How To Pick The Best Regression Equation: A Review And Comparison Of Model Selection Algorithms

This paper reviews and compares twenty-one different model selection algorithms (MSAs) representing a diversity of approaches, including (i) information criteria such as AIC and SIC; (ii) selection of a “portfolio” or best subset of models; (iii) general-to-specific algorithms, (iv) forward-stepwise regression approaches; (v) Bayesian Model Averaging; and (vi) inclusion of all variables. We use coefficient unconditional mean-squared error (UMSE) as the basis for our measure of MSA performance. Our main goal is to identify the factors that determine MSA performance. Towards this end, we conduct Monte Carlo experiments across a variety of data environments. Our experiments show that MSAs differ substantially with respect to their performance on relevant and irrelevant variables. We relate this to their associated penalty functions, and a bias-variance tradeoff in coefficient estimates. It follows that no MSA will dominate under all conditions. However, when we restrict our analysis to conditions where automatic variable selection is likely to be of greatest value, we find that two general-to-specific MSAs, Autometrics, do as well or better than all others in over 90% of the experiments.Model selection algorithms; Information Criteria; General-to-Specific modeling; Bayesian Model Averaging; Portfolio Models; AIC; SIC; AICc; SICc; Monte Carlo Analysis; Autometrics

WEST NILE VIRUS ANTIBODIES IN BREEDING NORTH DAKOTA ICTERIDS

Exotic infectious diseases can have devastating effects on the distribution and abundance ofnaYve wildlife species (Friend et al. 2001). West Nile Virus (WNV) is an exotic disease that was introduced into North America in 1999 and has resulted in the deaths of tens of thousands of birds (Marra et al. 2004). The natural cycle of WNV involves Culex spp. mosquitoes as principle vectors and birds as principle hosts, although humans, horses, and other mammals can become incidental hosts (Lanciotti et al. 2000). Because the virus can be fatal, outbreaks have become a national health concern for the human population, an economic concern for domestic animal losses, and a conservation concern for the status of free-living wildlife populations (Campbell et al. 2002). For birds, WNV infection can be lethal, but the degree to which birds are adversely affected varies among species and even between individuals within species (Komar et al. 2003). In light of concerns regarding the status of North American bird populations, we captured adult, juvenile, and nestling icterids in central North Dakota and tested them for WNV -specific antibodies. Specifically, we wanted to determine if antibody positive blackbirds were present during the early summer breeding season prior to the peak of mosq~ito populations that typically occurs later in the summer. Sampling during the icterid breeding season also allowed us to test the hypothesis that nestling blackbirds are particularly vulnerable to the virus because they are confined to the nest, lack protective feathers, and have naive immune systems. We also trapped mosquitoes to determine if Culex tarsalis, a known WNV vector in North Dakota (Bell et al. 2005), was present in our study area

A data-driven approach for exploiting enzyme promiscuity as a means to predict novel biochemical reactions

Systems metabolic engineering has been widely used to produce chemicals of high commercial value from low cost substrates. But this process has challenges for some applications, such as harnessing lignocellulosic biomass for biofuel and biochemical production, due to our limited metabolic knowledgebase. With current advances in protein engineering, it is possible to exploit substrate promiscuity of enzymes to enable novel biochemical reactions. Nevertheless, performing experiments to determine what substrates an enzyme can act on can be time consuming and it is not always clear what potential substrates to test. So, the current work aims to employ machine learning approaches for identifying novel substrates and in turn, predicting novel reactions that are more promising than the putative reactions predicted simply based on compound similarity measures (e.g., Tanimoto coefficient). A highly accurate (up to 88.3%) machine learning model was developed to identify candidate substrates for alcohol dehydrogenase (ADH) using a dataset consisting of 23 metabolites (with 8 of them being known positives) and 46 chemo-informatics based molecular descriptors (e.g., topology, stereochemistry, and electronic features). In addition, support vector regression proved to be a useful method for estimating enzyme kinetics (characterized by Michaelis-Menten constants, Km and Vmax) for a variety of oxidoreductases that are typically found in biofuel biosynthesis pathways. Such machine learning methods can be applied to other classes of enzymes and hence, used as a tool to expand the knowledgebase of metabolic reactions paving the way for next generation of metabolic/ pathway engineering. Please click Additional Files below to see the full abstract

Farmwork-Related Injury Among Farmers 50 Years of Age and Older in Kentucky and South Carolina: A Cohort Study, 2002-2005

Farmers in the U.S. are becoming more diverse; the average age of the farmer is increasing, as is the number of women and minority farm operators. There is limited research on injury risk factors in these special populations of farmers. It is especially important to study the risk factors for injury in these growing and at-risk groups. A longitudinal survey was conducted of farmers (n = 1,394) age 50 and older who resided in Kentucky and South Carolina. The questionnaire was administered by telephone and mail surveys four times between 2002 and 2005 to the fixed cohort of farmers, obtained by convenience sample. Approximately half of the cohort was female, and the majority of the cohort worked less than 40 hours per week. This cohort reported a crude, non-fatal injury rate of 9.3 injured farmers per 100 per year. Farmers reporting chronic bronchitis/emphysema (estimated odds ratio [EOR] = 1.57), back problems (EOR = 1.37), arthritis (EOR = 1.31), 3 to 4 restless nights in the past week (EOR = 2.02), or 5 to 7 restless nights in the past week (EOR = 1.82) were at significantly higher odds of sustaining a farmwork-related injury as calculated by the generalized estimating equations (GEE) regression method Farmers operating equipment on highways (EOR = 1.51) or climbing higher than eight feet (EOR = 1.69) were at significantly higher odds of sustaining a farmwork-related injury, and females were at higher risk of injury when performing animal-related tasks (EOR = 3.00) or crop-related tasks (EOR = 2.21). Identified factors associated with farmwork-related injury should better inform agricultural health policies and guidelines for older farmers, such as policies governing the allowable number of hours worked per week and rest breaks, guidelines that advise appropriate types of farm tasks, and ergonomic engineering advances on farming equipment

Effects of Dietary Sodium Intake on Blood Flow Regulation During Exercise in Salt Resistant Individuals

PURPOSE: Dietary sodium intake guidelines is ≤2,300 mg/day, yet is exceeded by 90% of Americans. This study examined the impact of a high sodium diet on blood flow regulation during exercise. METHODS: Six males (25 ± 2 years) consumed dietary sodium intake guidelines for two weeks, with one week salt-capsule supplemented (HS: 6,900 mg/day of sodium) and the other week placebo-capsule supplemented (LS: 2,300 mg/day of sodium). At the end of each week, peripheral hemodynamic measurements [blood flow (BF), shear rate (SR), and flow mediated dilation (FMD)/SR)] of the brachial and superficial femoral artery were taken during handgrip (HG) and plantar flexion (PF) exercise, respectively. Each exercise workload was 3 minutes and progressed by 8 kilograms until exhaustion. RESULTS: There were no differences between LS and HS in blood pressure (82 ± 4 v 80 ± 5 mmHg; p = 0.3) or heart rate (56 ± 6 v 59 ± 10 bpm; p = 0.4). HG and PF exercise increased BF, SR, and FMD/SR across workload (p \u3c 0.03 for all), but no difference between diets (p \u3e 0.05 for all). CONCLUSION: Despite previous reports that HS impairs resting vascular function, this study revealed that peripheral vascular function and blood flow regulation during exercise is not impacted by a HS diet.https://scholarscompass.vcu.edu/gradposters/1082/thumbnail.jp

Analysis of Beam Deflection Measurements in the Presence of Linear Absorption

We develop a series of analytical approximations allowing for rapid extraction of the nonlinear parameters from beam deflection measurements. We then apply these approximations to the analysis of cadmium silicon phosphide and compare the results against previously published parameter extraction methods and find good agreement for typical experimental conditions

Genome -Scale Reconstruction of Metabolic Networks of Lactobacillus casei ATCC 334 and 12A

Lactobacillus casei strains are widely used in industry and the utility of this organism in these industrial applications is straindependent. Hence, tools capable of predicting strain specific phenotypes would have utility in the selection of strains forspecific industrial processes. Genome-scale metabolic models can be utilized to better understand genotype-phenotyperelationships and to compare different organisms. To assist in the selection and development of strains with enhancedindustrial utility, genome-scale models for L. casei ATCC 334, a well characterized strain, and strain 12A, a corn silage isolate,were constructed. Draft models were generated from RAST genome annotations using the Model SEED database and refined by evaluating ATP generating cycles, mass-and-charge-balances of reactions, and growth phenotypes. After the validation process was finished, we compared the metabolic networks of these two strains to identify metabolic, genetic and ortholog differences that may lead to different phenotypic behaviors. We conclude that the metabolic capabilities of the two networks are highly similar. The L. casei ATCC 334 model accounts for 1,040 reactions, 959 metabolites and 548 genes, while the L. casei 12A model accounts for 1,076 reactions, 979 metabolites and 640 genes. The developed L. casei ATCC 334 and 12A metabolic models will enable better understanding of the physiology of these organisms and be valuable tools in the development and selection of strains with enhanced utility in a variety of industrial applications

The InfraRed Imaging Spectrograph (IRIS) for TMT: photometric precision and ghost analysis

The InfraRed Imaging Spectrograph (IRIS) is a first-light instrument for the Thirty Meter Telescope (TMT) that will be used to sample the corrected adaptive optics field by NFIRAOS with a near-infrared (0.8 - 2.4 $\mu$m) imaging camera and Integral Field Spectrograph (IFS). In order to understand the science case specifications of the IRIS instrument, we use the IRIS data simulator to characterize photometric precision and accuracy of the IRIS imager. We present the results of investigation into the effects of potential ghosting in the IRIS optical design. Each source in the IRIS imager field of view results in ghost images on the detector from IRIS's wedge filters, entrance window, and Atmospheric Dispersion Corrector (ADC) prism. We incorporated each of these ghosts into the IRIS simulator by simulating an appropriate magnitude point source at a specified pixel distance, and for the case of the extended ghosts redistributing flux evenly over the area specified by IRIS's optical design. We simulate the ghosting impact on the photometric capabilities, and found that ghosts generally contribute negligible effects on the flux counts for point sources except for extreme cases where ghosts coalign with a star of $\Delta$m$>$2 fainter than the ghost source. Lastly, we explore the photometric precision and accuracy for single sources and crowded field photometry on the IRIS imager.Comment: SPIE 2018, 14 pages, 14 figures, 4 tables, Proceedings of SPIE 10702-373, Ground-based and Airborne Instrumentation for Astronomy VII, 10702A7 (16 July 2018