A proto-understanding of absent reference (reference to absent entities) emerges around 12 months provided with richcontextual support, infants look and point to the location of a displaced object. When can infants understand absent ref-erence without contextual support? We modified the procedure from Hendrickson and Sundara (2017) who showed thatwith very minimal pre-exposure and no demonstration of referent displacement, 14-month-olds identify absent referents offamiliar words. Fourteen- and 16-month-olds first listened to passages containing target words, while viewing a checker-board. Then, two objects the referent and a distractor appeared on the screen. We analyzed infants’ looking to the targetduring 3 seconds from the onset of image display. Only 16-month-olds looked significantly above chance, suggestingthat listening to the passages activated their representations of the referents. These results are the first to show that by 16months, infants can retrieve mental representations of objects upon just hearing their labels.

A nascent understanding of absent reference emerges around 12months: provided with rich contextual support, infants look and pointto the location of a displaced object. When can infants understandabsent reference without contextual support? Using a proceduremodified from Hendrickson and Sundara (2017), 13- and 16-month-olds first listened to utterances containing familiar target words, whileviewing a checkerboard. Then, two objects – a referent and a distractor(e.g., a cup and a shoe) – appeared on the screen. Only 16-month-oldsdemonstrated a reliable looking preference for the referents, suggestingthat listening to the utterances activated their mental images of thereferents. These results establish that at 16 months, infants comprehendreference to absent entities without any contextual support.

It is generally accepted that infants initially discriminate native and non-native contrasts and that perceptual reorganization within the first year of life results in decreased discrimination of non-native contrasts, and improved discrimination of native contrasts. However, recent findings from Narayan, Werker, and Beddor (2010) surprisingly suggested that some acoustically subtle native-language contrasts might not be discriminated until the end of the first year of life. We first provide countervailing evidence that young English-learning infants can discriminate the Filipino contrast tested by Narayan et al. when tested in a more sensitive paradigm. Next, we show that young infants learning either English or French can also discriminate comparably subtle non-native contrasts from Tamil. These findings show that Narayan et al.'s null findings were due to methodological choices and indicate that young infants are sensitive to even subtle acoustic contrasts that cue phonetic distinctions cross-linguistically. Based on experimental results and acoustic analyses, we argue that instead of specific acoustic metrics, infant discrimination results themselves are the most informative about the salience of phonetic distinctions.

Primary open-angle glaucoma (POAG), is a heritable common cause of blindness world-wide. To identify risk loci, we conduct a large multi-ethnic meta-analysis of genome-wide association studies on a total of 34,179 cases and 349,321 controls, identifying 44 previously unreported risk loci and confirming 83 loci that were previously known. The majority of loci have broadly consistent effects across European, Asian and African ancestries. Cross-ancestry data improve fine-mapping of causal variants for several loci. Integration of multiple lines of genetic evidence support the functional relevance of the identified POAG risk loci and highlight potential contributions of several genes to POAG pathogenesis, including SVEP1, RERE, VCAM1, ZNF638, CLIC5, SLC2A12, YAP1, MXRA5, and SMAD6. Several drug compounds targeting POAG risk genes may be potential glaucoma therapeutic candidates.

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.