Crown Ether-Modified Clays and their Polystyrene Nanocomposites

Crown ether-modified clays were obtained by the combination of sodium and potassium clays with crown ethers and cryptands. Polystyrene nanocomposites were prepared by bulk polymerization in the presence of these clays. The structures of nanocomposites were characterized by X-ray diffraction and transmission electron microscopy. Their thermal stability and flame retardancy were measured by thermogravimetric analysis and cone calorimetry, respectively. Nanocomposites can be formed only from the potassium clays; apparently the sodium clays are not sufficiently organophilic to enable nanocomposite formation. The onset temperature of the degradation is higher for the nanocomposites compared to virgin polystyrene, and the peak heat release rate is decreased by 25% to 30%

Tutorial on Using Regression Models with Count Outcomes using \u3cb\u3eR\u3c/b\u3e

Education researchers often study count variables, such as times a student reached a goal, discipline referrals, and absences. Most researchers that study these variables use typical regression methods (i.e., ordinary least-squares) either with or without transforming the count variables. In either case, using typical regression for count data can produce parameter estimates that are biased, thus diminishing any inferences made from such data. As count-variable regression models are seldom taught in training programs, we present a tutorial to help educational researchers use such methods in their own research. We demonstrate analyzing and interpreting count data using Poisson, negative binomial, zero-inflated Poisson, and zero-inflated negative binomial regression models. The count regression methods are introduced through an example using the number of times students skipped class. The data for this example are freely available and the R syntax used run the example analyses are included in the Appendix. Accessed 16,559 times on https://pareonline.net from February 02, 2016 to December 31, 2019. For downloads from January 1, 2020 forward, please click on the PlumX Metrics link to the right

Likelihood ratios for genome medicine

Patients are beginning to present to healthcare providers with the results of high-throughput individualized genotyping, and interpreting these results in the context of the explosive growth of literature linking individual variants with disease may seem daunting. However, we suggest that results of a personal genomic analysis may be viewed as a panel of many tests for multiple diseases. By using well-established methods of evidence based medicine, these very many parallel tests may be combined using likelihood ratios to report a post-test probability of disease for use in patient assessment

Investigation of nanodispersion in polystyrene-montmorillonite nanocomposites by solid state NMR

Nanocomposites result from combinations of materials with vastly different properties in the nanometer scale. These materials exhibit many unique properties such as improved thermal stability, reduced flammability, and improved mechanical properties. Many of the properties associated with polymer–clay nanocomposites are a function of the extent of exfoliation of the individual clay sheets or the quality of the nanodispersion. This work demonstrates that solid-state NMR can be used to characterize, quantitatively, the nanodispersion of variously modified montmorillonite (MMT) clays in polystyrene (PS) matrices. The direct influence of the paramagnetic Fe3, embedded in the aluminosilicate layers of MMT, on polymer protons within about 1 nm from the clay surfaces creates relaxation sources, which, via spin diffusion, significantly shorten the overall proton longitudinal relaxation time (T1 H). Deoxygenated samples were used to avoid the particularly strong contribution to the T1 H of PS from paramagnetic molecular oxygen. We used T1 H as an indicator of the nanodispersion of the clay in PS. This approach correlated reasonably well with X-ray diffraction and transmission electron microscopy (TEM) data. A model for interpreting the saturation-recovery data is proposed such that two parameters relating to the dispersion can be extracted. The first parameter, f, is the fraction of the potentially available clay surface that has been transformed into polymer–clay interfaces. The second parameter is a relative measure of the homogeneity of the dispersion of these actual polymer–clay interfaces. Finally, a quick assay of T1 H is reported for samples equilibrated with atmospheric oxygen. Included are these samples as well as 28 PS/MMT nanocomposite samples prepared by extrusion. These measurements are related to the development of highthroughput characterization techniques. This approach gives qualitative indications about dispersion; however, the more time-consuming analysis, of a few deoxygenated samples from this latter set, offers significantly greater insight into the clay dispersion. A second, probably superior, rapid-analysis method, applicable to oxygen-containing samples, is also demonstrated that should yield a reasonable estimate of the f parameter. Thus, for PS/MMT nanocomposites, one has the choice of a less complete NMR assay of dispersion that is significantly faster than TEM analysis, versus a slower and more complete NMR analysis with sample times comparable to TEM, information rivaling that of TEM, and a substantial advantage that this is a bulk characterization method. © 2003 Wiley Periodicals, Inc.* J Polym Sci Part B: Polym Phys 41: 3188–3213, 200

Data preparation and interannotator agreement: BioCreAtIvE Task 1B

<p>Abstract</p> <p>Background</p> <p>We prepared and evaluated training and test materials for an assessment of text mining methods in molecular biology. The goal of the assessment was to evaluate the ability of automated systems to generate a list of unique gene identifiers from PubMed abstracts for the three model organisms Fly, Mouse, and Yeast. This paper describes the preparation and evaluation of answer keys for training and testing. These consisted of lists of normalized gene names found in the abstracts, generated by adapting the gene list for the full journal articles found in the model organism databases. For the training dataset, the gene list was pruned automatically to remove gene names not found in the abstract; for the testing dataset, it was further refined by manual annotation by annotators provided with guidelines. A critical step in interpreting the results of an assessment is to evaluate the quality of the data preparation. We did this by careful assessment of interannotator agreement and the use of answer pooling of participant results to improve the quality of the final testing dataset.</p> <p>Results</p> <p>Interannotator analysis on a small dataset showed that our gene lists for Fly and Yeast were good (87% and 91% three-way agreement) but the Mouse gene list had many conflicts (mostly omissions), which resulted in errors (69% interannotator agreement). By comparing and pooling answers from the participant systems, we were able to add an additional check on the test data; this allowed us to find additional errors, especially in Mouse. This led to 1% change in the Yeast and Fly "gold standard" answer keys, but to an 8% change in the mouse answer key.</p> <p>Conclusion</p> <p>We found that clear annotation guidelines are important, along with careful interannotator experiments, to validate the generated gene lists. Also, abstracts alone are a poor resource for identifying genes in paper, containing only a fraction of genes mentioned in the full text (25% for Fly, 36% for Mouse). We found that there are intrinsic differences between the model organism databases related to the number of synonymous terms and also to curation criteria. Finally, we found that answer pooling was much faster and allowed us to identify more conflicting genes than interannotator analysis.</p

Tryptic digestion coupled with ambient DESI and LESA mass spectrometry enables identification of skeletal muscle proteins in mixtures and distinguishes between beef, pork, horse, chicken and turkey meat

The use of ambient desorption electrospray ionization (DESI-MS) mass spectrometry and liquid extraction surface analysis mass spectrometry (LESA-MS) is explored for the first time to analyse skeletal muscle proteins obtained from mixture of standard proteins and raw meat. Single proteins and mixtures of up to five proteins (myoglobin, troponin C, actin, BSA, tropomyosin) were deposited onto a polymer surface, followed by in-situ tryptic digestion and comparative analysis using DESI-MS and LESA-MS using tandem electrospray MS. Peptide peaks specific to individual proteins were readily distinguishable with good signal-to-noise ratio in the five-component mixture. LESA-MS gave a more stable analysis and greater sensitivity compared with DESI-MS. Meat tryptic digests were subjected to peptidomics analysis by DESI-MS and LESA-MS. Bovine, horse, pig, chicken and turkey muscle digests were clearly discriminated using multivariate data analysis (MVA) of the peptidomic datasets. The most abundant skeletal muscle proteins were identified and correctly classified according to the species following MS/MS analysis. The study shows, for the first time, that ambient ionization techniques such as DESI-MS and LESA-MS have great potential for species-specific analysis and differentiation of skeletal muscle proteins by direct surface desorption

Ambient DESI and LESA-MS analysis of proteins adsorbed to a biomaterial surface using in-situ surface tryptic digestion

The detection and identification of proteins adsorbed onto biomaterial surfaces under ambient conditions has significant experimental advantages but has proven to be difficult to achieve with conventional measuring technologies. In this study, we present an adaptation of desorption electrospray ionization (DESI) and liquid extraction surface analysis (LESA) mass spectrometry (MS) coupled with in-situ surface tryptic digestion to identify protein species from a biomaterial surface. Cytochrome c, myoglobin, and BSA in a combination of single and mixture spots were printed in an array format onto Permanox slides, followed by in-situ surface digestion and detection via MS. Automated tandem MS performed on surface peptides was able to identify the proteins via MASCOT. Limits of detection were determined for DESI-MS and a comparison of DESI and LESA-MS peptide spectra characteristics and sensitivity was made. DESI-MS images of the arrays were produced and analyzed with imaging multivariate analysis to automatically separate peptide peaks for each of the proteins within a mixture into distinct components. This is the first time that DESI and LESA-MS have been used for the in-situ detection of surface digested proteins on biomaterial surfaces and presents a promising proof of concept for the use of ambient MS in the rapid and automated analysis of surface proteins

Translational bioinformatics in the cloud: an affordable alternative

With the continued exponential expansion of publicly available genomic data and access to low-cost, high-throughput molecular technologies for profiling patient populations, computational technologies and informatics are becoming vital considerations in genomic medicine. Although cloud computing technology is being heralded as a key enabling technology for the future of genomic research, available case studies are limited to applications in the domain of high-throughput sequence data analysis. The goal of this study was to evaluate the computational and economic characteristics of cloud computing in performing a large-scale data integration and analysis representative of research problems in genomic medicine. We find that the cloud-based analysis compares favorably in both performance and cost in comparison to a local computational cluster, suggesting that cloud computing technologies might be a viable resource for facilitating large-scale translational research in genomic medicine