3 research outputs found

    Does Learnability Predict Syntactic Universals? An Investigation Using Artificial Languages

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    Ph.D.Universals in natural language have long been a focus of the generative syntactic and typological literature. However, the source of these universals is not clear. Within Chomskyan generative syntactic literature, it is assumed that children are endowed with innate knowledge of language structure (see for example, Lightfoot 1999). However, research on language acquisition has shown that learning biases may partially explain these universals (Newport 1981; Morgan, Meier & Newport, 1987; Culbertson et al., 2012; Culbertson & Newport, 2015; Culbertson & Newport, 2017). In a series of artificial language experiments, we have attempted to add further evidence to the hypothesis that biases in learning mechanisms give rise to language universals. In our first study, we ask whether children can use distributional information to acquire syntactic categories, phrases, and the sentence structure of a language. Our results show children are able to use statistical learning mechanisms to acquire syntax from distributional cues to phrase structure. We then ask whether the addition of local asymmetry in a phrase structure grammar results in enhanced learning of sentence structure. Our results show that participants exposed to the locally asymmetric grammar learned the sentence structure better than those exposed to the control grammar, providing evidence for our hypothesis that local asymmetries bolster learning of sentence structure. We follow up on this finding by asking whether other local asymmetries are equally learnable. Our results suggest that only head-initial patterning is of additional learning benefit. We argue this is due to native-language induced biases, in line with previous research by Onnis & Thiessen (2013). Finally, we attempt to address the question of whether the Final-Over-Final Constraint (Holmberg, 2000) (FOFC) is a result of a bias in learning mechanisms. Our results showed that while there was no significant difference between the four grammars, the disallowed grammar resulted in weaker learning when compared to the three allowed grammars. We argue that the work included in this dissertation presents a partial confirmation of our hypothesis. We conclude by discussing our findings and propose future work to build upon the foundation we present here

    Examination of Adverse Reactions After COVID-19 Vaccination Among Patients With a History of Multisystem Inflammatory Syndrome in Children

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    ImportanceData are limited regarding adverse reactions after COVID-19 vaccination in patients with a history of multisystem inflammatory syndrome in children (MIS-C). The lack of vaccine safety data in this unique population may cause hesitancy and concern for many families and health care professionals.ObjectiveTo describe adverse reactions following COVID-19 vaccination in patients with a history of MIS-C.Design, Setting, and ParticipantsIn this multicenter cross-sectional study including 22 North American centers participating in a National Heart, Lung, and Blood Institute, National Institutes of Health–sponsored study, Long-Term Outcomes After the Multisystem Inflammatory Syndrome in Children (MUSIC), patients with a prior diagnosis of MIS-C who were eligible for COVID-19 vaccination (age ≥5 years; ≥90 days after MIS-C diagnosis) were surveyed between December 13, 2021, and February 18, 2022, regarding COVID-19 vaccination status and adverse reactions.ExposuresCOVID-19 vaccination after MIS-C diagnosis.Main Outcomes and MeasuresThe main outcome was adverse reactions following COVID-19 vaccination. Comparisons were made using the Wilcoxon rank sum test for continuous variables and the χ2 or Fisher exact test for categorical variables.ResultsOf 385 vaccine-eligible patients who were surveyed, 185 (48.1%) received at least 1 vaccine dose; 136 of the vaccinated patients (73.5%) were male, and the median age was 12.2 years (IQR, 9.5-14.7 years). Among vaccinated patients, 1 (0.5%) identified as American Indian/Alaska Native, non-Hispanic; 9 (4.9%) as Asian, non-Hispanic; 45 (24.3%) as Black, non-Hispanic; 59 (31.9%) as Hispanic or Latino; 53 (28.6%) as White, non-Hispanic; 2 (1.1%) as multiracial, non-Hispanic; and 2 (1.1%) as other, non-Hispanic; 14 (7.6%) had unknown or undeclared race and ethnicity. The median time from MIS-C diagnosis to first vaccine dose was 9.0 months (IQR, 5.1-11.9 months); 31 patients (16.8%) received 1 dose, 142 (76.8%) received 2 doses, and 12 (6.5%) received 3 doses. Almost all patients received the BNT162b2 vaccine (347 of 351 vaccine doses [98.9%]). Minor adverse reactions were observed in 90 patients (48.6%) and were most often arm soreness (62 patients [33.5%]) and/or fatigue (32 [17.3%]). In 32 patients (17.3%), adverse reactions were treated with medications, most commonly acetaminophen (21 patients [11.4%]) or ibuprofen (11 [5.9%]). Four patients (2.2%) sought medical evaluation, but none required testing or hospitalization. There were no patients with any serious adverse events, including myocarditis or recurrence of MIS-C.Conclusions and RelevanceIn this cross-sectional study of patients with a history of MIS-C, no serious adverse events were reported after COVID-19 vaccination. These findings suggest that the safety profile of COVID-19 vaccination administered at least 90 days following MIS-C diagnosis appears to be similar to that in the general population.</jats:sec

    Ethnic and racial differences in children and young people with respiratory and neurological post-acute sequelae of SARS-CoV-2: an electronic health record-based cohort study from the RECOVER InitiativeResearch in context

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    Summary: Background: Children from racial and ethnic minority groups are at greater risk for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, but it is unclear whether they have increased risk for post-acute sequelae of SARS-CoV-2 (PASC). Our objectives were to assess whether the risk of respiratory and neurologic PASC differs by race/ethnicity and social drivers of health. Methods: We conducted a retrospective cohort study of individuals <21 years seeking care at 24 health systems across the U.S, using electronic health record (EHR) data. Our cohort included those with a positive SARS-CoV-2 molecular, serology or antigen test, or with a COVID-19, multisystem inflammatory disease in children, or PASC diagnosis from February 29, 2020 to August 1, 2022. We identified children/youth with at least 2 codes associated with respiratory and neurologic PASC. We measured associations between sociodemographic and clinical characteristics and respiratory and neurologic PASC using odds ratios and 95% confidence intervals estimated from multivariable logistic regression models adjusted for other sociodemographic characteristics, social vulnerability index or area deprivation index, time period of cohort entry, presence and complexity of chronic respiratory (respectively, neurologic) condition and healthcare utilization. Findings: Among 771,725 children in the cohort, 203,365 (26.3%) had SARS-CoV-2 infection. Among children with documented infection, 3217 children had respiratory PASC and 2009 children/youth had neurologic PASC. In logistic regression models, children <5 years (Odds Ratio [OR] 1.78, 95% CI 1.62–1.97), and of Hispanic White descent (OR 1.19, 95% CI 1.05–1.35) had higher odds of having respiratory PASC. Children/youth living in regions with higher area deprivation indices (OR 1.25, 95% CI 1.10–1.420 for 60–79th percentile) and with chronic complex respiratory conditions (OR 3.28, 95% CI 2.91–3.70) also had higher odds of respiratory PASC. In contrast, older (OR 1.57, 95% CI 1.40–1.77 for those aged 12–17 years), non-Hispanic White individuals and those with chronic pre-existing neurologic conditions (OR 2.04, 95% CI 1.78–2.35) were more likely to have a neurologic PASC diagnosis. Interpretation: Racial and ethnic differences in healthcare utilization for neurologic and respiratory PASC may reflect social drivers of health and inequities in access to care. Funding: National Institutes of Health
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