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Background: Parkinsonism, including Parkinson’s disease (PD) are progressive neurological conditions. As these condition progress, individuals will need more support with their care needs to maintain independent community-living. Care needs are mainly met by unpaid, informal caregivers, usually close family members or friends. Caregiver strain is thought to lead to the need for care home placement when the caregiver can no longer cope. Objective: To understand predictors of caregiver strain and its influence on care home placement for people with moderate to advanced Parkinsonism. Methods: This is a convergent mixed methods study. Quantitative data, following an adapted stress-appraisal model, were collected on caregiver profile, tasks performed and causes of caregiver strain. Semi-structured, in-depth interviews were conducted with caregivers of people with PD (PwP) who went into a care home during the study period, to develop a deeper understanding of the caregiver role and the factors influencing caregiver strain. Results: Quantitative data were collected from 115 patient caregiver dyads. Interviews were conducted with 10 caregivers. A model to predict caregiver strain was developed and predictors of caregiver strain were identified, such as functional disability and poor caregiver sleep. Conclusion: Our findings further demonstrate the complexity of carer strain. Particular dimensions are identified that need to be addressed within clinical practice to reduce carer strain and support people with Parkinsonism to remain within their own home for as long as possible.

Potted poinsettia (Euphorbia pulcherrima) is an important commercial commodity for the U.S. floriculture industry. The production of poinsettia demands intensively managed light control, heat, fertilizer, and water; inhibiting elongation with plant growth regulators, and protecting plants from diseases and pests with pesticide applications. Excessive irrigation creates pollution, promotes disease, and is expensive. Sensor-based control systems can optimize irrigation schedules. Irrigation management is crucial in nursery production of poinsettias because water is a limited resource and agricultural runoff is monitored in many states across the United States. By pairing environmental sensors with sensors that continuously monitor plant transpiration, we can determine how plant water use and water stress fluctuate with environmental and physiological demands. We hypothesized that continual measurements of sap flow could be correlated with environmental sensors to develop a new water stress index (WSI), which can deliver the benefits of detecting water stress that might affect the quality of potted poinsettias. To test this hypothesis, rooted cuttings of poinsettia (E. pulcherrima cv. Prestige Red) were individually potted into twelve 11-L black plastic nursery pots. Potted plants were grown in a naturally illuminated temperature-controlled glasshouse. The 12 plants were randomly assigned one of three watering treatments: weekly, biweekly, and triweekly irrigation. From the data collected, we were able to create a WSI that correlated available soil moisture with the difference between the expected transpiration with actual transpiration rates. Our results suggest that the plants in the weekly treatment group did not experience water stress until 0.3 m3·m–3 volume water content indicated by 3·m–3 can be stressful soil moisture conditions for greenhouse-grown crops. Results also show that for substrates with similar substrates that irrigation set points can be reduced to 0.2 m3·m–3 for improved irrigation efficiency.

Image registration is an important preprocessing step prior to detecting changes using multi-temporal image data, which is increasingly accomplished using automated methods. In high spatial resolution imagery, shadows represent a major source of illumination variation, which can reduce the performance of automated registration routines. This study evaluates the statistical relationship between shadow presence and image registration accuracy, and whether masking and normalizing shadows leads to improved automatic registration results. Eighty-eight bitemporal aerial image pairs were co-registered using software called Scale Invariant Features Transform (SIFT) and Random Sample Consensus (RANSAC) Alignment (SARA). Co-registration accuracy was assessed at different levels of shadow coverage and shadow movement within the images. The primary outcomes of this study are (1) the amount of shadow in a multi-temporal image pair is correlated with the accuracy/success of automatic co-registration; (2) masking out shadows prior to match point select does not improve the success of image-to-image co-registration; and (3) normalizing or brightening shadows can help match point routines find more match points and therefore improve performance of automatic co-registration. Normalizing shadows via a standard linear correction provided the most reliable co-registration results in image pairs containing substantial amounts of relative shadow movement, but had minimal effect for pairs with stationary shadows.

Wine grapes are an important agricultural commodity in the Pacific Northwest, where grape powdery mildew (GPM) is one of the main disease problems. The efficacy of various sulfur concentrations and output volumes from an air blast sprayer retrofitted with the Intelligent Spray System (ISS) were evaluated for the management of GPM. The ISS consists of a LiDAR sensor, Doppler speed sensor, embedded computer, flow controller, and individual pulse-width-modulation solenoid valves at each nozzle. GPM cluster severity ranged from 55 to 75% across all trials in the study when the ISS was used at its default spray rate of 62.5 ml/m3 with micronized sulfur at 6 g/liter, which was significantly higher than all other fungicide treatments but lower than nontreated controls. Similarly, leaf incidence values were highest on nontreated vines, followed by micronized sulfur at 6 g/liter applied at 62.5 ml/m3, with all other fungicide treatments being significantly lower in all trials. Using the ISS at the 62.5 ml/m3 rate and a rotation of locally systemic fungicides resulted in the lowest observed GPM leaf incidence and average cluster severity of 11% in both 2019 and 2020, the lowest cluster severity of all fungicide treatments tested. GPM control with the ISS and micronized sulfur was equivalent to a constant-rate air blast treatment at 6 g/liter when the spray rate of the ISS was increased to 125 ml/m3 or the concentration of sulfur was increased to 24 g/liter. In those cases, the amount of sulfur applied to vines was at or above the minimum label rate from bloom until the end of the season, or the entire season, respectively. This study has shown that sufficient disease control cannot always be expected when pesticides are mixed at the same rate as would be used for a constant-rate sprayer in a variable rate sprayer, especially when contact fungicides such as sulfur are used. With appropriate adjustments, the variable-rate ISS can be a useful tool to reduce pesticide quantities, water needed for mixing, and as a result labor, because fewer trips to refill for a given spray event are needed.

Background Some interstitial lung disease (ILD) patients develop a progressive fibrosing-ILD phenotype (PF-ILD), with similar persistent lung function decline suggesting common molecular pathways involved. Nintedanib, a tyrosine kinase inhibitor targeting the PDGF, FGF, VEGF and M-CSF pathways, has shown comparable efficacy in idiopathic pulmonary fibrosis (IPF) and systemic sclerosis-associated ILD (SSc-ILD). We hypothesize that Nintedanib targeted molecular pathways will be augmented to a similar degree across PF-ILD regardless of aetiology. Methods We collected explanted lung tissue at the time of lung transplantation from 130 PF-ILD patients (99 (76%) IPF, 14 (11%) SSc-ILD, 17 (13%) other PF-ILD), and wedge biopsies from 200 donor lungs and measured PDGF, FGF, VEGF and M-CSF concentrations by Luminex. Findings The concentrations of PDGF-AA, PDGF-BB, FGF-2, VEGF and M-CSF were significantly increased in PF-ILD lungs compared to donor lungs (PDGF-AA 93·0 pg/ml [±97·2] vs. 37·5 pg/ml [±35·4], p

An important component of wildlife management and conservation is monitoring the health and population size of wildlife species. Monitoring the population size of an animal group can inform researchers of habitat use, potential changes in habitat and resulting behavioral adaptations, individual health, and the effectiveness of conservation efforts. Arboreal monkeys are difficult to monitor as their habitat is often poorly accessible and most monkey species have some degree of camouflage, making them hard to observe in and below the tree canopy. Surveys conducted using uninhabited aerial vehicles (UAVs) equipped with thermal infrared (TIR) cameras can help overcome these limitations by flying above the canopy and using the contrast between the warm body temperature of the monkeys and the cooler background vegetation, reducing issues with impassable terrain and animal camouflage. We evaluated the technical and procedural elements associated with conducting UAV-TIR surveys for arboreal and terrestrial macaque species. Primary imaging missions and analyses were conducted over a monkey park housing approximately 160 semi-free-ranging Japanese macaques (Macaca fuscata). We demonstrate Repeat Station Imaging (RSI) procedures using co-registered TIR image pairs facilitate the use of image differencing to detect targets that were moving during rapid sequence imaging passes. We also show that 3D point clouds may be generated from highly overlapping UAV-TIR image sets in a forested setting using structure from motion (SfM) image processing techniques. A point cloud showing area-wide elevation values was generated from TIR imagery, but it lacked sufficient point density to reliably determine the 3D locations of monkeys.