77 research outputs found
Mobile Application: Peril
In today’s world, phones and computers are widely used for various purposes, whether it would be using social media or using it for work. As they have become more popular, these devices have improved and continue to evolve, and as such, they have a wider range of uses. One such popular use of these devices is for entertainment, which includes watching content online or playing video games. With the rise of entertainment applications, I decided that I should learn how to make these applications. I ultimately decided on creating a video game for android systems which incorporated features from platformer and infinite runner games. The task of the game is to make it as far as the player can go without dying to the enemies or falling through a hole. Afterward, the players can compare scores through the scoreboard system. The game was programmed using C#, SQL, and was compiled using Unity
Modulation of Receptor Phosphorylation Contributes to Activation of Peroxisome Proliferator Activated Receptor α by Dehydroepiandrosterone and Other Peroxisome Proliferators
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Not AvailableDioxin-like molecules have been associated with endocrine disruption and liver disease. To better understand aryl hydrocarbon receptor (AHR) biology, metabolic phenotyping and liver proteomics were performed in mice following ligand-activation or whole-body genetic ablation of this receptor. Male wild type (WT) and Ahr–/– mice (Taconic) were fed a control diet and exposed to 3,3′,4,4′,5-pentachlorobiphenyl (PCB126) (61 nmol/kg by gavage) or vehicle for two weeks. PCB126 increased expression of canonical AHR targets (Cyp1a1 and Cyp1a2) in WT but not Ahr–/–. Knockouts had increased adiposity with decreased glucose tolerance; smaller livers with increased steatosis and perilipin-2; and paradoxically decreased blood lipids. PCB126 was associated with increased hepatic triglycerides in Ahr–/–. The liver proteome was impacted more so by Ahr–/– genotype than ligand-activation, but top gene ontology (GO) processes were similar. The PCB126-associated liver proteome was Ahr-dependent. Ahr principally regulated liver metabolism (e.g., lipids, xenobiotics, organic acids) and bioenergetics, but it also impacted liver endocrine response (e.g., the insulin receptor) and function, including the production of steroids, hepatokines, and pheromone binding proteins. These effects could have been indirectly mediated by interacting transcription factors or microRNAs. The biologic roles of the AHR and its ligands warrant more research in liver metabolic health and disease.Not Availabl
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Psychometric Approaches to Defining Cognitive Phenotypes in the Old Order Amish
Memory and cognitive problems are central to the diagnosis of Alzheimer's disease (AD). Psychometric approaches to defining phenotypes can aid in identify genetic variants associated with AD. However, these approaches have mostly been limited to affected individuals. Defining phenotypes of both affected and unaffected individuals may help identify genetic variants associated with both AD and healthy aging. This study compares psychometric methods for developing cognitive phenotypes that are more granular than clinical classifications.
682 older Old Order Amish individuals were included in the analysis. Adjusted Z-scores of cognitive tests were used to create four models including 1) global threshold scores or 2) memory threshold scores, and 3) global clusters and 4) memory clusters. An ordinal regression examined the coherence of the models with clinical classifications [cognitively impaired (CI), mildly impaired (MI), cognitively unimpaired (CU)], APOE-e4, sex, and age. An ANOVA examined the best model phenotypes for differences in clinical classification, APOE-e4, domain Z-scores (memory, language, executive function, and processing speed), sex, and age.
The memory cluster identified four phenotypes and had the best fit (χ
= 491.66). Individuals in the worse performing phenotypes were more likely to be classified as CI or MI and to have APOE-e4. Additionally, all four phenotypes performed significantly differently from one another on the domains of memory, language, and executive functioning.
Memory cluster stratification identified the cognitive phenotypes that best aligned with clinical classifications, APOE-e4, and cognitive performance We predict these phenotypes will prove useful in searching for protective genetic variants. This article is protected by copyright. All rights reserved
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Psychometric approaches to defining cognitive phenotypes in the Old Order Amish
Objective: Memory and cognitive problems are central to the diagnosis of Alzheimer's disease (AD). Psychometric approaches to defining phenotypes can aid in identify genetic variants associated with AD. However, these approaches have mostly been limited to affected individuals. Defining phenotypes of both affected and unaffected individuals may help identify genetic variants associated with both AD and healthy aging. This study compares psychometric methods for developing cognitive phenotypes that are more granular than clinical classifications.Methods: 682 older Old Order Amish individuals were included in the analysis. Adjusted Z-scores of cognitive tests were used to create four models including (1) global threshold scores or (2) memory threshold scores, and (3) global clusters and (4) memory clusters. An ordinal regression examined the coherence of the models with clinical classifications (cognitively impaired [CI], mildly impaired [MI], cognitively unimpaired), APOE-e4, sex, and age. An ANOVA examined the best model phenotypes for differences in clinical classification, APOE-e4, domain Z-scores (memory, language, executive function, and processing speed), sex, and age.Results: The memory cluster identified four phenotypes and had the best fit (?(2) = 491.66). Individuals in the worse performing phenotypes were more likely to be classified as CI or MI and to have APOE-e4. Additionally, all four phenotypes performed significantly differently from one another on the domains of memory, language, and executive functioning.Conclusions: Memory cluster stratification identified the cognitive phenotypes that best aligned with clinical classifications, APOE-e4, and cognitive performance We predict these phenotypes will prove useful in searching for protective genetic variants
Consequences of a Rare Complement Factor H Variant for Age-Related Macular Degeneration in the Amish
PURPOSE. Genetic variants in the complement factor H gene (CFH) have been consistently implicated in age-related macular degeneration (AMD) risk. However, their functional effects are not fully characterized. We previously identified a rare, AMD-associated variant in CFH (P503A, rs570523689) in 19 Amish individuals, but its functional consequences were not investigated. METHODS. We performed genotyping for CFH P503A in 1326 Amish individuals to identify additional risk allele carriers. We examined differences for age at AMD diagnosis between carriers and noncarriers. In blood samples from risk allele carriers and noncarriers, we quantified (i) CFH RNA expression, (ii) CFH protein expression, and (iii) C -reactive protein (CRP) expression. Potential changes to the CFH protein structure were interrogated computationally with Phyre2 and Chimera software programs. RESULTS. We identified 39 additional carriers from Amish communities in Ohio and Indiana. On average, carriers were younger than noncarriers at AMD diagnosis, but this difference was not significant. CFH transcript and protein levels in blood samples from Amish carriers and noncarriers were also not significantly different. CRP levels were also comparable in plasma samples from carriers and noncarriers. Computational protein modeling showed slight changes in the CFH protein conformation that were predicted to alter interactions between the CFH 503 residue and other neighboring residues. CONCLUSIONS. In total, we have identified 58 risk allele carriers for CFH P503A in the Ohio and Indiana Amish. Although we did not detect significant differences in age at AMD diagnosis or expression levels of CFH in blood samples from carriers and noncarriers, we observed modest structural changes to the CFH protein through in silico modeling. Based on our functional and computational observations, we hypothesize that CFH P503A may affect CFH binding or function rather than expression, which would require additional research to confirm
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