Konnektomik von viralen Tract-tracing Verbindungen des Nervensystems der Laborratte

In dieser Dissertation wurden erstmalig virale Tract-Tracing Konnektivitäten von adulten Laborratten in einer Metastudie methodisch zusammengefasst und anschließend mit dem Netzwerkanalyseprogramm "NeuroVIISAS" analysiert. Die Auswertung des Netzwerkes beinhaltet eine globale, lokale und differentielle Konnektomanalyse. Abschließend wird die Dissertation kritisch betrachtet, und es erfolgt ein Ausblick über zukünftige Entwicklungen in der Konnektomforschung

The EFSUMB Guidelines and Recommendations for the Clinical Practice of Elastography in Non-Hepatic Applications : Update 2018

Funding Information: Odd Helge Gilja: Advisory Board/Consultant fee from: AbbVie, Bracco, GE Healthcare, Samsung, and Takeda Paul S. Sidhu: Speaker honoraria, Bracco, Siemens, Samsung, Hiatchi, GE and Philips Christoph F. Dietrich: Speaker honoraria, Bracco, Hitachi, GE, Mindray, Supersonic, Pentax, Olympus, Fuji, Boston Scientific, AbbVie, Falk Foundation, Novartis, Roche; Advisory, Board Member, Hitachi, Mindray, Siemens; Research grant, GE, Mindray, SuperSonic Vito Cantisani: Speaker honoraria, Canon/Toshiba, Bracco, Samsung Dominique Amy: Speaker honoraria, Hitachi, Supersonic, EpiSonica Marco Brock: Speaker honoraria, Hitachi Fabrizio Calliada: Speaker honoraria, Bracco, Hitachi, Shenshen Mindray Dirk Andre Clevert: Speaker honoraria, Siemens, Samsung, GE, Bracco, Philips; Advisory Board, Siemens, Samsung, Bracco, Philips Jean-Michel Correas: Speaker honoraria, Hitachi-Aloka, Canon/Toshiba, Philips, Supersonic, Bracco, Guerbet; Research collaboration, Bracco Sonocap, Guerbet NsSafe and Secure protocols Mirko D’Onofrio: Speaker honoraria, Siemens, Bracco, Hitachi; Advisory Board Siemens, Bracco Andre Farrokh: Speaker honoraria, Hitachi Pietro Fusaroli: Speaker honoraria, Olympus Roald Flesland Havre: Speaker honoraria, GE Healthcare, Conference participation support from Pharmacosmos, Ultrasound equipment from Samsung Medison André Ignee: Speaker honoraria: Siemens, Canon/Toshiba, Hitachi, Boston Scientific, Bracco, Supersonic, Abbvie Christian Jenssen: Speaker honoraria, Bracco, Hitachi, Canon/Toshiba, Falk Foundation, Covidien; Research grant, Novartis Maija Radzina: Speaker honoraria, Bracco, Canon/Toshiba Luca Sconfienza: Travel grants from Bracco Imaging Italia Srl, Esaote SPA, Abiogen SPA, Fidia Middle East. Speaker honoraria from Fidia Middle East Ioan Sporea: Speaker honoraria, Philips, GE, Canon/Toshiba; Advisory Board Member, Siemens; Congress participation support, Siemens Mickael Tanter: Speaker honoraria, Supersonic; Co Founder and shareholder, Supersonic; Research collaboration, Supersonic Peter Vilmann: Speaker honoraria, Pentax, Norgine; Advisory Board, Boston Scientific; Consultancy MediGlobe The following members declared no conflicts of interest: Adrian Săftoiu, Michael Bachmann Nielsen, Flaviu Bob, Jörg Bojunga, Caroline Ewertsen, Michael Hocke, Andrea Klauser, Christian Kollmann, Kumar V Ramnarine, Carolina Solomon, Daniela Fodor, Horia Ștefănescu Publisher Copyright: © 2019 Georg Thieme Verlag KG Stuttgart New York.This manuscript describes the use of ultrasound elastography, with the exception of liver applications, and represents an update of the 2013 EFSUMB (European Federation of Societies for Ultrasound in Medicine and Biology) Guidelines and Recommendations on the clinical use of elastography.Peer reviewe

Can Survival Prediction Be Improved By Merging Gene Expression Data Sets?

BACKGROUND:High-throughput gene expression profiling technologies generating a wealth of data, are increasingly used for characterization of tumor biopsies for clinical trials. By applying machine learning algorithms to such clinically documented data sets, one hopes to improve tumor diagnosis, prognosis, as well as prediction of treatment response. However, the limited number of patients enrolled in a single trial study limits the power of machine learning approaches due to over-fitting. One could partially overcome this limitation by merging data from different studies. Nevertheless, such data sets differ from each other with regard to technical biases, patient selection criteria and follow-up treatment. It is therefore not clear at all whether the advantage of increased sample size outweighs the disadvantage of higher heterogeneity of merged data sets. Here, we present a systematic study to answer this question specifically for breast cancer data sets. We use survival prediction based on Cox regression as an assay to measure the added value of merged data sets. RESULTS:Using time-dependent Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) and hazard ratio as performance measures, we see in overall no significant improvement or deterioration of survival prediction with merged data sets as compared to individual data sets. This apparently was due to the fact that a few genes with strong prognostic power were not available on all microarray platforms and thus were not retained in the merged data sets. Surprisingly, we found that the overall best performance was achieved with a single-gene predictor consisting of CYB5D1. CONCLUSIONS:Merging did not deteriorate performance on average despite (a) The diversity of microarray platforms used. (b) The heterogeneity of patients cohorts. (c) The heterogeneity of breast cancer disease. (d) Substantial variation of time to death or relapse. (e) The reduced number of genes in the merged data sets. Predictors derived from the merged data sets were more robust, consistent and reproducible across microarray platforms. Moreover, merging data sets from different studies helps to better understand the biases of individual studies and can lead to the identification of strong survival factors like CYB5D1 expression

Modelling human choices: MADeM and decision‑making

Research supported by FAPESP 2015/50122-0 and DFG-GRTK 1740/2. RP and AR are also part of the Research, Innovation and Dissemination Center for Neuromathematics FAPESP grant (2013/07699-0). RP is supported by a FAPESP scholarship (2013/25667-8). ACR is partially supported by a CNPq fellowship (grant 306251/2014-0)

[Perspectives and Challenges of hand-held Ultrasound].

The use of handheld ultrasound devices from a technical and data protection point of view, device properties, functionality, documentation, indications, delegation of performance, applications by doctors, students and non-medical staff is examined and discussed

Räumliche und zeitliche Muster von Ellenberg-Nährstoffzahlen in Wäldern Deutschlands und angrenzender Gebiete : eine Untersuchung auf Grundlage pflanzensoziologischer Datenbanken

Within the last 30 years the role of nitrogen in Central European forests has changed fundamentally from limiting resource to environmental problem. As the retrospective tracking of nutrient availability by soil chemical and biogeochemical measurements faces serious problems, bioindication based on understorey species composition is indispensable for monitoring broad-scale eutrophication. Based on a broad survey of more than 100,000 forest vegetation plots accessible in electronic data-bases from Germany and adjacent countries, we calculated unweighted average Ellenberg nutrient values (mN) as a proxy of plant-available macronutrients. Based on the quantiles of the frequency distribution of mN in a regionally stratified sample, we define five trophic classes, which can be used to compare dimensionless mN values. We studied spatial patterns of average nutrient values within 17 regions and compared the periods from 1899 to 1975 and 1976 to 2006. After 1975 eutrophic (mN > 5.67) and hypertrophic (mN > 6.28) conditions were common everywhere except in the Alps and Saxony-Anhalt, but very oligotrophic conditions (mN < 3.44) were still widespread in regions with nutrient-poor bedrock. Before 1975 mN of plots had been lower than after 1975 in all but the southeastern regions. Between the pre- and post-1975 data the proportion of hypertrophic plots increased from 5.7 to 11.8%, and that of very oligo-trophic plots decreased from 14.6 to 8.3%. To remove bias resulting from uneven distribution, the dataset was stratified by five tree layer dominance types, period and region and resampled. In pre-1975 plots medians of mN increased in the order Pinus sylvestris, Quercus spp., Picea abies, Fagus sylvatica and Alnus spp, whereas the increase of mN was highest in forest types with historically low nutrient values. Therefore, the widespread change in mN must be attributed to the pronounced vegetation changes in Quercus and Pinus stands, indicating the importance of land-use change, i.e. recovery of nutrient cycles after hundreds of years of exploitation through coppicing, grazing and litter use. The analysis confirms eutrophication as a megatrend of modern vegetation change and demonstrates the high research potential of linking vegetation plot databases across large regions.Einleitung: In den vergangenen 30 Jahren hat sich die Wahrnehmung des Stickstoffs in mitteleuropäischen Wäldern grundlegend gewandelt (Bernhardt-Römermann & Ewald 2006). Einst Minimumfaktor, wurde N zu einem der wichtigsten Umweltprobleme. Angesichts der Schwierigkeit, das gesteigerte Nährstoffangebot bodenchemisch nachzuweisen, ist die Bioindikation an Hand von Vegetationsdaten unverzichtbar für den Nachweis von Eutrophierung. Die überregionale Verknüpfung von großen Vegetationsdatenbanken stellt einen wichtigen Ansatz zum Nachweis von zeitlichen Trends und räumlichen Mustern der Artenzusammensetzung dar (Jandt et al. 2011). Material und Methoden: Auf Basis einer Datensammlung mit mehr als 100.000 Vegetationsaufnahmen aus Deutschland und angrenzenden Gebieten (Niederlande, Belgien, östliches Frankreich, Schweiz, Österreich, Tschechien) wurden ungewichtete mittlere Ellenberg-Zeigerwerte für Nährstoffe (mN) als Surrogat für pflanzenverfügbare Makronährstoffe berechnet (Ellenberg et al. 2001). Fünf Trophie-Klassen wurden auf Grundlage der Quantile von mN in einer nach Regionen stratifizierten Stichprobe berechnet, um die Vergleichbarkeit der dimensionslosen Werte zu ermöglichen. Die Verteilung von mN in Wäldern des Untersuchungsraums wurde räumlich (17 Regionen) und zeitlich (1899–1975 und 1976–2006) differenziert dargestellt. Um statistische Verzerrungen zu vermeiden, wurden die Vegetationsaufnahmen nach dominierenden Baumarten (Waldkiefer, Eiche, Fichte, Buche, Erle), Region und Periode stratifiziert. Ergebnisse: Nach 1975 waren eutrophe (mN > 5,67) und hypertrophe (mN > 6,28) Zustände in allen Regionen mit Ausnahme der Alpen und Sachsen-Anhalts verbreitet anzutreffen. Stark oligotrophe Zustände (mN < 3,44) waren in Regionen mit armen Ausgangsteinen immer noch weit verbreitet. Vor 1975 war mN mit Ausnahme der südöstlichen Regionen geringer. Zwischen den Perioden erhöhte sich der Anteil hypertropher Aufnahmen von 5,7 auf 11,8 %, während derjenige stark oligotropher Aufnahmen sich von 14,6 auf 8,3 % verringerte. Für die bis einschließlich 1975 erhobene Vegetationsaufnahmen fanden wir die tiefsten Mediane von mN in Beständen von Pinus sylvestris, gefolgt von Quercus spp., Picea abies, Fagus sylvatica und Alnus spp. In den Aufnahmen nach 1975 zeigten die Mittelwerte bei den nährstoffreichsten Waldgesellschaften den geringsten und bei den oligotrophen Gesellschaften den größten Zuwachs. Diskussion: Die Studie bestätigt Eutrophierung als Megatrend des Vegetationswandels (Verheyen et al. 2012) und streicht das hohe Forschungspotenzial heraus, das in der Verknüpfung von Vegetationsdatenbanken über Ländergrenzen hinweg liegt (Dengler et al. 2011). Die weit verbreitete Zunahme von mN ist in erster Linie durch die markante Veränderung in Quercus- und Pinus-Beständen bedingt, was auf die Bedeutung des Nutzungswandels und die Erholung der Nährstoffkreisläufe nach Jahrhunderten der Niederwald-, Waldweide- und Streunutzung verweist (Glatzel 1991)