SimCorrMix: Simulation of Correlated Data with Multiple Variable Types Including Continuous and Count Mixture Distributions

The SimCorrMix package generates correlated continuous (normal, non-normal, and mixture), binary, ordinal, and count (regular and zero-inflated, Poisson and Negative Binomial) variables that mimic real-world data sets. Continuous variables are simulated using either Fleishman’s third-order or Headrick’s fifth-order power method transformation. Simulation occurs at the component level for continuous mixture distributions, and the target correlation matrix is specified in terms of correlations with components. However, the package contains functions to approximate expected correlations with continuous mixture variables. There are two simulation pathways which calculate intermediate correlations involving count variables differently, increasing accuracy under a wide range of parameters. The package also provides functions to calculate cumulants of continuous mixture distributions, check parameter inputs, calculate feasible correlation boundaries, and summarize and plot simulated variables. SimCorrMix is an important addition to existing R simulation packages because it is the first to include continuous mixture and zero-inflated count variables in correlated data sets

Integrated adaptive forecasting and energy aware scheduling model using genetic algorithm with dynamic stopping condition

Thesis (M.S.)-- Wichita State University, College of Engineering, Dept. of Industrial, Systems, and Manufacturing EngineeringAccording to Annual Energy Outlook 2022, published by the US Energy Information and Administration, manufacturing energy intensity is predicted to be reduced from 2018 to 2050. Despite near term uncertainty due to COVID-19 outbreak, this change can be attributed to the paradigm shift towards environmental sustainability and energy efficiency resulting in efforts by manufacturers to reduce costs, achieve carbon neutral status, and adhere to local, state, and international regulations. Manufacturers implement various demand side management projects for this purpose, one of them being energy efficient scheduling. The job shop scheduling model takes into consideration energy and demand costs, earliness and tardiness costs, worker costs, machine depreciation cost, and resource leveling for worker. Artificial Neural Network model is used to provide highly accurate prediction of energy prices and genetic algorithm is used to obtain job sequence and assignment in reasonable time for the job shop scheduling problem

Issues & Challenges in Genetic Analysis of Complex Disorders

The vast advances in molecular genetics over the last decade have opened new avenues to further explore the molecular basis of complex traits. The improvements in highthroughput genotyping techniques and accumulation of case-control or family–based data sets have allowed genome-wide screenings to identify genes associated/linked to disease susceptibility. The use of bi-allelic single nucleotide polymorphisms (SNPs) as markers for association/linkage studies has become more common due to their availability and high frequency throughout the genome. With the advent of new technology, it is possible to genotype about 1.6 million SNPs on whole genome on each individual using Illumina chip technology. However, this creates a new set of problems and challenges to analyze the data. In this talk, we will discuss issues and challenges those arise in analyzing the multidimensional data

Genetic risk scores associated with baseline lipoprotein subfraction concentrations do not associate with their responses to fenofibrate

Lipoprotein subclass concentrations are modifiable markers of cardiovascular disease risk. Fenofibrate is known to show beneficial effects on lipoprotein subclasses, but little is known about the role of genetics in mediating the responses of lipoprotein subclasses to fenofibrate. A recent genomewide association study (GWAS) associated several single nucleotide polymorphisms (SNPs) with lipoprotein measures, and validated these associations in two independent populations. We used this information to construct genetic risk scores (GRSs) for fasting lipoprotein measures at baseline (pre-fenofibrate), and aimed to examine whether these GRSs also associated with the responses of lipoproteins to fenofibrate. Fourteen lipoprotein subclass measures were assayed in 817 men and women before and after a three week fenofibrate trial. We set significance at a Bonferroni corrected alpha <0.05 (p < 0.004). Twelve subclass measures changed with fenofibrate administration (each p = 0.003 to <0.0001). Mixed linear models which controlled for age, sex, body mass index (BMI), smoking status, pedigree and study-center, revealed that GRSs were associated with eight baseline lipoprotein measures (p < 0.004), however no GRS was associated with fenofibrate response. These results suggest that the mechanisms for changes in lipoprotein subclass concentrations with fenofibrate treatment are not mediated by the genetic risk for fasting levels

A Method to Assess Linkage Disequilibrium between CNVs and SNPs Inside Copy Number Variable Regions

Since the discovery of the ubiquitous contribution of copy number variation to genetic variability, researchers have commonly used metrics such as r2 to quantify linkage disequilibrium (LD) between copy number variants (CNVs) and single nucleotide polymorphisms (SNPs). However, these reports have been restricted to SNPs outside copy number variable regions (CNVR) as current methods have not been adapted to account for SNPs displaying variable copy number. We show that traditional LD metrics inappropriately quantify SNP/CNV covariance when SNPs lie within CNVR. We derive a new method for measuring LD that solves this issue, and defaults to traditional metrics otherwise. Finally, we present a procedure to estimate CNV–SNP allele frequencies from unphased CNV–SNP genotypes. Our method allows researchers to include all SNPs in SNP/CNV LD measurements, regardless of copy number

STUDY ON EFFECT OF SELF-COMPACTING CONCRETE WITH PARTIAL REPLACEMENT OF MINERAL ADMIXTURES

This project is experimental investigation on self compacting concrete by using mineral additive such as Fly ash, Micro silica & Metakaolin. Self Compacting concrete is a concrete that exhibit the high flow ability and avoid the segregation and bleeding. The industrial waste such as fly ash use in this project as a partial replacement of cement to produce concrete, thus minimizes the amount of cement and reducing the cost.  Self−compacting concrete is one of "the most revolutionary developments" in concrete research; this concrete is able to flow and to fill the most restacked places of the form work without vibration. There are several methods for testing its properties in the fresh state: the most frequently used are Slump−flow test, L−box, U-box and V−funnel. This work presents properties of self−compacting concrete, mixed with different type’s additives: fly ash, micro silica, metakaolin. So we added admixture ac-hypercrete and ac-viscocrete about 0.5% and 0.2% of total cementatious content in every mix thereafter. The compressive strength carried in the compressive testing machine. The additions of fly ash were 20%, 25%, 30% and 35% of concrete. It was seen that increase the percentage of fly ash resulted in the decrease of compressive strength

Lipid changes due to fenofibrate treatment are not associated with changes in DNA methylation patterns in the GOLDN study

Fenofibrate lowers triglycerides (TG) and raises high density lipoprotein cholesterol (HDLc) in dyslipidemic individuals. Several studies have shown genetic variability in lipid responses to fenofibrate treatment. It is, however, not known whether epigenetic patterns are also correlated with the changes in lipids due to fenofibrate treatment. The present study was therefore undertaken to examine the changes in DNA methylation among the participants of Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study. A total of 443 individuals were studied for epigenome-wide changes in DNA methylation, assessed using the Illumina Infinium HumanMethylation450 array, before and after a 3-week daily treatment with 160 mg of fenofibrate. The association between the change in DNA methylation and changes in TG, HDLc, and low-density lipoprotein cholesterol (LDLc) were assessed using linear mixed models adjusted for age, sex, baseline lipids, and study center as fixed effects and family as a random effect. Changes in DNA methylation were not significantly associated with changes in TG, HDLc, or LDLc after 3 weeks of fenofibrate for any CpG. CpG changes in genes known to be involved in fenofibrate response, e.g., PPAR-α, APOA1, LPL, APOA5, APOC3, CETP, and APOB, also did not show evidence of association. In conclusion, changes in lipids in response to 3-week treatment with fenofibrate were not associated with changes in DNA methylation. Studies of longer duration may be required to detect treatment-induced changes in methylation