End-to-End Adversarial Retinal Image Synthesis.

In medical image analysis applications, the availability of the large amounts of annotated data is becoming increasingly critical. However, annotated medical data is often scarce and costly to obtain. In this paper, we address the problem of synthesizing retinal color images by applying recent techniques based on adversarial learning. In this setting, a generative model is trained to maximize a loss function provided by a second model attempting to classify its output into real or synthetic. In particular, we propose to implement an adversarial autoencoder for the task of retinal vessel network synthesis. We use the generated vessel trees as an intermediate stage for the generation of color retinal images, which is accomplished with a generative adversarial network. Both models require the optimization of almost everywhere differentiable loss functions, which allows us to train them jointly. The resulting model offers an end-to-end retinal image synthesis system capable of generating as many retinal images as the user requires, with their corresponding vessel networks, by sampling from a simple probability distribution that we impose to the associated latent space. We show that the learned latent space contains a well-defined semantic structure, implying that we can perform calculations in the space of retinal images, e.g., smoothly interpolating new data points between two retinal images. Visual and quantitative results demonstrate that the synthesized images are substantially different from those in the training set, while being also anatomically consistent and displaying a reasonable visual quality

Associação independente da variante rs1333049, no locus 9p21, com a doença coronária, numa população portuguesa

Funding: This study was supported by the European Regional Development Fund’s Operational Programme for the Enhancement of Economic Potential and Territorial Cohesion for the Autonomous Region of Madeira (INTERVIR+).Introduction: Recent genome-wide association studies have identified single-nucleotide polymorphisms (SNPs) at the 9p21 locus as risk factors for coronary artery disease (CAD). Among them, the SNP rs1333049 has demonstrated a consistent association with CAD, which has been successfully replicated in several populations. Aim: To investigate whether the SNP rsl333049 located on the 9p21 chromosome is an independent risk factor for CAD in a Portuguese population. Methods: We performed a case-control study which included 1406 individuals, 723 consecutive coronary patients (mean age 53.7±8.9 years, 79.9% male) and 683 controls without coronary disease (mean age 53.3±10.5 years, 73.9% male). Cases and controls were selected so as not to be significantly different in terms of gender and age. We studied the SNP rs1333049 at the 9p21 locus in all individuals, using standard PCR combined with the TaqMan technique (Applied Biosystems). The allelic and genotype distribution (C/G), odds ratios and corresponding confidence intervals for CAD risk were determined. A forward Wald logistic regression analysis model was constructed, adjusted for age, gender, conventional risk factors, biochemical markers and the genotypes under study, in order to determine which variables were linked significantly and independently with CAD. Results: The C allele was found in 60% of the CAD patients and 53% of the controls, with OR=1.33; p=0.0002. The CC genotype appeared in 35.7% of CAD patients, with OR=1.34, p=0.010. The heterozygous CG genotype was present in 48.1% of the CAD patients and 47% of the controls, and did not present vascular risk (OR=1.05, p=0.670). After logistic regression analysis, the CC genotype remained in the equation with 0R=1.7; p=0.018 and CG with OR=I.5, p=0.048. Conclusion: In the present study we replicated the coronary risk linked to the recently discovered variant rs1333049 on the 9p21 chromosome in a Portuguese population. Although the mechanism underlying the risk is still unknown, the robustness of this risk allele in risk stratification for CAD has been consistent, even in very different populations. The presence of the CC or CG genotype may thus prove to be useful for predicting the risk of developing CAD in the Portuguese population.publishersversionpublishe

Towards Adversarial Retinal Image Synthesis

Synthesizing images of the eye fundus is a challenging task that has been previously approached by formulating complex models of the anatomy of the eye. New images can then be generated by sampling a suitable parameter space. In this work, we propose a method that learns to synthesize eye fundus images directly from data. For that, we pair true eye fundus images with their respective vessel trees, by means of a vessel segmentation technique. These pairs are then used to learn a mapping from a binary vessel tree to a new retinal image. For this purpose, we use a recent image-to-image translation technique, based on the idea of adversarial learning. Experimental results show that the original and the generated images are visually different in terms of their global appearance, in spite of sharing the same vessel tree. Additionally, a quantitative quality analysis of the synthetic retinal images confirms that the produced images retain a high proportion of the true image set quality

Global CO2 emissions from dry inland waters share common drivers across ecosystems

Many inland waters exhibit complete or partial desiccation, or have vanished due to global change, exposing sediments to the atmosphere. Yet, data on carbon dioxide (CO2) emissions from these sediments are too scarce to upscale emissions for global estimates or to understand their fundamental drivers. Here, we present the results of a global survey covering 196 dry inland waters across diverse ecosystem types and climate zones. We show that their CO2 emissions share fundamental drivers and constitute a substantial fraction of the carbon cycled by inland waters. CO2 emissions were consistent across ecosystem types and climate zones, with local characteristics explaining much of the variability. Accounting for such emissions increases global estimates of carbon emissions from inland waters by 6% (~0.12 Pg C y−1). Our results indicate that emissions from dry inland waters represent a significant and likely increasing component of the inland waters carbon cycle

New insights into the genetic etiology of Alzheimer's disease and related dementias

Characterization of the genetic landscape of Alzheimer's disease (AD) and related dementias (ADD) provides a unique opportunity for a better understanding of the associated pathophysiological processes. We performed a two-stage genome-wide association study totaling 111,326 clinically diagnosed/'proxy' AD cases and 677,663 controls. We found 75 risk loci, of which 42 were new at the time of analysis. Pathway enrichment analyses confirmed the involvement of amyloid/tau pathways and highlighted microglia implication. Gene prioritization in the new loci identified 31 genes that were suggestive of new genetically associated processes, including the tumor necrosis factor alpha pathway through the linear ubiquitin chain assembly complex. We also built a new genetic risk score associated with the risk of future AD/dementia or progression from mild cognitive impairment to AD/dementia. The improvement in prediction led to a 1.6- to 1.9-fold increase in AD risk from the lowest to the highest decile, in addition to effects of age and the APOE ε4 allele