A Fluorogenic Probe for Cell Surface Phosphatidylserine Using an Intramolecular Indicator Displacement Sensing Mechanism.

The detection of externalized phosphatidylserine (PS) on the cell surface is commonly used to distinguish between living, apoptotic, and necrotic cells. The tools of choice for many researchers to study apoptosis are annexin V-fluorophore conjugates. However, the use of this 35 kDa protein is associated with several drawbacks, including temperature sensitivity, Ca2+ dependence, and slow binding kinetics. Herein, a fluorogenic probe for cell surface PS, P-IID, is described, which operates by an intramolecular indicator displacement (IID) mechanism. An intramolecularly bound coumarin indicator is released in the presence of cell surface PS, leading to a fluorescence "turn-on" response. P-IID demonstrates superior performance when compared to annexin V, for both fluorescence imaging and flow cytometry. This allows P-IID to be used in time-lapse imaging of apoptosis using confocal laser scanning microscopy and demonstrates the utility of the IID mechanism in live cells

An Area‐Specific, International Community‐Led Approach to Understanding and Addressing Equality, Diversity, and Inclusion Issues within Supramolecular Chemistry

Diversity, equality, and inclusion (DEI/EDI) are pressing issues in chemistry and the natural sciences. In this Essay we share how an area‐specific approach is “calling in” the community so that it can act to address EDI issues, and support those who are marginalised. Women In Supramolecular Chemistry (WISC) is an international network that aims to support equality, diversity, and inclusion within supramolecular chemistry. WISC has taken a field‐specific approach using qualitative research methods with scientists to identify the support that is needed and the problems the supramolecular community needs to address. Herein, we present survey data from the community which highlight the barriers that are faced by those who take career breaks for any reason, a common example is maternity leave, and the importance of mentoring to aid progression post‐PhD. In conclusion, we set out an interdisciplinary and creative approach to addressing EDI issues within supramolecular chemistry

Sparse PLS discriminant analysis: biologically relevant feature selection and graphical displays for multiclass problems

Background: Variable selection on high throughput biological data, such as gene expression or single nucleotide polymorphisms (SNPs), becomes inevitable to select relevant information and, therefore, to better characterize diseases or assess genetic structure. There are different ways to perform variable selection in large data sets. Statistical tests are commonly used to identify differentially expressed features for explanatory purposes, whereas Machine Learning wrapper approaches can be used for predictive purposes. In the case of multiple highly correlated variables, another option is to use multivariate exploratory approaches to give more insight into cell biology, biological pathways or complex traits.Results: A simple extension of a sparse PLS exploratory approach is proposed to perform variable selection in a multiclass classification framework.Conclusions: sPLS-DA has a classification performance similar to other wrapper or sparse discriminant analysis approaches on public microarray and SNP data sets. More importantly, sPLS-DA is clearly competitive in terms of computational efficiency and superior in terms of interpretability of the results via valuable graphical outputs. sPLS-DA is available in the R package mixOmics, which is dedicated to the analysis of large biological data sets

A systematic review of the effectiveness and cost-effectiveness of peer education and peer support in prisons.

BACKGROUND: Prisoners experience significantly worse health than the general population. This review examines the effectiveness and cost-effectiveness of peer interventions in prison settings. METHODS: A mixed methods systematic review of effectiveness and cost-effectiveness studies, including qualitative and quantitative synthesis was conducted. In addition to grey literature identified and searches of websites, nineteen electronic databases were searched from 1985 to 2012. Study selection criteria were: Population: Prisoners resident in adult prisons and children resident in Young Offender Institutions (YOIs). INTERVENTION: Peer-based interventions Comparators: Review questions 3 and 4 compared peer and professionally led approaches. OUTCOMES: Prisoner health or determinants of health; organisational/ process outcomes; views of prison populations. STUDY DESIGNS: Quantitative, qualitative and mixed method evaluations. RESULTS: Fifty-seven studies were included in the effectiveness review and one study in the cost-effectiveness review; most were of poor methodological quality. Evidence suggested that peer education interventions are effective at reducing risky behaviours, and that peer support services are acceptable within the prison environment and have a positive effect on recipients, practically or emotionally. Consistent evidence from many, predominantly qualitative, studies, suggested that being a peer deliverer was associated with positive effects. There was little evidence on cost-effectiveness of peer-based interventions. CONCLUSIONS: There is consistent evidence from a large number of studies that being a peer worker is associated with positive health; peer support services are also an acceptable source of help within the prison environment and can have a positive effect on recipients. Research into cost-effectiveness is sparse. SYSTEMATIC REVIEW REGISTRATION: PROSPERO ref: CRD42012002349

A flexible framework for sparse simultaneous component based data integration

<p>Abstract</p> <p>1 Background</p> <p>High throughput data are complex and methods that reveal structure underlying the data are most useful. Principal component analysis, frequently implemented as a singular value decomposition, is a popular technique in this respect. Nowadays often the challenge is to reveal structure in several sources of information (e.g., transcriptomics, proteomics) that are available for the same biological entities under study. Simultaneous component methods are most promising in this respect. However, the interpretation of the principal and simultaneous components is often daunting because contributions of each of the biomolecules (transcripts, proteins) have to be taken into account.</p> <p>2 Results</p> <p>We propose a sparse simultaneous component method that makes many of the parameters redundant by shrinking them to zero. It includes principal component analysis, sparse principal component analysis, and ordinary simultaneous component analysis as special cases. Several penalties can be tuned that account in different ways for the block structure present in the integrated data. This yields known sparse approaches as the lasso, the ridge penalty, the elastic net, the group lasso, sparse group lasso, and elitist lasso. In addition, the algorithmic results can be easily transposed to the context of regression. Metabolomics data obtained with two measurement platforms for the same set of <it>Escherichia coli </it>samples are used to illustrate the proposed methodology and the properties of different penalties with respect to sparseness across and within data blocks.</p> <p>3 Conclusion</p> <p>Sparse simultaneous component analysis is a useful method for data integration: First, simultaneous analyses of multiple blocks offer advantages over sequential and separate analyses and second, interpretation of the results is highly facilitated by their sparseness. The approach offered is flexible and allows to take the block structure in different ways into account. As such, structures can be found that are exclusively tied to one data platform (group lasso approach) as well as structures that involve all data platforms (Elitist lasso approach).</p> <p>4 Availability</p> <p>The additional file contains a MATLAB implementation of the sparse simultaneous component method.</p

The risk of misclassifying subjects within principal component based asset index

The asset index is often used as a measure of socioeconomic status in empirical research as an explanatory variable or to control confounding. Principal component analysis (PCA) is frequently used to create the asset index. We conducted a simulation study to explore how accurately the principal component based asset index reflects the study subjects’ actual poverty level, when the actual poverty level is generated by a simple factor analytic model. In the simulation study using the PC-based asset index, only 1% to 4% of subjects preserved their real position in a quintile scale of assets; between 44% to 82% of subjects were misclassified into the wrong asset quintile. If the PC-based asset index explained less than 30% of the total variance in the component variables, then we consistently observed more than 50% misclassification across quintiles of the index. The frequency of misclassification suggests that the PC-based asset index may not provide a valid measure of poverty level and should be used cautiously as a measure of socioeconomic status

From KIDSCREEN-10 to CHU9D: creating a unique mapping algorithm for application in economic evaluation

Background: The KIDSCREEN-10 index and the Child Health Utility 9D (CHU9D) are two recently developed generic instruments for the measurement of health-related quality of life in children and adolescents. Whilst the CHU9D is a preference based instrument developed specifically for application in cost-utility analyses, the KIDSCREEN-10 is not currently suitable for application in this context. This paper provides an algorithm for mapping the KIDSCREEN-10 index onto the CHU9D utility scores. Methods: A sample of 590 Australian adolescents (aged 11–17) completed both the KIDSCREEN-10 and the CHU9D. Several econometric models were estimated, including ordinary least squares estimator, censored least absolute deviations estimator, robust MM-estimator and generalised linear model, using a range of explanatory variables with KIDSCREEN-10 items scores as key predictors. The predictive performance of each model was judged using mean absolute error (MAE) and root mean squared error (RMSE). Results: The MM-estimator with stepwise-selected KIDSCREEN-10 items scores as explanatory variables had the best predictive accuracy using MAE, whilst the equivalent ordinary least squares model had the best predictive accuracy using RMSE. Conclusions: The preferred mapping algorithm (i.e. the MM-estimate with stepwise selected KIDSCREEN-10 item scores as the predictors) can be used to predict CHU9D utility from KIDSCREEN-10 index with a high degree of accuracy. The algorithm may be usefully applied within cost-utility analyses to generate cost per quality adjusted life year estimates where KIDSCREEN-10 data only are available