This investigation developed a self-report measure of empathy as experienced within digital communication – “digital empathy”. 72 items were developed using six validated empathy constructs as conceptual bases. In Study 1, exploratory factor analysis revealed the expected six factors underpinning 67 of the items. Six items per factor were chosen to create a parsimonious Digital Communication Empathy Scale (DCES). Study 2 verified the six-factor structure of the shorter, 36-item DCES using confirmatory factor analysis, and established preliminary validity evidence by examining its relationships with two variables often related to empathy: gender and social desirability. Similar to existing self-report empathy scales, females scored higher than males on five DCES subscales and the DCES total score. Relationships between DCES scores and social desirability were broadly consistent with those previously observed between existing self-report empathy scales and social desirability. The DCES exhibits good psychometrics, demonstrates preliminary validity evidence, and provides a multi-dimensional measure of digital empathy. Future research may extend this work by examining relationships between the DCES and digital behaviors like online helping and cyber-aggression.

MOTIVATION: Immunoglobulin heavy chain genes are formed by recombination of genes randomly selected from sets of IGHV, IGHD and IGHJ genes. Utilities have been developed to identify genes that contribute to observed VDJ rearrangements, but in the absence of datasets of known rearrangements, the evaluation of these utilities is problematic. We have analyzed thousands of VDJ rearrangements from an individual (S22) whose IGHV, IGHD and IGHJ genotype can be inferred from the dataset. Knowledge of this genotype means that the Stanford_S22 dataset can serve to benchmark the performance of IGH alignment utilities. RESULTS: We evaluated the performance of seven utilities. Failure to partition a sequence into genes present in the S22 genome was considered an error, and error rates for different utilities ranged from 7.1% to 13.7%. AVAILABILITY: Supplementary data includes the S22 genotypes and alignments. The Stanford_S22 dataset and an evaluation tool is available at http://www.emi.unsw.edu.au/~ihmmune/IGHUtilityEval/.

MOTIVATION: Immunoglobulin heavy chain (IGH) genes in mature B lymphocytes are the result of recombination of IGHV, IGHD and IGHJ germline genes, followed by somatic mutation. The correct identification of the germline genes that make up a variable VH domain is essential to our understanding of the process of antibody diversity generation as well as to clinical investigations of some leukaemias and lymphomas. RESULTS: We have developed iHMMune-align, an alignment program that uses a hidden Markov model (HMM) to model the processes involved in human IGH gene rearrangement and maturation. The performance of iHMMune-align was compared to that of other immunoglobulin gene alignment utilities using both clonally related and randomly selected IGH sequences. This evaluation suggests that iHMMune-align provides a more accurate identification of component germline genes than other currently available IGH gene characterization programs. AVAILABILITY: iHMMune-align cross-platform Java executable and web interface are freely available to academic users and can be accessed at http://www.emi.unsw.edu.au/~ihmmune/.