Targeted Economic Development in Tennessee: An Industrial Cluster Analysis of a Multi-County Upper-Cumberland Region

The purpose of this study was to identify and rank industrial clusters in a rural, multi-county region in Tennessee. Counties in the study region include Clay, Fentress, Jackson, Overton, Pickett, and Putnam. Industrial clusters were identified at the 3-digit level of the North American Industrial Classification System (NAICS). Five industrial clusters were identified in this region: food manufacturing, primary metal manufacturing, fabricated metal manufacturing, transportation equipment manufacturing, and truck transportation. These clusters were also analyzed for industrial characteristics, including average establishment size, employment growth, industrial multipliers, average worker wages and income, and revenue generated per worker. The differences in these characteristics were standardized and compiled into an equally weighted index

The effect of whole egg on DNA hypomethylation and disruption of one-carbon metabolism in insulin dependent diabetes mellitus

Diabetes mellitus is linked with a higher risk of cardiovascular disease and certain types of cancer [61-63]. A study of Type 1 and Type 2 diabetics in Australia showed multi-fold increases in the risk for several types of cancer. Diabetics are also two to four times more likely to have CVD than an adult without diabetes. The common thread among these three medical conditions is oxidative stress. To date, little work has been done on the impact of dietary intervention on the body’s ability to handle the increased oxidative stress caused by diabetes. In this study, male Sprague-Dawley rats were fed a high choline diet for 4 weeks (3 weeks acclimation and 1 week experimental). Diabetes was induced using STZ and after 1 week the rats were sacrificed and liver tissue was harvested for study. There was a significant decrease in GNMT abundance between the non-diabetic control and whole egg diet groups, but not between the diabetic control group and whole egg diet group. There was no attenuation of hepatic hypomethylation in the group fed the high choline diet in comparison to the diabetic control group (P = .121). No significant difference was seen in serum TG levels between groups (P = 0.462)

The thermodynamic landscape of carbon redox biochemistry

Redox biochemistry plays a key role in the transduction of chemical energy in all living systems. Observed redox reactions in metabolic networks represent only a minuscule fraction of the space of all possible redox reactions. Here we ask what distinguishes observed, natural redox biochemistry from the space of all possible redox reactions between natural and non-natural compounds. We generate the set of all possible biochemical redox reactions involving linear chain molecules with a fixed numbers of carbon atoms. Using cheminformatics and quantum chemistry tools we analyze the physicochemical and thermodynamic properties of natural and non-natural compounds and reactions. We find that among all compounds, aldose sugars are the ones with the highest possible number of connections (reductions and oxidations) to other molecules. Natural metabolites are significantly enriched in carboxylic acid functional groups and depleted in carbonyls, and have significantly higher solubilities than non-natural compounds. Upon constructing a thermodynamic landscape for the full set of reactions as a function of pH and of steady-state redox cofactor potential, we find that, over this whole range of conditions, natural metabolites have significantly lower energies than the non-natural compounds. For the set of 4-carbon compounds, we generate a Pourbaix phase diagram to determine which metabolites are local energetic minima in the landscape as a function of pH and redox potential. Our results suggest that, across a set of conditions, succinate and butyrate are local minima and would thus tend to accumulate at equilibrium. Our work suggests that metabolic compounds could have been selected for thermodynamic stability, and yields insight into thermodynamic and design principles governing nature’s metabolic redox reactions.https://www.biorxiv.org/content/10.1101/245811v1Othe

Eliminating Mole Size in Melanoma Classification

While skin cancer classification has been a popular and valuable deep learning application for years, there has been little consideration of the context in which testing images are taken. Traditional melanoma classifiers rely on the assumption that their testing environments are analogous to the structured images on which they are trained. This paper combats this notion, arguing that mole size, a vital attribute in professional dermatology, is a red herring in automated melanoma detection. Although malignant melanomas are consistently larger than benign melanomas, this distinction proves unreliable and harmful when images cannot be contextually scaled. This implementation builds a custom model that eliminates size as a training feature to prevent overfitting to incorrect parameters. Additionally, random rotation and contrast augmentations are performed to simulate the real-world use of melanoma detection applications. Several custom models with varying forms of data augmentation are implemented to demonstrate the most significant features of the generalization abilities of mole classifiers. These implementations show that user unpredictability is crucial when utilizing such applications. The caution required when manually modifying data is acknowledged, as data loss and biased conclusions are necessary considerations in this process. Additionally, mole size inconsistency and its significance are discussed in both the dermatology and deep learning communities