Copernicus is Europe's space-based Earth monitoring asset, which consists of a complex set of systems that collect data from different sources: remote sensing satellites (RS) and in-situ sensors such as ground stations, airborne and marine sensors. This study was originally prepared for the needs of the Czech agricultural community, where we provided an in-depth analysis of articles related to Earth observation in precision agriculture. At a later stage, we extended this study by comparing the recommendations of the European EO4Agri project and scientific articles published in MDPI. We had two important objectives, one was to validate the results of the EO4Agri project and the other was to look for gaps in current research and community needs. To recognize the importance of using Sentinel 1 data, we also added a specific analysis of methods for data fusion of Sentinel 1 and Sentinel 2 data.

We describe an efficient and cost effective data access mechanism for Sentinel-1 TOPS 1 mode bursts. Our data access mechanism enables burst-based data access and processing, thereby 2 eliminating ESA’s Sentinel-1 SLC data packaging conventions as a bottleneck to large scale processing. 3 Pipeline throughput is now determined by available compute resources and efficiency of the analysis 4 algorithms. For targeted infrastructure monitoring studies, we are able to generate coregistered, 5 geocoded stacks of SLCs for any AOI in the world in a few minutes. In addition, we describe our 6 global scale radar backscatter and interferometric products and associated pipeline design decisions 7 that ensure geolocation consistency across the suite of derived products from Sentinel-1 data. Finally, 8 we discuss the benefits and limitations of working with geocoded SAR SLC data.

The detection of waste plastics in the marine and terrestrial environment using satellite Earth Observation data offers the possibility of large-scale mapping, and reducing on-the-ground manual investigation. In addition, costs are kept to a minimum by utilizing free-to-access Copernicus data. A Machine Learning based classifier was developed to run on Sentinel-1 and -2 data. In support of the training and validation, a dataset was created with terrestrial and aquatic cases by manually digitizing varying landcover classes alongside plastics under the sub-categories of greenhouses, plastic, tyres and waste sites. The trained classifier, including an Artificial Neural Network and post-processing decision tree, was verified using five locations encompassing these different forms of plastic. Although exact matchups are challenging to digitize, the performance has generated high accuracy statistics, and the resulting land cover classifications have been used to map the occurrence of plastic waste in aquatic and terrestrial environments.

Knowledge of the spatio-temporal occurrence of avalanche activity is critical for avalanche forecasting and hazard mapping. We present a near-real time automatic avalanche monitoring system that outputs detected avalanche polygons within roughly 10 min after Sentinel- 1 SAR data download. Our avalanche detection algorithm has an average probability of detection of 67.2 % with a false alarm rate averaging 45.9, with maximum POD's over 85 % and minimum FAR's of 24.9 % compared to manual detection of avalanches. The high variability in performance stems from the dynamic nature of snow in the Sentinel-1 data. After tuning parameters of the detection algorithm, we processed five years of Sentinel-1 images acquired over a 150 x 100 km large area in Northern Norway, with the best setup. Compared to a dataset of field-observed avalanches, 77.3 % were manually detectable. Using these manual detections as benchmark, the avalanche detection algorithm achieved an accuracy of 79 % with high POD's in cases of medium to large wet snow avalanches. For the first time, we can present a dataset of spatiotemporal avalanche activity over several winters from a large region. This unique dataset allows for research into the relationship between avalanche activity and triggering meteorological factors, mapping of avalanche prone areas and near-real time avalanche activity monitoring to assist public avalanche forecasting. Currently, the Norwegian Avalanche Warning Service is using our processing system for pre-operational use in three regions in Norway.

Wetlands benefits can be summarized but are not limited to their ability to store floodwaters and improve water quality, providing habitats for wildlife and supporting biodiversity, as well as aesthetic values. Over the past few decades, remote sensing and geographical information technologies has proven to be a useful and frequent applications in monitoring and mapping wetlands. Combining both optical and microwave satellite data can give significant information about the biophysical characteristics of wetlands and wetlands` vegetation. Also, fusing data from different sensors, such as radar and optical remote sensing data, can increase the wetland classification accuracy. In this paper we investigate the ability of fusion two fine spatial resolution satellite data, Sentinel-2 and the Synthetic Aperture Radar Satellite, Sentinel-1, for mapping wetlands. As a study area in this paper, Balikdami wetland located in the Anatolian part of Turkey has been selected. Both Sentinel-1 and Sentinel-2 images require pre-processing before their use. After the pre-processing, several vegetation indices calculated from the Sentinel-2 bands were included in the data set. Furthermore, an object-based classification was performed. For the accuracy assessment of the obtained results, number of random points were added over the study area. In addition, the results were compared with data from Unmanned Aerial Vehicle collected on the same data of the overpass of the Sentinel-2, and three days before the overpass of Sentinel-1 satellite. The accuracy assessment showed that the results significant and satisfying in the wetland classification using both multispectral and microwave data. The statistical results of the fusion of the optical and radar data showed high wetland mapping accuracy, with an overall classification accuracy of approximately 90% in the object-based classification. Compared with the high resolution UAV data, the classification results give promising results for mapping and monitoring not just wetlands, but also the sub-classes of the study area. For future research, multi-temporal image use and terrain data collection are recommended.

Environmental effects and climate change are lately representing an increasing strain of the coastal areas which topography strongly depends on these conditions. However, the processes by which weather and environmental phenomena influence the highly variable beach morphology are still unknown. A continuous monitoring of the beach environment is necessary to implement protection strategies. This paper presents the results of an innovative study performed on a coastal area using satellite remote sensing data with the aim of understanding how environmental phenomena affect beaches. Two-years of synthetic aperture radar (SAR) Sentinel-1 images are used over a test area in Noordwijk, the Netherlands. At the same time as the SAR acquisitions, information on tidal and weather conditions are collected and integrated from nearby meteorological stations. Dedicated codes are implemented in order to understand the relationship between the SAR amplitude and the considered phenomena: wind, precipitation, tidal conditions. Surface roughness is taken into account. The results indicate a strong correlation between the amplitude and the wind. No particular correlation or trend could be noticed in the relation with the precipitation. The analysis of the amplitude also shows a decreasing trend moving from the dry area of the beach towards the sea and the correlation coefficient between the amplitude and the tide level gets negative with the increase of the water content.

A timely inventory of agricultural areas and crop types is an essential requirement for ensuring global food security. Satellite remote sensing has proven to be an increasingly more reliable tool to identify crop types. With the Copernicus program and its Sentinel satellites, a growing source of satellite remote sensing data is publicly available at no charge. Here we use joint Sentinel-1 radar and Sentinel-2 optical imagery to create a crop map for Belgium. To ensure homogenous radar and optical input across the country, Sentinel-1 12-day backscatter composites were created after incidence angle normalization, and Sentinel-2 NDVI images were smoothed to yield dekadal cloud-free composites. An optimized random forest classifier predicted the 8 crop types with a maximum accuracy of 82% and a kappa coefficient of 0.77. We found that a combination of radar and optical imagery always outperformed a classification based on single-sensor inputs, and that classification performance increased throughout the season until July, when differences between crop types are largest. Furthermore we showed that the concept of classification confidence derived from the random forest classifier provided insight in the reliability of the predicted class for each pixel, clearly showing that parcel borders have a lower classification confidence. We concluded that the synergistic use of radar and optical data for crop classification led to richer information increasing classification accuracies compared to optical-only classification. Further work should focus on object-level classification and crop monitoring to exploit the rich potential of combined radar and optical observations.

Satellite Crop Detection technologies are focused on detection of different types of crops on the field in the early stage before harvesting. Crop detection is usually done on a time series of satellite data by classification of the desired fields. Currently, data obtained from Remote Sensing (RS) are used to solve tasks related to the identification of the type of agricultural crops, also modern technologies using AI methods are desired in the postprocessing part. In this challenge Sentinel-1 and Sentinel-2 time series data were used due to their periodic availability. Our focus was to develop methodology for classification of time series of Sentinel 2 and Sentinel 1 data and compare how accuracy of classification can be increased, but also how to guarantee availability of data. We analyse phenology of single crops and on the basis of this analysis we started to provide crop classification. Original crop classifications were made from Enhanced Vegetation Index (EVI) layers made from Sentinel-2 time-series data and then we added also . To increase accuracy we also integrate into the process parcel borders and provide classification of fields..

Sentinel-1 SAR data preprocessing is essential for several earth observation applications, including land cover classification, change detection, vegetation monitoring, urban growth, natural hazards, etc. The information can be extracted from the 2x2 covariance matrix [C2] of Sentinel-1 dual-pol (VV-VH) acquisitions. To generate the covariance matrix from Sentinel-1 single look complex (SLC) data, several preprocessing steps are required. The ESA SNAP S-1 toolbox can be used to preprocess the data to generate a [C2] matrix. The polarimetric analysis in respective application fields often starts with the covariance matrix. However, due to limited availability of Sentinel-1 SLC data preprocessing workflow standards for polarimetric applications in contemporary research methods, downstream applications unable to comply with these workflows directly. In this paper, we propose a couple of generic practices to preprocess Sentinel-1 SLC data in SNAP S-1 toolbox, which would be beneficial for the radar remote sensing user community.

Soil moisture (SM) plays an essential role in environmental studies related to wetlands, an ecosystem sensitive to climate change. Hence, there is the need for its constant monitoring. SAR (Synthetic Aperture Radar) satellite imagery is the only mean to fulfill this objective regardless of the weather. The objective of the study was to develop the methodology for SM retrieval under wetland vegetation using Sentinel-1 (S-1) satellite data. The study was carried out during the years 2015–2017 in the Biebrza Wetlands, situated in northeastern Poland. At the Biebrza Wetlands, two Sentinel-1 validation sites were established, covering grassland and marshland biomes, where a network of 18 stations for soil moisture measurement was deployed. The sites were funded by the European Space Agency (ESA), and the collected measurements are available through the International Soil Moisture Network (ISMN). The NDVI (Normalized Difference Vegetation Index) was derived from the optical imagery of a MODIS (Moderate Resolution Imaging Spectroradiometer) sensor onboard the Terra satellite. The SAR data of the Sentinel-1 satellite with VH (vertical transmit and horizontal receive) and VV (vertical transmit and vertical receive) polarization were applied to soil moisture retrieval for a broad range of NDVI values and soil moisture conditions. The new methodology is based on research into the effect of vegetation on backscatter () changes under different soil moisture and vegetation (NDVI) conditions. It was found that the state of the vegetation may be described by the difference between  VH and  VV, or the ratio of  VV/VH, as calculated from the Sentinel-1 images. The most significant correlation coefficient for soil moisture was found for data that was acquired from the ascending tracks of the Sentinel-1 satellite, characterized by the lowest incidence angle, and SM at a depth of 5 cm. The study demonstrated that the use of the inversion approach, which was applied to the new developed models and includes the derived indices based on S-1, allowed the estimation of SM for peatlands with reasonable accuracy (RMSE ~ 10 vol. %). Due to the temporal frequency of the two S-1 satellites’ (S-1A and S-1B) acquisitions, it is possible to monitor SM changes every six days. The conclusion drawn from the study emphasizes a demand for the derivation of specific soil moisture retrieval algorithms that are suited for wetland ecosystems, where soil moisture is several times higher than in agricultural areas.

Space-borne Synthetic Aperture Radar (SAR) Interferometry (InSAR) is now a key geophysical tool for surface deformation studies. The European Commission’s Sentinel-1 Constellation began acquiring data systematically in late 2014. The data, which are free and open access, have global coverage at moderate resolution with a 6 or 12-day revisit, enabling researchers to investigate large-scale surface deformation systematically through time. However, full exploitation of the potential of Sentinel-1 requires specific processing approaches as well as the efficient use of modern computing and data storage facilities. Here we present LiCSAR, an operational system built for large-scale interferometric processing of Sentinel-1 data. LiCSAR is designed to automatically produce geocoded wrapped and unwrapped interferograms and coherence estimates, for large regions, at 0.001° resolution (WGS-84 system). The products are continuously updated in a frequency depending on prioritised regions (monthly, weekly or live update strategy). The products are open and freely accessible and downloadable through an online portal. We describe the algorithms, processing, and storage solutions implemented in LiCSAR, and show several case studies that use LiCSAR products to measure tectonic and volcanic deformation. We aim to accelerate the uptake of InSAR data by researchers as well as non-expert users by mass producing interferograms and derived products.

Contrary to optical images, Synthetic Aperture Radar (SAR) images are in different electromagnetic spectrum where the human visual system is not accustomed to. Thus, with more and more SAR applications, the demand for enhanced high-quality SAR images has increased considerably. However, high-quality SAR images entail high costs due to the limitations of current SAR devices and their image processing resources. To improve the quality of SAR images and to reduce the costs of their generation, we propose a Dialectical Generative Adversarial Network (Dialectical GAN) to generate high-quality SAR images. This method is based on the analysis of hierarchical SAR information and the “dialectical” structure of GAN frameworks. As a demonstration, a typical example will be shown where a low-resolution SAR image (e.g., a Sentinel-1 image) with large ground coverage is translated into a high-resolution SAR image (e.g., a TerraSAR-X image). Three traditional algorithms are compared, and a new algorithm is proposed based on a network framework by combining conditional WGAN-GP (Wasserstein Generative Adversarial Network - Gradient Penalty) loss functions and Spatial Gram matrices under the rule of dialectics. Experimental results show that the SAR image translation works very well when we compare the results of our proposed method with the selected traditional methods.

Landslide risk mitigation is limited by data scarcity. This could be improved using continuous landslide detection systems. In order to investigate which image types and machine learning (ML) models are most useful for landslide detection in a Norwegian setting, we compared the performance of five different ML models, for the Jølster case study (30-July-2019), in Western Norway. These included three globally pre-trained models; i) the Continuous Change Detection and Classification (CCDC) algorithm, ii) a combined k-means clustering and Random Forest classification model, and iii) a convolutional neural network (CNN), and two locally-trained models, including; iv) Classification and Regression Trees and v) a U-net CNN model. Images used included Sentinel-1, Sentinel-2, digital elevation model (DEM) and slope. The globally-trained models performed poorly in shadowed areas, and were all outperformed by the locally-trained models. A maximum Matthew’s correlation coefficient (MCC) score of 89% was achieved with model v, using combined Sentinel-1 and -2 images as input. This is one of the first attempts to apply deep-learning to detect landslides with both Sentinel-1 and -2 images. Using Sentinel-1 images only, the locally-trained deep-learning model significantly outperformed the conventional ML model. These findings contribute towards developing a national continuous monitoring system for landslides.

Surface soil moisture (SSM) is a significant factor affecting crop growth. This paper presents a method for retrieving SSM over wheat-covered areas using synergy dual-polarization C-band Sentinel-1 synthetic aperture radar and Sentinel-2 optical data. Firstly, a modified water cloud model (WCM) was proposed to remove the influence of vegetation from the backscattering coefficient of the radar data. The vegetation fraction was then introduced in this WCM, and the vegetation water content (VWC) was calculated using multiple linear regression model. Subsequently, the support vector regression technique was used to retrieve the SSM. This approach was validated using in-situ measurements of the wheat field in Hebi, in the north of Henan Province. The key findings of this study are as follows: (1) Based on vegetation indices obtained from Sentinel-2; the proposed VWC estimation model can effectively eliminate the influence of vegetation; (2) compared with vertical transmit and horizontal receive polarization, vertical transmit and vertical receive polarization is better for detecting changes in SSM at different growth stages of wheat; and, (3) the validation results indicated that the proposed approach, based on Sentinel-1 and Sentinel-2 data, successfully retrieved SSM in the study area.

This paper presents ensemble learning of multi-source satellite sensors dataset to obtain better predictive performance of the forest biomass. Spectral, spectral-indices, and spectral-textural features were generated from two optical satellite sensors, Landsat 8 Operational Land Imager (OLI) and Sentinel-2 Multispectral Instrument (MSI). In addition, two radar satellite sensors, Sentinel-1 C-band Synthetic Aperture Radar (CSAR), and Advanced Land Observing Satellite (ALOS-2) Phased Array type L-band Synthetic Aperture Radar (PALSAR-2) were utilized to generate backscattering and backscattering-textural features. The plot-wise above ground biomass data available from five forests in New England region were utilized. Ensemble learning of multi-source satellite sensors dataset was carried out by employing four machine learning regressors namely, Support Vector Machines (SVM), Random Forests (RF), Gradient Boosting (GB), and Multilayer Perceptron (MLP). A five-fold cross-validation method was used to evaluate predictive performance of the multi-source satellite sensors. The integration of multi-source satellite features, comprising of spectral, spectral-indices, backscattering, spectral-textural, and backscattering-textural information, through ensemble learning and cross-validation approach implemented in the research showed promising results (R² = 0.81, RMSE = 46.2 Mg/ha) for the estimation of plots-level forest biomass in New England region.

Sentinel-1A/B radar remote sensing data were applied for the first time to determine the sixth nuclear test, its underground explosion h-bomb location and affected zone in North Korea, on September 3, 2017. Location of epicenters nuclear test were found according to line-of-sight displacement images via its maximum value. Line-of-sight displacement images were obtained by processing in the GMTSAR package in the VirtualBox virtual machine of the Linux Ubuntu 16.04 operation system. In this research, three scenes Sentinel-B data with descending orbits were considered, one after and two before the event (the nuclear test date) scene were used.

We scrutinize the reliability of multilooked interferograms for deformation analysis. Designing a simple approach in the evaluation of the accuracy of the estimated deformation signals, we reveal a prominent bias in the deformation velocity maps. The bias is the result of propagation of small phase error of multilooked interferograms through the time series and can sum up to 6.5 mm/yr in case of using the error prone short temporal baseline interferograms. We further discuss the role of the phase estimation algorithms in reduction of the bias and put recommend a unified intermediate InSAR product for achieving high-precision deformation monitoring.

This study investigates the spatial signatures of seasonal snow in Synthetic Aperture Radar (SAR) observations at different spatial scales and for different physiographic regions. Sentinel-1 C-band (SAR) backscattering coefficients (BSC) were analyzed in the Swiss Alps (SA), in high elevation forest and grasslands in Grand Mesa (GM), Colorado, and in North Dakota (ND) croplands. GM BSC exhibit 10dB sensitivity to wetness at small scales (~100 m) over homogeneous grassland. Sensitivity decreases to 5 dB in the presence of trees, and it is demonstrated that VH BSC sensitivity enables wet snow mapping below the tree-line. Area-variance scaling relationships show minima at ~100 m and 150-250 m respectively in barren and grasslands in SA and GM, increasing up to 1 km and longer in GM forests and ND agricultural fields. The spatial organization of BSC (as described by 1D-directional BSC wavelength spectra) exhibits multi-scaling behavior in the 100 -1,000 m range with a break at (180-360 m) that is also present in UAVSAR L-band measurements in GM. Spectral slopes in GM forested areas steepen during accumulation and flatten in the melting season with mirror behavior for grasslands reflecting changes in scattering mechanisms with snow depth and wetness, and vegetation mass and structure. Overall, this study reveals persistent patterns of SAR scattering variability spatially organized by land-cover, topography and regional winds with large inter-annual variability tied to precipitation. This dynamic scaling behavior emerges as an integral physical expression of snowpack variability that can be used to model sub-km scales and for downscaling applications.

The rapid urbanization that takes place in developing regions such as Sub-Saharan Africa is associated with a large range of environmental and social issues. In this context, remote sensing is essential to provide accurate and up-to-date spatial information to support risk assessment and decision making. However, mapping urban areas remains a challenge because of their heterogeneity, especially in developing regions where the highest rates of misclassification are observed. Nevertheless, urban areas located in arid climates --- which are among the most vulnerables to anthropogenic impacts, suffer from the spectral confusion occurring between built-up and bare soil areas when using optical imagery. Today, the increasing availability of satellite imagery from multiple sensors allow to tackle the aforementioned issues by combining optical data with Synthetic Aperture Radar (SAR). In this paper, we assess the complementarity of the Landsat 8 and Sentinel-1 sensors to map built-up areas in twelve Sub-Saharan African urban areas, using a pixel-level supervised classification based on the Random Forest classifier. We make use of textural information extracted from SAR backscattering data in order to reduce the speckle noise and to introduce contextual information at the pixel level. Results suggest that combining both optical and SAR features consistently improves classification performances, mainly by enhancing the differentiation between built-up and bare lands. However, the fusion was less beneficial in mountainous case studies, suggesting that including features derived from a Digital Elevation Model (DEM) could improve the reliability of the proposed approach. As suggested by previous studies, combining features computed from both VV and VH polarizations consistently led to better classification performances. On the contrary, introducing textures computed from different spatial scales did not improve the classification performances.

The Grand Ethiopian Renaissance Dam (GERD), formerly known as the Millennium Dam, is currently under construction and has been filling at a fast rate without sufficient known analysis on possible impacts on the body of the structure. The filling of GERD not only has an impact on the Blue Nile Basin hydrology, water storages and flow but also pose massive risks in case of collapse. Rosaries Dam located in Sudan at only 116 km downstream of GERD, along with the 20 million Sudanese benefiting from that dam, would be seriously threatened in case of the collapse of GERD. In this study, through the analysis of Sentinal-1 satellite imagery we show concerning deformation patterns associated with different sections of the GERD’s Main Dam (structure RCC Dam type) and the Saddle Dam (Embankment Dam type). We processed 109 descending mode scenes from Sentinel-1 SAR imagery, from December 2016 to July 2021, using the Differential Synthetic Aperture Radar Interferometry technique to demonstrate the deformation trends of both - the GERD’s Main and Saddle Dams. The time-series generated from the analysis clearly indicates different displacement trends at various sections of the GERD as well as the Saddle Dam. Results of the multi temporal data analysis on and around the project area show inconsistent subsidence at the extremities of the GERD Main Dam, especially the west side of the dam where we recorded varying displacements in the range of 10 mm to 90 mm at the crest of the dam. We conducted the current analysis after masking the images with a coherence value of 0.9 and hence, the subsequent results are extremely reliable and accurate. Further decomposition of the subsiding rate has revealed higher vertical displacement over the west side of the GERD’s Main Dam as compared to the east side. The local geological structures consisting of weak zones under the GERD’s accompanying Saddle Dam adds further instability to its structure. We identified seven critical nodes on the Saddle Dam that match the tectonic faults lying underneath it, and which display a varying degree of vertical displacements. In fact, the nodes located next to each other displayed varying displacement trends: one or more nodes displayed subsidence since 2017 while the other node in the same section displayed uplift. The geological weak zones underneath and the weight of the Saddle Dam itself may somewhat explain this inconsistency and the non-uniform vertical displacements. For the most affected cells, we observed a total displacement value of ~90 mm during the whole study period (~20 mm/year) for the Main Dam while the value of the total displacement for the Saddle dam is ~380 mm during the same period (~85 mm/year). Analysis through CoastSat tool also suggested a non-uniformity in trends of surface water-edge at the two extremities of the Main Dam.

Floods are one of the most wide-spread, frequent, and devastating natural disasters that continue to increase in frequency and intensity. Remote sensing, specifically synthetic aperture radar (SAR), has been widely used to detect surface water inundation to provide retrospective and near-real time (NRT) information due to its high-spatial resolution, self-illumination, and low atmospheric attenuation. However, the efficacy of flood inundation mapping with SAR is susceptible to reflections and scattering from a variety of factors including dense vegetation and urban areas. In this study, the topographic dataset height above nearest drainage (HAND) was investigated as a potential supplement to Sentinel-1A C-Band SAR along with supervised machine learning to improve the detection of inundation in heterogeneous areas. Three machine learning classifiers were trained on two sets of features SAR only (VV & VH) and VV, VH & HAND to map inundated areas. Three study sites along the Neuse River in North Carolina, USA during the record flood of Hurricane Matthew in October 2016 were selected. The binary classification analysis (inundated as positive vs. non-inundated as negative) revealed significant improvements when incorporating HAND in several metrics including classification accuracy (ACC) (+37.1%), true positive rate (TPR) (+51.2%), and negative predictive value (NPV) (+23.7%), A marginal improvement of +1.4% was seen for positive predictive value (PPV), but true negative rate (TNR) fell -15.1%. By incorporating HAND, a significant number of areas with high SAR backscatter but low HAND values were detected as inundated which increased true positives. This in turn also increased the false positives detected but to a lesser extent as evident in the metrics. This study demonstrates that HAND could be considered a valuable feature to enhance SAR flood inundation mapping especially in areas with heterogeneous land covers with dense vegetation that interfere with SAR.

Easy to use satellite-based water quality visualizations are needed for monitoring and understanding coastal and inland waters, but to date, no publicly accessible real-time global visualization system was in place. Here we introduce the Ulyssys Water Quality Viewer (UWQV), a Sentinel Hub EO Browser Custom script designed for qualitative views of aquatic chlorophyll and suspended sediment concentrations. The viewer avoids unmixing of the chlorophyll and suspended sediment spectral signal by visualizing these parameters together, with high concentrations of suspended sediment obscuring chlorophyll if present. Cloud masking uses the Hollstein and Braaten algorithms (existing EO Browser custom script code), additionally water surfaces are masked using the Normalized Differential Water Index. Chlorophyll is estimated using reflectance line height-based indicators such as fluorescence line height and maximum chlorophyll index. Suspended sediment is visualized based on single-band reflectances at 620 or 700 nm. Data sources are Sentinel-2 and Sentinel-3 images, allowing either 20 m spatial resolution or up to daily imaging. This visualization system is easy to operate and interpret, and combined with the data service capacity of the Sentinel Hub, it is expected that UWQV will contribute to monitoring of remote water bodies and to our overall understanding of physical limnology and aquatic ecology.

Ho Chi Minh City (HCMC), the most crowded city and economic hub of Viet Nam, has been experiencing land subsidence over the past decades. This effort aims to contribute the spatial distribution of subsidence in HCMC in its horizontal and vertical components using synthetic aperture radar interferometry (InSAR) time series. To this purpose, an advanced Persistent Scatterers and Distributed Scatterers (PSDS) InSAR technique was applied to two European Space Agency (ESA) Sentinel-1 datasets consisting of 96 ascending and 202 descending images, acquired from 2014 to 2020 over the HCMC area. A time series of 33 COSMO-SkyMed ascending images was also used for comparison. The combination of ascending and descending satellite passes is used to decompose the light of sight velocities into horizontal east-west and vertical components. Taking into account the presence of east-west horizontal motion, our findings indicate that the accuracy of the decomposed vertical velocity can be improved by up to 3 mm/year for Sentinel-1 data. The obtained results revealed that subsidence is most pronounced in the areas along the Sai Gon River, in the northwest-southeast axis, and in the southwest of the city, with a maximum value of 80 mm/yr, which is in accordance with the findings of the literature. The amplitude of east-west horizontal velocities is relatively small and large-scale eastward movement can be observed in the west of the city at a rate of 3-5 mm/yr. This confirmed that the displacement in Ho Chi Minh City area is mainly vertical downward. Together, these results reinforced the remarkable suitability of ESA's SAR Sentinel-1 for subsidence applications, even for non-European countries such as Vietnam and Southeast Asia.

Delineating the cropping area of cocoa agroforests is a major challenge for quantifying the contribution of the land use expansion to tropical deforestation. Discriminating cocoa agroforests from tropical transition forests using multi-spectral optical images is difficult due to a similarity in the spectral characteristics of their canopy; moreover, optical sensors are largely impeded by the frequent cloud cover in the tropics. This study explores multi-season Sentinel-1 C-band SAR image to discriminate cocoa agroforests from transition forests for a heterogeneous landscape in central Cameroon. We use an ensemble classifier, random forest, to average SAR image texture features of GLCM (Grey Level Co-occurrence Matrix) across seasons; next, we compare classification performance with results from RapidEye optical data. Moreover, we assess the performance of GLCM texture feature extraction at four different grey level quantization: 32bits, 8bits, 6bits, and 4bits. The classification overall accuracy (OA) of texture-based maps outperformed that from an optical image; the highest OA of 88.8% was recorded at 6bits grey level. This quantization level, in comparison to the initial 32bits in SAR images, reduced the class prediction error by 2.9%. Although this prediction gain may be large for the landscape area, the resultant thematic map reveals the decrease and fragmentation of forest cover by cocoa agroforests. According to our classification validation, the Shannon entropy (H) or uncertainty provides a reliable validation for class predictions and reveals detail inference for discriminating inherently heterogeneous vegetation categories. The texture-based classification achieved a reliable accuracy considering the heterogeneity of the landscape and vegetation classes.

Consistent calibration and monitoring is a basic prerequisite for providing reliable time series of global and regional sea level variations from altimetry. The precision of sea level measurements and regional biases for six altimeter missions (Jason-1/2/3, Envisat, Saral, Sentinel-3A) is assessed at eleven GNSS-controlled tide gauge stations in the German Bight (SE North Sea) for the period 2002 to 2019. The gauges are partly located at the open water, partly at the coast close to mudflats. The altimetry is extracted at virtual stations with distances from 2 to 24 km from the gauges. The processing is optimized for the region and adjusted for the comparison with instantaneous tide gauges readings. An empirical correction is developed to account for mean height gradients and slight differences of the tidal dynamics between gauge and altimetry which improves the agreement between the two data sets by 15-75%. The precision of the altimeters is depending on location and mission and is shown to be at least 1.8 to 3.7 cm based on an assumed precision of 2 cm for the gauges. The accuracy of the regional mission biases is strongly dependent on the mean sea surface heights near the stations. The most consistent biases are obtained based on the CLS2011 model with mission dependent accuracies from 1.3 to 3.4 cm. Hence, the GNSS-controlled tide gauges operated operationally by WSV might complement the calibration and monitoring activities at dedicated CalVal stations.

HIV-1 transcribes only one kind of transcript – the full length genomic RNA. To make the mRNA transcripts for the accessory proteins Tat and Rev, the genomic RNA must completely splice. The mRNA transcripts for Vif, Vpr, and Env must splice but not completely. Genomic RNA (which also functions as mRNA for the Gag and Gag/Pro/Pol precursor polyproteins) must not splice at all. HIV-1 can tolerate a surprising range in the relative abundance of individual transcript types, and a surprising amount of aberrant and even odd splicing; however, it must not over-splice, which results in the loss of full length genomic RNA and has a dramatic fitness cost. Cells typically do not tolerate unspliced/incompletely spliced transcripts, so HIV-1 has to circumvent this cell policing mechanism to allow some splicing while suppressing most. Splicing is controlled by RNA secondary structure, cis-acting regulatory sequences which bind splicing factors, and the viral protein Rev. There is still much work to be done to clarify the combinatorial effects of these splicing regulators. These control mechanisms represent attractive targets to induce over-splicing as an antiviral strategy. Finally, splicing has been implicated in latency, but to date there is little supporting evidence for such a mechanism. In this review we apply what is known of cellular splicing to understand splicing in HIV-1, and also present data from our newer and more sensitive deep sequencing assays quantifying the different HIV-1 transcript types.

Sentinel-2 can provide multi-spectral optical remote sensing images in RGBN bands with a spatial resolution of 10m, but the spatial details provided are not enough for many applications. WorldView can provide HR multi-spectral images less than 2m, but it is a commercial paid resource with relatively high usage costs. In this paper, without any available reference images, Sentinel-2 images at 10m resolution are improved to a resolution of 2.5m through super-resolution (SR) based on deep learning technology. Our model, named DKN-SR-GAN, uses degradation kernel estimation and noise injection to construct a dataset of near-natural low-high-resolution (LHR) image pairs, with only low-resolution (LR) images and no high-resolution (HR) prior information. DKN-SR-GAN uses the Generative Adversarial Networks (GAN) combined of ESRGAN-type generator, PatchGAN-type discriminator and the VGG-19-type feature extractor, using perceptual loss to optimize the network, so as to obtain SR images with clearer details and better perceptual effects. Experiments demonstrate that in the quantitative comparison of the non-reference image quality assessment (NR-IQA) metrics like NIQE, BRISQUE and PIQE, as well as the intuitive visual effects of the generated images, compared with state-of-the-art models such as EDSR8-RGB, RCAN and RS-ESRGAN, our proposed model has obvious advantages.

This document describes the theoretical basis of the algorithms used to determine properties of snow and ice from the measurements of the Ocean and Land Color Instrument (OLCI) onboard Sentinel-3 satellites within the Pre-operational Sentinel-3 snow and ice products (SICE) project: http://snow.geus.dk/. The code used for the SICE retrieval and its documentation can be found at https://github.com/GEUS-SICE/pySICE. The algorithms were developed after the work from Kokhanovsky et al. (2018, 2019, 2020).

Advanced-Differential Interferometric SAR (A-DInSAR) has been used to monitor surface deformations in open pit mines and tailings dams. In this paper, ground deformations have been detected on the area of the tailings Dam-I at the Córrego do Feijão Mine (Brumadinho, Brazil) before its catastrophic failure occurred on 25 January 2019. Two techniques optimized for different scattering models, SBAS (Small BAseline Subset) and PSI (Persistent Scatterer Interferometry), were used to perform the analysis based on 26 Sentinel-1B images in IW mode, acquired on descending orbits from 03 March 2018 to 22 January 2019. A WorldDEM DSM product was used to remove the topographic phase component. The results provided by both techniques showed a synoptic and informative view of the deformation process affecting the study area, with a detection of persistent trend of deformations on the top, middle and bottom sectors of the dam face until its collapse, as well as the expected natural settlements on the tailings. It is worth noting the detection of an acceleration in the displacement time-series for a short period near the failure. The maximum accumulated displacements detected along the downstream slope face were -39 mm (SBAS) and -48 mm (PSI). It is reasonable to consider that Sentinel-1 would provide decision makers complementary motion information to the in-situ monitoring system for risk assessment and for a better understanding of on-going instability phenomena affecting the tailings dam.

Diagnosis of kidney diseases has recently become more comprehensive and accurate by using new renal markers. Despite the fact that creatinine and cystatin c have been sufficient in determining kidney function, they did not indicate the exact site of the damage and they were often insufficient in predicting the course of the disease. Aim of the study was to evaluate the potential correlations and differences in levels of six factors related to kidney function and injury: kidney injury molecule-1 (KIM-1), ncalbindin (CALB), glutathione S-transferase Pi (GST-Pi), calbindin and monocyte chemoattractant protein-1 (MCP-1), between renal patients with diabetic nephropathy (DM), congenital defects (CD) of the kidney and glomerulonephritis (GN). Study involved 75 patients: 49 with diabetic nephropathy, 12 with congenital defects and 14 with glomerulonephritis. Levels of renalase was measured using immunoenzymatic tests. Levels of other markers: calbindin, glutathione-S-transferase (GST-pi), interleukin-18 (IL-18), kidney injury molecule-1 (KIM-1) and monocyte chemoattractant protein-1 (MCP-1), were analyzed using Kidney Toxicity-1 Panel and BioPlex system, designed for analyses in urine and optimized by us for serum.From all analyzed markers, only levels of KIM-1 differed significantly between any subgroups, and that was for CD and DM. Renalase correlated significantly negatively with creatinine and positively with all other markers, apart from MCP-1. Obtained results indicate, that serum renalase, KIM-1, calbindin and GST-pi are related to kidney function, with KIM-1 being the most exact, while MCP-1 levels are unrelated to creatinine and glucose levels, does not differ between patients with diabetic nephropathy and other subgroups, and therefore seem to be independent of diabetes. Also, serum-optimized Kidney Toxicity Panel 1 kit for determination of selected markers gave results similar to previous ones and therefore the method can be valuable in determination of analyzed factors.

Crop yield data is critical for managing sustainable agriculture and assessing national food security. Current study aims to increase Peanut productivity from current levels by analyzing the yield gap of production potential between theoretical yield and actual farmers’ yields. The spatial yield gap of Peanut for Thiruvannamalai district of Tamil Nadu is examined in this paper by integrating the products of microwave remote sensing (SAR Sentinel-1A) with DSSAT CROPGRO peanut simulation model. CROPGRO Peanut model was calibrated and validated by conducting field experiment at Oilseeds Research Station, Tindivanam during Rabi 2019 for predominant cultivars viz. TMV 7, TMV 13, VRI 2 and G 7. Actual attainable yield was recorded by organizing CCE with help of Department of Agriculture Economics and Statistics in the respective monitoring Villages. Regression analysis between maximum recorded DSSAT Leaf Area Index (LAI) at peak flowering stage of peanut and yield recorded by Crop Cutting Experiment (CCE) for spatial yield estimation of Peanut in Thiruvannamalai district of Tamil Nadu during Rabi 2021 was carried out using ArcGIS 10.6 software. The results showed that the simulated potential yield ranged from 3194 to 4843 kg/ha, whereas actual yield ranged from 1228 to 3106 kg/ha, with a considerable disparity between the actual and potential yield levels (1217 to 2346 kg/ha) of the monitored locations. The minimum, maximum and average yield gaps in Peanut for Thiruvannamalai district was assessed as 1890, 2324 and 2134 kg/ha, respectively. To reduce the production difference (Yield gap) of Peanut cultivation, farmers should focus more on management issues such as time of sowing, irrigation or water management, quantity and sources of nutrients, cultivar selection and availability of quality seeds tailored to each region.

Soil salinity is a severe soil degradation problem mainly faced in arid and semi-arid regions. About 11 million ha of land in the arid, semi-arid, and desert parts of Ethiopia is salt-affected, especially in the Awash River basin, including Afambo irrigated area. Remote sensing approaches are significant tools for accurately predicting and modeling accurately predicting and modeling soil salinity in various world regions. This study aims to analyze and model soil salinity status in the case of Afambo irrigated areas using Landsat-8 and sentinel-2A, Afar region, Ethiopia, by applying remote sensing with field measurements. Thirty-two soil samples were collected from the topsoil (0-30 cm); out of these, 25 soil samples with various EC ranges were selected for modeling, and the remaining 7 samples were utilized to validate the model. Landsat-8 and Sentinel-2A images acquired in the same month were used to extract soil salinity indices. Linear regression analyses correlated the EC data with corresponding soil salinity spectral index values derived from satellite images. The best-performing model was selected for salinity mapping. The soil salinity indices extracted from both Landsat-8 and Sentinel-2A bands estimated soil salinity with high acceptable accuracy of R² values of SI, 0.78 and 0.81, respectively. The model results in three salinity classes with varying degree of salinity, namely, highly saline, moderately saline, and slightly saline, which covers 15.1%, 39.8% and 45.1% of the total area for Landsat-8, respectively and 26.1%, 32%, and 41.9% for sentinel 2A, respectively. Generally, the results revealed that the expansion rate of salt-affected soils has been increasing. From this study, it is possible to infer that if the present irrigation practice continues, it is expected that total the cultivated lands will become sterile within a short period. Thus, it needs to be monitored regularly to secure up-to-date knowledge of their extent to improve management practices and take appropriate actions.

Satellite altimeters have been used to monitor river and reservoir water levels, from which water storage estimates can be derived. Inland water altimetry can therefore play an important role in continental water resource management. Traditionally, satellite altimeters were designed to monitor homogeneous surfaces such as oceans or ice sheets, resulting in a poor performance over small inland water bodies due to the contribution from land contamination in the returned waveforms. The advent of synthetic aperture radar (SAR) altimetry (with its improved along-track spatial resolution) has enabled the measurement of inland water levels with a better accuracy and an increased spatial resolution. This paper presents three specialized algorithms or retrackers to retrieve water levels from SAR altimeter data over inland water bodies dedicated to minimizing land contamination from the waveforms. The performances of the proposed waveform portion selection method with three retrackers, namely, the threshold retracker, Offset Centre of Gravity (OCOG) retracker and 2-step physical-based retracker, are compared. Time series of water levels are retrieved for water bodies in the Ebro River basin (Spain). The results show good agreement with in situ measurements from the Ebro Reservoir (width is approximately 1.8 km) and Ribarroja Reservoir (width is approximately 400 m) with un-biased root-mean-square errors (RMSEs) of approximately 0.28 m and 0.16 m, respectively. The performances of all three retrackers are also compared with the European Space Agency’s ocean retracker in the Sentinel-3 Level-2 product.

This research is related to the eco-hydrological problems of herbaceous wetland drying and biodiversity loss in the floodplain lakes of the Middle Basin of the Biebrza river (Poland). An experiment was set up, whose main goals were: (i) mapping the vegetation types and the temporarily or permanently flooded areas, and (ii) comparing the usefulness of C-band Sentinel-1A (S1A) and X-band TerraSAR-X/TanDEM-X (TSX/TDX) for mapping purposes. The S1A imagery was acquired on a regular basis using the dual polarization VV/VH and the Interferometric Wide Swath Mode. The TSX/TDX data were acquired in quad-pol, a fully polarimetric mode, during the Science Phase. The paper addresses the following aspects: i) wetland mapping with S1A multi-temporal series; ii) wetland mapping with fully polarimetric TSX/TDX data; iii) comparing the wetland mapping using dual polarization TSX/TDX subsets, i.e. HH-HV, HH-VV and VV-VH; iv) comparing wetland mapping using S1A and TSX/TDX data based on the same polarization (VV-VH); v) studying the suitability of the Shannon Entropy for wetland mapping; and vi) assessing the contribution of interferometric coherence for wetland classification. The experimental results show main limitations of the S1A dataset, while they highlight the good accuracy that can be achieved using the TSX/TDX data, especially those taken in fully polarimetric mode.

Boosted by the progress in deep learning, Single Image Super-Resolution (SISR) has gained a lot of interest in the Remote Sensing community, who sees it as an oportunity to compensate for satellite's ever-limited spatial resolution with respect to end users needs. While there has been a great amount of work on network architures in the latest years, deep learning based SISR in remote sensing is still limited by the availability of the large training sets it requires. The lack of publicly available large datasets with the required variability in terms of landscapes and seasons pushes researchers to simulate their own dataset by means of downsampling. This may impair the applicability of the trained model on real world data at the target input resolution. In this paper, we propose an open-data licenced dataset composed of 10m and 20m cloud-free surface reflectance patches from Sentinel-2, with their reference spatially-registered surface reflectance patches at 5 meter resolution acquired on the same day by the VENµS satellite. This dataset covers 29 locations on earth with a total of 132 955 patches of 256x256 pixels at 5 meters resolution, and can be used for the training of super-resolution algorithms to bring the spatial resolution of 8 of the Sentinel-2 bands down to 5 meters.

Automatic ship detection provides an essential function towards maritime domain awareness for security or economic monitoring purposes. This work presents an approach for training a deep learning ship detector in Sentinel-2 multispectral images with few labeled examples. We design a network architecture for detecting ships with a backbone that can be pre-trained separately. By using Self Supervised Learning, an emerging unsupervised training procedure, we learn good features on Sentinel-2 images, without requiring labeling, to initialize our network’s backbone. The full network is then fine-tuned to learn to detect ships in challenging settings. We evaluated this approach versus pre-training on ImageNet and versus a classical image processing pipeline. We examined the impact of variations in the self-supervised learning step and we show that in the few-shot learning setting self-supervised pre-training achieves better results than ImageNet pre-training. When enough training data is available, our self-supervised approach is as good as ImageNet pre-training. We conclude that a better design of the self-supervised task and bigger non-annotated dataset sizes can lead to surpassing ImageNet pre-training performance without any annotation costs.

TRPOMI instrument aboard Sentinel-5P is a relatively new, high-resolution source of information about atmosphere composition. One of the primary atmospheric trace gases that we can observe through it is nitrogen dioxide. By now, we were using the chemical weather model (GEM-AQ) as a mean for estimating nitrogen dioxide concentration on a regional scale. Although well established in atmospheric science, the GEM-AQ simulations were always based on emission data, which in the case of the energy sector were reported by stack owners. In this paper, we attempted to compare the TROPOMI and GEM-AQ derived VCDs over Poland with a particular focus on large point emitters. We also checked how cloudy conditions influence TROPOMI results. Finally, we tried to link the NO2 column number densities with surface concentration using boundary layer height as an additional explanatory variable

Wind is one of the factors that has a great influence on suspended matter in lakes, especially in shallow lagoons. In order to know how wind affects the water in Albufera of Valencia, a shallow coastal lagoon, the measured variables of turbidity and transparency have been correlated with the estimates by processing Sentinel-2 satellite images with the Sen2Cor processor. Data from four years of study show that most of them are light to gentle easterly breezes and moderate to fresh westerly breezes. The results obtained show significant correlations between the measured variables and those obtained from the satellite images for total suspended matter and water transparency and with the average daily wind speed. There is no significant correlation between wind and chlorophyll a. Moderate to fresh breezes resuspend the fine sediment reaching concentration values from 100 to 300 mg L-1 according to satellite data. However, it is necessary to obtain field data for the values of moderate and fresh winds, as for now there are no experimental data to verify the validity of the satellite estimates.

Proper satellite-based crop monitoring applications at the farm-level often require near-daily imagery at medium to high spatial resolution. The synthesizing of ongoing satellite missions by ESA (Sentinel 2) and NASA (Landsat7/8) provides this unprecedented opportunity at a global scale; nonetheless, this is rarely implemented because these procedures are data demanding and computationally intensive. This study developed a complete stream processing in the Google Earth Engine cloud platform to generate harmonized surface reflectance images of Landsat7,8 and Sentinel 2 missions. The harmonized images were generated for two agriculture schemes in Bekaa (Lebanon) and Ninh Thuan (Vietnam) during the period 2018-2019. We evaluated the performance of several pre-processing steps needed for the harmonization including image co-registration, brdf correction, topographic correction, and band adjustment. This study found that the miss-registration between Landsat 8 and Sentinel 2 images, varied from 10 meters in Ninh Thuan, Vietnam to 32 meters in Bekaa, Lebanon, and if not treated, posed a great impact on the quality of the harmonized dataset. Analysis of a pair overlapped L8-S2 images over the Bekaa region showed that after the harmonization, all band-to-band spatial correlations were greatly improved from (0.57, 0.64, 0.67, 0.75, 0.76, 0.75, 0.79) to (0.87, 0.91, 0.92, 0.94, 0.97, 0.97, 0.96) in bands (blue, green, red, nir,swir1,swir2, ndvi) respectively. We demonstrated that dense observation of the harmonized dataset can be very helpful for characterizing cropland in highly dynamic areas. We detected unimodal, bimodal and trimodal shapes in the temporal NDVI patterns (likely cycles of paddy rice) in Ninh Thuan province only during the year 2018. We fitted the temporal signatures of the NDVI time series using harmonic (Fourier) analysis. Derived phase (angle from the starting point to the cycle's peak) and amplitude (the cycle's height) were combined with max-NDVI to generate an R-G-B image. This image highlighted croplands as colored pixels (high phase and amplitude) and other types of land as grey/dark pixels (low phase/amplitude). Generated harmonized datasets that contain surface reflectance images (bands blue, green, red, nir, swir1, swir2, and ndvi at 30 meters) over the two studied sites are provided for public usage and testing.

The Sentinel-2 satellite mission offers high resolution multispectral time series image data, enabling the production of detailed land cover maps globally. At this scale, the trade-off between processing time and result quality is a central design decision. Currently, this machine learning task is usually performed using pixelwise classification methods. The radical shift of the computer vision field away from hand engineered image features and towards more automation by representation learning comes with many promises, including higher quality results and less engineering effort. In this paper we assess fully convolutional neural networks architectures as replacements for a Random Forest classifier in an operational context for the production of high resolution land cover maps with Sentinel-2 time series at the country scale. Our contributions include a framework for working with Sentinel-2 L2A time series image data, an adaptation of the U-Net model for dealing with sparse annotation data while maintaining high resolution output, and an analysis of those results in the context of operational production of land cover maps.

Astronauts at are risk of losing 1.0 – 1.5% of their bone mass for every month they spend in space despite their adherence to high impact exercise training programs designed to preserve the musculoskeletal system. This article reviews the basics of bone formation and resorption and details how exposure to microgravity or simulated microgravity affects the structure and function of osteoblasts, osteocytes, osteoclasts, and their mesenchymal and hematologic stem cell precursors. It details the critical roles that insulin-like growth facor-1 and its receptor IGFR1 play in maintaining bone homeostasis and how exposure of bone cells to microgravity affects the function of these growth factors. Lastly, it discusses the potential of tumor necrosis factor-related apoptosis-inducing ligand, syncytin-A, and sclerostin inhibitors and recombinant IGF-1 as a bone-saving treatment for astronauts in space and during their colonization of the Moon.

Expression of Krüppel–like factor 15 (KLF15), a stress induced transcription factor, is induced during bovine herpesvirus 1 (BoHV-1) reactivation from latency, and KLF15 stimulates BoHV-1 replication. Transient transfection studies revealed KLF15 and glucocorticoid receptor (GR) cooperatively transactivate the BoHV-1 immediate early transcription unit 1 (IEtu1), herpes sim-plex virus type 1 (HSV-1) infected cell protein 0 (ICP0), and ICP4 promoter. The IEtu1 promoter drives expression of bICP0 and bICP4, two key BoHV-1 transcriptional regulatory proteins. Based on these studies, we hypothesized infection is a stressful stimulus that increases KLF15 ex-pression and enhances productive infection. New studies demonstrated that silencing KLF15 impaired HSV-1 productive infection and KLF15 steady state protein levels were increased at late stages of productive infection. KLF15 was primarily localized to the nucleus following in-fection of cultured cells with HSV-1, but not BoHV-1. When cells were transfected with a KLF15 promoter construct and then infected with HSV-1, promoter activity was significantly increased. The ICP0 gene and to a lesser extent bICP0 transactivated the KLF15 promoter in the absence of other viral proteins. In contrast, BoHV-1 or HSV-1 encoded VP16 had no effect on KLF15 pro-moter activity. Collectively, these studies revealed HSV-1 and BoHV-1 productive infection in-creased KLF15 steady state protein levels, which correlated with increased virus production.

Indigenous forests cover 24% of New Zealand and provide valuable ecosystem services. However, a national map of forest types, that is, physiognomic types, which would benefit conservation management, does not currently exist at an appropriate level of detail. While traditional forest classification approaches from remote sensing data are based on spectral information alone, the joint use of space-based optical imagery and structural information from synthetic aperture radar (SAR) and canopy metrics from air-borne Light Detection and Ranging (LiDAR) facilitates more detailed and accurate classifications of forest structure. We present a support vector machine (SVM) classification using data from ESA’s Sentinel-1 and 2 missions, ALOS PALSAR, and airborne LiDAR to produce a regional map of physiognomic types of indigenous forest in New Zealand. A five-fold cross-validation of ground data showed that the highest classification accuracy of 80.9% is achieved for bands 2, 3, 4, 5, 8, 11, and 12 from Sentinel-2, the ratio of bands VH and VV from Sentinel-1, HH from PALSAR, and mean canopy height and 97th percentile canopy height from LiDAR. The classification based on the optical bands alone was 73.1% accurate and the addition of structural metrics from SAR and LiDAR increased accuracy by 7.8%. The classification accuracy is sufficient for many management applications for indigenous forest in New Zealand, including biodiversity management, carbon inventory, pest control, ungulate management, and disease management. National application of the method will be possible in several years, once national LiDAR coverage is achieved, and a national canopy height model is available.

Crop-type mapping is an important intermediate step for cost-effective crop management at the field level, as an overview of all fields with a particular crop type can be used for monitoring or yield forecasting, for instance. Our study used a data set with 2400 fields and corresponding satellite observations from the federal state of Bavaria, Germany. The study classified corn, winter wheat, winter barley, sugar beet, potato, and winter rapeseed as the main crops grown in Upper Bavaria. We additionally experimented with a rejection class "Other", which summarised further crop types. Corresponding Sentinel-2 data included the normalised difference vegetation index (NDVI) and raw bands from 2016 to 2018 for each selected field. The influence of raw bands compared to NDVI was analysed and the classification algorithms, i.e. support vector machine (SVM) and random forest (RF), were compared. The study showed that the use of an index should be critically questioned and that raw bands provided a wider spectral bandwidth, which significantly improved the mapping of crop types. The results underline the use of RF with raw bands and achieved overall accuracies (OA) of up to 92%. We also predicted crop types in an unknown year with significantly different weather conditions and several months before the end of the growing season. Thus, the influence of climate anomalies and the accuracy depending on the time of prediction were assessed. The crop types of a test site and year without labels could be determined with an OA of up to 86%. The results demonstrate the usefulness of the proof-of-concept and its readiness for use in real applications.

For more than ten years, Central Chile faces drought conditions, which impact crop production and quality, increasing food security risk. Under this scenario, implementing management practices that allow increasing water use efficiency is urgent. The study was carried out in kiwifruit trees, located in the O’Higgins region, Chile; for season 2018-2019 and 2019-2020. We evaluate nine vegetation indices in the VNIR and SWIR regions derived from Sentinel-2 (A/B) satellites to know how much variability in the canopy water status could explain. Over the study's site were installed sensors that continuously measure the leaf's turgor pressure (Yara Water-Sensor). A strong correlation between turgor pressure and vegetation indices was obtained with the Spearman's rho coefficient ($\rho$). However, the NIR range's indices were influenced by the vegetative development of the crop rather than its water status. Red-edge showed better performance as the vegetative growth did not affect it. It is necessary to expand the study to consider higher variability in kiwifruit's water conditions and incorporate the sensitivity of different wavelengths.

This paper discusses the potential and limitations of the Normalized Difference Vegetation Index (NDVI) in environmental justice, health and inequality studies in urban areas. Very often the NDVI is correlated with socioeconomic and/or sociodemographic data to demonstrate the inequality in environmental settings that themselves influence individual health and questions of environmental justice. This paper addresses the limits of the NDVI for such applications and as well its potential, if applied properly. The overall goal is to make people of disciplines other than those that are geo-related aware of the characteristics, limits and potentials of satellite image-based information layers such as NDVI.

The main goal of our work is the revealing of problematic issues related to estimates of the absorption coefficient of colored organic matter in the northern seas from data of the Ocean and Land Color Instrument (OLCI) on the Sentinel-3 satellites. In particular, a comparison of the OLCI standard error estimates ADG443_NN_err., relating to the measurement and retrieval of the geophysical products, with the uncertainties in the real situation of the northern seas, where the natural conditions are extremely unfavorable (first of all, frequent cloudiness, low Sun heights). We conducted a comprehensive multi-sensor study of the uncertainties using various approaches, first at all, directly comparing the data from satellite (OLCI Sentinel-3 and four other ocean color sensors) and field measurements in five sea expeditions 2016-2019, by using the different processing algorithms. Our analysis has shown that the real uncertainties of the final product are significantly higher than the calculated errors of the ADG443_NN_err., which is 100% and ~10%. The main reason for that is the unsatisfactory atmospheric correction. We present the results of the analysis of the different effecting factors (satellite sensors, processing algorithms, use of the other parameters), and formulate the tasks of future work.

In Portugal, cork oak (Quercus suber L.) stands cover 737 Mha, being the most predominant species of the montado agroforestry system, contributing for the economic, social and environmental development of the country. Cork oak decline is a known problem since the late years of the 19th century that has recently worsen. The causes of oak decline seem to be a result of slow and cumulative processes, although the role of each environmental factor is not yet established. The availability of Sentinel-2 high spatial and temporal resolution dense time series enables gradual processes monitoring. These processes can be monitored using spectral vegetation indices (VI) once their temporal dynamics are expected to be related with green biomass and photosynthetic efficiency. The Normalized Difference Vegetation Index (NDVI) is sensitive to structural canopy changes, however it tends to saturate at moderate-to-dense canopies. Modified VI have been proposed to incorporate the reflectance in the red-edge spectral region, which is highly sensitive to chlorophyll content while largely unaffected by structural properties. In this research, in-situ data on the location and vitality status of cork oak trees are used to assess the correlation between chlorophyll indices (CI) and NDVI time series trends and cork oak vitality at the tree level. Preliminary results seem to be promising since differences between healthy and unhealthy (diseased/dead) trees were observed.

Sentinel lymph node dissection (sLND) using a magnetometer and superparamagnetic iron oxide nanoparticles (SPIONs) as a tracer was successfully applied in prostate cancer (PCa). Radioisotope-guided sLND combined with extended pelvic LND (ePLND) achieved better node removal, increasing the number of affected nodes or the detection of sentinel lymph nodes outside the established ePLND template. We determined the diagnostic value of additional magnetometer-guided sLND after intraprostatic SPION-injection in high-risk PCa. This retrospective study included 104 high-risk PCa patients (PSA >20 ng/ml and/or Gleason score ≥8 and/or cT2c) from a prospective cohort who underwent radical prostatectomy with magnetometer-guided sLND and ePLND. The diagnostic accuracy of sLND was assessed using ePLND as a reference standard. Lymph node metastases were found in 61 of 104 patients (58.7%). sLND had a 100% diagnostic rate, 96.6% sensitivity, 95.6% specificity, 96.6% positive predictive value, 95.6% negative predictive value, 3.4% false negative rate, and 4.4% false positive rate (detecting lymph node metastases outside the ePLND template). These findings demonstrate the high sensitivity and additional diagnostic value of magnetometer-guided sLND, exceeding that of ePLND through the individualized extension of PLND or the detection of sentinel lymph nodes / lymph node metastases outside the established node template in high-risk PCa.

Herpes simplex virus 1 (HSV-1) expresses a large number of miRNAs, and their function is still not completely understood. In addition, HSV-1 has been found to deregulate host miRNAs, which adds to the complexity of regulation of efficient virus replication. In this study, we comprehen-sively addressed the deregulation of host miRNAs by massive-parallel sequencing. We found that only miRNAs expressed from a single cluster, miR-183/96/182 are reproducibly deregulated dur-ing productive infection. These miRNAs are predicted to regulate a great number of potential tar-gets involved in different cellular processes and have only 33 shared targets. Among these, mem-bers of the FoxO family of proteins were identified as potential targets for all three miRNAs. However, our study shows that the upregulated miRNAs do not affect the expression of FoxO proteins, moreover these proteins were upregulated in HSV-1 infection. Furthermore, we show that the individual FoxO proteins are not required for efficient HSV-1 replication. Taken together, our results indicate a complex and redundant response of infected cells to the virus infection that is efficiently inhibited by the virus.

HIV-1 Tat protein interacts with TAR RNA and recruits CDK9/cyclin T1 and other host factors to induce HIV-1 transcription. Thus Tat-TAR RNA interaction, which is unique for HIV-1, represents an attractive target for anti-HIV-1 therapeutics. To target Tat-TAR RNA interaction, we used a crystal structure of TAR RNA with acetylpromazine bound to the bulge of TAR RNA, to dock compounds from Enamine database containing 1.6 million individual compounds. Docking identified 173 compounds that were analyzed for the inhibition of HIV-1 infection. Top ten inhibitory compounds with IC50 ≤ 6 µM were selected and the three least toxic compounds, T6780107 (IC50=2.97 μM), T0516-4834 (IC50=0.2 μM) and T5628834 (IC50=3.46 μM), were further tested for HIV-1 transcription inhibition. Only T0516-4834 compound showed selective inhibition of Tat-induced HIV-1 transcription, whereas T6780107 compound inhibited equally basal and Tat-induced transcription and T5628834 compound only inhibited basal HIV-1 transcription. The T0516-4834 compound also showed strongest inhibition of HIV-1 gag RNA expression and p24 production in CEM T cells infected with HIV-1 IIIB. Of the three compounds, only the T0516-4834 compound disrupted Tat-TAR RNA interaction indicating that it might target TAR RNA. Also, of the three tested compounds, T5628834 but not T6780107 or T0516-4834 disrupted Tat-CDK9/cyclin T1 interaction. Taken together, our study identified novel compound T0516-4834 that disrupted Tat-TAR RNA interaction and inhibited Tat-induced transcription and HIV-1 infection suggesting that this compound might serve as a new lead for anti-HIV-1 therapeutics.

Brazil, Russia, China, India, and the Republic of South Africa (BRICS) represent developing economies facing different energy and economic development challenges. The current study aims to forecast energy consumption in BRICS at aggregate and disaggregate levels using the annual time series data set from 1992 to 2019 and to compare results obtained from a set of models. The time-series data are from the British Petroleum (BP-2019) Statistical Review of World Energy. The forecasting methodology bases on a novel Fractional-order Grey Model (FGM) with different order parameters. This study contributes to the literature by comparing the forecasting accuracy and the forecasting ability of the FGM(1,1) with traditional ones, like standard GM(1,1) and ARIMA(1,1,1) models. Also, it illustrates the view of BRICS's nexus of energy consumption at aggregate and disaggregates levels using the latest available data set, which will provide a reliable and broader perspective. The Diebold-Mariano test results confirmed the equal predictive ability of FGM(1,1) for a specific range of order parameters and the ARIMA(1,1,1) model and the usefulness of both approaches for energy consumption efficient forecasting.

Tissue resident memory T cells (TRM) were first described in 2009. While initially the major focus was on CD8 TRM, there has been recently an increased interest in defining the phenotype and the role of CD4 TRM in diseases. Circulating CD4 T cells seed tissue CD4 TRM, but there also appears to be an equilibrium between CD4 TRM and blood CD4 T cells. CD4 TRM are more mobile than CD8 TRM, usually localized deeper within the dermis/lamina propria and yet may exhibit synergy with CD8 TRM in disease control. This has been demonstrated in herpes simplex infections in mice. In human recurrent herpes infections, both CD4 and CD8 TRM persisting between lesions may control asymptomatic shedding through interferon gamma secretion, although this has been more clearly shown for CD8 T cells. The exact role of the CD4/CD8 TRM axis in the trigeminal ganglia and/or cornea in controlling recurrent herpetic keratitis is unknown. In HIV, CD4 TRM have now been shown to be a major target for productive and latent infection in cervix. In HSV and HIV co-infections, CD4 TRM persisting in the dermis support HIV replication. Further understanding of the role of CD4 TRM and their induction by vaccines may help control sexual transmission by both viruses.

Solid tumor cells have an altered metabolism that can protect them from cytotoxic lymphocytes. The antidiabetic drug metformin modifies tumor cell metabolism and several clinical trials are testing its effectiveness for the treatment of solid cancers. The use of metformin in hematologic cancers has received much less attention, although allogeneic cytotoxic lymphocytes are very effective against these tumors. We show here that metformin induces expression of Natural Killer G2-D (NKG2D) ligands (NKG2DL) and intercellular adhesion molecule-1 (ICAM-1), a ligand of the lymphocyte function-associated antigen 1 (LFA-1). This leads to enhance sensitivity to cytotoxic lymphocytes. Overexpression of antiapoptotic Bcl-2 family members decrease both metformin effects. The sensitization to activated cytotoxic lymphocytes is mainly mediated by the increase on ICAM-1 levels, which favors cytotoxic lymphocytes binding to tumor cells. Finally, metformin decreases the growth of human hematological tumor cells in xenograft models, mainly in presence of monoclonal antibodies that recognize tumor antigens. Our results suggest that metformin could improve cytotoxic lymphocyte-mediated therapy.

Background: Multidrug-resistant bacteria present resistance mechanisms against β-lactam antibiotics, such as Extended-Spectrum Beta-lactamases (ESBL) and Metallo-β-lactamases enzymes (MBLs) operon encoded in Gram-negative species. Likewise, Gram-positive bacteria have evolved other mechanisms through mec genes, which encode modified penicillin-binding proteins (PBP2). This study aimed to determine the presence and spread of β-lactam antibiotic resistance genes and the microbiome circulating in Quito’s Public Transport (QTP). Methods: A total of 29 station turnstiles were swabbed to extract the surface environmental DNA. PCRs were performed to detect the presence of 13 antibiotic resistance genes and to identify 16S rDNA barcoding, followed by clone analysis, Sanger sequencing and BLAST search. Results: ESBL genes blaTEM-1 and blaCTX-M-1 and MBL genes blaOXA-181 and mecA were detected along QPT stations. Two subvariants were found for blaTEM-1, blaCTX-M-1, and blaOXA-181. Almost half of the circulating bacteria found at QPT stations were common human microbiota species including those classified by the WHO as pathogens of critical and high-priority surveillance. Conclusions: β-lactam antibiotic resistance genes are widely spread throughout QPT. This is the first report of blaOXA-181 in environmental samples in Ecuador. Moreover, we detected a new putative variant of this gene. Some commensal coagulase-negative bacteria may have a role as mecA resistance reservoirs

Vascular cell adhesion molecule-1 (VCAM-1) is closely associated with tumor progression and metastasis. However, the relevance and role of VCAM-1 in lung cancer have not been clearly elucidated. In this study, we found that VCAM-1 was highly overexpressed in lung cancer tissue compared with that of normal lung, and high VCAM-1 expression correlated with poor survival of lung cancer patients. VCAM-1 knockdown reduced invasion in A549 human lung cancer cells, and competitive blocking experiments targeting the Ig-like domain 6 of VCAM-1 (VCAM-1-D6) demonstrated that the VCAM-1-D6 domain was critical for VCAM-1-mediated A549 cell invasion. Next, we developed a human monoclonal antibody specific to human and mouse VCAM-1-D6 (VCAM-1-D6 huMab), which was isolated from a human synthetic antibody library using phage display technology. Finally, we showed that VCAM-1-D6 huMab had a nanomolar affinity for VCAM-1-D6 and that it potently suppressed invasion in A549 and NCI-H1299 lung cancer cell lines. Taken together, these results suggest that VCAM-1-D6 is a novel therapeutic target in VCAM-1-mediated lung cancer invasion and that our newly developed VCAM-1-D6 huMab will be a useful tool for inhibiting VCAM-1-expressing lung cancer cell invasion.

Conventional methods of crop mapping need ground truth information to train the classifier. Thanks to the frequent acquisition allowed by recent satellite missions (Sentinel 2), we can identify temporal patterns that depend on both phenology and crop management. Some of these patterns are specific to a given crop and thus can be used to map it. Thus, we can substitute ground truth information used in conventional methods with agronomic knowledge. This approach was applied to identify irrigated permanent grasslands (IPG) in the Crau area (Southern France) which play a crucial role in groundwater recharge. The grassland is managed by making three mows during the May-October period which leads to a specific temporal pattern of leaf area index (LAI). The mowing detection algorithm was designed using the temporal LAI signal derived from Sentinel 2 observations. The algorithm includes some filtering to remove noise in the signal that might lead to false mowing detection. A pixel is considered a grassland if the number of detected mows is greater than 1. A data set covering five years (2016-2020) was used. The detection mowing number was done at the pixel level and then results are aggregated at the plot level. A validation data set including 780 plots was used to assess the performances of the classification. We obtained a Kappa index ranging between 0.94-0.99 according to the year. These results were better than other supervised classification methods that include training data sets. The analysis of land-use changes shows that misclassified plots concern grasslands managed less intensively with strong intra-parcel heterogeneity due to irrigation defects or year-round grazing. Time series analysis, therefore, allows us to understand different management practices. Real land-use change in use can be observed, but long time series are needed to confirm the change and remove ambiguities with heterogeneous grasslands.

Water quality retrieval for small urban waterbodies by remote sensing get used to be difficult due to coarse spatial resolution of the remote sensing imagery. The recently launched Sentinel-2 produces imagery with a spatial resolution of 10 m. It provides an opportunity to solve the problem of retrieving water quality for small waterbodies. Additionally, many water management issues also require fine resolution of imagery, e.g. illegal discharge to an urban waterbody. Since illegal discharges are an important issue for urban water management, chemical oxygen demand (COD), total phosphorous (TP), and total nitrogen (TN) were chosen as the target parameters for water quality retrieval in this study. COD, TP and TN, however, are non-optically active parameters. There were limited studies in the past to retrieve these parameters in comparison with optically active parameters, e.g. Chlorophyll-A etc. This study compared three machine learning models, namely Random Forest (RF), Support Vector Regression (SVR), and Neural Networks (NN), to investigate the opportunity to retrieve the above non-optically active parameters. Results showed that R² of TP, TN, and COD by NN, RF and SVR were 0.94, 0.88, and 0.86, respectively. The performances of water quality retrieval for these non-optically active parameters were significantly improved by the optimized machine learning models. These models hence solved the problem to use remote sensing data to retrieve these non-optically active water quality parameters and provided a new monitoring strategy for small waterbodies. Water quality mapping obtained by Sentinel-2 imagery provided a full spatial coverage of the water quality characterization for the entire water surface. Compared with water samples collecting and testing, it greatly reduced labor cost, reagents cost, and waste treatment cost. It also may help identify illegal discharges to urban waterbodies. The method developed in this research provides a new practical and efficient water quality monitoring strategy in managing water with consideration of environmental sustainability.

Immune checkpoint blockade (ICB)-based therapy is revolutionizing cancer treatment by fostering successful immune surveillance and effector cell responses against various types of cancers. However, patients with HER2+ cancers are yet to benefit from this therapeutic strategy. Precisely, several questions regarding the right combination of drugs, drug modality, and effective dose recommendations pertaining to the use of ICB-based therapy for HER2+ patients remain unanswered. In this study, we use a mathematical modeling-based approach to quantify the growth inhibition of HER2+ breast cancer (BC) cell colonies (ZR75) when treated with anti-HER2; trastuzumab (TZ) and anti-PD-1/PD-L1 (BMS-202) agents. Our data show that a combination therapy of TZ and BMS-202 can significantly reduce the viability of ZR75 cells and trigger several morphological changes. The combination decreased the cell’s invasiveness along with altering several key pathways, such as Akt/mTor and ErbB2 compared to monotherapy. In addition, BMS-202 causes dose-dependent growth inhibition of HER2+ BC cell colonies alone, while this effect is significantly improved when used in combination with TZ. Based on the in-vitro monoculture experiments conducted, we argue that BMS-202 can cause tumor growth suppression not only by mediating immune response but also by interfering with the growth signaling pathways of HER2+ BC. Nevertheless, further studies are imperative to substantiate this argument and to uncover the potential crosstalk between PD-1/PD-L1 inhibitors and HER2 growth signaling pathways in breast cancer.

Observing cultivated crops and other forms of land use is an important environmental and economic concern for agricultural land management and crop classification. Crop categorization offers significant crop management data, ensuring food security, and developing agricultural policies. Remote sensing data, especially publicly available Sentinel 1 and 2 data, has effectively been used in crop mapping and classification in cloudy places because of their high spatial and temporal resolution. This study aimed to improve crop type classification by combining Sentinel-1 (Synthetic Aperture Rader (SAR)) data and the Sentinel-2 Multispectral Instrument (MSI) data. In the study, Random Forest (RF) and Classification and Regression Trees (CART) classier were used to classify grain crops (Barley and Wheat). The classification results based on the combination of Sentinel-2 and Sentinel-1 data indicated an overall accuracy (OA) of 93 % and a kappa coefficient (K) of 0.896 for RF and (89.15%, 0.84) for the CART classifier. It is suggested to employ a mix of radar and optical data to attain the highest level of classification accuracy since doing so improves the likelihood that the details will be observed in comparison to the single-sensor classification technique and yields more accurate results.

Multi- and hyper-spectral, multi-angular top-of-canopy reflectance data call for an efficient retrieval system which can improve the retrieval of standard canopy parameters (as albedo, LAI, fAPAR), and exploit the information to retrieve additional parameters (e.g. leaf pigments). Furthermore consistency between the retrieved parameters and quantification of uncertainties are required for many applications. % (2) methods We present a retrieval system for canopy and sub-canopy parameters (OptiSAIL), which is based on a model comprising SAIL, PROSPECT-D (leaf properties), TARTES (snow properties), a soil model (BRDF, moisture), and a cloud contamination model. The inversion is gradient based and uses codes % created by Automatic Differentiation. The full per pixel covariance-matrix of the retrieved parameters is computed. For this demonstration, single observation data from the Sentinel-3 SY_2_SYN (synergy) product is used. The results are compared with the MODIS 4-day LAI/fPAR product and PhenoCam site photography. OptiSAIL produces generally consistent and credible results, at least matching the quality of the technically quite different MODIS product. For most of the sites, the PhenoCam images support the OptiSAIL retrievals. The system is computationally efficient with a rate of 150 pixel per second (7 millisecond per pixel) for a single thread on a current desktop CPU using observations on 26 bands. Not all of the model parameters are well determined in all situations. Significant correlations between the parameters are found, which can change sign and magnitude over time. OptiSAIL appears to meet the design goals, puts real-time processing with this kind of system into reach, seamlessly extends to hyper-spectral and multi-sensor retrievals, and promises to be a good platform for sensitivity studies. The incorporated cloud and snow detection adds to the robustness of the system.

Targeted radioisotope-guided sentinel lymph node dissection (sLND) has shown high diagnostic accuracy in prostate cancer (PCa). To overcome the downsides of the radioactive tracers, magnetometer-guided sLND using superparamagnetic iron oxide nanoparticles (SPIONs) was successfully applied in PCa. This prospective study (SentiMag Pro II, DRKS00007671) determined the diagnostic accuracy of magnetometer-guided sLND in intermediate- and high-risk PCa. Fifty intermediate- or high-risk PCa patients (PSA≥10 ng/ml and/or Gleason score ≥7; median PSA 10.8 ng/ml, IQR 7.4–19.2 ng/ml) were enrolled. After intraprostatic SPIONs injection a day earlier, patients underwent magnetometer-guided sLND and eLND, followed by radical prostatectomy. SLNs were detected in vivo and in ex vivo samples. Diagnostic accuracy of sLND was assessed using eLND as the reference. SLNs were detected in all patients (detection rate 100%), with 447 SLNs (median 9, IQR 6–12) being identified and 966 LNs (median 18, IQR 15-23) being removed. Thirty-six percent (18/50) of patients had LN metastases (median 2, IQR 1–3). Magnetometer-guided sLND had 100% sensitivity, 97.0% specificity, 94.4% positive predictive value, 100% negative predictive value, 0.0% false negative rate, and 3.0% additional diagnostic value (LN metastases only in SLNs outside the eLND template). In vivo, one positive SLN/LN-positive patient was missed, resulting in a sensitivity of 94.4%. In conclusion, this new magnetic sentinel procedure has high accuracy for nodal staging in intermediate- and high-risk PCa. The reliability of intraoperative SLN detection using this magnetometer system requires verification in further multicentric studies.

Glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP) are secreted from the gut upon nutrient stimulation and regulate postprandial metabolism. These hormones are known as classical incretin hormones and are responsible for a major part of postprandial insulin release. The incretin effect is severely reduced in patients with type 2 diabetes, but it was discovered that administration of GLP-1 agonists was capable of normalizing glucose control in these patients. Over the last decades, much research has been focused on the development of incretin-based therapies for type 2 diabetes. These therapies include incretin receptor agonists and inhibitors of the incretin-degrading enzyme dipeptidyl peptidase-4. Especially the development of diverse GLP-1 receptor agonists has shown immense success, whereas studies of GIP monotherapy in patients with type 2 diabetes have consistently been disappointing. Interestingly, both GIP-GLP-1 co-agonists and GIP receptor antagonists administered in combination with GLP-1R agonists appear to be efficient with respect to both weight loss and control of diabetes, although the molecular mechanisms behind these effects remain unknown. This review describes our current knowledge of the two incretin hormones and the development of incretin-based therapies for treatment of type 2 diabetes.

Ulcerative colitis (UC) is a relatively frequent, chronic disease that impacts significantly the patient’s quality of life. Although many therapeutic options are available, additional approaches are needed because many patients either do not respond to current therapies or show significant side effects. Cardiotrophin-1 (CT-1) is a cytokine with potent cytoprotective, anti-inflammatory, and antiapoptotic properties. The purpose of this study was to assess if the administration of CT-1 could reduce colon damage in mice with experimental UC. UC was induced with 5% dextran sulfate sodium (DSS) in the drinking water. Some mice received i.v. dose of CT-1 (200 µg/kg) 2 hours before and 2 and 4 days after DSS administration. Animals were followed during 7 days after DSS. The severity of UC was measured by standard scores. Colon damage was assessed by histology and immunohistochemistry. Inflammatory mediators were measured by Western blot and PCR. CT-1 administration to DSS-treated mice ameliorated both the clinical course (disease activity index), histological damage, inflammation (colon expression of TNF-α, IL-17, IL-10, INF-γ, and iNOS), and apoptosis. Our results suggest that CT-1 administration before UC induction improves the clinical course, tissue damage and inflammation degree in DSS-induced UC in mice.

A 2-year prospective study carried out on ventilator-associated pneumonia (VAP) patients in the intensive care unit at King Khalid hospital, Hail, Kingdom of Saudi Arabia (KSA), revealed a high prevalence of extremely drug-resistant (XDR) Acinetobacter baumannii. About a 9% increase in the incidence rate of A. baumannii has occurred in the VAP patients between 2019 and 2020 (21.4% to 30.7%). In 2019 the isolates were positive for IMP-1 and VIM-2 (31.1% and 25.7%, respectively) as detected by PCR. In comparison, a higher proportion of isolates produced NDM-1 in 2020. Here, we observed a high resistant proportion of ICU isolates towards the most common antibiotics in use. Colistin sensitivity dropped to 91.4% in the year 2020 as compared to 2019 (100%). Thus, the finding of this study has a highly significant clinical implementation in the clinical management strategies for VAP patients. Furthermore, strict implementation of antibiotic stewardship policies, regular surveillance programs for antimicrobial resistance monitoring, and screening for genes encoding drug resistance phenotypes have become imperative.

Type 2 diabetes mellitus (T2D) is a metabolic disorder characterized by inappropriate insulin function. Despite wide progress in genome studies, defects in gene expression for diabetes prognosis still incompletely identified. Prolonged hyperglycemia activates NF-κB, which is a main player in vascular dysfunctions of diabetes. Activated NF-κB, triggers expression of various genes that promote inflammation and cell adhesion process. Alteration of pro-inflammatory and profibrotic gene expression contribute to the irreversible functional and structural changes in the kidney resulting in diabetic nephropathy (DN). To identify the effect of some important NF-κB related genes on mediation of DN progression, we divided our candidate genes on the basis of their function exerted in bloodstream into three categories (Proinflammatory; NF-κB, IL-1B, IL-6, TNF-α and VEGF); (Profibrotic; FN, ICAM-1, VCAM-1) and (Proliferative; MAPK-1 and EGF). We analyzed their expression profile in leukocytes of patients and explored their correlation to diabetic kidney injury features. Our data revealed the overexpression of both proinflammatory and profibrotic genes in DN group when compared to T2D group and were associated positively with each other in DN group indicating their possible role in DN progression. In DN patients, increased expression of proinflammatory genes correlated positively with glycemic control and inflammatory markers indicating their role in DN progression. Our data revealed that the persistent activation NF-κB and its related genes observed in hyperglycemia might contribute to DN progression and might be a good diagnostic and therapeutic target for DN progression. Large-scale studies are needed to evaluate the potential of these molecules to serve as disease biomarkers.

Stress Granules formation is a pro-survival mechanism helping cells to cope with environmental challenges. Stress Granules have been studied for two decades in fundamental research, and are now being examined in the context of human pathogenesis. Here, we review studies highlighting stress granules’ involvement in cancer development through translational pattern modification.

The effectiveness of immunotherapy for cervical adenocarcinoma (CA) has not been demonstrated yet. It may be possible for us to use programmed cell death 1 (PD-1), programmed cell death-ligand 1 (PD-L1), and CD8 as biomarkers of response to immune therapy in CA patients. In the present study, we aimed to investigate whether the expression levels of PD-1, PD-L1, and CD8 can predict the prognosis of CA patients and their response to ICI therapy. The levels of the PD-1, PD-L1, and CD8 proteins were analyzed by immunohistochemical analysis from formalin-fixed, paraffin-embedded tumor samples. The correlation between the expression levels and patient prognosis was analyzed by the Kaplan–Meier method and univariate and multivariate Cox proportional hazard regression model. We observed a significant inverse-correlation between the PD-1 and CD8 expression (p=0.001, chi square test). We also found a significant inverse-correlation between the PD-L1 and CD8 expression (p=0.027). The overall survival was significantly worse in patients with positive PD-1 expression (p=0.027). Similarly, the progression-free survival was also worse (p=0.087). Our results demonstrate that a high level of PD-1 expression is associated with a poor prognosis in CA patients. Further research is necessary to identify the molecular mechanisms that mediate this association.

(R)-1-(4-Amino-3,5-dichlorophenyl)-2-bromoethan-1-ol has been synthesised in 93% enantiomeric excess (ee) by asymmetric reduction of the corresponding ketone catalysed by a ketoreductase and NADPH as the co-factor in DMSO. (S)-N-(2,6-Dichloro-4-(1-hydroxyethyl)phenyl)acetamide has been synthesised in >98% ee by the same system. Both synthons are potential precursors for clenbuterol enantiomers. Clenbuterol is a β₂-agonist used in veterinary treatment of asthma in several countries. The drug is listed on the World Anti-doping Agency’s Prohibited list due to its effect on increased protein synthesis in the body. However, racemic clenbuterol has recently been shown to reduce the risk of Parkinson’s disease. In order to reveal which one (or both) of the enantiomers that cause this effect, the pure enantiomers need to be studied separately. Our biocatalytic approach in order to obtain enantiopure clenbuterol should be applicable to industrial scale.

Reliable estimates of poplar plantations area are not available at the French national scale due to the unsuitability and low update rate of existing forest databases for this short-rotation species. While supervised classification methods have been shown to be highly accurate in mapping forest cover from remotely sensed images, their performance depends to a great extent on the labelled samples used to build the models. In addition to their high acquisition cost, such samples are often scarce and not fully representative of the variability in class distributions. Consequently, when classification models are applied to large areas with high intra-class variance, they generally yield poor accuracies. In this paper, we propose the use of active learning (AL) to efficiently adapt a classifier trained on a source image to spatially distinct target images with minimal labelling effort and without sacrificing classification performance. The adaptation consists in actively adding to the initial local model, new relevant training samples from other areas, in a cascade that iteratively improves the generalisation capabilities of the classifier, leading to a global model tailored to different areas. This active selection relies on uncertainty sampling to directly focus on the most informative pixels for which the algorithm is the least certain of their class labels. Experiments conducted on Sentinel-2 time series showed that when the same number of training samples was used, active learning outperformed passive learning (random sampling) by up to 5% of overall accuracy and up to 12% of class F-score. In addition, and depending on the class considered, the random sampling required up to 50% more samples to achieve the same performance of an active learning-based model. Moreover, the results demonstrate the suitability of the derived global model to accurately map poplar plantations among other tree species with overall accuracy values up to 14% higher than those obtained with local models. The proposed approach paves the way for national-scale mapping in an operational context.

The incidence of Type 1 Diabetes (T1D) in the Arab world, particularly, oil and gas rich Gulf Cooperative Council (GCC) countries has more than doubled in the last twenty years. Therefore, there is a dire need for careful systematic familial cohort studies, especially in high-risk populations. Several immunogenetic factors affect the pathogenesis of the disease. Genes in the human leukocyte antigen (HLA) account for the major genetic susceptibility to the disease. The triggering agents initiate disease onset by destruction of pancreatic β-cells. The autoantibodies against glutamic acid decarboxylase (GADA), insulinoma antigen-2 (IA-2A), insulin (IAA), and zinc transporter-8 (ZnT-8A) comprise the most reliable biomarkers for T1D in both children and adults. Although three of the GCC countries, namely Kuwait, Saudi Arabia and Qatar are among the top 10 countries with high incidence rate of T1D, no proper diagnostic and prediction tools were applied in the region. Understanding the disease sequelae in a homogenous gene pool with high consanguinity in the GCC could help solve the challenges in understanding pathogenesis, as well as hasten the prevention of T1D. Arab states must incorporate T1D predictive and intervention policies on a war-footing basis to minimize the burden of this serious disease.

Infection by human immunodeficiency virus type I (HIV-1) requires virus particle binding to host cell-surface receptor CD4 via the viral envelope glycoprotein gp120. HIV-1 therapy and prevention efforts involve development of mimetic or recombinant gp120 vaccines or deployment of antiviral agents that target specific epitopes of gp120. The unliganded conformational state of gp120 is closed, whereas the CD4-bound state is open. However, in between, there exist dynamic conformational states, indicating intrinsically flexible region(s) of structural dynamics, imposing a structural challenge for developing drug or antibody targets. Known conformational states of gp120 were determined by X-ray crystallographic and cryo-electron microscopy, and neither method captures the population of gp120 species arising from conformational plasticity, motions, and transitions. gp120 plasticity brings up several important questions. How will differences in conformation affect receptor binding, antibody recognition, and neutralization? Which regions are crucial for gp120 structural plasticity? How could structural dynamics influence HIV-1 evasiveness against host immunity and drugs or vaccines, and facilitate the viral entry into its host? This review explores the structural constraints presented by conformational states of the glycoprotein to antibodies or drugs and how these conformational states provide structural avenues for the virus to escape neutralizing agents and evade host immunity.

Before the adaptive immune response is established, retroviruses can be targeted by several cellular host factors at different stages of the viral replication cycle. This intrinsic immunity relies on a large diversity of antiviral processes. In the case of HTLV-1 infection, these active innate host defence mechanisms are debated. Among these mechanisms, we focused on a RNA decay pathway called nonsense-mediated mRNA decay (NMD), which can target multiple viral RNAs, including HTLV-1 unspliced RNA, as it has been recently demonstrated. NMD is a cotranslational process that depends on the RNA helicase UPF1 and regulates the expression of multiple types of host mRNAs. RNA sensitivity to NMD depends on mRNA organization and the ribonucleoprotein (mRNP) composition. HTLV-1 has evolved several means to evade the NMD threat, leading to NMD inhibition. In the early steps of infection, NMD inhibition favours the production of HTLV-1 infectious particles, which may contribute to the survival of the most fit clones despite genome instability; however, its direct long-term impact remains to be investigated.

Purinergic receptors are inflammatory mediators activated by extracellular nucleotides released by dying or injured cells. Several studies have described an important role for these receptors in HIV-1 entry, particularly regarding their activity on HIV-1 viral membrane fusion. Several reports identify purinergic receptor antagonists that inhibit HIV-1 membrane fusion; these drugs are suspected to act through antagonizing Env-chemokine receptor interactions. They also appear to abrogate activity of downstream mediators that potentiate activation of the NLRP3 inflammasome pathway. Here we review the literature on purinergic receptors, the drugs that inhibit their function, and the evidence implicating these receptors in HIV-1 entry.

BACKGROUND: During the natural history, the incidence HTLV-1 related diseases were 0.5% until 10% after decades of infection. Despite relative low lethality, previous study observed that HTLV-1 infection is associated with significantly increased mortality. For example, the incidence of ATLL and HAM/TSP, co-infections with HIV and HCV, parasitic co-infection with Strongyloides stercoralis may increase morbidity and mortality. OBJECTIVE: Determine the mortality rate and its major variables as possible risk factors, analyzing the HTLV Clinic at Emilio Ribas Institute, a continuous open cohort of HTLV patients since 1997. METHODS: This cohort open cohort possesses new patients added at a rate of approximately 50 patients/year. There were 1100 HTLV-infected subjects identified by September 30th 2018. All clinical data including clinical and laboratory data, which have been updated on a regular basis over the last 22 years, were entered into a specific REDCap database. The Ethical Board of the IIER approved the protocol. RESULTS: Along 22 years of clinical care in the HTLV-out clinic, 727 HTLV-1-infected subjects and 248 HAM/TSP cases. Four-hundred sixty-eight patients of whom remaining under active follow up. The mean follow-up time of the cohort was 12 years. Twenty-seven patients died (median age of 51,5 years old). From this total, 13 was asymptomatic carriers and 14 HAM/TSP subjects, 12 of asymptomatic and seven HAM/TSP possess co-infected with HIV or/and HCV. Other seven presented HAM/TSP without co-infection, and all was female, and sepsis was the majority cases the majority of cases of sepsis were related to infection of the urinary tract (p=0.058) and to ulcers of decubitus (p=0.021), and it was associated with risk factors for mortality. CONCLUSIONS: Overall, the mortality rate for HAM-TSP patients was six percent. The mean life expectancy in Brazil is about 78 years in 2018, twenty-two years higher than HAM/TSP patients without any other coinfection. This study highlights the possibility of higher death risk among HTLV-1-infected patients and HAM/TSP subjects in Brazil, and identified some risk factors for this outcome. Also, these patients may possess higher risk for morbidity, usually urinary tract infections and decubitus scars, which could increase risk for death.

Plasma osteopontin (OPN) levels are elevated in mycobacterium tuberculosis patients and may involve granuloma formation. New inhibitors using brefelamide, an aromatic amide isolated from Dictyostelium cellular slim molds which may inhibit OPN transcription at concentration of 1M, were synthesized as compounds C, D and E. Their inhibitory activity against OPN synthesis in phorbol 12-myristate 13-acetate (PMA)-stimulated THP-1 cells was confirmed using enzyme-linked immunosorbent assay (ELISA), a multicolor immune-fluorescent microscope and western blot analysis. For the ELISA performed using the full-length OPN, each compound showed significant inhibition. Detailed analysis were done using C and D. They also showed inhibitory activity when used on another ELISA system to detect the immune-related form of OPN and their IC50 were 0.6 and 1.2 M for compounds C and D, respectively. Fluorescent particle count of stained cell numbers by O-17 showed the inhibition. Antibodies for O-17 and 34E3, which recognize OPN N-terminus and thrombin-cleaved site, respectively, detected distinct bands on the western blots following PMA stimulation. The decrease in full-length OPN detected by O-17 in the compound-treated cells was identified via western blot analysis. These newly-developed compounds may therefore be used in clinical trials for cancer and infectious diseases.

Data that have been processed to allow analysis with a minimum of additional user effort are often referred to as Analysis Ready Data (ARD). The ability to perform large scale Landsat analysis relies on the ability to access observations that are geometrically and radiometrically consistent, and have had non-target features (clouds) and poor quality observations flagged so that they can be excluded. The United States Geological Survey (USGS) has processed all of the Landsat 4 and 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) archive over the conterminous United States (CONUS), Alaska, and Hawaii, into Landsat ARD. The ARD are available to significantly reduce the burden of pre-processing on users of Landsat data. Provision of pre-prepared ARD is intended to make it easier for users to produce Landsat-based maps of land cover and land-cover change and other derived geophysical and biophysical products. The ARD are provided as tiled, georegistered, top of atmosphere and atmospherically corrected products defined in a common equal area projection, accompanied by spatially explicit quality assessment information, and

This paper deals with the merger of the two lightweight stream ciphers—A5/1 and Trivium. The idea is to make the key stream generation more secure and to remove the attacks of the individual algorithms. The bits generated by the Trivium cipher (output) will act as the input of the A5/1 cipher. The registers used in the A5/1 cipher will be filled by the output bits of the Trivium cipher. The three registers will then be connected to generate an output which will be our required key stream.

Water quality is the measure of chemical, physical and biological suitability of water in relation to natural effects and intended purpose which may affect human health and aquatic life. Assessment of water quality is very essential for the management of water resources and human health. Traditionally, in-situ measurements have been used to obtain the water quality parameters of the water bodies. However, with the availability of satellite images, researchers have shown that satellite images are a reliable tool that can be used to estimate water quality. Satellite image-derived water quality parameters provide extensive spatial extent and large temporal variations when compared to traditional in situ sample collection and laboratory measurements. The present work estimated several parameters for quality of water in the Kamuzu reservoir of Lilongwe River for the 2013-2020 period using Sentinel-2 and Landsat-8 satellite images. The band ratio algorithms were used to retrieve Chlorophyll a (Chl-a), Turbidity, Total Suspended Matter (TSM), Secchi depth, Coloured Dissolved Organic Matter (CDOM), and Cyanobacteria from the reservoir. Turbidity and TSM were compared with the in-situ data collected over the same period. The comparison indicated R² of 0.9 and 0.69 for TSM and Turbidity respectively from Sentinel-2 images whereas R² of 0.56 and 0.61 was obtained using Landsat 8 images which are quite encouraging. The other set of results included the spatial distribution maps of water quality parameters using Landsat-8 and Sentinel-2 satellite data. It was observed that the spatial distribution of water quality parameters, except for CDOM and Cyanobacteria, showed very good distribution and matches with the theoretical results. However, for CDOM and Cyanobacteria, the distribution was almost similar for the entire study area and the band ratio algorithm may not be able to estimate them quite reasonably. This research reiterates the need for the use of remote sensing in estimating the water quality parameters and may be a substitute to the in-situ data, in terms of spread and frequency, which is very common to most of the water bodies, across the globe.

A national green planning strategy has recently been introduced in the Italian urban planning sector, aimed at making all local initiatives undertaken nationwide consistent with each other. At a regional level, Friuli Venezia-Giulia has recently implemented a Landscaping Plan, which is of an urban planning and ecological nature at an intermediate level between national and local. This article describes the local green plan of Latisana, which has been entitled Ecopolitana, given that it is represents the experimental phase, at a regional level, of the possibilities offered by landscape planning and design. Specifically, it outlines the multi-disciplinary approach used, demonstrating how landscape planning can be compared to the sustainable development of cities, with specific regard to the agricultural sector. Regarding the agricultural sector, a low-intensity cropping model is also suggested, based on the principles of agroecology and landscape ecology, which has already been implemented in the historical rural landscape of Plasencis (UD) and developed through GIS analysis and remote sensing processes. Its aim is to be the starting point for the achievement of the goals set in the 2030 Agenda, and especially Goals 13 (Climate action) and 15 (Life on land), given the current scarcity of agroecological infrastructures in the area of Latisana (UD) and the high percentage of soil used for intensive cropping.

The objective of this study was to evaluate the efficacy of one-step nucleic acid amplification (OSNA) for the detection of sentinel lymph node (SLN) metastasis compared to standard pathological ultrastaging in patients with early-stage endometrial cancer (EC). A total of 526 SLNs from 191 patients with EC were included in the study. 379 SLNs (147 patients) were evaluated by both methods, OSNA and standard pathological ultrastaging. The central 1-mm portion of each lymph node was subjected to semi-serial sectioning at 200-μm intervals and examined by hematoxylin-eosin and immunohistochemistry with CK19; the remaining tissue was analysed by OSNA for CK19 mRNA. The OSNA assay detected metastases in 19.7% of patients (14.9% micrometastasis and 4.8% macrometastasis), whereas pathological ultrastaging detected metastasis in 8.8% of patients (3.4% micrometastasis and 5.4% macrometastasis). Using the established cut-off value for detecting SLN metastasis by OSNA in EC (250 copies/μl), the sensitivity of the OSNA assay was 92%; specificity was 82%; diagnostic accuracy was 83%, and the negative predictive value was 99%. Discordant results between both methods were recorded in 20 patients (13.6%). OSNA resulted in an upstaging in 12 patients (8.2%). OSNA could aid in the identification of patients requiring adjuvant treatment at the time of diagnosis.

We present a simplified atmospheric correction algorithm for the snow/ice albedo retrieval using single view satellite measurements. The validation of the technique is performed using Ocean and Land Colour Instrument (OLCI) on board Copernicus Sentinel - 3 satellite and ground spectral or broadband albedo measurements from locations on the Greenland ice sheet and in the French Alps. Through comparison with independent ground observations, the technique is shown to perform accurately in a range of conditions from a 2100 m elevation mid-latitude location in the French Alps to a network of 15 locations across a 2390 m elevation range in seven regions across the Greenland ice sheet. Retrieved broadband albedo is accurate within 5% over a wide (0.5) broadband albedo range of the (N = 4,155) Greenland observations and with no apparent bias.

The Sentinel Application Platform (SNAP) architecture facilitates Earth Observation data processing (http://step.esa.int/main/toolboxes/snap/). In this work we present results from a new Snow Processor for SNAP. We also describe physical principles behind the developed snow property retrieval technique based on the analysis of Ocean and Land Colour Instrument (OLCI) onboard Sentinel-3A/B measurements over clean and polluted snow fields. Using OLCI spectral reflectance measurements in the range 400-1020nm, we derive important snow properties such as spectral and broadband albedo, snow specific surface area, snow extent and grain size on the spatial grid of 300m. The algorithm also incorporates cloud screening and atmospheric correction procedures over snow surfaces. We present validation results using ground measurements from Antarctica, the Greenland ice sheet and the French Alps. We find the spectral albedo retrieved with accuracy of better than 3% on average, making our retrievals sufficient for a variety of applications. Broadband albedo is retrieved with the average accuracy of about 5% over snow. Therefore, the uncertainties of satellite retrievals are close to experimental errors of ground measurements. The retrieved surface grain size shows good agreement with ground observations. Snow specific surface area observations are also consistent with our OLCI retrievals. We present snow albedo and grain size mapping over the inland ice sheet of Greenland for areas including dry snow, melted/melting snow and impurity rich bare ice. The algorithm can be applied to OLCI Sentinel-3 measurements providing an opportunity for creation of long – term snow property records essential for climate monitoring and data assimilation studies - especially in the Arctic region, where we face rapid environmental changes including reduction of snow/ice extent and, therefore, planetary albedo.

Herpes simplex virus type 1 (HSV-1) is among the most widely spread viruses on the planet. However, the rate of binding to the receptor is not among the greatest ones. Gibbs energy of binding, which represents the driving force for antigen-receptor binding, of HSV-1 is less negative than can be expected. Furthermore, the Gibbs energy of biosynthesis of HSV-1 is among the most negative in nature. This implies high rate of multiplication of HSV-1. Obviously, HSV-1 uses specific strategy in virus-host interaction. While most other viruses increase infectivity through a high rate of antigen-receptor binding and cell entry/ highly negative Gibbs energy of binding, HSV-1 uses another strategy. It is related to a relatively slow reaction of antigen-receptor binding, which is followed by a higher rate of multiplication, which is a consequence of a highly negative Gibbs energy of biosynthesis. We can conclude empirically that both strategies are successful.

Theaflavins, the characteristic and bioactive polyphenols in black tea, possess the potential improvement effects on insulin resistance-associated metabolic abnormalities including obesity and type 2 diebetes. However, the molecular mechanisms of theaflavins improving insulin sensitivity are still not clear. In this study, we investigated the protective effects and mechanisms of theaflavins on palmitic acid-induced insulin resistance in HepG2 cells. Theaflavins could significantly increase glucose uptake of insulin-resistant cells at noncytotoxic doses. This activity was mediated by upregulating the glucose transporter 4 protein expression, increasing the phosphorylation of IRS-1 at Ser307, and reduced the phosphor-Akt (Ser473) level. Moreover, theaflavins were found to enhance mitochondrial DNA copy number through down-regulate the PGC-1β mRNA level and up-regulate PRC mRNA expression in insulin-resistant HepG2 cells. These results indicated that theaflavins could improve free fatty acid-induced hepatic insulin resistance by promoting mitochondrial biogenesis, and were promising functional food and medicines for insulin resistance-related disorders.

A finite-type immersion or smooth map is a nice tool to classify submanifolds of Euclidean space, which comes from eigenvalue problem of immersion. The notion of generalized 1-type is a natural generalization of those of 1-type in the usual sense and pointwise 1-type. We classify ruled surfaces with generalized 1-type Gauss map as part of a plane, a circular cylinder, a cylinder over a base curve of an infinite type, a helicoid, a right cone and a conical surface of $G$-type.

Let {$\Gamma(k)$} denote {$(k-1)!$}, and let {$\Gamma_{n}(k)$} denote {$(k-1)!$}, where {$n \in \{3,\ldots,16\}$} and {$k \in \{2\} \cup [2^{\textstyle 2^{n-3}}+1,\infty) \cap \mathbb N$}. For an integer {$n \in \{3,\ldots,16\}$}, let $\Sigma_n$ denote the following statement: if a system of equations {${\mathcal S} \subseteq \{\Gamma_{n}(x_i)=x_k:~i,k \in \{1,\ldots,n\}\} \cup \{x_i \cdot x_j=x_k:~i,j,k \in \{1,\ldots,n\}\}$} with $\Gamma$ instead of $\Gamma_{n}$ has only finitely many solutions in positive integers {$x_1,\ldots,x_n$}, then every tuple {$(x_1,\ldots,x_n) \in (\mathbb N \setminus \{0\})^n$} that solves the original system ${\mathcal S}$ satisfies {$x_1,\ldots,x_n \leqslant 2^{\textstyle 2^{n-2}}$}. Our hypothesis claims that the statements {$\Sigma_{3},\ldots,\Sigma_{16}$} are true. The statement {$\Sigma_6$} proves the following implication: if the equation {$x(x+1)=y!$} has only finitely many solutions in positive integers $x$ and $y$, then each such solution {$(x,y)$} belongs to the set {$\{(1,2),(2,3)\}$}. The statement {$\Sigma_6$} proves the following implication: if the equation {$x!+1=y^2$} has only finitely many solutions in positive integers $x$ and $y$, then each such solution {$(x,y)$} belongs to the set {$\{(4,5),(5,11),(7,71)\}$}. The statement {$\Sigma_9$} implies the infinitude of primes of the form {$n^2+1$}. The statement {$\Sigma_9$} implies that any prime of the form {$n!+1$} with {$n \geqslant 2^{\textstyle 2^{9-3}}$} proves the infinitude of primes of the form {$n!+1$}. The statement {$\Sigma_{14}$} implies the infinitude of twin primes. The statement {$\Sigma_{16}$} implies the infinitude of Sophie Germain primes.

In this research, new setting is introduced for new SuperHyperNotions, namely, an 1-failed SuperHyperForcing and Neutrosophic 1-failed SuperHyperForcing. Assume a SuperHyperGraph. Then an ``1-failed SuperHyperForcing'' $\mathcal{Z}(NSHG)$ for a neutrosophic SuperHyperGraph $NSHG:(V,E)$ is the maximum cardinality of a SuperHyperSet $S$ of black SuperHyperVertices (whereas SuperHyperVertices in $V(G) \setminus S$ are colored white) such that $V(G)$ isn't turned black after finitely many applications of ``the color-change rule'': a white SuperHyperVertex is converted to a black SuperHyperVertex if it is the only white SuperHyperNeighbor of a black SuperHyperVertex. The additional condition is referred by ``1-'' about the usage of any black SuperHyperVertex only once to act on white SuperHyperVertex to be black SuperHyperVertex; a ``neutrosophic 1-failed SuperHyperForcing'' $\mathcal{Z}_n(NSHG)$ for a neutrosophic SuperHyperGraph $NSHG:(V,E)$ is the maximum neutrosophic cardinality of a SuperHyperSet $S$ of black SuperHyperVertices (whereas SuperHyperVertices in $V(G) \setminus S$ are colored white) such that $V(G)$ isn't turned black after finitely many applications of ``the color-change rule'': a white SuperHyperVertex is converted to a black SuperHyperVertex if it is the only white SuperHyperNeighbor of a black SuperHyperVertex. The additional condition is referred by ``1-'' about the usage of any black SuperHyperVertex only once to act on white SuperHyperVertex to be black SuperHyperVertex. Assume a SuperHyperGraph. Then an ``$\delta-$1-failed SuperHyperForcing'' is a \underline{maximal} 1-failed SuperHyperForcing of SuperHyperVertices with \underline{maximum} cardinality such that either of the following expressions hold for the (neutrosophic) cardinalities of SuperHyperNeighbors of $s\in S:$ $~|S\cap N(s)| > |S\cap (V\setminus N(s))|+\delta,~|S\cap N(s)| < |S\cap (V\setminus N(s))|+\delta.$ The first Expression, holds if $S$ is an ``$\delta-$SuperHyperOffensive''. And the second Expression, holds if $S$ is an ``$\delta-$SuperHyperDefensive''; a``neutrosophic $\delta-$1-failed SuperHyperForcing'' is a \underline{maximal} neutrosophic 1-failed SuperHyperForcing of SuperHyperVertices with \underline{maximum} neutrosophic cardinality such that either of the following expressions hold for the neutrosophic cardinalities of SuperHyperNeighbors of $s\in S:$ $~|S\cap N(s)|_{neutrosophic} > |S\cap (V\setminus N(s))|_{neutrosophic}+\delta,~ |S\cap N(s)|_{neutrosophic} < |S\cap (V\setminus N(s))|_{neutrosophic}+\delta.$ The first Expression, holds if $S$ is a ``neutrosophic $\delta-$SuperHyperOffensive''. And the second Expression, holds if $S$ is a ``neutrosophic $\delta-$SuperHyperDefensive''. A basic familiarity with SuperHyperGraph theory and neutrosophic SuperHyperGraph theory are proposed.

Abstract The expression patterns of microRNAs (small non-coding RNAs) are altered in many biological processes such as myogenesis. In this study, we aimed to investigate the impact of predicted miR-202, its target genes Akt2 and Rock-1 as a potential regulator of myoblast in the myocyte differentiation process using the C2C12 cell line. After confirmation of the differentiation process induced by 3% horse serum, the expression level of miRNA and its targets were evaluated. In the following, a luciferase assay was conducted to approve the effect of miRNA on its target. Our results indicated that miR-202 and Akt2 were significantly up-regulated during differentiation, while Rock-1 was downregulated. Co-transfection of miRNA with psiCHECK2-Rock-1 significantly presented that Rock-1 was directly targeted by miR-202. On the contrary, miR-202 has failed to enforce its inhibitory effect on Akt2 expression. In particular, miR-202 seems to be a regulator of muscle differentiation pathway thought targeting Rock-1.

Abstract: Oxidative metabolism is crucial for leukemic stem cell (LSC) function and drug resistance in acute myeloid leukemia (AML). Mitochondrial metabolism also affects the immune system and therefore the antitumor response. Modulation of oxidative phosphorylation (OxPHOS) has emerged as a promising approach to improve therapy outcome for AML patients. However, the effect of mitochondrial inhibitors on the immune compartment in the context of AML is yet to be explored. Immune checkpoints such as the ecto-nucleotidase CD39 and programmed dead ligand 1 (PD-L1) have been reported to be expressed in AML and linked to chemoresistance and poor prognosis. In the present study, we first demonstrated that a novel selective electron transfer chain complex (ETC) I inhibitor, EVT-701, decreased OxPHOS metabolism of murine and human cytarabine (AraC)-resistant leukemic cell lines. Furthermore, we showed that, while AraC induced immune response regulation by increasing CD39 expression and by reinforcing interferon-γ/PD-L1 axis, EVT-701 reduced CD39 and PD-L1 expression in vitro in a panel of both murine and human AML cell lines, especially upon AraC treatment. Altogether, this work uncovers a non-canonical function of ETCI in controlling CD39 and PD-L1 immune checkpoints, thereby improving the anti-tumor response in AML.

Herpesviruses produce a plethora of pleomorphic and heterogeneous particle populations. The composition and biological role of these is not understood. Detailed analysis has been challenging due to the lack of multidimensional identification and purification methodologies. Fluorescence-activated cell sorting (FACS), originally developed to sort objects with at least ten thousand-fold larger volumes, has recently been applied to cellular exosomes as well as viral particles and has been dubbed nanoscale flow cytometry or “flow virometry”. In comparison to other nanoparticles, herpesvirus concentrations can be measured with high precision using simple culturing methods. Here, we used this unique capability to evaluate a standard FACS sorter. We demonstrate that detection and separation capabilities were insufficient to distinguish infectious fluorescent viral populations from populations lacking fluorescence and infectivity. Moreover, fluorescent populations did not contain single virus particles but mostly aggregates. On top, analysis of viral samples by flow cytometry was confounded by swarm detection, as multiple objects are measured simultaneously and interpreted as a single object. Despite these technical difficulties, comparison of crude supernatant to gradient purified HCMV revealed that infectious virus is a minor proportion of the particles released from infected cells. Our data stress the need for a set of standardized controls and protocols when applying FACS to biological nanoparticles and highlights technical challenges that need to be solved before flow virometry can achieve its full potential.

It has been long recognized that under hypoxia conditions cancer cells reprogram their metabolism through shift from oxidative phosphorylation (OXPHOS) to glycolysis to meet elevated requirements in energy and nutrients for proliferation, migration and survival. However, data accumulated over the last years increasingly evidence that cancer cells can revert from glycolysis to OXPHOS and maintain both reprogrammed and oxidative metabolism even in the same tumor. The phenomenon denoted as cancer cell metabolic plasticity or hybrid metabolism depends on a tumor micro-environment, which is highly heterogeneous and influenced by intensity of vasculature and blood flow, oxygen concentration, nutrient and energy supply, and requires regulatory interplay between multiple oncogenes, transcription factors, growth factors, reactive oxygen species (ROS), etc. Hypoxia-inducible factor-1 (HIF-1) and AMP-activated protein kinase (AMPK) represent key modulators of switch between reprogrammed and oxidative metabolism. The present review focuses on cross-talks between HIF-1, GLUTs, and AMPK and other regulatory proteins including oncogenes such as c-Myc, p53 and KRAS, growth factor-initiated PKB/Akt, PI3K and mTOR signaling pathways and tumor suppressors such as LKB1 and TSC1 in controlling cancer cell metabolism. The multiple switches between metabolic pathways can underlie chemo-resistance to conventional anti-cancer therapy and should be taken into account in choosing molecular targets to discovery novel anti-cancer drugs.

Postharvest 1-MCP treatment can inhibit lignification of fruit and vegetables. It has been suggested that the mode of action of 1-MCP is through inhibiting ethylene production, but the effect of 1-MCP and ethylene on lignification of common bean remain unknown. This work compared the effect of 0.5 μL L-1 1-MCP and 100 μL L-1 ethylene on lignification of common bean during storage. Postharvest 1-MCP significantly inhibited the increase of lignified cell group, sclerenchyma became thicker, vascular bundles thickened and lignified cells grew during storage, while ethylene is the opposite. 1-MCP inhibited the increase in respiration rate, sucrose phosphate synthase (SPS), sucrose synthase (SuSy), phenylalanine ammonialyase (PAL), cinnamyl-alcohol dehydrogenase (CAD), and peroxidase (POD) activities, whereas ethylene hastened all. Ethylene treatment can be stimulated and 1-MCP inhibited the decline of reducing sugar and cellulose content. Expression of genes, including PvACO1, PvAOG1, PvSuSy, PvPAL3, and Pv4CL1, PvCOMT1 together with lignin content were significantly increased in common bean during storage. 1-MCP treatment markedly inhibited expression of PvACO1, PvSuSy2, PvPAL3, Pv4CL1 and PvCOMT1 genes, while strengthened expression of PvETR1 and PvAOG1, while ethylene was opposite. This work provides evidence that ethylene plays a key role in regulating the lignin biosynthesis of common bean, and also provides strategies for the maintenance of fruit quality during storage.

Metastasis is a major cause factor for breast cancer (BC)-associated mortality. During the metastatic process, disseminated tumor cells (DTCs) detach from the primary sites, and enter the bloodstream and establish the secondary colonies. Recent studies have provided substantial evidence for the importance of Notch signaling in BC metastasis. Therefore, this review focuses on the mechanisms by which Notch contributes to the origin of BC DTCs, increases their motility, regulates their intravasation and extravasation, protects them from host surveillance, and finally facilitates colonization. Identification of the mechanisms underlying Notch-related BC metastasis will lead to the development of novel Notch-targeted therapeutic strategies to reduce metastasis and significantly improve outcomes.

Aims/Hypothesis): Type 1 diabetes is an immune-mediated disease with destruction of the pancreatic β-cells, a process that is conditioned by multiple genes and other factors. HLA counts as the major susceptibility gene. Significant variations in HLA genetic susceptibility to type 1 diabetes between Caucasians, African and Asian and other ethnic groups may have led to the variation in incidence of type 1 diabetes globally. Type 1 diabetes is characterized upon HLA identification. In this chapter we discuss global variations in genetic susceptibility of HLA with regard to type 1 diabetes globally with a particular attention to Arab population. Methods): Haplotype configuration of HLA class I A, B, C and Class II –DR/DQ/DP were studied in Caucasians, African and Asian and in Arab population to see if that is responsible for the exponential rise in the rate of type 1 diabetes. Results): Although Arabs have one of the highest global incidence and prevalence rates of type 1 diabetes, unfortunately, there is a dearth amount of information regarding HLA genetic susceptibility to type 1 diabetes in the Arab world. HLA haplotype configurations contribute to its risk value. However, out of an insufficient present study there are examples of misjudgment of HLA risk according to HLA alleles rather than haplotypes. Conclusion): To date HLA outlooks for the characterization of type 1 diabetes. There is an ethnicity difference in HLA characteristics which is responsible for variation in type 1 diabetes. Although Arab population have contributed heavily in the rise of burden of type 1 diabetes, however, there is significantly a dearth amount of studies on HLA in Arab population. Obviously, any future prediction, prevention or cure of the disease will be based on the HLA genetics. There is a dire need for a systematic screening of HLA for Arab population with type 1 diabetes, identification of Arab HLA-risk values and identify those who are prone to get the disease.

The scale and accessibility of passive global surveillance have rapidly increased over time. This provides an opportunity to calibrate the performance of models, algorithms, and reflectance ratios between remote sensing devices. Here we test the sensitivity and specificity of Eucalypt chlorophyll-a reflectance ratio (ECARR) and Eucalypt chlorophyll-b reflectance ratio (ECBRR) to remotely identify eucalypt vegetation in Queensland, Australia. We compare reflectance ratio values from Sentinel-2 and Planet imagery across four sites of known vegetation composition. All imagery was transformed to reflectance values and Planet imagery was additionally scaled to harmonize across Planet Scenes. ECARR can identify eucalypt vegetation remotely with high sensitivity, but shows low specificity and is impacted by the density of the vegetation. ECBRR reflectance ratios show similar sensitivity and specificity when identifying eucalypt vegetation but with values an order of magnitude smaller than ECARR. We find that ECARR was better at identifying eucalypt vegetation in the Sentinel-2 imagery than Planet imagery. ECARR can serve as a general chlorophyll indicator but is not a specific index to identify Eucalyptus vegetation with certainty.

The processing of cloud free geo-referenced imagery is one of the preliminary processing step of any land application. This letter describe the methodology developed to obtain a seamless cloud free composite of Africa for 2016 using Sentinel-2A data at 10 meters resolution freely available from the European Space Agency. The method is based on an hybrid method resulting from the merging of the two most robust time series methods namely the "darkest pixel" and the "maximum NDVI" previously developed with AVHRR time series.

In this study, a structural profiling of equid alphaherpesvirus 1 (EHV-1) transcriptome was carried out using next-generation (Illumina) and third-generation (Oxford Nanopore Technologies) sequencing platforms. We annotated the canonical mRNA molecules and their isoforms, including transcript start and end site isoforms, and splice variants. Additionally, a number of putative 5′-truncated mRNAs containing shorter in-frame ORFs were detected. We also demonstrated that EHV-1 produces a high number of non-coding transcripts, including antisense and intergenic RNAs. One of the most remarkable features of the EHV-1 is the generation of abundant fusion transcripts some of which encoding chimeric polypeptides. We observed a higher number of splicing and transcriptional overlaps than in related viruses. Additionally, we found that many upstream genes of tandem gene clusters have their own transcript end sites (TESs) besides the co-terminal TESs, which is rare in other alphaherpesviruses. We show here that the replication origins (OriS and OriL) of the virus are co-localized with promoter sequences and overlap with specific RNA molecules. Furthermore, we discovered a novel non-coding RNA (designated as NOIR) that overlaps the 5′-ends of the longer transcript variants encoded by the two main transactivator genes ORF64 and 65 bracketing the OriL. These all suggest the existence of a central regulatory system which controls the genome-wide transcription and the replication through a mechanism based on the interference between the machineries carrying out the synthesis of DNA and RNA.

Evidence has recently emerged on the influence of gender on the immune system. In this systematic review and meta-analysis of phase III randomized clinical trials (RCTs), we explored the impact of gender on survival in patients with advanced cancer treated with immune checkpoint inhibitors (ICIs). We performed a comprehensive search of the literature updated to April 2018, including the Cochrane Central Register of Controlled Trials, PubMed, and EMBASE. We extracted data on study characteristics and risk of bias in duplicate. Of 423 unique citations, 21 RCTs were included, inherently to 12,635 patients. Both males and females showed reduced risk of death associated with ICIs use (HR 0.73, p<0.001 and HR 0.77, p<0.001, respectively). Subgroup analyses by specific ICI showed similar OS in both genders for anti-PD-1/PDL-1. Anti-CTLA-4 use was associated with longer OS in men only (HR 0.77, p<0.012), with the exception of melanoma (in women, HR 0.80, p=0.006). PFS was longer in men than in women (HR 0.67, p<0.001 and HR 0.77, p=0.100, respectively). Conclusively, ICIs use was associated with more favorable outcomes in men, particularly for anti-CTLA-4 agents. In melanoma, not gender-related factors may influence the anti-tumor immune response evoked by ICIs.

Programmed cell death protein 1 (PD-1) is a biomarker on the surface of cells that has a role in promoting self-tolerance by suppressing the inflammatory activity of T cells. In this work, one peptide of PD-1 was used as the template in molecular imprinting. The magnetic peptide-imprinted poly(ethylene-co-vinyl alcohol) composite nanoparticles (MPIP NPs) were characterized by dynamic light scattering (DLS), high-performance liquid chromatography (HPLC), Brunauer-Emmett-Teller (BET) analysis and superconducting quantum interference device (SQUID) analysis. Natural killer-92 (NK-92) cells were added to these composite nanoparticles and then incubated with human hepatoma (HepG2) cells. The viability and apoptosis pathway of HepG2 were then studied using cell counting kit-8 (CCK8) and the quantitative real-time polymerase chain reaction (qRT-PCR), respectively. These nanoparticles were found significantly enhance the activity of natural killer cells toward HepG2 cells by increasing expression of NK-kB, caspase 8 and especially caspase 3.