Genomic selection methodologies and genome-wide association studies use powerful statistical procedures that correlate large amounts of high-density SNP genotypes and phenotypic data. Actual 305-d milk (MY), fat (FY), and protein (PY) yield data on 695 cows and 76,355 genotyping-by-sequencing-generated SNP marker genotypes from Canadian Holstein dairy cows were used to characterize linkage disequilibrium (LD) structure of Canadian Holstein cows. Also, the comparison of pedigree-based BLUP, genomic BLUP (GBLUP), and Bayesian (BayesB) statistical methods in the genomic selection methodologies and the comparison of Bayesian ridge regression and BayesB statistical methods in the genome-wide association studies were carried out for MY, FY, and PY. Results from LD analysis revealed that as marker distance decreases, LD increases through chromosomes. However, unexpected high peaks in LD were observed between marker pairs with larger marker distances on all chromosomes. The GBLUP and BayesB models resulted in similar heritability estimates through 10-fold cross-validation for MY and PY; however, the GBLUP model resulted in higher heritability estimates than BayesB model for FY. The predictive ability of GBLUP model was significantly lower than that of BayesB for MY, FY, and PY. Association analyses indicated that 28 high-effect markers and markers on Bos taurus autosome 14 located within 6 genes (DOP1B, TONSL, CPSF1, ADCK5, PARP10, and GRINA) associated significantly with FY.

This study assessed the predictive ability of genomic best linear unbiased prediction (GBLUP) and Bayesian regularization for feed-forward neural networks (BRNN-s1-s3-neuron) with one to three neurons using genomic relationship based on single nucleotide polymorphisms markers. Simulated and actual datasets were used to compare models and choose the better fit model. A five-generation simulated dataset consisted of 3,226 individuals with 10,031 single nucleotide polymorphism (SNP) were taken from the 14th QTL-MAS workshop. Actual mice dataset included body weights (BW) at the age of 6 weeks (g) obtained from 1904 animals genotyped at 10345 SNP loci (aa, Aa, and AA, genotypes were coded as 0, 1 and 2, respectively) and variables of gender of animal, month of birth, year of birth, coat color, cage density, litter. Predictive performance of GBLUP and BRNN-s1-s3-neuron models was investigated by examining the correlations from the cross-validation datasets. In the 14th QTL-MAS validation dataset, the correlations between the simulated true genetic and predicted phenotypic values were 0.607 for GBLUP model and 0.559, 0.353, and 0.288 for BRNN-s1-s3-neuron models. In the 10-fold cross-validation mice datasets, the overall predictive ability of models was low and average of the correlations were 0.419 for GBLUP, 0.336 for BRNN-s1, 0.256 for BRNN-s2, and 0.250 for BRNN-s3-neuron models. In this study, correlation results from the BRNN-s2 and BRNN-s3-neuron models indicated overfitting problem in training datasets as the number of neurons and parameters rises and this led to worse predictions in the validation datasets. The correlations from the GBLUP and BRNN-s1-s3-neuron models for the simulated and actual mice datasets indicated that there was no superiority of the BRNN-s1-s3-neuron models over the GBLUP model for predictive performance. The BRNN model with one neuron had less parameters and resulted in predictive performance similar with those from the GBLUP model.

The purpose of the present study was to estimate dimensional measure properties of T-shirts made up of Single Jersey and interlock fabrics through Artificial Neural Networks (ANN). To that end, 72 different types of t-shirts were manufactured under 2 different fabric groups, each was consisting of 2 groups: one with elastane and the other without. Each of these groups were manufactured from six different materials in three different densities through two different knitting techniques of single jersey and interlock. For estimation of dimensional changes in these T-shirts, models including feed-forward, back-propagated, the momentum learning rule and sigmoid transfer function were utilized. As a result of the present study, the ANN system was found to be successful in estimation of pattern measures of garments. The prediction of dimensional properties produced by the neural network model proved to be highly reliable (R2> 0.99).

A simulation study was carried out to determine the genomic prediction performance of Artificial Neural Network model with 1 to 10 neurons (ANN-1-10) using 3361 SNP markers from BovineSNP50 (Infinium BeadChip, Illumina, San Diego, CA) on the first chromosome of Brangus beef cattle as a pilot study for two traits with heritabilities of 25% (ℎ 1 2 = 0.25) and 50% (ℎ 2 2 = 0.5) determined either by 50, 100, 250 or 500 QTL selected randomly from SNP markers. QTL effects were sampled from a multivariate normal distribution. Genomic predictions were carried out by Feed Forward Multi-Layer Perceptron ANN-1-10 with the back-propagation of errors algorithm employing the Levenberg-Marquardt algorithm to locate the optimal solution. Three sets of SNP panels were used for genomic prediction: only QTL (Panel-1), all SNP markers, including the QTL (Panel-2), and all SNP, excluding the QTL (Panel-3). Correlations between true genetic merits (breeding values) and predicted phenotypes from 10-fold disjoint cross-validation were used to assess predictive ability of ANN-1-10. Results indicated that an increase in heritability resulted in an increased predictive performance of ANN-1-10 for all scenarios. SNP Panels had a greater chance of including markers in LD with QTL, allowed the possibility of predicting the effect of each QTL from the collective action of several markers and performed better than the Panel including only QTL. In the other Panels, predictive performance of ANN-1-10 increased inconsistently with the number of neurons, which indicated that a few numbers of neurons were not be enough to learn specification of data and could cause the under fitting problem. Therefore, high number of neurons could be needed to learn relevant details of the data in the applications of ANN.

ÖzTemel hava parametreleri olan sıcaklık, hava kalitesi indeksi ve ultraviyole indeksi insan sağlığını önemli derecede etkilemektedir. İnsan sağlığını birincil dereceden etkileyen bu faktörlere yönelik alınması gereken tedbirler için günümüz teknolojileri kullanılarak bilgilendirilme yapılması önemlidir. Bu çalışma kapsamında Aydın ilinde yaşayan insanların bu üç önemli parametre ile ilgili farkındalıklarını arttırma, onları bilgilendirme ve uyarma üzerine bir yazılım gerçekleştirilmiştir. Elde edilen veriler kullanıcıların erişebileceği bir web sitesinde gösterilmiş ve daha önceden belirlenmiş şartlara göre kullanıcılara bilgilendirme mesajı ve elektronik posta gönderilmesi gerçekleştirilmiştir. Bunun yanında Aydın ili için toplanan hava parametreleri yapay sinir ağı ile analiz edilerek ileriye dönük bu parametrelerin tahmin edilmesi amaçlanmıştır. Tahmin edilen ile gerçek veriler arasındaki ilişkinin analiz edilmesi sonucunda hava kalitesi indeksi ve scaklık tahmininde yüksek verimli sonuçlar verdiği gözlemlenmiştir. AbstractTemperature, air quality index and ultraviolet index, which are basic air parameters, affect human health significantly. It is important to inform people about the precautions to be taken for these factors affecting human health at the primary level using today's technology. In this study, a software was developed for increasing the awareness of the people living in Aydın and informing them about these three important parameters. The obtained data was showed in an accessible website for the users and an informing message and an electronic mail are sent to the users according to predetermined conditions. Besides, air parameters, which were collected for Aydin province, are analyzed with artificial neural network for predicting of forward-looking these parameters. The result of analysis of the relationship between the predicted and real data has been observed to yield highly efficient results in the prediction of air quality index and temperature.