Search

The satellite-based techniques for the monitoring of extreme ice features (EIFs) in the Canadian Arctic were investigated and demonstrated using synthetic aperture radar (SAR) and electro-optical data sources. The main EIF types include large ice islands and ice-island fragments, multiyear hummock fields (MYHF) and other EIFs, such as fragments of MYHF and large, newly formed hummock fields. The main objectives for the paper included demonstration of various satellite capabilities over specific regions in the Canadian Arctic to assess their utility to detect and characterize EIFs. Stereo pairs of very-high-resolution (VHR) imagery provided detailed measurements of sea ice topography and were used as validation information for evaluation of the applied techniques. Single-pass interferometric SAR (InSAR) data were used to extract ice topography including hummocks and ice islands. Shape from shading and height from shadow techniques enable us to extract ice topography relying on a single image. A new method for identification of EIFs in sea ice based on the thermal infrared band of Landsat 8 was introduced. The performance of the methods for ice feature height estimation was evaluated by comparing with a stereo or InSAR digital elevation models (DEMs). Full polarimetric RADARSAT-2 data were demonstrated to be useful for identification of ice islands. [ABSTRACT FROM AUTHOR]

Mapping underwater aquatic vegetation (UVeg) is crucial for understanding the dynamics of freshwater ecosystems. The advancement of artificial intelligence (AI) techniques has shown great potential in improving the accuracy and efficiency of UVeg mapping using remote sensing data. This paper presents a comparative study of the performance of classical and modern AI tools, including logistic regression, random forest, and a visual-prompt-tuned foundational model, the Segment Anything model (SAM), for mapping UVeg by analyzing air- and space-borne images in the few-shot learning regime, i.e., using limited annotations. The findings demonstrate the effectiveness of the SAM foundation model in air-borne imagery ( G S D = 3–6 cm) with an F 1 score of 86.5 % ± 4.1 % when trained with as few as 40 positive/negative pairs of pixels, compared to 54.0 % ± 9.2 % using the random forest model and 42.8 % ± 6.2 % using logistic regression models. However, adapting SAM to space-borne images (WorldView-2 and Sentinel-2) remains challenging, and could not outperform classical pixel-wise random forest and logistic regression methods in our task. The findings presented provide valuable insights into the strengths and limitations of AI models for UVeg mapping, aiding researchers and practitioners in selecting the most suitable tools for their specific applications. [ABSTRACT FROM AUTHOR]

Our objective was to develop a method for the assessment of forest area and structural variables for cases in which the availability of representative ground reference data is poor and these data are not collected from the whole area of interest. We implemented two independent approaches to the estimation of the forest variables of a European boreal forest: (i) the computation of wall-to-wall estimates using moderate- to low-resolution VIIRS imagery from the Suomi NPP mission; and (ii) the visual interpretation of plots of samples from very high resolution (VHR) satellite data obtained via a two-stage design. Our focus was on the statistical comparison of forest resources at a country or larger level. The study area was boreal forest ranging from Norway to the Ural Mountains in Russia. We computed a seamless mosaic from 111 VIIRS images. From the mosaic, we computed predictions for the forest area, growing stock volume, height of the dominating tree layer, proportion of conifers and broadleaved trees, site fertility class, and leaf area index. The reference data for the VIIRS imagery were national forest inventory (NFI)-based raster maps from Finland. The first stage sample of VHR data included 42 images; of these, a second stage sample of 2690 plots was visually interpreted for the same variables. The forest area prediction from VIIRS for the whole study area was 1.2% higher than the VHR-based result. All other structural variable predictions using VIIRS fitted within the 95% confidence intervals computed from the VHR sample except for estimates of the main tree species groups, which were outside the limits. A comparison of VIIRS-based forest area estimates using Finnish and Swedish NFI data indicated overestimations of 10.0% points and 4.6% points, whereas the total growing stock volumes were overestimated by 8% and underestimated by 3.4%, respectively. The correlation coefficients between the VIIRS and VHR image predictions at the 42 VHR image locations varied from 0.70 to 0.85. The VIIRS maps strongly averaged the local predictions due to their coarse spatial resolutions. Based on our findings, the approach using two independent estimations yielded similar figures for the central forest variables for the European boreal forest. A model computed using reference data from a small part of the area of interest can provide satisfactory predictions for a much larger area with a similar biome. Therefore, our concept is applicable to the estimation and overall mapping of a forest area and central structural variables at regional to national levels. [ABSTRACT FROM AUTHOR]

Land Use/Land Cover (LULC) mapping is the first step in monitoring urban sprawl and its environmental, economic and societal impacts. While satellite imagery and vegetation indices are commonly used for LULC mapping, the limited resolution of these images can hamper object recognition for Geographic Object-Based Image Analysis (GEOBIA). In this study, we utilize very high-resolution (VHR) optical imagery with a resolution of 50 cm to improve object recognition for GEOBIA LULC classification. We focused on the city of Nice, France, and identified ten LULC classes using a Random Forest classifier in Google Earth Engine. We investigate the impact of adding Gray-Level Co-Occurrence Matrix (GLCM) texture information and spectral indices with their temporal components, such as maximum value, standard deviation, phase and amplitude from the multi-spectral and multi-temporal Sentinel-2 imagery. This work focuses on identifying which input features result in the highest increase in accuracy. The results show that adding a single VHR image improves the classification accuracy from 62.62% to 67.05%, especially when the spectral indices and temporal analysis are not included. The impact of the GLCM is similar but smaller than the VHR image. Overall, the inclusion of temporal analysis improves the classification accuracy to 74.30%. The blue band of the VHR image had the largest impact on the classification, followed by the amplitude of the green-red vegetation index and the phase of the normalized multi-band drought index. [ABSTRACT FROM AUTHOR]

Urban tree canopy (UTC) area is an important index for evaluating the urban ecological environment; the very high resolution (VHR) images are essential for improving urban tree canopy survey efficiency. However, the traditional image classification methods often show low robustness when extracting complex objects from VHR images, with insufficient feature learning, object edge blur and noise. Our objective was to develop a repeatable method—superpixel-enhanced deep neural forests (SDNF)—to detect the UTC distribution from VHR images. Eight data expansion methods was used to construct the UTC training sample sets, four sample size gradients were set to test the optimal sample size selection of SDNF method, and the best training times with the shortest model convergence and time-consumption was selected. The accuracy performance of SDNF was tested by three indexes: F1 score (F1), intersection over union (IoU) and overall accuracy (OA). To compare the detection accuracy of SDNF, the random forest (RF) was used to conduct a control experiment with synchronization. Compared with the RF model, SDNF always performed better in OA under the same training sample size. SDNF had more epoch times than RF, converged at the 200 and 160 epoch, respectively. When SDNF and RF are kept in a convergence state, the training accuracy is 95.16% and 83.16%, and the verification accuracy is 94.87% and 87.73%, respectively. The OA of SDNF improved 10.00%, reaching 89.00% compared with the RF model. This study proves the effectiveness of SDNF in UTC detection based on VHR images. It can provide a more accurate solution for UTC detection in urban environmental monitoring, urban forest resource survey, and national forest city assessment. [ABSTRACT FROM AUTHOR]

Dual‐specificity phosphatase 3 (DUSP3), also known as Vaccinia H1‐related phosphatase, is a protein tyrosine phosphatase that typically performs its major role in the regulation of multiple cellular functions through the dephosphorylation of its diverse and constantly expanding range of substrates. Many of the substrates described so far as well as alterations in the expression or the activity of DUSP3 itself are associated with the development and progression of various types of neoplasms, indicating that DUSP3 may be an important player in oncogenesis and a promising therapeutic target. This review focuses exclusively on DUSP3's contribution to either benign or malignant melanocytic oncogenesis, as many of the established culprit pathways and mechanisms constitute DUSP3's regulatory targets, attempting to synthesize the current knowledge on the matter. The spectrum of the DUSP3 interactions analysed in this review covers substrates implicated in cellular growth, cell cycle, proliferation, survival, apoptosis, genomic stability/repair, adhesion and migration of tumor melanocytes. Furthermore, the speculations raised, based on the evidence to date, may be considered a fundament for potential research regarding the oncogenesis, evolution, management and therapeutics of melanocytic tumors. [ABSTRACT FROM AUTHOR]

The thermal behavior of two copper alloys, with 0.2 and 0.5 mass % of Mg, was analyzed after severe plastic deformation processing by Equal Channel Angular Pressing (ECAP). Both alloys were forced to be passed through a 90º inner angle ECAP die at room temperature up to 16 passes following route Bc. The thermal stability was analyzed in terms of the recrystallization kinetics by using the Various Heating Rates method, derived from the Johnson–Mehl–Avrami–Kolmogorov equation, after analyzing the Differential Scanning Calorimetry peaks for both alloys at any pass of ECAP. The calculated recrystallization parameters included the activation energies (E (kJ mol−1) ECuMg02 = 42.6 ± 19.8 ECuMg05 = 52.7 ± 13 kJ mol−1) and the kinetic exponent n which took an average value of ≈ 1.75, irrespective of the considered alloy. [ABSTRACT FROM AUTHOR]

On board the IKONOS satellite there are sensors operating in the panchromatic and multispectral range: the geometric resolution of the acquired images is higher in the first case (1 m) than in the second one (4 m); on the contrary, panchromatic images have lower spectral resolution than the latter. Pan-sharpening methods allow to reduce the pixel dimensions of the multispectral images to comply with the panchromatic resolution. In this way, it is possible to obtain enhanced detailed data in both geometric and spectral resolution. This work aims to compare the results obtained from the application of eight different pan-sharpening methods, which are totally carried out by using the raster calculator in QGIS: Multiplicative, Simple Mean, Brovey Transformation, Brovey Transformation Fast, Intensity Hue Saturation (IHS), IHS Fast, Gram-Schmidt, and Gram-Schmidt Fast. Each resulting dataset is compared with the original one to evaluate the performance of each method by the following quality indices: Correlation Coefficient (CC), Universal Image Quality Index (UIQI), Relative Average Spectral Error (RASE), Erreur Relative Global Adimensionnelle de Synthèse (ERGAS), Spatial Correlation Coefficient (SCC) and Spatial ERGAS (SERGAS); however, this is a difficult task because the quality of the fused image depends on the considered datasets. Finally, a comparison the various between methods is carried out. [ABSTRACT FROM AUTHOR]

In this paper, we present LC compositions having novel reactive mesogen (RM) carrying a chalcone unit and fluorinated diluter 3HFFH3 showed excellent voltage holding ratio (VHR) after photo irradiation for RM polymerization. On the other hand, LC composition having diluter substituted by alkenyl group showed lower VHR compared with our novel LC composition. [ABSTRACT FROM AUTHOR]

Purpose: Sarcopenia, or loss of muscle mass, is associated with increased morbidity and mortality in oncologic resections and several other major surgeries. Complex ventral hernia repairs (VHRs) and abdominal wall reconstruction are often performed in patients at high risk for morbidity and recurrence, though limited data exist on outcomes related to sarcopenia. We aimed to determine if sarcopenia is associated with worse outcomes in patients undergoing VHR.Methods: We reviewed patients undergoing VHRs from 2014 to 2015. Preoperative CT images were analyzed for cross-sectional muscle mass. Patients with and without sarcopenia underwent statistical analysis to evaluate differences in perioperative morbidity and hernia recurrence. Muscle indices were analyzed independently for outcomes.Results: 135 patients underwent VHR with/without fistula takedown, staged repairs or other concomitant procedures. 27% had sarcopenia (age 34-84, BMI 27-33, 62% male). Postoperative complications occurred in 43% of sarcopenic patients and 47% of non-sarcopenic patients (p = 0.70). Surgical site infections (SSI) were seen in 16% of sarcopenic patients compared to 29% without sarcopenia (p = 0.14). There was no difference in hernia recurrence between groups (p = 0.90). However, after adjusting for diabetes and BMI, a 10 cm2/m2 decrease in muscle index had 1.44 OR of postoperative complications (p < 0.05).Conclusions: Though prevalent in our population, sarcopenia was not associated with an increase in postoperative complications, surgical site occurences/infections,  or hernia recurrence when previously published oncologic sarcopenia cutoffs were utilized. Previously established sarcopenia outcomes in malignancy may be attributable to an altered metabolic state that is not present in hernia repair patients. Larger-scale studies are recommended to establish new sarcopenia cutoffs for VHRs. [ABSTRACT FROM AUTHOR]

Ionizing radiation dosimetry with thermoluminescence (TL) materials based on silicon or glass can be interesting in its potential use in radiation monitoring as the solution to the constant looking of development of new radiation detectors. In this work, TL characteristics of sodium silicate exposed to beta radiation effects were studied. TL response beta irradiated exhibited a glow curve with two peaks centered at 398 K and 473 K. Samples showed linearity from 0.55 to 13.2 Gy. TL readings after 10 times showed a repeatability with an error of less than 1%. Remain information showed significant losses during the first 24 h, but its information was almost constant after 72 h of storage. The T max -T stop method exhibited three peaks which were mathematically analyzed with a general order deconvolution finding kinetic orders close to the second order for the first peak, meanwhile the kinetic order for the second peak and third peak are close to second order. Finally, the VHR method showed anomalous TL glow curve behavior with an increasing intensity TL as the heating rate increased. • Sodium Silicate Glass is good candidate for beta radiation monitoring applications. • Tl glow curve has two main peaks. They are composed of shallow and deep traps. • Trivalent ions Pr3+ modified the TL intensity of Sodium Silicate Glass. • TL glow curve deconvolution shows three peaks. • TL behavior observed is anomalous when varying the heating rate VHR. [ABSTRACT FROM AUTHOR]

The application potential of very high resolution (VHR) remote sensing imagery has been boosted by recent developments in the data acquisition and processing ability of aerial photogrammetry. However, shadows in images contribute to problems such as incomplete spectral information, lower intensity brightness, and fuzzy boundaries, which seriously affect the efficiency of the image interpretation. In this paper, to address these issues, a simple and automatic method of shadow detection is presented. The proposed method combines the advantages of the property-based and geometric-based methods to automatically detect the shadowed areas in VHR imagery. A geometric model of the scene and the solar position are used to delineate the shadowed and non-shadowed areas in the VHR image. A matting method is then applied to the image to refine the shadow mask. Different types of shadowed aerial orthoimages were used to verify the effectiveness of the proposed shadow detection method, and the results were compared with the results obtained by two state-of-the-art methods. The overall accuracy of the proposed method on the three tests was around 90%, confirming the effectiveness and robustness of the new method for detecting fine shadows, without any human input. The proposed method also performs better in detecting shadows in areas with water than the other two methods. [ABSTRACT FROM AUTHOR]

Background: Complex ventral hernia repair (VHR) is a common surgical operation but carries a risk of complications from surgical site infections (SSI) and occurrences (SSO). We aimed to create a predictive risk score to identify patients at increased risk for SSO or SSI within 30 days of surgery.Methods: Data were prospectively collected on all patients undergoing VHR between January 2008 and February 2015 by a single surgeon. Multivariable logistic regression was used to identify independent factors predictive of SSO and SSI. Significant predictors of SSO and SSI were assigned point values based on their odds ratios to create a novel risk score, the Hopkins ventral hernia repair SSO/SSI risk score; predicted and actual rates of outcomes were then compared using weighted regression.Results: During the study period, 362 patients underwent open VHR. Thirty-day SSO and SSI occurred in 18.5 and 10% of patients, respectively. After risk adjustment, ASA class ≥3 (1 point), operative time ≥4 h (2 points), and the absence of a postoperative wound vacuum dressing (1 point) were predictive of 30-day SSO. Predicted risk of SSO utilizing this scoring system was 9.7, 19.4, 29.1, and 38.8% for 1, 2, 3, and 4 points (AUC = 0.73). For SSI, operative time ≥4 h (1 point) and the lack of a wound vacuum dressing (1 point) were predictive. Predicted risk of SSI based on this scoring system was 12.5% for 1 point and 25% for 2 points (AUC = 0.71). Actual vs. predicted rates of SSO and SSI correlated strongly for risk model with a coefficient of determination (R 2) of 0.92 and 0.91, respectively.Conclusion: The novel Hopkins ventral hernia repair risk score accurately predicts risk of SSO and SSI after complex VHR. Further studies using a prospective randomized controlled trial will be needed to further validate our findings. [ABSTRACT FROM AUTHOR]

Recent advances in remote sensing technologies and the cost reduction of surveying, along with the importance of natural resources management, present new opportunities for mapping land cover at a very high resolution over large areas. This paper proposes and applies a framework to update hyperspatial resolution (<1 m) land thematic mapping over large areas by handling multi-source and heterogeneous data. This framework deals with heterogeneity both from observation and the targeted features. First, observation diversity comes from the different platform and sensor types (25-cm passive optical and 1-m LiDAR) as well as the different instruments (three cameras and two LiDARs) used in heterogeneous observation conditions (date, time, and sun angle). Second, the local heterogeneity of the targeted features results from their within-type diversity and neighborhood effects. This framework is applied to surface water bodies in the southern part of Belgium (17,000 km2). This makes it possible to handle both observation and landscape contextual heterogeneity by mapping observation conditions, stratifying spatially and applying ad hoc classification procedures. The proposed framework detects 83% of the water bodies—if swimming pools are not taken into account—and more than 98% of those water bodies greater than 100 m2, with an edge accuracy below 1 m over large areas. [ABSTRACT FROM AUTHOR]

In this study, the potential of using very high resolution Pléiades imagery to estimate a number of common forest attributes for 10-m plots in boreal forest was examined, when a high-resolution terrain model was available. The explanatory variables were derived from three processing alternatives. Height metrics were extracted from image matching of the images acquired from different incidence angles. Spectral derivatives were derived by performing principal component analysis of the spectral bands and lastly, second order textural metrics were extracted from a gray-level co-occurrence matrix, computed with an 11 x 11 pixels moving window. The analysis took place at two Swedish test sites, Krycklan and Remningstorp, containing boreal and hemi-boreal forest. The lowest RMSE was estimated with 1.4 m (7.7%) for Lorey's mean height, 1.7 m (10%) for airborne laser scanning height percentile 90, 5.1 m⋅ ha-1 (22%) for basal area, 66 m² ⋅ ha-1 (27%) for stem volume, and 26 tons ⋅ ha-1 (26%) for above-ground biomass, respectively. It was found that the image-matched height metrics were most important in all models, and that the spectral and textural metrics contained similar information. Nevertheless, the best estimations were obtained when all three explanatory sources were used. To conclude, image-matched height metrics should be prioritised over spectral metrics when estimation of forest attributes is concerned. [ABSTRACT FROM AUTHOR]

Arterial thrombosis is the primary cause of most cases of myocardial infarction and stroke, the leading causes of death in the developed world. Platelets, highly specialized cells of the circulatory system, are key contributors to thrombotic events. Antiplatelet drugs, which prevent platelets from aggregating, have been very effective in reducing the mortality and morbidity of these conditions. However, approved antiplatelet therapies have adverse side effects, most notably the increased risk of bleeding. Moreover, there remains a considerable incidence of arterial thrombosis in a subset of patients receiving currently available drugs. Thus, there is a pressing medical need for novel antiplatelet agents with a more favorable safety profile and less patient resistance. The discovery of novel antiplatelet targets is the matter of intense ongoing research. Recent findings demonstrate the potential of targeting key signaling molecules, including kinases and phosphatases, to prevent platelet activation and aggregation. Here, we offer perspectives to targeting members of the protein tyrosine phosphatase (PTP) superfamily, a major class of enzymes in signal transduction. We give an overview of previously identified PTPs in platelet signaling, and discuss their potential as antiplatelet drug targets. We also introduce VHR (DUSP3), a PTP that we recently identified as a major player in platelet biology and thrombosis. We review our data on genetic deletion as well as pharmacological inhibition of VHR, providing proof-of-principle for a novel and potentially safer VHR-based antiplatelet therapy. [ABSTRACT FROM AUTHOR]

Recent advances in remote sensing technologies have facilitated the generation of very high resolution (VHR) environmental data. Exploratory studies suggested that, if used in species distribution models (SDMs), these data should enable modelling species’ micro-habitats and allow improving predictions for fine-scale biodiversity management. In the present study, we tested the influence, in SDMs, of predictors derived from a VHR digital elevation model (DEM) by comparing the predictive power of models for 239 plant species and their assemblages fitted at six different resolutions in the Swiss Alps. We also tested whether changes of the model quality for a species is related to its functional and ecological characteristics. Refining the resolution only contributed to slight improvement of the models for more than half of the examined species, with the best results obtained at 5 m, but no significant improvement was observed, on average, across all species. Contrary to our expectations, we could not consistently correlate the changes in model performance with species characteristics such as vegetation height. Temperature, the most important variable in the SDMs across the different resolutions, did not contribute any substantial improvement. Our results suggest that improving resolution of topographic data only is not sufficient to improve SDM predictions – and therefore local management – compared to previously used resolutions (here 25 and 100 m). More effort should be dedicated now to conduct finer-scale in-situ environmental measurements (e.g. for temperature, moisture, snow) to obtain improved environmental measurements for fine-scale species mapping and management. [ABSTRACT FROM AUTHOR]

Abstract: Protein tyrosine phosphatases (PTPs) play an essential role in maintaining the proper tyrosine phosphorylation state of proteins. Abnormal tyrosine phosphorylation has been implicated in diseases as diverse as type 2 diabetes, cancer, immune disorders and neurological disorders, and thus inhibitors of PTPs have been investigated as potential treatments of these diseases. Natural products are widely regarded to be privileged structures in drug discovery efforts, and are therefore a good starting point for the development of PTP inhibitors. Here we describe reported natural product PTP inhibitors as well as methods to screen for natural product PTP inhibitors using bioassay-guided fractionation. These methods are illustrated using the example of a family of bromotyrosine-derived PTP inhibitors isolated from two marine sponges. We also identify potential pitfalls and false-positives, in particular compounds that are oxidizing agents that react irreversibly with the PTP. [Copyright &y& Elsevier]

Abstract: The validity of training samples collected in field campaigns is crucial for the success of land use classification models. However, such samples often suffer from a sample selection bias and do not represent the variability of spectra that can be encountered in the entire image. Therefore, to maximize classification performance, one must perform adaptation of the first model to the new data distribution. In this paper, we propose to perform adaptation by sampling new training examples in unknown areas of the image. Our goal is to select these pixels in an intelligent fashion that minimizes their number and maximizes their information content. Two strategies based on uncertainty and clustering of the data space are considered to perform active selection. Experiments on urban and agricultural images show the great potential of the proposed strategy to perform model adaptation. [Copyright &y& Elsevier]

The air index (ψiair) of the PARIS II software (Environmental Protection Agency), the Indiana Relative Chemical Hazard Score (IRCHS), and the Final Hazard Score (FHS) used in the P2OASys system (Toxics Use Reduction Institute) are comprehensive hazard screening indices that can be used in solvent substitution. The objective of this study was to evaluate these indices using a list of 67 commonly used or recommended solvents. The indices ψiair, IRCHS and FHS were calculated considering 9, 13, and 33 parameters, respectively, that summarized health and safety hazards, and environmental impacts. Correlation and sensitivity analyses were performed. The vapor hazard ratio (VHR) was used as a reference point. Two good correlations were found: (1) between VHR and ψiair (ρ = 0.84), (2) and between IRCHS and FHS (ρ = 0.81). Values of sensitivity ratios above 0.2 were found with ψiair (4 of 9 parameters) and IRCHS (3 of 13 parameters), but not with FHS. Overall, the three indices exhibited important differences in the way they integrate key substitution factors, such as volatility, occupational exposure limit, skin exposure, flammability, carcinogenicity, photochemical oxidation potential, atmospheric global effects, and environmental terrestrial and aquatic effects. These differences can result in different choices of alternatives between indices, including the VHR. IRCHS and FHS are the most comprehensive indices but are very tedious and complex to use and lack sensitivity to several solvent-specific parameters. The index ψiair is simpler to calculate but does not cover some parameters important to solvents. There is presently no suitably comprehensive tool available for the substitution of solvents. A two-tier approach for the selection of solvents is recommended to avoid errors that could be made using only a global index or the consideration of the simple VHR. As a first tier, one would eliminate solvent candidates having crucial impacts. As a second tier, other parameters would be considered, with emphasis on the VHR. [ABSTRACT FROM AUTHOR]

Vapour Hazard Ratio (VHR) is used in solvent substitution to select the best replacement option regarding overexposure potential of solvents. However, VHR calculations are limited by the availability of Occupational Exposure Limits (OELs). The overall objective of this study was to develop quantitative property-property relationship (QPPR) approaches for computing OELs, in view of supporting the derivation of VHRs for solvents without OELs. QPPRs were developed for estimating OELs using a database of 88 solvents which have health-based Time-Weighted Average (TWA) OELs published by the American Conference of Governmental Industrial Hygienists (ACGIH). Three surrogates of biotic lipid : air partition coefficients [n-octanol : air (Koa), olive oil : air (Koila) and fat : air (Kfa)] were selected for evaluating the descriptive/predictive relationship with OELs for solvents with local modes of action. For solvents with systemic modes of action, the prediction of OEL needs to consider quantitative differences in toxicokinetics (i.e. kinetic variability factor, KVF) and toxicological potency (i.e. effective internal concentration, EIC). The n-octanol : water (Kow), the oil : water (Koilw) and the fat : water (Kfw) partition coefficients were selected for evaluating the relationship with EICs. For local modes of action, Koa is the most accurate predictor of OELs [OEL (ppm) = [image omitted]; n = 21, r2 = 0.71, PRESS/SSY = 0.36, F = 45.5 with p < 0.001] and the mean (±SD) (range) of the recommended to predicted OELs was 1.04 ± 0.61 (0.2-2.5). For systemic modes of action, KVFs and EICs vary in a range from 0.73 to 41.4 µmol L-1 and 1.20-848 µmol L-1, respectively. Kow is an accurate predictor of calculated EICs [EIC (µmol L-1) = [image omitted]; n = 27, r2 = 0.88, PRESS/SSY = 0.12, F = 181 with p < 0.001] and 50% of the predicted OEL values were within a factor of two of the recommended TWA OELs. Overall, 61% and 87% of the predicted VHRs were within a factor of two and five, respectively, of the calculated VHRs. The QPPR models developed in this study represent potentially useful tools for estimating provisional OELs for solvents lacking such guideline values. These provisional OELs are developed only to support initial estimations of VHR for dealing with the challenge of solvent substitution where relative values rather than absolute values of OEL and vapour pressure guide the hygienist in making pragmatic decisions for managing occupational health hazards. [ABSTRACT FROM AUTHOR]

This paper discusses the challenges in operational crisis management and describes the role of information and geo-spatial technologies in meeting those challenges. The paper discusses two main sources of data, Web and very high resolution (VHR) earth observation sensors, in terms of relevance to crisis management and techniques for information extraction and analysis. Although research in information text extraction and analysis is more advanced than in information image extraction and analysis, further research is required in both these fields to take advantage of the increasing complexity but richness of open source and VHR satellite data. The paper also discusses the use of Web, GIS and Digital Earth technologies in facilitating collaborative work, decision-making and information sharing in crisis management. Despite exciting and relevant advances in information sources, information extraction and analysis methods, and collaborative crisis technologies, the main challenge remains to convince stakeholders in operational crisis management that the adoption of these technologies will lead to enhanced and effective crisis management. [ABSTRACT FROM AUTHOR]

The paper discusses the potential of very high resolution (VHR) satellite imagery for post-earthquake damage assessment in comparison with the role of aerial photographs. Post-disaster optical and radar satellite data are assessed for their ability to resolve collapsed buildings, destroyed transportation infrastructure, and specific land cover changes. Optical VHR imagery has shown to be effective in quantifying building stock and for assessing damage at the building level. High-resolution synthetic aperture radar (SAR) imagery requires further research to identify optimum information extraction procedures for rapid assessment of affected buildings. Based on current technical and operational capabilities increasing efforts should be devoted to the generation of spatial datasets for disaster preparedness. [ABSTRACT FROM AUTHOR]

As an important application in remote sensing, landcover classification remains one of the most challenging tasks in very-high-resolution (VHR) image analysis. As the rapidly increasing number of Deep Learning (DL) based landcover methods and training strategies are claimed to be the state-of-the-art, the already fragmented technical landscape of landcover mapping methods has been further complicated. Although there exists a plethora of literature review work attempting to guide researchers in making an informed choice of landcover mapping methods, the articles either focus on the review of applications in a specific area or revolve around general deep learning models, which lack a systematic view of the ever advancing landcover mapping methods. In addition, issues related to training samples and model transferability have become more critical than ever in an era dominated by data-driven approaches, but these issues were addressed to a lesser extent in previous review articles regarding remote sensing classification. Therefore, in this paper, we present a systematic overview of existing methods by starting from learning methods and varying basic analysis units for landcover mapping tasks, to challenges and solutions on three aspects of scalability and transferability with a remote sensing classification focus including (1) sparsity and imbalance of data; (2) domain gaps across different geographical regions; and (3) multi-source and multi-view fusion. We discuss in detail each of these categorical methods and draw concluding remarks in these developments and recommend potential directions for the continued endeavor. [ABSTRACT FROM AUTHOR]

Using site-directed mutagenesis and steady-state kinetic measurements, the functional role of the conserved glycine 127 in a human vaccinia H1-related phosphatase (VHR) was investigated. The mutations of Gly127 to Ala and Pro resulted in a significant decrease in kcat/Km, and increase in Ki for arsenate, indicating that flexibility at the Gly127 site has a large effect on substrate binding and catalytic activity. No substantial decrease in kcat/Km and increase in Ki values were observed for G127 deletion mutant. This showed the conformational flexibility of the PTP loop also affected the enzymatic activity of VHR. Our data suggest that the flexibility of the PTP loop in VHR is probably controlled by Gly127, and that even subtle changes in the loop flexibility may interfere with substrate binding and enzymatic reaction. [ABSTRACT FROM AUTHOR]

Biological evaluation of 96 analogs and synthetic intermediates of the naphthyridinomycin-type antitumor antibiotic Dnacin led to the identification of several low-micromolar inhibitors of dual specificity phosphatases, specifically Cdc25A1, Cdc25B2, and VHR, as well as the tyrosine phosphatase PTP1B. While the parent Dnacins are potent DNA cleavage agents, most of the analog structures, even those that retained significant phosphatase inhibitory activities, did not lead to plasmid DNA cleavage. Thus, the DNA-targeting and the phosphatase-inhibitory activities of Dnacins can be assigned to different pharmacophores. [Copyright &y& Elsevier]

While remotely sensed images of various resolutions have been widely used in identifying changes in urban and peri-urban environments, only very high resolution (VHR) imagery is capable of providing the information needed for understanding the changes taking place in remote rural environments, due to the small footprints and low density of man-made structures in these settings. However, limited by data availability, mapping man-made structures and conducting subsequent change detections in remote areas are typically challenging and thus require a certain level of flexibility in algorithm design that takes into account the specific environmental and image conditions. In this study, we mapped all buildings and corrals for two remote villages in Mozambique based on two single-date VHR images that were taken in 2004 and 2012, respectively. Our algorithm takes advantage of the presence of shadows and, through a fusion of both spectra- and object-based analysis techniques, is able to differentiate buildings with metal and thatch roofs with high accuracy (overall accuracy of 86% and 94% for 2004 and 2012, respectively). The comparison of the mapping results between 2004 and 2012 reveals multiple lines of evidence suggesting that both villages, while differing in many aspects, have experienced substantial increases in the economic status. As a case study, our project demonstrates the capability of a coupling of VHR imagery with locally adjusted classification algorithms to infer the economic development of small, remote rural settlements. [ABSTRACT FROM AUTHOR]

Fully convolutional networks (FCN) such as UNet and DeepLabv3+ are highly competitive when being applied in the detection of earthquake-damaged buildings in very high-resolution (VHR) remote sensing images. However, existing methods show some drawbacks, including incomplete extraction of different sizes of buildings and inaccurate boundary prediction. It is attributed to a deficiency in the global context-aware and inaccurate correlation mining in the spatial context as well as failure to consider the relative positional relationship between pixels and boundaries. Hence, a detection method for earthquake-damaged buildings based on the object contextual representations (OCR) and boundary enhanced loss (BE loss) was proposed. At first, the OCR module was separately embedded into high-level feature extractions of the two networks DeepLabv3+ and UNet in order to enhance the feature representation; in addition, a novel loss function, that is, BE loss, was designed according to the distance between the pixels and boundaries to force the networks to pay more attention to the learning of the boundary pixels. Finally, two improved networks (including OB-DeepLabv3+ and OB-UNet) were established according to the two strategies. To verify the performance of the proposed method, two benchmark datasets (including YSH and HTI) for detecting earthquake-damaged buildings were constructed according to the post-earthquake images in China and Haiti in 2010, respectively. The experimental results show that both the embedment of the OCR module and application of BE loss contribute to significantly increasing the detection accuracy of earthquake-damaged buildings and the two proposed networks are feasible and effective. [ABSTRACT FROM AUTHOR]

Agricultural land abandonment is an increasing problem in Europe. The Comunitat Valenciana Region (Spain) is one of the most important citrus producers in Europe suffering this problem. This region characterizes by small sized citrus plots and high spatial fragmentation which makes necessary to use Very High-Resolution images to detect abandoned plots. In this paper spectral and Gray Level Co-Occurrence Matrix (GLCM)-based textural information derived from the Normalized Difference Vegetation Index (NDVI) are used to map abandoned citrus plots in Oliva municipality (eastern Spain). The proposed methodology is based on three general steps: (a) extraction of spectral and textural features from the image, (b) pixel-based classification of the image using the Random Forest algorithm, and (c) assignment of a single value per plot by majority voting. The best results were obtained when extracting the texture features with a 9 × 9 window size and the Random Forest model showed convergence around 100 decision trees. Cross-validation of the model showed an overall accuracy of the pixel-based classification of 87% and an overall accuracy of the plot-based classification of 95%. All the variables used are statistically significant for the classification, however the most important were contrast, dissimilarity, NIR band (720 nm), and blue band (620 nm). According to our results, 31% of the plots classified as citrus in Oliva by current methodology are abandoned. This is very important to avoid overestimating crop yield calculations by public administrations. The model was applied successfully outside the main study area (Oliva municipality); with a slightly lower accuracy (92%). This research provides a new approach to map small agricultural plots, especially to detect land abandonment in woody evergreen crops that have been little studied until now. [ABSTRACT FROM AUTHOR]

This research investigates the use of scale-space theory to detect individual trees in orchards from very-high resolution (VHR) satellite images. Trees are characterized by blobs, for example, bell-shaped surfaces. Their modeling requires the identification of local maxima in Gaussian scale space, whereas location of the maxima in the scale direction provides information about the tree size. A two-step procedure relates the detected blobs to tree objects in the field. First, a Gaussian blob model identifies tree crowns in Gaussian scale space. Second, an improved tree crown model modifies this model in the scale direction. The procedures are tested on the following three representative cases: an area with vitellaria trees in Mali, an orchard with walnut trees in Iran, and one case with oil palm trees in Indonesia. The results show that the refined Gaussian blob model improves upon the traditional Gaussian blob model by effectively discriminating between false and correct detections and accurately identifying size and position of trees. A comparison with existing methods shows an improvement of 10–20% in true positive detections. We conclude that the presented two-step modeling procedure of tree crowns using Gaussian scale space is useful to automatically detect individual trees from VHR satellite images for at least three representative cases. [ABSTRACT FROM AUTHOR]

The increasing availability of very-high resolution (VHR; <2 m) imagery has the potential to enable agricultural monitoring at increased resolution and cadence, particularly when used in combination with widely available moderate-resolution imagery. However, scaling limitations exist at the regional level due to big data volumes and processing constraints. Here, we demonstrate the Fusion Approach for Remotely-Sensed Mapping of Agriculture (FARMA), using a suite of open source software capable of efficiently characterizing time-series field-scale statistics across large geographical areas at VHR resolution. We provide distinct implementation examples in Vietnam and Senegal to demonstrate the approach using WorldView VHR optical, Sentinel-1 Synthetic Aperture Radar, and Sentinel-2 and Sentinel-3 optical imagery. This distributed software is open source and entirely scalable, enabling large area mapping even with modest computing power. FARMA provides the ability to extract and monitor sub-hectare fields with multisensor raster signals, which previously could only be achieved at scale with large computational resources. Implementing FARMA could enhance predictive yield models by delineating boundaries and tracking productivity of smallholder fields, enabling more precise food security observations in low and lower-middle income countries. [ABSTRACT FROM AUTHOR]

There is a growing demand in both food quality and quantity, but as of now, one-third of all food produced for human consumption is lost due to pests and other pathogens accounting for roughly 40% of pre-harvest loss in potatoes. Pathogens in potato plants, like the Erwinia bacteria and the PVYNTN virus for example, exhibit symptoms of varying severity that are not easily captured by pixel-based classes (as these ignore shape, texture, and context in general). The aim of this research is to develop an object-based image analysis (OBIA) method for trait retrieval of individual potato plants that maximizes information output from Unmanned Aerial Vehicle (UAV) RGB very high resolution (VHR) imagery and its derivatives, to be used for disease detection of the Solanum tuberosum. The approach proposed can be split in two steps: (1) object-based mapping of potato plants using an optimized implementation of large scale mean-shift segmentation (LSMSS), and (2) classification of disease using a random forest (RF) model for a set of morphological traits computed from their associative objects. The approach was proven viable as the associative RF model detected presence of Erwinia and PVY pathogens with a maximum F1 score of 0.75 and an average Matthews Correlation Coefficient (MCC) score of 0.47. It also shows that low-altitude imagery acquired with a commercial UAV is a viable off-the-shelf tool for precision farming, and potato pathogen detection. [ABSTRACT FROM AUTHOR]