Subgroup analysis for the functional linear model

Classical functional linear regression models the relationship between a scalar response and a functional covariate, where the coefficient function is assumed to be identical for all subjects. In this paper, the classical model is extended to allow heterogeneous coefficient functions across different subgroups of subjects. The greatest challenge is that the subgroup structure is usually unknown to us. To this end, we develop a penalization-based approach which innovatively applies the penalized fusion technique to simultaneously determine the number and structure of subgroups and coefficient functions within each subgroup. An effective computational algorithm is derived. We also establish the oracle properties and estimation consistency. Extensive numerical simulations demonstrate its superiority compared to several competing methods. The analysis of an air quality dataset leads to interesting findings and improved predictions.Comment: 24 pages, 9 figure

Starch nanoparticles: modifications, toxicity, and drug loading

Nanoparticles as drug delivery vehicles have been well tested and transferred into clinical practice in the past few decades. The success of most nanocarriers is attributed to their biocompatibility, controlled release and unique size-dependent properties. In this regard, I proposed that starch nanoparticles (SNPs) might be a good candidate for drug delivery due to their excellent biocompatibility. The crosslinked SNPs supplied by EcoSynthetix Inc. are nanogel-like materials with many potential advantages, including good biocompatibility, biodegradability, and high capacity for loading drugs. In addition, SNPs can be engineered to achieve targeted delivery and sustained release of drugs. So far the SNPs from EcoSynthetix are only used commercially in the paper coating industry. The chemical modifications of SNPs are expected to generate new materials that can be used for drug and gene delivery. Since the safety of nanomaterial is of great concern in biomedical applications, one objective of this research is to study the toxicity of the unmodified and modified SNPs. Through my research, the reason for the toxicity is attributed to free crosslinker molecules present in the sample, and they can be removed by washing. For drug loading study, doxorubicin (Dox), a highly effective clinical anticancer drug, was chosen as the model drug. Due to the significant side effects of Dox, it is important to develop targeted delivery vehicles to decrease its toxicity to the healthy tissues. While several biocompatible nanocarriers have been developed to deliver Dox, the synthesis of these vehicles is often complicated and expensive. Compared to these delivery platforms, the SNPs are renewable and can be produced commercially on a large scale. Therefore, the second objective is to synthesize and characterize carboxyl-modified SNPs for Dox loading. The drug loading/release kinetics and the efficacy of drug complex were studied and compared to that of the free drug. Finally, cationic SNPs developed by a co-worker were evaluated and utilized for DNA delivery. DNA adsorption onto the cationic SNPs was investigated, and then the cellular uptake efficiency of the DNA/SNPs complexes was were tested and formed to be comparable to a commercial gene transfection agent. This new generation of SNP platform holds great promise for the treatment of cancer in the future

Mechanisms of DNA Sensing on Graphene Oxide

This document is the Accepted Manuscript version of a Published Work that appeared in final form in Analytical Chemistry copyright © American Chemical Society after peer review and technical editing by publisher. To access the final edited and published work see Liu, B., Sun, Z., Zhang, X., & Liu, J. (2013). Mechanisms of DNA Sensing on Graphene Oxide. Analytical Chemistry, 85(16), 7987–7993. https://doi.org/10.1021/ac401845pAdsorption of a fluorophore-labeled DNA probe by graphene oxide (GO) produces a sensor that gives fluorescence enhancement in the presence of its complementary DNA (cDNA). While many important analytical applications have been demonstrated, it remains unclear how DNA hybridization takes place in the presence of GO, hindering further rational improvement of sensor design. For the first time, we report a set of experimental evidence to reveal a new mechanism involving nonspecific probe displacement followed by hybridization in the solution phase. In addition, we show quantitatively that only a small portion of the added cDNA molecules undergo hybridization while most are adsorbed by GO to play the displacement role. Therefore, it is possible to improve signaling by raising the hybridization efficiency. A key innovation herein is using probes and cDNA with a significant difference in their adsorption energy by GO. This study offers important mechanistic insights into the GO/DNA system. At the same time, it provides simple experimental methods to study the biomolecular reaction dynamics and mechanism on a surface, which may be applied for many other biosensor systems.University of Waterloo || Canadian Foundation for Innovation || Natural Sciences and Engineering Research Council || Ontario Ministry of Research and Innovation |

Regional Analysis to Delineate Intrasample Heterogeneity With RegionalST

MOTIVATION: Spatial transcriptomics has greatly contributed to our understanding of spatial and intra-sample heterogeneity, which could be crucial for deciphering the molecular basis of human diseases. Intra-tumor heterogeneity, e.g. may be associated with cancer treatment responses. However, the lack of computational tools for exploiting cross-regional information and the limited spatial resolution of current technologies present major obstacles to elucidating tissue heterogeneity. RESULTS: To address these challenges, we introduce RegionalST, an efficient computational method that enables users to quantify cell type mixture and interactions, identify sub-regions of interest, and perform cross-region cell type-specific differential analysis for the first time. Our simulations and real data applications demonstrate that RegionalST is an efficient tool for visualizing and analyzing diverse spatial transcriptomics data, thereby enabling accurate and flexible exploration of tissue heterogeneity. Overall, RegionalST provides a one-stop destination for researchers seeking to delve deeper into the intricacies of spatial transcriptomics data. AVAILABILITY AND IMPLEMENTATION: The implementation of our method is available as an open-source R/Bioconductor package with a user-friendly manual available at https://bioconductor.org/packages/release/bioc/html/RegionalST.htm

Clustered Federated Learning based on Nonconvex Pairwise Fusion

This study investigates clustered federated learning (FL), one of the formulations of FL with non-i.i.d. data, where the devices are partitioned into clusters and each cluster optimally fits its data with a localized model. We propose a novel clustered FL framework, which applies a nonconvex penalty to pairwise differences of parameters. This framework can automatically identify clusters without a priori knowledge of the number of clusters and the set of devices in each cluster. To implement the proposed framework, we develop a novel clustered FL method called FPFC. Advancing from the standard ADMM, our method is implemented in parallel, updates only a subset of devices at each communication round, and allows each participating device to perform a variable amount of work. This greatly reduces the communication cost while simultaneously preserving privacy, making it practical for FL. We also propose a new warmup strategy for hyperparameter tuning under FL settings and consider the asynchronous variant of FPFC (asyncFPFC). Theoretically, we provide convergence guarantees of FPFC for general nonconvex losses and establish the statistical convergence rate under a linear model with squared loss. Our extensive experiments demonstrate the advantages of FPFC over existing methods.Comment: 46 pages, 9 figure

Locally Adaptive Algorithms for Multiple Testing with Network Structure, with Application to Genome-Wide Association Studies

Linkage analysis has provided valuable insights to the GWAS studies, particularly in revealing that SNPs in linkage disequilibrium (LD) can jointly influence disease phenotypes. However, the potential of LD network data has often been overlooked or underutilized in the literature. In this paper, we propose a locally adaptive structure learning algorithm (LASLA) that provides a principled and generic framework for incorporating network data or multiple samples of auxiliary data from related source domains; possibly in different dimensions/structures and from diverse populations. LASLA employs a $p$-value weighting approach, utilizing structural insights to assign data-driven weights to individual test points. Theoretical analysis shows that LASLA can asymptotically control FDR with independent or weakly dependent primary statistics, and achieve higher power when the network data is informative. Efficiency again of LASLA is illustrated through various synthetic experiments and an application to T2D-associated SNP identification.Comment: 33 pages, 7 figure

Does urbanization have spatial spillover effect on poverty reduction: empirical evidence from rural China

In light of a scarcity of research on the spatial effects of urbanization on poverty reduction, this study uses panel data on 30 provinces in China from 2009 to 2019 to construct a system of indices to assess poverty that spans the four dimensions of the economy, education, health, and living. We use the spatial autocorrelation test and the spatial Durbin model (SDM) to analyze the spatial effects of urbanization on poverty reduction in these different dimensions. The main conclusions are as follows: (a) China’s urbanization has the characteristics of spatial aggregation and a spatial spillover effect. (b) Different dimensions of poverty had the attributes of spatial agglomeration, and Moran’s index of a reduction in economic poverty was the highest. Under the SDM, the different dimensions of poverty also showed a significant positive spatial correlation. (c) Urbanization has a significant effect on poverty reduction along the dimensions of the economy, education, and living, but has little effect on reducing health poverty. It has a spatial spillover effect on poverty reduction in economic and living contexts. (d) There were spatial differences in the effect of urbanization on relieving economic and living-related poverty

Metabolic reprogramming in esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is a malignancy with high incidence in China. Due to the lack of effective molecular targets, the prognosis of ESCC patients is poor. It is urgent to explore the pathogenesis of ESCC to identify promising therapeutic targets. Metabolic reprogramming is an emerging hallmark of ESCC, providing a novel perspective for revealing the biological features of ESCC. In the hypoxic and nutrient-limited tumor microenvironment, ESCC cells have to reprogram their metabolic phenotypes to fulfill the demands of bioenergetics, biosynthesis and redox homostasis of ESCC cells. In this review, we summarized the metabolic reprogramming of ESCC cells that involves glucose metabolism, lipid metabolism, and amino acid metabolism and explore how reprogrammed metabolism provokes novel opportunities for biomarkers and potential therapeutic targets of ESCC

A General Implicit Framework for Fast NeRF Composition and Rendering

A variety of Neural Radiance Fields (NeRF) methods have recently achieved remarkable success in high render speed. However, current accelerating methods are specialized and incompatible with various implicit methods, preventing real-time composition over various types of NeRF works. Because NeRF relies on sampling along rays, it is possible to provide general guidance for acceleration. To that end, we propose a general implicit pipeline for composing NeRF objects quickly. Our method enables the casting of dynamic shadows within or between objects using analytical light sources while allowing multiple NeRF objects to be seamlessly placed and rendered together with any arbitrary rigid transformations. Mainly, our work introduces a new surface representation known as Neural Depth Fields (NeDF) that quickly determines the spatial relationship between objects by allowing direct intersection computation between rays and implicit surfaces. It leverages an intersection neural network to query NeRF for acceleration instead of depending on an explicit spatial structure.Our proposed method is the first to enable both the progressive and interactive composition of NeRF objects. Additionally, it also serves as a previewing plugin for a range of existing NeRF works.Comment: 7 pages for main conten