Quality-by-Design II: Application of Quantitative Risk Analysis to the Formulation of Ciprofloxacin Tablets

Qualitative risk assessment methods are often used as the first step to determining design space boundaries; however, quantitative assessments of risk with respect to the design space, i.e., calculating the probability of failure for a given severity, are needed to fully characterize design space boundaries. Quantitative risk assessment methods in design and operational spaces are a significant aid to evaluating proposed design space boundaries. The goal of this paper is to demonstrate a relatively simple strategy for design space definition using a simplified Bayesian Monte Carlo simulation. This paper builds on a previous paper that used failure mode and effects analysis (FMEA) qualitative risk assessment and Plackett-Burman design of experiments to identity the critical quality attributes. The results show that the sequential use of qualitative and quantitative risk assessments can focus the design of experiments on a reduced set of critical material and process parameters that determine a robust design space under conditions of limited laboratory experimentation. This approach provides a strategy by which the degree of risk associated with each known parameter can be calculated and allocates resources in a manner that manages risk to an acceptable level.     

A life-like virtual cell membrane using discrete automata

A framework is presented that captures the discrete and probabilistic nature of molecular transport and reaction kinetics found in a living cell as well as formally representing the spatial distribution of these phenomena. This particle or agent-based approach is computationally robust and complements established methods. Namely it provides a higher level of spatial resolution than formulations based on ordinary differential equations (ODE) while offering significant advantages in computational efficiency over molecular dynamics (MD). Using this framework, a model cell membrane has been constructed with discrete particle agents that respond to local component interactions that resemble flocking or herding behavioural cues in animals. Results from simulation experiments are presented where this model cell exhibits many of the characteristic behaviours associated with its biological counterpart such as lateral diffusion, response to osmotic pressure gradients, membrane growth and cell division. Lateral diffusion rates and estimates for the membrane modulus of elasticity derived from these simple experiments fall well within a biologically relevant range of values. More importantly, these estimates were obtained by applying a simple qualitative tuning of the model membrane. Membrane growth was simulated by injecting precursor molecules into the proto-cell at different rates and produced a variety of morphologies ranging from a single large cell to a cluster of cells. The computational scalability of this methodology has been tested and results from benchmarking experiments indicate that real-time simulation of a complete bacterial cell will be possible within 10 years.     

Measuring lymphocyte proliferation, survival and differentiation using CFSE time-series data

Cellular proliferation is an essential feature of the adaptive immune response. The introduction of the division tracking dye carboxyfluorescein diacetate succinimidyl ester (CFSE) has made it possible to monitor the number of cell divisions during proliferation and to examine the relationship between proliferation and differentiation. Although qualitative examination of CFSE data may be useful, substantially more information about division and death rates can be extracted from quantitative CFSE time-series experiments. Quantitative methods can reveal in detail how lymphocyte proliferation and survival are regulated and altered by signals such as those received from co-stimulatory molecules, drugs and genetic polymorphisms. In this protocol, we present a detailed method for examining time-series data using graphical and computer-based procedures available to all experimenters.     

Unscheduled DNA synthesis: Some statistical thoughts

Statistical interpretation of results of experiments involving unscheduled DNA synthesis is examined from a design standpoint. Most appropriate methods currently in use are evaluated and some modifications and extensions are suggested. Concerns about replication and/or interaction errors are evaluated and methods for their appropriate handling are discussed. It is suggested that methods incorporating both dose-response and heterogeneity statistics should be considered in treating results from unscheduled DNA synthesis experiments. Proper designs for such experiments are emphasized.Abbreviations ANOVA analysis of variance - MSE mean square error - UDS unscheduled DNA synthesis     

A probabilistic methodology for integrating knowledge and experiments on biological networks.

Biological systems are traditionally studied by focusing on a specific subsystem, building an intuitive model for it, and refining the model using results from carefully designed experiments. Modern experimental techniques provide massive data on the global behavior of biological systems, and systematically using these large datasets for refining existing knowledge is a major challenge. Here we introduce an extended computational framework that combines formalization of existing qualitative models, probabilistic modeling, and integration of high-throughput experimental data. Using our methods, it is possible to interpret genomewide measurements in the context of prior knowledge on the system, to assign statistical meaning to the accuracy of such knowledge, and to learn refined models with improved fit to the experiments. Our model is represented as a probabilistic factor graph, and the framework accommodates partial measurements of diverse biological elements. We study the performance of several probabilistic inference algorithms and show that hidden model variables can be reliably inferred even in the presence of feedback loops and complex logic. We show how to refine prior knowledge on combinatorial regulatory relations using hypothesis testing and derive p-values for learned model features. We test our methodology and algorithms on a simulated model and on two real yeast models. In particular, we use our method to explore uncharacterized relations among regulators in the yeast response to hyper-osmotic shock and in the yeast lysine biosynthesis system. Our integrative approach to the analysis of biological regulation is demonstrated to synergistically combine qualitative and quantitative evidence into concrete biological predictions.     

Measuring the costs of reproduction

The measurement of costs of reproduction is of interest because such costs are generally assumed by life history theory. There is some controversy concerning how to measure costs: common methods include experimental manipulations of life history, such as preventing some individuals from reproducing, or estimates of genetic correlations. These two methods often yield similar results, suggesting that one can serve as a substitute for the other. There are now experiments which demonstrate that there are different mechanisms underlying the response to an experimental manipulation versus a genetic correlation, so the two methods are not equivalent in estimating costs.     

An evaluation of video playback using Xiphophorus helleri

Video playback is being increasingly used as a technique for behavioural research. The importance of critically evaluating the effectiveness of video playback is clear, as available video technology is not designed for nonhuman visual systems. We discuss several aspects concerning the perception of video images that could lead to inconclusive or erroneous results. Researchers should verify that behaviour observed in response to video playback is comparable to behaviour observed in response to live animals. We conducted such a verification using live and video playback methods to measure female response to swords of varying lengths in the green swordtail, Xiphophorus helleri. Using both methods, female response appeared to be an increasing function of male sword length. Females did not differ in their response to live and video versions of noncourting, noninteractive males, however, females tended to prefer video playbacks of males with longer swords, a result that has also been found in experiments using live males. These results suggest that females express the same qualitative mating preference, but not necessarily the same quantitative preference, for sword length when viewing video stimuli. Several methodological factors that may contribute to an apparent difference in the strength of the preference are discussed. Despite these differences, both methods produced comparable results; female response to sworded males tended to increase as sword length increased. These experiments demonstrate that video playback is an effective method to measure female preferences accurately in X. helleri and provide an example of how video playback can be evaluated in other species. Copyright 2000 The Association for the Study of Animal Behaviour.     

Determination of ochratoxin A in food: comparison of a stable isotope dilution assay,liquid chromatography-fluorescence detection and an enzyme-linked immunosorbent assay

Quantitative results for the mycotoxin ochratoxin A (OTA), obtained by a stable isotope dilution assay (SIDA) were compared with two commonly used analytical methods for OTA quantitation. For this, different types of food, such as wheat, coffee, sultanas, and blood sausages, were analyzed. Because results obtained by the SIDA method were closest to the certified contents of an OTA reference material, data obtained by this method were considered as reference data. For liquid chromatography-fluorescence detection, a clean-up by solid phase extraction on silica was found to be necessary, and a correction for recovery had to be performed to match the data from the SIDA experiments. The enzyme-linked immunosorbent assay (ELISA) strongly overestimated the OTA content in coffee and nutmeg therefore an extract clean-up by immunoaffinity chromatography had to be used to match the SIDA results. Following this sample preparation, ELISA gave correct qualitative and semiquantitative results, and proved to be a suitable screening method. SIDA was also established as a valuable tool to quantify OTA in meat products, when using a clean-up procedure developed recently for blood samples.     

Inferring qualitative relations in genetic networks and metabolic pathways

MOTIVATION: Inferring genetic network architecture from time series data of gene expression patterns is an important topic in bioinformatics. Although inference algorithms based on the Boolean network were proposed, the Boolean network was not sufficient as a model of a genetic network. RESULTS: First, a Boolean network model with noise is proposed, together with an inference algorithm for it. Next, a qualitative network model is proposed, in which regulation rules are represented as qualitative rules and embedded in the network structure. Algorithms are also presented for inferring qualitative relations from time series data. Then, an algorithm for inferring S-systems (synergistic and saturable systems) from time series data is presented, where S-systems are based on a particular kind of nonlinear differential equation and have been applied to the analysis of various biological systems. Theoretical results are shown for Boolean networks with noises and simple qualitative networks. Computational results are shown for Boolean networks with noises and S-systems, where real data are not used because the proposed models are still conceptual and the quantity and quality of currently available data are not enough for the application of the proposed methods.     

Bacterial discrimination by means of a universal array approach mediated by LDR (ligase detection reaction)

Background  

PCR amplification of bacterial 16S rRNA genes provides the most comprehensive and flexible means of sampling bacterial communities. Sequence analysis of these cloned fragments can provide a qualitative and quantitative insight of the microbial population under scrutiny although this approach is not suited to large-scale screenings. Other methods, such as denaturing gradient gel electrophoresis, heteroduplex or terminal restriction fragment analysis are rapid and therefore amenable to field-scale experiments. A very recent addition to these analytical tools is represented by microarray technology.     

LSimpute: accurate estimation of missing values in microarray data with least squares methods

Microarray experiments generate data sets with information on the expression levels of thousands of genes in a set of biological samples. Unfortunately, such experiments often produce multiple missing expression values, normally due to various experimental problems. As many algorithms for gene expression analysis require a complete data matrix as input, the missing values have to be estimated in order to analyze the available data. Alternatively, genes and arrays can be removed until no missing values remain. However, for genes or arrays with only a small number of missing values, it is desirable to impute those values. For the subsequent analysis to be as informative as possible, it is essential that the estimates for the missing gene expression values are accurate. A small amount of badly estimated missing values in the data might be enough for clustering methods, such as hierachical clustering or K-means clustering, to produce misleading results. Thus, accurate methods for missing value estimation are needed. We present novel methods for estimation of missing values in microarray data sets that are based on the least squares principle, and that utilize correlations between both genes and arrays. For this set of methods, we use the common reference name LSimpute. We compare the estimation accuracy of our methods with the widely used KNNimpute on three complete data matrices from public data sets by randomly knocking out data (labeling as missing). From these tests, we conclude that our LSimpute methods produce estimates that consistently are more accurate than those obtained using KNNimpute. Additionally, we examine a more classic approach to missing value estimation based on expectation maximization (EM). We refer to our EM implementations as EMimpute, and the estimate errors using the EMimpute methods are compared with those our novel methods produce. The results indicate that on average, the estimates from our best performing LSimpute method are at least as accurate as those from the best EMimpute algorithm.     

QUANTITATIVE CHARACTERS IN PHYLOGENETIC ANALYSIS

Abstract— When analysing phylogcnetic relationships at low laxonomic levels it is often the ease that many of the features that can be used to separate taxa show continuous variation. The theoretical and practical problems for the use of such quantitative characters in phylogenetic analysis arc examined. Three methods of coding continuous data into discrete characters are assessed in detail: simple gap-coding, generalised gap-coding and segment-coding, a form of range-coding. The methods are applied to a data set gathered for Eucatyptus L'Hérit. informal subgenus Symphyomyrtus section Maidenana (Myrtaceae). Each method is capable of distorting relative differences between taxa, but segment-coding produces the least amount of distortion, provided the range of variation of the character is divided into a sufficient number of character states.
Continuous quantitative characters provide data for phylogenetic analysis that arc more noisy than those provided by discrete qualitative characters and should, therefore, only be used when the number of qualitative characters is insufficient for resolution of relationships. The results of such analyses should be recognised as provisional pending the discovery of more readily informative characters.     

The ABRF Proteomics Research Group studies: educational exercises for qualitative and quantitative proteomic analyses

Resource (core) facilities have played an ever-increasing role in furnishing the scientific community with specialized instrumentation and expertise for proteomics experiments in a cost-effective manner. The Proteomics Research Group (PRG) of the Association of Biomolecular Resource Facilities (ABRF) has sponsored a number of research studies designed to enable participants to try new techniques and assess their capabilities relative to other laboratories analyzing the same samples. Presented here are results from three PRG studies representing different samples that are typically analyzed in a core facility, ranging from simple protein identification to targeted analyses, and include intentional challenges to reflect realistic studies. The PRG2008 study compares different strategies for the qualitative characterization of proteins, particularly the utility of complementary methods for characterizing truncated protein forms. The use of different approaches for determining quantitative differences for several target proteins in human plasma was the focus of the PRG2009 study. The PRG2010 study explored different methods for determining specific constituents while identifying unforeseen problems that could account for unanticipated results associated with the different samples, and included (15) N-labeled proteins as an additional challenge. These studies provide a valuable educational resource to research laboratories and core facilities, as well as a mechanism for establishing good laboratory practices.     

Algorithms for ultrasound elastography: a survey

Elastography is a useful and interesting technique that can be used to infer stiffness information from ultrasound medical images. In one decade of activity, the scientific community has developed this technique to a more and more mature stage, such that it has evolved into a fruitful application for clinical practice. During this decade, the evolution has proceeded from qualitative stiffness information to numerical quantification using different algorithms and approaches, based on both iterative and direct approaches. Moreover, different post- and pre-processing techniques as well as evaluation methods have been implemented. This work presents a survey on the methods developed by giving a short overview of algorithms and mathematical solutions and by analysing results and comparing methodologies.     

Estimating mutation rates in low-replication experiments

Since mutation rate is a key biological parameter, its proper estimation has received great attention for decades. However, instead of the mutation rate, many authors opt for reporting the average mutant frequency, a less meaningful quantity. This is because the standard methods to estimate the mutation rate, derived from the Luria and Delbrück's fluctuation analysis, ideally require high-replication experiments to be applied; a requirement often unattainable due to constraints of time, budget or sample availability. But the main problem with mutant frequency, apart from being less informative, is its poor reproducibility; an especially marked defect when the chosen average is the arithmetic mean. Several authors tried to avoid this by employing other averages (such as the median or the geometric mean) or discarding outliers, though as far as we know nobody has evaluated which method performs best under low-replication settings. Here we use computer simulations to compare the performance of different methods used in low-replication experiments (≤4 cultures). Besides the customary averages of mutant frequency, we also tested two well-known fluctuation methods. Contrary to common practice, our results support that fluctuation methods should be applied in such circumstances, as they perform as well as or better than any average of mutant frequency. In particular, experimentalists will benefit from using MSS maximum likelihood in low-replication experiments because it: (i) provides more reproducible results, (ii) allows for direct estimation of mutation rate and (iii) allows for the application of conventional statistics.     

An Innovative Method for Exosome Quantification and Size Measurement

Although the biological importance of exosomes has recently gained an increasing amount of scientific and clinical attention, much is still unknown about their complex pathways, their bioavailability and their diverse functions in health and disease. Current work focuses on the presence and the behavior of exosomes (in vitro as well as in vivo) in the context of different human disorders, especially in the fields of oncology, gynecology and cardiology.Unfortunately, neither a consensus regarding a gold standard for exosome isolation exists, nor is there an agreement on such a method for their quantitative analysis. As there are many methods for the purification of exosomes and also many possibilities for their quantitative and qualitative analysis, it is difficult to determine a combination of methods for the ideal approach. Here, we demonstrate nanoparticle tracking analysis (NTA), a semi-automated method for the characterization of exosomes after isolation from human plasma by ultracentrifugation. The presented results show that this approach for isolation, as well as the determination of the average number and size of exosomes, delivers reproducible and valid data, as confirmed by other methods, such as scanning electron microscopy (SEM).     

MRI denoising using nonlocal neutrosophic set approach of Wiener filtering

In this paper, a new filtering method is presented to remove the Rician noise from magnetic resonance images (MRI) acquired using single coil MRI acquisition system. This filter is based on nonlocal neutrosophic set (NLNS) approach of Wiener filtering. A neutrosophic set (NS), a part of neutrosophy theory, studies the origin, nature, and scope of neutralities, as well as their interactions with different ideational spectra. Now, we apply the neutrosophic set into image domain and define some concepts and operators for image denoising. First, the nonlocal mean is applied to the noisy MRI. The resultant image is transformed into NS domain, described using three membership sets: true (T), indeterminacy (I) and false (F). The entropy of the neutrosophic set is defined and employed to measure the indeterminacy. The ω-Wiener filtering operation is used on T and F to decrease the set indeterminacy and to remove the noise. The experiments have been conducted on simulated MR images from Brainweb database and clinical MR images. The results show that the NLNS Wiener filter produces better denoising results in terms of qualitative and quantitative measures compared with other denoising methods, such as classical Wiener filter, the anisotropic diffusion filter, the total variation minimization and the nonlocal means filter. The visual and the diagnostic quality of the denoised image are well preserved.