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Crop diversity (e.g. the number of agricultural crop types and the level of evenness in area distribution) in the agricultural systems of arid Central Asia has recently been increased mainly to achieve food security of the rural population, however, not throughout the irrigation system. Site-specific factors that promote or hamper crop diversification after the dissolvent of the Soviet Union have hardly been assessed yet. While tapping the potential of remote sensing, the objective was to map and explain spatial patterns of current crop diversity by the example of the irrigated agricultural landscapes of the Fergana Valley, Uzbekistan. Multi-temporal Landsat and RapidEye satellite data formed the basis for creating annual and multi-annual crop maps for 2010–2012 while using supervised classifications. Applying the Simpson index of diversity (SID) to circular buffers with radii of 1.5 and 5 km elucidated the spatial distribution of crop diversity at both the local and landscape spatial scales. A variable importance analysis, rooted in the conditional forest algorithm, investigated potential environmental and socio-economic drivers of the spatial patterns of crop diversity. Overall accuracy of the annual crop maps ranged from 0.84 to 0.86 whilst the SID varied between 0.1 and 0.85. The findings confirmed the existence of areas under monocultures as well as of crop diverse patches. Higher crop diversity occurred in the more distal parts of the irrigation system and sparsely settled areas, especially due to orchards. In contrast, in water-secure and densely settled areas, cotton-wheat rotations dominated due to the state interventions in crop cultivation. Distances to irrigation infrastructure, settlements and the road network influenced crop diversity the most. Spatial explicit information on crop diversity per se has the potential to support policymaking and spatial planning towards crop diversification. Driver analysis as exemplified at the study region in Uzbekistan can help reaching the declared policy to increase crop diversity throughout the country and even beyond. [ABSTRACT FROM AUTHOR]

Summary Tuberous sclerosis complex (TSC) is a neurodevelopmental disease caused by TSC1 or TSC2 mutations and subsequent activation of the mTORC1 kinase. Upon mTORC1 activation, anabolic metabolism, which requires mitochondria, is induced, yet at the same time the principal pathway for mitochondrial turnover, autophagy, is compromised. How mTORC1 activation impacts mitochondrial turnover in neurons remains unknown. Here, we demonstrate impaired mitochondrial homeostasis in neuronal in vitro and in vivo models of TSC. We find that Tsc1/2 -deficient neurons accumulate mitochondria in cell bodies, but are depleted of axonal mitochondria, including those supporting presynaptic sites. Axonal and global mitophagy of damaged mitochondria is impaired, suggesting that decreased turnover may act upstream of impaired mitochondrial metabolism. Importantly, blocking mTORC1 or inducing mTOR-independent autophagy restores mitochondrial homeostasis. Our study clarifies the complex relationship between the TSC-mTORC1 pathway, autophagy, and mitophagy, and defines mitochondrial homeostasis as a therapeutic target for TSC and related diseases. [ABSTRACT FROM AUTHOR]

[Display omitted] • GDV is found in low permeable HSU with surficial groundwater circulation. • GDV classes got validated with an ecohydrological index from botanical in situ data. • Differences in in situ vitality exist between Quercus species in GDV and non-GDV. Groundwater dependent ecosystems (GDEs) are biodiversity hotspots and provide important ecosystem services. This study presents a novel multi-instrument concept for the local identification of groundwater dependent vegetation (GDV) in the Mediterranean. The concept integrates high-resolution Sentinel-2 remote sensing data with available geodata and requires in situ vegetation data for validation and calibration. The approach combines five criteria to identify GDV: 1) high vitality, and wetness during dry period, 2) low seasonal changes in vitality and leaf area, 3) low interannual changes in vitality, 4) high topographic potential of water accumulation and low water table depth, 5) high potential inflow dependency. Iso Cluster Unsupervised Classification (ICUC) was applied to identify GDV in the study area (Campania, Italy). Botanical field mapping was utilized for validating the remote sensing approach, as it exhibited significant differences between GDV and Non-GDV in terms of ecohydrological indicator values, leaf anatomy and phreatophyte coverage. According to a new simple ecohydrological rule set that considers phreatophyte cover and mean moisture value of non-phreatophyte species, 9% of vegetation plots are considered GDV and 33% likely GDV. 80% of all GDV derived from classification occur in hydrostratigraphic units (HSU) that are characterized by surficial groundwater circulation and low permeability. The overall accuracy of classifying likelihoods is 62.7%. For 14.6% of the plots, non-GDVs were classified as GDVs (false positives), and only one GDV plot has been classified falsely as non-GDV (false negative). Local results on GDV locations can be overlayed with aquifer use or aquifer reaction to climate change in order to identify GDV under threat and implement sustainable managements of groundwater resources. [ABSTRACT FROM AUTHOR]

Abstract: The sustainability of irrigated croplands in Central Asia is threatened by degradation of their productive function. To quantify the extent of the cropland degradation, this paper combines object-based change detection and spectral mixture analysis for vegetation cover decline mapping in irrigated agro-ecosystems in Uzbekistan based on multitemporal Landsat TM images from 1998 to 2009. The results of the change detection reveal that the cadastral field parcels, characterized by vegetation cover decrease, occupied 18% (52,938 ha) of the cropland area. Spatial distribution of fields with decreased vegetation cover was mainly associated with abandoned cropland and land with inherently low-fertility soils located on the outreaches of the irrigation system and bordering natural sandy deserts. The comparison with the land degradation map based on trend analyses of 250 m MODIS NDVI time series 2000–2010 yielded an overall agreement of 93%. The proposed approach is a useful supplement to the commonly applied trend analysis for detecting land degradation in cases when plot-specific data are needed but satellite time series of high spatial resolution are not available. The derived parcel-specific, spatial information supports better informed decisions on cropland rehabilitation measures. [Copyright &y& Elsevier]

Abstract: Evaluations of existing land cover maps have revealed that high landscape heterogeneity and small patch sizes are a major reason for misclassification. These problems globally occur in biomes of mixed vegetation structure and are particularly relevant for African savannas. This paper presents a multi-resolution approach to derive fractional cover of vegetation growth forms at sub-pixel level, aiming at an improved mapping of land cover in the African grassland, savanna and shrubland biome. Fractional cover is delineated for woody growth forms (trees and shrubs), herbaceous growth forms, and bare surface. The approach incorporates very high resolution (QuickBird/IKONOS, 0.6–1m), high resolution (Landsat TM/ETM+, 30m), and medium resolution data (MODIS, 250m). While QuickBird/IKONOS data are classified into discrete classes, at Landsat and MODIS resolutions, sub-pixel cover is delineated using non-parametric ensemble regression trees from the random forest family. The propagation of errors in the hierarchical multi-resolution approach is assessed with Monte Carlos simulations. The multi-resolution approach allows the adequate description of the heterogeneous vegetation structure in selected study regions of Southern Africa. The RMSE of the delineated fractional cover values range between 3.1% and 8.2% when compared with higher resolution data and between 4.4% and 9.9% when compared with field surveys. Errors at the Landsat resolution show minor influence on the accuracy of the MODIS results. Regarding the inter-resolution error propagation, for 90% of the Monte Carlo simulations, errors at the Landsat resolution resulted in RMSEs for MODIS increased by less than 4% (woody vegetation), 3.5% (herbaceous vegetation) and 2% (bare surface). [Copyright &y& Elsevier]

Saitohin is a gene unique to humans and their closest relatives, the function of which is not yet known. Saitohin contains a single polymorphism (QTR), and its Q and R alleles belong to the H1 and H2 tau haplotype, respectively. The Saitohin Q allele confers susceptibility to several neurodegenerative diseases. To get a handle on Saitohin function, we used it as a bait in a yeast two-hybrid screen. By this assay and subsequent co-immunoprecipitation and glutathione S-transferase pull-down assays, we discovered and confirmed that Saitohin interacts with peroxiredoxin 6, a unique member of that family that is bifunctional and the levels of which increase in Pick disease. The strength of the interaction appeared to be allele-specific, giving the first distinction between the two forms of Saitohin. [ABSTRACT FROM AUTHOR]

Rural communities in Ghana, dependent on agriculture and lacking resources and infrastructure, are highly vulnerable to climate and environmental change. Internal migration is often considered as a strategy to mitigate local livelihood constraints. Understanding the challenges of rural communities requires knowledge of local conditions. As only few studies have mapped vulnerable areas in the context of migration in Ghana at a spatially explicit and nationwide level, this study provides a geodata-based examination of how rural areas in Ghana are vulnerable to multiple, co-occurring socio-economic and environmental factors influencing migration. A multifactorial and expert-based weighted overlay analysis was applied, integrating diverse data sources including climate, remote sensing, and recent census data from Ghana. Bivariate maps visualize vulnerable areas where a high impact of the factors coincides with a high rural population density. High levels of factor impact are observed in the northern regions and coastal areas of Ghana. Relatively low impact is found in more central parts of the country. The results align with current net migration rates, confirming the applicability of our method for assessing rural internal migration. This method enhances the understanding of migration dynamics in Ghana and emphasizes the role of spatial data in migration studies. • Spatial data is used to map vulnerability and assess rural migration in Ghana. • The study integrates expert-weighted socio-economic and environmental factors. • The results of the weighted overlay analysis are combined with rural population density. • High migration likelihood is found in vulnerable areas in northern and coastal regions. • The results largely align with current net migration rates, confirming the method's applicability. [ABSTRACT FROM AUTHOR]

In many regions worldwide, cropland abandonment is growing, which has strong and known environmental and socio-economic consequences. Yet, spatially explicit information on the spatial pattern of abandonment is sparse, particularly in post-Soviet countries of Central Asia. When thriving reaching for key Millennium Development Goals such as food security and poverty reduction, the issue of cropland abandonment is critical and therefore must be monitored and limited, or land use transformed into an alternative one. Central Asia experienced large changes of its agricultural system after the collapse of the Soviet Union in 1991. Land degradation, which started already before independence, and cropland abandonment is growing in extent, but their spatial pattern remains ill-understood. The objective of this study was to map and analyse agricultural land use in the irrigated areas of Kyzyl-Orda, southern Kazakhstan, Central Asia. For mapping land use and identifying abandoned agricultural land, an object-based classification approach was applied. Random forest (RF) and support vector machines (SVM) algorithms permitted classifying Landsat and RapidEye data from 2009 to 2014. Overlaying these maps with information about irrigated land parcels, installed during the Soviet period, allowed indicating abandoned fields. Fusing the results of the two approaches, RF and SVM, resulted in classification accuracies of up to 97%. This was statistically significantly higher than with RF or SVM alone. Through the analysis of the land use trajectories, abandoned agricultural fields and a clear indication of abandoned land were identified on almost 50% of all fields in Kyzyl-Orda with an accuracy of approximately 80%. The outputs of this study may provide valuable information for planners, policy- and decision-makers to support better-informed decision-making like reducing possible environmental impacts of land abandonment, or identifying areas for sustainable intensification or re-cultivation. [ABSTRACT FROM AUTHOR]

In a host of neurodegenerative diseases Tau, a microtubule-associated protein, aggregates into insoluble lesions within neurons. Previous studies have utilized cyanine dyes as Tau aggregation inhibitors in vitro. Herein we utilize cyanine dye 3,3'-diethyl-9-methyl-thiacarbocyanine iodide (C11) to modulate Tau polymerization in two model systems, an organotypic slice culture model derived from Tau transgenic mice and a split green fluorescent protein complementation assay in Tau-expressing cells. In slice cultures, submicromolar concentrations (0.001 μM) of Cli produced a significant reduction of aggregatedTau and a corresponding increase in unpolymerized Tau. In contrast, treatment with a 1 μM dose promoted aggregation of Tau. These results were recapitulated in the complementation assay where administration of 1 μM C11 produced a significant increase in polymerized Tau relative to control, whereas treatment of cells with 0.01 μM C11 resulted in a marked reduction of aggregated Tau. In the organotypic slice cultures, modulation of Tau aggregation was independent of changes in phosphorylation at disease and microtubule binding relevant epitopes for both dosing regimes. Furthermore, treatment with 0.00 1 μM C11 resulted in a decrease in both total filament mass and number. There was no evidence of apoptosis or loss of synaptic integrity at either dose, however, whereas submicromolar concentrations of C11 did not interfere with microtubule binding, higher doses resulted in a decrease in the levels of microtubule-bound Tau. Overall, a cyanine dye can dissociate aggregated Tau in an ex vivo model of tauopathy with little toxicity and exploration of the use of these type of dyes as therapeutic agents is warranted. [ABSTRACT FROM AUTHOR]