The Genotypes of α-Thalassemia and Genotypes Frequencies of α- Thalassemia in Western Aegean Region

Yaygın görülen bir tek gen hastalığı olan Alfa talasemi, α-globin zincirinin kusurlu sentezi ile ortaya çıkar. Globin genlerindeki bozukluklara bağlı olarak çok geniş bir klinik spektruma yayılan bu hastalıkta çok sayıda belirlenmemiş taşıyıcı olduğu düşünülmektedir. Klinik semptomu olmayan sessiz taşıyıcılardan, rahim içinde ölüme yol açan şiddetli anemi ile kendini gösteren, çok değişken bulgulara sahip genetik bir hastalıktır. Bu çalışmada bu amaçla alfa globin gen mutasyonu sıklığının ve tiplerinin bulunması ve varyasyon saptanan bireylerdeki fenotipik etkiyi görmek amaçlandı. Gereç ve Yöntemler: HBA1 ve HBA2 genlerindeki intron bölgelerini çevreleyen tüm kodlama bölgesi sanger dizileme ile tespit edildi. Delesyonlar ve duplikasyonlar multipleks ligasyona bağımlı prob amplifikasyonu (MLPA) ile mutasyonlar tespit edildi. Bulgular: Bölgemizde en sık rastlanan mutasyon tipi olan -3,7 / (%23,18), 3.7 kb’lık delesyon çalışmamızda da en sık olarak görülürken, diğer mutasyonların dağılımı ise --3,7 (%6,82), -3,7/-- MED (%0,91), --MED (%6,82), --20,5 (3,15), --SEA (%1,36), -4,2 (%0,95), triplikasyon (%0,45) ve nükleotid değişimleri (%4,55) olarak tespit edilmiştir. Sonuç: Mevcut bilgiler ışığında genotipin fenotipe yansımasının da farklılıklar olması nedeniyle taşıyıcı bireylerin tesbit edilmesi ve genotip fenotip ilişkisinin netleştirilmesi açısından daha geniş popülasyon taramasına ihtiyaç duyulmaktadır. Toplumu alfa talasemi ve ağır klinik seyreden genetik hastalıklar hakkında bilinçlendirmek için taşıyıcı bireylere genetik danışmanlık verilmesi ve genetik çalışmalara ağırlık verilmesi bir gerekliliktir.Alpha-thalassemia, a common single gene disorder, is caused by defective synthesis of the α-globin chain. It is thought to have a wide clinical spectrum due to defects in globin genes and a large number of indeterminate carriers. It is a genetic disease with highly variable findings ranging from silent carriers with no clinical symptoms to severe anemia leading to in utero death. In this study, we aimed to determine the frequency and types of alpha globin gene mutations and to observe the phenotypic effect in individuals with mutations. Material and Methods: The coding and intron regions of HBA1 and HBA2 genes were determined by Sanger sequencing. Deletions and duplications were detected by multiplex ligation-dependent probe amplification (MLPA). Results: This research shows that -3,7 / (%23.18),3.7 kb is the most common mutation type in our deletion research/analysis, and distribution of other mutations is as follows: --3,7 (%6.82), -3,7 / -- MED (%0,91), --MED (%6.82), --20,5 (3.18), --SEA (%1.36), -4,2 (%0.95). This research also demonstrates that triplication is at %0.45 and nucleotide mutation is %4.55. Conclusion: In the light of the available information, since there are differences in the reflection of genotype to phenotype, a larger population screening is needed to identify carrier individuals and to clarify the genotype-phenotype relationship. In order to raise public awareness about alpha thalassemia and genetic diseases with severe clinical course, it is a necessity to provide genetic counseling to carrier individuals and to focus on genetic studies

İlinti temelli uyarlanır rezonans kuramı kullanarak sıradüzensel davranış sınıflandırma.

This thesis introduces a novel behavior categorization model that can be used for behavior recognition and learning. Correlation Based Adaptive Resonance Theory (CobART) network, which is a kind of self organizing and unsupervised competitive neural network, is developed for this purpose. CobART uses correlation analysis methods for category matching. It has modular and simple architecture. It can be adapted to different categorization tasks by changing the correlation analysis methods used when needed. CobART networks are integrated hierarchically for an adequate categorization of behaviors. The hierarchical model is developed by adding a second layer CobART network on top of first layer networks. The first layer CobART networks categorize self behavior data of a robot or an object in the environment. The second layer CobART network receives first layer CobART network categories as an input, and categorizes them to elicit the robot's behavior with respect to its effect on the object. Besides, the second layer network back-propagates the matching information to the first layer networks in order to find the relation between the first layer categories. The performance of the hierarchical model is compared with that of different neural network based models. Experiments show that the proposed model generates reasonable categorization of behaviors being tested. Moreover, it can learn different forms of the behaviors, and it can detect the relations between them. In essence, the model has an expandable architecture and it contains reusable parts. The first layer CobART networks can be integrated with other CobART networks for another categorization task. Hence, the model presents a way to reveal all behaviors performed by the robot at the same time.Ph.D. - Doctoral Progra

Behavior Categorization Using Correlation Based Adaptive Resonance Theory

This paper presents a new method of categorizing robot behavior, which is based on a variation of Correlation Based Adaptive Resonance Theory (CobART) learning. CobART is a type of ART 2 network and its main contribution is the usage of correlation analysis methods for category matching. This study uses derivation based correspondence and Euclidian distance as correlation analysis methods for behavior categorization. Tests show that the proposed method generates better results than ART 2 categorization even when a priori SOM (Self-Organizing Map) categorization is combined with ART 2 categorization

Hierarchical behavior categorization using correlation based adaptive resonance theory

This paper introduces a new model for robot behavior categorization. Correlation based adaptive resonance theory (CobART) networks are integrated hierarchically in order to develop an adequate categorization, and to elicit various behaviors performed by the robot. The proposed model is developed by adding a second layer CobART network which receives first layer CobART network categories as an input, and back-propagates the matching information to the first layer networks. The first layer CobART networks categorize self-behavior data of a robot or an object in the environment while the second layer CobART network categorizes the robot's behavior with respect to its effect on the object. Experiments show that the proposed model generates reasonable categorization of behaviors being tested. Moreover, it can learn different forms of the behaviors, and it can detect the relations between them. In essence, the model has an expandable architecture and it contains reusable parts. The first layer CobART networks can be integrated with other CobART networks for another categorization task. Hence, the model presents a way to reveal all behaviors performed by the robot at the same time

CobART: Correlation Based Adaptive Resonance Theory

This paper introduces a new type of ART 2 network that performs satisfactory categorization for a domain where the patterns are constructed from consecutive analog inputs. The main contribution relies on the correlation analysis methods used for category-matching. The resulting network model is named as Correlation Based Adaptive Resonance Theory (CobART). Correlation waveform analysis and Euclidian distance methods are used to elicit correlation between the learned categories and the data fed to the network