Parameter Estimation in Enzyme-Kinetics with Consideration of Heteroscedasticity and Low Dose Data

In this paper we propose a simulation study in order to discuss four statistical models dealing with the problem of parameter estimation in enzyme-kinetics. The pseudo-maximum-likelihood estimators for the transform-both-sides-model and the weighted TBS-model are compared with least-square-estimators of the classical nonlinear regression model and the linearized Eadie-Hofstee-plot. Due to heteroscedasticity of enzyme-kinetic data in low dose experiments the proposed estimators are investigated

Adjuncts and Additives to Regional Anesthesia

With regional anesthesia becoming increasingly popular, an emphasis has been placed on choosing appropriate adjunct medications to local anesthetics for prolonging peripheral nerve blocks- lengthening postoperative pain control and decreasing perioperative narcotic usage. This project aimed to increase provider knowledge of the proven efficacy of block and adjunct selection for breast, shoulder, and knee surgeries with intended results being prolonged block duration and increased patient satisfaction post-operatively. A focused lecture along with a Likert-scale questionnaire and quiz were administered to a small sample of anesthesia providers at a rural Illinois hospital. Half (55.6%) of the providers showed evidence of knowledge gained in PNB choice as well as adjunct selection. The remaining 44.4% did not reach out after the presentation and post-presentation knowledge was unable to be assessed. Thus, the results of the post-test and efficacy of the training may not be applicable to larger populations

Sequence-Structure Alignment Using a Statistical Analysis of Core Models and Dynamic Programming

The expanding availability of protein data enforces the application of empirical methods necessary to recognize protein structures. In this paper a sequence-structure alignment method is described and applied to various Ubiquitin-like folded Ras-binding domains. On the basis of two probability functions that evaluate similarities between the occurrence of amino-acids in the primary and secondary protein structure, different versions of simple scoring functions are proposed. The application of the program ’PLACER’ that uses a dynamic programming approach enables the search for an optimal sequence-structure alignment and the prediction of the secondary structure

Genetical and statistical aspects of polymerase chain reactions

In this paper we describe the principles of polymerase chain reaction (PCR) and its expanding use in molecular genetic research and molecular medicine. A short introduction of exemplary applications of the PCR is connected with a discussion of the lack of PCR accuracy. We give a statistical model for the PCR and discuss estimation methods in order to quantify the lack of PCR accuracy

Statistical analysis of Sequence-Structure Alignment Scores

The structural analysis of proteins is fundamental to the analysis of protein functions. In this context, sequence-structure alignment methods are important among the different empirical methods. In order to assess the quality of sequence-structure alignments, a statistical method using a Bayesian approach proposed by Lathrop et al. (1998) will be presented. Finally, the results of a developed statistical analysis of scores of RDP(recursive dynamic programming)-sequence-structure alignments (Thiele et al., 1999) according to data of six proteins will be described

Quantitative Nachweismethoden für Proteine und DNA aus gentechnisch veränderten Organismen in Lebensmitteln

Hoher Probendurchsatz mit einem Ja-/Nein-Ergebnis und präzise Gehaltsbestimmung GVO-positiver Lebensmittel sind Ziele bei der Kennzeichnung gentechnisch veränderter Organismen (GVOs). Die Bestimmung eines für viele GVOs charakteristischen Proteins (Neomycinphosphotransferase Typ II, NPTII) erfolgte dazu nach Antikörpererzeugung immunologisch durch Western-Blot oder Sandwich-ELISA. Sensitive Detektion von GVO- und Spezies-DNA (CaMV35S-Promotor, Roundup Ready-Konstrukt, Soja-Lektingen) wurde dagegen durch Vervielfältigung in der PCR (normal/kompetitiv) in Kombination mit einem PCR-ELISA (Hybridisierung) erreicht. Dieses Verfahren wurden in einem Ringversuch (13 Teilnehmer) getestet (Schwellenwert-Beurteilung) und erwies sich als unkompliziert, robust und korrekt; gerade in Hinblick auf das hohe Probenaufkommen in der Routineanalytik ist es gewinnbringend einsetzbar. Absolute Quantifizierung war durch Einsatz von Doppelkompetitor- und Normierungsplasmiden und nach Validierung der Gleichheit von Amplifikations-Effizienzen möglich. Eine Überprüfung fand durch Realzeit-PCR und zertifizierte Standards statt. Screening of food components using a polymerase chain reaction (PCR) for the presence of genetic elements, such as the widespread cauliflower mosaic virus 35S promoter introduced into genetically modified organisms (GMOs), has become a routine method in modern food analysis. With the aim of developing a high throughput method suitable for automation we established a PCR-enzyme-linked immunosorbent assay (PCR-ELISA). It is based on specific hybridization of an immobilized, biotinylated PCR product with a digoxigenin-labelled internal probe; the label then serves in colorimetric immunodetection. With this fast and convenient method laborious blotting procedures and the use of hazardous ethidium bromide in gel staining are avoided. The optimized protocol for this PCR-ELISA allows the detection of as little as 0.1 ng amplicon in only 2 h. With this new technique we analyzed whole Roundup Ready soybeans as well as soybean flour with GMO contents ranging from 0.1% to 2%

Modeling Big Data Systems by Extending the Palladio Component Model

ABSTRACT The growing availability of big data has induced new storing and processing techniques implemented in big data systems such as Apache Hadoop or Apache Spark. With increased implementations of these systems in organizations, simultaneously, the requirements regarding performance qualities such as response time, throughput, and resource utilization increase to create added value. Guaranteeing these performance requirements as well as efficiently planning needed capacities in advance is an enormous challenge. Performance models such as the Palladio component model (PCM) allow for addressing such problems. Therefore, we propose a metamodel extension for PCM to be able to model typical characteristics of big data systems. The extension consists of two parts. First, the meta-model is extended to support parallel computing by forking an operation multiple times on a computer cluster as intended by the single instruction, multiple data (SIMD) architecture. Second, modeling of computer clusters is integrated into the meta-model so operations can be properly scheduled on contained computing nodes