Evaluation of saliva collection devices for the analysis of proteins

Background: Human saliva mirrors the body's health and can be collected non-invasively, does not require specialized skills and is suitable for large population based screening programs. The aims were twofold: to evaluate the suitability of commercially available saliva collection devices for quantifying proteins present in saliva and to provide levels for C-reactive protein (CRP), myoglobin, and immunoglobin E (IgE) in saliva of healthy individuals as a baseline for future studies. Methods: Saliva was collected from healthy volunteers (n = 17, ages 18-33 years). The following collection methods were evaluated: drool; Salimetrics (R) Oral Swab (SOS); Salivette (R) Cotton and Synthetic (Sarstedt) and Greiner Bio-One Saliva Collection System (GBO SCS (R)). We used AlphaLISA (R) assays to measure CRP, IgE and myoglobin levels in human saliva. Results: Significant (p<0.05) differences in the salivary flow rates were observed based on the method of collection, Le. salivary flow rates were significantly lower (p<0.05) in unstimulated saliva (Le. drool and SOS), when compared with mechanically stimulated methods (p<0.05) (Salivette (R) Cotton and Synthetic) and acid stimulated method (p<0.05) (SCS (R)). Saliva collected using SOS yielded significantly (p<0.05) lower concentrations of myoglobin and CRP, whilst, saliva collected using the Salivette (R) Cotton and Synthetic swab yielded significantly (p<0.05) lower myoglobin and IgE concentrations respectively. Conclusions: The results demonstrated significantly relevant differences in analyte levels based on the collection method. Significant differences in the salivary flow rates were also observed depending on the saliva collection method. The data provide preliminary baseline values for salivary CRP, myoglobin, and IgE levels in healthy participants and based on the collection method. (C) 2012 Elsevier B.V. All rights reserved.

Acoustic identification of 12 species of echolocating bat by discriminant function analysis and artificial neural networks

We recorded echolocation calls from 14 sympatric species of bat in Britain. Once digitised, one temporal and four spectral features were measured from each call. The frequency-time course of each call was approximated by fitting eight mathematical functions, and the goodness of fit, represented by the mean-squared error, was calculated. Measurements were taken using an automated process that extracted a single call from background noise and measured all variables without intervention. Two species of Rhinolophus were easily identified from call duration and spectral measurements. For the remaining 12 species, discriminant function analysis and multilayer back-propagation perceptrons were used to classify calls to species level. Analyses were carried out with and without the inclusion of curve-fitting data to evaluate its usefulness in distinguishing among species. Discriminant function analysis achieved an overall correct classification rate of 79% with curve-fitting data included, while an artificial neural network achieved 87%. The removal of curve-fitting data improved the performance of the discriminant function analysis by 2 %, while the performance of a perceptron decreased by 2 %. However, an increase in correct identification rates when curve-fitting information was included was not found for all species. The use of a hierarchical classification system, whereby calls were first classified to genus level and then to species level, had little effect on correct classification rates by discriminant function analysis but did improve rates achieved by perceptrons. This is the first published study to use artificial neural networks to classify the echolocation calls of bats to species level. Our findings are discussed in terms of recent advances in recording and analysis technologies, and are related to factors causing convergence and divergence of echolocation call design in bats.

Acoustic identification of twelve species of echolocating bat by discriminant function analysis and artificial neural networks

We recorded echolocation calls from 14 sympatric species of bat in Britain. Once digitised, one temporal and four spectral features were measured from each call. The frequency-time course of each call was approximated by fitting eight mathematical functions, and the goodness of fit, represented by the mean-squared error, was calculated. Measurements were taken using an automated process that extracted a single call from background noise and measured all variables without intervention. Two species of Rhinolophus were easily identified from call duration and spectral measurements. For the remaining 12 species, discriminant function analysis and multilayer back-propagation perceptrons were used to classify calls to species level. Analyses were carried out with and without the inclusion of curve-fitting data to evaluate its usefulness in distinguishing among species. Discriminant function analysis achieved an overall correct classification rate of 79% with curve-fitting data included, while an artificial neural network achieved 87%. The removal of curve-fitting data improved the performance of the discriminant function analysis by 2 %, while the performance of a perceptron decreased by 2 %. However, an increase in correct identification rates when curve-fitting information was included was not found for all species. The use of a hierarchical classification system, whereby calls were first classified to genus level and then to species level, had little effect on correct classification rates by discriminant function analysis but did improve rates achieved by perceptrons. This is the first published study to use artificial neural networks to classify the echolocation calls of bats to species level. Our findings are discussed in terms of recent advances in recording and analysis technologies, and are related to factors causing convergence and divergence of echolocation call design in bats.

Efficiency of parallelisation of genetic algorithms in the data analysis context

Most real-life data analysis problems are difficult to solve using exact methods, due to the size of the datasets and the nature of the underlying mechanisms of the system under investigation. As datasets grow even larger, finding the balance between the quality of the approximation and the computing time of the heuristic becomes non-trivial. One solution is to consider parallel methods, and to use the increased computational power to perform a deeper exploration of the solution space in a similar time. It is, however, difficult to estimate a priori whether parallelisation will provide the expected improvement. In this paper we consider a well-known method, genetic algorithms, and evaluate on two distinct problem types the behaviour of the classic and parallel implementations.

Inventory of pedagogy and practice: Confirmatory factor analysis of multiple facets of teaching

Student perceptions of teaching have often been used in tertiary education for evaluation purposes. However, there is a paucity of research on the validity, reliability, and applicability of instruments that cover a wide range of student perceptions of pedagogies and practices in high school settings for descriptive purposes. The study attempts to validate an inventory of pedagogy and practice (IPP) that provides researchers and practitioners with a psychometrically sound instrument that covers the most salient factors related to teaching. Using a sample of students (N = 1515) from 39 schools in Singapore, 14 factors about teaching in English lessons from the students’ perspective were tested with confirmatory factor analysis (classroom task goal, structure and clarity, curiosity and interest, positive class climate, feedback, questioning, quality homework, review of students’ work, conventional teaching, exam preparation, behaviour management, maximizing learning time, student-centred pedagogy, and subject domain teaching). Two external criterion factors were used to further test the IPP factor structure. The inventory will enable teachers to understand more about their teaching and researchers to examine how teaching may be related to learning outcomes.

Multi-site genetic analysis of diffusion images and voxelwise heritability analysis: A pilot project of the ENIGMA-DTI working group

The ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) Consortium was set up to analyze brain measures and genotypes from multiple sites across the world to improve the power to detect genetic variants that influence the brain. Diffusion tensor imaging (DTI) yields quantitative measures sensitive to brain development and degeneration, and some common genetic variants may be associated with white matter integrity or connectivity. DTI measures, such as the fractional anisotropy (FA) of water diffusion, may be useful for identifying genetic variants that influence brain microstructure. However, genome-wide association studies (GWAS) require large populations to obtain sufficient power to detect and replicate significant effects, motivating a multi-site consortium effort. As part of an ENIGMA-DTI working group, we analyzed high-resolution FA images from multiple imaging sites across North America, Australia, and Europe, to address the challenge of harmonizing imaging data collected at multiple sites. Four hundred images of healthy adults aged 18-85 from four sites were used to create a template and corresponding skeletonized FA image as a common reference space. Using twin and pedigree samples of different ethnicities, we used our common template to evaluate the heritability of tract-derived FA measures. We show that our template is reliable for integrating multiple datasets by combining results through meta-analysis and unifying the data through exploratory mega-analyses. Our results may help prioritize regions of the FA map that are consistently influenced by additive genetic factors for future genetic discovery studies. Protocols and templates are publicly available at (http://enigma.loni.ucla.edu/ongoing/dti-working-group/).

An analysis of ethical issues in using wastewater analysis to monitor illicit drug use

Aims To discuss ethical issues that may arise in using WWA to monitor illicit drug use in the general population and in entertainment precincts, prisons, schools and work-places. Method Review current applications of WWA and identify ethical and social issues that may be raised with current and projected future uses of this method. Results Wastewater analysis (WWA) of drug residues is a promising method of monitoring illicit drug use that may overcome some limitations of other monitoring methods. When used for monitoring purposes in large populations, WWA does not raise major ethical concerns because individuals are not identified and the prospects of harming residents of catchment areas are remote. When WWA is used in smaller catchment areas (entertainment venues, prisons, schools or work-places) their results could, possibly, indirectly affect the occupants adversely. Researchers will need to take care in reporting their results to reduce media misreporting. Fears about possible use of WWA for mass individual surveillance by drug law enforcement officials are unlikely to be realized, but will need to be addressed because they may affect public support adversely for this type of research. Conclusions Using wastewater analysis to monitor illicit drug use in large populations does not raise major ethical concerns, but researchers need to minimize possible adverse consequences in studying smaller populations, such as workers, prisoners and students.

Assessing police classifications of sexual assault reports: A meta-analysis of false reporting rates

The objective of the study was to determine, through meta-analysis, the rate of confirmed false reports of sexual assault to police. The meta-analysis initially involved a search for relevant articles. The search revealed seven studies where researchers or their trained helpers evaluated reported sexual assault cases to determine the rate of confirmed false reports. The meta-analysis calculated an overall rate and tested for possible moderators of effect size. The meta-analytic rate of false reports of sexual assault was .052 (95% CIs .030, .089). The rates for the individual studies were heterogeneous, suggesting the possibility of moderators of rate. However, the four possible moderators examined, year of publication, whether the data set used had information in addition to police reports, whether the study was completed in the U.S. or elsewhere, and whether inter-rater reliabilities were reported, were all not significant. The meta-analysis of seven relevant studies shows that confirmed false allegations of sexual assault made to police occur at a significant rate. The total false reporting rate, including both confirmed and equivocal cases, would be greater than the 5 percent rate found here.

Investigating limitations of SURF approach for thermal imaging analysis and mapping

Wildlife conservation involves an understanding of a specific animal, its environment and the interaction within a local ecosystem. Unmanned Aerial Vehicles (UAVs) present cost effective, non-intrusive solution for detecting animals over large areas and the use thermal imaging cameras offer the ability detect animals that would otherwise be concealed to visible light cameras. This report examines some of limitations on using SURF for the development of large maps using multiple stills images extracted from the thermal imaging video camera which contain wildlife (eg. Koala in them).