Controlling complexity: the clinical relevance of mouse complex genetics.

Experimental animal models are essential to obtain basic knowledge of the underlying biological mechanisms in human diseases. Here, we review major contributions to biomedical research and discoveries that were obtained in the mouse model by using forward genetics approaches and that provided key insights into the biology of human diseases and paved the way for the development of novel therapeutic approaches.

Advances in MRI-based computational neuroanatomy: from morphometry to in-vivo histology.

PURPOSE OF REVIEW: Current computational neuroanatomy based on MRI focuses on morphological measures of the brain. We present recent methodological developments in quantitative MRI (qMRI) that provide standardized measures of the brain, which go beyond morphology. We show how biophysical modelling of qMRI data can provide quantitative histological measures of brain tissue, leading to the emerging field of in-vivo histology using MRI (hMRI). RECENT FINDINGS: qMRI has greatly improved the sensitivity and specificity of computational neuroanatomy studies. qMRI metrics can also be used as direct indicators of the mechanisms driving observed morphological findings. For hMRI, biophysical models of the MRI signal are being developed to directly access histological information such as cortical myelination, axonal diameters or axonal g-ratio in white matter. Emerging results indicate promising prospects for the combined study of brain microstructure and function. SUMMARY: Non-invasive brain tissue characterization using qMRI or hMRI has significant implications for both research and clinics. Both approaches improve comparability across sites and time points, facilitating multicentre/longitudinal studies and standardized diagnostics. hMRI is expected to shed new light on the relationship between brain microstructure, function and behaviour, both in health and disease, and become an indispensable addition to computational neuroanatomy.

Hire me: Computational inference of hirability in employment interviews based on nonverbal behavior

Understanding the basis on which recruiters form hirability impressions for a job applicant is a key issue in organizational psychology and can be addressed as a social computing problem. We approach the problem from a face-to-face, nonverbal perspective where behavioral feature extraction and inference are automated. This paper presents a computational framework for the automatic prediction of hirability. To this end, we collected an audio-visual dataset of real job interviews where candidates were applying for a marketing job. We automatically extracted audio and visual behavioral cues related to both the applicant and the interviewer. We then evaluated several regression methods for the prediction of hirability scores and showed the feasibility of conducting such a task, with ridge regression explaining 36.2% of the variance. Feature groups were analyzed, and two main groups of behavioral cues were predictive of hirability: applicant audio features and interviewer visual cues, showing the predictive validity of cues related not only to the applicant, but also to the interviewer. As a last step, we analyzed the predictive validity of psychometric questionnaires often used in the personnel selection process, and found that these questionnaires were unable to predict hirability, suggesting that hirability impressions were formed based on the interaction during the interview rather than on questionnaire data.

Estimation of reference curves for the urinary steroid metabolome in the first year of life in healthy children : Tracing the complexity of human postnatal steroidogenesis.

CONTEXT: Complex steroid disorders such as P450 oxidoreductase deficiency or apparent cortisone reductase deficiency may be recognized by steroid profiling using chromatographic mass spectrometric methods. These methods are highly specific and sensitive, and provide a complete spectrum of steroid metabolites in a single measurement of one sample which makes them superior to immunoassays. The steroid metabolome during the fetal-neonatal transition is characterized by (a) the metabolites of the fetal-placental unit at birth, (b) the fetal adrenal androgens until its involution 3-6 months postnatally, and (c) the steroid metabolites produced by the developing endocrine organs. All these developmental events change the steroid metabolome in an age- and sex-dependent manner during the first year of life. OBJECTIVE: The aim of this study was to provide normative values for the urinary steroid metabolome of healthy newborns at short time intervals in the first year of life. METHODS: We conducted a prospective, longitudinal study to measure 67 urinary steroid metabolites in 21 male and 22 female term healthy newborn infants at 13 time-points from week 1 to week 49 of life. Urine samples were collected from newborn infants before discharge from hospital and from healthy infants at home. Steroid metabolites were measured by gas chromatography-mass spectrometry (GC-MS) and steroid concentrations corrected for urinary creatinine excretion were calculated. RESULTS: 61 steroids showed age and 15 steroids sex specificity. Highest urinary steroid concentrations were found in both sexes for progesterone derivatives, in particular 20α-DH-5α-DH-progesterone, and for highly polar 6α-hydroxylated glucocorticoids. The steroids peaked at week 3 and decreased by ∼80% at week 25 in both sexes. The decline of progestins, androgens and estrogens was more pronounced than of glucocorticoids whereas the excretion of corticosterone and its metabolites and of mineralocorticoids remained constant during the first year of life. CONCLUSION: The urinary steroid profile changes dramatically during the first year of life and correlates with the physiologic developmental changes during the fetal-neonatal transition. Thus detailed normative data during this time period permit the use of steroid profiling as a powerful diagnostic tool.

Complexity of the Paradigms Present in Quality Criteria of Qualitative Research Grids

With qualitative methods being increasingly used in health science fields, numerous grids proposing criteria to evaluate the quality of this type of research have been produced. Expert evaluators deem that there is a lack of consensual tools to evaluate qualitative research. Based on the review of 133 quality criteria grids for qualitative research in health sciences, the authors present the results of a computerized lexicometric analysis, which confirms the variety of intra- and inter-grid constructions, including within the same field. This variety is linked to the authors' paradigmatic references underlying the criteria proposed. These references seem to be built intuitively, reflecting internal representations of qualitative research, thus making the grids and their criteria hard to compare. Consequently, the consensus on the definitions and the number of criteria becomes problematic. The paradigmatic and theoretical references of the grids should be specified so that users could better assess their contributions and limitations.

Predicted decrease in global climate suitability masks regional complexity of invasive fruit fly species response to climate change

Climate change affects the rate of insect invasions as well as the abundance, distribution and impacts of such invasions on a global scale. Among the principal analytical approaches to predicting and understanding future impacts of biological invasions are Species Distribution Models (SDMs), typically in the form of correlative Ecological Niche Models (ENMs). An underlying assumption of ENMs is that species-environment relationships remain preserved during extrapolations in space and time, although this is widely criticised. The semi-mechanistic modelling platform, CLIMEX, employs a top-down approach using species ecophysiological traits and is able to avoid some of the issues of extrapolation, making it highly applicable to investigating biological invasions in the context of climate change. The tephritid fruit flies (Diptera: Tephritidae) comprise some of the most successful invasive species and serious economic pests around the world. Here we project 12 tephritid species CLIMEX models into future climate scenarios to examine overall patterns of climate suitability and forecast potential distributional changes for this group. We further compare the aggregate response of the group against species-specific responses. We then consider additional drivers of biological invasions to examine how invasion potential is influenced by climate, fruit production and trade indices. Considering the group of tephritid species examined here, climate change is predicted to decrease global climate suitability and to shift the cumulative distribution poleward. However, when examining species-level patterns, the predominant directionality of range shifts for 11 of the 12 species is eastward. Most notably, management will need to consider regional changes in fruit fly species invasion potential where high fruit production, trade indices and predicted distributions of these flies overlap.