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Speaker: Professor Dr. Hans-Thomas Janka, Max Planck Institute for Astrophysics, Garching
Title: 3D core-collapse supernova modeling and applications to Cas A and other supernova remnants, Presentation in English
Date: Monday, 15 October 2018, 2:00 p.m.
Location: Carl-Bosch-Auditorium, Studio Villa Bosch, Schloss-Wolfsbrunnenweg 33, 69118 Heidelberg (Studio entrance between Villa Bosch and HITS)
Parking: Parking garage "Unter der Boschwiese" (free of charge)
Abstract:
First three-dimensional, first-principle simulations of core-collapse supernovae have become possible in the recent past. They demonstrate the basic viability of the neutrino-driven mechanism for powering the explosions of the majority of supernova progenitors. Although a number of open questions remain to be settled, the explosion models are now sufficiently mature to strive for detailed comparisons against observations, for example considering well studied, nearby supernovae and young supernova remnants. This talk will review our basic understanding of the explosion mechanism and report some results of such observational tests.
Curriculum vitae:
Please see: https://wwwmpa.mpa-garching.mpg.de/~thj/about-en.html
Contact:
Benedicta Frech (Diese E-Mail-Adresse ist vor Spambots geschützt! Zur Anzeige muss JavaScript eingeschaltet sein!, phone: 06221-533-263)
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Am 28. September 2018 findet die diesjährige Nacht der Forschung Heidelberg / Mannheim statt. Das ZITI beteiligt sich mit einer interaktiven Roboterausstellung sowie mit Demos im Motion Capture Bewegungslabor. Sie finden uns in den Foyers im UG und im EG des Mathematikons, INF 205, ab 18.00 bis 22:00 Uhr.
Weitere Informationen dazu finden Sie hier.
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Das Hochschulteam der Bundesagentur für Arbeit Heidelberg bietet umfassend Beratung für Studierenden und Absolventen sowie Absolventinnen rund um Arbeitsmarkt und Berufseinstieg.
Das Semesterprogramm für das WS 2018/19 finden Sie hier.
Weitere Informationen entnehmen Sie bitte der Homepage der Agentur für Arbeit: Link
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Speaker: Professor Dr. Kai Johnsson, Max Planck Institute for Medical Research, Dept. of Chemical Biology, Heidelberg
Title: Artificial Sensor Proteins for Applications in Basic Research and Medicine, Presentation in English
Date: Monday, 17 September 2018, 11:00 a.m.
Location: Carl-Bosch-Auditorium, Studio Villa Bosch, Schloss-Wolfsbrunnenweg 33, 69118 Heidelberg (Studio entrance between Villa Bosch and HITS)
Parking: Parking garage "Unter der Boschwiese" (free of charge)
Abstract:
Monitoring drug or metabolite concentrations at the point-of-care could improve the diagnosis and management of numerous diseases. Yet for most medically relevant molecules, such assays are not available. Using a combination of synthetic chemistry and protein engineering, we have generated light-emitting sensor proteins for use in paper-based assays. The analyte induces a change in the color of the emitted light, enabling its quantification using a digital camera. The approach makes numerous medically relevant molecules candidates for quantitative point-of-care assays, as shown for the anti-cancer drug methotrexate. Methotrexate serum levels were analyzed at the point-of-care within minutes using only minute amounts of sample. The approach should be important for the diagnosis and management of numerous diseases and furthermore underlines how the synergy between synthetic chemistry and protein engineering can be exploited to create artificial biomolecules with highly unusual properties.
Kai Johnsson is Director at the Max Planck Institute for Medical Research, Department of Chemical Biology since 2017. He was appointed after being Full Professor at the Institute of Chemical Sciences and Engineering of the École Polytechnique Fédérale de Lausanne (EPFL). His current research interests focus on the development of chemical approaches to visualize and manipulate biochemical activities in living cells. His past achievements include the introduction of methods to specifically label proteins in living cells (i.e. SNAP-tag and CLIP-tag), the development of new fluorescent probes and sensors as well as studies on the mechanism of action of drugs and drug candidates.
Kai Johnsson is Associate Editor of ACS Chemical Biology since 2005. He is member of the Editorial Advisory Board of Science and of the Research Council of the Swiss National Science Foundation. He received the Prix APLE for the invention of the year 2003 of EPFL, the Novartis Lectureship Award 2012/13, the Karl-Heinz Beckurts Prize 2016 and is elected member of EMBO.
Contact:
Benedicta Frech (Diese E-Mail-Adresse ist vor Spambots geschützt! Zur Anzeige muss JavaScript eingeschaltet sein!, phone: 06221-533-263)
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Speaker: Prof. Dr. Sergei L. Kosakovsky Pond, Temple University, Dep. of Biology, Philadelphia, USA
Title: Beyond software tuning: scaling up comparative coding sequence analysis using approximations and models that adapt their complexity to the data (Presentation in English)
Date: Monday, 28 May 2018, 11:00 a.m.
Location: Carl-Bosch-Auditorium, Studio Villa Bosch, Schloss-Wolfsbrunnenweg 33, 69118 Heidelberg (Studio entrance between Villa Bosch and HITS)
Parking: Parking garage "Unter der Boschwiese" (free of charge)
Abstract:
Genetic sequence data are being generated at an ever-increasing pace, while many analytical techniques that are commonly used to make biologically meaningful infer-ences on these data are still “stuck” in the “small data” age. For example, a practical upper bound on the number of sequences that can be analyzed with many popular comparative phylogenetic methods is 1000, especially if codon-substitution models are used. These types of models are an essential tool for deciphering the action of natural selection on genetic sequences, and have been used extensively in biomedical and basic science applications, for example to quantify pathogen evolution: drug re-sistance, zoonotic adaptation, immune escape.
We show how his number can be raised by several orders of magnitude, enabling in-depth study of gene-sized alignments with 10000 – 100000 sequences, much more extensive model testing, or the implementation of more realistic models with added complexity. This can be accomplished via an adaptation of machine learning tech-niques originally developed in the context of large-scale data mining (latent Dirichlet allocation models), and for variable selection.
Specifically, we describe a relatively general approximation technique to limit the num-ber of expensive likelihood function evaluations a priori, by discretizing a part of the parameter space to a fixed grid, estimating other parameters using much faster sim-pler models, and integrating over the grid using MCMC or a variational Bayes ap-proach. We demonstrate how this technique can achieve 100× or greater speedups for detecting sites subject to positive selection, while improving statistical performance. Other analyses where there are only a 2-3 parameters of interest (e.g. detection of directional selection in protein sequences) can be accommodated. When discretization is not appropriate, it is often possible to develop methods that employ variable para-metric complexity chosen with an information theoretic criterion. For example, in the Adaptive Branch Site Random Effects model, we quickly select and apply models of different complexity to different branches in the phylogeny, and deliver statistical per-formance matching or exceeding best-in-class existing approaches, while running an order of magnitude faster.
Curriculum vitae: Please see: http://spond.github.io/CV.js/cv.html
Contact: Benedicta Frech (Diese E-Mail-Adresse ist vor Spambots geschützt! Zur Anzeige muss JavaScript eingeschaltet sein!, phone: 06221-533-263)
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