Bayesian learning of joint distributions of objects

There is increasing interest in broad application areas in defining flexible joint models for data having a variety of measurement scales, while also allowing data of complex types, such as functions, images and documents. We consider a general framework for nonparametric Bayes joint modeling through mixture models that incorporate dependence across data types through a joint mixing measure. The mixing measure is assigned a novel infinite tensor factorization (ITF) prior that allows flexible dependence in cluster allocation across data types. The ITF prior is formulated as a tensor product of stick-breaking processes. Focusing on a convenient special case corresponding to a Parafac factorization, we provide basic theory justifying the flexibility of the proposed prior and resulting asymptotic properties. Focusing on ITF mixtures of product kernels, we develop a new Gibbs sampling algorithm for routine implementation relying on slice sampling. The methods are compared with alternative joint mixture models based on Dirichlet processes and related approaches through simulations and real data applications.Comment: Appearing in Proceedings of the 16th International Conference on Artificial Intelligence and Statistics (AISTATS) 2013, Scottsdale, AZ, US

Bayesian Knockoff Generators for Robust Inference Under Complex Data Structure

The recent proliferation of medical data, such as genetics and electronic health records (EHR), offers new opportunities to find novel predictors of health outcomes. Presented with a large set of candidate features, interest often lies in selecting the ones most likely to be predictive of an outcome for further study such that the goal is to control the false discovery rate (FDR) at a specified level. Knockoff filtering is an innovative strategy for FDR-controlled feature selection. But, existing knockoff methods make strong distributional assumptions that hinder their applicability to real world data. We propose Bayesian models for generating high quality knockoff copies that utilize available knowledge about the data structure, thus improving the resolution of prognostic features. Applications to two feature sets are considered: those with categorical and/or continuous variables possibly having a population substructure, such as in EHR; and those with microbiome features having a compositional constraint and phylogenetic relatedness. Through simulations and real data applications, these methods are shown to identify important features with good FDR control and power

Stronger Baseline Models -- A Key Requirement for Aligning Machine Learning Research with Clinical Utility

Machine Learning (ML) research has increased substantially in recent years, due to the success of predictive modeling across diverse application domains. However, well-known barriers exist when attempting to deploy ML models in high-stakes, clinical settings, including lack of model transparency (or the inability to audit the inference process), large training data requirements with siloed data sources, and complicated metrics for measuring model utility. In this work, we show empirically that including stronger baseline models in healthcare ML evaluations has important downstream effects that aid practitioners in addressing these challenges. Through a series of case studies, we find that the common practice of omitting baselines or comparing against a weak baseline model (e.g. a linear model with no optimization) obscures the value of ML methods proposed in the research literature. Using these insights, we propose some best practices that will enable practitioners to more effectively study and deploy ML models in clinical settings.18 pages, 6 figure

Adult Zymosan Re-Exposure Exacerbates the Molecular Alterations in the Brainstem Rostral Ventromedial Medulla of Rats With Early Life Zymosan-Induced Cystitis

Recent evidence suggests that the descending modulatory pathways from the brainstem rostral ventromedial medulla (RVM) are important for bladder inflammatory pain. This study aimed to identify the long-term molecular changes in RVM neurons due to early life cystitis during neuronal development and the effect of reexposure later in adulthood. RVM tissues from two treatment protocols were used: (1) neonatal zymosan exposures with acute adult rechallenge (RC) and (2) only neonatal zymosan exposures (NRC). RNAseq analysis showed upregulation of several genes associated with synaptic plasticity (Grin1, Grip2, Notch1, Arc, and Scn2b) in the cystitis groups compared to controls in both protocols. The RC protocol exhibited a stronger treatment effect with significantly higher fold differences between the groups compared to the NRC protocol (p \u3c 0.001, fold differences RC vs NRC). In microarrays, miR-34a-5p showed cystitis-induced downregulation in both protocols. Bioinformatics analysis identified multiple 3′UTRs complementary binding sites for miR-34a-5p on Grin2b, Notch1, Grip2, Scn2b, and Arc genes. The enhanced response in the RC protocol indicates a possible priming effect of early life cystitis on rechallenge in adulthood. These long-term molecular alterations may play a critical role in the development of chronic bladder pain conditions as seen in patients with Interstitial Cystitis/Bladder pain syndrome

Virtual reality-based training to augment recovery of hand dexterity after surgery for degenerative cervical myelopathy

Degenerative cervical myelopathy (DCM), the leading cause of non-traumatic spinal cord injury, frequently results in impaired hand dexterity. While surgical decompression is the primary treatment, over 40% of patients report residual hand disability after surgery. There are no therapies to restore hand function after surgery for DCM. In this single-arm clinical trial, post-surgical DCM participants (within 12 months after surgery) underwent a 4-week VR training protocol using the Virtual Keyboard system, which promotes practice of finger individuation. Assessments of hand dexterity were performed at baseline (at week 1), post-training (at week 6) and follow-up (at week 10). The primary outcome measure for hand dexterity assessment was the Jebsen-Taylor Hand Function Test (JTHFT). Twenty-two post-surgical DCM participants were included in the final analysis. Statistically significant improvement in the JTHFT was observed at both post-training (p < 0.001, Δ= -15.21s) and follow-up (p < 0.001, Δ= -17.84s), with changes exceeding the Minimal Clinically Important Difference (MCID) at both time points. VR hand training also produced significant, sustained and clinically meaningful improvements in quantitative hand dexterity tests and health-related quality of life. The results of this uncontrolled, single-arm study demonstrate the feasibility and efficacy of targeted neurorehabilitation to augment post-surgical neurological recovery in people with DCM

Stay on track: A pilot randomized control trial on the feasibility of a diet and exercise intervention in patients with breast cancer receiving radiotherapy

PURPOSE: Among patients with breast cancer undergoing radiotherapy, posttreatment cardiovascular disease and worsened quality of life (QoL) are leading causes of morbidity and mortality. To overcome these negative radiotherapy effects, this prospective, randomized clinical trial pilots a 12-week Stay on Track exercise and diet intervention for overweight patients with nonmetastatic breast cancer undergoing whole-breast radiotherapy. EXPERIMENTAL DESIGN: The intervention group (n = 22) participated in three personal exercise and dietary counseling sessions, and received three text reminders/week to adhere to recommendations. The control group (n = 22) was administered a diet/exercise information binder. All patients received a Fitbit, and at baseline, 3 months, and 6 months, measurements of biomarkers, dual-energy X-ray absorptiometry scans, QoL and physical activity surveys, and food frequency questionnaires were obtained. A satisfaction survey was administered at 3 months. RESULTS: Stay on Track was well received, with high rates of adherence and satisfaction. The intervention group showed an increase in self-reported physical activity and preserved QoL, a decrease in body mass index and visceral fat, and higher American Cancer Society/American Institute of Cancer Research dietary adherence. The control participants had reduced QoL, anti-inflammatory markers, and increased metabolic syndrome markers. Both groups had decreased overall body mass. These changes were within group effects. When comparing the intervention and control groups over time, there were notable improvements in dietary adherence in the intervention group. CONCLUSIONS: Targeted lifestyle interventions during radiotherapy are feasible and could decrease cardiovascular comorbidities in patients with breast cancer. Larger-scale implementation with longer follow-up can better determine interventions that influence cardiometabolic health and QoL. SIGNIFICANCE: This pilot study examines cardiometabolic benefits of a combined diet and exercise intervention for patients with breast cancer undergoing radiotherapy. The intervention included an activity tracker (FitBit) and text message reminders to promote adherence to lifestyle interventions. Large-scale implementation of such programs may improve cardiometabolic outcomes and overall QoL among patients with breast cancer

Efficacy of a Weight Loss Intervention for African American Breast Cancer Survivors

African American women with breast cancer have higher cancer-specific and overall mortality rates. Obesity is common among African American women and contributes to breast cancer progression and numerous chronic conditions. Weight loss interventions among breast cancer survivors positively affect weight, behavior, biomarkers, and psychosocial outcomes, yet few target African Americans. This article examines the effects of Moving Forward, a weight loss intervention for African American breast cancer survivors (AABCS) on weight, body composition, and behavior

Noninvasive Autopsy-Validated Tumor Probability Maps Identify Glioma Invasion Beyond Contrast Enhancement

Background and objectivesThis study identified a clinically significant subset of patients with glioma with tumor outside of contrast enhancement present at autopsy and subsequently developed a method for detecting nonenhancing tumor using radio-pathomic mapping. We tested the hypothesis that autopsy-based radio-pathomic tumor probability maps would be able to noninvasively identify areas of infiltrative tumor beyond traditional imaging signatures.MethodsA total of 159 tissue samples from 65 subjects were aligned to MRI acquired nearest to death for this retrospective study. Demographic and survival characteristics for patients with and without tumor beyond the contrast-enhancing margin were computed. An ensemble algorithm was used to predict pixelwise tumor presence from pathological annotations using segmented cellularity (Cell), extracellular fluid, and cytoplasm density as input (6 train/3 test subjects). A second level of ensemble algorithms was used to predict voxelwise Cell, extracellular fluid, and cytoplasm on the full data set (43 train/22 test subjects) using 5-by-5 voxel tiles from T1, T1 + C, fluid-attenuated inversion recovery, and apparent diffusion coefficient as input. The models were then combined to generate noninvasive whole brain maps of tumor probability.ResultsTumor outside of contrast was identified in 41.5% of patients, who showed worse survival outcomes (hazard ratio = 3.90, P < .001). Tumor probability maps reliably tracked nonenhancing tumor on a range of local and external unseen data, identifying tumor outside of contrast in 69% of presurgical cases that also showed reduced survival outcomes (hazard ratio = 1.67, P = .027).ConclusionThis study developed a multistage model for mapping gliomas using autopsy tissue samples as ground truth, which was able to identify regions of tumor beyond traditional imaging signatures

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements