Food insecurity in veteran households: findings from nationally representative data

OBJECTIVE: The present study is the first to use nationally representative data to compare rates of food insecurity among households with veterans of the US Armed Forces and non-veteran households. DESIGN: We used data from the 2005-2013 waves of the Current Population Survey - Food Security Supplement to identify rates of food insecurity and very low food security in veteran and non-veteran households. We estimated the odds and probability of food insecurity in veteran and non-veteran households in uncontrolled and controlled models. We replicated these results after separating veteran households by their most recent period of service. We weighted models to create nationally representative estimates. SETTING: Nationally representative data from the 2005-2013 waves of the Current Population Survey - Food Security Supplement. SUBJECTS: US households (n 388 680). RESULTS: Uncontrolled models found much lower rates of food insecurity (8·4 %) and very low food security (3·3 %) among veteran households than in non-veteran households (14·4 % and 5·4 %, respectively), with particularly low rates among households with older veterans. After adjustment, average rates of food insecurity and very low food security were not significantly different for veteran households. However, the probability of food insecurity was significantly higher among some recent veterans and significantly lower for those who served during the Vietnam War. CONCLUSIONS: Although adjusting eliminated many differences between veteran and non-veteran households, veterans who served from 1975 and onwards may be at higher risk for food insecurity and should be the recipients of targeted outreach to improve nutritional outcomes

Rapid response tools and datasets for post-fire modeling: linking Earth Observations and process-based hydrological models to support post-fire remediation

Preparation is key to utilizing Earth Observations and process-based models to support post-wildfire mitigation. Post-fire flooding and erosion can pose a serious threat to life, property and municipal water supplies. Increased runoff and sediment delivery due to the loss of surface cover and fire-induced changes in soil properties are of great concern. Remediation plans and treatments must be developed and implemented before the first major storms in order to be effective. One of the primary sources of information for making remediation decisions is a soil burn severity map derived from Earth Observation data (typically Landsat) that reflects fire induced changes in vegetation and soil properties. Slope, soils, land cover and climate are also important parameters that need to be considered. Spatially-explicit process-based models can account for these parameters, but they are currently under-utilized relative to simpler, lumped models because they are difficult to set up and require spatially-explicit inputs (digital elevation models, soils, and land cover). Our goal is to make process-based models more accessible by preparing spatial inputs before a fire, so that datasets can be rapidly combined with soil burn severity maps and formatted for model use. We are building an online database (http://geodjango.mtri.org/geowepp /) for the continental United States that will allow users to upload soil burn severity maps. The soil burn severity map is combined with land cover and soil datasets to generate the spatial model inputs needed for hydrological modeling of burn scars. Datasets will be created to support hydrological models, post-fire debris flow models and a dry ravel model. Our overall vision for this project is that advanced GIS surface erosion and mass failure prediction tools will be readily available for post-fire analysis using spatial information from a single online site

Escitalopram restores reversal learning impairments in rats with lesions of orbital frontal cortex

This study was funded by H. Lundbeck A/S.The term ‘cognitive structures’ is used to describe the fact that mental models underlie thinking, reasoning and representing. Cognitive structures generally improve the efficiency of information processing by providing a situational framework within which there are parameters governing the nature and timing of information and appropriate responses can be anticipated. Unanticipated events that violate the parameters of the cognitive structure require the cognitive model to be updated, but this comes at an efficiency cost. In reversal learning a response that had been reinforced is no longer reinforced, while an alternative is now reinforced, having previously not been (A+/B− becomes A−/B+). Unanticipated changes of contingencies require that cognitive structures are updated. In this study, we examined the effect of lesions of the orbital frontal cortex (OFC) and the effects of the selective serotonin reuptake inhibitor (SSRI), escitalopram, on discrimination and reversal learning. Escitalopram was without effect in intact rats. Rats with OFC lesions had selective impairment of reversal learning, which was ameliorated by escitalopram. We conclude that reversal learning in OFC-lesioned rats is an easily administered and sensitive test that can detect effects of serotonergic modulation on cognitive structures that are involved in behavioural flexibility.Publisher PD

Mapping Kenyan grassland heights across large spatial scales with combined optical and radar satellite imagery

Grassland monitoring can be challenging because it is time-consuming and expensive to measure grass condition at large spatial scales. Remote sensing offers a time- and cost-effective method for mapping and monitoring grassland condition at both large spatial extents and fine temporal resolutions. Combinations of remotely sensed optical and radar imagery are particularly promising because together they can measure differences in moisture, structure, and reflectance among land cover types. We combined multi-date radar (PALSAR-2 and Sentinel-1) and optical (Sentinel-2) imagery with field data and visual interpretation of aerial imagery to classify land cover in the Masai Mara National Reserve, Kenya using machine learning (Random Forests). This study area comprises a diverse array of land cover types and changes over time due to seasonal changes in precipitation, seasonal movements of large herds of resident and migratory ungulates, fires, and livestock grazing. We classified twelve land cover types with user’s and producer’s accuracies ranging from 66%–100% and an overall accuracy of 86%. These methods were able to distinguish among short, medium, and tall grass cover at user’s accuracies of 83%, 82%, and 85%, respectively. By yielding a highly accurate, fine-resolution map that distinguishes among grasses of different heights, this work not only outlines a viable method for future grassland mapping efforts but also will help inform local management decisions and research in the Masai Mara National Reserve

Development of a bi-national Great Lakes coastal wetland and land use map using three-season PALSAR and landsat imagery

Methods using extensive field data and three-season Landsat TM and PALSAR imagery were developed to map wetland type and identify potential wetland stressors (i.e., adjacent land use) for the United States and Canadian Laurentian coastal Great Lakes. The mapped area included the coastline to 10 km inland to capture the region hydrologically connected to the Great Lakes. Maps were developed in cooperation with the overarching Great Lakes Consortium plan to provide a comprehensive regional baseline map suitable for coastal wetland assessment and management by agencies at the local, tribal, state, and federal levels. The goal was to provide not only land use and land cover (LULC) baseline data at moderate spatial resolution (20–30 m), but a repeatable methodology to monitor change into the future. The prime focus was on mapping wetland ecosystem types, such as emergent wetland and forested wetland, as well as to delineate wetland monocultures (Typha, Phragmites, Schoenoplectus) and differentiate peatlands (fens and bogs) from other wetland types. The overall accuracy for the coastal Great Lakes map of all five lake basins was 94%, with a range of 86% to 96% by individual lake basin (Huron, Ontario, Michigan, Erie and Superior)

Climate, wildfire, and erosion ensemble foretells more sediment in western USA watersheds

The area burned annually by wildfires is expected to increase worldwide due to climate change. Burned areas increase soil erosion rates within watersheds, which can increase sedimentation in downstream rivers and reservoirs. However, which watersheds will be impacted by future wildfires is largely unknown. Using an ensemble of climate, fire, and erosion models, we show that postfire sedimentation is projected to increase for nearly nine tenths of watersheds by \u3e10% and for more than one third of watersheds by \u3e100% by the 2041 to 2050 decade in the western USA. The projected increases are statistically significant for more than eight tenths of the watersheds. In the western USA, many human communities rely on water from rivers and reservoirs that originates in watersheds where sedimentation is projected to increase. Increased sedimentation could negatively impact water supply and quality for some communities, in addition to affecting stream channel stability and aquatic ecosystems