Polylactic acid (PLA)/Silver-NP/VitaminE bionanocomposite electrospun nanofibers with antibacterial and antioxidant activity

Cataloged from PDF version of article.The antibacterial property of silver nanoparticles (Ag-NPs) and the antioxidant activity of Vitamin E have been combined by incorporation of these two active components within polylactic acid (PLA) nanofibers via electrospinning (PLA/Ag-NP/VitaminE nanofibers). The morphological and structural characterizations of PLA/Ag-NP/VitaminE nanofibers were performed by Scanning Electron Microscopy (SEM), Transmission Electron Microscopy and X-ray diffraction. The average fiber diameter was 140 ± 60 nm, and the size of the Ag-NP was 2.7 ± 1.5 nm. PLA/Ag-NP/VitaminE nanofibers inhibited growth of Escherichia coli, Listeria monocytogenes and Salmonella typhymurium up to 100 %. The amount of released Ag ions from the nanofibers immersed in aqueous solution was determined by Inductively Coupled Plasma Mass Spectrometry, and it has been observed that the release of Ag ions was kept approximately constant after 10 days of immersion. The antioxidant activity of PLA/Ag-NP/VitaminE nanofibers was evaluated according to DPPH (2,2-diphenyl-1-picrylhydrazyl) method and determined as 94 %. The results of the tests on fresh apple and apple juice indicated that the PLA/Ag/VitaminE nanofiber membrane actively reduced the polyphenol oxidase activity. The multifunctional electrospun PLA nanofibers incorporating Ag-NP and Vitamin E may be quite applicable in food packaging due to the extremely large surface area of nanofibers along with antibacterial and antioxidant activities. These materials could find application in food industry as a potential preservative packaging for fruits and juices. © 2014, Springer Science+Business Media Dordrecht

Using Very-High-Resolution Multispectral Classification to Estimate Savanna Fractional Vegetation Components

&nbsp; Characterizing compositional and structural aspects of vegetation is critical to effectively assessing land function. When priorities are placed on ecological integrity, remotely sensed estimates of fractional vegetation components (FVCs) are useful for measuring landscape-level habitat structure and function. In this study, we address whether FVC estimates, stratified by dominant vegetation type, vary with different classification approaches applied to very-high-resolution small unoccupied aerial system (UAS)-derived imagery. Using Parrot Sequoia imagery, flown on a DJI Mavic Pro micro-quadcopter, we compare pixel- and segment-based random forest classifiers alongside a vegetation height-threshold model for characterizing the FVC in a southern African dryland savanna. Results show differences in agreement between each classification method, with the most disagreement in shrub-dominated sites. When compared to vegetation classes chosen by visual identification, the pixel-based random forest classifier had the highest overall agreement and was the only classifier not to differ significantly from the hand-delineated FVC estimation. However, when separating out woody biomass components of tree and shrub, the vegetation height-threshold performed better than both random-forest approaches. These findings underscore the utility and challenges represented by very-high-resolution multispectral UAS-derived data (~10 cm ground resolution) and their uses to estimate FVC. Semi-automated approaches statistically differ from by-hand estimation in most cases; however, we present insights for approaches that are applicable across varying vegetation types and structural conditions. Importantly, characterization of savanna land function cannot rely only on a &ldquo;greenness&rdquo; measure but also requires a structural vegetation component. Underscoring these insights is that the spatial heterogeneity of vegetation structure on the landscape broadly informs land management, from land allocation, wildlife habitat use, natural resource collection, and as an indicator of overall ecosystem function.</div

Modeling Community-Scale Natural Resource Use in a Transboundary Southern African Landscape: Integrating Remote Sensing and Participatory Mapping

Remote sensing analyses focused on non-timber forest product (NTFP) collection and grazing are current research priorities of land systems science. However, mapping these particular land use patterns in rural heterogeneous landscapes is challenging because their potential signatures on the landscape cannot be positively identified without fine-scale land use data for validation. Using field-mapped resource areas and household survey data from participatory mapping research, we combined various Landsat-derived indices with ancillary data associated with human habitation to model the intensity of grazing and NTFP collection activities at 100-m spatial resolution. The study area is situated centrally within a transboundary southern African landscape that encompasses community-based organization (CBO) areas across three countries. We conducted four iterations of pixel-based random forest models, modifying the variable set to determine which of the covariates are most informative, using the best fit predictions to summarize and compare resource use intensity by resource type and across communities. Pixels within georeferenced, field-mapped resource areas were used as training data. All models had overall accuracies above 60% but those using proxies for human habitation were more robust, with overall accuracies above 90%. The contribution of Landsat data as utilized in our modeling framework was negligible, and further research must be conducted to extract greater value from Landsat or other optical remote sensing platforms to map these land use patterns at moderate resolution. We conclude that similar population proxy covariates should be included in future studies attempting to characterize communal resource use when traditional spectral signatures do not adequately capture resource use intensity alone. This study provides insights into modeling resource use activity when leveraging both remotely sensed data and proxies for human habitation in heterogeneous, spectrally mixed rural land areas &nbsp;</p

Listeria monocytogenes in Milk Products

peer-reviewedMilk and milk products are frequently identified as vectors for transmission of Listeria monocytogenes. Milk can be contaminated at farm level either by indirect external contamination from the farm environment or less frequently by direct contamination of the milk from infection in the animal. Pasteurisation of milk will kill L. monocytogenes, but post-pasteurisation contamination, consumption of unpasteurised milk and manufacture of unpasteurised milk products can lead to milk being the cause of outbreaks of listeriosis. Therefore, there is a concern that L. monocytogenes in milk could lead to a public health risk. To protect against this risk, there is a need for awareness surrounding the issues, hygienic practices to reduce the risk and adequate sampling and analysis to verify that the risk is controlled. This review will highlight the issues surrounding L. monocytogenes in milk and milk products, including possible control measures. It will therefore create awareness about L. monocytogenes, contributing to protection of public health

Increasing the Accuracy of Runoff and Streamflow Simulation in the Nzoia Basin, Western Kenya, through the Incorporation of Satellite-Derived CHIRPS Data

Hydrologic models will be an increasingly important tool for water resource managers as water availability dwindles and water security concerns become more pertinent in data-scarce regions. Fortunately, newly available satellite remote sensing technology provides an opportunity for improving the spatial resolution and quality of input data to hydrologic models in such regions. In particular, the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) dataset provides quasi-global high resolution precipitation information derived from a blend of in situ and active and passive remote sensing data sources. We piloted the incorporation of the CHIRPS dataset into the Soil and Water Assessment Tool (SWAT), a hydrologic model. Comparisons of results between estimation of streamflow using in situ rainfall gauge station data, the Climate Forecast System Reanalysis (CFSR) dataset, and the CHIRPS dataset in the data-scarce Nzoia Basin in western Kenya over the temporal range 1990–2000 were reported. Simulated streamflow estimates were poor with rainfall gauge station data but improved significantly with the CFSR and CHIRPS datasets. However, the use of the CHIRPS dataset in comparison with the CFSR dataset provided an improved statistical performance following model calibration with the exception of one streamflow gauge station in higher elevation regions. Overall, the use of the CHIRPS dataset had the greatest linear correlation, relative variability, and normalized bias despite overall average Nash-Sutcliffe Efficiency (NSE) and R2 values

A Conceptual Approach towards Improving Monitoring of Living Conditions for Populations Affected by Desertification, Land Degradation, and Drought

Addressing the global challenges of desertification, land degradation, and drought (DLDD), and their impacts on achieving sustainable development goals for coupled human-environmental systems is a key component of the 2030 Agenda for Sustainable Development. In particular, Sustainable Development Goal (SDG) 15.3 aims to, “by 2030, combat desertification, restore degraded land and soil, including land affected by desertification, drought and floods, and strive to achieve a land degradation-neutral world”. Addressing this challenge is essential for improving the livelihoods of those most affected by DLDD and for safeguarding against the most extreme effects of climate change. This paper introduces a conceptual framework for improved monitoring of DLDD in the context of United Nations Convention to Combat Desertification (UNCCD) Strategic Objective 2 (SO2) and its expected impacts: food security and adequate access to water for people in affected areas are improved; the livelihoods of people in affected areas are improved and diversified; local people, especially women and youth, are empowered and participate in decision-making processes in combating DLDD; and migration forced by desertification and land degradation is substantially reduced. While it is critical to develop methods and tools for assessing DLDD, work is needed first to provide a conceptual roadmap of the human dimensions of vulnerability in relation to DLDD, especially when attempting to create a globally standardized monitoring approach

Thermal Imaging of Beach-Nesting Bird Habitat with Unmanned Aerial Vehicles: Considerations for Reducing Disturbance and Enhanced Image Accuracy

Knowledge of temperature variation within and across beach-nesting bird habitat, and how such variation may affect the nesting success and survival of these species, is currently lacking. This type of data is furthermore needed to refine predictions of population changes due to climate change, identify important breeding habitat, and guide habitat restoration efforts. Thermal imagery collected with unmanned aerial vehicles (UAVs) provides a potential approach to fill current knowledge gaps and accomplish these goals. Our research outlines a novel methodology for collecting and implementing active thermal ground control points (GCPs) and assess the accuracy of the resulting imagery using an off-the-shelf commercial fixed-wing UAV that allows for the reconstruction of thermal landscapes at high spatial, temporal, and radiometric resolutions. Additionally, we observed and documented the behavioral responses of beach-nesting birds to UAV flights and modifications made to flight plans or the physical appearance of the UAV to minimize disturbance. We found strong evidence that flying on cloudless days and using sky-blue camouflage greatly reduced disturbance to nesting birds. The incorporation of the novel active thermal GCPs into the processing workflow increased image spatial accuracy an average of 12 m horizontally (mean root mean square error of checkpoints in imagery with and without GCPs was 0.59 m and 23.75 m, respectively). The final thermal indices generated had a ground sampling distance of 25.10 cm and a thermal accuracy of less than 1 &deg;C. This practical approach to collecting highly accurate thermal data for beach-nesting bird habitat while avoiding disturbance is a crucial step towards the continued monitoring and modeling of beach-nesting birds and their habitat

A Conceptual Approach towards Improving Monitoring of Living Conditions for Populations Affected by Desertification, Land Degradation, and Drought

Addressing the global challenges of desertification, land degradation, and drought (DLDD), and their impacts on achieving sustainable development goals for coupled human-environmental systems is a key component of the 2030 Agenda for Sustainable Development. In particular, Sustainable Development Goal (SDG) 15.3 aims to, “by 2030, combat desertification, restore degraded land and soil, including land affected by desertification, drought and floods, and strive to achieve a land degradation-neutral world”. Addressing this challenge is essential for improving the livelihoods of those most affected by DLDD and for safeguarding against the most extreme effects of climate change. This paper introduces a conceptual framework for improved monitoring of DLDD in the context of United Nations Convention to Combat Desertification (UNCCD) Strategic Objective 2 (SO2) and its expected impacts: food security and adequate access to water for people in affected areas are improved; the livelihoods of people in affected areas are improved and diversified; local people, especially women and youth, are empowered and participate in decision-making processes in combating DLDD; and migration forced by desertification and land degradation is substantially reduced. While it is critical to develop methods and tools for assessing DLDD, work is needed first to provide a conceptual roadmap of the human dimensions of vulnerability in relation to DLDD, especially when attempting to create a globally standardized monitoring approach

Climate-Related Child Undernutrition in the Lake Victoria Basin: An Integrated Spatial Analysis of Health Surveys, NDVI, and Precipitation Data

Despite growing research into the socio-economic aspects of vulnerability [1]-[4], relatively little work has linked population dynamics with climate change beyond the complex relationship between migration and climate change [5]. It is likely, however, that most people experience climate change in situ, so understanding the role of population dynamics remains critical. How a given number of people, in a given location and with varying population characteristics may exacerbate or mitigate the impacts of climate change or how, conversely, they may be vulnerable to climate change impacts are basic questions that remain largely unresolved [6]. This paper explores where and to what extent population dynamics intersect with high exposure to climate change. Specifically, in Eastern Africa's Lake Victoria Basin (LVB), a climate change/health vulnerability hotspot we have identified in prior research [7], we model child undernutrition vulnerability indices based on climate variables, including proxy measures (NDVI) derived from satellite imagery, at a 5-km spatial resolution. Results suggest that vegetation changes associated with precipitation decline in rural areas of sub-Saharan Africa can help predict deteriorating child health

A spatial analysis of climate-related child malnutrition in the Lake Victoria Basin

International Geoscience and Remote Sensing Symposium, 2015, Milan, ItalyDespite growing research into the socio-economic aspects of vulnerability [1-3], relatively little work has linked population dynamics with climate change. Understanding the role of population dynamics remains critical. How a given number of people, in a given location and with varying population characteristics may exacerbate or mitigate the impacts of climate change or how, conversely, they may be vulnerable to climate change impacts are basic questions that remain largely unresolved [4]. This paper explores where and to what extent population dynamics intersect with high exposure to climate change. Specifically, in Eastern Africa's Lake Victoria Basin (LVB), a climate change/health vulnerability hotspot we have identified in prior research [5], we model child malnutrition vulnerability indices based on climate variables at a 5km spatial resolution