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Hiperspektral görüntü sınıflandırma, uzaktan algılanan görüntülerin analizi için yaygın olarak kullanılmaktadır. Bir hiperspektral görüntü, uygulamalarda büyük potansiyele sahip olan yer nesnelerinin zengin spektral bilgilerini ve uzamsal bilgilerini içermektedir. Spektral uzamsal bilgi kullanımı hiperspektral görüntü sınıflandırmasının performansını önemli ölçüde arttırmaktadır. Hiperspektral görüntüler, 3B küpler biçiminde gösterilmektedir. Bu nedenle, 3B uzamsal filtreleme, bu tür görüntülerdeki spektral uzamsal özellikleri eşzamanlı olarak çıkarmak için doğal olarak basit ve etkili bir yöntem sunmaktadır. Bu çalışmada, hiperspektral görüntü sınıflandırması için bir 3B evrişimli sinir ağı (3B ESA) yöntemi önerilmiştir. Önerilen yöntem, derin spektral uzamsal birleştirilmiş özellikleri etkin bir şekilde çıkarmaktadır. Aynı zamanda herhangi bir ön işleme veya son işleme dayanmadan hiperspektral görüntü küpü verileri toplu olarak görüntülemektedir. Hiperspektral görüntü küpü önce küçük üst üste binen 3B parçalara bölünmektedir. Daha sonra bu parçalar, spektral bilgileri de koruyan birden çok bitişik bant üzerinde bir 3B çekirdek işlevi kullanarak 3B özellik haritaları oluşturmak için işlenmektedir. Önerilen yöntem indian pines, pavia üniversitesi ve botswana veri setleri ile test edilmiştir. Deneysel çalışmalar sonucunda, indian pines için %99,35, pavia üniversitesi için %99,90 ve botswana için ise %99,59 genel doğruluk sonuçları elde edilmiştir. Sonuçlar, 4 farklı derin öğrenme tabanlı yöntemle karşılaştırılmıştır. Deneysel sonuçlardan, önerilen 3B ESA yöntemimizin daha iyi performans gösterdiği görülmektedir.

Convolutional neural networks (CNNs) are one of the popular deep learning methods used to solve the hyperspectral image classification (HSIC) problem. CNN has a strong feature learning ability that can ensure more distinctive features for higher quality HSIC. The traditional CNN-based methods mainly use the 2D CNN for HSIC. However, with 2D CNN, only spatial features are extracted in HSI. Good feature maps cannot be extracted from spectral dimensions with the use of 2D CNN alone. By using 3D CNN, spatial-spectral features are extracted simultaneously. However, 3D CNN is computationally complex. In this study, a hybrid CNN method, which is a combination of 3D CNN and 2D CNN, is improved to solve the two problems described above. Using hybrid CNN decreases the complexity of the method compared to using only 3D CNN and can perform well against a limited number of training samples. On the other hand, in Hybrid CNN, depthwise separable convolution (DSC) is used, which decreases computational cost, prevents overfitting and enables more spatial feature extraction. By adding DSC to the developed hybrid CNN, a hybrid depthwise separable convolutional neural network is obtained. Extensive applications on frequently used HSI benchmark datasets show that the classification performance of the proposed network is better than compared methods.

Hyperspectral image classification is widely used in the analysis of remote sensing images. Recently, deep learning has been seen as the most effective method for hyperspectral image classification. Especially, Convolutional neural networks (CNN) are getting more and more attention in this field. CNN provides automated approaches that can learn more abstract features of hyperspectral images from spectral, spatial or spectral-spatial fields. In this study, a 3D CNN based MiniVGGNet network is proposed to take full advantage of the relationships between hyperspectral features and to increase the classification accuracy. With 3D CNN, spectral-spatial features are extracted simultaneously. With MiniVGGNet, the number of trainable parameters is reduced and the training time is shortened. In addition, principal component analysis (PCA) is used as a preprocessing method to reduce the computational complexity caused by the high dimensionality of hyperspectral images. In order to test the performance of the proposed method, applications were performed on remote sensing datasets of Indian Pines, University of Pavia and Salinas. The results were compared with different deep learning-based methods. Better classification performance is obtained by using the proposed method for hyperspectral image classification., Hiperspektral görüntü sınıflandırma uzaktan algılanan görüntülerin analizinde yaygın olarak kullanılmaktadır. Son zamanlarda, derin öğrenme hiperspektral görüntü sınıflandırmasında en etkili yöntem olarak görülmektedir. Özellikle evrişimsel sinir ağları (ESA) bu alanda giderek daha fazla ilgi görmektedir. ESA, spektral, uzamsal veya spektral-uzamsal alanlardan hiperspektral görüntülerin daha soyut özelliklerini öğrenebilen otomatik yaklaşımlar sağlamaktadır. Bu çalışma kapsamında, hiperspektral özellikler arasındaki ilişkilerden tam olarak yararlanmak ve sınıflandırma doğruluğunu arttırmak için 3B ESA tabanlı MiniVGGNet ağı önerilmektedir. 3B ESA ile spektral-uzamsal özellikler eş zamanlı olarak çıkarılmaktadır. MiniVGGNet ile de eğitilebilir parametre sayısı azaltılmakta ve eğitim süresi kısaltılmaktadır. Ayrıca, hiperspektral görüntülerin yüksek boyutluluğundan kaynaklanan hesaplama karmaşıklığını azaltmak için ön işleme yöntemi olarak temel bileşen analizi kullanılmaktadır. Önerilen yöntemin performansını test etmek için Indian Pines, Pavia Üniversitesi ve Salinas uzaktan algılama veri kümeleri üzerinde uygulamalar gerçekleştirilmiştir. Sonuçlar, farklı derin öğrenme tabanlı yöntemlerle karşılaştırılmıştır. Hiperspektral görüntü sınıflandırması için önerilen yöntem kullanılarak daha iyi sınıflandırma performansı elde edilmiştir.

Hiperspektral görüntüler (HG), iki uzamsal ve bir spektral boyuta sahip 3 boyutlu (3B) görüntü küpleridir. Derin öğrenme yöntemlerinin gelişimi, HG sınıflandırmada önemli bir etki oluşturmuştur. Özellikle evrişimsel sinir ağı (ESA) tabanlı yöntemler bu alanda daha fazla ilgi görmektedir. Bu çalışmada, HG sınıflandırma problemi için ESA’nın başarılı örnekleri arasında olan LeNet5, AlexNet, VGG16, GoogleNet ve ResNet50 derin öğrenme mimarilerinden yararlanıyoruz. Bu mimarileri kullanırken 3B ESA tabanlı hibrit bir yaklaşım kullanmaktayız. Çünkü, 3B ESA kullanılarak, spektral-uzamsal özellikler eş zamanlı olarak çıkarılmaktadır. Bu durumda elde edilen spektral-uzamsal tabanlı derin öğrenme mimarisi ile HG’lerin sınıflandırma doğruluğu arttırılmaktadır. Bununla birlikte, önerilen modelde, HG'lerden optimal bant çıkarımı için bir ön işleme tekniği olarak temel bileşen analizi (TBA) kullanılmaktadır. TBA uygulandıktan sonra komşuluk çıkarımı ile 3B küpler elde edilmekte ve derin öğrenme mimarilerinin girişine verilmektedir. 3B ESA tabanlı derin öğrenme mimarilerinin sınıflandırma performanslarını karşılaştırmak için Indian pines, Salinas, Botswana ve HyRANK-Loukia verisetleri kullanılmıştır. Gerçekleştirilen uygulamalar sonucunda, en iyi sınıflandırma doğruluğu Indian pines verisetinde VGG16, Botswana verisetinde ResNet50, HyRANK-Loukia verisetinde VGG16, Salinas verisetinde LeNet5 ve VGG16 mimarileri ile elde edilmiştir.

Bu calismada, alt sol dolgu, ilk uygun azalan ve uygun olmayan cokgen sezgisel algoritmalari kullanilarak iki boyutlu dikdortgen serit paketleme probleminin cozumu uzerine bir yontem onerilmektedir. Iki boyutlu dikdortgen serit paketleme problemi (2D-SPP), sabit genislik ve sonsuz yukseklige sahip bir serit uzerine bir dizi dikdortgen parcanin yerlestirilmesidir. Amac, tum dikdortgenleri tamamen seridin icine yerlestirmek icin gereken yuksekligi en aza indirgemektir. Bu problemin cozumunde, yerlestirme islemi icin alt sol dolgu algoritmasi, dikdortgen parcalar arasinda olusabilecek cakismayi onlemek icin uygun olmayan cokgen yontemi kullanilmistir. Ayrica, parcalar alanlarina gore azalan sirada siralandiktan sonra yerlestirme islemi icin kullanilacak olan secim algoritmasi olarak da ilk uygun azalan sezgisel algoritmasi kullanilmistir. 21 farkli veri seti uzerinde calismalar gerceklestirilmis ve performans degerlendirilmesi yapilmistir. Gerceklestirilen deneysel calismalar sonucunda optimal cozume yakin sonuclar elde edilmistir. Deneysel sonuclar, 2D-SPP icin onerilen sezgisel yontemlerin etkinligini gostermektedir .

The cutting and packing problem is the process ofcutting small pieces of certain sizes and proportions from materials used fordifferent purposes in industries. Because this problem cannot be expressed bymathematical models, combinational optimization in multidimensional space isutilized for the solution. The aim of this problem is to increase the usabilityof the material used for the placement process and to minimize the trim loss.In this study, a solution is presented to two-dimensional regular cutting andpacking problem by a combined method consisting of improved bottom-left,bottom-left fill placement algorithms, no-fit polygon and first fit decreasingheuristic algorithms. The improved bottom-left placement algorithm for theplacement of parts starting from the bottom-left part according to a certainpermutation order, bottom-left fill algorithm for the placement of suitablepieces to the available free spaces in placement model, no-fit polygon methodfor preventing the geometric overlap between the parts and the first fitdecreasing heuristic algorithm is used as the selection algorithm afterordering from large to small according to the parts areas. Placement processand performance evaluation was performed for 11 different test data. As aresult of the studies carried out with combined heuristic methods, it is seenthat there is a placement without any waste in P2 and P10 placement models.This shows that the optimal solution is obtained. In other placement models, atrim loss was obtained between 4.54% and 16.7%. The experimental results showthe effectiveness of the proposed heuristic methods for the solution of thecutting and packing problem., Kesme ve paketleme problemi,endüstrilerde farklı amaçlar için kullanılan malzemelerden belirli büyüklük veoranlarda küçük parçaların kesilmesi işlemidir. Bu problem, matematikselmodellerle ifade edilemediğinden dolayı, çözüm için çok boyutlu uzaydakombinasyonel optimizasyon kullanılır. Bu problemin amacı, yerleştirme işlemiiçin kullanılan malzemenin kullanılabilirliğini arttırmak ve fire oranınıminimize etmektir. Bu çalışmada, iki boyutlu düzenli kesme ve paketleme problemine,geliştirilmiş alt-sol, alt-sol dolgu yerleşim algoritmaları, uygun olmayançokgen ve ilk uygun azalan sezgisel algoritmalarından oluşan birleştirilmiş biryöntem ile çözüm sunulmuştur. Parçaların belirli bir permütasyon sırasına görealt-sol kısımdan başlayarak yerleşimi için geliştirilmiş alt-sol yerleşimalgoritması, yerleşim modelinde mevcut boş alanlara uygun parçalarınyerleştirilmesi için alt-sol dolgu algoritması, parçalar arasında geometrikçakışmayı önlemek için uygun olmayan çokgen yöntemi ve parçalar alanlarına görebüyükten küçüğe doğru sıralandıktan sonra seçim algoritması olarak da ilk uygunazalan sezgisel algoritması kullanılmaktadır. 11 farklı test verisi içinyerleştirme işlemi gerçekleştirilmiş ve performans değerlendirilmesi yapılmıştır.Birleştirilmiş sezgisel yöntemlerle gerçekleştirilen çalışmalar sonucunda P2 veP10 yerleşim modellerinde firesiz bir yerleşim olduğu görülmektedir. Bu daoptimal çözümün elde edildiğini göstermektedir. Diğer yerleşim modellerindeise, % 4.54 ile % 16.7 aralığında fire oranı elde edilmiştir. Deneyselsonuçlar, kesme ve paketleme probleminin çözümü için önerilen sezgiselyöntemlerin etkinliğini göstermektedir.

Hiperspektral görüntüler, uzaktan algılama uygulamalarında yaygın olarak kullanılan bitişik bant görüntüleridir. Derin öğrenme hiperspektral görüntü sınıflandırmasını iyileştirmek için kullanılan etkili bir yöntemdir. Evrişimli sinir ağları ise hiperspektral görüntü sınıflandırma alanında başarıyla kullanılan derin öğrenme yöntemlerinden biridir. Spektral, uzamsal veya spektral uzamsal alanlardan hiperspektral görüntülerin daha soyut özelliklerini öğrenebilen otomatik yaklaşımlar sağlamaktadır. Hiperspektral görüntülerin yüksek boyutsallığı hesaplama karmaşıklığını arttırmaktadır. Bu nedenle, geliştirilen evrişimli sinir ağ modellerinin çoğu, bir ön işleme adımı olarak boyut indirgeme gerçekleştirmektedir. Hiperspektral görüntü sınıflandırmasındaki diğer bir problem ise, doğru sonuçlar elde etmek için hem uzamsal hem de spektral özelliklerin dikkate alınması gerekliliğidir. Çünkü, hiperspektral görüntü sınıflandırma performansı büyük ölçüde hem spektral hem de uzamsal bilgilere bağlıdır. Bu çalışmada, hiperspektral görüntü sınıflandırması için yeni bir 3B evrişimli sinir ağı modeli önerilmiştir. Önerilen yöntem, hiperspektral görüntülerdeki spektral-uzamsal özellikleri eşzamanlı olarak çıkarmak için basit ve etkili bir yöntem sağlamaktadır. Ağ, giriş verileri olarak 3B hiperspektral küpü kullanmaktadır. Hiperspektral küpteki boyutsal fazlalığı gidermek için temel bileşen analizi ile boyut indirgeme yapılmaktadır. Daha sonra komşuluk çıkarımı kullanılarak, spektral uzamsal özellikler etkin bir şekilde çıkarılmaktadır. Önerilen yöntem indian pines, pavia üniversitesi, salinas ve kennedy uzay merkezi veri setleri ile test edilmiştir. Deneysel çalışmalar sonucunda, indian pines için %99,93, pavia üniversitesi için %99,99, salinas için %100 ve kennedy uzay merkezi için ise %99,81 genel doğruluk sonuçları elde edilmiştir. Sonuçlar, 7 farklı derin öğrenme tabanlı yöntemle karşılaştırılmıştır. Deneysel sonuçlardan, 4CF-Net yöntemimizin daha iyi performans gösterdiği görülmektedir.

Myocardial fibrosis refers to a variety of quantitative and qualitative changes in the interstitial myocardial collagen network that occur in response to cardiac ischaemic insults, systemic diseases, drugs, or any other harmful stimulus affecting the circulatory system or the heart itself. Myocardial fibrosis alters the architecture of the myocardium, facilitating the development of cardiac dysfunction, also inducing arrhythmias, influencing the clinical course and outcome of heart failure patients. Focusing on myocardial fibrosis may potentially improve patient care through the targeted diagnosis and treatment of emerging fibrotic pathways. The European Commission funded the FIBROTARGETS consortium as a multinational academic and industrial consortium with the primary aim of performing a systematic and collaborative search of targets of myocardial fibrosis, and then translating these mechanisms into individualized diagnostic tools and specific therapeutic pharmacological options for heart failure. This review focuses on those methodological and technological aspects considered and developed by the consortium to facilitate the transfer of the new mechanistic knowledge on myocardial fibrosis into potential biomedical applications.

Background: Rheumatoid Arthritis (RA) is a chronic, progressive, inflammatory autoimmune disease associated with articular, extra-articular and systemic effects leading to joint destruction. T cells, B cells and the orchestrated interaction of pro-inflammatory cytokines play key roles in the pathophysiology of RA. Better comprehension of interaction between cytokines and their signaling pathways are key for the development of new strategies with small molecules or biologicals. Today, new technologies allow the specific investigation of inflammatory pathways at mRNA level without extraction step directly from the blood. The BIOPRED panel, Disease activity specific gene enrichment studies in RA & other autoimmune-inflammatory disorders would help pharmaceutical industry tailor target specific therapies and would help clinicians choose optimal & personalized therapy for their patients. Therefore, we have identified active biological pathways in RA patients associated with different disease activity status. Objectives: By using Firalis’ BIOPRED assay, a targeted gene sequencing panel including 2155 mRNAs from inflammatory and immune pathways, we aimed to identify active biological pathways in function to different disease activity status of RA and Healthy volunteer (HV) subjects. Methods: Paxgene samples of active RA patients with DAS28>3.2 (n=178) and HV (n=25) are directly profiled without RNA extraction with BIOPRED assay and 2155 mRNA were quantified on HTG EdgeSeq platform, a combination of a nuclease protection assay & next generation sequencing (NGS). Subjects are categorized into three groups; High disease activity (HDA) group with DAS28 >5.1, Moderate disease activity (MDA) group with DAS28 between 3.2 and 5.1, and Healthy volunteer (HEV) group. Results: After transformation and normalization of the gene expression data, 22 mRNA genes are found to be significantly upregulated in RA (p-value 2) as compared to HV group. After ANOVA analysis on three groups as stated above, 351 mRNA targets are significantly regulated (p-value Conclusion: Our results identify a list of mRNAs relevant to RA pathology and pathways that have the potential to be candidate biomarkers for disease activity, disease severity or as therapeutic targets. Moreover, BIOPRED panel accurately measures 2155 mRNA (Coefficient of Variation is less than 20% for more than 1100 mRNAs) from inflammatory and immune pathways and can be further used to study pathway analysis in autoimmune-inflammatory disorders such as RA. Disclosure of Interests: None declared

Background: Today the objective for efficient Rheumatoid Arthritis (RA) therapy is not restricted to reduced synovial inflammation but to induce immunological and clinicaly lasting remission. Despite improvement in therapy of RA, anti-TNF alpha or anti-IL-6 (Tocilizumab) biologicals provide less than 20% of clinical remission. Interestingly, the complete remission can last up to 12 months in about 40% of these patients despite discontinuation of the above biologicals. In order to set up optimized strategy to obtain persistent clinical remission, new predictive markers of complete remission are needed. Objectives: By using Firalis’ BIOPRED panel, an innovative targeted gene sequencing panel of 2155 mRNA targets associated with immune-inflammatory pathways, we identified candidate biomarkers that have the potential to stratify patients for personalized therapy. Methods: Paxgene RNA samples obtained from 18 RA patients treated with Etanercept (n=11) and Tocilizumab (n=7) who had achieved remission (DAS28 Results: In total, 37 mRNA targets are found to be significantly up- or down-regulated in Etanercept groups as compared to Tocilizumab group (p-value 2 (p-value Conclusion: Our preliminary analysis shows that a panel of candidate predictive markers for biological specific (Etanercept) remission has the potential to stratify patients for personalized therapy in RA patients. Longitudinal studies are required with RA patients treated with all biologicals to validate this signature and explore further dimensions. Disclosure of Interests: None declared

The rectangle packing area minimization problem (RPAMP) has a wide range of applications in industry. The purpose of RPAMP is to ensure maximum use of the materials used in industries and minimize the trim loss. The yield from materials is significant issue. Because a small change in placement can result in a significant reduction in production costs and material savings. In this study, 2D-RPAMP, which is widely seen in the manufacturing industry, has been investigated. The solution approaches have been developed by using heuristic methods such as no-fit polygon and bottom-left fill and metaheuristic method such as Simulated Annealing (SA). In experimental studies, the bottom-left fill method is used for the placement process, no-fit polygon method is used in order to avoid overlapping between the parts placed, and the SA algorithm is employed for the faster and efficient packing process. As a result of experiments using the hybrid methodology consisting of the SA algorithm can reach reasonable level of the trim loss varies that is between 0,21% and 9,1% for most industry. The experimental results show the efficiency of the packing process with hybrid methodology.

2019 International Conference on Artificial Intelligence and Data Processing Symposium, IDAP 2019 -- 21 September 2019 through 22 September 2019 -- -- 153040, Fabric defect detection is one of the most important areas for quality control of products in the textile industry. Many different studies have developed methods for this problem. In this study, an automatic thresholding method developed with entropy has been proposed. Due to the low cost of calculation, the proposed automatic thresholding method will be very suitable for real-Time applications. In this study, automatic thresholding method which is supported by 4 different entropy method was compared with otsu method which is one of automatic thresholding methods. Various tests have been made on different fabric types for comparisons. As a result of experimental studies, successful results of automatic thresholding methods supported with entropy were obtained for fabric defect detection. Renyi entropy method was the most successful result among the proposed methods. © 2019 IEEE.

Bu çalışmada, farklı ortam koşullarının ve farklı fiber kombinasyonlarının hibrit kompozitlerin burkulma davranışı üzerine etkileri deneysel olarak araştırılmıştır. Fiber olarak dimi (twill) dokuma karbon, dimi (twill) dokuma E-cam ve dimi (twill) dokuma aramid kumaşlar, matris olarak da epoksi reçinesi kullanılarak, [(0/90)3]s, [(30/-60)3]s, [(45/-45)3]s simetrik ve [(0/90)3]as antisimetrik oryantasyona sahip 12 tabakalı hibrit kompozit plakalar üretilmiştir. Üretilen plakalar 7 ayrı gruba ayrılarak farklı ortam (oda koşulları, Akdeniz suyu, Karadeniz suyu ve soğuk ortam) koşullarında bekletilmiştir. 90 gün oda koşulları, 90 gün soğutucu (-18 °C), 90 gün Karadeniz suyu, 90 gün Akdeniz suyu, 150 gün soğutucu, 150 gün Akdeniz suyu ve 90 gün süresince 30 gün Akdeniz suyu-30 gün soğutucu -30 gün Akdeniz suyunda bekletilen hibrit kompozit numuneler burkulma ve yanal burkulma deneylerine tabi tutulmuşlardır. Bütün ortam koşulları içinde, en yüksek kritik burkulma yükünün 150 gün soğuk ortamda (-18 °C) bekletilen simetrik [(0/90)3]s dizilimli CAG (karbon-aramid-cam) konfügürasyonunda oluştuğu, en düşük kritik burkulma yükünün ise 150 gün Akdeniz suyuna maruz bırakılan [(45/-45)3]s dizilimli CAG45 konfügürasyonunda oluştuğu belirlenmiştir. En yüksek kritik yanal burkulma yükü 150 gün soğuk ortamda (-18 °C) bekletilen simetrik [(30/-60)3]s dizilimli CAG30'da, en düşük kritik yanal burkulma yükü ise 150 gün Akdeniz suyuna maruz bırakılan [(0/90)3]s dizilimli GAC'da oluşmuştur. Deniz suyu ortamında bekletilen hibrit kompozit malzemelerin mekanik özelliklerinin ve kritik burkulma yüklerinin oda koşullarına göre azaldığı görülmüştür. Soğuk ortamda bekletilen hibrit kompozit malzemelerin mekanik özellikleri ve kritik burkulma yüklerinin ise oda koşullarına göre arttığı gözlemlenmiştir. [(0/90)3]s, [(30/-60)3]s ve [(45/-45)3]s istifleme dizilimli malzemeler karşılaştırıldığında, tüm ortam koşulları için [(0/90)3]s dizilimli malzemelerin kritik burkulma yüklerinin en fazla olduğu gözlemlenmiştir. Simetrik dizilimli [(0/90)3]s hibrit kompozit numuneler ile antisimetrik dizilimli [(0/90)3]as hibrit kompozit numunelerin kritik burkulma yükleri karşılaştırıldığında; simetrik dizilimli hibrit kompozitlerin kritik burkulma yüklerinin antisimetrik dizilimli hibrit kompozitlere göre daha fazla olduğu görülmüştür. Hibrit kompozitlerin burkulma davranışları incelendiğinde; [(0/90)3]s istifleme dizilimine sahip hibrit kompozitlerin burkulma dayanımının [(30/-60)3]s ve [(45/-45)3]s istifleme dizilimine sahip hibrit kompozitlerin burkulma dayanımlarına göre daha yüksek olduğu görülmüştür. Hibrit kompozitlerin yanal burkulma davranışları incelendiğinde ise [(30/-60)3]s ve [(45/-45)3]s istifleme dizilimine sahip hibrit kompozitlerin yanal burkulma dayanımlarının [(0/90)3]s istifleme dizilimine sahip hibrit kompozitlerin yanal burkulma dayanımına göre daha yüksek olduğu görülmüştür. Hibrit kompozitlerin fiber istifleme dizilimleri karşılaştırıldığında; tüm ortam koşullarında dış tabakalarda karbon fiber bulunan CAG (karbon/aramid/cam) hibrit konfügürasyonunun burkulma yükünün, dış tabakalarda cam fiber bulunan GAC (cam/aramid/karbon) hibrit konfügürasyonunun burkulma yüküne nazaran daha yüksek olduğu belirlenmiştir.Farklı ortam koşullarının, farklı istifleme diziliminin ve farklı fiber oryantasyon açılarının hibrit kompozit malzemelerin burkulma ve yanal burkulma davranışlarını doğrudan etkilediği sonucuna varılmıştır. In this study, effects of different fiber combinations and different environmental conditions on buckling and lateral buckling of hybrid composite plates have been investigated. 12-layers of hybrid composite plates have been produced using different combinations of twill weave carbon, twill weave E-glass and dimi twill weave aramid fiber fabrics with epoxy resin (as a matrix). The hybrid composite plates have been prepared having [(0/90)3]s, [(30/-60)3]s, [(45/-45)3]s and [(0/90)3]us symmetrical and antisymmetrical ply orientations. Produced plates were separated in 7 different groups and kept in different environment conditions (room conditions, Mediterranean water, Black Sea water and cold environment). Hybrid composite configurations which have the smallest and the highest buckling and lateral buckling strength have been determined.The highest critical buckling load is in CAG (carbon-aramid-glass) with a symmetrical [(0/90)3]s stacking sequences exposed to cold environment (-18°C) for 150 days and the lowest critical buckling load is exposed to Mediterranean water for 150 days [(0/90)3]s stacking sequences GAC (glass-aramid-carbon). The sample with the highest critical lateral buckling load is in CAG30 with a symmetrical [(30/-60)3]s sequence exposed to cold environment (-18°C) for 150 days, while the lowest critical buckling load is exposed to Mediterranean water for 150 days [45/-45)3]s sequence CAG45. It has been observed that the mechanical properties and critical buckling loads of the materials kept in the sea water environment are decreased, the mechanical properties of the materials kept in the cold environment and the increases in the critical buckling loads are observed. When [(0/90)3]s symmetrical array materials [(30/-60)3]s and [(45/-45)3]s stacking materials are compared for all environment conditions [(0/90)3]s symmetrical array materials it is observed that the critical buckling load is the greatest. When the critical buckling loads of hybrid composite specimens having [(0/90)3]s symmetrical stacking sequences and hybrid composite specimens having [(0/90)3]as antisymmetric stacking sequences are compared; it has been found that the critical buckling loads of symmetric sequential samples are higher than the critical buckling loads of the antisymmetric sequential samples. Hybrid composite plates having [(0/90)3]s stacking sequences have higher buckling strength with respect to configurations which having [(30/-60)3]s and [(45/-45)3]s stacking sequences. When the lateral buckling strengths according to the stacking sequences are compared, it is seen that the lateral buckling strengths of the hybrid composite plates having [(30/-60)3]s and [(45/-45)3]s stacking sequences were found to be higher with respect to the lateral buckling strength of configurations which having [(0/90)3]s stacking sequences. When the fiber stacking sequences of the hybrid composites are compared, it has been observed that the CAG (carbon/aramid/glass) hybrid configuration with carbon fiber in the outer layers has a higher the buckling load than the GAC (glass/aramid/glass) hybrid configuration with glass fiber in the outer layers for all environment conditions.It has been concluded that different environmental conditions and different fiber orientation angles and different stacking sequences are directly affect the buckling and the lateral buckling behaviors of hybrid composite materials. 159

Background Anti-TNF alpha biologicals are an important breakthrough in the treatment of Rheumatoid Arthritis (RA) patients. However, 30–40% of RA patients do not respond to these therapies. Therefore, there is an unmet need for a tool to predict treatment response that would help clinicians choose an optimal treatment for RA patients. Objectives Under the framework of Horizone2020 SME Instrument of European Commission, Firalis has identified and developed a panel of 2159 mRNA genes which can predict non-response to anti-TNF alpha therapy using the HTG EdgeSeq platform, an innovative combination of a nuclease protection assay & next generation sequencing (NGS). Methods RABIOPRED assay is a proprietary panel of Firalis signatures, which also includes targets selected by the BTCure consortium , to predict treatment response of anti-TNF alpha biologicals. In total 2175 targets were selected for the development and 2159 are successfully included in the panel. Each oligonucleotide is a 100-mer comprising a 25-mer “wing” at the 59 end and 39 end, and a 50-mer sequence in between that is complementary to the target mRNA. QC is checked for secondary structure and absence of homology with other sequences. Analytical parameters are assessed and repeatability of the RABIOPRED assay is validated on both Paxgene and purified RNA samples. Sample input is set at 32 μl for Paxgene RNA blood and 25 ng for extracted RNA. Results Mean correlation factor for 12 samples on 8 replicates for Paxgene and RNA samples are R 2 >0.97 and R 2 >0.99 respectively. First analysis and predictive modelling shows an AUC over 0.95 for the prediction of non-response to anti-TNF alpha. In the present work, we disclose the performance of the CE-IVD RABIOPRED assay based on more than 200 samples obtained from the prospective clinical studies, PRINT and RA-TNF. The algorithm will be further validated within the ongoing RABIOPRED Proof-of-Performance study (ClinicalTrials.gov Identifier: NCT03016260) in 720 patients treated by anti-TNF alpha biologicals (5 originators and 3 biosimilars) launched in December 2016. First version of the CE-IVD RABIOPRED assay will be available during Q2 2017 and open for testing. Conclusions We are showing that we can accurately measure mRNA expression with RABIOPRED assay using HTG-EdgeSeq NGS platform. Preliminary performance of the assay shows that it can efficiently predict treatment response to anti-TNF alpha biologicals. The algorithm will be later on validated in a multi-centric proof-of-performance clinical study. Disclosure of Interest None declared

The importance of using translational safety biomarkers that can predict, detect and monitor drug-induced toxicity during human trials is becoming increasingly recognized. However, suitable processes to qualify biomarkers in clinical studies have not yet been established. There is a need to define clear scientific guidelines to link biomarkers to clinical processes and clinical endpoints. To help define the operational approach for the qualification of safety biomarkers the IMI SAFE-T consortium has established a generic qualification strategy for new translational safety biomarkers that will allow early identification, assessment and management of drug-induced injuries throughout R&D.

Multiple sclerosis (MS) is a demyelinating inflammatory disease of the CNS. Though originally believed to be CD4-mediated, additional immune effector mechanisms, including myelin-specific CD8+ T cells, are now proposed to participate in the pathophysiology of MS. To study the immunologic and encephalitogenic behavior of HLA-A*0201-binding myelin-derived epitopes in vivo, we used a humanized HLA-A*0201-transgenic mouse model. Eight HLA-A*0201-binding peptides derived from myelin oligodendrocyte glycoprotein (MOG), an immunodominant myelin self-Ag, were identified in silico. After establishing their relative affinity for HLA-A*0201 and their capacity to form stable complexes with HLA-A*0201 in vitro, their immunological characteristics were studied in HLA-A*0201-transgenic mice. Five MOG peptides, which bound stably to HLA-A*0201 exhibited strong immunogenicity by inducing a sizeable MOG-specific HLA-A*0201-restricted CD8+ T cell response in vivo. Of these five candidate epitopes, four were processed by MOG-transfected RMA target cells and two peptides proved immunodominant in vivo in response to a plasmid-encoding native full-length MOG. One of the immunodominant MOG peptides (MOG181) generated a cytotoxic CD8+ T cell response able to aggravate CD4+-mediated EAE. Therefore, this detailed in vivo characterization provides a hierarchy of candidate epitopes for MOG-specific CD8+ T cell responses in HLA-A*0201 MS patients identifying the encephalitogenic MOG181 epitope as a primary candidate.

Crystallographic analysis of human Hfe has documented an overall structure similar to classical (class Ia) MHC molecules with a peptide binding groove deprived of ligand. Thus, to address the question of whether αβ T cells could recognize MHC molecules independently of bound ligands, we studied human and mouse Hfe interactions with T lymphocytes. We provide formal evidence of direct cytolytic recognition of human Hfe by mouse αβ T cell receptors (TCR) in HLA-A * 0201 transgenic mice and that this interaction results in ZAP-70 phosphorylation. Furthermore, direct recognition of mouse Hfe molecules by cytotoxic T lymphocytes (CTLs) was demonstrated in DBA/2 Hfe knockout mice. These CTLs express predominantly two T cell antigen receptor α variable gene segments (AV6.1 and AV6.6). Interestingly, in wild-type mice we identified a subset of CD8 + T cells positively selected by Hfe that expresses the AV6.1/AV6.6 gene segments. T cell antigen receptor recognition of MHC molecules independently of bound ligand has potential general implications in alloreactivity and identifies in the Hfe case a cognitive link supporting the concept that the immune system could be involved in the control of iron metabolism.

The tetracycline-controlled transcription system (Tet-on) is widely used to regulate gene expression in mammalian cells. In gene therapy applications, immune responses to Tet-on proteins such as the rtTA transcription factor have been reported, raising concerns about their occurrence in humans. To monitor the HLA class I cytolytic responses against Tet-on regulators, we characterized the immunogenic CD8+ epitopes within rtTA and tTS regulators using HLA-A*0201 class I transgenic mice. Epitope prediction programs, HLA-A*0201 binding assays, and peptide immunization were used to select a set of immunogenic peptides within rtTA and tTS sequences. To identify further the rejection epitopes, we expressed Tet-on protein components in vivo and found a single dominant rtTA186 CTL epitope in the rtTA tetracycline repressor domain. Target cells expressing rtTA were susceptible to CTL lysis, and rtTA expression compromised muscle transgene engraftment. To reduce the occurrence of immune responses to rtTA protein, we mutated the dominant rtTA186 epitope and found that this leads to the appearance of subdominant epitopes. As a result, we think that an epitope modification strategy is not applicable to blunt the immune response in this model. Moreover, the identification of HLA-A*0201 rtTA epitopes allowed us to demonstrate here that the delivery of the Tet-on system with weakly immunogenic rAAV vectors does not trigger primary CTL responses in mice, in contrast to DNA transfer. Altogether, the existence of HLA-A*0201 rtTA epitopes may lead to the occurrence of immune responses depending on vectors and local inflammation in gene therapy applications involving rtTA-based regulatory systems.

Most of the human tumor-associated antigens (TAAs) characterized thus far are derived from nonmutated “self”-proteins. Numerous strategies have been developed to break tolerance to TAAs, combining various forms of antigens with different vectors and adjuvants. However, no study has yet determined how to select epitopes within a given TAA to induce the highest antitumor effector response. We addressed this question by evaluating in HLA-A*0201-transgenic HHD mice the antitumor vaccination efficacy of high- and low-affinity epitopes from the naturally expressed murine telomerase reverse transcriptase (mTERT). Immunity against low-affinity epitopes was induced with heteroclitical variants. We show here that the CTL repertoire against high-affinity epitopes is partially tolerized, while that against low-affinity epitopes is composed of frequent CTLs with high avidity. The high-affinity p797 and p545 mTERT epitopes are not able to protect mice from a lethal challenge with the mTERT-expressing EL4-HHD tumor. In contrast, mice developing CTL responses against the p572 and p988 low-affinity epitopes exhibit potent antitumor immunity and no sign of autoimmune reactivity against TERT-expressing normal tissues. Our results strongly argue for new TAA epitope selection and modification strategies in antitumor immunotherapy applications in humans.

Dystrophin-based gene therapy treatments aimed at correcting the Duchenne muscular dystrophy phenotype require stable expression of normal dystrophin (DYST) protein in myocytes without immune responses, which would compromise long-term expression. To predict cytotoxic T-cell-mediated responses elicited by transgenes, we used here H-2-negative HLA-A*0201 transgenic mice and identified human DYST epitopes, which elicit HLA-A*0201-restricted cytotoxic T cell activities. Among a series of eight peptides predicted from the human DYST sequence, not shared with the endogenous mouse DYST sequence, four of them were able to bind to HLA-A*0201 molecules and to induce cytotoxic T lymphocyte (CTL) responses. After human DYST DNA transfer in muscle of HLA-A*0201 mice, only the human DYST1281 epitope, located in the spectrin-like repeat 9 domain, induced strong CD8(+) CTL responses. Using the corresponding human DYST1281 peptide/HLA-A*0201 tetramer, we detected human DYST1281-specific CD8(+) T cells in peripheral lymphoid organs and blood of HLA-A*0201 mice injected with human DYST DNA. Our results demonstrate that muscle injection with human DYST DNA systematically triggers CTL responses against this HLA-A*0201-restricted human DYST1281 peptide, which is present in long human DYST isoforms. Identification of such immunodominant human DYST epitopes and use of peptide/HLA tetramers will allow the immunomonitoring of CTL responses in HLA-phenotyped Duchenne muscular dystrophy patients undergoing gene therapy. Finally, the knowledge of HLA-A*0201-restricted human DYST peptides will be of importance to test, in mouse models, new immunomodulatory interventions allowing long-term engraftment of human dystrophin.

Parathyroid hormone-related protein (PTH-rP), a secreted protein produced by prostate carcinoma and other epithelial cancers, is considered a key agent for the development of bone metastases. We investigated the construct GC90/IRIV, composed of immunopotentiating reconstituted influenza virosomes (IRIV) containing PTH-rP gene plasmids (GC90), as a potential tool for human anticancer immunotherapy into humanised mice transgenic for HLA-A(*)02.01, the human-beta2 microglobulin, and the human CD8alpha molecule. Intranasal administration of GC90/IRIV resulted in the induction of a PTH-rP-specific multiepitope cytotoxic T-cell (CTL) response. Cytotoxic T cells derived from vaccinated mice were capable of lysing in vitro syngenic murine PTH-rP transfectants and human HLA-A((*))02.01(+)/PTH-rP(+) prostate carcinoma LNCaP cells as well. The immune response capacity and the absence of any sign of toxicity and/or autoimmunity in vivo suggest the GC90/IRIV vaccine as a valid tool for active specific immunotherapy of human cancers and metastases overexpressing PTH-rP.

HLA-B*0702 transgenic mice (expressing a chimeric heavy chain with a murine alpha 3 domain: HLA-B7(m alpha 3)) in which the H-2K(b) and H-2D(b) class I-a (Cl I-a(-/-)) genes have been inactivated were compared with H-2K(b)D(b) Cl I-a(+/+) positive controls. Expression of the HLA-B7(m alpha 3) molecules resulted in a 3- to 4-fold increase in peripheral CD8(+) T lymphocyte numbers compared to H-2 Cl I-a(-/-) knockout mice. These cells show a diversified TCR repertoire. Following influenza infection, a significant improvement in HLA-B0702-restricted cytotoxic T lymphocyte (CTL) responses was observed in HLA-B7(m alpha 3), H-2 Cl I-a(-/-) compared to HLA-B7(m alpha 3), H-2 Cl I-a(+/+) mice. The CTL response of infected HLA-B7(m alpha 3), H-2 Cl I-a(-/-) mice was directed against the nucleoprotein (NP) 418-426 epitope in which mutations have accumulated. Whereas all NP 418-426 variant peptides induced a CTL response, cross-reactivity to the variants was affected. These NP mutations could have been selected over time in humans for the virus to escape HLA-B0702-restricted CTL responses since a similar response was seen in humans with, as in mice, altered cross-recognition of the NP 418-426 variants. These animals may prove a suitable model to study HLA-B0702-restricted CTL responses.

Rheumatoid arthritis (RA) is a severe autoimmune systemic disease. Chronic synovial inflammation results in destruction of the joints. No conventional treatment is efficient in RA. Gene therapy of RA targets mainly the players of inflammation or articular destruction: TNF-α or IL-1 blocking agents (such as anti-TNF-α monoclonal antibodies, soluble TNF-α receptor, type II soluble receptor of IL-1, IL-1 receptor antagonist), antiinflammatory cytokines (such as IL-4, IL-10, IL-1), and growth factors. In this polyarticular disease, the vector expressing the therapeutic protein can be administered as a local (intra-articular injection) or a systemic treatment (extra-articular injection). All the main vectors have been used in experimental models, including the more recent lentivirus and adeno-associated virus. Ex vivo gene transfer was performed with synovial cells, fibroblasts, T cells, dendritic cells, and different cells from xenogeneic origin. In vivo gene therapy is simpler, although a less controlled method. Clinical trials in human RA have started with ex vivo retrovirus-expressing IL-1 receptor antagonists and have demonstrated the feasibility of the strategy of gene therapy. The best target remains to be determined and extensive research has to be conducted in preclinical studies. Copyright © 2002 John Wiley & Sons, Ltd.

BACKGROUND: A central triple-stranded DNA structure created during HIV-1 reverse transcription, the central flap, acts as a cis-active nuclear import determinant of the HIV-1 DNA genome. Insertion of the sequences responsible for formation of the central DNA flap into an HIV-1-derived vector strongly enhances its transduction efficiency. METHODS: HIV-1 vectors with or without inclusion of the DNA flap and encoding the same melanoma polyepitope were constructed to transduce dendritic cells (DCs) and to evaluate their capacity for induction of melanoma-specific cytotoxic T-lymphocyte (CTL) responses ex vivo and in vivo. RESULTS: HIV-1 vectors including the DNA flap transduced up to 100% of immature mouse and human DCs. Inoculation of HLA-A2.1 transgenic mice with this flap vector elicited vigorous and multi-specific long-term anti-melanoma CTL responses, whereas the parental vector lacking the flap sequence was less efficient. Furthermore, human DCs transduced ex vivo with the recombinant DNA flap vector displayed efficient multi-specific primary human CTL responses against melanoma. CONCLUSION: Lentiviral vectors including the DNA flap should be powerful tools both for active immunization and for the ex vivo priming of CTL for tumor immunotherapy.

Drug-induced vascular injury (DIVI) is a common preclinical toxicity usually characterized by hemorrhage, vascular endothelial and smooth muscle damage, and inflammation. DIVI findings can cause delays or termination of drug candidates due to low safety margins. The situation is complicated by the absence of sensitive, noninvasive biomarkers for monitoring vascular injury and the uncertain relevance to humans. The Safer And Faster Evidence-based Translation (SAFE-T) consortium is a public–private partnership funded within the European Commission’s Innovative Medicines Initiative (IMI) aiming to accelerate drug development by qualifying biomarkers for drug-induced organ injuries, including DIVI. The group is using patients with vascular diseases that have key histomorphologic features (endothelial damage, smooth muscle damage, and inflammation) in common with those observed in DIVI, and has selected candidate biomarkers associated with these features. Studied populations include healthy volunteers, patients with spontaneous vasculitides and other vascular disorders. Initial results from studies with healthy volunteers and patients with vasculitides show that a panel of biomarkers can successfully discriminate the population groups. The SAFE-T group plans to seek endorsement from health authorities (European Medicines Agency and Food and Drug Administration) to qualify the biomarkers for use in regulatory decision-making processes.

HER-2/neu is a tumor-associated antigen overexpressed in a large variety of human tumors. Eight HER-2/neu peptides displaying HLA-A*0201 anchoring motifs were selected and tested for their binding affinity to HLA-A*0201 and their capacity to elicit cytotoxic T lymphocyte (CTL) responses in both HLA-A*0201 transgenic mice and in HLA-A*0201(+) healthy donors. Two high-affinity (p5 and p48) and one intermediate-affinity (p1023) peptides triggered CTL responses in both transgenic mice and humans, comparable to those observed for the well-known HER2/neu dominant peptide p369. CTL induced in transgenic mice lysed HLA-A*0201(+) RMA cells infected with recombinant HER-2/neu but not cells infected with wild-type vaccinia virus. Human CTL lysed HLA-A*0201(+) HER-2/neu(+) tumor cells of different origins (breast, colon, lung and renal cancer) irrespective of the expression levels of HER-2/neu. Importantly, primed CTL specific for these epitopes were detected in freshly isolated tumor-infiltrating lymphocytes from three renal cell carcinoma patients. Therefore, the HER-2/neu peptides p5, p48 and p1023 may be good candidates for immunotherapy of a broad spectrum of tumors, including renal cell carcinoma.