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Nocardia is an environmental aerobic actinobacterium (Actinomycete) that stains positive on Gram stain and forms commonly in soil and water. Infection is primarily acquired through inhalation; however, it may also [...]

Semi‐arid ecosystems, common across the Australian continent, strongly influence the inter‐annual variability and trend in the global terrestrial net carbon sink. Here we explore the future Australian terrestrial carbon cycle using the CMIP6 ensemble, and the dynamic global vegetation model LPJ‐GUESS. Uncertainty in Australia's carbon storage in vegetation ranged between 6 and 49 PgC at the end of the century and was strongly linked to biases in the meteorological forcing. Using LPJ‐GUESS with bias‐corrected meteorological forcing reduced uncertainty in the vegetation carbon storage to between 14 and 20 PgC, with the remaining range linked to model sensitivities to rising atmospheric CO2 concentration, temperature, and precipitation variability. Reducing this uncertainty will require improved terrestrial biosphere models, but also major improvements in the simulation of regional precipitation by Global Climate Models. Plain Language Summary: Vegetation on land takes up about one‐third of greenhouse gas emissions associated with human activities and strongly influences the year‐to‐year variability in the atmospheric growth rate of CO2. Semi‐arid areas in the Southern Hemisphere, for example, in Australia, dominate the variability and trend in the net carbon sink. We use both coupled climate‐carbon cycle and uncoupled carbon cycle simulations to study future changes in the Australian carbon cycle and provide the first comprehensive regional analysis of future carbon cycle trends for Australia accounting for Global Climate Model uncertainty. Biases in the simulated climate lead to large uncertainty in the simulated carbon cycle but this uncertainty can be reduced when the biases are corrected. We identify areas of uncertainty in the process representation in the vegetation models, specifically carbon residence time, and a discrepancy in the sensitivity to three forcing variables (atmospheric CO2 concentration, temperature, and precipitation). Our study highlights that reducing precipitation biases in Global Climate Models would lead to a better understanding of future changes to the regional carbon cycle and help inform climate mitigation strategies. Key Points: A key area of future uncertainty in regional terrestrial carbon cycle projections is linked to biases in climate projectionsWe provide a comprehensive regional analysis of future carbon cycle trends for Australia accounting for uncertainty in climate projectionsApplying bias‐correction to the climate projections reduces the uncertainty in projected vegetation carbon from 6 – 49 PgC to 14–20 PgC [ABSTRACT FROM AUTHOR]

Objectives: Retrograde Cricopharyngeal Dysfunction (RCPD) is treated by botulinum toxin (BTX) injection into the cricopharyngeus. This prospective study compares the effectiveness and side effects of operating room (OR) and in‐office (IO) injections. Methods: Patients over 18 years of age with inability to burp, abdominal, thoracic, or cervical gurgling sounds, bloating, and excessive flatulence were diagnosed with RCPD and included in the study. Injections were performed in the OR (80U) or IO (30U) by the senior author. An RCPD questionnaire quantifying major and minor symptoms on a Likert scale, Eating Assessment Tool‐10 (EAT‐10), and Generalized Anxiety Score−7 (GAD‐7), were completed preinjection; at 1, 2, and 3 weeks; and 3 months postoperatively. Linear mixed models were used to analyze effects of BTX injection on RCPD symptoms, the EAT‐10, and the GAD‐7. Results: 108 (55 M/53F) patients completed the pretreatment survey, 53 (31 OR vs. 22 IO) completed the 3‐week follow‐up, and 36 (22 OR vs. 14 IO) completed the 3‐month questionnaire. Average posttreatment RCPD scores were significantly lower in both groups at 3 weeks and 3 months (p < 0.0001), There was no difference between IO or OR (p = 0.4924). GAD‐7 scores were significantly lower in both groups at week 3 (p = 0.0018) and month 3 (p = 0.0012). Postinjection EAT‐10 scores were significantly higher in OR compared with IO (p = 0.0379). Conclusion: OR and IO injections are equally effective in the treatment of RCPD. Postinjection dysphagia is more severe after the OR injections which may be related to higher doses of BTX used. General anxiety levels decrease with treatment. Level of Evidence: 2 Laryngoscope, 134:4614–4619, 2024 [ABSTRACT FROM AUTHOR]

Accurate topographic data acquired at appropriate spatio‐temporal resolution is often the cornerstone of geomorphic research. Recent decades have seen advances in our ability to generate highly accurate topographic data, primarily through the application of remote sensing techniques. Structure from Motion‐Multi View Stereo (SfM‐MVS) and lidar have revolutionised the spatial resolution of surveys across large spatial extents. Technological developments have led to commercialisation of small form factor (SFF) 3D lidar sensors that are suited to deployment on both mobile (e.g., uncrewed aerial systems), and in fixed semi‐permanent installations. Whilst the former has been adopted, the potential for the latter to generate data suitable for geomorphic investigations has yet to be assessed. We address this gap here in the context of a 3‐month deployment where channel change is assessed in an adjusting fluvial system. We find that SFF 3D lidar sensors generate change detection products comparable to those generated using a conventional lidar system. Areas of no geomorphic change are characterised as such (mean 3D change of 0.014 m compared with 0.0014 m for the Riegl VZ‐4000), with differences in median change in eroding sections of between 0.02 and 0.04 m. We illustrate that this data enables: (a) accurate characterisation of river channel adjustments through extraction of bank long‐profiles; (b) the assessment of bank retreat patterns which help elucidate failure mechanics; and (c) the extraction of water surface elevations. The deployment of this technology will enable a better understanding of processes across a variety of geomorphic systems, as data can be captured in 4D with near real‐time processing. Plain Language Summary: This research demonstrates how instruments that were initially developed for the automotive and robotics industry can be used to provide detailed information about the shape and size of landforms, and how they change over time. The instruments that we use are small, low‐cost, lidar sensors (which use laser beams to measure distances). We deployed the sensors over a 3‐month period, where they were used to assess how the banks of a river channel changed (eroded and deposited sediment). The river bank of interest was located approximately 30‐m away from the lidar sensors and we found them to generate results comparable to those obtained from conventional lidar systems. The small sensors accurately identified areas where no changes occurred, with minor differences in estimates for areas experiencing change. The fixed installation allowed for high‐resolution data to be collected every 2‐hr. Using this new technology, we were able to accurately study the change of the river channel, measure the patterns of bank retreat, understand the failure mechanisms and extract elevations of the water surface. This new technology has the potential to enhance our understanding of various environments that are undergoing change by capturing detailed data in three‐dimensional space over time. Key Points: Fixed monitoring platforms using small form lidar sensors offer insight into hydrogeomorphic processes at user defined temporal resolutionChange detection products are within 2–4 cm of those produced using conventional lidar systems at river bank sectionsApplication provides quantified insight into bank retreat processes and river water levels at high spatio‐temporal resolution [ABSTRACT FROM AUTHOR]

A brief functional analysis was conducted in an analogue setting to identify the maintaining variables for upright walking in a woman with developmental disabilities supported in an outpatient setting. All conditions required modification, since treatment outcomes were intended to increase, rather than decrease, the target behavior. The participant displayed a significantly greater duration of upright walking during the attention condition, which initially indicated that the desired behavior was maintained by positive reinforcement. Treatment of socially stigmatizing ambulation was implemented during natural transition times in a reversal design and consisted of differential reinforcement of an incompatible response. Results of treatment indicated that physical assistance delivered by staff produced an immediate and large increase in upright walking while verbal attention from staff was ineffective. During treatment conditions, duration of socially stigmatizing ambulation was significantly greater in baseline. These results indicate that socially stigmatizing ambulation was maintained by automatic reinforcement, rather than socially or access-based positive reinforcement, and treatment initially chosen to function as differential reinforcement of incompatible behavior may have also functioned as sensory matching and sensory extinction. (Contains 3 figures.)

Climate change has accelerated the frequency of catastrophic wildfires; however, the drivers that control the time‐to‐recover of forests are poorly understood. We integrated remotely sensed data, climate records, and landscape features to identify the causes of variability in the time‐to‐recover of canopy leaf area in southeast Australian eucalypt forests. Approximately 97% of all observed burns between 2001 and 2014 recovered to a pre‐fire leaf area index (±0.25 sd) within six years. Time‐to‐recover was highly variable within individual wildfires (ranging between ≤1 and ≥5 years), across burn seasons (90% longer January to September), and year of fire (median time‐to‐recover varying four‐fold across fire years). We used the logistic growth function to estimate the leaf area recovery rate, burn severity, and the long‐term carrying capacity of leaf area. Time‐to‐recover was most correlated with the leaf area recovery rate. The leaf area recovery rate was largest in areas that experienced high burn severity, and smallest in areas of intermediate to low burn severity. The leaf area recovery rate was also strongly accelerated by anomalously high post‐fire precipitation, and delayed by post‐fire drought. Finally we developed a predictive machine‐learning model of time‐to‐recover (R2: 0.68). Despite the exceptionally high burn severity of the 2019–2020 Australian megafires, we forecast the time‐to‐recover to be only 15% longer than the average of previous fire years. Plain Language Summary: Australian eucalypt forests have evolved different strategies to recover from fire. While the meteorological drivers of bushfire are reasonably well understood, the various processes explaining how long a forest takes to recover from fire are not. We investigated a range of static (landscape) and dynamic (vegetation condition or meteorological) factors that could influence how long a forest's canopy leaf area would take to recover from fire. "Time‐to‐recover" after fire is highly variable, ranging from less than 1 year to more than 5 years even within an individual burn location. More intense fires cause greater forest canopy damage and generally (but not always) lead to longer recovery times, whereas wetter conditions after the fire can accelerate recovery. Using these factors and others, we developed a model capable of predicting the time‐to‐recover and applied it to the 2019–2020 Australian megafires. Our analysis suggests the canopy damage caused by these fires was far more severe than fires in years prior. This would normally lead to a prolonged time‐to‐recover, however we predict that anomalously high rainfall in the year following the fires will shorten recovery time, compensating for the high burn severity. Ultimately we predict the time‐to‐recover will be only slightly longer than average. Key Points: Pre‐fire leaf area, burn severity, and post‐fire meteorological conditions combine to determine time‐to‐recover after fireLarge geographic variation in time‐to‐recover can be explained by mean climate and landscape differencesTime‐to‐recover can be predicted with high accuracy using information limited to the first year following fire [ABSTRACT FROM AUTHOR]