STOCHASTIC MODELS FOR RESOURCE ALLOCATION, SERIES PATIENTS SCHEDULING, AND INVESTMENT DECISIONS

We develop stochastic models to devise optimal or near-optimal policies in three different areas: resource allocation in virtual compute labs (VCL), appointment scheduling in healthcare facilities with series patients, and capacity management for competitive investment. A VCL consists of a large number of computers (servers), users arrive and are given access to severs with user-specified applications loaded onto them. The main challenge is to decide how many servers to keep “on”, how many of them to preload with specific applications (so users needing these applications get immediate access), and how many to be left flexible so that they can be loaded with any application on demand, thus providing delayed access. We propose dynamic policies that minimize costs subject to service performance constraints and validate them using simulations with real data from the VCL at NC State. In the second application, we focus on healthcare facilities such as physical therapy (PT) clinics, where patients are scheduled for a series of appointments. We use Markov Decision Processes to develop the optimal policies that minimize staffing, overtime, overbooking and delay costs, and develop heuristic secluding policies using the policy improvement algorithm. We use the data from a local PT center to test the effectiveness of our proposed policies and compare their performance with other benchmark policies. In the third application, we study a strategic capacity investment problem in a duopoly model with an unknown market size. A leader chooses its capacity to enter a new market. In a continuous-time Bayesian setting, a competitive follower dynamically learns about the favorableness of the new market by observing the performance of the leader, and chooses its capacity and timing of investment. We show that an increase in the probability of a favorable market can strictly decrease the leaders expected discounted profit due to non-trivial interplay between leaders investment capacity and timing of the dynamically-learning follower.Doctor of Philosoph

First-time homebuying: attitudes and behaviors of low-income renters through the financial crisis

We use psychological theory to investigate how attitudes toward homebuying relate to first-time home purchases over the past decade. Homeownership rates in the US have dropped to 20-year lows, but whether views toward homebuying shifted due to the financial crisis is not known because studies have not compared attitudes for the same respondents pre- and post-crisis. We address this gap with 2004–2014 panel data from low-income renters. We find that a negative shift in homebuying attitudes is associated with a decline in first-time home purchases. Older renters aged more than 35 years at baseline report the greatest declines in homebuying intentions. Younger renters aged 18–34 also report diminished homebuying intentions, yet express highest overall levels of homebuying intentions pre- and post-crisis. Blacks report greater homebuying intentions although their odds of home purchase are 29 per cent lower than whites. Homebuying norms and favorability are associated with homebuying intentions but not with actual purchases, while perceived control over homebuying influences both outcomes

A semi-quantitative upconversion nanoparticle-based immunochromatographic assay for SARS-CoV-2 antigen detection

The unprecedented public health and economic impact of the coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has been met with an equally unprecedented scientific response. Sensitive point-of-care methods to detect SARS-CoV-2 antigens in clinical specimens are urgently required for the rapid screening of individuals with viral infection. Here, we developed an upconversion nanoparticle-based lateral flow immunochromatographic assay (UCNP-LFIA) for the high-sensitivity detection of SARS-CoV-2 nucleocapsid (N) protein. A pair of rabbit SARS-CoV-2 N-specific monoclonal antibodies was conjugated to UCNPs, and the prepared UCNPs were then deposited into the LFIA test strips for detecting and capturing the N protein. Under the test conditions, the limit of detection (LOD) of UCNP-LFIA for the N protein was 3.59 pg/mL, with a linear range of 0.01–100 ng/mL. Compared with that of the current colloidal gold-based LFIA strips, the LOD of the UCNP-LFIA-based method was increased by 100-fold. The antigen recovery rate of the developed method in the simulated pharyngeal swab samples ranged from 91.1 to 117.3%. Furthermore, compared with the reverse transcription-polymerase chain reaction, the developed UCNP-LFIA method showed a sensitivity of 94.73% for 19 patients with COVID-19. Thus, the newly established platform could serve as a promising and convenient fluorescent immunological sensing approach for the efficient screening and diagnosis of COVID-19

Genome-Wide Analysis of DNA Methylation and Cigarette Smoking in a Chinese Population

Background: Smoking is a risk factor for many human diseases. DNA methylation has been related to smoking, but genome-wide methylation data for smoking in Chinese populations is limited. Objectives: We aimed to investigate epigenome-wide methylation in relation to smoking in a Chinese population. Methods: We measured the methylation levels at > 485,000 CpG sites (CpGs) in DNA from leukocytes using a methylation array and conducted a genome-wide meta-analysis of DNA methylation and smoking in a total of 596 Chinese participants. We further evaluated the associations of smoking-related CpGs with internal polycyclic aromatic hydrocarbon (PAH) biomarkers and their correlations with the expression of corresponding genes. Results: We identified 318 CpGs whose methylation levels were associated with smoking at a genome-wide significance level (false discovery rate < 0.05), among which 161 CpGs annotated to 123 genes were not associated with smoking in recent studies of Europeans and African Americans. Of these smoking-related CpGs, methylation levels at 80 CpGs showed significant correlations with the expression of corresponding genes (including RUNX3, IL6R, PTAFR, ANKRD11, CEP135 and CDH23), and methylation at 15 CpGs was significantly associated with urinary 2-hydroxynaphthalene, the most representative internal monohydroxy-PAH biomarker for smoking. Conclusion: We identified DNA methylation markers associated with smoking in a Chinese population, including some markers that were also correlated with gene expression. Exposure to naphthalene, a byproduct of tobacco smoke, may contribute to smoking-related methylation. Citation: Zhu X, Li J, Deng S, Yu K, Liu X, Deng Q, Sun H, Zhang X, He M, Guo H, Chen W, Yuan J, Zhang B, Kuang D, He X, Bai Y, Han X, Liu B, Li X, Yang L, Jiang H, Zhang Y, Hu J, Cheng L, Luo X, Mei W, Zhou Z, Sun S, Zhang L, Liu C, Guo Y, Zhang Z, Hu FB, Liang L, Wu T. 2016. Genome-wide analysis of DNA methylation and cigarette smoking in Chinese. Environ Health Perspect 124:966–973; http://dx.doi.org/10.1289/ehp.150983

Three new shRNA expression vectors targeting the CYP3A4 coding sequence to inhibit its expression.

RNA interference (RNAi) is useful for selective gene silencing. Cytochrome P450 3A4 (CYP3A4), which metabolizes approximately 50% of drugs in clinical use, plays an important role in drug metabolism. In this study, we aimed to develop a short hairpin RNA (shRNA) to modulate CYP3A4 expression. Three new shRNAs (S1, S2 and S3) were designed to target the coding sequence (CDS) of CYP3A4, cloned into a shRNA expression vector, and tested in different cells. The mixture of three shRNAs produced optimal reduction (55%) in CYP3A4 CDS-luciferase activity in both CHL and HEK293 cells. Endogenous CYP3A4 expression in HepG2 cells was decreased about 50% at both mRNA and protein level after transfection of the mixture of three shRNAs. In contrast, CYP3A5 gene expression was not altered by the shRNAs, supporting the selectivity of CYP3A4 shRNAs. In addition, HepG2 cells transfected with CYP3A4 shRNAs were less sensitive to Ginkgolic acids, whose toxic metabolites are produced by CYP3A4. These results demonstrate that vector-based shRNAs could modulate CYP3A4 expression in cells through their actions on CYP3A4 CDS, and CYP3A4 shRNAs may be utilized to define the role of CYP3A4 in drug metabolism and toxicity

Three new shRNA expression vectors targeting the CYP3A4 coding sequence to inhibit its expression.

RNA interference (RNAi) is useful for selective gene silencing. Cytochrome P450 3A4 (CYP3A4), which metabolizes approximately 50% of drugs in clinical use, plays an important role in drug metabolism. In this study, we aimed to develop a short hairpin RNA (shRNA) to modulate CYP3A4 expression. Three new shRNAs (S1, S2 and S3) were designed to target the coding sequence (CDS) of CYP3A4, cloned into a shRNA expression vector, and tested in different cells. The mixture of three shRNAs produced optimal reduction (55%) in CYP3A4 CDS-luciferase activity in both CHL and HEK293 cells. Endogenous CYP3A4 expression in HepG2 cells was decreased about 50% at both mRNA and protein level after transfection of the mixture of three shRNAs. In contrast, CYP3A5 gene expression was not altered by the shRNAs, supporting the selectivity of CYP3A4 shRNAs. In addition, HepG2 cells transfected with CYP3A4 shRNAs were less sensitive to Ginkgolic acids, whose toxic metabolites are produced by CYP3A4. These results demonstrate that vector-based shRNAs could modulate CYP3A4 expression in cells through their actions on CYP3A4 CDS, and CYP3A4 shRNAs may be utilized to define the role of CYP3A4 in drug metabolism and toxicity

Manipulation of Photoelectrochemical Water Splitting by Controlling Direction of Carrier Movement Using InGaN/GaN Hetero-Structure Nanowires

We report the improvement in photoelectrochemical water splitting (PEC-WS) by controlling migration kinetics of photo-generated carriers using InGaN/GaN hetero-structure nanowires (HSNWs) as a photocathode (PC) material. The InGaN/GaN HSNWs were formed by first growing GaN nanowires (NWs) on an Si substrate and then forming InGaN NWs thereon. The InGaN/GaN HSNWs can cause the accumulation of photo-generated carriers in InGaN due to the potential barrier formed at the hetero-interface between InGaN and GaN, to increase directional migration towards electrolyte rather than the Si substrate, and consequently to contribute more to the PEC-WS reaction with electrolyte. The PEC-WS using the InGaN/GaN-HSNW PC shows the current density of 12.6 mA/cm2 at −1 V versus reversible hydrogen electrode (RHE) and applied-bias photon-to-current conversion efficiency of 3.3% at −0.9 V versus RHE. The high-performance PEC-WS using the InGaN/GaN HSNWs can be explained by the increase in the reaction probability of carriers at the interface between InGaN NWs and electrolyte, which was analyzed by electrical resistance and capacitance values defined therein

Cross-Entropy Method for Content Placement and User Association in Cache-Enabled Coordinated Ultra-Dense Networks

Due to the high splitting-gain of dense small cells, Ultra-Dense Network (UDN) is regarded as a promising networking technology to achieve high data rate and low latency in 5G mobile communications. In UDNs, each User Equipment (UE) may receive signals from multiple Base Stations (BSs), which impose severe interference in the networks and in turn motivates the possibility of using Coordinated Multi-Point (CoMP) transmissions to further enhance network capacity. In CoMP-based Ultra-Dense Networks, a great challenge is to tradeoff between the gain of network throughput and the worsening backhaul latency. Caching popular files on BSs has been identified as a promising method to reduce the backhaul traffic load. In this paper, we investigated content placement strategies and user association algorithms for the proactive caching ultra dense networks. The problem has been formulated to maximize network throughput of cell edge UEs under the consideration of backhaul load, which is a constrained non-convex combinatorial optimization problem. To decrease the complexity, the problem is decomposed into two suboptimal problems. We first solved the content placement algorithm based on the cross-entropy (CE) method to minimize the backhaul load of the network. Then, a user association algorithm based on the CE method was employed to pursue larger network throughput of cell edge UEs. Simulation were conducted to validate the performance of the proposed cross-entropy based schemes in terms of network throughput and backhaul load. The simulation results show that the proposed cross-entropy based content placement scheme significantly outperform the conventional random and Most Popular Content placement schemes, with with 50% and 20% backhaul load decrease respectively. Furthermore, the proposed cross-entropy based user association scheme can achieve 30% and 23% throughput gain, compared with the conventional N-best, No-CoMP, and Threshold based user association schemes