Illness Perceptions Predict Cognitive Performance Validity

© 2018 The International Neuropsychological Society. Objectives: The aim of this study was to investigate the relationship of psychological variables to cognitive performance validity test (PVT) results in mixed forensic and nonforensic clinical samples. Methods: Participants included 183 adults who underwent comprehensive neuropsychological examination. Criterion groups were formed, that is, Credible Group or Noncredible Group, based upon their performance on the Word Memory Test and other stand-alone and embedded PVT measures. Results: Multivariate logistic regression analysis identified three significant predictors of cognitive performance validity. These included two psychological constructs, for example, Cogniphobia (perception that cognitive effort will exacerbate neurological symptoms), and Symptom Identity (perception that current symptoms are the result of illness or injury), and one contextual factor (forensic). While there was no interaction between these factors, elevated scores were most often observed in the forensic sample, suggesting that these independently contributing intrinsic psychological factors are more likely to occur in a forensic environment. Conclusions: Illness perceptions were significant predictors of cognitive performance validity particularly when they reached very elevated levels. Extreme elevations were more common among participants in the forensic sample, and potential reasons for this pattern are explored

precisionFDA Truth Challenge V2: Calling variants from short- and long-reads in difficult-to-map regions

The precisionFDA Truth Challenge V2 aimed to assess the state-of-the-art of variant calling in difficult-to-map regions and the Major Histocompatibility Complex (MHC). Starting with FASTQ files, 20 challenge participants applied their variant calling pipelines and submitted 64 variant callsets for one or more sequencing technologies (~35X Illumina, ~35X PacBio HiFi, and ~50X Oxford Nanopore Technologies). Submissions were evaluated following best practices for benchmarking small variants with the new GIAB benchmark sets and genome stratifications. Challenge submissions included a number of innovative methods for all three technologies, with graph-based and machine-learning methods scoring best for short-read and long-read datasets, respectively. New methods out-performed the 2016 Truth Challenge winners, and new machine-learning approaches combining multiple sequencing technologies performed particularly well. Recent developments in sequencing and variant calling have enabled benchmarking variants in challenging genomic regions, paving the way for the identification of previously unknown clinically relevant variants

PrecisionFDA Truth Challenge V2: Calling variants from short and long reads in difficult-to-map regions

The precisionFDA Truth Challenge V2 aimed to assess the state of the art of variant calling in challenging genomic regions. Starting with FASTQs, 20 challenge participants applied their variant-calling pipelines and submitted 64 variant call sets for one or more sequencing technologies (Illumina, PacBio HiFi, and Oxford Nanopore Technologies). Submissions were evaluated following best practices for benchmarking small variants with updated Genome in a Bottle benchmark sets and genome stratifications. Challenge submissions included numerous innovative methods, with graph-based and machine learning methods scoring best for short-read and long-read datasets, respectively. With machine learning approaches, combining multiple sequencing technologies performed particularly well. Recent developments in sequencing and variant calling have enabled benchmarking variants in challenging genomic regions, paving the way for the identification of previously unknown clinically relevant variants