University of Zurich

About: University of Zurich is a education organization based out in Zurich, Switzerland. It is known for research contribution in the topics: Population & Medicine. The organization has 50842 authors who have published 124042 publications receiving 5304521 citations. The organization is also known as: UZH & Uni Zurich.

Computational prediction of proteotypic peptides for quantitative proteomics

Abstract: Mass spectrometry-based quantitative proteomics has become an important component of biological and clinical research. Although such analyses typically assume that a protein's peptide fragments are observed with equal likelihood, only a few so-called 'proteotypic' peptides are repeatedly and consistently identified for any given protein present in a mixture. Using >600,000 peptide identifications generated by four proteomic platforms, we empirically identified >16,000 proteotypic peptides for 4,030 distinct yeast proteins. Characteristic physicochemical properties of these peptides were used to develop a computational tool that can predict proteotypic peptides for any protein from any organism, for a given platform, with >85% cumulative accuracy. Possible applications of proteotypic peptides include validation of protein identifications, absolute quantification of proteins, annotation of coding sequences in genomes, and characterization of the physical principles governing key elements of mass spectrometric workflows (e.g., digestion, chromatography, ionization and fragmentation).

EFNS guideline on the drug treatment of migraine--revised report of an EFNS task force.

Abstract: Migraine is one of the most frequent disabling neurological conditions with a major impact on the patients' quality of life. To give evidence-based or expert recommendations for the different drug treatment procedures of the different migraine syndromes based on a literature search and an consensus in an expert panel. All available medical reference systems were screened for all kinds of clinical studies on migraine with and without aura and on migraine-like syndromes. The findings in these studies were evaluated according to the recommendations of the EFNS resulting in level A,B, or C recommendations and good practice points. For the acute treatment of migraine attacks, oral non-steroidal anti-inflammatory drugs (NSAIDs) and triptans are recommended. The administration should follow the concept of stratified treatment. Before intake of NSAIDs and triptans, oral metoclopramide or domperidon is recommended. In very severe attacks, intravenous acetylsalicylic acid or subcutaneous sumatriptan are drugs of first choice. A status migrainosus can probably be treated by steroids. For the prophylaxis of migraine, betablockers (propranolol and metoprolol), flunarizine, valproic acid, and topiramate are drugs of first choice. Drugs of second choice for migraine prophylaxis are amitriptyline, naproxen, petasites, and bisoprolol.

Optimal Transport for Domain Adaptation

Abstract: Domain adaptation is one of the most challenging tasks of modern data analytics. If the adaptation is done correctly, models built on a specific data representation become more robust when confronted to data depicting the same classes, but described by another observation system. Among the many strategies proposed, finding domain-invariant representations has shown excellent properties, in particular since it allows to train a unique classifier effective in all domains. In this paper, we propose a regularized unsupervised optimal transportation model to perform the alignment of the representations in the source and target domains. We learn a transportation plan matching both PDFs, which constrains labeled samples of the same class in the source domain to remain close during transport. This way, we exploit at the same time the labeled samples in the source and the distributions observed in both domains. Experiments on toy and challenging real visual adaptation examples show the interest of the method, that consistently outperforms state of the art approaches. In addition, numerical experiments show that our approach leads to better performances on domain invariant deep learning features and can be easily adapted to the semi-supervised case where few labeled samples are available in the target domain.

Phosphorylation of Nedd4‐2 by Sgk1 regulates epithelial Na+ channel cell surface expression

Abstract: The epithelial Na+ channel (ENaC) plays an essential role in the regulation of whole body Na+ balance and blood pressure. The cell surface expression of this channel, a complex of three subunits (α, β and γENaC), has been shown to be regulated by hormones such as aldosterone and vasopressin and by intracellular signaling, including ubiquitylation and/or phosphorylation. However, the molecular mechanisms involving phosphorylation in the regulation of ENaC are unclear. Here we show by expression studies in Xenopus laevis oocytes that the aldosterone-induced Sgk1 kinase interacts with the ubiquitin protein ligase Nedd4-2 in a PY motif-dependent manner and phosphorylates Nedd4-2 on Ser444 and, to a lesser extent, Ser338. Such phosphorylation reduces the interaction between Nedd4-2 and ENaC, leading to elevated ENaC cell surface expression. These data show that phosphorylation of an enzyme involved in the ubiquitylation cascade (Nedd4-2) controls cell surface density of ENaC and propose a paradigm for the control of ion channels. Moreover, they suggest a novel and complete signaling cascade for aldosterone-dependent regulation of ENaC.