TY - JOUR A1 - Dejung, Mario A1 - Subota, Ines A1 - Bucerius, Ferdinand A1 - Dindar, Gülcin A1 - Freiwald, Anja A1 - Engstler, Markus A1 - Boshart, Michael A1 - Butter, Falk A1 - Janzen, Chistian J. T1 - Quantitative proteomics uncovers novel factors involved in developmental differentiation of Trypanosoma brucei JF - PLoS Pathogens N2 - Developmental differentiation is a universal biological process that allows cells to adapt to different environments to perform specific functions. African trypanosomes progress through a tightly regulated life cycle in order to survive in different host environments when they shuttle between an insect vector and a vertebrate host. Transcriptomics has been useful to gain insight into RNA changes during stage transitions; however, RNA levels are only a moderate proxy for protein abundance in trypanosomes. We quantified 4270 protein groups during stage differentiation from the mammalian-infective to the insect form and provide classification for their expression profiles during development. Our label-free quantitative proteomics study revealed previously unknown components of the differentiation machinery that are involved in essential biological processes such as signaling, posttranslational protein modifications, trafficking and nuclear transport. Furthermore, guided by our proteomic survey, we identified the cause of the previously observed differentiation impairment in the histone methyltransferase DOT1B knock-out strain as it is required for accurate karyokinesis in the first cell division during differentiation. This epigenetic regulator is likely involved in essential chromatin restructuring during developmental differentiation, which might also be important for differentiation in higher eukaryotic cells. Our proteome dataset will serve as a resource for detailed investigations of cell differentiation to shed more light on the molecular mechanisms of this process in trypanosomes and other eukaryotes. KW - cell differentiation KW - cell cycle and cell division KW - parasitic cell cycles KW - proteomes KW - chromatin KW - parasitic life cycles KW - transcriptome analysis KW - host-pathogen interactions Y1 - 2016 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-146362 VL - 12 IS - 2 ER - TY - BOOK A1 - Falk, Michael A1 - Marohn, Frank A1 - Michel, René A1 - Hofmann, Daniel A1 - Macke, Maria A1 - Spachmann, Christoph A1 - Englert, Stefan T1 - A First Course on Time Series Analysis : Examples with SAS [Version 2012.August.01] N2 - The analysis of real data by means of statistical methods with the aid of a software package common in industry and administration usually is not an integral part of mathematics studies, but it will certainly be part of a future professional work. The present book links up elements from time series analysis with a selection of statistical procedures used in general practice including the statistical software package SAS. Consequently this book addresses students of statistics as well as students of other branches such as economics, demography and engineering, where lectures on statistics belong to their academic training. But it is also intended for the practician who, beyond the use of statistical tools, is interested in their mathematical background. Numerous problems illustrate the applicability of the presented statistical procedures, where SAS gives the solutions. The programs used are explicitly listed and explained. No previous experience is expected neither in SAS nor in a special computer system so that a short training period is guaranteed. This book is meant for a two semester course (lecture, seminar or practical training) where the first three chapters can be dealt within the first semester. They provide the principal components of the analysis of a time series in the time domain. Chapters 4, 5 and 6 deal with its analysis in the frequency domain and can be worked through in the second term. In order to understand the mathematical background some terms are useful such as convergence in distribution, stochastic convergence, maximum likelihood estimator as well as a basic knowledge of the test theory, so that work on the book can start after an introductory lecture on stochastics. Each chapter includes exercises. An exhaustive treatment is recommended. Chapter 7 (case study) deals with a practical case and demonstrates the presented methods. It is possible to use this chapter independent in a seminar or practical training course, if the concepts of time series analysis are already well understood. This book is consecutively subdivided in a statistical part and an SAS-specific part. For better clearness the SAS-specific parts are highlighted. This book is an open source project under the GNU Free Documentation License. KW - Zeitreihenanalyse KW - Box-Jenkins-Verfahren KW - SAS KW - Zustandsraummodelle KW - Time Series Analysis KW - State-Space Models KW - Frequency Domain KW - Box–Jenkins Program Y1 - 2012 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-72617 N1 - Version: 2012-August-01 ER - TY - BOOK A1 - Falk, Michael A1 - Marohn, Frank A1 - Michel, René A1 - Hofmann, Daniel A1 - Macke, Maria A1 - Tewes, Bernward A1 - Dinges, Peter A1 - Spachmann, Christoph A1 - Englert, Stefan T1 - A First Course on Time Series Analysis : Examples with SAS N2 - The analysis of real data by means of statistical methods with the aid of a software package common in industry and administration usually is not an integral part of mathematics studies, but it will certainly be part of a future professional work. The present book links up elements from time series analysis with a selection of statistical procedures used in general practice including the statistical software package SAS. Consequently this book addresses students of statistics as well as students of other branches such as economics, demography and engineering, where lectures on statistics belong to their academic training. But it is also intended for the practician who, beyond the use of statistical tools, is interested in their mathematical background. Numerous problems illustrate the applicability of the presented statistical procedures, where SAS gives the solutions. The programs used are explicitly listed and explained. No previous experience is expected neither in SAS nor in a special computer system so that a short training period is guaranteed. This book is meant for a two semester course (lecture, seminar or practical training) where the first three chapters can be dealt within the first semester. They provide the principal components of the analysis of a time series in the time domain. Chapters 4, 5 and 6 deal with its analysis in the frequency domain and can be worked through in the second term. In order to understand the mathematical background some terms are useful such as convergence in distribution, stochastic convergence, maximum likelihood estimator as well as a basic knowledge of the test theory, so that work on the book can start after an introductory lecture on stochastics. Each chapter includes exercises. An exhaustive treatment is recommended. Chapter 7 (case study) deals with a practical case and demonstrates the presented methods. It is possible to use this chapter independent in a seminar or practical training course, if the concepts of time series analysis are already well understood. This book is consecutively subdivided in a statistical part and an SAS-specific part. For better clearness the SAS-specific parts are highlighted. This book is an open source project under the GNU Free Documentation License. KW - Zeitreihenanalyse KW - Box-Jenkins-Verfahren KW - SAS KW - Zustandsraummodelle KW - Time Series Analysis KW - State-Space Models KW - Frequency Domain KW - Box–Jenkins Program Y1 - 2011 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-56489 N1 - Version: 2011-March-01 ER - TY - JOUR A1 - Hornburg, Daniel A1 - Drepper, Carsten A1 - Butter, Falk A1 - Meissner, Felix A1 - Sendtner, Michael A1 - Mann, Matthias T1 - Deep Proteomic Evaluation of Primary and Cell Line Motoneuron Disease Models Delineates Major Differences in Neuronal Characteristics* JF - Molecular & Cellular Proteomics : MCP N2 - The fatal neurodegenerative disorders amyotrophic lateral sclerosis and spinal muscular atrophy are, respectively, the most common motoneuron disease and genetic cause of infant death. Various in vitro model systems have been established to investigate motoneuron disease mechanisms, in particular immortalized cell lines and primary neurons. Using quantitative mass-spectrometry-based proteomics, we compared the proteomes of primary motoneurons to motoneuron-like cell lines NSC-34 and N2a, as well as to non-neuronal control cells, at a depth of 10,000 proteins. We used this resource to evaluate the suitability of murine in vitro model systems for cell biological and biochemical analysis of motoneuron disease mechanisms. Individual protein and pathway analysis indicated substantial differences between motoneuron-like cell lines and primary motoneurons, especially for proteins involved in differentiation, cytoskeleton, and receptor signaling, whereas common metabolic pathways were more similar. The proteins associated with amyotrophic lateral sclerosis also showed distinct differences between cell lines and primary motoneurons, providing a molecular basis for understanding fundamental alterations between cell lines and neurons with respect to neuronal pathways with relevance for disease mechanisms. Our study provides a proteomics resource for motoneuron research and presents a paradigm of how mass-spectrometry-based proteomics can be used to evaluate disease model systems. Y1 - 2014 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-120954 SN - 1535-9484 N1 - This research was originally published in Molecular & Cellular Proteomics. Daniel Hornburg, Carsten Drepper, Falk Butter, Felix Meissner, Michael Sendtner, and Matthias Mann. Deep Proteomic Evaluation of Primary and Cell Line Motoneuron Disease Models Delineates Major Differences in Neuronal Characteristics*. Molecular & Cellular Proteomics. 2014; 13:3410–3420. © the American Society for Biochemistry and Molecular Biology. VL - 13 IS - 12 ER - TY - JOUR A1 - Falk, Michael A1 - Marohn, Frank T1 - Von Mises condition revisited N2 - It is shown that the rate of convergence in the von Mises conditions of extreme value theory determines the distance of the underlying distribution function F from a generalized Pareto distribution. The distance is measured in terms of the pertaining densities with the limit being ultimately attained if and only if F is ultimately a generalized Pareto distribution. Consequently, the rate of convergence of the extremes in an lid sample, whether in terms of the distribution of the largest order statistics or of corresponding empirical truncated point processes, is determined by the rate of convergence in the von Mises condition. We prove that the converse is also true. KW - Von Mises conditions KW - extreme value theory KW - extreme value distribution KW - extreme order statistics KW - generalized Pareto distribution Y1 - 1993 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-45790 ER - TY - JOUR A1 - Falk, Michael A1 - Marohn, Frank T1 - Asymptotically optimal tests for conditional distributions N2 - No abstract available Y1 - 1993 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-45823 ER - TY - CHAP A1 - Falk, Michael A1 - Marohn, Frank T1 - Laws of small numbers : Some applications to conditional curve estimation N2 - No abstract available KW - Gesetz der kleinen Zahlen KW - Poisson-Prozess KW - Regressionsanalyse KW - Extremwertstatistik Y1 - 1992 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-45841 N1 - Essays to the memory of Professor Jozsef Mogyorodi ER - TY - JOUR A1 - Franke, Werner W. A1 - Berger, S. A1 - Falk, Heinz A1 - Spring, H. A1 - Scheer, Ulrich A1 - Trendelenburg, Michael F. A1 - Schweiger, H. G. A1 - Herth, W. T1 - Morphology of the nucleo-cytoplasmic interactions during the development of Acetabularia cells. I. The vegetative phase N2 - The ultrastructure of th e growin g and ma turing primary nucleus of Acetabularia medite rranea and Acetabularia major has been studied with the use of various fi xation procedures. Particular interest has been focused on the deta ils of the nuclear periphery and the perinuclear region. It is demonstrated that early in nuclear grow th a characteristic perinucl ear structura l complex is formed which is, among the eukaryotic cells, unique to Acetabularia and re lated genera. This perinuclear system consists essentially of a) the nuclear envelope with a very hi gh pore frequency and various pore complex assoc iat ion s w ith granular and/or threadlike structures some of which are continuous with the nucleolus; b) an approx imate ly 100 nm thick intermediate zone densely filled with a filam entOus material and occasional sma ll membraneous structures from which the typical cytOplasmic and nuclear organe lles and particles are excl ud ed ; c) an adjacent Iacunar labyrinthum which is interrupted by many plasmatic junction channels between the intermed iate zone and the free cytOplasm; d) numerous dense perinuclear bodies in the juxtanuclear cytOplasm which a re especia lly frequent at the junction channels and reveal a composition of aggregated fibrillar and granul ar structures; e) very dense exclusively fibrill ar agg regates which occur either in assoc iation with t he perinuclear region of the lacunar labyrinthum or, somewhat further out, in the cytOplasmic strands between the bra nches of the lacun ar labyrinthum in the form of slender, characteristic rods or "sausages". Y1 - 1974 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-32363 ER - TY - JOUR A1 - Dittrich, Falk A1 - Thoenen, Hans A1 - Sendtner, Michael T1 - Ciliary neurotrophic factor: pharmacokinetics and acute-phase response in rat N2 - No abstract available Y1 - 1994 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-42639 ER - TY - BOOK A1 - Falk, Michael A1 - Marohn, Frank A1 - Michel, René A1 - Hofmann, Daniel A1 - Macke, Maria A1 - Tewes, Bernward A1 - Dinges, Peter T1 - A First Course on Time Series Analysis : Examples with SAS N2 - The analysis of real data by means of statistical methods with the aid of a software package common in industry and administration usually is not an integral part of mathematics studies, but it will certainly be part of a future professional work. The present book links up elements from time series analysis with a selection of statistical procedures used in general practice including the statistical software package SAS Statistical Analysis System). Consequently this book addresses students of statistics as well as students of other branches such as economics, demography and engineering, where lectures on statistics belong to their academic training. But it is also intended for the practician who, beyond the use of statistical tools, is interested in their mathematical background. Numerous problems illustrate the applicability of the presented statistical procedures, where SAS gives the solutions. The programs used are explicitly listed and explained. No previous experience is expected neither in SAS nor in a special computer system so that a short training period is guaranteed. This book is meant for a two semester course (lecture, seminar or practical training) where the first two chapters can be dealt with in the first semester. They provide the principal components of the analysis of a time series in the time domain. Chapters 3, 4 and 5 deal with its analysis in the frequency domain and can be worked through in the second term. In order to understand the mathematical background some terms are useful such as convergence in distribution, stochastic convergence, maximum likelihood estimator as well as a basic knowledge of the test theory, so that work on the book can start after an introductory lecture on stochastics. Each chapter includes exercises. An exhaustive treatment is recommended. This book is consecutively subdivided in a statistical part and an SAS-specific part. For better clearness the SAS-specific part, including the diagrams generated with SAS, always starts with a computer symbol, representing the beginning of a session at the computer, and ends with a printer symbol for the end of this session. This book is an open source project under the GNU Free Documentation License. KW - Zeitreihenanalyse KW - SAS KW - Zeitreihenanalyse KW - SAS KW - Time series analyses KW - SAS Y1 - 2005 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-12593 ER - TY - BOOK A1 - Falk, Michael A1 - Marohn, Frank A1 - Michel, René A1 - Hofmann, Daniel A1 - Macke, Maria A1 - Tewes, Bernward A1 - Dinges, Peter T1 - A First Course on Time Series Analysis : Examples with SAS N2 - The analysis of real data by means of statistical methods with the aid of a software package common in industry and administration usually is not an integral part of mathematics studies, but it will certainly be part of a future professional work. The present book links up elements from time series analysis with a selection of statistical procedures used in general practice including the statistical software package SAS Statistical Analysis System). Consequently this book addresses students of statistics as well as students of other branches such as economics, demography and engineering, where lectures on statistics belong to their academic training. But it is also intended for the practician who, beyond the use of statistical tools, is interested in their mathematical background. Numerous problems illustrate the applicability of the presented statistical procedures, where SAS gives the solutions. The programs used are explicitly listed and explained. No previous experience is expected neither in SAS nor in a special computer system so that a short training period is guaranteed. This book is meant for a two semester course (lecture, seminar or practical training) where the first two chapters can be dealt with in the first semester. They provide the principal components of the analysis of a time series in the time domain. Chapters 3, 4 and 5 deal with its analysis in the frequency domain and can be worked through in the second term. In order to understand the mathematical background some terms are useful such as convergence in distribution, stochastic convergence, maximum likelihood estimator as well as a basic knowledge of the test theory, so that work on the book can start after an introductory lecture on stochastics. Each chapter includes exercises. An exhaustive treatment is recommended. This book is consecutively subdivided in a statistical part and an SAS-specific part. For better clearness the SAS-specific part, including the diagrams generated with SAS, always starts with a computer symbol, representing the beginning of a session at the computer, and ends with a printer symbol for the end of this session. This book is an open source project under the GNU Free Documentation License. KW - Zeitreihenanalyse KW - SAS KW - Zeitreihenanalyse KW - SAS KW - Time series analyses KW - SAS Y1 - 2006 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-16919 ER - TY - JOUR A1 - Falk, Michael A1 - Fuller, Timo T1 - New characterizations of multivariate Max-domain of attraction and D-Norms JF - Extremes N2 - In this paper we derive new results on multivariate extremes and D-norms. In particular we establish new characterizations of the multivariate max-domain of attraction property. The limit distribution of certain multivariate exceedances above high thresholds is derived, and the distribution of that generator of a D-norm on R\(^{d}\), whose components sum up to d, is obtained. Finally we introduce exchangeable D-norms and show that the set of exchangeable D-norms is a simplex. KW - extremal exchangeable D-norms KW - multivariate extreme value theory KW - multivariate max-domain of attraction KW - D-norm KW - generator of D-norm KW - multivariate exceedance KW - co-extremality coefficient KW - exchangeable D-norms Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-269071 SN - 1572-915X VL - 24 IS - 4 ER - TY - JOUR A1 - Herrmann, Johannes A1 - Lotz, Christopher A1 - Karagiannidis, Christian A1 - Weber-Carstens, Steffen A1 - Kluge, Stefan A1 - Putensen, Christian A1 - Wehrfritz, Andreas A1 - Schmidt, Karsten A1 - Ellerkmann, Richard K. A1 - Oswald, Daniel A1 - Lotz, Gösta A1 - Zotzmann, Viviane A1 - Moerer, Onnen A1 - Kühn, Christian A1 - Kochanek, Matthias A1 - Muellenbach, Ralf A1 - Gaertner, Matthias A1 - Fichtner, Falk A1 - Brettner, Florian A1 - Findeisen, Michael A1 - Heim, Markus A1 - Lahmer, Tobias A1 - Rosenow, Felix A1 - Haake, Nils A1 - Lepper, Philipp M. A1 - Rosenberger, Peter A1 - Braune, Stephan A1 - Kohls, Mirjam A1 - Heuschmann, Peter A1 - Meybohm, Patrick T1 - Key characteristics impacting survival of COVID-19 extracorporeal membrane oxygenation JF - Critical Care N2 - Background Severe COVID-19 induced acute respiratory distress syndrome (ARDS) often requires extracorporeal membrane oxygenation (ECMO). Recent German health insurance data revealed low ICU survival rates. Patient characteristics and experience of the ECMO center may determine intensive care unit (ICU) survival. The current study aimed to identify factors affecting ICU survival of COVID-19 ECMO patients. Methods 673 COVID-19 ARDS ECMO patients treated in 26 centers between January 1st 2020 and March 22nd 2021 were included. Data on clinical characteristics, adjunct therapies, complications, and outcome were documented. Block wise logistic regression analysis was applied to identify variables associated with ICU-survival. Results Most patients were between 50 and 70 years of age. PaO\(_{2}\)/FiO\(_{2}\) ratio prior to ECMO was 72 mmHg (IQR: 58–99). ICU survival was 31.4%. Survival was significantly lower during the 2nd wave of the COVID-19 pandemic. A subgroup of 284 (42%) patients fulfilling modified EOLIA criteria had a higher survival (38%) (p = 0.0014, OR 0.64 (CI 0.41–0.99)). Survival differed between low, intermediate, and high-volume centers with 20%, 30%, and 38%, respectively (p = 0.0024). Treatment in high volume centers resulted in an odds ratio of 0.55 (CI 0.28–1.02) compared to low volume centers. Additional factors associated with survival were younger age, shorter time between intubation and ECMO initiation, BMI > 35 (compared to < 25), absence of renal replacement therapy or major bleeding/thromboembolic events. Conclusions Structural and patient-related factors, including age, comorbidities and ECMO case volume, determined the survival of COVID-19 ECMO. These factors combined with a more liberal ECMO indication during the 2nd wave may explain the reasonably overall low survival rate. Careful selection of patients and treatment in high volume ECMO centers was associated with higher odds of ICU survival. KW - Covid-19 KW - extracorporeal membrane oxygenation (ECMO) KW - intensive care unit Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-299686 VL - 26 IS - 1 ER -