The Spatial Extent of Pain Is Associated with Pain Intensity, Catastrophizing and Some Measures of Central Sensitization in People with Frozen Shoulder

The aim of this cross-sectional study was to explore the spatial extent of pain and its association with clinical symptoms, psychological features, and pain sensitization in people with frozen shoulder (FS). Forty-eight individuals with FS completed pain drawings (PDs) and reported their clinical symptoms including pain intensity (Visual Analogue Scale) and shoulder disability (Shoulder Pain and Disability Index). Moreover, pain sensitization measurements (pressure pain thresholds, temporal summation, conditioned pain modulation, and Central Sensitization Inventory (CSI)) were assessed. Psychological features were assessed by Pain Catastrophizing Scale (PCS) and Pain Vigilance and Awareness Questionnaire. Pain frequency maps were generated, Margolis rating scale was used for pain location, and Spearman correlation coefficients were computed. The mean (SD) pain extent was 12.5% (6.7%) and the most common painful area was the anterolateral shoulder region (100%). Women presented a more widespread pain distribution compared with men. Significant positive associations were obtained between pain extent and current pain intensity (rs = 0.421, p < 0.01), PCS (rs = 0.307, p < 0.05) and CSI (rs = 0.358, p < 0.05). The anterolateral region of the shoulder was the most common painful area in people with FS. Women with FS presented more extended areas of pain; and a more widespread distribution of pain was correlated with higher levels of pain, pain catastrophizing and pain sensitization.

Reconnecting the Elwha River: Spatial Patterns of Fish Response to Dam Removal

The removal of two large dams on the Elwha River was completed in 2014 with a goal of restoring anadromous salmonid populations. Using observations from ongoing field studies, we compiled a timeline of migratory fish passage upstream of each dam. We also used spatially continuous snorkeling surveys in consecutive years before (2007, 2008) and after (2018, 2019) dam removal during summer baseflow to assess changes in fish distribution and density over 65 km of the mainstem Elwha River. Before dam removal, anadromous fishes were limited to the 7.9 km section of river downstream of Elwha Dam, potamodromous species could not migrate throughout the river system, and resident trout were the most abundant species. After dam removal, there was rapid passage into areas upstream of Elwha Dam, with 8 anadromous species (Chinook, Coho, Sockeye, Pink, Chum, Winter Steelhead, Summer Steelhead, Pacific Lamprey, and Bull Trout) observed within 2.5 years. All of these runs except Chum Salmon were also observed in upper Elwha upstream of Glines Canyon Dam within 5 years. The spatial extent of fish passage by adult Chinook Salmon and Summer Steelhead increased by 50 km and 60 km, respectively, after dam removal. Adult Chinook Salmon densities in some previously inaccessible reaches in the middle section of the river exceeded the highest densities observed in the lower section of the river prior to dam removal. The large number (&gt;100) of adult Summer Steelhead in the upper river after dam removal was notable because it was among the rarest anadromous species in the Elwha River prior to dam removal. The spatial extent of trout and Bull Trout remained unchanged after dam removal, but their total abundance increased and their highest densities shifted from the lower 25 km of the river to the upper 40 km. Our results show that reconnecting the Elwha River through dam removal provided fish access to portions of the watershed that had been blocked for nearly a century.

Unoccupied aerial vehicle-assisted monitoring of benthic vegetation in the coastal zone enhances the quality of ecological data

Traditional monitoring (e.g., in-water based surveys) of eelgrass meadows and perennial macroalgae in coastal areas is time and labor intensive, requires extensive equipment, and the collected data has a low temporal resolution. Further, divers and Remotely Operated Vehicles (ROVs) have a low spatial extent that cover small fractions of full systems. The inherent heterogeneity of eelgrass meadows and macroalgae assemblages in these coastal systems makes interpolation and extrapolation of observations complicated and, as such, methods to collect data on larger spatial scales whilst retaining high spatial resolution is required to guide management. Recently, the utilization of Unoccupied Aerial Vehicles (UAVs) has gained popularity in ecological sciences due to their ability to rapidly collect large amounts of area-based and georeferenced data, making it possible to monitor the spatial extent and status of SAV communities with limited equipment requirements compared to ROVs or diver surveys. This paper is focused on the increased value provided by UAV-based, data collection (visual/Red Green Blue imagery) and Object Based Image Analysis for gaining an improved understanding of eelgrass recovery. It is demonstrated that delineation and classification of two species of SAV ( Fucus vesiculosus and Zostera marina) is possible; with an error matrix indicating 86–92% accuracy. Classified maps also highlighted the increasing biomass and areal coverage of F. vesiculosus as a potential stressor to eelgrass meadows. Further, authors derive a statistically significant conversion of percentage cover to biomass ( R2 = 0.96 for Fucus vesiculosus, R2 = 0.89 for Zostera marina total biomass, and R2 = 0.94 for AGB alone, p < 0.001). Results here provide an example of mapping cover and biomass of SAV and provide a tool to undertake spatio-temporal analyses to enhance the understanding of eelgrass ecosystem dynamics.

Agricultural drought : Assessment & monitoring

Drought is a creeping natural disaster with long lasting effects on ecology as well as economy. Monitoring and assessment of drought is a very critical component of the drought management strategy aimed at mitigation of its adverse impacts. Spatial extent, intensity and duration of drought related information is essentially needed for taking the choicest rational decision making in the field of agriculture. Satellite remote sensing enables deriving indicators that explain the prevalence, severity, persistence and spatial extent of the area affected by drought. New satellite missions coupled with novel information extraction techniques are opening new vistas towards monitoring and assessment of drought. Aspects related to agricultural drought are discussed in this paper.

Non-flooded riparian Amazon trees are a regionally significant methane source

Inundation-adapted trees were recently established as the dominant egress pathway for soil-produced methane (CH 4 ) in forested wetlands. This raises the possibility that CH 4 produced deep within the soil column can vent to the atmosphere via tree roots even when the water table (WT) is below the surface. If correct, this would challenge modelling efforts where inundation often defines the spatial extent of ecosystem CH 4 production and emission. Here, we examine CH 4 exchange on tree, soil and aquatic surfaces in forest experiencing a dynamic WT at three floodplain locations spanning the Amazon basin at four hydrologically distinct times from April 2017 to January 2018. Tree stem emissions were orders of magnitude larger than from soil or aquatic surface emissions and exhibited a strong relationship to WT depth below the surface (less than 0). We estimate that Amazon riparian floodplain margins with a WT < 0 contribute 2.2–3.6 Tg CH 4 yr −1 to the atmosphere in addition to inundated tree emissions of approximately 12.7–21.1 Tg CH 4 yr −1 . Applying our approach to all tropical wetland broad-leaf trees yields an estimated non-flooded floodplain tree flux of 6.4 Tg CH 4 yr −1 which, at 17% of the flooded tropical tree flux of approximately 37.1 Tg CH 4 yr −1 , demonstrates the importance of these ecosystems in extending the effective CH 4 emitting area beyond flooded lands. This article is part of a discussion meeting issue 'Rising methane: is warming feeding warming? (part 2)'.

Land Cover Dynamics on the Lower Ganges–Brahmaputra Delta: Agriculture–Aquaculture Transitions, 1972–2017

Aquaculture in tropical and subtropical developing countries has expanded in recent years. This practice is controversial due to its potential for serious economic, food security, and environmental impacts—especially for intensive operations in and near mangrove ecosystems, where many shrimp species spawn. While considerable effort has been directed toward understanding aquaculture impacts, maps of spatial extent and multi-decade spatiotemporal dynamics remain sparse. This is in part because aquaculture ponds (ghers) can be challenging to distinguish from other shallow water targets on the basis of water-leaving radiance alone. Here, we focus on the Lower Ganges–Brahmaputra Delta (GBD), one of the most expansive areas of recent aquaculture growth on Earth and adjacent to the Sundarbans mangrove forest, a biodiversity hotspot. We use a combination of MODIS 16-day EVI composites and 45 years (1972–2017) of Landsat observations to characterize dominant spatiotemporal patterns in the vegetation phenology of the area, identify consistent seasonal optical differences between flooded ghers and other land uses, and quantify the multi-decade expansion of standing water bodies. Considerable non-uniqueness exists in the spectral signature of ghers on the GBD, propagating into uncertainty in estimates of spatial extent. We implement three progressive decision boundaries to explicitly quantify this uncertainty and provide liberal, moderate, and conservative estimates of flooded gher extent on three different spatial scales. Using multiple extents and multiple thresholds, we quantify the size distribution of contiguous regions of flooded gher extent at ten-year intervals. The moderate threshold shows standing water area within Bangladeshi polders to have expanded from less than 300 km2 in 1990 to over 1400 km2 in 2015. At all three scales investigated, the size distribution of standing water bodies is increasingly dominated by larger, more interconnected networks of flooded areas associated with aquaculture. Much of this expansion has occurred in immediate proximity to the Bangladeshi Sundarbans.

Machine-learning approach to crop yield prediction with the spatial extent of drought

Crop yield is one of the variables used to assess the impact of droughts on agriculture. Crop growth models calculate yield and variables related to plant development and become more suitable for crop yield estimation. However, these models are limited in that specific data are needed for computation. Given this limitation, machine learning (ML) models are often widely utilised instead, but their use with the spatial characteristics of droughts as input data is limited. This research explored the spatial extent of drought (area) as input data for building an approach to predict seasonal crop yield. This ML approach is made up of two components. The first includes polynomial regression (PR) models, and the second considers artificial neural network (ANN) models. In this approach, the purpose was to evaluate both types of ML models (PR and ANN) and integrate them into one operational tool. The logic is as follows: ANN models determine the most accurate predictions, but in practice, issues regarding data retrieval and processing can make the use of equations, i.e. PR, preferable. The proposed approach provides these PR equations to perform such calculations with early and preliminary input. The estimates can be further improved when the ANN models are run with the final input data. The results indicated that the empirical equations (PR) produced good predictions when using drought area as the input. ANN provides better estimates, in general. This research will improve drought monitoring systems for assessing drought effects. Since it is currently possible to calculate drought areas within these systems, the direct application of the prediction of drought effects is possible to integrate by following approaches such as the one presented or similar.

Subseasonal prediction of the 2020 Great Barrier Reef and Coral Sea marine heatwave

The 2020 marine heatwave in the Great Barrier Reef and Coral Sea led to mass coral bleaching. Sea surface temperature anomalies reached +1.7°C for the whole of the Great Barrier Reef and Coral Sea and exceeded +2°C across broad regions (referenced to 1990-2012). The marine heatwave reached Category 2 (Strong) and warm anomalies peaked between mid-February and mid-March 2020. The marine heatwave’s peak intensity aligned with regions of reduced cloud cover and weak wind speeds. We used a marine heatwave framework to assess the ability of an operational coupled ocean-atmosphere prediction system (ACCESS-S1) to capture the marine heatwave’s severity, duration, and spatial extent. For initial week predictions, the predicted marine heatwave severity generally agreed with the magnitude and spatial extent of the observed severity for that week. The model ensemble mean did not capture the marine heatwave’s development phase at lead times beyond the first week. The model underestimated the marine heatwave’s spatial extent, which reached up to 95% of the study area with at least Moderate severity and up to 43% with at least Strong severity. However, most forecast ensemble members correctly predicted the period of Strong severity in the first week of the model forecast. The model correctly predicted marine heatwave conditions to persist from mid-February to mid-March but did not capture the end of the marine heatwave. The inability to predict the end of the event and other periods of less skilful prediction were related to subseasonal variability owing to weather systems, including the passage of tropical cyclones not simulated in the model. On subseasonal timescales, evaluating daily to weekly forecasts of ocean temperature extremes is an important step toward implementing methods for developing operational forecast extremes products for use in early warning systems.

Spatial Extent and Distribution of Ambient Airborne Particulate Matter (PM2.5) in Selected Land Use Sites in Nairobi, Kenya

Air pollution is one of the most important environmental and public health concerns worldwide. Urban air pollution has been increasing since the industrial revolution due to rapid industrialization, mushrooming of cities, and greater dependence on fossil fuels in urban centers. Particulate matter (PM) is considered to be one of the main aerosol pollutants that causes a significant adverse impact on human health. Low-cost air quality sensors have attracted attention recently to curb the lack of air quality data which is essential in assessing the health impacts of air pollutants and evaluating land use policies. This is mainly due to their lower cost in comparison to the conventional methods. The aim of this study was to assess the spatial extent and distribution of ambient airborne particulate matter with an aerodynamic diameter less than 2.5 μm (PM2.5) in Nairobi City County. Seven sites were selected for monitoring based on the land use type: high- and low-density residential, industrial, agricultural, commercial, road transport, and forest reserve areas. Calibrated low-cost sensors and cyclone samplers were used to monitor PM2.5 concentration levels and gravimetric measurements for elemental composition of PM2.5, respectively. The sensor percentage accuracy for calibration ranged from 81.47% to 98.60%. The highest 24-hour average concentration of PM2.5 was observed in Viwandani, an industrial area (111.87 μg/m³), and the lowest concentration at Karura (21.25 μg/m³), a forested area. The results showed a daily variation in PM2.5 concentration levels with the peaks occurring in the morning and the evening due to variation in anthropogenic activities and the depth of the atmospheric boundary layer. Therefore, the study suggests that residents in different selected land use sites are exposed to varying levels of PM2.5 pollution on a regular basis, hence increasing the potential of causing long-term health effects.