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Introduction WHO recommends exclusive breast feeding from birth to 6 months. However, to monitor populations, it recommends using the proportion of infants under 6 months who were exclusively breastfed during the previous 24 hours. To assess the usefulness of 24-hour recall, we (1) compared the prevalence of exclusive breast feeding measured by since-birth recall to the prevalence measured by 24-hour recall and (2) quantified each indicator’s association with WHO-recommended, well-established methods for in-hospital breastfeeding support.Methods We conducted two online surveys of mothers in Japan (total n=4247) who had a healthy singleton delivery in the previous 25 months. They reported on their breast feeding (a) from birth to 5 months; or (b) during the previous 24 hours, for those with infants under 5 months; or (c) both, for those who participated in the initial survey and also in the follow-up survey. All mothers also reported on their in-hospital support. The strength of each indicator’s association with provision of in-hospital support was quantified as the area under the curve (AUC).Results The prevalences of exclusive breast feeding by since-birth recall were 4.4% (first survey) and 2.5% (second survey). By 24-hour recall, the prevalence appeared to be 29.8%. More in-hospital support was moderately well associated with more exclusive breast feeding measured by since-birth recall: AUC 0.72 (95%CI 0.66 to 0.78). That association is consistent with the known benefits of in-hospital support. In contrast, when exclusive breast feeding was measured by 24-hour recall, its association with in-hospital support appeared to be extremely weak: AUC 0.59 (95% CI 0.54 to 0.65).Conclusion Using 24-hour recall substantially overestimates the prevalence of exclusive breast feeding since birth, and it conceals the benefits of in-hospital breastfeeding support. To monitor population achievement of exclusive breast feeding for the first 6 months, or to evaluate breastfeeding interventions, 24-hour recall of exclusive breast feeding should not be used alone.

Abstract Ultrafast 3D imaging is indispensable for visualizing complex and dynamic biological processes. Conventional scanning-based techniques necessitate an inherent trade-off between acquisition speed and space-bandwidth product (SBP). Emerging single-shot 3D wide-field techniques offer a promising alternative but are bottlenecked by the synchronous readout constraints of conventional CMOS systems, thus restricting data throughput to maintain high SBP at limited frame rates. To address this, we introduce EventLFM, a straightforward and cost-effective system that overcomes these challenges by integrating an event camera with Fourier light field microscopy (LFM), a state-of-the-art single-shot 3D wide-field imaging technique. The event camera operates on a novel asynchronous readout architecture, thereby bypassing the frame rate limitations inherent to conventional CMOS systems. We further develop a simple and robust event-driven LFM reconstruction algorithm that can reliably reconstruct 3D dynamics from the unique spatiotemporal measurements captured by EventLFM. Experimental results demonstrate that EventLFM can robustly reconstruct fast-moving and rapidly blinking 3D fluorescent samples at kHz frame rates. Furthermore, we highlight EventLFM’s capability for imaging of blinking neuronal signals in scattering mouse brain tissues and 3D tracking of GFP-labeled neurons in freely moving C. elegans. We believe that the combined ultrafast speed and large 3D SBP offered by EventLFM may open up new possibilities across many biomedical applications.

BackgroundNystagmus identification and interpretation is challenging for non-experts who lack specific training in neuro-ophthalmology or neuro-otology. This challenge is magnified when the task is performed via telemedicine. Deep learning models have not been heavily studied in video-based eye movement detection.MethodsWe developed, trained, and validated a deep-learning system (aEYE) to classify video recordings as normal or bearing at least two consecutive beats of nystagmus. The videos were retrospectively collected from a subset of the monocular (right eye) video-oculography (VOG) recording used in the Acute Video-oculography for Vertigo in Emergency Rooms for Rapid Triage (AVERT) clinical trial (#NCT02483429). Our model was derived from a preliminary dataset representing about 10% of the total AVERT videos (n = 435). The videos were trimmed into 10-sec clips sampled at 60 Hz with a resolution of 240 × 320 pixels. We then created 8 variations of the videos by altering the sampling rates (i.e., 30 Hz and 15 Hz) and image resolution (i.e., 60 × 80 pixels and 15 × 20 pixels). The dataset was labeled as “nystagmus” or “no nystagmus” by one expert provider. We then used a filtered image-based motion classification approach to develop aEYE. The model's performance at detecting nystagmus was calculated by using the area under the receiver-operating characteristic curve (AUROC), sensitivity, specificity, and accuracy.ResultsAn ensemble between the ResNet-soft voting and the VGG-hard voting models had the best performing metrics. The AUROC, sensitivity, specificity, and accuracy were 0.86, 88.4, 74.2, and 82.7%, respectively. Our validated folds had an average AUROC, sensitivity, specificity, and accuracy of 0.86, 80.3, 80.9, and 80.4%, respectively. Models created from the compressed videos decreased in accuracy as image sampling rate decreased from 60 Hz to 15 Hz. There was only minimal change in the accuracy of nystagmus detection when decreasing image resolution and keeping sampling rate constant.ConclusionDeep learning is useful in detecting nystagmus in 60 Hz video recordings as well as videos with lower image resolutions and sampling rates, making it a potentially useful tool to aid future automated eye-movement enabled neurologic diagnosis.