Plug-and-Display: decoration of Virus-Like Particles via isopeptide bonds for modular immunization.

Virus-like particles (VLPs) are non-infectious self-assembling nanoparticles, useful in medicine and nanotechnology. Their repetitive molecularly-defined architecture is attractive for engineering multivalency, notably for vaccination. However, decorating VLPs with target-antigens by genetic fusion or chemical modification is time-consuming and often leads to capsid misassembly or antigen misfolding, hindering generation of protective immunity. Here we establish a platform for irreversibly decorating VLPs simply by mixing with protein antigen. SpyCatcher is a genetically-encoded protein designed to spontaneously form a covalent bond to its peptide-partner SpyTag. We expressed in E. coli VLPs from the bacteriophage AP205 genetically fused to SpyCatcher. We demonstrated quantitative covalent coupling to SpyCatcher-VLPs after mixing with SpyTag-linked to malaria antigens, including CIDR and Pfs25. In addition, we showed coupling to the VLPs for peptides relevant to cancer from epidermal growth factor receptor and telomerase. Injecting SpyCatcher-VLPs decorated with a malarial antigen efficiently induced antibody responses after only a single immunization. This simple, efficient and modular decoration of nanoparticles should accelerate vaccine development, as well as other applications of nanoparticle devices.

Gymnasts and orienteers display better mental rotation performance than nonathletes

The aim of this study was to examine whether athletes differ from nonathletes regarding their mental rotation performance. Furthermore, it investigated whether athletes doing sports requiring distinguishable levels of mental rotation (orienteering, gymnastics, running), as well as varying with respect to having an egocentric (gymnastics) or an allocentric perspective (orienteering), differ from each other. Therefore, the Mental Rotations Test (MRT) was carried out with 20 orienteers, 20 gymnasts, 20 runners, and 20 nonathletes. The results indicate large differences in mental rotation performance, with those actively doing sports outperforming the nonathletes. Analyses for the specific groups showed that orienteers and gymnasts differed from the nonathletes, whereas endurance runners did not. Contrary to expectations, the mental rotation performance of gymnasts did not differ from that of orienteers. This study also revealed gender differences in favor of men. Implications regarding a differentiated view of the connection between specific sports and mental rotation performance are discussed.

Developing an alternative response format for the script concordance test

Introduction: Clinical reasoning is essential for the practice of medicine. In theory of development of medical expertise it is stated, that clinical reasoning starts from analytical processes namely the storage of isolated facts and the logical application of the ‘rules’ of diagnosis. Then the learners successively develop so called semantic networks and illness-scripts which finally are used in an intuitive non-analytic fashion [1], [2]. The script concordance test (SCT) is an example for assessing clinical reasoning [3]. However the aggregate scoring [3] of the SCT is recognized as problematic [4]. The SCT`s scoring leads to logical inconsistencies and is likely to reflect construct-irrelevant differences in examinees’ response styles [4]. Also the expert panel judgments might lead to an unintended error of measurement [4]. In this PhD project the following research questions will be addressed: 1. How does a format look like to assess clinical reasoning (similar to the SCT but) with multiple true-false questions or other formats with unambiguous correct answers, and by this address the above mentioned pitfalls in traditional scoring of the SCT? 2. How well does this format fulfill the Ottawa criteria for good assessment, with special regards to educational and catalytic effects [5]? Methods: 1. In a first study it shall be assessed whether designing a new format using multiple true-false items to assess clinical reasoning similar to the SCT-format is arguable in a theoretically and practically sound fashion. For this study focus groups or interviews with assessment experts and students will be undertaken. 2. In an study using focus groups and psychometric data Norcini`s and colleagues Criteria for Good Assessment [5] shall be determined for the new format in a real assessment. Furthermore the scoring method for this new format shall be optimized using real and simulated data.

LORENZ: Stata module to estimate and display Lorenz curves and concentration curves

Lorenz estimates Lorenz and concentration curves from individual-level data and, optionally, displays the results in a graph. Relative as well as generalized, absolute, unnormalized, or custom-normalized Lorenz or concentration curves are supported, and tools for computing contrasts between different subpopulations or outcome variables are provided. Variance estimation for complex samples is fully supported.

MOREMATA: Stata module (Mata) to provide various functions

This package includes various Mata functions. kern(): various kernel functions; kint(): kernel integral functions; kdel0(): canonical bandwidth of kernel; quantile(): quantile function; median(): median; iqrange(): inter-quartile range; ecdf(): cumulative distribution function; relrank(): grade transformation; ranks(): ranks/cumulative frequencies; freq(): compute frequency counts; histogram(): produce histogram data; mgof(): multinomial goodness-of-fit tests; collapse(): summary statistics by subgroups; _collapse(): summary statistics by subgroups; gini(): Gini coefficient; sample(): draw random sample; srswr(): SRS with replacement; srswor(): SRS without replacement; upswr(): UPS with replacement; upswor(): UPS without replacement; bs(): bootstrap estimation; bs2(): bootstrap estimation; bs_report(): report bootstrap results; jk(): jackknife estimation; jk_report(): report jackknife results; subset(): obtain subsets, one at a time; composition(): obtain compositions, one by one; ncompositions(): determine number of compositions; partition(): obtain partitions, one at a time; npartitionss(): determine number of partitions; rsubset(): draw random subset; rcomposition(): draw random composition; colvar(): variance, by column; meancolvar(): mean and variance, by column; variance0(): population variance; meanvariance0(): mean and population variance; mse(): mean squared error; colmse(): mean squared error, by column; sse(): sum of squared errors; colsse(): sum of squared errors, by column; benford(): Benford distribution; cauchy(): cumulative Cauchy-Lorentz dist.; cauchyden(): Cauchy-Lorentz density; cauchytail(): reverse cumulative Cauchy-Lorentz; invcauchy(): inverse cumulative Cauchy-Lorentz; rbinomial(): generate binomial random numbers; cebinomial(): cond. expect. of binomial r.v.; root(): Brent's univariate zero finder; nrroot(): Newton-Raphson zero finder; finvert(): univariate function inverter; integrate_sr(): univariate function integration (Simpson's rule); integrate_38(): univariate function integration (Simpson's 3/8 rule); ipolate(): linear interpolation; polint(): polynomial inter-/extrapolation; plot(): Draw twoway plot; _plot(): Draw twoway plot; panels(): identify nested panel structure; _panels(): identify panel sizes; npanels(): identify number of panels; nunique(): count number of distinct values; nuniqrows(): count number of unique rows; isconstant(): whether matrix is constant; nobs(): number of observations; colrunsum(): running sum of each column; linbin(): linear binning; fastlinbin(): fast linear binning; exactbin(): exact binning; makegrid(): equally spaced grid points; cut(): categorize data vector; posof(): find element in vector; which(): positions of nonzero elements; locate(): search an ordered vector; hunt(): consecutive search; cond(): matrix conditional operator; expand(): duplicate single rows/columns; _expand(): duplicate rows/columns in place; repeat(): duplicate contents as a whole; _repeat(): duplicate contents in place; unorder2(): stable version of unorder(); jumble2(): stable version of jumble(); _jumble2(): stable version of _jumble(); pieces(): break string into pieces; npieces(): count number of pieces; _npieces(): count number of pieces; invtokens(): reverse of tokens(); realofstr(): convert string into real; strexpand(): expand string argument; matlist(): display a (real) matrix; insheet(): read spreadsheet file; infile(): read free-format file; outsheet(): write spreadsheet file; callf(): pass optional args to function; callf_setup(): setup for mm_callf().

The Plasmodium serine-type SERA proteases display distinct expression patterns and non-essential in vivo roles during life cycle progression of the malaria parasite

Parasite proteases play key roles in several fundamental steps of the Plasmodium life cycle, including haemoglobin degradation, host cell invasion and parasite egress. Plasmodium exit from infected host cells appears to be mediated by a class of papain-like cysteine proteases called 'serine repeat antigens' (SERAs). A SERA subfamily, represented by Plasmodium falciparum SERA5, contains an atypical active site serine residue instead of a catalytic cysteine. Members of this SERAser subfamily are abundantly expressed in asexual blood stages, rendering them attractive drug and vaccine targets. In this study, we show by antibody localization and in vivo fluorescent tagging with the red fluorescent protein mCherry that the two P. berghei serine-type family members, PbSERA1 and PbSERA2, display differential expression towards the final stages of merozoite formation. Via targeted gene replacement, we generated single and double gene knockouts of the P. berghei SERAser genes. These loss-of-function lines progressed normally through the parasite life cycle, suggesting a specialized, non-vital role for serine-type SERAs in vivo. Parasites lacking PbSERAser showed increased expression of the cysteine-type PbSERA3. Compensatory mechanisms between distinct SERA subfamilies may thus explain the absence of phenotypical defect in SERAser disruptants, and challenge the suitability to develop potent antimalarial drugs based on specific inhibitors of Plasmodium serine-type SERAs.