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Cities are facing too many challenges. Urban vegetation, in particular trees, are essential as they provide services in terms of air pollution mitigation, freshness, biodiversity, and citizens' well-being. Accurate data on location, species, and structural characteristics are essential for quantifying their benefits. However, the cost of measuring thousands of individual trees through field campaigns can be prohibitive and reliable information on domestic gardens is lacking due to difficulties in acquiring systematic data. The main objective of this study was to investigate the suitability of very-high resolution satellite imagery, e.g., WorldView-2, for detecting, delineating, and classifying the urban plant species in both public and private areas. The characterization of urban vegetation is difficult due to the complexity of the urban environment (buildings, shadows, open courtyards), the diversity of species and the spatial proximity between trees. To overcome these constraints, an object-based classification was developed with the selection of new relevant spectral and texture-based features for each plant species. Four spectral bands (blue, green, yellow, red) and four texture features (i.e., energy, entropy, inverse difference moment, Haralick correlation) were found to be the most efficient attributes for object-based classification from WV-2 images. Then, a classification of plant species, by using a Random Forest classifier, and ground validation were performed. In the two study areas, Aix-en-Provence (France) and Florence (Italy), 22 and 20 dominant plant species, and grassland, were identified and classified with an overall accuracy of 84% and 83%, respectively. The highest classification accuracy was obtained for Pinus spp. and Platanus acerifolia in Aix-en-Provence, and for Celtis australis and Cupressus sempervirens in Florence. The lowest classification accuracy was obtained for Quercus spp. in Aix-en-Provence, and Magnolia grandiflora in Florence. • Full inventory is needed to quantify the benefits provided by urban vegetation • Suitability of very-high resolution satellite imagery to detect plant species in cities. • Object-based classification applied for identifying tree species and grassland. • Most of urban plants (>85%) grow in private areas. • In both cities, 22 and 20 plant species are identified with an accuracy of 84% and 83%. [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]

In this paper, we present a proposition of a fully automatic classification of VHR satellite images. Unlike the most widespread approaches: supervised classification, which requires prior defining of class signatures, or unsupervised classification, which must be followed by an interpretation of its results, the proposed method requires no human intervention except for the setting of the initial parameters. The presented approach bases on both spectral and textural analysis of the image and consists of 3 steps. The first step, the analysis of spectral data, relies on NDVI values. Its purpose is to distinguish between basic classes, such as water, vegetation and non-vegetation, which all differ significantly spectrally, thus they can be easily extracted basing on spectral analysis. The second step relies on granulometric maps. These are the product of local granulometric analysis of an image and present information on the texture of each pixel neighbourhood, depending on the texture grain. The purpose of texture analysis is to distinguish between different classes, spectrally similar, but yet of different texture, e.g. bare soil from a built-up area, or low vegetation from a wooded area. Due to the use of granulometric analysis, based on mathematical morphology opening and closing, the results are resistant to the border effect (qualifying borders of objects in an image as spaces of high texture), which affect other methods of texture analysis like GLCM statistics or fractal analysis. Therefore, the effectiveness of the analysis is relatively high. Several indices based on values of different granulometric maps have been developed to simplify the extraction of classes of different texture. The third and final step of the process relies on a vegetation index, based on near infrared and blue bands. Its purpose is to correct partially misclassified pixels. All the indices used in the classification model developed relate to reflectance values, so the preliminary step of recalculation of pixel DNs to reflectance is required. Thanks to this, the proposed approach is in theory universal, and might be applied to different satellite system images of different acquisition dates. The test data consists of 3 Pleiades images captured on different dates. Research allowed to determine optimal indices values. Using the same parameters, we obtained a very good accuracy of extraction of 5 land cover/use classes: water, low vegetation, bare soil, wooded area and built-up area in all the test images (kappa from 87% to 96%). What constitutes important, even significant changes in parameter values, did not cause a significant declination of classification accuracy, which demonstrates how robust the proposed method is. [ABSTRACT FROM AUTHOR]

High resolution level in satellite imagery came with its fundamental problem as big amount of telemetry data which is to be stored after the downlink operation. Moreover, later the post-processing and image enhancement steps after the image is acquired, the file sizes increase even more and then it gets a lot harder to store and consume much more time to transmit the data from one source to another; hence, it should be taken into account that to save even more space with file compression of the raw and various levels of processed data is a necessity for archiving stations to save more space. Lossless data compression algorithms that will be examined in this study aim to provide compression without any loss of data holding spectral information. Within this objective, well-known open source programs supporting related compression algorithms have been implemented on processed GeoTIFF images of Airbus Defence & Spaces SPOT 6 & 7 satellites having 1.5 m. of GSD, which were acquired and stored by ITU Center for Satellite Communications and Remote Sensing (ITU CSCRS), with the algorithms Lempel-Ziv-Welch (LZW), Lempel-Ziv-Markov chain Algorithm (LZMA & LZMA2), Lempel-Ziv-Oberhumer (LZO), Deflate & Deflate 64, Prediction by Partial Matching (PPMd or PPM2), Burrows-Wheeler Transform (BWT) in order to observe compression performances of these algorithms over sample datasets in terms of how much of the image data can be compressed by ensuring lossless compression. [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 (

Post-disaster very high resolution (VHR) satellite data are potential sources to provide detailed information on damage and geological changes for a large area in a short time. In this paper, we studied landslides triggered by the M w 6.9 earthquake in Sikkim, India which occurred on 18 September 2011 using VHR data from Cartosat-1, GeoEye-1, QuickBird-2 and WorldView-2 satellites. Since the earthquake-affected area is located in mostly inaccessible Himalayan terrain, VHR data from these satellites provided a unique opportunity for quick and synoptic assessment of the damage. Using visual change analysis technique through comparison of pre- and post-earthquake images, we assessed the damage caused by the event. A total of 123 images acquired from eight satellites, covering an area of 4105 km 2 were analysed and 1196 new landslides triggered by the earthquake were mapped. Road blockages and severely affected villages were also identified. Geological assessment of the terrain highlighted linear disposition of landslides along existing fault scarps, suggesting a reactivation of fault. The landslide inventory map prepared from VHR images also showed a good correlation with the earthquake shake map. Results showed that several parts of north Sikkim, particularly Mangan and Chungthang, which are close to the epicentre, were severely affected by the earthquake, and that the event-based landslide inventory map can be used in future earthquake-triggered landslide susceptibility assessment studies. [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]

Copyright of Journal of Entrepreneurship, Management & Innovation is the property of Wyzsza Szkola Biznesu-National Louis University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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 × 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 m2·ha−1 (22%) for basal area, 66 m3·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.

Canopy height is one of the strongest predictors of biomass and carbon in forested ecosystems. Additionally, mangrove ecosystems represent one of the most concentrated carbon reservoirs that are rapidly degrading as a result of deforestation, development, and hydrologic manipulation. Therefore, the accuracy of Canopy Height Models (CHM) over mangrove forest can provide crucial information for monitoring and verification protocols. We compared four CHMs derived from independent remotely sensed imagery and identified potential errors and bias between measurement types. CHMs were derived from three spaceborne datasets; Very-High Resolution (VHR) stereophotogrammetry, TerraSAR-X add-on for Digital Elevation Measurement, and Shuttle Radar Topography Mission (TanDEM-X), and lidar data which was acquired from an airborne platform. Each dataset exhibited different error characteristics that were related to spatial resolution, sensitivities of the sensors, and reference frames. Canopies over 10 m were accurately predicted by all CHMs while the distributions of canopy height were best predicted by the VHR CHM. Depending on the guidelines and strategies needed for monitoring and verification activities, coarse resolution CHMs could be used to track canopy height at regional and global scales with finer resolution imagery used to validate and monitor critical areas undergoing rapid changes.

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]

This paper describes an improved concept for the mapping of tropical forest classes with ALOS AVNIR and ALOS PALSAR data. The improvement comes from a combination of a sample of very high resolution (VHR) satellite images with medium resolution wall-to-wall mapping in a statistical sampling framework. The approach developed makes it possible to obtain reliable information on mapping accuracy over the whole area of interest. [ABSTRACT FROM PUBLISHER]

Disturbance of the dynamic balance between protein tyrosine phosphorylation and dephosphorylation, modulated by protein tyrosine kinases (PTKs) and protein tyrosine phosphatases (PTPs), is known to be crucial for the development of many human diseases. The discovery of agents that restore this balance has been the subject of many drug research efforts, most of which have focused on tyrosine kinase inhibitors (TKIs), resulting in the development of more than 50 FDA-approved TKIs during the past two decades. More recently, accumulating evidence has suggested that members of the PTP superfamily are also promising drug targets, and efforts to discover tyrosine phosphatase inhibitors (TPIs) have increased dramatically. Here, we provide protocols for determining the potency of TPIs in vitro. We focus on the use of fluorescence-based substrates, which exhibit a dramatic increase in fluorescence emission when dephosphorylated by the PTP, and thus allow setting up highly sensitive and miniaturized phosphatase activity assays using 384-well or 1536-well microplates and a continuous (kinetic) assay format. The protocols cover PTP specific activity assays, Michaelis-Menten kinetics, dose-response inhibition assays, and dose-response data analysis for determining IC 50 values. Potential pitfalls are also discussed. While advanced instrumentation is utilized for compound spotting and liquid dispensing, all the assays can be adapted to existing equipment in most laboratories. Assays are described for selected PTP drug targets, including SHP2 ( PTPN11 ), PTP1B ( PTPN1 ), STEP ( PTPN5 ), and VHR ( DUSP3 ). However, all protocols are applicable to members of the PTP enzyme family in general. Graphical abstract., Competing Interests: Competing interests The authors declare that they have no conflicts of interest with the contents of this article., (Copyright © 2022 The Authors; exclusive licensee Bio-protocol LLC.)

Abstract: The progresses of statistical texture analysis have been playing a more and more important roles in remote sensing mapping, pattern recognition and computer vision. The achievements and research approaches have found important applications in many fields, including industry inspection, remote sensed data analysis and mapping, medical imaging, textile defect detection, video image analysis, food grading, and natural texture recognition and retrieval, object recognition, etc.[1] This paper starts with the principles and algorithms of second order statistics. Based on the theoretical analysis, we selected 5 typical texture class samples from Quick Bird RGB fused data with 0.61m resolution. We used GLCMs to quantitatively calculate texture features, which parameter values are suitable for the specific texture classifications. GLCMs of 5 typical texture class samples from the data set were calculated. Six statistical features for every class sample in four orientations and 1 pixel of pair-wise distance were obtained, including: energy, entropy, contrast, homogeneity, correlation, and dissimilarity respectively. The average values in four directions were computed and compared. The results show that dissimilarity and entropy have biggest value differences among six samples. They are the most important features for classification or recognition of class samples. The statistics of dissimilarity, entropy, homogeneity, contrast have been demonstrated a decrease in classification ability. But the average contrast can discriminate the complex building sample from other textures in spite of small differences with others. The results of the research supplied important references for the quantitative interpretation of VHR Quick Bird imagery in the applications of land cover/use classification and mapping. [Copyright &y& Elsevier]

Abstract: In this paper, we proposed a contextual and Object-based Image Analysis (OBIA) classification approach through the experiment, which was using Cognition Network Language (CNL) of commercial software eCognition Developer. Combination of spectrum and texture explored the rich information contents in the Very High-spatial Resolution (VHR) satellite imagery. Meanwhile, contextual relation was adopted in the experiment to improve the classification accuracy. In the research, a few land cover characteristics (contrast, entropy, etc.) were studied for the local land covers. By the experiment, six different classes were extracted: building, road, forest, water, farmland and bare soil. 94.05% classification overall accuracy and 0.92 Kappa coefficient were achieved by the approach for the complex land covers. Data set was five bands QuickBird imagery located nearby Kunming Dianchi Lake (KDL), which is a very important highland lake in China, also in the world. The results may be used in lake protection, environment protection, land planning and land use management, and government decision making around KDL. [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]

Negative dielectric liquid crystal (nLC) represents lower resistibility than positive dielectric liquid crystal in spite of its higher transmittance in AH-IPS mode. Based on the analysis between transmittance under DC signal and resistivity of LC, DC resistibility is increased by the new nLC with high VHR and thus flickering phenomenon could be improved. [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]

Malignant prostate tissues have markedly reduced zinc (Zn2+) contents in comparison to non-malignant tissues. In this study, we restored a high intracellular Zn2+ level to LNCaP prostate cancer cells by culturing the cells in a growth medium supplemented with a supraphysiological concentration of Zn2+ (10 μg/ml) over 5 weeks. The intracellular Zn2+ level increased in the Zn2+-treated cells, and there was a marked increase in the presence of zincosomes, a Zn2+-specific intracellular organelle. The proliferation rate of the Zn2+-treated cells was markedly reduced. There was also a significant increase (36.6% ± 6.4%) in the total tyrosine phosphorylated proteins. Vaccinia H1-related (VHR) phosphatase, zeta chain-associated protein-70 (ZAP-70) kinase and phosphorylated extracellular signal-regulated protein kinase 1 and 2 (p-ERK 1 and 2) were also present in higher abundance. Treatment with TPEN, which chelates Zn2+, reduced the abundance of VHR phosphatase and ZAP-70 kinase, but increased the abundance of p-ERK 1. However, the TPEN treatment restored the Zn2+-treated LNCaP cell proliferation to a rate comparable to that of the non Zn2+-treated cells. These results highlight the importance of a high intracellular Zn2+ content and the VHR/ZAP-70-associated pathways in the modulation of LNCaP prostate cancer cell growth. [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]

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]