Bayesian Modeling in Personalized Medicine with Applications to N-of-1 Trials

The ultimate goal of personalized or precision medicine is to identify the best treatment for each patient. An N-of-1 trial is a multiple-period crossover trial performed within a single individual, which focuses on individual outcome instead of population or group mean responses. As in a conventional crossover trial, it is critical to understand carryover effects of the treatment in an N-of-1 trial, especially in situations where there are no washout periods between treatment periods and high volume of measurements are made during the study. Existing statistical methods for analyzing N-of-1 trials include nonparametric tests, mixed effect models and autoregressive models. These methods may fail to simultaneously handle measurements autocorrelation and adjust for potential carryover effects. Distributed lag model is a regression model that uses lagged predictors to model the lag structure of exposure effects. In the dissertation, we first introduce a novel Bayesian distributed lag model that facilitates the estimation of carryover effects for single N-of-1 trial, while accounting for temporal correlations using an autoregressive model. In the second part, we extend the single N-of-1 trial model to multiple N-of-1 trials scenarios. In the third part, we again focus on single N-of-1 trials. But instead of modeling comparison with one treatment and one placebo (or active control), multiple treatments and one placebo (or active control) is considered. In the first part, we propose a Bayesian distributed lag model with autocorrelated errors (BDLM-AR) that integrate prior knowledge on the shape of distributed lag coefficients and explicitly model the magnitude and duration of carryover effect. Theoretically, we show the connection between the proposed prior structure in BDLM-AR and frequentist regularization approaches. Simulation studies were conducted to compare the performance of our proposed BDLM-AR model with other methods and the proposed model is shown to have better performance in estimating total treatment effect, carryover effect and the whole treatment effect coefficient curve under most of the simulation scenarios. Data from two patients in the light therapy study was utilized to illustrate our method. In the second part, we extend the single N-of-1 trial model to multiple N-of-1 trials model and focus on estimating population level treatment effect and carryover effect. A Bayesian hierarchical distributed lag model (BHDLM-AR) is proposed to model the nested structure of multiple N-of-1 trials within the same study. The Bayesian hierarchical structure also improve estimates for individual level parameters by borrowing strength from the N-of-1 trials of others. We show through simulation studies that BHDLM-AR model has best average performance in terms of estimating both population level and individual level parameters. The light therapy study is revisited and we applied the proposed model to all patients’ data. In the third part, we extend BDLM-AR model to multiple treatments and one placebo (or active control) scenario. We designed prior precision matrix on each treatment. We demonstrated the application of the proposed method using a hypertension study, where multiple guideline recommended medications were involved in each single N-of-1 trial

Multiple View Geometry Transformers for 3D Human Pose Estimation

In this work, we aim to improve the 3D reasoning ability of Transformers in multi-view 3D human pose estimation. Recent works have focused on end-to-end learning-based transformer designs, which struggle to resolve geometric information accurately, particularly during occlusion. Instead, we propose a novel hybrid model, MVGFormer, which has a series of geometric and appearance modules organized in an iterative manner. The geometry modules are learning-free and handle all viewpoint-dependent 3D tasks geometrically which notably improves the model's generalization ability. The appearance modules are learnable and are dedicated to estimating 2D poses from image signals end-to-end which enables them to achieve accurate estimates even when occlusion occurs, leading to a model that is both accurate and generalizable to new cameras and geometries. We evaluate our approach for both in-domain and out-of-domain settings, where our model consistently outperforms state-of-the-art methods, and especially does so by a significant margin in the out-of-domain setting. We will release the code and models: https://github.com/XunshanMan/MVGFormer.Comment: 14 pages, 8 figure

Uncertainty-aware 3D Object-Level Mapping with Deep Shape Priors

3D object-level mapping is a fundamental problem in robotics, which is especially challenging when object CAD models are unavailable during inference. In this work, we propose a framework that can reconstruct high-quality object-level maps for unknown objects. Our approach takes multiple RGB-D images as input and outputs dense 3D shapes and 9-DoF poses (including 3 scale parameters) for detected objects. The core idea of our approach is to leverage a learnt generative model for shape categories as a prior and to formulate a probabilistic, uncertainty-aware optimization framework for 3D reconstruction. We derive a probabilistic formulation that propagates shape and pose uncertainty through two novel loss functions. Unlike current state-of-the-art approaches, we explicitly model the uncertainty of the object shapes and poses during our optimization, resulting in a high-quality object-level mapping system. Moreover, the resulting shape and pose uncertainties, which we demonstrate can accurately reflect the true errors of our object maps, can also be useful for downstream robotics tasks such as active vision. We perform extensive evaluations on indoor and outdoor real-world datasets, achieving achieves substantial improvements over state-of-the-art methods. Our code will be available at https://github.com/TRAILab/UncertainShapePose.Comment: Manuscript submitted to ICRA 202

Make Your Brief Stroke Real and Stereoscopic: 3D-Aware Simplified Sketch to Portrait Generation

Creating the photo-realistic version of people sketched portraits is useful to various entertainment purposes. Existing studies only generate portraits in the 2D plane with fixed views, making the results less vivid. In this paper, we present Stereoscopic Simplified Sketch-to-Portrait (SSSP), which explores the possibility of creating Stereoscopic 3D-aware portraits from simple contour sketches by involving 3D generative models. Our key insight is to design sketch-aware constraints that can fully exploit the prior knowledge of a tri-plane-based 3D-aware generative model. Specifically, our designed region-aware volume rendering strategy and global consistency constraint further enhance detail correspondences during sketch encoding. Moreover, in order to facilitate the usage of layman users, we propose a Contour-to-Sketch module with vector quantized representations, so that easily drawn contours can directly guide the generation of 3D portraits. Extensive comparisons show that our method generates high-quality results that match the sketch. Our usability study verifies that our system is greatly preferred by user.Comment: Project Page on https://hangz-nju-cuhk.github.io

Bioactive peptides: an alternative therapeutic approach for cancer management

Cancer is still considered a lethal disease worldwide and the patients’ quality of life is affected by major side effects of the treatments including post-surgery complications, chemo-, and radiation therapy. Recently, new therapeutic approaches were considered globally for increasing conventional cancer therapy efficacy and decreasing the adverse effects. Bioactive peptides obtained from plant and animal sources have drawn increased attention because of their potential as complementary therapy. This review presents a contemporary examination of bioactive peptides derived from natural origins with demonstrated anticancer, ant invasion, and immunomodulation properties. For example, peptides derived from common beans, chickpeas, wheat germ, and mung beans exhibited antiproliferative and toxic effects on cancer cells, favoring cell cycle arrest and apoptosis. On the other hand, peptides from marine sources showed the potential for inhibiting tumor growth and metastasis. In this review we will discuss these data highlighting the potential befits of these approaches and the need of further investigations to fully characterize their potential in clinics

The calcimimetic R-568 induces apoptotic cell death in prostate cancer cells

<p>Abstract</p> <p>Background</p> <p>Increased serum level of parathyroid hormone (PTH) was found in metastatic prostate cancers. Calcimimetic R-568 was reported to reduce PTH expression, to suppress cell proliferation and to induce apoptosis in parathyroid cells. In this study, we investigated the effect of R-568 on cellular survival of prostate cancer cells.</p> <p>Methods</p> <p>Prostate cancer cell lines LNCaP and PC-3 were used in this study. Cellular survival was determined with MTT, trypan blue exclusion and fluorescent Live/Death assays. Western blot assay was utilized to assess apoptotic events induced by R-568 treatment. JC-1 staining was used to evaluate mitochondrial membrane potential.</p> <p>Results</p> <p>In cultured prostate cancer LNCaP and PC-3 cells, R-568 treatment significantly reduced cellular survival in a dose- and time-dependent manner. R-568-induced cell death was an apoptotic event, as evidenced by caspase-3 processing and PARP cleavage, as well as JC-1 color change in mitochondria. Knocking down calcium sensing receptor (CaSR) significantly reduced R-568-induced cytotoxicity. Enforced expression of Bcl-xL gene abolished R-568-induced cell death, while loss of Bcl-xL expression led to increased cell death in R-568-treated LNCaP cells,.</p> <p>Conclusion</p> <p>Taken together, our data demonstrated that calcimimetic R-568 triggers an intrinsic mitochondria-related apoptotic pathway, which is dependent on the CaSR and is modulated by Bcl-xL anti-apoptotic pathway.</p