The COVID-19 pandemic has sparked unprecedented public health and social measures (PHSM) by national and local governments, including border restrictions, school closures, mandatory facemask use and stay at home orders. Quantifying the effectiveness of these interventions in reducing disease transmission is key to rational policy making in response to the current and future pandemics. In order to estimate the effectiveness of these interventions, detailed descriptions of their timelines, scale and scope are needed. The Health Intervention Tracking for COVID-19 (HIT-COVID) is a curated and standardized global database that catalogues the implementation and relaxation of COVID-19 related PHSM. With a team of over 200 volunteer contributors, we assembled policy timelines for a range of key PHSM aimed at reducing COVID-19 risk for the national and first administrative levels (e.g. provinces and states) globally, including details such as the degree of implementation and targeted populations. We continue to maintain and adapt this database to the changing COVID-19 landscape so it can serve as a resource for researchers and policymakers alike.
Predictive coding suggests that the brain infers the causes of its sensations by combining sensory evidence with internal predictions based on available prior knowledge. However, the neurophysiological correlates of (pre)activated prior knowledge serving these predictions are still unknown. Based on the idea that such preactivated prior knowledge must be maintained until needed, we measured the amount of maintained information in neural signals via the active information storage (AIS) measure. AIS was calculated on whole-brain beamformer-reconstructed source time courses from MEG recordings of 52 human subjects during the baseline of a Mooney face/house detection task. Preactivation of prior knowledge for faces showed as α-band-related and β-band-related AIS increases in content-specific areas; these AIS increases were behaviorally relevant in the brain's fusiform face area. Further, AIS allowed decoding of the cued category on a trial-by-trial basis. Our results support accounts indicating that activated prior knowledge and the corresponding predictions are signaled in low-frequency activity (<30 Hz). Our perception is not only determined by the information our eyes/retina and other sensory organs receive from the outside world, but strongly depends also on information already present in our brains, such as prior knowledge about specific situations or objects. A currently popular theory in neuroscience, predictive coding theory, suggests that this prior knowledge is used by the brain to form internal predictions about upcoming sensory information. However, neurophysiological evidence for this hypothesis is rare, mostly because this kind of evidence requires strong a priori assumptions about the specific predictions the brain makes and the brain areas involved. Using a novel, assumption-free approach, we find that face-related prior knowledge and the derived predictions are represented in low-frequency brain activity.
The purpose of this study was to improve the morphological analysis of microvascular networks depicted in three-dimensional (3D) super-resolution ultrasound (SR-US) images. This was supported by qualitative and quantitative validation by comparison to matched brightfield microscopy and traditional B-mode ultrasound (US) images. Contrast-enhanced US (CEUS) images were collected using a preclinical US scanner (Vevo 3100, FUJIFILM VisualSonics Inc.) equipped with an MX250 linear array transducer. CEUS imaging was performed after administration of a microbubble (MB) contrast agent into the vitelline network of a developing chicken embryo. Volume data was collected by mechanically scanning the US transducer throughout a tissue volume-of-interest in 90 μm step increments. CEUS images were collected at each increment and stored as in-phase/quadrature data (2000 frames at 152 frames per sec). SR-US images were created for each cross-sectional plane using established data processing methods. All SR-US images were then used to reconstruct a final 3D volume for vessel diameter (VD) quantification and for surface rendering. VD quantification from the 3D SR-US data exhibited an average error of 6.1% ± 6.0% when compared with matched brightfield microscopy images, whereas measurements from B-mode US images had an average error of 77.1% ± 68.9%. Volume and surface renderings in 3D space enabled qualitative validation and improved visualization of small vessels below the axial resolution of the US system. Overall, 3D SR-US image reconstructions depicted the microvascular network of the developing chicken embryos. Improved visualization of isolated vessels and quantification of microvascular morphology from SR-US images achieved a considerably greater accuracy compared to B-mode US measurements.
PurposeNeural networks are potentially valuable for many challenges associated with MRS data. The purpose of this manuscript is to describe the AGNOSTIC dataset, which contains 259,200 synthetic MRS examples. To demonstrate the utility, we use AGNOSTIC to train two Convolutional Neural Networks (CNNs) to address out-of-voxel (OOV) echoes. A Detection Network was trained to identify the point-wise presence of OOV echoes, providing proof of concept for real-time detection. A Prediction Network was trained to reconstruct OOV echoes, allowing subtraction during post-processing.MethodsAGNOSTIC was created using 270 basis sets that were simulated across 18 field strengths and 15 echo times. The synthetic examples were produced to resemblein vivobrain data with combinations of metabolite, macromolecule, and residual water signals, and noise.Complex OOV signals were mixed into 85% of synthetic examples to train two separate U-net CNNs for the detection and prediction of OOV signals.ResultsAGNOSTIC is available through Dryad and all Python 3 code is available through GitHub. The Detection network was shown to perform well, identifying 95% of OOV echoes. Traditional modeling of these detected OOV signals was evaluated and may prove to be an effective method during linear-combination modeling. The Prediction Network greatly reduces OOV echoes within FIDs and achieved a median log10normed-MSE of –1.79, an improvement of almost two orders of magnitude.ConclusionThe AGNOSTIC benchmark dataset for MRS is introduced and various dataset features are described. As an exemplar use of AGNOSTIC, two CNNs were developed to detect and predict OOV echoes.
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