Air pollution has been shown to impact multiple measures of neurodevelopment in young children. Its effects on particularly vulnerable populations, such as ethnic minorities, however, is less studied. To address this gap in the literature, we assess the associations between infant non-nutritive suck (NNS), an early indicator of central nervous system integrity, and air pollution exposures in Puerto Rico. Among infants aged 0-3 months enrolled in the Center for Research on Early Childhood Exposure and Development (CRECE) cohort from 2017 to 2019, we examined associations between exposure to fine particulate matter (PM) and its components on infant NNS in Puerto Rico. NNS was assessed using a pacifier attached to a pressure transducer, allowing for real-time visualization of NNS amplitude, frequency, duration, cycles/burst, cycles/min and bursts/min. These data were linked to 9-month average prenatal concentrations of PM and components, measured at three community monitoring sites. We used linear regression to examine the PM-NNS association in single pollutant models, controlling for infant sex, maternal age, gestational age, and season of birth in base and additionally for household smoke exposure, age at testing, and NNS duration in full models. Among 198 infants, the average NNS amplitude and burst duration was 17.1 cmHO and 6.1 s, respectively. Decreased NNS amplitude was consistently and significantly associated with 9-month average exposure to sulfur (-1.026 ± 0.507), zinc (-1.091 ± 0.503), copper (-1.096 ± 0.535) vanadium (-1.157 ± 0.537), and nickel (-1.530 ± 0.501). Decrements in NNS frequency were associated with sulfur exposure (0.036 ± 0.018), but not other examined PM components. Our findings provide new evidence that prenatal maternal exposure to specific PM components are associated with impaired neurodevelopment in Puerto Rican infants soon after birth.
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http://dx.doi.org/10.1016/j.scitotenv.2021.148008 | DOI Listing |
Cleft Palate Craniofac J
November 2024
Department of Communication Sciences and Disorders, Northeastern University, Boston, MA, USA.
Objective: To compare non-nutritive sucking (NNS) and caregiver-reported feeding skills in infants with cleft lip and/or palate (CL/P) to a control group of typically developing infants without CL/P. To examine differences in NNS patterns and feeding behavior between cleft phenotypes.
Design: Prospective, cross-sectional study comparing infants born with CL/P to an age-matched control group with no congenital anomalies.
We propose a complex-amplitude diffractive processor based on diffractive deep neural networks (DNNs). By precisely controlling the propagation of an optical field, it can effectively remove the motion blur in numeral images and realize the restoration. Comparative analysis of phase-only, amplitude-only, and complex-amplitude diffractive processor reveals that the complex-amplitude network significantly enhances the performance of the processor and improves the peak signal-to-noise ratio (PSNR) of the images.
View Article and Find Full Text PDFMetabolites
June 2024
Magnetic Resonance Imaging and Spectroscopy Lab, Department of Biomedical Engineering, The University of Memphis, Memphis, TN 38152, USA.
Neural networks (NNs) are emerging as a rapid and scalable method for quantifying metabolites directly from nuclear magnetic resonance (NMR) spectra, but the nonlinear nature of NNs precludes understanding of how a model makes predictions. This study implements an explainable artificial intelligence algorithm called integrated gradients (IG) to elucidate which regions of input spectra are the most important for the quantification of specific analytes. The approach is first validated in simulated mixture spectra of eight aqueous metabolites and then investigated in experimentally acquired lipid spectra of a reference standard mixture and a murine hepatic extract.
View Article and Find Full Text PDFNeural network (NN)-based equalizers have been widely applied for dealing with nonlinear impairments in intensity-modulated direct detection (IM/DD) systems due to their excellent performance. However, the computational complexity (CC) is a major concern that limits the real-time application of NN-based receivers. In this Letter, we propose, to our knowledge, a novel weight-adaptive joint mixed-precision quantization and pruning approach to reduce the CC of NN-based equalizers, where only integer arithmetic is taken into account instead of floating-point operations.
View Article and Find Full Text PDFJ Vis Exp
April 2024
Department of Communication Sciences and Disorders, Northeastern University;
The non-nutritive suck (NNS) device is a transportable, user-friendly pressure transducer system that quantifies infants' NNS behavior on a pacifier. Recording and analysis of the NNS signal using our system can provide measures of an infant's NNS burst duration (s), amplitude (cmH2O), and frequency (Hz). Accurate, reliable, and quantitative assessment of NNS has immense value in serving as a biomarker for future feeding, speech-language, cognitive, and motor development.
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