as promising for three-dimensional proliferation assays. A low-cost impedance-based solution, easily integrable with multi-well plates, is here presented. Sensors were developed using biocompatible carbon-based ink on foldable polyimide substrates by means of a novel aerosol jet printing technique. The setup was tested to monitor the proliferation of human mesenchymal stromal cells into previously validated gelatin-chitosan hybrid hydrogel scaffolds. Reliability of the methodology was assessed comparing variations of the electrical impedance parameters with the outcomes of enzymatic proliferation assay. Results obtained showed a magnitude increase and a phase angle decrease at 4 kHz (maximum of 2.5 kΩ and -9 degrees) and an exponential increase of the modeled resistance and capacitance components due to the cell proliferation (maximum of 1.5 kΩ and 200 nF). A statistically significant relationship with enzymatic assay outcomes could be detected for both phase angle and electric model parameters. Overall, these findings support the potentiality of this non-invasive approach for continuous monitoring of scaffold-based cultures, being also promising in the perspective of optimizing the scaffold-culture system.

Increasing Promotion of Women Faculty in Academic Medicine: Impact of National Career Development Programs.

Background: Three national career development programs (CDPs)-Early and Mid-Career Programs sponsored by the Association of American Medical Colleges and the Hedwig van Ameringen Executive Leadership in Academic Medicine sponsored by Drexel University-seek to expand gender diversity in faculty and institutional leadership of academic medical centers. Over 20 years of success and continued need are evident in the sustained interest and investment of individuals and institutions. However, their impact on promotion in academic rank remains unknown. The purpose of the study is to compare promotion rates of women CDP participants and other faculty of similar institutional environment and initial career stage. Methods: The study examined retrospective cohorts of 2,719 CDP participants, 12,865 nonparticipant women, and 26,810 men, from the same institutions, with the same degrees, and first years of appointment in rank. Rates of promotion to Associate and Full Professor ranks in respective cohorts of Assistant and of Associate Professors were compared using Kaplan-Meier survival curves and log-rank tests, and logistic regression adjusting for other predictors of academic success. Results: In adjusted analyses, participants were more likely than men and non-participant women to be promoted to Associate Professor and as likely as men and more likely than non-participant women to be promoted to Full Professor within 10 years. Within 5 years, CDP participants were more likely than nonparticipant women to be promoted to Associate Professor and as likely as to be promoted to Full Professor; in the same interval, participants were promoted to both higher ranks at the same rates as men. For both intervals, nonparticipant women were significantly less likely than men to be promoted to either rank. Conclusions: The higher rates of promotion for women participating in national CDPs support the effectiveness of these programs in building capacity for academic medicine.

Beneficial Effects of Melatonin on Apolipoprotein-E Knockout Mice by Morphological and 18F-FDG PET/CT Assessments.

Atherosclerosis represents one of the main risk factors for the development of cardiovascular diseases. Their etiologies have been studied in recent years in order to better define therapeutic targets for intervention and to identify diagnostic methods. Two different subtypes of macrophages, M1 and M2, have been described in physiological conditions. They can also be found in the atherosclerotic process, where they both have opposite roles in disease progression. Perivascular brown adipose tissue is also involved in inflammation and endothelial damage. In this work, we provide insights into the protective role of melatonin in the atherosclerotic process by morphological and 18F-FDG-PET/CT analyses. In particular, we examined the effects of melatonin on pathways that are linked to atherosclerosis development. We showed that melatonin, by suppressing M1 activity, reduced inflammation and directed macrophage polarization toward the M2 macrophage subtype. Moreover, melatonin preserved the activity of perivascular brown adipose tissue. In addition, 18F-FDG uptake is very high in mice treated with melatonin, confirming that other factors may alter 18F-FDG distribution. In conclusion, we showed that melatonin affects inflammatory pathways that have been linked to atherosclerosis, assessed the relationships of the 18F-FDG PET/CT parameters with macrophage markers and the production of their cytokines, which that have been defined by morphological evaluations.

Qualitative evaluation of MRI features of lipoma and atypical lipomatous tumor: results from a multicenter study.

OBJECTIVES: The objectives of the study are (1) to distinguish lipoma (L) from atypical lipomatous tumor (ALT) using MRI qualitative features, (2) to assess the value of contrast enhancement, and (3) to evaluate the reproducibility and confidence level of radiological readings. MATERIALS AND METHODS: Patients with pathologically proven L or ALT, who underwent MRI within 3 months from surgical excision were included in this retrospective multicenter international study. Two radiologists independently reviewed MRI centrally. Impressions were recorded as L or ALT. A third radiologist was consulted for discordant readings. The two radiologists re-read all non-contrast sequences; impression was recorded; then post-contrast images were reviewed and any changes were recorded. RESULTS: A total of 246 patients (135 females; median age, 59 years) were included. ALT was histopathologically confirmed in 70/246 patients. In multivariable analysis, in addition to the lesion size, deep location, proximal lower limb lesions, demonstrating incomplete fat suppression, or increased architectural complexity were the independent predictive features of ALT; but not the contrast enhancement. Post-contrast MRI changed the impression in a total of 5 studies (3 for R1 and 4 for R2; 2 studies are common); all of them were incorrectly changed from Ls to ALTs. Overall, inter-reader kappa agreement was 0.42 (95% CI 0.39-0.56). Discordance between the two readers was statistically significant for both pathologically proven L (p < 0.001) and ALT (p = 0.003). CONCLUSION: Most qualitative MR imaging features can help distinguish ALTs from BLs. However, contrast enhancement may be limited and occasionally misleading. Substantial discordance on MRI readings exists between radiologists with a relatively high false positive and negative rates.

Bayesian negative binomial mixture regression models for the analysis of sequence count and methylation data.

In this article, we develop a Bayesian hierarchical mixture regression model for studying the association between a multivariate response, measured as counts on a set of features, and a set of covariates. We have available RNA-Seq and DNA methylation data measured on breast cancer patients at different stages of the disease. We account for the heterogeneity and over-dispersion of count data (here, RNA-Seq data) by considering a mixture of negative binomial distributions and incorporate the covariates (here, methylation data) into the model via a linear modeling construction on the mean components. Our modeling construction includes several innovative characteristics. First, it employs selection techniques that allow the identification of a small subset of features that best discriminate the samples while simultaneously selecting a set of covariates associated to each feature. Second, it incorporates known dependencies into the feature selection process via the use of Markov random field (MRF) priors. On simulated data, we show how incorporating existing information via the prior model can improve the accuracy of feature selection. In the analysis of RNA-Seq and DNA methylation data on breast cancer, we incorporate knowledge on relationships among genes via a gene-gene network, which we extract from the KEGG database. Our data analysis identifies genes which are discriminatory of cancer stages and simultaneously selects significant associations between those genes and DNA methylation sites. A biological interpretation of our findings reveals several biomarkers that can help understanding the effect of DNA methylation on gene expression transcription across cancer stages.