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Low temperatures largely determine the geographic limits of plant species by reducing survival and growth. Inter-specific differences in the geographic distribution of mangrove species have been associated with cold tolerance, with exclusively tropical species being highly cold-sensitive and subtropical species being relatively cold-tolerant. To identify species-specific adaptations to low temperatures, we compared the chilling stress response of two widespread Indo-West Pacific mangrove species from Rhizophoraceae with differing latitudinal range limits—Bruguiera gymnorhiza (L.) Lam. ex Savigny (subtropical range limit) and Rhizophora apiculata Blume (tropical range limit). For both species, we measured the maximum photochemical efficiency of photosystem II (Fv/Fm) as a proxy for the physiological condition of the plants and examined gene expression profiles during chilling at 15 and 5 °C. At 15 °C, B. gymnorhiza maintained a significantly higher Fv/Fm than R. apiculata. However, at 5 °C, both species displayed equivalent Fv/Fm values. Thus, species-specific differences in chilling tolerance were only found at 15 °C, and both species were sensitive to chilling at 5 °C. At 15 °C, B. gymnorhiza downregulated genes related to the light reactions of photosynthesis and upregulated a gene involved in cyclic electron flow regulation, whereas R. apiculata downregulated more RuBisCo-related genes. At 5 °C, both species repressed genes related to CO2 assimilation. The downregulation of genes related to light absorption and upregulation of genes related to cyclic electron flow regulation are photoprotective mechanisms that likely contributed to the greater photosystem II photochemical efficiency of B. gymnorhiza at 15 °C. The results of this study provide evidence that the distributional range limits and potentially the expansion rates of plant species are associated with differences in the regulation of photosynthesis and photoprotective mechanisms under low temperatures.

 

We consider the cybersecurity challenges arising from communications between autonomous vehicles and smart infrastructures. In particular, we consider coordination between vehicles and Reduced Speed Work Zones (RSWZ). Malicious or tampered communica- tions between these entities can have catastrophic consequences. We discuss methods for the analysis of such attacks. In particular, we show how to generate congurable, eective vehicular trajecto- ries for exploring such attacks and how to utilize such trajectories in identifying impactful attacks and evaluating defenses. 

Phase variable control based on global tibia kinematics holds promise for predicting gait cycle progression to continuously control robotic transtibial prostheses. Calibration of the phase variable is critical to ensure its monotonic behavior, to approach a linear relationship with gait percentage, and to accurately predict the percentage of gait. This paper compares four calibration approaches using data from 22 able-bodied subjects walking at 14 speeds. The typical pure centering (PC) approach employed for thigh-based phase variables is not viable, yielding monotonic phase progression in fewer than half of the cases. An optimization (OPT) approach found monotonic calibrations in 305/308 cases with high linearity (average R^2 of 0.91). Critical point centering (CPC) approximates the OPT performance, with 274/308 monotonic calibrations and an average R^2 of 0.85, whereas the related vertical weighted average (VWA) approach was only slightly better than PC. All four approaches are similarly accurate in predicting gait percentage, staying within 5% at least 92.7% of the time. 

Solar energetic particle (SEP) events and their major subclass, solar proton events (SPEs), can have unfavorable consequences on numerous aspects of life and technology, making them one of the most harmful effects of solar activity. Garnering knowledge preceding such events by studying operational data flows is essential for their forecasting. Considering only solar cycle (SC) 24 in our previous study, we found that it may be sufficient to only utilize proton and soft X-ray (SXR) parameters for SPE forecasts. Here, we report a catalog recording ≥10 MeV ≥10 particle flux unit SPEs with their properties, spanning SCs 22–24, using NOAA’s Geostationary Operational Environmental Satellite flux data. We report an additional catalog of daily proton and SXR flux statistics for this period, employing it to test the application of machine learning (ML) on the prediction of SPEs using a support vector machine (SVM) and extreme gradient boosting (XGBoost). We explore the effects of training models with data from oneandtwo SCs, evaluating how transferable a model might be across different time periods. XGBoost proved to be more accurate than SVMs for almost every test considered, while also outperforming operational SWPC NOAA predictions and a persistence forecast. Interestingly, training done with SC 24 produces weaker true skill statistic and Heidke skill scores₂, even when paired with SC 22 or SC 23, indicating transferability issues. This work contributes toward validating forecasts using long-spanning data—an understudied area in SEP research that should be considered to verify the cross cycle robustness of ML-driven forecasts.