Waning immunity to inactive SARS-CoV-2 vaccine in healthcare workers: Booster required

Aims Despite high vaccination rates, increasing case numbers continue to be reported with the identification of new variants of concern, and the issue of durability of the vaccine-induced immune response remains hot topic. Real-life data regarding time-dependent immunogenicity of inactivated COVID-19 vaccines are scarce. We aimed to investigate the changes in the antibody at the different times after the second dose of the CoronaVac vaccine. Methods The study included 175 HCWs vaccinated with inactive CoronaVac (Sinovac Life Sciences, China) SARS-CoV-2 vaccine in two doses. Anti-spike/RBD IgG levels were measured first, third, and sixth months after the second dose. Chemiluminescent microparticle immunoassay (IgG II Quant test, Abbott, USA), which is 100% compatible with plaque reduction neutralization test, was used. Results Mean age of the participants was 38 +/- 11.23 years (range between 22 and 66) of whom 119 (63.9%) were female, and 56 (32%) were male. Dramatic reductions were demonstrated in median antibody levels particularly in the infection-naive group, comprising 138 HCWs compared to those with prior history of COVID-19 infection (n = 37) (p < 0.001). There was no difference between the two groups in terms of age, gender, blood groups, BMI, and comorbid diseases. Conclusions While antibody positivity remained above 90% in the 6th month after two doses of inactivated vaccine in HCWs, the median titers of neutralizing antibodies decreased rapidly. The decrease was more rapid and significant in those with no history of prior COVID-19 infection. In this critical phase of the pandemic, where we are facing the dominance of the Omicron variant after Delta, booster doses have become vital.IU-Cerrahpasa Scientific Research Projects Uni

Servikal kökenli baş ağrıları ile ganglion spinale II ilişkisinin anatomik incelemesi

ÖZET Bu çalışma ganglion spinale IFnin anatomik yapısını ve komşuluklarını tanımlamak ve servikal kökenli baş ağrılarında oynadığı rolü açıklığa kavuşturabilmek amacıyla yapılmıştır. Ege Üniversitesi Tıp Fakültesi Anatomi Anabilim Dalı bünyesinde, 20 kadavrada toplam 40 ganglion üzerinde yapılan ölçümler değerlendirilmiştir. Disseksiyonlann çeşitli aşamalarında 15x mercekli Bausch ; Lomb (0,7x - 3x) disseksiyon mikroskobu kullanılmıştır. Ölçüm işlemi sırasında plastik hamur yönteminden yararlanılmıştır. Yapılan ölçümler sağ ve sol tarafta ganglion spinale IFnin vertikal yüksekliği ve arcus posterior atlantis ile lamina axis arasındaki vertikal mesafeyi içermektedir. Arcus posterior atlantis ile lamina axis arasındaki mesafe hem nötral pozisyonda hem de hiperekstensiyon ile beraber karşı tarafa rotasyon pozisyonlarında ayrı ayrı ölçülmüştür. Bu ölçümlere göre ganglion spinale IFnin vertikal yüksekliği sağ tarafta 4,97 ± 0,92 mm, sol tarafta 4,6 ± 0,84 mm dir. Normal anatomik pozisyonda (nötral) arcus posterior atlantis ile lamina axis arasındaki mesafe sağ tarafta 9,74 ± 1,77 mm, sol tarafta 9,64 ± 1,47 mm iken, hiperekstensiyon ile beraber karşıtarafa rotasyon pozisyonunda sağ taraftaki mesafe 7,48 ±144 mm, sol taraftaki mesafe 7,12 ± 0,96 mm olarak bulunmuştur. Bu ölçümler sonucunda arcus posterior atlantis ile lamina axis arasındaki mesafenin en az olduğu hiperekstensiyon +ile beraber karşı tarafa rotasyon pozisyonunda dahi ganglion spinale II için yeterli alan bulunmaktadır. Yani ganglion spinale II'ye herhangi bir kemik yapı temas etmemekte veya bası yapmamaktadır. Servikal kökenli başağrılarmın çeşitli etiyoloj ilerinden ve bu konularda yapılmış olan çalışmalardan da metin içerisinde söz edilmiştir. 4

Tarihsel boyutta Avustralya’daki Türk diasporası

ÖZETTARİHSEL BOYUTTA AVUSTRALYA'DAKİ TÜRK DİASPORASI (1950 – 2004) Avustralya’daki Türk Diasporasının Tarihsel Varlığı (1950-2004) adlı bu çalışmada, Avustralya’daki Türk Toplumu’nun 19. Yüzyılın ikinci yarısından 20. Yüzyılın sonuna kadar olan tarihsel süreci incelenmektedir. 19. Yüzyılın ikinci yarısında ağırlıklı olarak bireysel göçlerle oluşmaya başlayan Avustralya Türk Toplumu, 1950’li yıllara kadar Avustralya coğrafyasında kendini yeterli ölçüde gösterememiştir. 1950’li yıllarda Kıbrıs Türkleri’nin Avustralya’ya göçlerinin başlaması, bu süreci takiben Bulgaristan’dan Batı Trakya’dan ve 5 Ekim 1967 tarihinde Türkiye ile Avustralya arasında imzalanan Türklerin Avustralya’da İkamet ve Çalışmaları Hakkında Türkiye Cumhuriyeti Hükümeti ile Avustralya Milletler Topluluğu Hükümeti Arasında Antlaşma adlı antlaşma ile Türkiye’den Avustralya’ya gerçekleşen göçler Avustralya’daki Türk Diasporası’nı oluşturmaktadır. 1970’li yıllar Avustralya Türk Diasporası’nın her anlamda kurumsallaşmaya başladığı yıllardır. 2000’li yıllara uzanan süreçte okyanus ötesi yurttaşlık kimliği kazanan Avustralya Türk Diasporası, ekonomik, sosyal, dini ve kültürel yönleriyle kurumsallaşmıştır. Bu çalışma, bireysel göçmenlikten okyanus ötesi toplumsal yapıya uzanan Avustralya Türk Diasporası’nın tarihsel süreci ele almamıştır. ABSTRACTAT THE HISTORICAL DIMENSION OF THE TURKISH DIASPORA IN AUSTRALIA (1950-2004) This study titled At The Historical Dimension of the Turkish Diaspora in Australia (1950-2004) examines the history of the Turkish community in Commonwealth of Australia spanning from the second half of the 19th Century until the end of the 20th Century. Beginning to evolve in the second half of the 19th Century mainly through individual migration, the Australian Turkish Community could not have been able to make its presence as a community felt until 1950s. With the beginning of migration of the Cyprus Turks to the Australia in 1950s, and subsequently from Bulgaria and Western Thrace, and enactment of the Bilateral Migration and Settlement Agreement (Agreement Between the Government of The Commonwealth of Australia and Government of The Republic of Turkey Concerning the Resident and Employment of Turkish Citizens in Australia) between the Turkish Republic and the Commonwealth Australia for settlement and employment of the Turkish nationality migrants in Australia on October 5th 1967, the Turkish Diaspora in Australia has been created. The 1970s are the years when the Turkish Diaspora in Australia has become institutionalized in all respects. In the period stretching to 2000 the Australian Turkish Diaspora having gained the identity of trans-oceanic nationality, has become institutionalized in economical, social, religious and cultural aspects. This study attempts to investigate the chronicle of the Australian Turkish Diaspora starting from individual migration to grow into a full community structure

Hiperspektral görüntülerde optimizasyon ve derin öğrenme tabanlı çok modelli bolluk tahmini ve ayrıştırma algoritmaları

Hyperspectral unmixing aims to identify the materials within the pixels of an image and estimate the corresponding abundance values of these materials. This thesis proposes an optimizationbased abundance estimation method for the case where the spectral signatures of the materials are available, and a deep learning based hyperspectral unmixing method for the case where the spectral signatures of the materials are unavailable. The proposed abundance estimation algorithm assumes that real data can contain complex interactions that cannot be modeled with a single model, and therefore,use multiple mixing models for determining the abundance of real data. The proposed optimization-based coarse-to-fine estimation algorithm first adopts a linear mixing model for the tested pixel until the error between the reconstructed and original pixel is smaller than a threshold. The algorithm then proceeds by integrating the other nonlinear mixing modelsto the cost function. Among various utilized optimization algorithms and metrics, the proposed solution with the sequential quadratic programming and spectral angle mapper combination is found more successful than other search methods and baseline algorithms. As the second contribution of this thesis, a new 3D convolutional encoder based deep learning method is proposed for hyperspectral unmixing by observing that the local neighborhood information is not sufficiently used for the unmixing problem in hyperspectral images. Given that nonlinear mixing has not been adequately covered in deep learning based hyperspectral unmixing literature, the proposed method is especially designed to solve thenonlinear mixture models with the 3D convolutional encoder structure. The proposed method gives better performance than the well-known pure material extraction and abundance detection algorithms on synthetic and real dataHiperspektralayrıştırma, görüntünün içindeki malzemeleri tanımlamayı ve bu malzemelerekarşılık gelen bolluk değerlerini tahmin etmeyi amaçlamaktadır. Bu tez, malzemelerin spektral imzalarının mevcut olduğu durum için optimizasyona dayalı bir bolluk tahmin yöntemi ve malzemelerin spektral imzalarının olmadığı durumlar için derin öğrenme tabanlı bir hiperspektral ayrıştırma yöntemi önermektedir.İlk çalışmada sunulan bolluk tespit algoritması gerçek verilerin tek bir modelle ifade edilemeyecek kadar karmaşık etkileşimler içerebilmesi varsayımına dayanmaktadır. Bu nedenle, gerçek verilerde bolluk tespiti yapılırken çoklu model kullanılması hedeflenmiştir. Önerilen optimizasyon tabanlı bolluk tespit algoritması, hedef pikseleyakın bir hata oranına ulaşılana kadar doğrusal karışım modelini varsayan bir yaklaşımı benimser. Optimizasyon algoritması daha sonra maliyet fonksiyonunu, olası karışım modelleri için yeniden tanımlayarak işleme devam eder. Kullanılan çeşitli optimizasyon algoritmaları ve uzaklık metrikleriarasında, sıralı ikinci dereceden programlama ve spektral açı haritalama kombinasyonu ile önerilen çözüm, diğer arama yöntemleri ve temel algoritmalardan daha başarılı bulunmuştur. Bu tezin ikinci katkısı olarak, hiperspektral görüntülerde komşuluk bilgisinin ayrıştırma problemi için yeterince kullanılmadığı gözlemlenerek hiperspektral ayrıştırmaiçin yeni bir 3 boyutlu evrişimli kodlayıcı tabanlı derin öğrenme yöntemi önerilmiştir. Doğrusal olmayan karıştırmanın daha önce sunulmuş derin öğrenme tabanlı hiperspektral ayrıştırma çalışmalarında yeterince ele alınmadığı göz önüne alındığında, önerilen yöntem doğrusal olmayan karışım modellerini 3D evrişimli kodlayıcı yapısıyla çözmek için tasarlanmıştır. Önerilen yöntem, sentetik ve gerçek veriler üzerinde iyi bilinen saf malzeme çıkarma ve bolluk tahmini algoritmalarından daha iyi performans göstermiştir.Ph.D. - Doctoral Progra

Uzaktan algılama için hiperspektral imge sınıflandırıcıları üzerine bir inceleme.

Hyperspectral image processing is improved by the capabilities of multispectral image processing with high spectral resolution. In this thesis, we explored hyperspectral classification with Support Vector Machines (SVM), Maximum Likelihood (ML) and KNearest Neighborhood algorithms. We analyzed the effect of training data on classification accuracy. For this purpose, we implemented three different training data selection methods; first N sample selection, randomly N sample selection and uniformly N sample selection methods. We employed Principal Component Analysis (PCA) as preprocessing method and conducted experiments with different number of principal components for all three classification algorithms. As a post-processing method following pixelwise classification, filtering with 3x3 window and majority voting with meanshift segmentation methods are used to incorporate spatial information over spectral information. The experiments showed that without using pre-processing and post-processing SVM procures better classification accuracies than the other algorithms for all training data sizes. ML is inferior for lower number of training data samples but improves its performance with lower number of principal components. K-NN algorithm provides almost the same accuracies for more than 10 principal components. PCA usage does not improve SVM performance but decreases classification time for larger scenes. Filtering with 3x3 window method improves the classification accuracy by 4-5%. However, spatial information usage by employing majority voting with meanshift segmentation method performs better than filtering 3x3 window. Classification with both pre-processing and post-processing improves classification accuracy and decreases classification time. The largest improvement is for the ML method with lower number of training data.M.S. - Master of Scienc

Fireproof plastinates

Plastination is a method that has grown in popularity over time, used for the preparation of educational and exhibition sam- ples. We have created many and various anatomical samples to be used in the education of our students in the plastination laboratory we have established since 2010. In this Article, it is aimed to explain how our 10-year plastination collection was affected by the fire that broke out in our laboratory building in June 2020 and to bring this information to the literatu

Improved Hyperspectral Vegetation Detection Using Neural Networks with Spectral Angle Mapper

Hyperspectral images have been used in many areas including city planning, mining and military decision support systems. Hyperspectral image analysis techniques have a great potential for vegetation detection and classification with their capability to identify the spectral differences across the electromagnetic spectrum due to their ability to provide information about the chemical compositions of materials. This study introduces a vegetation detection method employing Artificial Neural Network (ANN) over hyperspectral imaging. The algorithm employed backpropagation MLP algorithm for training neural networks. The performance of ANN is improved by the joint use with Spectral Angle Mapper (SAM). The algorithm first obtains the certainty measure from ANN, following the completion of this process, every pixels' angular distance is computed by SAM. The certainty measure is divided by angular distance. Results from ANN, SAM and Support Vector Machine (SVM) algorithms are compared and evaluated with the result of the algorithm. Limited number of training samples are used for training. The results demonstrate that joint use of ANN and SAM significantly improves classification accuracy for smaller training sample

3D-CNN and Autoencoder-Based Gas Detection in Hyperspectral Images

The detection of gas emission levels is a crucial problem for ecology and human health. Hyperspectral image analysis offers many advantages over traditional gas detection systems with its detection capability from safe distances. Observing that the existing hyperspectral gas detection methods in the thermal range neglect the fact that the captured radiance in the longwave infrared (LWIR) spectrum is better modeled as a mixture of the radiance of background and target gases, we propose a deep learning-based hyperspectral gas detection method in this article, which combines unmixing and classification. The proposed method first converts the radiance data to luminance-temperature data. Then, a 3-D convolutional neural network (CNN) and autoencoder-based network, which is specially designed for unmixing, is applied to the resulting data to acquire abundances and endmembers for each pixel. Finally, the detection is achieved by a three-layer fully connected network to detect the target gases at each pixel based on the extracted endmember spectra and abundance values. The superior performance of the proposed method with respect to the conventional hyperspectral gas detection methods using spectral angle mapper and adaptive cosine estimator is verified with LWIR hyperspectral images including methane and sulfur dioxide gases. In addition, the ablation study with respect to different combinations of the proposed structure including direct classification and unmixing methods has revealed the contribution of the proposed system