QUALITY AND COST ASSOCIATED WITH CANCER CARE

Cancer has been one of the leading causes of death for decades in the United States. It poses a significant burden to patients, stakeholders, and society in both economic (e.g., healthcare costs) and human (e.g., health-related quality of life) terms. This dissertation aimed at filling gaps in the literature on the cost and quality associated with cancer care. First, a retrospective cross-sectional study was conducted to evaluate the effect of an alternative payment model - accountable care organizations (ACOs) - on health-related quality of life (HRQOL) and healthcare cost among patients with cancer. Findings from this study indicate that although ACOs enrollment didn’t influence healthcare expenditures, cancer survivors cared for by providers in ACOs were more likely to have greater Mental Component Summary (MCS) scores and lower Physical Component Summary (PCS) scores of HRQOL. Second, a systematic literature review synthesized and appraised current knowledge on end-of-life (EOL) care in terms of quality indicators among adult patients with hematologic malignancies (HM). The results demonstrate that death in hospital and admission to the ICU in the last 30 days of life were the most commonly reported quality indicators of EOL for patients with HM. Additionally, this review provides evidence that a significant proportion of patients with HM receive aggressive care during the EOL period. Finally, machine learning algorithms including Regression and Classification Trees, Random Forests, and Gradient Boosting Machine were adopted to build and optimize models to predict high-cost cancer patients during the EOL period in national Medicare data. Among all the models, the Random Forests model was the top-performing model. Study findings of this dissertation on the cost and quality of cancer care are valuable for healthcare professionals, payers, and policy makers to improve the HRQOL, reduce the healthcare costs, and improve EOL care management for patients with cancer

LightSleepNet: Design of a Personalized Portable Sleep Staging System Based on Single-Channel EEG

This paper proposed LightSleepNet - a light-weight, 1-d Convolutional Neural Network (CNN) based personalized architecture for real-time sleep staging, which can be implemented on various mobile platforms with limited hardware resources. The proposed architecture only requires an input of 30s single-channel EEG signal for the classification. Two residual blocks consisting of group 1-d convolution are used instead of the traditional convolution layers to remove the redundancy in the CNN. Channel shuffles are inserted into each convolution layer to improve the accuracy. In order to avoid over-fitting to the training set, a Global Average Pooling (GAP) layer is used to replace the fully connected layer, which further reduces the total number of the model parameters significantly. A personalized algorithm combining Adaptive Batch Normalization (AdaBN) and gradient re-weighting is proposed for unsupervised domain adaptation. A higher priority is given to examples that are easy to transfer to the new subject, and the algorithm could be personalized for new subjects without re-training. Experimental results show a state-of-the-art overall accuracy of 83.8% with only 45.76 Million Floating-point Operations per Second (MFLOPs) computation and 43.08 K parameters.Comment: 5 pages, 3 figures, published by IEEE TCAS-I

Missense mutations in CRX homeodomain cause dominant retinopathies through two distinct mechanisms

Homeodomain transcription factors (HD TFs) are instrumental to vertebrate development. Mutations in HD TFs have been linked to human diseases, but their pathogenic mechanisms remain elusive. Here, we us

CMOS Ising Machines with Coupled Bistable Nodes

Ising machines use physics to naturally guide a dynamical system towards an optimal state which can be read out as a heuristical solution to a combinatorial optimization problem. Such designs that use nature as a computing mechanism can lead to higher performance and/or lower operation costs. Quantum annealers are a prominent example of such efforts. However, existing Ising machines are generally bulky and energy intensive. Such disadvantages might lead to intrinsic advantages at some larger scale in the future. But for now, integrated electronic designs allow more immediate applications. We propose one such design that uses bistable nodes, coupled with programmable and variable strengths. The design is fully CMOS compatible for on-chip applications and demonstrates competitive solution quality and significantly superior execution time and energy.Comment: 11 pages, 12 figures, 2 tables, 5 sections

CompeteAI: Understanding the Competition Behaviors in Large Language Model-based Agents

Large language models (LLMs) have been widely used as agents to complete different tasks, such as personal assistance or event planning. While most work has focused on cooperation and collaboration between agents, little work explores competition, another important mechanism that fosters the development of society and economy. In this paper, we seek to examine the competition behaviors in LLM-based agents. We first propose a general framework to study the competition between agents. Then, we implement a practical competitive environment using GPT-4 to simulate a virtual town with two types of agents, including restaurant agents and customer agents. Specifically, restaurant agents compete with each other to attract more customers, where the competition fosters them to transform, such as cultivating new operating strategies. The results of our experiments reveal several interesting findings ranging from social learning to Matthew Effect, which aligns well with existing sociological and economic theories. We believe that competition between agents deserves further investigation to help us understand society better. The code will be released soon.Comment: Technical report; 21 page

Use of gene therapy for optic nerve protection: Current concepts

Gene therapy has become an essential treatment for optic nerve injury (ONI) in recent years, and great strides have been made using animal models. ONI, which is characterized by the loss of retinal ganglion cells (RGCs) and axons, can induce abnormalities in the pupil light reflex, visual field defects, and even vision loss. The eye is a natural organ to target with gene therapy because of its high accessibility and certain immune privilege. As such, numerous gene therapy trials are underway for treating eye diseases such as glaucoma. The aim of this review was to cover research progress made in gene therapy for ONI. Specifically, we focus on the potential of gene therapy to prevent the progression of neurodegenerative diseases and protect both RGCs and axons. We cover the basic information of gene therapy, including the classification of gene therapy, especially focusing on genome editing therapy, and then we introduce common editing tools and vector tools such as Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) -Cas9 and adeno-associated virus (AAV). We also summarize the progress made on understanding the roles of brain derived neurotrophic factor (BDNF), ciliary neurotrophic factor (CNTF), phosphatase-tensin homolog (PTEN), suppressor of cytokine signal transduction 3 (SOCS3), histone acetyltransferases (HATs), and other important molecules in optic nerve protection. However, gene therapy still has many challenges, such as misalignment and mutations, immunogenicity of AAV, time it takes and economic cost involved, which means that these issues need to be addressed before clinical trials can be considered