Owner-Intruder Contests with Information Asymmetry

Owner-Intruder Contests with Information Asymmetry Faheem Farooq, Depts. of Biology and Chemistry, Jay Bisen, Manaeil Hasan, and Akhil Patel, with Dr. Jan Rychtar, Dept. of Mathematics and Discrete Mathematics, and Dr. Dewey T. Taylor, Dept. of Mathematics and Discrete Mathematics We consider kleptoparasitic interactions between two individuals - Owner and Intruder - and model the situation as a sequential game in an extensive form. Owner is in a possession of a valuable resource when it spots Intruder. Owner has to decide whether to defend the resource; if the Owner defends, the Intruder has to decide whether to fight with the Owner. The individuals may value the resource differently and we distinguish three information cases: (a) both individuals know resource values to both of them, (b) individuals know only their own valuation, (c) individuals do not know the value at all. We solve the game in all three cases. We find that it is typically beneficial for the individuals to know as much information as possible. However, we identify several scenarios where knowing less seems better. We also show that an individual may or may not benefit from their opponent knowing less. Finally, we consider the same kind of interactions but with the reversed order of decisions. We find that typically the individual initiating the interaction has an advantage. However, when individuals know only their own valuation and not the valuations to their opponents, it is sometimes better when the opponent initiates.https://scholarscompass.vcu.edu/uresposters/1298/thumbnail.jp

A simple atomic beam oven with a metal thermal break

We report the design and construction of a simple, easy to machine high-temperature oven for generating an atomic beam in laser cooling experiments. This design eliminates the problem of thermal isolation of the oven region from the rest of the vacuum system without using a glass or ceramic thermal break. This design simplifies the construction and operation of high-temperature ovens for elements having low vapor pressure. We demonstrate the functionality of such a source for Strontium (Sr) atoms. We generate a high flux of Sr atoms for use in laser cooling and trapping experiments. The optimization of the design of the metal thermal break is done using a finite element analysis.Comment: 5 pages,6 figure

Assessing Information Congruence of Documented Cardiovascular Disease between Electronic Dental and Medical Records

Dentists are more often treating patients with Cardiovascular Diseases (CVD) in their clinics; therefore, dentists may need to alter treatment plans in the presence of CVD. However, it’s unclear to what extent patient-reported CVD information is accurately captured in Electronic Dental Records (EDRs). In this pilot study, we aimed to measure the reliability of patient-reported CVD conditions in EDRs. We assessed information congruence by comparing patients’ self-reported dental histories to their original diagnosis assigned by their medical providers in the Electronic Medical Record (EMR). To enable this comparison, we encoded patients CVD information from the free-text data of EDRs into a structured format using natural language processing (NLP). Overall, our NLP approach achieved promising performance extracting patients’ CVD-related information. We observed disagreement between self-reported EDR data and physician-diagnosed EMR data

Leveraging Electronic Dental Record Data to Classify Patients Based on Their Smoking Intensity

Background Smoking is an established risk factor for oral diseases and, therefore, dental clinicians routinely assess and record their patients' detailed smoking status. Researchers have successfully extracted smoking history from electronic health records (EHRs) using text mining methods. However, they could not retrieve patients' smoking intensity due to its limited availability in the EHR. The presence of detailed smoking information in the electronic dental record (EDR) often under a separate section allows retrieving this information with less preprocessing. Objective To determine patients' detailed smoking status based on smoking intensity from the EDR. Methods First, the authors created a reference standard of 3,296 unique patients’ smoking histories from the EDR that classified patients based on their smoking intensity. Next, they trained three machine learning classifiers (support vector machine, random forest, and naïve Bayes) using the training set (2,176) and evaluated performances on test set (1,120) using precision (P), recall (R), and F-measure (F). Finally, they applied the best classifier to classify smoking status from an additional 3,114 patients’ smoking histories. Results Support vector machine performed best to classify patients into smokers, nonsmokers, and unknowns (P, R, F: 98%); intermittent smoker (P: 95%, R: 98%, F: 96%); past smoker (P, R, F: 89%); light smoker (P, R, F: 87%); smokers with unknown intensity (P: 76%, R: 86%, F: 81%), and intermediate smoker (P: 90%, R: 88%, F: 89%). It performed moderately to differentiate heavy smokers (P: 90%, R: 44%, F: 60%). EDR could be a valuable source for obtaining patients’ detailed smoking information. Conclusion EDR data could serve as a valuable source for obtaining patients' detailed smoking information based on their smoking intensity that may not be readily available in the EHR

Hybrid-aligned nematic liquid-crystal modulators fabricated on VLSI circuits

A new method for fabricating analog light modulators on VLSI devices is described. The process is fully compatible with devices fabricated by commercial VLSI foundries, and the assembly of the modulator structures requires a small number of simple processing steps. The modulators are capable of analog amplitude or phase modulation and can operate at video rates and at low voltages (2.2 V). The modulation mechanism and the process yielding the modulator structures are described. Experimental data are presented

Limited processivity of single motors improves overall transport flux of self-assembled motor-cargo complexes

Single kinesin molecular motors can processively move along a microtubule (MT) a few micrometers on average before dissociating. However, cellular length scales over which transport occurs are several hundred microns and more. Why seemingly unreliable motors are used to transport cellular cargo remains poorly understood. We propose a new theory for how low processivity, the average length of a single bout of directed motion, can enhance cellular transport when motors and cargoes must first diffusively self assemble into complexes. We employ stochastic modeling to determine the effect of processivity on overall cargo transport flux. We show that, under a wide range of physiologically relevant conditions, possessing "infinite" processivity does not maximize flux along MTs. Rather, we find that low processivity i.e., weak binding of motors to MTs, is optimal. These results shed light on the relationship between processivity and transport efficiency and offer a new theory for the physiological benefits of low motor processivity

Cardiovascular Risk Factors as Differential Predictors of Incident Atypical and Typical Major Depressive Disorder in U.S. Adults

Objectives While the association between major depressive disorder (MDD) and future cardiovascular disease (CVD) is established, less is known about the relationship between CVD risk factors and future depression, and no studies have examined MDD subtypes. Our objective was to determine whether hypertension, tobacco use, and body mass index (BMI) differentially predict atypical and typical MDD in a national sample of U.S. adults. Methods We examined prospective data from 22,915 adults with no depressive disorder history at baseline who participated in Wave 1 (2001-2002) and Wave 2 (2004-2005) of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC). CVD risk factors (Wave 1) and incident MDD subtypes (Wave 2) were determined by structured interviews. Results There were 252 atypical and 991 typical MDD cases. In fully-adjusted models, baseline hypertension (OR=0.58, 95% CI: 0.43-0.76), former tobacco use (OR=1.46, 95% CI: 1.20-1.78), and BMI (OR=1.32, 95% CI: 1.25-1.40; all p’s<0.001) predicted incident atypical MDD versus no MDD, whereas no CVD risk factor predicted incident typical MDD. Baseline hypertension (OR=0.52, 95% CI: 0.39-0.70), former tobacco use (OR=1.53, 95% CI: 1.22-1.93), and BMI (OR=1.26, 95% CI: 1.18-1.36; all p’s<0.001) also predicted incident atypical MDD versus typical MDD. Conclusions Our study is the first to report that CVD risk factors differentially predict MDD subtypes, with hypertension (protective factor), former tobacco use (risk factor), and BMI (risk factor) being stronger predictors of incident atypical versus typical MDD. Such evidence could provide insights into the etiologies of MDD subtypes and inform interventions tailored to MDD subtype