Scoring a forced-choice image-based assessment of personality: A comparison of machine learning, regression, and summative approaches

Recent years have seen rapid advancements in the way that personality is measured, resulting in a number of innovative predictive measures being proposed, including using features extracted from videos and social media profiles. In the context of selection, game- and image-based assessments of personality are emerging, which can overcome issues like social desirability bias, lack of engagement and low response rates that are associated with traditional self-report measures. Forced-choice formats, where respondents are asked to rank responses, can also mitigate issues such as acquiescence and social desirability bias. Previously, we reported on the development of a gamified forced-choice image-based assessment of the Big Five personality traits created for use in selection, using Lasso regression for the scoring algorithms. In this study, we compare the machine-learning-based Lasso approach to ordinary least squares regression, as well as the summative approach that is typical of forced-choice formats. We find that the Lasso approach performs best in terms of generalisability and convergent validity, although the other methods have greater discriminate validity. We recommend the use of predictive Lasso regression models for scoring forced-choice image-based measures of personality over the other approaches. Potential further studies are suggested

FoxP2 isoforms delineate spatiotemporal transcriptional networks for vocal learning in the zebra finch.

Human speech is one of the few examples of vocal learning among mammals yet ~half of avian species exhibit this ability. Its neurogenetic basis is largely unknown beyond a shared requirement for FoxP2 in both humans and zebra finches. We manipulated FoxP2 isoforms in Area X, a song-specific region of the avian striatopallidum analogous to human anterior striatum, during a critical period for song development. We delineate, for the first time, unique contributions of each isoform to vocal learning. Weighted gene coexpression network analysis of RNA-seq data revealed gene modules correlated to singing, learning, or vocal variability. Coexpression related to singing was found in juvenile and adult Area X whereas coexpression correlated to learning was unique to juveniles. The confluence of learning and singing coexpression in juvenile Area X may underscore molecular processes that drive vocal learning in young zebra finches and, by analogy, humans

Expansion of a novel population of NK cells with low ribosome expression in juvenile dermatomyositis

Juvenile dermatomyositis (JDM) is a pediatric autoimmune disease associated with characteristic rash and proximal muscle weakness. To gain insight into differential lymphocyte gene expression in JDM, peripheral blood mononuclear cells from 4 new-onset JDM patients and 4 healthy controls were sorted into highly enriched lymphocyte populations for RNAseq analysis. NK cells from JDM patients had substantially greater differentially expressed genes (273) than T (57) and B (33) cells. Upregulated genes were associated with the innate immune response and cell cycle, while downregulated genes were associated with decreased ribosomal RNA. Suppressed ribosomal RNA in JDM NK cells was validated by measuring transcription and phosphorylation levels. We confirmed a population of low ribosome expressing NK cells in healthy adults and children. This population of low ribosome NK cells was substantially expanded in 6 treatment-naïve JDM patients and was associated with decreased NK cell degranulation. The enrichment of this NK low ribosome population was completely abrogated in JDM patients with quiescent disease. Together, these data suggest NK cells are highly activated in new-onset JDM patients with an increased population of low ribosome expressing NK cells, which correlates with decreased NK cell function and resolved with control of active disease

Nighttime is the worst time: Parental fear of hypoglycemia in young children with type 1 diabetes

BACKGROUND: Fear of hypoglycemia is common in parents of young children with type 1 diabetes (T1D), but little is known about the specific fears that parents most often experience. Hypoglycemia fear has been associated with poorer glycemic control in older children, though not yet studied in a large cohort of very young children. MATERIALS AND METHODS: Parents of 549 children <7 years (mean 5.2 ± 1.2 years [19% <3 years]) with a mean diabetes duration of 2.4 ± 1.0 years (range 1-6 years) and mean HbA1c 8.2% ± 1.1% (66 ± 12 mmol/mol) registered in the T1D Exchange completed the worry scale of the Hypoglycemia Fear Survey modified for parents (HFS-P). RESULTS: Mean parental fear of hypoglycemia worry score was 36.1 ± 23.1 (possible range 0-100), with most frequent worries related to the child having a low while asleep and the child not recognizing a low. The mean worry score was not associated with the child's age, glycemic control, or recent severe hypoglycemic event. Parental worries about lows while sleeping were significantly higher in pump users than non-users (61% vs. 45%; P < .001), and tended to be higher in CGM users than non-users (62% vs 51%; P = .02). CONCLUSIONS: The greatest worries of parents of young children with T1D were related to hypoglycemia during sleep and other times/circumstances during which it would be difficult to detect hypoglycemia. Using advanced diabetes technologies may be an effort to temper fears about hypoglycemia during sleep, though the directionality of this relationship is undetermined. Additional studies can clarify this association and leverage use of diabetes technologies to improve glycemic control

Laterally Orienting C. elegans Using Geometry at Microscale for High-Throughput Visual Screens in Neurodegeneration and Neuronal Development Studies

C. elegans is an excellent model system for studying neuroscience using genetics because of its relatively simple nervous system, sequenced genome, and the availability of a large number of transgenic and mutant strains. Recently, microfluidic devices have been used for high-throughput genetic screens, replacing traditional methods of manually handling C. elegans. However, the orientation of nematodes within microfluidic devices is random and often not conducive to inspection, hindering visual analysis and overall throughput. In addition, while previous studies have utilized methods to bias head and tail orientation, none of the existing techniques allow for orientation along the dorso-ventral body axis. Here, we present the design of a simple and robust method for passively orienting worms into lateral body positions in microfluidic devices to facilitate inspection of morphological features with specific dorso-ventral alignments. Using this technique, we can position animals into lateral orientations with up to 84% efficiency, compared to 21% using existing methods. We isolated six mutants with neuronal development or neurodegenerative defects, showing that our technology can be used for on-chip analysis and high-throughput visual screens

Spinodal Decomposition and Inflation: Dynamics and Metric Perturbations

We analyse the dynamics of spinodal decomposition in inflationary cosmology using the closed time path formalism of out of equilibrium quantum field theory combined with the non-perturbative Hartree approximation. In addition to a general analysis, we compute the detailed evolution of two inflationary models of particular importance: lambda Phi^4 new inflation and natural inflation. We compute the metric fluctuations resulting from inflationary phase transitions in the slow roll approximation, showing that there exists a regime for which quantum fluctuations of the inflaton field result in a significant deviation in the predictions of the spectrum of primordial density perturbations from standard results. We provide case examples for which a blue tilt to the power spectrum (i.e. n_s > 1) results from the evolution of a single inflaton field, and demonstrate that field fluctuations may result in a scalar amplitude of fluctuations significantly below standard predictions, resulting in a slight alleviation of the inflationary fine tuning problem. We show explicitly that the metric perturbation spectrum resulting from inflation depends upon the state at the outset of the inflationary phase.Comment: 26 pages, 19 figure

Large-scale associations between the leukocyte transcriptome and BOLD responses to speech differ in autism early language outcome subtypes.

Heterogeneity in early language development in autism spectrum disorder (ASD) is clinically important and may reflect neurobiologically distinct subtypes. Here, we identified a large-scale association between multiple coordinated blood leukocyte gene coexpression modules and the multivariate functional neuroimaging (fMRI) response to speech. Gene coexpression modules associated with the multivariate fMRI response to speech were different for all pairwise comparisons between typically developing toddlers and toddlers with ASD and poor versus good early language outcome. Associated coexpression modules were enriched in genes that are broadly expressed in the brain and many other tissues. These coexpression modules were also enriched in ASD-associated, prenatal, human-specific, and language-relevant genes. This work highlights distinctive neurobiology in ASD subtypes with different early language outcomes that is present well before such outcomes are known. Associations between neuroimaging measures and gene expression levels in blood leukocytes may offer a unique in vivo window into identifying brain-relevant molecular mechanisms in ASD