Assessment center redux: there’s no “one best way”

This session will review current research on the assessment center method. Topics will include AC validity and usefulness, proper design and application of the AC method through alignment with broader talent management strategies, differences in perspectives on focal constructs, and creating ACs to meet client needs while respecting current research

Autism Spectrum Disorder in an Unselected Cohort of Children with Neurofibromatosis Type 1 (NF1)

In a non-selected sample of children with Neurofibromatosis type 1 (NF1) the prevalence rate of autism spectrum disorder (ASD) and predictive value of an observational (ADOS)—and questionnaire-based screening instrument were assessed. Complete data was available for 128 children. The prevalence rate for clinical ASD was 10.9%, which is clearly higher than in the general population. This prevalence rate is presumably more accurate than in previous studies that examined children with NF1 with an ASD presumption or solely based on screening instruments. The combined observational- and screening based classifications demonstrated the highest positive predictive value for DSM-IV diagnosis, highlighting the importance of using both instruments in children with NF1

The validity of assessment centres for the prediction of supervisory performance ratings: A meta-analysis

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Examination of the genetic factors underlying the cognitive variability associated with neurofibromatosis type 1

Purpose: Neurofibromatosis type 1 (NF1) is an autosomal dominant disorder associated with cognitive deficits. The NF1 cognitive phenotype is generally considered to be highly variable, possibly due to the observed T2-weighted hyperintensities, loss of heterozygosity, NF1-specific genetic modifiers, or allelic imbalance. Methods: We investigated cognitive variability and assessed the contribution of genetic factors by performing a retrospective cohort study and a monozygotic twin case series. We included data of 497 children with genetically confirmed NF1 and an IQ assessment, including 12 monozygotic twin and 17 sibling sets. Results: Individuals carrying an NF1 chromosomal microdeletion showed significant lower full-scale IQ (FSIQ) scores than individuals carrying intragenic pathogenic NF1 variants. For the intragenic subgroup, the variability in cognitive ability and the correlation of IQ between monozygotic NF1 twin pairs or between NF1 siblings is similar to the general population. Conclusions: The variance and heritability of IQ in individuals with NF1 are similar to that of the general population, and hence mostly driven by genetic background differences. The only factor that significantly attenuates IQ in NF1 individuals is the NF1 chromosomal microdeletion genotype. Implications for clinical management are that individuals with intragenic NF1 variants that score <1.5–2 SD below the mean of the NF1 population should be screened for additional causes of cognitive disability

TRY plant trait database – enhanced coverage and open access

Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives