Phosphorus Use Efficiency of Safflower (Carthamus tinctorius L.) and Sunflower (Helianthus annuus L.) Studied in Nutrient Solution

Safflower represents an important oil crop internationally and may have a production potential under low input conditions, but its putatively high phosphorous use efficiency is not sustained. This study aims to directly compare safflower with sunflower in terms of phosphorus use efficiency in nutrient solution under controlled conditions. Growth of both species responded strongly to increasing P supply. Safflower recovers less proportion of added P than sunflower. External P requirement ((g P supply (100 g dry matter (DM) produced)-1) was higher in safflower than sunflower. The efficiency of the crops for DM production based on accumulated P (mg P pot-1, efficiency ratio), and P concentration in DM ((mg P (g DM)-1), utilization index) were interpreted using Michaelis-Menten kinetics as growth response curves. Accordingly, Km constant was lower in sunflower compared to safflower in terms of utilization index, but both were similar in terms of efficiency ratio. High Km constant in safflower in terms of utilization index indicates the high P concentration in tissues to produce 50% of potential maximum DM, consequently less efficient crop. Utilization efficiency contributed more than uptake efficiency in overall PUE in the efficient cultivar and could be the cause of its superiority in PUE. It can be concluded that safflower has a high requirement for P with respect to growth, sunflower is more efficient in terms of uptake and utilization of P at optimal and sub-optimal P supplies indicating that safflower can not be considered a low nutrient input crop compared to sunflower with respect to phosphorus.The authors wish to express their gratitude to the German Academic Exchange Service (DAAD) for financial support through a Ph.D. scholarship (A/03/33909)

Ambivalencia y ambigüedad en la obra de Eduardo Gasca

Con juegos de palabras, alusiones y datos ocultos, Eduardo Gasca logra un estilo caracterizado por la levedad y por tenues matices, para, con todo ello, construir poemas sólidamente estructurados. Vamos a revisar algunos de los más relevantes aspectos de esta obra poco común en la poesía venezolana. Este es un extracto de un texto más amplio, que forma parte de un libro en preparación y que responde al nombre de Literatura Venezolana contemporánea (1951-...), cuya fecha de cierre aún se no se cierra. ,

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

General and disease-specific quality of life in patients with chronic suppurative otitis media - a prospective study

Background: Chronic suppurative otitis media (CSOM) is frequently associated with symptoms of inflammation like discharge from the ear or pain. In many cases, patients suffer from hearing loss causing communication problems and social withdrawal. The objective of this work was to collect prospective audiological data and data on general and disease-specific quality of life with validated quality of life measurement instruments to assess the impact of the disease on health-related quality of life (HR-QOL). Methods: 121 patients were included in the study. Patients were clinically examined in the hospital before and 6 months after surgery including audiological testing. They filled in the quality of life questionnaires SF-36 and Chronic Otitis Media Outcome Test 15 (COMOT-15) pre-operatively and 6 and 12 months post-operatively, respectively. Results: Complete data records from 90 patients were available for statistical analysis. Disease-specific HR-QOL in patients with CSOM improved after tympanoplasty in all the scales of the COMOT-15. There was no difference in HR-QOL assessment between patients with mesotympanic respectively epitympanic CSOM. However, we did find the outcome to be worse in patients who received revision surgery compared with those receiving primary surgery. Audiometric findings correlated very well with the subscale hearing function from the COMOT-15 questionnaire. General HR-QOL measured with the SF-36 was not significantly changed by tympanoplasty. Conclusions: Tympanoplasty did lead to a significant improvement of disease-specific HR-QOL in patients with CSOM while general HR-QOL did not change. Very well correlations were found between the subscale hearing function from the COMOT-15 questionnaire and audiological findings. Revision surgery seems to be a predictor for a worse outcome

A Combined Finite Element-Multiple Criteria Optimization Approach for Materials Selection of Gas Turbine Components

The design of critical components for aerospace applications involves a number of conflicting functional requirements: reducing fuel consumption, cost, and weight, while enhancing performance, operability and robustness. As several materials systems and concepts remain competitive, a new approach that couples finite element analysis (FEA) and established multicriteria optimization protocols is developed in this paper. To demonstrate the approach, a prototypical materials selection problem for gas turbine combustor liners is chosen. A set of high temperature materials systems consisting of superalloys and thermal barrier coatings is considered as candidates. A thermo-mechanical FEA model of the combustor liner is used to numerically predict the response of each material system candidate. The performance of each case is then characterized by considering the material cost, manufacturability, oxidation resistance, damping behavior, thermomechanical properties, and the FEA postprocessed parameters relating to fatigue and creep. Using the obtained performance values as design criteria, an ELECTRE multiple attribute decision-making (MADM) model is employed to rank and classify the alternatives. The optimization model is enhanced by incorporating the relative importance (weighting factors) of the selection criteria, which is determined by multiple designers via a group decision-making process.Engineering and Applied Science

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