New Algorithms for D-optimal Designs under General Parametric Models

In this dissertation, we utilize the optimal design theories to develop two algorithms and provide theoretical justifications for the optimality of our algorithms. The first algorithm is a constrained adaptation of the lift-one algorithm, tailored for sampling in paid research studies. The second algorithm focuses on D-optimal designs for experiments with mixed factors, aiming to reduce distinct experimental settings while maintaining high efficiency. In Chapter 1, I introduce the fundamental elements of optimal designs, generalized linear models(GLMs), and multinomial linear models(MLMs) along with their respective Fisher in- formation matrices. This chapter also provides a concise overview of the preceding research this dissertation work built upon, such as the optimal designs for multinomial logistic models (1; 2; 3), the optimal design for generalized linear models (4; 5), and lift-one algorithm (6; 7). In Chapter 2, we consider constrained sampling problems in paid research studies or clinical trials. When there are more qualified volunteers than the budget allowed, we recommend a D- optimal sampling strategy based on the optimal design theory and develop a constrained lift-one algorithm to find the optimal allocation. Unlike the literature, which mainly deals with linear models, our solution solves the constrained sampling problem under fairly general statistical models, including generalized linear models and multinomial logistic models, and with more general constraints. We justify theoretically the optimality of our sampling strategy and show, by simulation studies and real-world examples, the advantages of simple random sampling and proportionally stratified sampling strategies. In Chapter 3 and Chapter 4, we address the problem of designing an experiment with both discrete and continuous factors under fairly general parametric statistical models. We propose a new algorithm, named ForLion, to search for optimal designs under the D-criterion. The algorithm performs an exhaustive search in a design space with mixed factors while keeping high efficiency and reducing the number of distinct experimental settings. Its optimality is guaranteed by the general equivalence theorem. We demonstrate its superiority over state-of- the-art design algorithms using real-life experiments under multinomial logistic models (MLM) and generalized linear models (GLM). Our simulation studies show that the ForLion algorithm could reduce the number of experimental settings by 25% or improve the relative efficiency of the designs by 17.5% on average. Our algorithm can help the experimenters reduce the time cost, the usage of experimental devices, and thus the total cost of their experiments while preserving high efficiencies of the designs. I conclude the dissertation work in Chapter 5. Following that, I also propose several prospec- tive directions for future research, building upon the methodologies and results presented herein. Those potential extensions offer future exploration of the framework and applications introduced in this work

Constrained D-optimal Design for Paid Research Study

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Table_2_Mass cytometry reveals the corneal immune cell changes at single cell level in diabetic mice.docx

IntroductionDiabetic ocular complications include sight-threatening consequences and decreased corneal sensitivity, characterized by decreased tear production, corneal sensitivity and delayed corneal epithelial wound healing. The pathogenesis of diabetic corneal disorders remains largely unknown. Growing evidence implies the participation of immune cells in the development of diabetic corneal diseases. Nonetheless, the immunological changes that result in diabetic corneal problems are largely unknown.MethodsMass cytometry by time of flight (CyTOF) was used to investigate immune cell cluster alterations associated with diabetic corneal disorders. CyTOF test was performed on corneal cells at a single level from 21-week-old diabetic (db/db) and non-diabetic (db/m) mice. A panel of 41 immune-related markers monitored different immune cell types in diabetic corneas. To investigate the proportion of each immune cell subpopulation, an unsupervised clustering method was employed, and T-distributed stochastic neighbor embedding was used to visualize the distinctions between different immune cell subsets.ResultsThrough CyTOF test, we identified 10 immune cell subsets in the corneal tissues. In a novel way, we discovered significant immune alterations in diabetic corneas, including pronounced alterations in T cells and myeloid cell subgroups in diabetic corneas linked to potential biomarkers, including CD103, CCR2, SiglecF, Ly6G, and CD172a. Comprehensive immunological profiling indicated remarkable changes in the immune microenvironment in diabetic corneas, characterized by a notable decrease in CD103+CD8+ tissue-resident memory T (TRM) cells and Tregs, as well as a dramatic increase of γδT cells and subsets of CD11b+Ly6G+ myeloid-derived suppressor cells (MDSCs).ConclusionCyTOF analysis revealed significant alterations in the immune microenvironment during the development of diabetic corneal complications. This study mapped the immune microenvironment landscape of type 2 diabetic corneas, providing a fundamental understanding of immune-driven diabetic corneal disorders.</p

Table_1_Mass cytometry reveals the corneal immune cell changes at single cell level in diabetic mice.docx

IntroductionDiabetic ocular complications include sight-threatening consequences and decreased corneal sensitivity, characterized by decreased tear production, corneal sensitivity and delayed corneal epithelial wound healing. The pathogenesis of diabetic corneal disorders remains largely unknown. Growing evidence implies the participation of immune cells in the development of diabetic corneal diseases. Nonetheless, the immunological changes that result in diabetic corneal problems are largely unknown.MethodsMass cytometry by time of flight (CyTOF) was used to investigate immune cell cluster alterations associated with diabetic corneal disorders. CyTOF test was performed on corneal cells at a single level from 21-week-old diabetic (db/db) and non-diabetic (db/m) mice. A panel of 41 immune-related markers monitored different immune cell types in diabetic corneas. To investigate the proportion of each immune cell subpopulation, an unsupervised clustering method was employed, and T-distributed stochastic neighbor embedding was used to visualize the distinctions between different immune cell subsets.ResultsThrough CyTOF test, we identified 10 immune cell subsets in the corneal tissues. In a novel way, we discovered significant immune alterations in diabetic corneas, including pronounced alterations in T cells and myeloid cell subgroups in diabetic corneas linked to potential biomarkers, including CD103, CCR2, SiglecF, Ly6G, and CD172a. Comprehensive immunological profiling indicated remarkable changes in the immune microenvironment in diabetic corneas, characterized by a notable decrease in CD103+CD8+ tissue-resident memory T (TRM) cells and Tregs, as well as a dramatic increase of γδT cells and subsets of CD11b+Ly6G+ myeloid-derived suppressor cells (MDSCs).ConclusionCyTOF analysis revealed significant alterations in the immune microenvironment during the development of diabetic corneal complications. This study mapped the immune microenvironment landscape of type 2 diabetic corneas, providing a fundamental understanding of immune-driven diabetic corneal disorders.</p

Efficient Quantum Imaginary Time Evolution by Drifting Real-Time Evolution: An Approach with Low Gate and Measurement Complexity

Quantum imaginary time evolution (QITE) is one of the promising candidates for finding the eigenvalues and eigenstates of a Hamiltonian on a quantum computer. However, the original proposal suffers from large circuit depth and measurements due to the size of the Pauli operator pool and Trotterization. To alleviate the requirement for deep circuits, we propose a time-dependent drifting scheme inspired by the qDRIFT algorithm [Campbell, E. Phys. Rev. Lett. 2019, 123, 070503]. We show that this drifting scheme removes the depth dependency on the size of the operator pool and converges inversely with respect to the number of steps. We further propose a deterministic algorithm that selects the dominant Pauli term to reduce the fluctuation for the ground state preparation. We also introduce an efficient measurement reduction scheme across Trotter steps that removes its cost dependence on the number of iterations. We analyze the main source of error for our scheme both theoretically and numerically. We numerically test the validity of depth reduction, convergence performance of our algorithms, and the faithfulness of the approximation for our measurement reduction scheme on several benchmark molecules. In particular, the results on the LiH molecule give circuit depths comparable to that of the advanced adaptive variational quantum eigensolver (VQE) methods while requiring much fewer measurements

Interconnected Porous Polymers with Tunable Pore Throat Size Prepared via Pickering High Internal Phase Emulsions

Interconnected macroporous polymers were prepared by copolymerizing methyl acrylate (MA) via Pickering high internal phase emulsion (HIPE) templates with modified silica particles. The pore structure of the obtained polymer foams was observed by field-emission scanning electron microscopy (FE-SEM). Gas permeability was characterized to evaluate the interconnectivity of macroporous polymers. The polymerization shrinkage of continuous phase tends to form open pores while the solid particles surrounding the droplets act as barriers to produce closed pores. These two conflicting factors are crucial in determining the interconnectivity of macroporous polymers. Thus, poly-Pickering HIPEs with high permeability and well-defined pore structure can be achieved by tuning the MA content, the internal phase fraction, and the content of modified silica particles

In Situ Electrochemical Reconstitution of CF–CuO/CeO₂ for Efficient Active Species Generation

Achievement of the intrinsic activity by in situ electrochemical reconstruction has been becoming a great challenge for designing a catalyst. Herein, an effective electrochemical strategy is proposed to reconstruct the surface of the CF–CuO/CeO2 precursor. Under the stimulation of oxidative/reductive potential, abundant active sites were successfully generated on the surface of CF–CuO/CeO2. Remarkably, the implantation of oxygen vacancy-rich CeO2 synergistically optimizes the chemical composition and electronic structure of CF–CuO/CeO2, greatly promoting the generation of active species. Systematic electrochemical experiments indicate that the superior catalytic performance of reconstructed CF–CuO/CeO2 could be attributed to CuOOH/CeO2 and Cu2O/Ce2O3 active species, respectively. The oxidative-/reductive-activated CF–CuO/CeO2 was further employed in a paired cell for the synergistic catalysis of hydroxymethylfurfural oxidation with 4-nitrophenol hydrogenation. As a result, nearly 100% Faraday efficiency for furandicarboxylic acid/4-aminophenol production was achieved in the paired system (−0.9 V vs Ag/AgCl, 1.5 h). Therefore, the electrochemical reconstruction via oxidative/reductive activation has been confirmed as a feasible approach to significantly excite the intrinsic activity of a catalyst

Additional file 4 of Predicting central lymph node metastasis in patients with papillary thyroid carcinoma based on ultrasound radiomic and morphological features analysis

Supplementary Material

Reactive Oxygen SpeciesResponsive Lipid Nanoparticles for Effective RNAi and Corneal Neovascularization Therapy

Corneal neovascularization (CNV) is a common disease that affects the vision ability of more than 1 million people annually. Small interfering RNA (siRNA) delivery nanoparticle platforms are a promising therapeutic modality for CNV treatment. However, the efficient delivery of siRNA into cells and the effective release of siRNA from delivery vehicles in a particular cell type challenge effective RNAi clinical application for CNV suppression. This study reports the design of a novel reactive oxygen species (ROS)-responsive lipid nanoparticle for siRNA delivery into corneal lesions for enhanced RNAi as a potential CNV treatment. We demonstrated that lipid nanoparticles could efficiently deliver siRNA into human umbilical vein endothelial cells and release siRNA for enhanced gene silencing by using the upregulated ROS of CNV to promote lipid nanoparticle degradation. Moreover, the subconjunctival injection of siRNA nanocomplexes into corneal lesions effectively knocked down vascular endothelial growth factor expression and suppressed CNV formation in an alkali burn model. Thus, we believe that the strategy of using ROS-responsive lipid nanoparticles for enhanced RNAi in CNV could be further extended to a promising clinical therapeutic approach to attenuate CNV formation

Additional file 1 of Predicting central lymph node metastasis in patients with papillary thyroid carcinoma based on ultrasound radiomic and morphological features analysis

Supplementary Material