Testing a computational model of causative overgeneralizations: Child judgment and production data from English, Hebrew, Hindi, Japanese and K'iche'.

How do language learners avoid the production of verb argument structure overgeneralization errors ( *The clown laughed the man c.f. The clown made the man laugh), while retaining the ability to apply such generalizations productively when appropriate? This question has long been seen as one that is both particularly central to acquisition research and particularly challenging. Focussing on causative overgeneralization errors of this type, a previous study reported a computational model that learns, on the basis of corpus data and human-derived verb-semantic-feature ratings, to predict adults' by-verb preferences for less- versus more-transparent causative forms (e.g., * The clown laughed the man vs The clown made the man laugh) across English, Hebrew, Hindi, Japanese and K'iche Mayan. Here, we tested the ability of this model (and an expanded version with multiple hidden layers) to explain binary grammaticality judgment data from children aged 4;0-5;0, and elicited-production data from children aged 4;0-5;0 and 5;6-6;6 ( N=48 per language). In general, the model successfully simulated both children's judgment and production data, with correlations of r=0.5-0.6 and r=0.75-0.85, respectively, and also generalized to unseen verbs. Importantly, learners of all five languages showed some evidence of making the types of overgeneralization errors - in both judgments and production - previously observed in naturalistic studies of English (e.g., *I'm dancing it). Together with previous findings, the present study demonstrates that a simple learning model can explain (a) adults' continuous judgment data, (b) children's binary judgment data and (c) children's production data (with no training of these datasets), and therefore constitutes a plausible mechanistic account of the acquisition of verbs' argument structure restrictions

Biomedical informatics and translational medicine

Biomedical informatics involves a core set of methodologies that can provide a foundation for crossing the "translational barriers" associated with translational medicine. To this end, the fundamental aspects of biomedical informatics (e.g., bioinformatics, imaging informatics, clinical informatics, and public health informatics) may be essential in helping improve the ability to bring basic research findings to the bedside, evaluate the efficacy of interventions across communities, and enable the assessment of the eventual impact of translational medicine innovations on health policies. Here, a brief description is provided for a selection of key biomedical informatics topics (Decision Support, Natural Language Processing, Standards, Information Retrieval, and Electronic Health Records) and their relevance to translational medicine. Based on contributions and advancements in each of these topic areas, the article proposes that biomedical informatics practitioners ("biomedical informaticians") can be essential members of translational medicine teams

Increased type IIA secretory phospholipase A(2) expression contributes to oxidative stress in end-stage renal disease

End-stage renal disease (ESRD) patients exhibit increased in vivo oxidative stress conceivably contributing to cardiovascular mortality. The type IIA secretory phospholipase A(2) (sPLA(2)) has proatherogenic activity. We explored the hypothesis that sPLA(2) contributes to oxidative stress generation and endothelial dysfunction in ESRD patients and transgenic (tg) mice. Patients with ESRD had increased in vivo oxidative stress as assessed by plasma isoprostane levels (p <0.001). Active sPLA(2) in plasma was substantially increased compared with healthy controls (1,156 +/- 65 versus 184 +/- 5 ng/dL, p <0.001) and correlated with plasma isoprostanes (r = 0.61, p <0.001). Correspondingly, human sPLA(2) tg mice display increased generation of reactive oxygen species within aortic vascular smooth muscle cells, leading to severe endothelial dysfunction (maximal vasodilation in response to 10 A mu mol/L acetylcholine, sPLA(2) 36 A +/- 8%, controls 80 A +/- 2% of phenylephrine-induced vasoconstriction). Increased vascular oxidative stress in sPLA(2) tg mice is dependent on the induction of vascular cyclooxygenase (COX)2 expression. Conversely, ESRD patients show increased formation of COX2-derived prostaglandins (p <0.05) correlated with plasma sPLA(2) (r = 0.71, p <0.05). Our data indicate that increased expression of sPLA(2) might represent a novel causative risk factor contributing to the increased cardiovascular disease morbidity and mortality in ESRD