Feasibility and acceptability of a personalized, pre-filled online grocery cart to improve benefit redemption and diet quality of grocery purchases among participants of the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC).

Most participants in the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) do not fully redeem their benefits due to barriers like transportation, confusing product eligibility, and unclear labeling. Online food shopping enables choice architecture to promote benefit redemption and maximize diet quality. We conducted a mixed-methods pilot randomized-controlled trial to assess the feasibility and acceptability of a pre-filled online grocery shopping cart to improve WIC benefit redemption and diet quality of grocery purchases. Rhode Island WIC participants (n = 24, mean age 29.4 ± 1.1 years, 75% Hispanic, 54% had never grocery shopped online) completed a baseline questionnaire and a simulated shopping episode (SSE), buying WIC and non-WIC items. After a week, we randomized participants into the intervention (personalized, modifiable carts pre-filled with 100% of the 2022 proposed WIC packages) or control (selected their items individually) groups before the second SSE. Both groups had WIC labels. We assessed feasibility using process data and percent agreement to feasibility questions, and acceptability via percent agreement to acceptability questions and post-intervention qualitative interviews. We conducted exploratory analyses to examine differences within and between groups at each timepoint for percent WIC benefit redemption and diet quality of grocery purchases, evaluated using the Grocery Purchase Quality Index-2016 (GPQI-2016) scores. Quantitative study measures suggest that the intervention was feasible and that the personalized, modifiable pre-filled cart was acceptable. These findings were supported during qualitative interviews, where participants highlighted time-savings, flexibility, and WIC labels as facilitators of WIC online shopping. Exploratory results showed significant increases in mean percent redemption of most WIC food categories and non-significant moderate increases in most GPQI-2016 scores. These measures are vital for the future adaptation of a full-scale efficacy trial in real-life settings.

Fast and robust active neuron segmentation in two-photon calcium imaging using spatiotemporal deep learning.

Calcium imaging records large-scale neuronal activity with cellular resolution in vivo. Automated, fast, and reliable active neuron segmentation is a critical step in the analysis workflow of utilizing neuronal signals in real-time behavioral studies for discovery of neuronal coding properties. Here, to exploit the full spatiotemporal information in two-photon calcium imaging movies, we propose a 3D convolutional neural network to identify and segment active neurons. By utilizing a variety of two-photon microscopy datasets, we show that our method outperforms state-of-the-art techniques and is on a par with manual segmentation. Furthermore, we demonstrate that the network trained on data recorded at a specific cortical layer can be used to accurately segment active neurons from another layer with different neuron density. Finally, our work documents significant tabulation flaws in one of the most cited and active online scientific challenges in neuron segmentation. As our computationally fast method is an invaluable tool for a large spectrum of real-time optogenetic experiments, we have made our open-source software and carefully annotated dataset freely available online.

Deep learning based detection of cone photoreceptors with multimodal adaptive optics scanning light ophthalmoscope images of achromatopsia.

Fast and reliable quantification of cone photoreceptors is a bottleneck in the clinical utilization of adaptive optics scanning light ophthalmoscope (AOSLO) systems for the study, diagnosis, and prognosis of retinal diseases. To-date, manual grading has been the sole reliable source of AOSLO quantification, as no automatic method has been reliably utilized for cone detection in real-world low-quality images of diseased retina. We present a novel deep learning based approach that combines information from both the confocal and non-confocal split detector AOSLO modalities to detect cones in subjects with achromatopsia. Our dual-mode deep learning based approach outperforms the state-of-the-art automated techniques and is on a par with human grading.