Isolation and detection of Listeria spp, Salmonella spp and Yersinia spp using a simultaneous enrichment step followed by a surface adhesion immunofluorescent technique

There has been a proliferation of techniques and methods reported for analysis of water samples to determine the presence of the protozoan pathogens Cryptosporidium parvum and Giardia lamblia. Many of the proposed methods are presented as complete procedures, which include sampling, processing, staining, or detection steps while other methods are not complete. Some proposed methods have been extensively tested in multi-laboratory settings, however, others are still in the developmental stage. A set of evaluation criteria has been developed to evaluate the many proposed methods. These criteria have been applied as an example, to an existing method. These criteria should be useful to individuals attempting to evaluate methods developed for detecting protozoa in water, and conversely, they should serve as a guideline for individuals interested in developing methods, allowing them to gather data with and about their methods, and present this data in a manner that is both logical and easily evaluated.     

Regression Analysis with a Misclassified Covariate from a Current Status Observation Scheme

Summary Naive use of misclassified covariates leads to inconsistent estimators of covariate effects in regression models. A variety of methods have been proposed to address this problem including likelihood, pseudo‐likelihood, estimating equation methods, and Bayesian methods, with all of these methods typically requiring either internal or external validation samples or replication studies. We consider a problem arising from a series of orthopedic studies in which interest lies in examining the effect of a short‐term serological response and other covariates on the risk of developing a longer term thrombotic condition called deep vein thrombosis. The serological response is an indicator of whether the patient developed antibodies following exposure to an antithrombotic drug, but the seroconversion status of patients is only available at the time of a blood sample taken upon the discharge from hospital. The seroconversion time is therefore subject to a current status observation scheme, or Case I interval censoring, and subjects tested before seroconversion are misclassified as nonseroconverters. We develop a likelihood‐based approach for fitting regression models that accounts for misclassification of the seroconversion status due to early testing using parametric and nonparametric estimates of the seroconversion time distribution. The method is shown to reduce the bias resulting from naive analyses in simulation studies and an application to the data from the orthopedic studies provides further illustration.     

Comparative methods as a statistical fix: the dangers of ignoring an evolutionary model

Abstract Comparative methods are widely used in ecology and evolution. The most frequently used comparative methods are based on an explicit evolutionary model. However, recent approaches have been popularized that are without an evolutionary basis or an underlying null model. Here we highlight the limitations of such techniques in comparative analyses by using simulations to compare two commonly used comparative methods with and without evolutionary basis, respectively: generalized least squares (GLS) and phylogenetic eigenvector regression (PVR). We find that GLS methods are more efficient at estimating model parameters and produce lower variance in parameter estimates, lower phylogenetic signal in residuals, and lower Type I error rates than PVR methods. These results can very likely be generalized to eigenvector methods that control for space and both space and phylogeny. We highlight that GLS methods can be adapted in numerous ways and that the variance structure used in these models can be flexibly optimized to each data set.     

Rooting a phylogenetic tree with nonreversible substitution models

Background  

We compared two methods of rooting a phylogenetic tree: the stationary and the nonstationary substitution processes. These methods do not require an outgroup.     

Fuzzy connectives as a combination tool in a hybrid multi-neural system

The set of fuzzy connectives can be seen as an important combination tool, such as in combining the antecedent sets of the rules, in multi-criteria decision making and in combining the outputs of neural classifiers in a multi-neural system. This papers investigates the performance of some fuzzy combination schemes applied to a multi hybrid neural system which is composed of neural and fuzzy neural networks. An empirical evaluation in a handwritten numeral recognition task is used to investigate the performance of the presented fuzzy methods with some existing combination methods.     

Enhancing P300 Detection Using a Band-Selective Filter Bank for a Visual P300 Speller

Background: An open challenge of P300-based BCI systems focuses on recognizing ERP signals using a reduced number of trials with enough classification rate.Methods: Three novel methods based on Filter Bank and Canonical Correlation Analysis (CCA) are proposed for the recognition of P300 ERPs using a reduced number of trials. The proposed methods were evaluated with two freely available EEG datasets based on 6x6 speller and were compared with five standard methods: Mean-Amplitude, Step-Wise, Principal Component Analysis, Peak, and CCA.Results: The proposed methods outperform significantly standard algorithms for P300 identification with a maximum AUC of 0.93 and 0.98, and an average of 0.73 and 0.76, using a single trial.Conclusion: Proposed methods based on Filter Bank are robust for the identification of P300 using a reduced number of trials, which could be used in real-time BCI spellers for rehabilitation engineering.     

Assessment of rate constants for isolating a metabolite by a model-independent method]

A model-independent method for estimating an elimination rate constant of a metabolite of exogenous substance is suggested as an alternative to known methods. The new method (named the initial slope method) uses blood (plasma) concentration-time data of both the substance and the metabolite obtained after an extravascular impulse input of the substance. The metabolite input is not needed substantially facilitating the experiment. The method is based upon the assessment of areas under the substance and metabolite concentration-time curves, the initial substance concentration, and the initial slope of the metabolite concentration-time curve. The method was tested using artificial data generated on the basis of a compartment model for the substance and metabolite kinetics. It was shown providing nonbiased estimates of a true metabolite elimination rate constant irrespective of the structure of the model used to generate data. Other methods failed to provide such estimates.     

Extracting multiple structural alignments from pairwise alignments: a comparison of a rigorous and a heuristic approach

MOTIVATION: Multiple structural alignments (MSTAs) provide position-specific information on the sequence variability allowed by protein folds. This information can be exploited to better understand the evolution of proteins and the physical chemistry of polypeptide folding. Most MSTA methods rely on a pre-computed library of pairwise alignments. This library will in general contain conflicting residue equivalences not all of which can be realized in the final MSTA. Hence to build a consistent MSTA, these methods have to select a conflict-free subset of equivalences. RESULTS: Using a dataset with 327 families from SCOP 1.63 we compare the ability of two different methods to select an optimal conflict-free subset of equivalences. One is an implementation of Reinert et al.'s integer linear programming formulation (ILP) of the maximum weight trace problem (Reinert et al., 1997, Proc. 1st Ann. Int. Conf. Comput. Mol. Biol. (RECOMB-97), ACM Press, New York). This ILP formulation is a rigorous approach but its complexity is difficult to predict. The other method is T-Coffee (Notredame et al., 2000) which uses a heuristic enhancement of the equivalence weights which allow it to use the speed and simplicity of the progressive alignment approach while still incorporating information of all alignments in each step of building the MSTA. We find that although the ILP formulation consistently selects a more optimal set of conflict-free equivalences, the differences are small and the quality of the resulting MSTAs are essentially the same for both methods. Given its speed and predictable complexity, our results show that T-Coffee is an attractive alternative for producing high-quality MSTAs.     

Teaching Density with a Little Drama

This article provides an example of an innovative science activity applied in a science methods course for future elementary teachers at a small university in northeastern Turkey. The aim of the activity is to help prospective elementary teachers understand the density concept in a simple way and see an innovative teaching example.     

Dynamic measurements of three-dimensional scapular kinematics: a validation study

The validation of two noninvasive methods for measuring the dynamic three-dimensional kinematics of the human scapula with a magnetic tracking device is presented. One method consists of simply fixing a sensor directly to the acromion and the other consists of mounting a sensor to an adjustable plastic jig that fits over the scapular spine and acromion. The concurrent validity of both methods was assessed separately by comparison with data collected simultaneously from an invasive approach in which pins were drilled directly into the scapula. The differences between bone and skin based measurements represents an estimation of skin motion artifact. The average motion pattern of each surface method was similar to that measured by the invasive technique, especially below 120 degrees of elevation. These results indicate that with careful consideration, both methods may offer reasonably accurate representations of scapular motion that may be used to study shoulder pathologies and help develop computational models.     

Profile-profile methods provide improved fold-recognition: a study of different profile-profile alignment methods

To improve the detection of related proteins, it is often useful to include evolutionary information for both the query and target proteins. One method to include this information is by the use of profile-profile alignments, where a profile from the query protein is compared with the profiles from the target proteins. Profile-profile alignments can be implemented in several fundamentally different ways. The similarity between two positions can be calculated using a dot-product, a probabilistic model, or an information theoretical measure. Here, we present a large-scale comparison of different profile-profile alignment methods. We show that the profile-profile methods perform at least 30% better than standard sequence-profile methods both in their ability to recognize superfamily-related proteins and in the quality of the obtained alignments. Although the performance of all methods is quite similar, profile-profile methods that use a probabilistic scoring function have an advantage as they can create good alignments and show a good fold recognition capacity using the same gap-penalties, while the other methods need to use different parameters to obtain comparable performances.     

Diversity and evolutionary origins of fungi associated with seeds of a neotropical pioneer tree: a case study for analysing fungal environmental samples

Fungi associated with seeds of tropical trees pervasively affect seed survival and germination, and thus are an important, but understudied, component of forest ecology. Here, we examine the diversity and evolutionary origins of fungi isolated from seeds of an important pioneer tree (Cecropia insignis, Cecropiaceae) following burial in soil for five months in a tropical moist forest in Panama. Our approach, which relied on molecular sequence data because most isolates did not sporulate in culture, provides an opportunity to evaluate several methods currently used to analyse environmental samples of fungi. First, intra- and interspecific divergence were estimated for the nu-rITS and 5.8S gene for four genera of Ascomycota that are commonly recovered from seeds. Using these values we estimated species boundaries for 527 isolates, showing that seed-associated fungi are highly diverse, horizontally transmitted, and genotypically congruent with some foliar endophytes from the same site. We then examined methods for inferring the taxonomic placement and phylogenetic relationships of these fungi, evaluating the effects of manual versus automated alignment, model selection, and inference methods, as well as the quality of BLAST-based identification using GenBank. We found that common methods such as neighbor-joining and Bayesian inference differ in their sensitivity to alignment methods; analyses of particular fungal genera differ in their sensitivity to alignments; and numerous and sometimes intricate disparities exist between BLAST-based versus phylogeny-based identification methods. Lastly, we used our most robust methods to infer phylogenetic relationships of seed-associated fungi in four focal genera, and reconstructed ancestral states to generate preliminary hypotheses regarding the evolutionary origins of this guild. Our results illustrate the dynamic evolutionary relationships among endophytic fungi, pathogens, and seed-associated fungi, and the apparent evolutionary distinctiveness of saprotrophs. Our study also elucidates the diversity, taxonomy, and ecology of an important group of plant-associated fungi and highlights some of the advantages and challenges inherent in the use of ITS data for environmental sampling of fungi.     

Latent-Model Robustness in Joint Models for a Primary Endpoint and a Longitudinal Process

Summary .  Joint modeling of a primary response and a longitudinal process via shared random effects is widely used in many areas of application. Likelihood-based inference on joint models requires model specification of the random effects. Inappropriate model specification of random effects can compromise inference. We present methods to diagnose random effect model misspecification of the type that leads to biased inference on joint models. The methods are illustrated via application to simulated data, and by application to data from a study of bone mineral density in perimenopausal women and data from an HIV clinical trial.     

Fungal molecular diagnostics: a mini review

Conventional methods to identify fungi have often relied on identification of disease symptoms, isolation and culturing of environmental organisms, and laboratory identification by morphology and biochemical tests. Although these methods are still fundamental there is an increasing move towards molecular diagnostics of fungi in all fields. In this review, some of the molecular approaches to fungal diagnostics based on polymerase chain reaction (PCR) and DNA/RNA probe technology are discussed. This includes several technological advances in PCR-based methods for the detection, identification and quantification of fungi including real-time PCR which has been successfully used to provide rapid, quantitative data on fungal species from environmental samples. PCR and probe based methods have provided new tools for the enumeration of fungal species, but it is still necessary to combine the new technology with more conventional methods to gain a fuller understanding of interactions occurring in the environment. Since its introduction in the mid 1980's PCR has provided many molecular diagnostic tools, some of which are discussed within this review, and with the advances in micro-array technology and real-time PCR methods the future is bright for the development of accurate, quantitative diagnostic tools that can provide information not only on individual fungal species but also on whole communities.     

Fast and accurate population admixture inference from genotype data from a few microsatellites to millions of SNPs

Model-based (likelihood and Bayesian) and non-model-based (PCA and K-means clustering) methods were developed to identify populations and assign individuals to the identified populations using marker genotype data. Model-based methods are favoured because they are based on a probabilistic model of population genetics with biologically meaningful parameters and thus produce results that are easily interpretable and applicable. Furthermore, they often yield more accurate structure inferences than non-model-based methods. However, current model-based methods either are computationally demanding and thus applicable to small problems only or use simplified admixture models that could yield inaccurate results in difficult situations such as unbalanced sampling. In this study, I propose new likelihood methods for fast and accurate population admixture inference using genotype data from a few multiallelic microsatellites to millions of diallelic SNPs. The methods conduct first a clustering analysis of coarse-grained population structure by using the mixture model and the simulated annealing algorithm, and then an admixture analysis of fine-grained population structure by using the clustering results as a starting point in an expectation maximisation algorithm. Extensive analyses of both simulated and empirical data show that the new methods compare favourably with existing methods in both accuracy and running speed. They can analyse small datasets with just a few multiallelic microsatellites but can also handle in parallel terabytes of data with millions of markers and millions of individuals. In difficult situations such as many and/or lowly differentiated populations, unbalanced or very small samples of individuals, the new methods are substantially more accurate than other methods.Subject terms: Population genetics, Evolutionary ecology     

Superhelical turns of closed circular DNA in solution and in a gel: evidence for a conformational difference

We have compared the number of superhelical turns, tau, in circular covalently closed plasmid pBR beta G DNA obtained by four different methods, each based on one particular distinguishing principle. Three of the methods allow an unequivocal determination of tau under gel electrophoresis conditions, whilst the fourth enables us to determine its value in solution. We were able to detect a significant difference between the two environments, corresponding to an unwinding of the DNA duplex angle by 0.3 degrees when a sample is transferred from solution to gel. The possible existence of such an effect has been generally overlooked by previous investigators. Our result suggests that the previously reported value for the number of base-pairs per helical turn should be adjusted downwards by about 0.10, so that it applies to conditions in solution.     

Pathogen detection: a perspective of traditional methods and biosensors

The detection of pathogenic bacteria is key to the prevention and identification of problems related to health and safety. Legislation is particularly tough in areas such as the food industry, where failure to detect an infection may have terrible consequences. In spite of the real need for obtaining analytical results in the shortest time possible, traditional and standard bacterial detection methods may take up to 7 or 8 days to yield an answer. This is clearly insufficient, and many researchers have recently geared their efforts towards the development of rapid methods. The advent of new technologies, namely biosensors, has brought in new and promising approaches. However, much research and development work is still needed before biosensors become a real and trustworthy alternative.This review not only offers an overview of trends in the area of pathogen detection but it also describes main techniques, traditional methods, and recent developments in the field of pathogen bacteria biosensors.     

OBIYagns: a grid-based biochemical simulator with a parameter estimator

SUMMARY: OBIYagns (yet another gene network simulator) is a biochemical system simulator that comprises a multiple-user Web-based graphical interface, an ordinary differential equation solver and a parameter estimators distributed over an open bioinformatics grid (OBIGrid). This grid-based biochemical simulation system can achieve high performance and provide a secure simulation environment for estimating kinetic parameters in an acceptable time period. OBIYagns can be applied to larger system biology-oriented simulation projects. AVAILABILITY: OBIYagns example models, methods and user guide are available at https://access.obigrid.org/yagns/ SUPPLEMENTARY INFORMATION: Please refer to Bioinformatics online.     

Calibration of a 220Rn flow-through source

Two reference methods have been established to calibrate the yield of a self-made ²²⁰Rn flow-through source. These reference methods include (a) a small scintillation cell flow-through method and (b) a gamma spectrometry method that takes advantage of an activated charcoal. The methods were validated theoretically and experimentally including intercomparison measurements. It is shown that the source is stable with a relative deviation less than 2.5% over 5 years, when the relative humidity ranged from 50 to 90%, and the temperature from 5 to 38°C. The ²²⁰Rn monitor used in these reference methods is calibrated without a ²²⁰Rn gas source with determinate yield. On the basis of the calibration of the ²²⁰Rn source by the methods mentioned above, the accurate calibration of any ²²⁰Rn monitor by the self-made ²²⁰Rn source with stable yield is feasible.