Harmonization guidelines for HLA-peptide multimer assays derived from results of a large scale international proficiency panel of the Cancer Vaccine Consortium.

PURPOSE: The Cancer Vaccine Consortium of the Cancer Research Institute (CVC-CRI) conducted a multicenter HLA-peptide multimer proficiency panel (MPP) with a group of 27 laboratories to assess the performance of the assay. EXPERIMENTAL DESIGN: Participants used commercially available HLA-peptide multimers and a well characterized common source of peripheral blood mononuclear cells (PBMC). The frequency of CD8+ T cells specific for two HLA-A2-restricted model antigens was measured by flow cytometry. The panel design allowed for participants to use their preferred staining reagents and locally established protocols for both cell labeling, data acquisition and analysis. RESULTS: We observed significant differences in both the performance characteristics of the assay and the reported frequencies of specific T cells across laboratories. These results emphasize the need to identify the critical variables important for the observed variability to allow for harmonization of the technique across institutions. CONCLUSIONS: Three key recommendations emerged that would likely reduce assay variability and thus move toward harmonizing of this assay. (1) Use of more than two colors for the staining (2) collect at least 100,000 CD8 T cells, and (3) use of a background control sample to appropriately set the analytical gates. We also provide more insight into the limitations of the assay and identified additional protocol steps that potentially impact the quality of data generated and therefore should serve as primary targets for systematic analysis in future panels. Finally, we propose initial guidelines for harmonizing assay performance which include the introduction of standard operating protocols to allow for adequate training of technical staff and auditing of test analysis procedures.

Multi-scale support vector algorithms for hot spot detection and modelling

The algorithmic approach to data modelling has developed rapidly these last years, in particular methods based on data mining and machine learning have been used in a growing number of applications. These methods follow a data-driven methodology, aiming at providing the best possible generalization and predictive abilities instead of concentrating on the properties of the data model. One of the most successful groups of such methods is known as Support Vector algorithms. Following the fruitful developments in applying Support Vector algorithms to spatial data, this paper introduces a new extension of the traditional support vector regression (SVR) algorithm. This extension allows for the simultaneous modelling of environmental data at several spatial scales. The joint influence of environmental processes presenting different patterns at different scales is here learned automatically from data, providing the optimum mixture of short and large-scale models. The method is adaptive to the spatial scale of the data. With this advantage, it can provide efficient means to model local anomalies that may typically arise in situations at an early phase of an environmental emergency. However, the proposed approach still requires some prior knowledge on the possible existence of such short-scale patterns. This is a possible limitation of the method for its implementation in early warning systems. The purpose of this paper is to present the multi-scale SVR model and to illustrate its use with an application to the mapping of Cs137 activity given the measurements taken in the region of Briansk following the Chernobyl accident.

Validation of the German version of the Career Adapt-Abilities Scale and its relation to orientations to happiness and work stress

Career adapt-ability has recently gained momentum as a psychosocial construct that not only has much to offer the field of career development, but also contributes to positive coping, adjustment and self-regulation through the four dimensions of concern, control, curiosity and confidence. The positive psychology movement, with concepts such as the orientations to happiness, explores the factors that contribute to human flourishing and optimum functioning. This research has two main contributions; 1) to validate a German version of the Career Adapt-Abilities Scale (CAAS), and 2) to extend the contribution of adapt-abilities to the field of work stress and explore its mediating capacity in the relation between orientations to happiness and work stress. We used a representative sample of the German-speaking Swiss working population including 1204 participants (49.8% women), aged between 26 and 56 (Mage = 42.04). Results indicated that the German version of the CAAS is valid, with overall high levels of model fit suggesting that the conceptual structure of career adapt-ability replicates well in this cultural context. Adapt-abilities showed a negative relationship to work stress, and a positive one with orientations to happiness. The engagement and pleasure scales of orientations to happiness also correlated negatively with work stress. Moreover, career adapt-ability mediates the relationship between orientations to happiness and work stress. In depth analysis of the mediating effect revealed that control is the only significant mediator. Thus control may be acting as a mechanism through which individuals attain their desired life at work subsequently contributing to reduced stress levels.

Structural Validity of the Wechsler Intelligence Scale for Children (WISC-IV) in a French-speaking Swiss sample

The Wechsler Intelligence Scale for Children-fourth edition (i.e. WISC-IV) recognizes a four-factor scoring structure in addition to the Full Scale IQ (FSIQ) score: Verbal Comprehension (VCI), Perceptual Reasoning (PRI), Working Memory (WMI), and Processing Speed (PSI) indices. However, several authors suggested that models based on the Cattell-Horn-Carroll (CHC) theory with 5 or 6 factors provided a better fit to the data than does the current four-factor solution. By comparing the current four-factor structure to CHC-based models, this research aimed to investigate the factorial structure and the constructs underlying the WISC-IV subtest scores with French-speaking Swiss children (N = 249). To deal with this goal, confirmatory factor analyses (CFAs) were conducted. Results showed that a CHC-based model with five factors better fitted the French-Swiss data than did the current WISC-IV scoring structure. All together, these results support the hypothesis of the appropriateness of the CHC model with French-speaking children.

A new alternative method for testing skin irritation using a human skin model: a pilot study.

Studies assessing skin irritation to chemicals have traditionally used laboratory animals; however, such methods are questionable regarding their relevance for humans. New in vitro methods have been validated, such as the reconstructed human epidermis (RHE) model (Episkin®, Epiderm®). The comparison (accuracy) with in vivo results such as the 4-h human patch test (HPT) is 76% at best (Epiderm®). There is a need to develop an in vitro method that better simulates the anatomo-pathological changes encountered in vivo. To develop an in vitro method to determine skin irritation using human viable skin through histopathology, and compare the results of 4 tested substances to the main in vitro methods and in vivo animal method (Draize test). Human skin removed during surgery was dermatomed and mounted on an in vitro flow-through diffusion cell system. Ten chemicals with known non-irritant (heptylbutyrate, hexylsalicylate, butylmethacrylate, isoproturon, bentazon, DEHP and methylisothiazolinone (MI)) and irritant properties (folpet, 1-bromohexane and methylchloroisothiazolinone (MCI/MI)), a negative control (sodiumchloride) and a positive control (sodiumlaurylsulphate) were applied. The skin was exposed at least for 4h. Histopathology was performed to investigate irritation signs (spongiosis, necrosis, vacuolization). We obtained 100% accuracy with the HPT model; 75% with the RHE models and 50% with the Draize test for 4 tested substances. The coefficients of variation (CV) between our three test batches were <0.1, showing good reproducibility. Furthermore, we reported objectively histopathological irritation signs (irritation scale): strong (folpet), significant (1-bromohexane), slight (MCI/MI at 750/250ppm) and none (isoproturon, bentazon, DEHP and MI). This new in vitro test method presented effective results for the tested chemicals. It should be further validated using a greater number of substances; and tested in different laboratories in order to suitably evaluate reproducibility.

Broad-scale adaptive genetic variation in alpine plants is driven by temperature and precipitation.

Identifying adaptive genetic variation is a challenging task, in particular in non-model species for which genomic information is still limited or absent. Here, we studied distribution patterns of amplified fragment length polymorphisms (AFLPs) in response to environmental variation, in 13 alpine plant species consistently sampled across the entire European Alps. Multiple linear regressions were performed between AFLP allele frequencies per site as dependent variables and two categories of independent variables, namely Moran's eigenvector map MEM variables (to account for spatial and unaccounted environmental variation, and historical demographic processes) and environmental variables. These associations allowed the identification of 153 loci of ecological relevance. Univariate regressions between allele frequency and each environmental factor further showed that loci of ecological relevance were mainly correlated with MEM variables. We found that precipitation and temperature were the best environmental predictors, whereas topographic factors were rarely involved in environmental associations. Climatic factors, subject to rapid variation as a result of the current global warming, are known to strongly influence the fate of alpine plants. Our study shows, for the first time for a large number of species, that the same environmental variables are drivers of plant adaptation at the scale of a whole biome, here the European Alps.