Pyrosequenzierungsbasierte Analyse von SNP-Loci zur Diagnostik des Heterozygotieverlust auf Chromosom 3 im uvealen malignen Melanom

Im Rahmen der Dissertation wurde ein Verfahren zur Quantifizierung monosomer Zellpopulationen innerhalb eines disomen Normalgewebes auf Basis der Pyrosequenzierung von Einzelbasenmutationen etabliert und hinsichtlich seiner Genauigkeit untersucht. Dabei liegt ein besonderer Schwerpunkt auf der Entwicklung eines Verfahrens zur Festlegung von Grenzwerten für die Detektion monosomer Population sowie für genetisch heterogene Subpopulationen. Zur Bestimmung der Genauigkeit wurden Mischreihen von DNA zweier Genotypen angefertigt und das Allelverhältnis durch Pyrosequenzierung gemessen. Diese Ergebnisse wurden genutzt, um Grenzwerte für die Detektion von LOH3-positiven Zellen im UMM estzulegen. In diesen Vorversuchen konnte die Anwendbarkeit der Analysemethode für Proben aus UMM sowohl aus Enukleations wie auch aus Feinnadelaspirationspräparaten demonstriert werden. Es wurde dann in einem weiteren Schritt analysiert, wie viele differente Loci für eine korrekte Diskriminierung zweier Genotypen analysiert werden müssen. Hier wurde gezeigt, dass zum einen die Anzahl der untersuchten SNP aber auch das gemessene Allelverhältnis maßgeblichen Einfluss auf die Genauigkeit der Analyse haben. Basierend auf diesen Daten wurde ein Verfahren entwickelt, das aus der gewünschten Genauigkeit eine Berechnung des Umfangs eines zu etablierenden SNP-Panels ermöglichte

Automatic glossary term extraction from large-scale requirements specifications

Creating glossaries for large corpora of requirments is an important but expensive task. Glossary term extraction methods often focus on achieving a high recall rate and, therefore, favor linguistic proecssing for extracting glossary term candidates and neglect the benefits from reducing the number of candidates by statistical filter methods. However, especially for large datasets a reduction of the likewise large number of candidates may be crucial. This paper demonstrates how to automatically extract relevant domain-specific glossary term candidates from a large body of requirements, the CrowdRE dataset. Our hybrid approach combines linguistic processing and statistical filtering for extracting and reducing glossary term candidates. In a twofold evaluation, we examine the impact of our approach on the quality and quantity of extracted terms. We provide a ground truth for a subset of the requirements and show that a substantial degree of recall can be achieved. Furthermore, we advocate requirements coverage as an additional quality metric to assess the term reduction that results from our statistical filters. Results indicate that with a careful combination of linguistic and statistical extraction methods, a fair balance between later manual efforts and a high recall rate can be achieved

Supporting the Development of Cyber-Physical Systems with Natural Language Processing: A Report

Software has become the driving force for innovations in any technical system that observes the environment with different sensors and influence it by controlling a number of actuators; nowadays called Cyber-Physical System (CPS). The development of such systems is inherently inter-disciplinary and often contains a number of independent subsystems. Due to this diversity, the majority of development information is expressed in natural language artifacts of all kinds. In this paper, we report on recent results that our group has developed to support engineers of CPSs in working with the large amount of information expressed in natural language. We cover the topics of automatic knowledge extraction, expert systems, and automatic requirements classification. Furthermore, we envision that natural language processing will be a key component to connect requirements with simulation models and to explain tool-based decisions. We see both areas as promising for supporting engineers of CPSs in the future

A Subtle Interplay Between Three Pex11 Proteins Shapes De Novo Formation and Fission of Peroxisomes

The organization of eukaryotic cells into membrane-bound compartments must be faithfully sustained for survival of the cell. A subtle equilibrium exists between the degradation and the proliferation of organelles. Commonly, proliferation is initiated by a membrane remodeling process. Here, we dissect the function of proteins driving organelle proliferation in the particular case of peroxisomes. These organelles are formed either through a growth and division process from existing peroxisomes or de novo from the endoplasmic reticulum (ER). Among the proteins involved in the biogenesis of peroxisomes, peroxins, members of the Pex11 protein family participate in peroxisomal membrane alterations. In the yeast Saccharomyces cerevisiae, the Pex11 family consists of three proteins, Pex11p, Pex25p and Pex27p. Here we demonstrate that yeast mutants lacking peroxisomes require the presence of Pex25p to regenerate this organelle de novo. We also provide evidence showing that Pex27p inhibits peroxisomal function and illustrate that Pex25p initiates elongation of the peroxisomal membrane. Our data establish that although structurally conserved each of the three Pex11 protein family members plays a distinct role. While ScPex11p promotes the proliferation of peroxisomes already present in the cell, ScPex25p initiates remodeling at the peroxisomal membrane and ScPex27p acts to counter this activity. In addition, we reveal that ScPex25p acts in concert with Pex3p in the initiation of de novo peroxisome biogenesis from the ER

Expression Profiling of Single Mammalian Cells – Small is Beautiful

Increasingly mRNA expression patterns established using a variety of molecular technologies such as cDNA microarrays, SAGE and cDNA display are being used to identify potential regulatory genes and as a means of providing valuable insights into the biological status of the starting sample. Until recently, the application of these techniques has been limited to mRNA isolated from millions or, at very best, several thousand cells thereby restricting the study of small samples and complex tissues. To overcome this limitation a variety of amplification approaches have been developed which are capable of broadly evaluating mRNA expression patterns in single cells. This review will describe approaches that have been employed to examine global gene expression patterns either in small numbers of cells or, wherever possible, in actual isolated single cells. The first half of the review will summarize the technical aspects of methods developed for single-cell analysis and the latter half of the review will describe the areas of biological research that have benefited from single-cell expression analysis

Hidden localization motifs: naturally occurring peroxisomal targeting signals in non-peroxisomal proteins

BACKGROUND: Can sequence segments coding for subcellular targeting or for posttranslational modifications occur in proteins that are not substrates in either of these processes? Although considerable effort has been invested in achieving low false-positive prediction rates, even accurate sequence-analysis tools for the recognition of these motifs generate a small but noticeable number of protein hits that lack the appropriate biological context but cannot be rationalized as false positives. RESULTS: We show that the carboxyl termini of a set of definitely non-peroxisomal proteins with predicted peroxisomal targeting signals interact with the peroxisomal matrix protein receptor peroxin 5 (PEX5) in a yeast two-hybrid test. Moreover, we show that examples of these proteins - chicken lysozyme, human tyrosinase and the yeast mitochondrial ribosomal protein L2 (encoded by MRP7) - are imported into peroxisomes in vivo if their original sorting signals are disguised. We also show that even prokaryotic proteins can contain peroxisomal targeting sequences. CONCLUSIONS: Thus, functional localization signals can evolve in unrelated protein sequences as a result of neutral mutations, and subcellular targeting is hierarchically organized, with signal accessibility playing a decisive role. The occurrence of silent functional motifs in unrelated proteins is important for the development of sequence-based function prediction tools and the interpretation of their results. Silent functional signals have the potential to acquire importance in future evolutionary scenarios and in pathological conditions

Convergent evidence for validity of a performance-based ICT skills test

The goal of this study was to investigate sources of evidence of convergent validity supporting the construct interpretation of scores on a simulation-based ICT skills test. The construct definition understands ICT skills as reliant on ICT-specific knowledge as well as comprehension and problem-solving skills. On the basis of this, a validity argument comprising three claims was formulated and tested. (1) In line with the classical nomothetic span approach, all three predictor variables explained task success positively across all ICT skills items. As ICT tasks can vary in the extent to which they require construct-related knowledge and skills and in the way related items are designed and implemented, the effects of construct-related predictor variables were expected to vary across items. (2) A task-based analysis approach revealed that the item-level effects of the three predictor variables were in line with the targeted construct interpretation for most items. (3) Finally, item characteristics could significantly explain the random effect of problem-solving skills, but not comprehension skills. Taken together, the obtained results generally support the validity of the construct interpretation

Climate and parameter sensitivity and induced uncertainties in carbon stock projections for European forests (using LPJ-GUESS 4.0)

Understanding uncertainties and sensitivities of projected ecosystem dynamics under environmental change is of immense value for research and climate change policy. Here, we analyze sensitivities (change in model outputs per unit change in inputs) and uncertainties (changes in model outputs scaled to uncertainty in inputs) of vegetation dynamics under climate change, projected by a state-of-the-art dynamic vegetation model (LPJ-GUESS v4.0) across European forests (the species Picea abies, Fagus sylvatica and Pinus sylvestris), considering uncertainties of both model parameters and environmental drivers. We find that projected forest carbon fluxes are most sensitive to photosynthesis-, water-, and mortality-related parameters, while predictive uncertainties are dominantly induced by environmental drivers and parameters related to water and mortality. The importance of environmental drivers for predictive uncertainty increases with increasing temperature. Moreover, most of the interactions of model inputs (environmental drivers and parameters) are between environmental drivers themselves or between parameters and environmental drivers. In conclusion, our study highlights the importance of environmental drivers not only as contributors to predictive uncertainty in their own right but also as modifiers of sensitivities and thus uncertainties in other ecosystem processes. Reducing uncertainty in mortality-related processes and accounting for environmental influence on processes should therefore be a focus in further model development

Classes of depression symptom trajectories in patients with major depression receiving a collaborative care intervention

Purpose Collaborative care is effective in improving symptoms of patients with depression. The aims of this study were to characterize symptom trajectories in patients with major depression during one year of collaborative care and to explore associations between baseline characteristics and symptom trajectories. Methods We conducted a cluster-randomized controlled trial in primary care. The collaborative care intervention comprised case management and behavioral activation. We used the Patient Health Questionnaire-9 (PHQ-9) to assess symptom severity as the primary outcome. Statistical analyses comprised latent growth mixture modeling and a hierarchical binary logistic regression model. Results We included 74 practices and 626 patients (310 intervention and 316 control recipients) at baseline. Based on a minimum of 12 measurement points for each intervention recipient, we identified two latent trajectories, which we labeled \u27fast improvers\u27 (60.5%) and \u27slow improvers\u27 (39.5%). At all measurements after baseline, \u27fast improvers\u27 presented higher PHQ mean values than \u27slow improvers\u27. At baseline, \u27fast improvers\u27 presented fewer physical conditions, higher health-related quality of life, and had made fewer suicide attempts in their history. Conclusions A notable proportion of 39.5% of patients improved only \u27slowly\u27 and probably needed more intense treatment. The third follow-up in month two could well be a sensible time to adjust treatment to support \u27slow improvers\u27. (DIPF/Orig.