University of Freiburg

About: University of Freiburg is a education organization based out in Freiburg, Baden-Württemberg, Germany. It is known for research contribution in the topics: Population & Transplantation. The organization has 41992 authors who have published 77296 publications receiving 2896269 citations. The organization is also known as: alberto-ludoviciana & Albert-Ludwigs-Universität Freiburg.

Hirnpotentialänderungen bei Willkürbewegungen und passiven Bewegungen des Menschen: Bereitschaftspotential und reafferente Potentiale

Abstract: Eine Methode zur chronologischen Datenspeicherung und Ruckwartsanalyse hirnelektrischer Begleitvorgange wiederholter Willkurbewegungen beim Menschen wird beschrieben.

The PIN auxin efflux facilitator network controls growth and patterning in Arabidopsis roots

Abstract: Local accumulation of the plant growth regulator auxin mediates pattern formation in Arabidopsis roots and influences outgrowth and development of lateral root- and shoot-derived primordia. However, it has remained unclear how auxin can simultaneously regulate patterning and organ outgrowth and how its distribution is stabilized in a primordium-specific manner. Here we show that five PIN genes collectively control auxin distribution to regulate cell division and cell expansion in the primary root. Furthermore, the joint action of these genes has an important role in pattern formation by focusing the auxin maximum and restricting the expression domain of PLETHORA (PLT) genes, major determinants for root stem cell specification. In turn, PLT genes are required for PIN gene transcription to stabilize the auxin maximum at the distal root tip. Our data reveal an interaction network of auxin transport facilitators and root fate determinants that control patterning and growth of the root primordium.

Decoupled Weight Decay Regularization.

Abstract: L$_2$ regularization and weight decay regularization are equivalent for standard stochastic gradient descent (when rescaled by the learning rate), but as we demonstrate this is \emph{not} the case for adaptive gradient algorithms, such as Adam. While common implementations of these algorithms employ L$_2$ regularization (often calling it "weight decay" in what may be misleading due to the inequivalence we expose), we propose a simple modification to recover the original formulation of weight decay regularization by \emph{decoupling} the weight decay from the optimization steps taken w.r.t. the loss function. We provide empirical evidence that our proposed modification (i) decouples the optimal choice of weight decay factor from the setting of the learning rate for both standard SGD and Adam and (ii) substantially improves Adam's generalization performance, allowing it to compete with SGD with momentum on image classification datasets (on which it was previously typically outperformed by the latter). Our proposed decoupled weight decay has already been adopted by many researchers, and the community has implemented it in TensorFlow and PyTorch; the complete source code for our experiments is available at this https URL

The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2016 update

Abstract: High-throughput data production technologies, particularly 'next-generation' DNA sequencing, have ushered in widespread and disruptive changes to biomedical research. Making sense of the large datasets produced by these technologies requires sophisticated statistical and computational methods , as well as substantial computational power. This has led to an acute crisis in life sciences, as researchers without informatics training attempt to perform computation-dependent analyses. Since 2005, the Galaxy project has worked to address this problem by providing a framework that makes advanced computational tools usable by non experts. Galaxy seeks to make data-intensive research more accessible , transparent and reproducible by providing a Web-based environment in which users can perform computational analyses and have all of the details automatically tracked for later inspection, publication , or reuse. In this report we highlight recently added features enabling biomedical analyses on a large scale.

Common variants at ABCA7, MS4A6A/MS4A4E, EPHA1, CD33 and CD2AP are associated with Alzheimer's disease.

Abstract: We sought to identify new susceptibility loci for Alzheimer's disease through a staged association study (GERAD+) and by testing suggestive loci reported by the Alzheimer's Disease Genetic Consortium (ADGC) in a companion paper. We undertook a combined analysis of four genome-wide association datasets (stage 1) and identified ten newly associated variants with P ≤ 1 × 10−5. We tested these variants for association in an independent sample (stage 2). Three SNPs at two loci replicated and showed evidence for association in a further sample (stage 3). Meta-analyses of all data provided compelling evidence that ABCA7 (rs3764650, meta P = 4.5 × 10−17; including ADGC data, meta P = 5.0 × 10−21) and the MS4A gene cluster (rs610932, meta P = 1.8 × 10−14; including ADGC data, meta P = 1.2 × 10−16) are new Alzheimer's disease susceptibility loci. We also found independent evidence for association for three loci reported by the ADGC, which, when combined, showed genome-wide significance: CD2AP (GERAD+, P = 8.0 × 10−4; including ADGC data, meta P = 8.6 × 10−9), CD33 (GERAD+, P = 2.2 × 10−4; including ADGC data, meta P = 1.6 × 10−9) and EPHA1 (GERAD+, P = 3.4 × 10−4; including ADGC data, meta P = 6.0 × 10−10).