University of Graz

About: University of Graz is a education organization based out in Graz, Steiermark, Austria. It is known for research contribution in the topics: Population & Quantum chromodynamics. The organization has 17934 authors who have published 37489 publications receiving 1110980 citations. The organization is also known as: Carolo Franciscea Graecensis & Karl Franzens Universität.

A dual role for autophagy in a murine model of lung cancer

Abstract: Autophagy prolongs the survival of cells in stressful conditions but its role in cancer is unclear. Here, Rao et al. show that loss of the autophagic protein Atg5 enhanced cancer incidence but impaired tumour progression in a mouse model of lung cancer.

Online or Face-to-Face? Students' Experiences and Preferences in E-Learning.

Abstract: Which aspects of e-learning courses do students experience as being favorable for learning? When do students prefer online or face-to-face learning components? These questions were the subject of a research study in a sample of 2196 students from 29 Austrian universities. The students completed a questionnaire on their experiences attending an e-learning course, on their perceived achievements, and on their preferences for online or face-to-face learning components. Students appreciated online learning for its potential in providing a clear and coherent structure of the learning material, in supporting self-regulated learning, and in distributing information. They preferred face-to-face learning for communication purposes in which a shared understanding has to be derived or in which interpersonal relations are to be established. An especially important result concerns students' perceptions of their learning achievements: When conceptual knowledge in the subject matter or skills in the application of one's knowledge are to be acquired, students prefer face-to-face learning. However, when skills in self-regulated learning are to be acquired, students advocate online learning.

Risk factors for progression of brain atrophy in aging: six-year follow-up of normal subjects.

Abstract: Objectives: To determine the rate of brain atrophy in neurologically asymptomatic elderly and to investigate the impact of baseline variables including conventional cerebrovascular risk factors, APOE e4, and white matter hyperintensity (WMH) on its progression. Methods: We assessed the brain parenchymal fraction at baseline and subsequent annual brain volume changes over 6 years for 201 participants (F/M = 96/105; 59.8 ± 5.9 years) in the Austrian Stroke Prevention Study from 1.5-T MRI scans using SIENA (structural image evaluation using normalization of atrophy)/SIENAX (an adaptation of SIENA for cross-sectional measurement)(www.fmrib.ox.ac.uk/fsl). Hypertension, cardiac disease, diabetes mellitus, smoking, and regular alcohol intake were present in 64 (31.8%), 60 (29.9%), 5 (2.5%), 70 (39.3%), and 40 (20.7%) subjects, respectively. Plasma levels of fasting glucose (93.7 ± 18.6 mg/dL), glycated hemoglobin A (HbA 1c ; 5.6 ± 0.7%), total cholesterol (228.3 ± 40.3 mg/dL), and triglycerides (127.0 ± 75.2 mg/dL) were determined. WMH was rated as absent (n = 56), punctate (n = 120), early confluent (n = 14), and confluent (n = 11). Results: The baseline brain parenchymal fraction of the entire cohort was 0.80 ± 0.02 with a mean annual brain volume change of −0.40 ± 0.29%. Univariate analysis demonstrated a higher rate of brain atrophy in older subjects ( p = 0.0001), in those with higher HbA 1c ( p = 0.0001), higher body mass index ( p = 0.02), high alcohol intake ( p = 0.04), severe WMH ( p = 0.03), and in APOE e4 carriers ( p = 0.07). Multivariate analysis suggested that baseline brain parenchymal fraction, HbA 1c , and WMH score explain a major proportion of variance in the rates of brain atrophy in the cohort (corrected R 2 = 0.27; p = 0.0001). Conclusions: Neurologically asymptomatic elderly experience continuing brain volume loss, which appears to accelerate with age. Glycated hemoglobin A (HbA 1c ) was identified as a risk factor for a greater rate of brain atrophy. Clustering of factors associated with the so-called metabolic syndrome in subjects with high HbA 1c suggests a link between this syndrome and late-life brain tissue loss.

Imaging of the active B1 field in vivo.

Abstract: The authors describe a method for accurate in vivo multislice imaging of the active component of the B1 field which is based on a previously proposed method, which uses the signal intensity ratio of two images measured with different excitation angles, and a repetition time TR 5 > or = 5 T1. The new method essentially reduces repetition and scan time by means of an additional compensating pulse. The suppression of T1 effects by this pulse are verified with simulations and measurements. Further investigations concerned the influence of slice selective excitation and magnetization transfer in multislice imaging to the B1 field determination. The stability and accuracy of the presented method is shown by several phantom and in vivo measurements. With the described method the active B1 field can be determined in vivo in 23 cross-sections in less than 6 min.

Learning a Variational Network for Reconstruction of Accelerated MRI Data

Abstract: Purpose: To allow fast and high-quality reconstruction of clinical accelerated multi-coil MR data by learning a variational network that combines the mathematical structure of variational models with deep learning. Theory and Methods: Generalized compressed sensing reconstruction formulated as a variational model is embedded in an unrolled gradient descent scheme. All parameters of this formulation, including the prior model defined by filter kernels and activation functions as well as the data term weights, are learned during an offline training procedure. The learned model can then be applied online to previously unseen data. Results: The variational network approach is evaluated on a clinical knee imaging protocol. The variational network reconstructions outperform standard reconstruction algorithms in terms of image quality and residual artifacts for all tested acceleration factors and sampling patterns. Conclusion: Variational network reconstructions preserve the natural appearance of MR images as well as pathologies that were not included in the training data set. Due to its high computational performance, i.e., reconstruction time of 193 ms on a single graphics card, and the omission of parameter tuning once the network is trained, this new approach to image reconstruction can easily be integrated into clinical workflow.