Correlation functions quantify super-resolution images and estimate apparent clustering due to over-counting

We present an analytical method to quantify clustering in super-resolution localization images of static surfaces in two dimensions. The method also describes how over-counting of labeled molecules contributes to apparent self-clustering and how the effective lateral resolution of an image can be determined. This treatment applies to clustering of proteins and lipids in membranes, where there is significant interest in using super-resolution localization techniques to probe membrane heterogeneity. When images are quantified using pair correlation functions, the magnitude of apparent clustering due to over-counting will vary inversely with the surface density of labeled molecules and does not depend on the number of times an average molecule is counted. Over-counting does not yield apparent co-clustering in double label experiments when pair cross-correlation functions are measured. We apply our analytical method to quantify the distribution of the IgE receptor (Fc{\epsilon}RI) on the plasma membranes of chemically fixed RBL-2H3 mast cells from images acquired using stochastic optical reconstruction microscopy (STORM) and scanning electron microscopy (SEM). We find that apparent clustering of labeled IgE bound to Fc{\epsilon}RI detected with both methods arises from over-counting of individual complexes. Thus our results indicate that these receptors are randomly distributed within the resolution and sensitivity limits of these experiments.Comment: 22 pages, 5 figure

Synthesis and Self-Assembly of Well-Defined Block Copolypeptides via Controlled NCA Polymerization

This article summarizes advances in the synthesis of well-defined polypeptides and block copolypeptides. Traditional methods used to polymerize α-amino acid-N-carboxyanhydrides (NCAs) are described, and limitations in the utility of these systems for the preparation of polypeptides are discussed. Improved initiators and methods that allow polypeptide synthesis with good control over chain length, chain length distribution, and chain-end functionality are also discussed. Using these methods, block and random copolypeptides of controlled dimensions (including molecular weight, sequence, composition, and molecular weight distribution) can now be prepared. The ability of well-defined block copolypeptides to assemble into supramolecular copolypeptide micelles, copolypeptide vesicles, and copolypeptide hydrogels is described. Many of these assemblies have been found to possess unique properties that are derived from the amino acid building blocks and ordered conformations of the polypeptide segments. © Springer-Verlag Berlin Heidelberg 2013

Can mental health diagnoses in administrative data be used for research? A systematic review of the accuracy of routinely collected diagnoses

BACKGROUND: There is increasing availability of data derived from diagnoses made routinely in mental health care, and interest in using these for research. Such data will be subject to both diagnostic (clinical) error and administrative error, and so it is necessary to evaluate its accuracy against a reference-standard. Our aim was to review studies where this had been done to guide the use of other available data. METHODS: We searched PubMed and EMBASE for studies comparing routinely collected mental health diagnosis data to a reference standard. We produced diagnostic category-specific positive predictive values (PPV) and Cohen’s kappa for each study. RESULTS: We found 39 eligible studies. Studies were heterogeneous in design, with a wide range of outcomes. Administrative error was small compared to diagnostic error. PPV was related to base rate of the respective condition, with overall median of 76 %. Kappa results on average showed a moderate agreement between source data and reference standard for most diagnostic categories (median kappa = 0.45–0.55); anxiety disorders and schizoaffective disorder showed poorer agreement. There was no significant benefit in accuracy for diagnoses made in inpatients. CONCLUSIONS: The current evidence partly answered our questions. There was wide variation in the quality of source data, with a risk of publication bias. For some diagnoses, especially psychotic categories, administrative data were generally predictive of true diagnosis. For others, such as anxiety disorders, the data were less satisfactory. We discuss the implications of our findings, and the need for researchers to validate routine diagnostic data. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12888-016-0963-x) contains supplementary material, which is available to authorized users