Diversity and Activity of Denitrifiers of Chilean Arid Soil Ecosystems

The Chilean sclerophyllous matorral is a Mediterranean semiarid ecosystem affected by erosion, with low soil fertility, and limited by nitrogen. However, limitation of resources is even more severe for desert soils such as from the Atacama Desert, one of the most extreme arid deserts on Earth. Topsoil organic matter, nitrogen and moisture content were significantly higher in the semiarid soil compared to the desert soil. Although the most significant loss of biologically preferred nitrogen from terrestrial ecosystems occurs via denitrification, virtually nothing is known on the activity and composition of denitrifier communities thriving in arid soils. In this study we explored denitrifier communities from two soils with profoundly distinct edaphic factors. While denitrification activity in the desert soil was below detection limit, the semiarid soil sustained denitrification activity. To elucidate the genetic potential of the soils to sustain denitrification processes we performed community analysis of denitrifiers based on nitrite reductase (nirK and nirS) genes as functional marker genes for this physiological group. Presence of nirK-type denitrifiers in both soils was demonstrated but failure to amplify nirS from the desert soil suggests very low abundance of nirS-type denitrifiers shedding light on the lack of denitrification activity. Phylogenetic analysis showed a very low diversity of nirK with only three distinct genotypes in the desert soil which conditions presumably exert a high selection pressure. While nirK diversity was also limited to only few, albeit distinct genotypes, the semiarid matorral soil showed a surprisingly broad genetic variability of the nirS gene. The Chilean matorral is a shrub land plant community which form vegetational patches stabilizing the soil and increasing its nitrogen and carbon content. These islands of fertility may sustain the development and activity of the overall microbial community and of denitrifiers in particular

Using CycleGANs for effectively reducing image variability across OCT devices and improving retinal fluid segmentation

Optical coherence tomography (OCT) has become the most important imaging modality in ophthalmology. A substantial amount of research has recently been devoted to the development of machine learning (ML) models for the identification and quantification of pathological features in OCT images. Among the several sources of variability the ML models have to deal with, a major factor is the acquisition device, which can limit the ML model's generalizability. In this paper, we propose to reduce the image variability across different OCT devices (Spectralis and Cirrus) by using CycleGAN, an unsupervised unpaired image transformation algorithm. The usefulness of this approach is evaluated in the setting of retinal fluid segmentation, namely intraretinal cystoid fluid (IRC) and subretinal fluid (SRF). First, we train a segmentation model on images acquired with a source OCT device. Then we evaluate the model on (1) source, (2) target and (3) transformed versions of the target OCT images. The presented transformation strategy shows an F1 score of 0.4 (0.51) for IRC (SRF) segmentations. Compared with traditional transformation approaches, this means an F1 score gain of 0.2 (0.12).Comment: * Contributed equally (order was defined by flipping a coin) --------------- Accepted for publication in the "IEEE International Symposium on Biomedical Imaging (ISBI) 2019

On orthogonal projections for dimension reduction and applications in augmented target loss functions for learning problems

The use of orthogonal projections on high-dimensional input and target data in learning frameworks is studied. First, we investigate the relations between two standard objectives in dimension reduction, preservation of variance and of pairwise relative distances. Investigations of their asymptotic correlation as well as numerical experiments show that a projection does usually not satisfy both objectives at once. In a standard classification problem we determine projections on the input data that balance the objectives and compare subsequent results. Next, we extend our application of orthogonal projections to deep learning tasks and introduce a general framework of augmented target loss functions. These loss functions integrate additional information via transformations and projections of the target data. In two supervised learning problems, clinical image segmentation and music information classification, the application of our proposed augmented target loss functions increase the accuracy

Long-term comparative analysis of no evidence of disease activity (NEDA-3) status between multiple sclerosis patients treated with natalizumab and fingolimod for up to 4 years

Comparative effectiveness of natalizumab and fingolimod over a follow-up longer than 2 years has been not addressed yet

Improving foveal avascular zone segmentation in fluorescein angiograms by leveraging manual vessel labels from public color fundus pictures

In clinical routine, ophthalmologists frequently analyze the shape and size of the foveal avascular zone (FAZ) to detect and monitor retinal diseases. In order to extract those parameters, the contours of the FAZ need to be segmented, which is normally achieved by analyzing the retinal vasculature (RV) around the macula in fluorescein angiograms (FA). Computer-aided segmentation methods based on deep learning (DL) can automate this task. However, current approaches for segmenting the FAZ are often tailored to a specific dataset or require manual initialization. Furthermore, they do not take the variability and challenges of clinical FA into account, which are often of low quality and difficult to analyze. In this paper we propose a DL-based framework to automatically segment the FAZ in challenging FA scans from clinical routine. Our approach mimics the workflow of retinal experts by using additional RV labels as a guidance during training. Hence, our model is able to produce RV segmentations simultaneously. We minimize the annotation work by using a multi-modal approach that leverages already available public datasets of color fundus pictures (CFPs) and their respective manual RV labels. Our experimental evaluation on two datasets with FA from 1) clinical routine and 2) large multicenter clinical trials shows that the addition of weak RV labels as a guidance during training improves the FAZ segmentation significantly with respect to using only manual FAZ annotations.Fil: Hofer, Dominik. Medizinische Universität Wien; AustriaFil: Schmidt Erfurth, Ursula. Medizinische Universität Wien; AustriaFil: Orlando, José Ignacio. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Grupo de Plasmas Densos Magnetizados. Provincia de Buenos Aires. Gobernación. Comision de Investigaciones Científicas. Grupo de Plasmas Densos Magnetizados; Argentina. Medizinische Universität Wien; AustriaFil: Goldbach, Felix. Medizinische Universität Wien; AustriaFil: Gerendas, Bianca S.. Medizinische Universität Wien; AustriaFil: Seeböck, Philipp. Medizinische Universität Wien; Austri

An amplified-target loss approach for photoreceptor layer segmentation in pathological OCT scans

Segmenting anatomical structures such as the photoreceptor layer in retinal optical coherence tomography (OCT) scans is challenging in pathological scenarios. Supervised deep learning models trained with standard loss functions are usually able to characterize only the most common disease appeareance from a training set, resulting in suboptimal performance and poor generalization when dealing with unseen lesions. In this paper we propose to overcome this limitation by means of an augmented target loss function framework. We introduce a novel amplified-target loss that explicitly penalizes errors within the central area of the input images, based on the observation that most of the challenging disease appeareance is usually located in this area. We experimentally validated our approach using a data set with OCT scans of patients with macular diseases. We observe increased performance compared to the models that use only the standard losses. Our proposed loss function strongly supports the segmentation model to better distinguish photoreceptors in highly pathological scenarios.Comment: Accepted for publication at MICCAI-OMIA 201

U2-Net: A Bayesian U-Net model with epistemic uncertainty feedback for photoreceptor layer segmentation in pathological OCT scans

In this paper, we introduce a Bayesian deep learning based model for segmenting the photoreceptor layer in pathological OCT scans. Our architecture provides accurate segmentations of the photoreceptor layer and produces pixel-wise epistemic uncertainty maps that highlight potential areas of pathologies or segmentation errors. We empirically evaluated this approach in two sets of pathological OCT scans of patients with age-related macular degeneration, retinal vein oclussion and diabetic macular edema, improving the performance of the baseline U-Net both in terms of the Dice index and the area under the precision/recall curve. We also observed that the uncertainty estimates were inversely correlated with the model performance, underlying its utility for highlighting areas where manual inspection/correction might be needed.Comment: Accepted for publication at IEEE International Symposium on Biomedical Imaging (ISBI) 201

CIÊNCIA EM DESENVOLVIMENTO: DIPLOMAR, TITULAR E DESCOBRIR

O presente artigo apresenta dados referentes a constituição do grupo de professores que compõem o Departamento de Educação Física (DEF) e seu Programa de pós-graduação em Educação Física (PPGEF) da Universidade Federal do Paraná (UFPR) sob a ótica dos títulos acadêmicos, tendo como objetivo descrever sua configuração no ano de 2015. A pesquisa foi realizada em duas etapas: a) levantamento quantitativo dos nomes dos professores do DEF da UFPR e, destes, quem compõem o PPGEDF; b) busca de informações sobre a formação acadêmica destes professores, através da plataforma Lattes. Os resultados indicaram através de uma análise temporal como os sujeitos se estruturam e como decorreu a configuração atual do Departamento de Educação Física da UFPR. Conclui-se que a criação do programa de pós-graduação é um marco histórico no processo de constituição do corpo docente que compõe o DEF e o PPGEDF

Ciência em desenvolvimento: diplomar, titular e descobrir

This article presents data on the constitution of the group of teachers who are part of the Department of Physical Education (DEF) and its Program for Graduate Studies in Physical Education (PPGEF) of the Federal University of Paraná (UFPR). It was considered the perspective of academic degrees, aiming to describe their state in 2015. The survey was conducted in two stages: a) a quantitative survey of DEF teachers’ names and among them who are part of the PPGEDF; b) academic information about these teachers through the Lattes Platform. The results indicated how they become structured through time and how the current configuration of UFPR Department of Physical Education took place. It was concluded that the creation of the Graduation Program is a landmark for the constitution of the faculty that are part of the DEF and of the PPGEDF.Este artículo presenta datos respecto a la constitución del grupo de docentes que componen el Departamento de Educación Física (DEF) y su programa de Estudios de Posgrado en Educación Física (PPGEF) de la Universidad Federal de Paraná (UFPR) desde la perspectiva de los títulos académicos, con el objetivo de describir su configuración en el año 2015. La investigación se realizó en dos etapas: a) relevamiento cuantitativo de los nombres de profesores del DEF de la UFPR y entre éstos, aquellos que componen el PPGEDF; b) la búsqueda de información sobre la formación académica de estos profesores, mediante la plataforma Lattes. Los resultados muestran a partir de un análisis temporal como los sujetos se estructuran y cómo se conformó la configuración actual del Departamento de Educación Física de la UFPR. Se concluyó que la creación del programa de Estudios de Posgrado es una marca histórica en el proceso de constitución del profesorado que compone el DEF y el PPGEDF.O presente artigo apresenta dados referentes a constituição do grupo de professores que compõem o Departamento de Educação Física (DEF) e seu Programa de pós-graduação em Educação Física (PPGEF) da Universidade Federal do Paraná (UFPR) sob a ótica dos títulos acadêmicos, tendo como objetivo descrever sua configuração no ano de 2015. A pesquisa foi realizada em duas etapas: a) levantamento quantitativo dos nomes dos professores do DEF da UFPR e destes, quais compõem o PPGEDF; b) busca de informações sobre a formação acadêmica destes professores, através da plataforma Lattes. Os resultados indicaram através de uma análise temporal como os sujeitos se estruturam e como decorreu a configuração atual do Departamento de Educação Física da UFPR. Conclui-se que a criação do programa de pós-graduação é um marco histórico no processo de constituição do corpo docente que compõe o DEF e o PPGEDF