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Institution

Zonguldak Karaelmas University

About: Zonguldak Karaelmas University is a based out in . It is known for research contribution in the topics: Population & Copolymer. The organization has 1939 authors who have published 4296 publications receiving 62466 citations.


Papers
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Journal ArticleDOI
13 Nov 2014-Nature
TL;DR: It is estimated that LGD mutation in about 400 genes can contribute to the joint class of affected females and males of lower IQ, with an overlapping and similar number of genes vulnerable to contributory missense mutation.
Abstract: Whole exome sequencing has proven to be a powerful tool for understanding the genetic architecture of human disease. Here we apply it to more than 2,500 simplex families, each having a child with an autistic spectrum disorder. By comparing affected to unaffected siblings, we show that 13% of de novo missense mutations and 43% of de novo likely gene-disrupting (LGD) mutations contribute to 12% and 9% of diagnoses, respectively. Including copy number variants, coding de novo mutations contribute to about 30% of all simplex and 45% of female diagnoses. Almost all LGD mutations occur opposite wild-type alleles. LGD targets in affected females significantly overlap the targets in males of lower intelligence quotient (IQ), but neither overlaps significantly with targets in males of higher IQ. We estimate that LGD mutation in about 400 genes can contribute to the joint class of affected females and males of lower IQ, with an overlapping and similar number of genes vulnerable to contributory missense mutation. LGD targets in the joint class overlap with published targets for intellectual disability and schizophrenia, and are enriched for chromatin modifiers, FMRP-associated genes and embryonically expressed genes. Most of the significance for the latter comes from affected females.

2,124 citations

Journal ArticleDOI
TL;DR: Five pre-trained convolutional neural network-based models have been proposed for the detection of coronavirus pneumonia-infected patient using chest X-ray radiographs and it has been seen that the pre- trained ResNet50 model provides the highest classification performance.
Abstract: The 2019 novel coronavirus disease (COVID-19), with a starting point in China, has spread rapidly among people living in other countries, and is approaching approximately 34,986,502 cases worldwide according to the statistics of European Centre for Disease Prevention and Control. There are a limited number of COVID-19 test kits available in hospitals due to the increasing cases daily. Therefore, it is necessary to implement an automatic detection system as a quick alternative diagnosis option to prevent COVID-19 spreading among people. In this study, five pre-trained convolutional neural network based models (ResNet50, ResNet101, ResNet152, InceptionV3 and Inception-ResNetV2) have been proposed for the detection of coronavirus pneumonia infected patient using chest X-ray radiographs. We have implemented three different binary classifications with four classes (COVID-19, normal (healthy), viral pneumonia and bacterial pneumonia) by using 5-fold cross validation. Considering the performance results obtained, it has seen that the pre-trained ResNet50 model provides the highest classification performance (96.1% accuracy for Dataset-1, 99.5% accuracy for Dataset-2 and 99.7% accuracy for Dataset-3) among other four used models.

1,040 citations

Journal ArticleDOI
TL;DR: It is suggested that QE treatment has protective effect in diabetes by decreasing oxidative stress and preservation of pancreatic beta-cell integrity.

782 citations

Journal ArticleDOI
TL;DR: In this paper, five pre-trained convolutional neural network-based models were proposed for the detection of coronavirus pneumonia-infected patient using chest X-ray radiographs.
Abstract: The 2019 novel coronavirus disease (COVID-19), with a starting point in China, has spread rapidly among people living in other countries and is approaching approximately 101,917,147 cases worldwide according to the statistics of World Health Organization. There are a limited number of COVID-19 test kits available in hospitals due to the increasing cases daily. Therefore, it is necessary to implement an automatic detection system as a quick alternative diagnosis option to prevent COVID-19 spreading among people. In this study, five pre-trained convolutional neural network-based models (ResNet50, ResNet101, ResNet152, InceptionV3 and Inception-ResNetV2) have been proposed for the detection of coronavirus pneumonia-infected patient using chest X-ray radiographs. We have implemented three different binary classifications with four classes (COVID-19, normal (healthy), viral pneumonia and bacterial pneumonia) by using five-fold cross-validation. Considering the performance results obtained, it has been seen that the pre-trained ResNet50 model provides the highest classification performance (96.1% accuracy for Dataset-1, 99.5% accuracy for Dataset-2 and 99.7% accuracy for Dataset-3) among other four used models.

769 citations

Journal ArticleDOI
TL;DR: In this paper, the effects of Poisson's ratio in the elastic deformation of materials, intact rocks, and rock masses are briefly reviewed, and the reported values of POI for some elements, materials, and minerals are compiled while typical ranges of values are presented for some rocks and granular soils.

669 citations


Authors

Showing all 1939 results

NameH-indexPapersCitations
Ramón Martínez-Máñez7354924257
Roy L. Johnston5529013604
Riccardo Ferrando5025613688
Alessandro Fortunelli472779080
Levent Altinay441555164
Mehmet Kanter401486045
Shuanggen Jin403745024
Chandra M. Sehgal392075270
Giovanni Barcaro361323778
Baki Hazer361944420
Ferah Armutcu33653630
Ahmet Gürel33983525
Christine Mottet31614108
Michael P. Shaver301143014
Ahmet Avcı291903087
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20228
2021383
2020411
2019305
2018256
2017280