Deep Learning-based Approach for the Semantic Segmentation of Bright Retinal Damage

Regular screening for the development of diabetic retinopathy is imperative for an early diagnosis and a timely treatment, thus preventing further progression of the disease. The conventional screening techniques based on manual observation by qualified physicians can be very time consuming and prone to error. In this paper, a novel automated screening model based on deep learning for the semantic segmentation of exudates in color fundus images is proposed with the implementation of an end-to-end convolutional neural network built upon UNet architecture. This encoder-decoder network is characterized by the combination of a contracting path and a symmetrical expansive path to obtain precise localization with the use of context information. The proposed method was validated on E-OPHTHA and DIARETDB1 public databases achieving promising results compared to current state-of-theart methods.This paper was supported by the European Union’s Horizon 2020 research and innovation programme under the Project GALAHAD [H2020-ICT2016-2017, 732613]. The work of Adri´an Colomer has been supported by the Spanish Government under a FPI Grant [BES-2014-067889]. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.Silva, C.; Colomer, A.; Naranjo Ornedo, V. (2018). Deep Learning-based Approach for the Semantic Segmentation of Bright Retinal Damage. En Intelligent Data Engineering and Automated Learning – IDEAL 2018. Springer. 164-173. https://doi.org/10.1007/978-3-030-03493-1_18S164173World Health Organization: Diabetes fact sheet. Sci. Total Environ. 20, 1–88 (2011)Verma, L., Prakash, G., Tewari, H.K.: Diabetic retinopathy: time for action. No complacency please! Bull. 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Comparison of Local Analysis Strategies for Exudate Detection in Fundus Images

Diabetic Retinopathy (DR) is a severe and widely spread eye disease. Exudates are one of the most prevalent signs during the early stage of DR and an early detection of these lesions is vital to prevent the patient’s blindness. Hence, detection of exudates is an important diagnostic task of DR, in which computer assistance may play a major role. In this paper, a system based on local feature extraction and Support Vector Machine (SVM) classification is used to develop and compare different strategies for automated detection of exudates. The main novelty of this work is allowing the detection of exudates using non-regular regions to perform the local feature extraction. To accomplish this objective, different methods for generating superpixels are applied to the fundus images of E-OPHTA database and texture and morphological features are extracted for each of the resulting regions. An exhaustive comparison among the proposed methods is also carried out.This paper was supported by the European Union’s Horizon 2020 research and innovation programme under the Project GALAHAD [H2020-ICT2016-2017, 732613]. The work of Adri´an Colomer has been supported by the Spanish Government under a FPI Grant [BES-2014-067889]. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.Pereira, J.; Colomer, A.; Naranjo Ornedo, V. (2018). Comparison of Local Analysis Strategies for Exudate Detection in Fundus Images. En Intelligent Data Engineering and Automated Learning – IDEAL 2018. Springer. 174-183. https://doi.org/10.1007/978-3-030-03493-1_19S174183Sidibé, D., Sadek, I., Mériaudeau, F.: Discrimination of retinal images containing bright lesions using sparse coded features and SVM. Comput. Biol. 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Mechanisms of telomerase inhibition by oxidized and therapeutic dNTPs

Telomerase enzymes add telomeric repeats to the end of linear chromosomes. Here the authors reveal mechanisms by which oxidized dNTPs and therapeutic dNTPs inhibit telomerase-mediated telomere elongation

Altered Nucleotide Insertion Mechanisms of Disease-Associated TERT Variants

Telomere biology disorders (TBDs) are a spectrum of diseases that arise from mutations in genes responsible for maintaining telomere integrity. Human telomerase reverse transcriptase (hTERT) adds nucleotides to chromosome ends and is frequently mutated in individuals with TBDs. Previous studies have provided insight into how relative changes in hTERT activity can lead to pathological outcomes. However, the underlying mechanisms describing how disease-associated variants alter the physicochemical steps of nucleotide insertion remain poorly understood. To address this, we applied single-turnover kinetics and computer simulations to the Tribolium castaneum TERT (tcTERT) model system and characterized the nucleotide insertion mechanisms of six disease-associated variants. Each variant had distinct consequences on tcTERT’s nucleotide insertion mechanism, including changes in nucleotide binding affinity, rates of catalysis, or ribonucleotide selectivity. Our computer simulations provide insight into how each variant disrupts active site organization, such as suboptimal positioning of active site residues, destabilization of the DNA 3′ terminus, or changes in nucleotide sugar pucker. Collectively, this work provides a holistic characterization of the nucleotide insertion mechanisms for multiple disease-associated TERT variants and identifies additional functions of key active site residues during nucleotide insertion

Altered Nucleotide Insertion Mechanisms of Disease-Associated TERT Variants

Telomere biology disorders (TBDs) are a spectrum of diseases that arise from mutations in genes responsible for maintaining telomere integrity. Human telomerase reverse transcriptase (hTERT) adds nucleotides to chromosome ends and is frequently mutated in individuals with TBDs. Previous studies have provided insight into how relative changes in hTERT activity can lead to pathological outcomes. However, the underlying mechanisms describing how disease-associated variants alter the physicochemical steps of nucleotide insertion remain poorly understood. To address this, we applied single-turnover kinetics and computer simulations to the Tribolium castaneum TERT (tcTERT) model system and characterized the nucleotide insertion mechanisms of six disease-associated variants. Each variant had distinct consequences on tcTERT’s nucleotide insertion mechanism, including changes in nucleotide binding affinity, rates of catalysis, or ribonucleotide selectivity. Our computer simulations provide insight into how each variant disrupts active site organization, such as suboptimal positioning of active site residues, destabilization of the DNA 3′ terminus, or changes in nucleotide sugar pucker. Collectively, this work provides a holistic characterization of the nucleotide insertion mechanisms for multiple disease-associated TERT variants and identifies additional functions of key active site residues during nucleotide insertion