Recommended Practice Regarding Selection of Sensitivity Analysis Methods Applied to Microbial Food Safety Process Risk Models

A guideline is presented for selection of sensitivity analysis methods applied to microbial food safety process risk (MFSPR) models. The guideline provides useful boundaries and principles for selecting sensitivity analysis methods for MSFPR models. Although the guideline is predicated on a specific branch of risk assessment models related to food-borne diseases, the principles and recommendations provided are typically generally applicable to other types of risk models. Applicable situations include: prioritizing potential critical control points; identifying key sources of variability and uncertainty; and refinement, verification, and validation of a model. Based on the objective of the analysis, characteristics of the model under study, amount of detail expected from sensitivity analysis, and characteristics of the sensitivity analysis method, recommendations for selection of sensitivity analysis methods are provided. A decision framework for method selection is introduced. The decision framework can substantially facilitate the process of selecting a sensitivity analysis method.     

Detection of operons

Operons are clusters of genes that are transcribed as a single message, and regulated by the same gene expression machinery. They are found primarily in prokaryotic genomes. Because genes in the same operon are likely to have related functions, identification of the operon structure is potentially useful for assigning gene function. We report the development and benchmarking of two different methods for detecting operons, based on an analysis of 42 fully sequenced prokaryotic organisms. The Gene Neighbor method (GNM) utilizes the relatively high conservation of gene order in operons, compared with genes in general. The Gene Gap Method (GGM) makes use of the relatively short gap between genes in operons compared with that otherwise found between adjacent genes. The methods have been benchmarked using KEGG pathway data and RegulonDB Escherichia coli operon data. With optimum parameters, the specificity of the GNM is 93% and the sensitivity is 70%. For the GGM, the specificity is 95% and the sensitivity is 68%. Together, the two methods have a sensitivity of 87.2%, while joint predictions have a sensitivity of 50% and a specificity of 98%. The methods are used to infer possible functions for some hypothetical genes in prokaryotic genomes. The methods have proven a useful addition to structure information in deriving protein function in a structural genomics project.     

Synchrotron radiation for direct analysis of metalloproteins on electrophoresis gels

Metalloproteomics requires analytical techniques able to assess and quantify the inorganic species in metalloproteins. The most widely used methods are hyphenated techniques, based on the coupling of a high resolution chromatographic method with a high sensitivity method for metal analysis in solution. An alternative approach is the use of methods for solid sample analysis, combining metalloprotein separation by gel electrophoresis and direct analysis of the gels. Direct methods are based on beam analysis, such as lasers, ion beams or synchrotron radiation beams. The aim of this review article is to present the main features of synchrotron radiation based methods and their applications for metalloprotein analysis directly on electrophoresis gels. Synchrotron radiation X-ray fluorescence has been successfully employed for sensitive metal identification, and X-ray absorption spectroscopy for metal local structure speciation in proteins. Synchrotron based methods will be compared to ion beam and mass spectrometry for direct analysis of metalloproteins in electrophoresis gels.     

Comparative study of parameter sensitivity analyses of the TCR-activated Erk-MAPK signalling pathway

Parameter estimation is a major challenge for mathematical modelling of biological systems. Given the uncertainties associated with model parameters, it is important to understand how sensitive the model output is to variations in parameter values. A local sensitivity analysis determines the model sensitivity to parameter variations over a localised region around the nominal parameter values, whereas a global sensitivity analysis (GSA) investigates the sensitivity over the entire parameter space. Using a T-cell receptor-activated Erk-MAPK signalling pathway model as an example, the authors present a comparative study of a variety of different sensitivity analysis techniques. These techniques include: local sensitivity analysis, existing GSA methods of partial rank correlation coefficient, Sobol's, extended Fourier amplitude sensitivity test, as well as a weighted average of local sensitivities and a new GSA method to extract global parameter sensitivities from a parameter identification routine. Results of this study revealed critical reactions in the signalling pathway and their impact on the signalling dynamics and provided insights into embedded regulatory mechanisms such as feedback loops in the pathway. From this study, a recommendation emerges for a general sensitivity analysis strategy to efficiently and reliably infer quantitative, dynamic as well as topological properties from systems biology models.     

Identification and quantification of polyphenol phytoestrogens in foods and human biological fluids

We review the methods used to measure phytoestrogens (genistein, daidzein, lignans and their derivatives) in foods and biological fluids, and discuss advantages and disadvantages of each. The range of detection limits reported varies widely between individual laboratories, but generally the best reported sensitivity is as follows: immunoassay>HPLC-mass spectrometry=HPLC-multichannel electrochemical detection (coularray)>GC-single ion monitoring-mass spectrometry>HPLC-UV diode array>HPLC-single channel electrochemical detection. The best sensitivity reported so far is 0.002 pmol per assay for daidzein by radioimmunoassay. HPLC with UV diode array detection is the most commonly employed, but is the least sensitive and specific. GC and HPLC coupled with mass spectrometry or electrochemical detection are the most accurate and reproducible methods for a wide variety of analytes. Generally most methods, with the exception of immunoassay, have not been correlated with other methods. Recoveries from extraction methods, limits of detection, nature of compounds analysed and the internal standards used are summarised for more than 90 reports in the literature. From this data, it is clear that an inter-laboratory validation and correlation between a wide range of methods for phytoestrogen analysis is required. One underdeveloped area that requires particular attention is the analysis of plant lignans.     

Evaluating the Use of Global Sensitivity Analysis in Dynamic MFA

Dynamic material flow analysis (MFA) provides information about material usage over time and consequent changes in material stocks and flows. In order to understand the effect of limited data quality and model assumptions on MFA results, the use of sensitivity analysis methods in dynamic MFA studies has been on the increase. So far, sensitivity analysis in dynamic MFA has been conducted by means of a one‐at‐a‐time method, which tests parameter perturbations individually and observes the outcomes on output. In contrast to that, variance‐based global sensitivity analysis decomposes the variance of the model output into fractions caused by the uncertainty or variability of input parameters. The present study investigates interaction and time‐delay effects of uncertain parameters on the output of an archetypal input‐driven dynamic material flow model using variance‐based global sensitivity analysis. The results show that determining the main (first‐order) effects of parameter variations is often sufficient in dynamic MFA because substantial effects attributed to the simultaneous variation of several parameters (higher‐order effects) do not appear for classical setups of dynamic material flow models. For models with time‐varying parameters, time‐delay effects of parameter variation on model outputs need to be considered, potentially boosting the computational cost of global sensitivity analysis. Finally, the implications of exploring the sensitivities of model outputs with respect to parameter variations in the archetypical model are used to derive model‐ and goal‐specific recommendations on choosing appropriate sensitivity analysis methods in dynamic MFA.     

Sensitivity of diagnostic tests for human soil-transmitted helminth infections: a meta-analysis in the absence of a true gold standard

Reliable, sensitive and practical diagnostic tests are an essential tool in disease control programmes for mapping, impact evaluation and surveillance. To provide a robust global assessment of the relative performance of available diagnostic tools for the detection of soil-transmitted helminths, we conducted a meta-analysis comparing the sensitivities and the quantitative performance of the most commonly used copro-microscopic diagnostic methods for soil-transmitted helminths, namely Kato-Katz, direct microscopy, formol-ether concentration, McMaster, FLOTAC and Mini-FLOTAC. In the absence of a perfect reference standard, we employed a Bayesian latent class analysis to estimate the true, unobserved sensitivity of compared diagnostic tests for each of the soil-transmitted helminth species Ascaris lumbricoides, Trichuris trichiura and the hookworms. To investigate the influence of varying transmission settings we subsequently stratified the analysis by intensity of infection. Overall, sensitivity estimates varied between the different methods, ranging from 42.8% for direct microscopy to 92.7% for FLOTAC. The widely used double slide Kato-Katz method had a sensitivity of 74–95% for the three soil-transmitted helminth species at high infection intensity, however sensitivity dropped to 53–80% in low intensity settings, being lowest for hookworm and A. lumbricoides. The highest sensitivity, overall and in both intensity groups, was observed for the FLOTAC method, whereas the sensitivity of the Mini-FLOTAC method was comparable with the Kato-Katz method. FLOTAC average egg count estimates were significantly lower compared with Kato-Katz, while the compared McMaster counts varied. In conclusion, we demonstrate that the Kato-Katz and Mini-FLOTAC methods had comparable sensitivities. We further show that test sensitivity of the Kato-Katz method is reduced in low transmission settings.     

Evaluation of Sampling-Based Methods for Sensitivity Analysis: Case Study for the E. coli Food Safety Process Risk Model

This article evaluates selected sensitivity analysis methods applicable to risk assessment models with two-dimensional probabilistic frameworks, using a microbial food safety process risk model as a test-bed. Six sampling-based sensitivity analysis methods were evaluated including Pearson and Spearman correlation, sample and rank linear regression, and sample and rank stepwise regression. In a two-dimensional risk model, the identification of key controllable inputs that can be priorities for risk management can be confounded by uncertainty. However, despite uncertainty, results show that key inputs can be distinguished from those that are unimportant, and inputs can be grouped into categories of similar levels of importance. All selected methods are capable of identifying unimportant inputs, which is helpful in that efforts to collect data to improve the assessment or to focus risk management strategies can be prioritized elsewhere. Rank-based methods provided more robust insights with respect to the key sources of variability in that they produced narrower ranges of uncertainty for sensitivity results and more clear distinctions when comparing the importance of inputs or groups of inputs. Regression-based methods have advantages over correlation approaches because they can be configured to provide insight regarding interactions and nonlinearities in the model.     

Reverse staining of sodium dodecyl sulfate polyacrylamide gels by imidazole-zinc salts: sensitive detection of unmodified proteins.

We report here a modification to copper and zinc chloride staining methods. The introduction of a preincubation of the gels, prior to metal staining, with 0.2 M imidazole allows the formation of a homogeneous background for the subsequent precipitation of the metal chelate. The reported imidazole-zinc staining takes minutes, resulting in reproducible staining patterns with only slightly lower sensitivity than silver staining. The method allows efficient recovery of proteins from previously stained gels and is compatible with immunoidentification on Western blots and also with amino acid analysis and NH2-terminal sequence analysis of transferred proteins. A mechanism is proposed to explain the observed improvement in reproducibility and sensitivity of imidazole preincubation to zinc staining.     

Chemical derivatization of phosphoserine and phosphothreonine containing peptides to increase sensitivity for MALDI-based analysis and for selectivity of MS/MS analysis

Protein phosphorylation is one of the most important and common ways of regulating protein function in cells. However, phosphopeptides are difficult to analyse, ionising poorly under standard MALDI conditions. Several methods have been developed to deal with the low sensitivity and specificity of phosphopeptide analysis. Here, we show an approach using a simple one-step beta-elimination/Michael addition reaction for the derivatization of phosphoserine and phosphothreonine. The substitution of the negatively charged phosphate group by a positively charged S-ethylpyridyl group greatly improves the ionisation of the modified peptides, especially in MALDI MS, increasing the sensitivity of the analysis. The modification allows the formation of a unique fragment ion at m/z 106 under mild collisional activation conditions, which can be used for parent (precursor) ion scanning in order to improve both the sensitivity and the selectivity of the analysis. The optimisation of the approach is described for a standard model peptide and protein and then applied to phosphorylation analysis in two biologically derived proteins purified from different experimental systems.     

生态模型的灵敏度分析

灵敏度分析用于定性或定量地评价模型参数误差对模型结果产生的影响,是模型参数化过程和模型校正过程中的有用工具,具有重要的生态学意义．灵敏度分析包括局部灵敏度分析和全局灵敏度分析．局部灵敏度分析只检验单个参数的变化对模型结果的影响程度;全局灵敏度分析则检验多个参数的变化对模型运行结果总的影响,并分析每一个参数及其参数之间相互作用对模型结果的影响．目前,在对生态模型的灵敏度分析中,越来越倾向于使用全局灵敏度分析的方法．但国内仍多采用局部灵敏度分析方法,很少采用全局灵敏度分析方法．文中详细论述了局部灵敏分析和全局灵敏度分析的主要方法(一次变换法、多元回归法、Morris法、Sobol’法、傅里叶幅度灵敏度检验法和傅里叶幅度灵敏度检验扩展法),希望能为国内生态模型的发展提供一个比较完善的灵敏度分析方法库．结合国内外的灵敏度分析发展现状,指出联合灵敏度研究、灵敏度共性研究及空间直观景观模型的灵敏度分析将为生态模型灵敏度分析研究中的热点和难点．     

Application of mass spectrometry for identification and structural studies of flavonoid glycosides

Mass spectrometry is an important tool for the identification and structural determination of flavonoid glycosides. The advantages of mass spectrometry are high sensitivity and possibilities of hyphenation with liquid chromatographic methods for the analysis of mixtures of compounds. Different desorption ionization methods allow the analysis of underivatized glycosides. A review of mass spectrometric techniques applied to the identification and structural studies of flavonoid glycosides is presented.     

Partitioning stomatal and non-stomatal limitations to photosynthesis

Abstract. Plant scientists concerned both with crop improvement and with understanding the control mechanisms of complex processes such as photosynthesis need to identify those processes that are most important in restricting the overall rate and to quantify the relative importance of different components. The techniques that have been used for quantifying the relative importance of component processes in limiting net assimilation rate are reviewed and related to a fundamental definition based on sensitivity analysis. It is concluded that many methods currently in use, including standard resistance analysis, frequently give very misleading answers.
In addition, possible methods for apportioning the contributions of different component processes to observed changes in net photosynthetic rate (for example after stress) are also reviewed and compared against a fundamental approach based on sensitivity analysis. Unfortunately, the detailed time course of changes in mesophyll and stomatal properties that is required for application of the basic sensitivity analysis is seldom likely to be available, so that it is usually necessary to adopt an approximate method. The standard approximation that is recommended for calculating the contributions of different component processes to a change in assimilation rate, involves measurements at the initial and final states only. The various methods discussed in this paper are compared using published photosynthetic data for a range of species.     

Detection and enumeration of airborne biocontaminants

The sampling and analysis of airborne microorganisms has received attention in recent years owing to concerns with mold contamination in indoor environments and the threat of bioterrorism. Traditionally, the detection and enumeration of airborne microorganisms has been conducted using light microscopy and/or culture-based methods; however, these analyses are time-consuming, laborious, subjective and lack sensitivity and specificity. The use of molecular methods, such as quantitative polymerase chain reaction amplification, can enhance monitoring strategies by increasing sensitivity and specificity, while decreasing the time required for analysis.     

The enzymatic measurement of adenine nucleotides and P-creatine in picomole amounts

Enzymatic methods are described for the analysis of ATP, ATP + ADP, total adenylates, or P-creatine in biological samples. The methods include (i) direct fluorometric procedures for the measurement of 0.1–10 nmol using hexokinase and glucose-6-P-dehydrogenase as the indicator step; (ii) an enzymatic cycling procedure with a sensitivity of 1–50 pmol; and (iii) the measurement of light emission in the luciferin-luciferase system with a sensitivity of 0.1–80 pmol.     

Therapeutic mechanism of intracranial infection in patients with hydrocephalus after craniocerebral injury based on decompressive craniectomy

The objective of this study is to analyze the treatment mechanism of decompressive craniectomy for intracranial infection in patients with hydrocephalus after craniocerebral injury, and to provide a treatment plan for intracranial infection in patients with hydrocephalus after craniocerebral injury. In this study, literature screening and data acquisition were carried out firstly based on the research content, and then heterogeneity analysis, Meta-analysis, sensitivity analysis, and publication bias analysis were performed using statistical methods for the unilateral and bilateral decompressive craniectomy. Heterogeneity analysis, Meta-analysis and sensitivity analysis of indiscriminate unilateral decompressive craniectomy was performed; heterogeneity analysis, Meta-analysis, cumulative Meta-analysis, and sensitivity analysis for bilateral decompressive craniectomy were performed. In this study, the order of influence on patients with hydrocephalus after brain injury was as follows: bilateral decompressive craniectomy > unilateral and bilateral decompressive decompression > indiscriminate unilateral decompressive. Intracranial infection in patients with hydrocephalus after the craniocerebral injury should be comprehensively evaluated before the surgery and given clinical treatment in time.     

Hybridization analysis of nucleic acids using stained latex]

Method of the latex hybridization analysis has been developed: after hybridization of NA-target with biotinylated probe visualization of hybrids was carried out using latex particles, containing fluorescent dye pyronin G and coated with streptavidin. Due to encapsulation of the fluorescent dye in polymer particles sensitivity of the analysis was increased by several orders of magnitude in comparison with methods, using fluorescently labelled probes. Possessing a number of advantages, the method yields to none of any other methods of NA hybridization analysis in sensitivity.     

Serum protein profiling by solid phase extraction and mass spectrometry: A future diagnostics tool?

Serum protein profiling by MS is a promising method for early detection of disease. Important characteristics for serum protein profiling are preanalytical factors, analytical reproducibility and high throughput. Problems related to preanalytical factors can be overcome by using standardized and rigorous sample collection and sample handling protocols. The sensitivity of the MS analysis relies on the quality of the sample; consequently, the blood sample preparation step is crucial to obtain pure and concentrated samples and enrichment of the proteins and peptides of interest. This review focuses on the serum sample preparation step prior to protein profiling by MALDI MS analysis, with particular focus on various SPE methods. The application of SPE techniques with different chromatographic properties such as RP, ion exchange, or affinity binding to isolate specific subsets of molecules (subproteomes) is advantageous for increasing resolution and sensitivity in the subsequent MS analysis. In addition, several of the SPE sample preparation methods are simple and scalable and have proven easy to automate for higher reproducibility and throughput, which is important in a clinical proteomics setting.     

Bayesian influence measures for joint models for longitudinal and survival data

Summary This article develops a variety of influence measures for carrying out perturbation (or sensitivity) analysis to joint models of longitudinal and survival data (JMLS) in Bayesian analysis. A perturbation model is introduced to characterize individual and global perturbations to the three components of a Bayesian model, including the data points, the prior distribution, and the sampling distribution. Local influence measures are proposed to quantify the degree of these perturbations to the JMLS. The proposed methods allow the detection of outliers or influential observations and the assessment of the sensitivity of inferences to various unverifiable assumptions on the Bayesian analysis of JMLS. Simulation studies and a real data set are used to highlight the broad spectrum of applications for our Bayesian influence methods.     

Identification of Bicluster Regions in a Binary Matrix and Its Applications

Biclustering has emerged as an important approach to the analysis of large-scale datasets. A biclustering technique identifies a subset of rows that exhibit similar patterns on a subset of columns in a data matrix. Many biclustering methods have been proposed, and most, if not all, algorithms are developed to detect regions of “coherence” patterns. These methods perform unsatisfactorily if the purpose is to identify biclusters of a constant level. This paper presents a two-step biclustering method to identify constant level biclusters for binary or quantitative data. This algorithm identifies the maximal dimensional submatrix such that the proportion of non-signals is less than a pre-specified tolerance δ. The proposed method has much higher sensitivity and slightly lower specificity than several prominent biclustering methods from the analysis of two synthetic datasets. It was further compared with the Bimax method for two real datasets. The proposed method was shown to perform the most robust in terms of sensitivity, number of biclusters and number of serotype-specific biclusters identified. However, dichotomization using different signal level thresholds usually leads to different sets of biclusters; this also occurs in the present analysis.