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The capacity for rangeland stakeholders, including land managers, financiers and regulators, to regularly assess impacts of management practices on grazed landscapes has potential benefits. This paper describes the development of ground cover maintenance (GCM) spatial layers for a large study area in the catchment of the Great Barrier Reef in Queensland, Australia. GCM layers are an experimental product designed to benchmark the direction and strength of annual change in remotely sensed total ground cover (ΔTGC). This was achieved by predicting ΔTGC per pixel in a multivariate model, then using the quantile of the observed ΔTGC within its modelled prediction interval to benchmark observed ΔTGC. Under this approach, pixels with higher quantiles are those with a more positive annual observed ΔTGC after rainfall and other predictors in the multivariate model are taken into account. We then mapped these quantiles annually (2011–2021) across the study area and the annual spatial distribution of these quantiles is what we call the GCM layers. We identified two important issues to be addressed in future iterations of this work, namely, the potentially confounding impact of fire on GCM layers and their interpretation, and a need for more predictive skill in the underlying random forest model. Because management variables were not part of the underlying multivariate model but management practices can affect ΔTGC, we were interested in whether patterns in the mapped GCM values correlated with any known management practices or management-practice effects in the study area. We tested this idea on three datasets. In one, we compared GCM values from 12 well managed and 12 poorly managed grazing sites, finding no significant differences between the two groups. Another analysis looked at the relationship between grazing land condition and cumulative GCM values at two sets of sites (n = 110 and n = 189). Land condition and cumulative GCM values correlated significantly, although in only one of these data sets. Overall, we conclude that the developed GCM layers require further refinement to fit their desired purpose, but have potential to produce a number of benefits if current limitations can be addressed.

Osteoporosis affects over 200 million women worldwide, one-third of whom are predicted to suffer from an osteoporotic fracture in their lifetime. The most promising anabolic drugs involve administration of expensive antibodies. Because mechanical loading stimulates bone formation, our current data, using a mouse model, replicates the anabolic effects of loading in humans and may identify novel pathways amenable to oral treatment. Murine tibial compression produces axially varying deformations along the cortical bone, inducing highest strains at the mid-diaphysis and lowest at the metaphyseal shell. To test the hypothesis that load-induced transcriptomic responses at different axial locations of cortical bone would vary as a function of strain magnitude, we loaded the left tibias of 10-week-old female C57Bl/6 mice in vivo in compression, with contralateral limbs as controls. Animals were euthanized at 1, 3, or 24 hours post-loading or loaded for 1 week (n = 4-5/group). Bone marrow and cancellous bone were removed, cortical bone was segmented into the metaphyseal shell, proximal diaphysis, and mid-diaphysis, and load-induced differential gene expression and enriched biological processes were examined for the three segments. At each time point, the mid-diaphysis (highest strain) had the greatest transcriptomic response. Similarly, biological processes regulating bone formation and turnover increased earlier and to the greatest extent at the mid-diaphysis. Higher strain induced greater levels of osteoblast and osteocyte genes, whereas expression was lower in osteoclasts. Among the top differentially expressed genes at 24-hours post-loading, 17 had known functions in bone biology, of which 12 were present only in osteoblasts, 3 exclusively in osteoclasts, and 2 were present in both cell types. Based on these results, we conclude that murine tibial loading induces spatially unique transcriptomic responses correlating with strain magnitude in cortical bone. © 2022 American Society for Bone and Mineral Research (ASBMR).

One of the most important elements for market acceptance of new technologies is ensuring reliability. Nowhere is this truer than in the shift from well characterized fossil fuel technologies to newer renewable and sustainable energy technologies. The key enabling technology driving these shifts is the development of power converters and inverters. Conventional approaches to assess reliability of these devices have severe drawbacks. Frequent redesigns, often with new parts having no historical data, limit the usefulness of methods based on historical data. Conversely, physics-of-failure approaches often do not capture the most relevant failure mechanisms, including those related to operationally induced electrical overstress and software. In this paper, we will discuss a revolutionary new reliability assessment approach that utilizes advancements in artificial intelligence (AI), machine learning, and data analytics, along with new techniques for characterizing and modeling failure mechanisms to improve power electronics reliability. The reliability assessment method combines AI and machine learning algorithms for analyzing field failure data, with top down models that translate the impacts of grid-connected and grid-parallel mode dynamics and mode-transition dynamics on power systems, and reliability physics degradation models for key failure mechanisms that simulate the effects of both electrical and environmental degradation under field operational stresses. These models can be embedded in digital twins created specifically to replicate the design of current and new inverters. The output of these digital twins reflects the effects of aging and component degradation on system performance and will be transferable to multiple power electronic systems and platforms.

Millions of people across the globe have been infected with coronavirus disease (COVID-19), and many lives have been lost in the process. As a result, vaccines are being developed to protect people from COVID-19 morbidity and mortality. Therefore, this study was conducted to assess the coverage rate for the COVID-19 vaccine in Oyo State.A descriptive secondary analysis of COVID-19 immunization data was done between March and April 2021. Data were extracted from the original paper format and entered into Excel sheets. Charts and line graphs were plotted to determine the coverage rates.The overall coverage rate for the State was 81.0%. The highest and lowest coverage rates were 243.0% and 39.0% for Ibadan North and Iseyin Local Government Areas (LGAs), respectively. The proportion of female health workers vaccinated in the State was 64.5%. The proportion of male strategic leaders and male frontline workers was 62.5% and 55.7%, respectively. Akinyele and Egbeda LGAs recorded the same highest number of cases (27) of adverse events following immunization (AEFI).The study highlights the high proportion of vaccinated people in the State, while there was a low proportion of vaccinees in some LGAs. Therefore, effort to scale-up coverage across all the LGAs is recommended.Des millions de personnes dans le monde ont été infectées par le COVID-19 et de nombreuses vies ont été perdues dans ce processus. En conséquence, des vaccins sont en cours de développement pour protéger les personnes contre la morbidité et la mortalité liées au COVID-19. Cette étude a donc été menée pour évaluer le taux de couverture du vaccin COVID-19 dans l'Etat d'Oyo.Une analyse secondaire descriptive des données de vaccination COVID-19 a été réalisée entre mars et avril 2021. Les données ont été extraites du format papier original et saisies dans des feuilles Excel. Des diagrammes et des graphiques linéaires ont été tracés pour déterminer les taux de couverture.Le taux de couverture global de l'État était de 81,0 %. Les taux de couverture les plus élevés et les plus faibles étaient respectivement de 243,0 % et 39,0 % pour les zones de gouvernement local (LGA) d'Ibadan Nord et d'Iseyin. La proportion d'agents de santé féminins vaccinés dans l'État était de 64,5 %. La proportion d'hommes leaders stratégiques et d'hommes travailleurs de première ligne était respectivement de 62,5 % et 55,7 %. Les LGA d'Akinyele et d'Egbeda ont enregistré le même nombre élevé de cas (27) d'événements indésirables après la vaccination (EIAS).L'étude met en évidence la forte proportion de personnes vaccinées dans l'état, alors qu'il y avait une faible proportion de vaccinés dans certaines zone de gouvernement local. Il est donc recommandé de déployer des efforts pour augmenter la couverture vaccinale dans toutes les AGL.Épidémiologie, COVID-19, vaccin, première phase, Nigéria.

Parathyroid hormone (PTH) is an anabolic osteoporosis treatment that increases bone mass and reduces fracture risk. Clinically, the effects of PTH are site-specific, increasing bone mass more at the spine than the hip and not increasing bone mass at the radius. Differences in local loading environment between the spine, hip, and radius may help explain the variation in efficacy, as PTH and mechanical loading have been shown to synergistically increase bone mass. We hypothesized that differences in loading mode might further explain these variations. Owing to the curvature of the mouse tibia, cyclic compression of the hindlimb causes bending at the tibial midshaft, placing the anterior surface under tension and the posterior surface under compression. We investigated the combination of PTH treatment and tibial loading in an osteoblast-specific estrogen receptor-alpha knockout mouse model of low bone mass (pOC-ERαKO) and their littermate controls (LCs) and analyzed bone morphology in the tensile, compressive, and neutral regions of the tibial midshaft. We also hypothesized that pretreating wild-type C57Bl/6J (WT) mice with PTH prior to mechanical loading would enhance the synergistic anabolic effects. Compression was more anabolic than tension, and PTH enhanced the effect of loading, particularly under compression. PTH pretreatment maintained the synergistic anabolic effect for longer durations than concurrent treatment and loading alone. Together these data provide insights into more effective physical therapy and exercise regimens for patients receiving PTH treatment. © 2022 American Society for Bone and Mineral Research (ASBMR).