A genetically informed study of the associations between maternal age at childbearing and adverse perinatal outcomes

We examined associations of maternal age at childbearing (MAC) with gestational age and fetal growth (i.e., birth weight adjusting for gestational age), using two genetically informed designs (cousin and sibling comparisons) and data from two cohorts, a population-based Swedish sample and a nationally representative United States sample. We also conducted sensitivity analyses to test limitations of the designs. The findings were consistent across samples and suggested that, associations observed in the population between younger MAC and shorter gestational age were confounded by shared familial factors; however, associations of advanced MAC with shorter gestational age remained robust after accounting for shared familial factors. In contrast to the gestational age findings, neither early nor advanced MAC was associated with lower fetal growth after accounting for shared familial factors. Given certain assumptions, these findings provide support for a causal association between advanced MAC and shorter gestational age. The results also suggest that there are not causal associations between early MAC and shorter gestational age, between early MAC and lower fetal growth, and between advanced MAC and lower fetal growth.NonePublishe

Extending Knowledge Graphs with Subjective Influence Networks for personalized fashion

International audienceThis chapter shows Stitch Fix's industry case as an applied fashion application in cognitive cities. Fashion goes hand in hand with the economic development of better methods in smart and cognitive cities, leisure activities and consumption. However, extracting knowledge and actionable insights from fashion data still presents challenges due to the intrinsic subjectivity needed to effectively model the domain. Fashion ontologies help address this, but most existing such ontologies are "clothing" ontologies, which consider only the physical attributes of garments or people and often model subjective judgements only as opaque categorizations of entities. We address this by proposing a supplementary ontological approach in the fashion domain based on subjective influence networks. We enumerate a set of use cases this approach is intended to address and discuss possible classes of prediction questions and machine learning experiments that could be executed to validate or refute the model. We also present a case study on business models and monetization strategies for digital fashion, a domain that is fast-changing and gaining the battle in the digital domain

A family-based study of the association between labor induction and offspring attention-deficit hyperactivity disorder and low academic achievement

The current study examined associations between labor induction and both (1) offspring attention-deficit hyperactivity disorder (ADHD) diagnosis in a Swedish birth cohort born 1992-2005 (n = 1,085,008) and (2) indices of offspring low academic achievement in a sub-cohort born 1992-1997 (n = 489,196). Associations were examined in the entire sample (i.e., related and unrelated individuals) with adjustment for measured covariates and, in order to account for unmeasured confounders shared within families, within differentially exposed cousins and siblings. We observed an association between labor induction and offspring ADHD diagnosis and low academic achievement in the population. However, these associations were fully attenuated after adjusting for measured covariates and unmeasured factors that cousins and siblings share. The results suggest that observed associations between labor induction and ADHD and low academic achievement may be due to genetic and/or shared environmental factors that influence both mothers' risk of labor induction and offspring neurodevelopment.NoneAccepte

Distinct transcriptome responses to water limitation in isohydric and anisohydric grapevine cultivars

Background: Grapevine (Vitis vinifera L.) is an economically important crop with a wide geographical distribution, reflecting its ability to grow successfully in a range of climates. However, many vineyards are located in regions with seasonal drought, and these are often predicted to be global climate change hotspots. Climate change affects the entire physiology of grapevine, with strong effects on yield, wine quality and typicity, making it difficult to produce berries of optimal enological quality and consistent stability over the forthcoming decades. Results: Here we investigated the reactions of two grapevine cultivars to water stress, the isohydric variety Montepulciano and the anisohydric variety Sangiovese, by examining physiological and molecular perturbations in the leaf and berry. A multidisciplinary approach was used to characterize the distinct stomatal behavior of the two cultivars and its impact on leaf and berry gene expression. Positive associations were found among the photosynthetic, physiological and transcriptional modifications, and candidate genes encoding master regulators of the water stress response were identified using an integrated approach based on the analysis of topological co-expression network properties. In particular, the genome-wide transcriptional study indicated that the isohydric behavior relies upon the following responses: i) faster transcriptome response after stress imposition; ii) faster abscisic acid-related gene modulation; iii) more rapid expression of heat shock protein (HSP) genes and iv) reversion of gene-expression profile at rewatering. Conversely, that reactive oxygen species (ROS)-scavenging enzymes, molecular chaperones and abiotic stress-related genes were induced earlier and more strongly in the anisohydric cultivar. Conclusions: Overall, the present work found original evidence of a molecular basis for the proposed classification between isohydric and anisohydric grapevine genotypes

Physiological Correlates of Volunteering

We review research on physiological correlates of volunteering, a neglected but promising research field. Some of these correlates seem to be causal factors influencing volunteering. Volunteers tend to have better physical health, both self-reported and expert-assessed, better mental health, and perform better on cognitive tasks. Research thus far has rarely examined neurological, neurochemical, hormonal, and genetic correlates of volunteering to any significant extent, especially controlling for other factors as potential confounds. Evolutionary theory and behavioral genetic research suggest the importance of such physiological factors in humans. Basically, many aspects of social relationships and social activities have effects on health (e.g., Newman and Roberts 2013; Uchino 2004), as the widely used biopsychosocial (BPS) model suggests (Institute of Medicine 2001). Studies of formal volunteering (FV), charitable giving, and altruistic behavior suggest that physiological characteristics are related to volunteering, including specific genes (such as oxytocin receptor [OXTR] genes, Arginine vasopressin receptor [AVPR] genes, dopamine D4 receptor [DRD4] genes, and 5-HTTLPR). We recommend that future research on physiological factors be extended to non-Western populations, focusing specifically on volunteering, and differentiating between different forms and types of volunteering and civic participation

Study of the B-c(+) -> J/psi D-s(+) and Bc(+) -> J/psi D-s*(+) decays with the ATLAS detector

The decays B-c(+) -> J/psi D-s(+) and B-c(+) -> J/psi D-s*(+) are studied with the ATLAS detector at the LHC using a dataset corresponding to integrated luminosities of 4.9 and 20.6 fb(-1) of pp collisions collected at centre-of-mass energies root s = 7 TeV and 8 TeV, respectively. Signal candidates are identified through J/psi -> mu(+)mu(-) and D-s(()*()+) -> phi pi(+)(gamma/pi(0)) decays. With a two-dimensional likelihood fit involving the B-c(+) reconstructed invariant mass and an angle between the mu(+) and D-s(+) candidate momenta in the muon pair rest frame, the yields of B-c(+) -> J/psi D-s(+) and B-c(+) -> J/psi D-s*(+), and the transverse polarisation fraction in B-c(+) -> J/psi D-s*(+) decay are measured. The transverse polarisation fraction is determined to be Gamma +/-+/-(B-c(+) -> J/psi D-s*(+))/Gamma(B-c(+) -> J/psi D-s*(+)) = 0.38 +/- 0.23 +/- 0.07, and the derived ratio of the branching fractions of the two modes is B-Bc+ -> J/psi D-s*+/B-Bc+ -> J/psi D-s(+) = 2.8(-0.8)(+1.2) +/- 0.3, where the first error is statistical and the second is systematic. Finally, a sample of B-c(+) -> J/psi pi(+) decays is used to derive the ratios of branching fractions B-Bc+ -> J/psi D-s*+/B-Bc+ -> J/psi pi(+) = 3.8 +/- 1.1 +/- 0.4 +/- 0.2 and B-Bc+ -> J/psi D-s*+/B-Bc+ -> J/psi pi(+) = 10.4 +/- 3.1 +/- 1.5 +/- 0.6, where the third error corresponds to the uncertainty of the branching fraction of D-s(+) -> phi(K+ K-)pi(+) decay. The available theoretical predictions are generally consistent with the measurement

Legal Tech : Cognitive Computing in der juristischen Praxis

Die Rechtswissenschaft und juristische Praxis zeichnen sich durch das Beherrschen der natürlichen Sprache aus. Sachverhalte und Argumente müssen möglichst präzise beschrieben werden. Das Recht, bestehend aus Gesetzestexten und Gerichtsurteilen, stellt einen grossen, weitgehend unstrukturierten Datensatz dar. Der Artikel zeichnet nach, wie es dennoch möglich ist, Recht maschinenlesbar zu machen beziehungsweise wie Computersysteme das Recht interpretieren und JuristInnen unterstützen können. Methoden, die im Sinne des Cognitive Computings natürliche Sprache ohne viel menschliches Zutun auswerten, stecken noch in den Kinderschuhen, weisen aber Potenzial auf. Mittels Analyse von Metadaten und gezielten Suchen ist es möglich, Zusammenhänge zwischen Rechtsquellen herzustellen oder Gerichtsurteile zu einem gewissen Grad vorherzusagen. Das Kapitel zeigt dabei die eingesetzten datengetriebenen oder regelorientierten Ansätze auf und setzt sich mit den Möglichkeiten, Grenzen und Risiken dieser Methoden auseinander

Digital Human Models for Automated Ultrasound User Interface Design

The purpose of this theoretical paper is to describe the development of a new technology for the automated analysis and design definition of Ultrasound (US) system User Interfaces (UI) and US transducers. US examination is a real-time multi-factor approach, which involves the whole sonographer’s body; its automated evaluation, analysis and design must take into account many different factors and aspects which need to be evaluated and implemented. The proposed technology, based on Digital Human Modeling (DHM) systems, would get input from multi- factor technologies such as Motion Analysis, Eye Tracking, Superficial Electromyography, Stereo Imaging and also physical information such as temperature, ECG, respiration activity, etc., applied to different US users for different clinical applications and protocols. The utilization of DHM to manage and analyze these diverse requirements would drive the automated optimization of system design, in terms of ergonomics and workflow