scholarly journals Genetic Variants in the SORL1 Gene Are Associated with Age at Onset of Alzheimer Disease: A Survival Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-6
Author(s):  
Ke-Sheng Wang ◽  
Xuefeng Liu ◽  
Liang Wang ◽  
David Briones ◽  
Chun Xu
Keyword(s):  
Survival Analysis ◽  
Alzheimer Disease ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Genetic Variants ◽  
Cox Regression ◽  
Cox Model ◽  
Age At Onset ◽  
Logrank Test

Few studies focused on the association of SORL1 with the age at onset (AAO) of Alzheimer disease (AD). This study investigated the association of 43 SNPs in SORL1 with the AAO of AD by using the Kaplan-Meier survival analysis and the Cox proportional hazards model in SAS version 9.2 and linear regression model in PLINK software (791 AD patients and 782 controls). Both logrank test and Cox regression model showed that five SNPs (rs1784934, rs676759, rs560573, rs593769, and rs11218313) were associated with the AAO of AD in the male sample, while one SNP (rs17125558) was associated with the AAO of AD in the female sample (P<0.05). SNP rs560573, previously associated with the risk of late-onset AD, showed the most association with the AAO in the male sample (P=0.0077 for logrank test and P=0.0117 in the Cox model). The mean AAO was approximately 2.5 years earlier in individuals who were homozygous for the minor allele compared with those who had at least one major allele. Linear regression model showed that rs2282649 and rs726601 were associated with AAO in the whole sample (P=0.0374 and 0.0367, resp.). These findings provide evidence of several genetic variants in SORL1 influencing the AAO of AD.

scholarly journals Genetic Variants in the Hedgehog Interacting Protein Gene Are Associated with the FEV1/FVC Ratio in Southern Han Chinese Subjects with Chronic Obstructive Pulmonary Disease

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Zili Zhang ◽  
Jian Wang ◽  
Zeguang Zheng ◽  
Xindong Chen ◽  
Xiansheng Zeng ◽  
...  
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Genetic Variants ◽  
Smoking Status ◽  
Statistical Significance ◽  
Significant Snps ◽  
Han Chinese ◽  
Chronic Obstructive ◽  
Risk Alleles

Background. Convincing evidences have demonstrated the associations between HHIP and FAM13a polymorphisms and COPD in non-Asian populations. Here genetic variants in HHIP and FAM13a were investigated in Southern Han Chinese COPD. Methods. A case-control study was conducted, including 989 cases and 999 controls. The associations between SNPs genotypes and COPD were performed by a logistic regression model; for SNPs and COPD-related phenotypes such as lung function, COPD severity, pack-year of smoking, and smoking status, a linear regression model was employed. Effects of risk alleles, genotypes, and haplotypes of the 3 significant SNPs in the HHIP gene on FEV1/FVC were also assessed in a linear regression model in COPD. Results. The mean FEV1/FVC% value was 46.8 in combined COPD population. None of the 8 selected SNPs apparently related to COPD susceptibility. However, three SNPs (rs12509311, rs13118928, and rs182859) in HHIP were associated significantly with the FEV1/FVC% (Pmax = 4.1 × 10−4) in COPD adjusting for gender, age, and smoking pack-years. Moreover, statistical significance between risk alleles and the FEV1/FVC% (P = 2.3 × 10−4), risk genotypes, and the FEV1/FVC% (P = 3.5 × 10−4) was also observed in COPD. Conclusions. Genetic variants in HHIP were related with FEV1/FVC in COPD. Significant relationships between risk alleles and risk genotypes and FEV1/FVC in COPD were also identified.

Physico-Chemical Analysis of Groundwater and Effect of Climatic Change in Angul-Talcher Area

2018 ◽  
Vol 6 (12) ◽  
pp. 26
Author(s):  
Aliva Bera ◽  
D.P. Satapathy
Keyword(s):  
Water Resources ◽  
Linear Regression ◽  
Regression Model ◽  
Climatic Change ◽  
Linear Regression Model ◽  
Ann Model ◽  
Quality Of Water ◽  
Physico Chemical ◽  
Simulation Based

In this paper, the linear regression model using ANN and the linear regression model using MS Excel were developed to estimate the physico-chemical concentrations in groundwater using pH, EC, TDS, TH, HCO3 as input parameters and Ca, Mg and K as output parameters. A comparison was made which indicated that ANN model had the better ability to estimate the physic-chemical concentrations in groundwater. An analytical survey along with simulation based tests for finding the climatic change and its effect on agriculture and water bodies in Angul-Talcher area is done. The various seasonal parameters such as pH, BOD, COD, TDS,TSS along with heavy elements like Pb, Cd, Zn, Cu, Fe, Mn concentration in water resources has been analyzed. For past 30 years rainfall data has been analyzed and water quality index values has been studied to find normal and abnormal quality of water resources and matlab based simulation has been done for performance analysis. All results has been analyzed and it is found that the condition is stable. 

Prediction of Surface Roughness and Optimization of Cutting Parameters in CNC Turning of Rotational Features

2020 ◽  
Vol 38 (8A) ◽  
pp. 1143-1153
Author(s):  
Yousif K. Shounia ◽  
Tahseen F. Abbas ◽  
Raed R. Shwaish
Keyword(s):  
Surface Roughness ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Feed Rate ◽  
Cutting Parameters ◽  
Spindle Speed ◽  
Depth Of Cut ◽  
Non Linear ◽  
Optimization Of Cutting Parameters

This research presents a model for prediction surface roughness in terms of process parameters in turning aluminum alloy 1200. The geometry to be machined has four rotational features: straight, taper, convex and concave, while a design of experiments was created through the Taguchi L25 orthogonal array experiments in minitab17 three factors with five Levels depth of cut (0.04, 0.06, 0.08, 0.10 and 0.12) mm, spindle speed (1200, 1400, 1600, 1800 and 2000) r.p.m and feed rate (60, 70, 80, 90 and 100) mm/min. A multiple non-linear regression model has been used which is a set of statistical extrapolation processes to estimate the relationships input variables and output which the surface roughness which prediction outside the range of the data. According to the non-linear regression model, the optimum surface roughness can be obtained at 1800 rpm of spindle speed, feed-rate of 80 mm/min and depth of cut 0.04 mm then the best surface roughness comes out to be 0.04 μm at tapper feature at depth of cut 0.01 mm and same spindle speed and feed rate pervious which gives the error of 3.23% at evolution equation.

An Enhanced Multiple Linear Regression Model for Seasonal Rainfall Prediction

2020 ◽  
Vol 10 (4) ◽  
pp. 473-483
Author(s):  
Pundra Chandra Shaker Reddy ◽  
Alladi Sureshbabu
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Weather Prediction ◽  
Big Data Analytics ◽  
Multiple Linear Regression Model ◽  
Seasonal Rainfall ◽  
Climate Data ◽  
Rainfall Forecasting

Aims & Background: India is a country which has exemplary climate circumstances comprising of different seasons and topographical conditions like high temperatures, cold atmosphere, and drought, heavy rainfall seasonal wise. These utmost varieties in climate make us exact weather prediction is a challenging task. Majority people of the country depend on agriculture. Farmers require climate information to decide the planting. Weather prediction turns into an orientation in farming sector to deciding the start of the planting season and furthermore quality and amount of their harvesting. One of the variables are influencing agriculture is rainfall. Objectives & Methods: The main goal of this project is early and proper rainfall forecasting, that helpful to people who live in regions which are inclined natural calamities such as floods and it helps agriculturists for decision making in their crop and water management using big data analytics which produces high in terms of profit and production for farmers. In this project, we proposed an advanced automated framework called Enhanced Multiple Linear Regression Model (EMLRM) with MapReduce algorithm and Hadoop file system. We used climate data from IMD (Indian Metrological Department, Hyderabad) in 1901 to 2002 period. Results: Our experimental outcomes demonstrate that the proposed model forecasting the rainfall with better accuracy compared with other existing models. Conclusion: The results of the analysis will help the farmers to adopt effective modeling approach by anticipating long-term seasonal rainfall.

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