Hemoglobin A1c Levels Modify Associations between Dietary Acid Load and Breast Cancer Recurrence.

BackgroundMetabolic acidosis promotes cancer metastasis. No prospective studies have examined the association between dietary acid load and breast cancer recurrence among breast cancer survivors, who are susceptible to metabolic acidosis. Hyperglycemia promotes cancer progression and acid formation; however, researchers have not examined whether hyperglycemia can modify the association between dietary acid load and breast cancer recurrence.MethodsWe studied 3081 early-stage breast cancer survivors enrolled in the Women's Healthy Eating and Living study who provided dietary information through 24-h recalls at baseline and during follow-up and had measurements of hemoglobin A1c (HbA1c) at baseline. We assessed dietary acid load using two common dietary acid load scores, potential renal acid load (PRAL) score and net endogenous acid production (NEAP) score.ResultsAfter an average of 7.3 years of follow-up, dietary acid load was positively associated with recurrence when baseline HbA1c levels were ≥ 5.6% (median level) and ≥5.7% (pre-diabetic cut-point). In the stratum with HbA1c ≥ 5.6%, comparing the highest to the lowest quartile of dietary acid load, the multivariable-adjusted hazard ratio was 2.15 (95% confidence interval [CI] 1.34-3.48) for PRAL and was 2.31 (95% CI 1.42-3.74) for NEAP. No associations were observed in the stratum with HbA1c levels were <5.6%. P-values for interactions were 0.01 for PRAL and 0.05 for NEAP.ConclusionsOur study demonstrated for the first time that even at or above normal to high HbA1c levels, dietary acid load was associated with increased risk of breast cancer recurrence among breast cancer survivors.ImpactsOur study provides strong evidence for developing specific dietary acid load guidelines based on HbA1c levels

A Flexible Zero-Inflated Poisson-Gamma model with application to microbiome read counts

In microbiome studies, it is of interest to use a sample from a population of microbes, such as the gut microbiota community, to estimate the population proportion of these taxa. However, due to biases introduced in sampling and preprocessing steps, these observed taxa abundances may not reflect true taxa abundance patterns in the ecosystem. Repeated measures including longitudinal study designs may be potential solutions to mitigate the discrepancy between observed abundances and true underlying abundances. Yet, widely observed zero-inflation and over-dispersion issues can distort downstream statistical analyses aiming to associate taxa abundances with covariates of interest. To this end, we propose a Zero-Inflated Poisson Gamma (ZIPG) framework to address the aforementioned challenges. From a perspective of measurement errors, we accommodate the discrepancy between observations and truths by decomposing the mean parameter in Poisson regression into a true abundance level and a multiplicative measurement of sampling variability from the microbial ecosystem. Then, we provide flexible modeling by connecting both mean abundance and the variability to different covariates, and build valid statistical inference procedures for both parameter estimation and hypothesis testing. Through comprehensive simulation studies and real data applications, the proposed ZIPG method provides significant insights into distinguished differential variability and abundance

Associations between Dietary Acid Load and Biomarkers of Inflammation and Hyperglycemia in Breast Cancer Survivors.

Metabolic acidosis can lead to inflammation, tissue damage, and cancer metastasis. Dietary acid load contributes to metabolic acidosis if endogenous acid-base balance is not properly regulated. Breast cancer survivors have reduced capacities to adjust their acid-base balance; yet, the associations between dietary acid load and inflammation and hyperglycemia have not been examined among them. We analyzed data collected from 3042 breast cancer survivors enrolled in the Women's Healthy Eating and Living (WHEL) Study who had provided detailed dietary intakes and measurements of plasma C-reactive protein (CRP) and hemoglobin A1c (HbA1c). Using a cross-sectional design, we found positive associations between dietary acid load and plasma CRP and HbA1c. In the multivariable-adjusted models, compared to women with the lowest quartile, the intakes of dietary acid load among women with the highest quartile showed 30-33% increases of CRP and 6-9% increases of HbA1c. Our study is the first to demonstrate positive associations between dietary acid load and CRP and HbA1c in breast cancer survivors. Our study identifies a novel dietary factor that may lead to inflammation and hyperglycemia, both of which are strong risk factors for breast cancer recurrence and comorbidities

A unified quantile framework reveals nonlinear heterogeneous transcriptome-wide associations

Transcriptome-wide association studies (TWAS) are powerful tools for identifying putative causal genes by integrating genome-wide association studies and gene expression data. However, most TWAS methods focus on linear associations between genes and traits, ignoring the complex nonlinear relationships that exist in biological systems. To address this limitation, we propose a novel quantile transcriptomics framework, QTWAS, that takes into account the nonlinear and heterogeneous nature of gene-trait associations. Our approach integrates a quantile-based gene expression model into the TWAS model, which allows for the discovery of nonlinear and heterogeneous gene-trait associations. By conducting comprehensive simulations and examining various psychiatric and neurodegenerative traits, we demonstrate that the proposed model outperforms traditional techniques in identifying gene-trait associations. Additionally, QTWAS can uncover important insights into nonlinear relationships between gene expression levels and phenotypes, complementing traditional TWAS approaches. We further show applications to 100 continuous traits from the UK Biobank and 10 binary traits related to brain disorders

Action and Trajectory Planning for Urban Autonomous Driving with Hierarchical Reinforcement Learning

Reinforcement Learning (RL) has made promising progress in planning and decision-making for Autonomous Vehicles (AVs) in simple driving scenarios. However, existing RL algorithms for AVs fail to learn critical driving skills in complex urban scenarios. First, urban driving scenarios require AVs to handle multiple driving tasks of which conventional RL algorithms are incapable. Second, the presence of other vehicles in urban scenarios results in a dynamically changing environment, which challenges RL algorithms to plan the action and trajectory of the AV. In this work, we propose an action and trajectory planner using Hierarchical Reinforcement Learning (atHRL) method, which models the agent behavior in a hierarchical model by using the perception of the lidar and birdeye view. The proposed atHRL method learns to make decisions about the agent's future trajectory and computes target waypoints under continuous settings based on a hierarchical DDPG algorithm. The waypoints planned by the atHRL model are then sent to a low-level controller to generate the steering and throttle commands required for the vehicle maneuver. We empirically verify the efficacy of atHRL through extensive experiments in complex urban driving scenarios that compose multiple tasks with the presence of other vehicles in the CARLA simulator. The experimental results suggest a significant performance improvement compared to the state-of-the-art RL methods.Comment: ICML Workshop on New Frontiers in Learning, Control, and Dynamical System

Deep N-ary Error Correcting Output Codes

Ensemble learning consistently improves the performance of multi-class classification through aggregating a series of base classifiers. To this end, data-independent ensemble methods like Error Correcting Output Codes (ECOC) attract increasing attention due to its easiness of implementation and parallelization. Specifically, traditional ECOCs and its general extension N-ary ECOC decompose the original multi-class classification problem into a series of independent simpler classification subproblems. Unfortunately, integrating ECOCs, especially N-ary ECOC with deep neural networks, termed as deep N-ary ECOC, is not straightforward and yet fully exploited in the literature, due to the high expense of training base learners. To facilitate the training of N-ary ECOC with deep learning base learners, we further propose three different variants of parameter sharing architectures for deep N-ary ECOC. To verify the generalization ability of deep N-ary ECOC, we conduct experiments by varying the backbone with different deep neural network architectures for both image and text classification tasks. Furthermore, extensive ablation studies on deep N-ary ECOC show its superior performance over other deep data-independent ensemble methods.Comment: EAI MOBIMEDIA 202