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1. Molecular Targets of Cannabidiol warn against its consumption during pregnancy

   https://doi.org/10.18103/mra.v12i5.5450  

   Bates, Emily; Oh, Won (January 2024, Medical Research Archives)

   People use cannabidiol (CBD), the primary non-psychoactive cannabinoid of cannabis, as a treatment for symptoms that are commonly associated with pregnancy including nausea, pain, and anxiety. Many people believe CBD is safe to take during pregnancy. However, CBD crosses the placenta and affects the activity of protein targets that are expressed in the fetal brain. Cannabidiol alters the activity of ion channels including voltage- gated sodium, potassium, and calcium channels that control the electrical activity of neurons. Abnormal electrical activity could disrupt brain function via changes in axon growth and synapse structure and function. Furthermore, CBD alters the activity of G- protein coupled receptors that are expressed in the fetal brain and are important for axon growth and guidance suggesting that fetal exposure could prevent axons from reaching their correct targets. Indeed, cannabidiol exposure reduces axon growth in vitro and in vivo. This raises the possibility that CBD consumption during pregnancy could disrupt fetal brain development. Recent studies show that oral cannabidiol consumption during pregnancy alters the excitability of the pyramidal neurons of the prefrontal cortex and affects postnatal cognitive function in mouse offspring. Furthermore, fetal CBD exposure increases thermal pain sensitivity in offspring. Gestational cannabidiol exposure affects compulsivity and memory in a different rodent model. Here, we discuss how CBD affects various ion channels and G-protein coupled receptors, the roles of these proteins in neurodevelopment, and evidence that CBD affects brain development. 

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2. On Partial Adoption of Vehicle-To-Vehicle Communication: When Should Cars Warn Each Other of Hazards?

   Gould, Brendan T; Brown, Philip N. (June 2022, 2022 American Control Conference)
3. Using Machine Learning to Generate Storm-Scale Probabilistic Guidance of Severe Weather Hazards in the Warn-on-Forecast System

   https://doi.org/10.1175/MWR-D-20-0194.1  

   Flora, Montgomery L.; Potvin, Corey K.; Skinner, Patrick S.; Handler, Shawn; McGovern, Amy (May 2021, Monthly Weather Review)

   Abstract A primary goal of the National Oceanic and Atmospheric Administration Warn-on-Forecast (WoF) project is to provide rapidly updating probabilistic guidance to human forecasters for short-term (e.g., 0–3 h) severe weather forecasts. Postprocessing is required to maximize the usefulness of probabilistic guidance from an ensemble of convection-allowing model forecasts. Machine learning (ML) models have become popular methods for postprocessing severe weather guidance since they can leverage numerous variables to discover useful patterns in complex datasets. In this study, we develop and evaluate a series of ML models to produce calibrated, probabilistic severe weather guidance from WoF System (WoFS) output. Our dataset includes WoFS ensemble forecasts available every 5 min out to 150 min of lead time from the 2017–19 NOAA Hazardous Weather Testbed Spring Forecasting Experiments (81 dates). Using a novel ensemble storm-track identification method, we extracted three sets of predictors from the WoFS forecasts: intrastorm state variables, near-storm environment variables, and morphological attributes of the ensemble storm tracks. We then trained random forests, gradient-boosted trees, and logistic regression algorithms to predict which WoFS 30-min ensemble storm tracks will overlap a tornado, severe hail, and/or severe wind report. To provide rigorous baselines against which to evaluate the skill of the ML models, we extracted the ensemble probabilities of hazard-relevant WoFS variables exceeding tuned thresholds from each ensemble storm track. The three ML algorithms discriminated well for all three hazards and produced more reliable probabilities than the baseline predictions. Overall, the results suggest that ML-based postprocessing of dynamical ensemble output can improve short-term, storm-scale severe weather probabilistic guidance. 

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4. Gridded Severe Hail Nowcasting Using 3D U-Nets, Lightning Observations, and the Warn-on-Forecast System

   https://doi.org/10.1175/AIES-D-24-0026.1  

   Schmidt, Tobias G; McGovern, Amy; Allen, John T; Potvin, Corey K; Chase, Randy J; Wiley, Chad M; McGovern-Fagg, William R; Flora, Montgomery L; Homeyer, Cameron R; Williams, John K (October 2024, Artificial Intelligence for the Earth Systems)

   Abstract Hailstorms cause billions of dollars in damage across the United States each year. Part of this cost could be reduced by increasing warning lead times. To contribute to this effort, we developed a nowcasting machine learning model that uses a 3D U-Net to produce gridded severe hail nowcasts for up to 40 min in advance. The three U-Net dimensions uniquely incorporate one temporal and two spatial dimensions. Our predictors consist of a combination of output from the National Severe Storms Laboratory Warn-on-Forecast System (WoFS) numerical weather prediction ensemble and remote sensing observations from Vaisala’s National Lightning Detection Network (NLDN). Ground truth for prediction was derived from the maximum expected size of hail calculated from the gridded NEXRAD WSR-88D radar (GridRad) dataset. Our U-Net was evaluated by comparing its test set performance against rigorous hail nowcasting baselines. These baselines included WoFS ensemble Hail and Cloud Growth Model (HAILCAST) and a logistic regression model trained on WoFS 2–5-km updraft helicity. The 3D U-Net outperformed both these baselines for all forecast period time steps. Its predictions yielded a neighborhood maximum critical success index (max CSI) of ∼0.48 and ∼0.30 at forecast minutes 20 and 40, respectively. These max CSIs exceeded the ensemble HAILCAST max CSIs by as much as ∼0.35. The NLDN observations were found to increase the U-Net performance by more than a factor of 4 at some time steps. This system has shown success when nowcasting hail during complex severe weather events, and if used in an operational environment, may prove valuable. 

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5. Assessing the Fidelity of Landfalling Tropical Cyclone Convective‐Scale Environments in the Warn‐On‐Forecast System Using Radiosondes

   https://doi.org/10.1029/2023JD040473  

   Schenkel, Benjamin A; Jones, Thomas; Waugh, Sean (June 2024, Journal of Geophysical Research: Atmospheres)

   Forecasts of tropical cyclone (TC) tornadoes are less skillful than their non‐TC counterparts at all lead times. The development of a convection‐allowing regional ensemble, known as the Warn‐on‐Forecast System (WoFS), may help improve short‐fused TC tornado forecasts. As a first step, this study investigates the fidelity of convective‐scale kinematic and thermodynamic environments to a preliminary set of soundings from WoFS forecasts for comparison with radiosondes for selected 2020 landfalling TCs. Our study shows reasonable agreement between TC convective‐scale kinematic environments in WoFS versus observed soundings at all forecast lead times. Nonetheless, WoFS is biased toward weaker than observed TC‐relative radial winds, and stronger than observed near‐surface tangential winds with weaker winds aloft, during the forecast. Analysis of storm‐relative helicity (SRH) shows that WoFS underestimates extreme observed values. Convective‐scale thermodynamic environments are well simulated for both temperature and dewpoint at all lead times. However, WoFS is biased moister with steeper lapse rates compared to observations during the forecast. Both CAPE and, to a lesser extent, 0–3‐km CAPE distributions are narrower in WoFS than in radiosondes, with an underestimation of higher CAPE values. Together, these results suggest that WoFS may have utility for forecasting convective‐scale environments in landfalling TCs with lead times of several hours. 

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