Non-stationarity in peaks-over-threshold river flows:a regional random effects model

Under the influence of local- and large-scale climatological processes, extreme river flow events often show long-term trends, seasonality, inter-year variability and other characteristics of temporal non-stationarity. Properly accounting for this non-stationarity is vital for making accurate predictions of future floods. In this paper, a regional model based on the generalised Pareto distribution is developed for peaks-over-threshold river flow data sets when the event sizes are non-stationary. If observations are non-stationary and covariates are available then extreme value (semi-)parametric regression models may be used. Unfortunately the necessary covariates are rarely observed and, if they are, it is often not clear which process, or combination of processes, to include in the model. Within the statistical literature, latent process (or random effects) models are often used in such scenarios. We develop a regional time-varying random effects model which allows identification of temporal non-stationarity in event sizes by pooling information across all sites in a spatially homogeneous region. The proposed model, which is an instance of a Bayesian hierarchical model, can be used to predict both unconditional extreme events such as the m-year maximum, as well as extreme events that condition on being in a given year. The estimated random effects may also tell us about likely candidates for the climatological processes which cause non-stationarity in the flood process. The model is applied to UK flood data from 817 stations spread across 81 hydrometric regions

Global predictability of temperature extremes

Extreme temperatures are one of the leading causes of death and disease in both developed and developing countries, and heat extremes are projected to rise in many regions. To reduce risk, heatwave plans and cold weather plans have been effectively implemented around the world. However, much of the world’s population is not yet protected by such systems, including many data-scarce but also highly vulnerable regions. In this study, we assess at a global level where such systems have the potential to be effective at reducing risk from temperature extremes, characterizing (1) long-term average occurrence of heatwaves and coldwaves, (2) seasonality of these extremes, and (3) short-term predictability of these extreme events three to ten days in advance. Using both the NOAA and ECMWF weather forecast models, we develop global maps indicating a first approximation of the locations that are likely to benefit from the development of seasonal preparedness plans and/or short-term early warning systems for extreme temperature. The extratropics generally show both short-term skill as well as strong seasonality; in the tropics, most locations do also demonstrate one or both. In fact, almost 5 billion people live in regions that have seasonality and predictability of heatwaves and/or coldwaves. Climate adaptation investments in these regions can take advantage of seasonality and predictability to reduce risks to vulnerable populations

Resistance Exercise Reduces Skeletal Muscle Cachexia and Improves Muscle Function in Rheumatoid Arthritis

Rheumatoid arthritis (RA) is a chronic, systemic, autoimmune, inflammatory disease associated with cachexia (reduced muscle and increased fat). Although strength-training exercise has been used in persons with RA, it is not clear if it is effective for reducing cachexia. A 46-year-old woman was studied to determine: (i) if resistance exercise could reverse cachexia by improving muscle mass, fiber cross-sectional area, and muscle function; and (2) if elevated apoptotic signaling was involved in cachexia with RA and could be reduced by resistance training. A needle biopsy was obtained from the vastus lateralis muscle of the RA subject before and after 16 weeks of resistance training. Knee extensor strength increased by 13.6% and fatigue decreased by 2.8% Muscle mass increased by 2.1%. Average muscle fiber cross-sectional area increased by 49.7%, and muscle nuclei increased slightly after strength training from 0.08 to 0.12 nuclei/μm2. In addition, there was a slight decrease (1.6%) in the number of apoptotic muscle nuclei after resistance training. This case study suggests that resistance training may be a good tool for increasing the number of nuclei per fiber area, decreasing apoptotic nuclei, and inducing fiber hypertrophy in persons with RA, thereby slowing or reversing rheumatoid cachexia

Capturing Client Feedback for the Reopening of a Medical Fitness Facility During the COVID-19 Pandemic

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