Wave steepness is presented as an extension and a valuable add-on to the conventional set of sea state parameters retrieved from satellite altimetry data. Following physical model based on recent advances of weak turbulence theory wave steepness is estimated from directly measured spatial gradient of wave height. In this way the method works with altimetry trajectories rather than with point-wise data. Moreover, in contrast to widely used parametric models this approach provides us with instantaneous values of wave steepness and period. Relevance of single-track estimates of wave steepness (period) is shown for wave climate studies and confirmed by a simple probabilistic model. The approach is verified via comparison against buoy and satellite data including crossover points for standard 1 second data of Ku-band altimeters. High quality of the physical model and robustness of the parametric ones are examined in terms of global wave statistics. Prospects and relevance of both approaches in the ocean wave climate studies are discussed.

Radar altimetry provides valuable measurements to characterize the state and the evolution of the Antartica and Greenland ice sheet cover. Global Navigation Satellite System Reflectometry (GNSS-R) has the potential capacity of complementing the dedicated radar altimeters incrementing the temporal and spatial resolution of the surface height measurements. In this work we perform an study of the Greenland ice sheet using data obtained by the GNSS-R instrument aboard the British TechDemoSat-1 (TDS-1) satellite mission, designed primarily to provide sea state information, like sea surface roughness or wind, but not altimetric products. The data has been analyzed with altimetric methodologies, already proved in aircraft based experiments, to extract signal delay observables to be used to infer the topography of the Greenland cover. The penetration depth of the GNSS signals into ice has also considered. The topographic signal obtained is consistent with those obtained with other passive or active microwave sensors. The main conclusion derived from this work is that GNSS-R also provides valuable measurements of the ice sheet cover and, as taken at a variety of geometries and at least two frequency bands, they prospect different depths into the ice. They have thus potential to complement our understanding of the ice firn and its evolution.

This study investigates the long-term stability of the Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) moderate-resolution Thermal Emissive Bands (M TEBs; M12 – M16) covering a period from February 2012 to August 2020. It also assesses inter-sensor consistency of the VIIRS M TEBs among three satellites-S-NPP, NOAA-20, and NOAA-21 over eight months spanning from March 18 to November 30, 2023. The field of interesting is limited to the ocean surface between 60°S and 60°N, specifically under clear-sky conditions. Taking radiative transfer modeling (RTM) as the transfer reference, we employed the Community Radiative Transfer Model (CRTM) to simulate VIIRS TEB brightness temperature (BTs), incorporating European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis data as inputs. Our results reveal two key findings. Firstly, the reprocessed S-NPP VIIRS TEBs exhibit a robust long-term stability, as demonstrated through analyses of the observation minus background BT differences (O-B ∆BTs) between VIIRS measurements (O) and CRTM simulations (B). The drifts of the O-B BT differences are consistently less than 0.105 K/Decade across all S-NPP VIIRS M TEB bands. Notably, observations from VIIRS M14 and M16 stand out with drifts well within 0.04 K/Decade, reinforcing their exceptional reliability for climate change studies. Secondly, excellent inter-sensor consistency among these three VIIRS instruments is confirmed through the double-difference analysis method (O-O). This method relies on the O-B BT differences obtained from daily data. The mean inter-VIIRS O-O BT differences remain within 0.08 K for all M TEBs, except for M13. Even in the case of M13, the O-O BT differences between NOAA-21 and NOAA-20/S-NPP have values of 0.312 K and 0.234 K, respectively, which are comparable to the 0.2 K difference between some TEBs among VIIRS and MODIS. These disparities are primarily attributed to the significant differences in the Spectral Response Function (SRF) of NOAA-21 compared to NOAA-20 and S-NPP. Our study confirms the RTM-based TEB quality evaluation method’s versatility and effectiveness in assessing long-term sensor stability and inter-sensor consistency. The double-difference approach effectively mitigates uncertainties and biases inherent to CRTM simulations, establishing itself a robust mechanism for assessing inter-sensor consistency. In addition, we’ve observed that, except for M13, M12 always exhibits larger spatial variations of O-O BT differences with greater uncertainties compared to the other M TEBs. The influence of solar contribution through sea surface reflection on the Top of Atmosphere (TOA) radiance measurements during daytime in M12 should not be underestimated.

In this study, a new approach for estimating volume variations of lakes and reservoirs using water levels from satellite altimetry and surface areas from optical imagery is presented. Both input data sets, namely water level time series and surface area time series, are provided by DGFI-TUM‘s Database of Hydrological Time Series of Inland Waters (DAHITI). The approach is divided into three parts. In the first part, a hypsometry model based on the new modified Strahler approach is computed by combining water levels and surface areas. The hypsometry model describes the dependency between water levels and surface areas of lakes and reservoirs. In the second part, a bathymetry between minimum and maximum surface area is computed. For this purpose, DAHITI land-water masks are stacked using water levels derived from the hypsometry model. Finally, water levels and surface areas are intersected with the bathymetry to estimate a time series of volume variations in relation to the minimum observed surface area. The results are validated with volume time series derived from in-situ water levels in combination with bathymetric surveys. In this study, 28 lakes and reservoirs located in Texas are investigated. The absolute volumes of the investigated lakes and reservoirs vary between 0.099 km³ and 6.448 km³. The correlation coefficients of the resulting volume variation time series with validation data vary between 0.80 and 0.99. Overall, the relative errors with respect to volume variations vary between 2.8% and 14.9% with an average of 8.3% for all 28 investigated lakes and reservoirs. When comparing the resulting RMSE with absolute volumes, the absolute errors vary between 1.5% and 6.4% with an average of 3.1%. This study shows that volume variations can be calculated with a high accuracy which depends essentially on the quality of the used water levels and surface areas. In addition, this study provides a hypsometry model, high-resolution bathymetry and water level time series derived from surface areas based on the hypsometry model. All data sets are publicly available on the Database of Hydrological Time Series of Inland Waters (DAHITI, https://dahiti.dgfi.tum.de).

An improved FSM method is used in geophysical and environmental corrections to enhance the final product of the along track Jason-2 SLA data and extend it near the Red Sea borders. In this study the ionospheric correction range, wet tropospheric correction range, sea state bias correction range and dry tropospheric correction range are enhanced and improved using FSM01, which helped to retrieve three more tracks (106, 170 and 234), earlier neglected by the distribution centers, and extend the tracks towards the coast. The FSM01 SLA is compared with Jason-2 SLA and AVISO SLA for the available 5 tracks, in which the FSM01 SLA show a good agreement and higher correlation with the Jason-2 SLA compared with that of AVISO, in addition to that it fills the gaps in the times series of all tracks. The new retrieved tracks also compared with those retrieved by AVISO, both data show similar variability, with FSM01 SLA show no gaps in the time series. The FSM01 SLA also extended towards the coast and show high correlation with the coastal tide measurements.

This study introduces a technique to uncover concealed patterns in satellite altimetry data, reflecting sea level variations in inland water bodies. We applied the methodology to the Caspian Sea, using altimetry data from four satellite missions over 27 years (1993-2020): TOPEX/Poseidon, Jason1, Jason2, and Jason3. The approach involves two steps: estimating sea level trends and identifying breakpoints that indicate trend shifts; and leveraging time lags between breakpoints at various locations to classify water drainage areas according to their degree of influence on each period. By revealing concealed patterns in the altimetry data, this technique can provide insights to understand the impacts of global warming and local climate changes on sea level fluctuations in the Caspian Sea. We hope that our study can facilitate subsequent interdisciplinary research on the intricate interplay between climate change and hydrological processes in inland water bodies.

Remote sensing data are essential for monitoring Earth’s surface waters, especially since the number of publicly available in-situ data is declining. Satellite altimetry provides valuable information on water level and its variations for lakes, reservoirs, and rivers. In combination with satellite imagery, the derived time series allow for monitoring lake storage changes and river discharge. However, satellite altimetry is limited in spatial resolution due to its measurement geometry, only providing information in nadir direction beneath the satellite’s orbit. In a case study in the Mississippi River Basin (MRB), this study investigates the potential and the limitations of past and current satellite missions to monitor basin-wide storage changes. For that purpose an automated target detection is developed and the extracted lake surfaces are merged with the satellites’ tracks. This reveals that the current altimeter configuration misses about 80% of all lakes larger than 0.1 km2 in the MRB, and still 20% of lakes larger than 10 km2, corresponding to 30% and 7% of surface area, respectively. Past altimetry configurations perform even worse. From the water bodies represented by a global hydrology model, at least 91% of targets and 98% of storage changes are captured by the current altimeter configuration. This will significantly improve with the launch of the planned SWOT mission.

Dry intrusion is an important mid-latitude atmosphere phenomenon within the upper troposphere and lower stratosphere. It is always found to be related to the cyclogenesis, rainstorm, as well as convection generation and precipitation enhancement. Since the atmosphere environment for any of these above-mentioned weather is terribly complicated, those preexisting popular schemes which takes no account of water vapor may not suitable for detecting the dry intrusion related to these weathers. With regard to the merits and demerits of the current preexisting schemes, a new scheme based on Fengyun-2E geo-stationary satellite data is presented in this study to detect the atmospheric dry intrusion. The scheme is set up based on the statistical relationship between water vapor at high level troposphere, the general moist potential vorticity, ozone concentration and upper-level jet. After using the total amount of ozone and ozone profile operational products retrieved by Fengyun-3 Polar Orbiting Meteorological Satellites and the potential vorticity calculated by ECMWF Interim data for validation, this scheme is applied to analyze two typical middle-latitude weather processes. One is the famous Beijing extreme rainfall of 21 July 2012 and the other is a hailstorm occurred on the eastern China during March 19, 2014. A good application effect in both cases suggests that our new method of detecting dry intrusion is feasible and can be helpful in middle-latitude disastrous weather monitoring and forecasting.

The three-dimensional structure of Gulf of Mexico’s warm-core rings, detaching from the Loop Current, is investigated using satellite altimetry and a large set of ARGO float profiles. Reconstruction of the Loop Current rings (LCRs) vertical structure from sea surface height observations is made possible by the use of the gravest empirical modes method (GEM). The GEMs are transfer functions that associate a value of temperature and salinity for each variable pair {dynamic height;pressure}, and are computed by estimating an empirical relationship between dynamic height and the vertical thermohaline structure of the ocean. Between 1993 and 2021, 40 LCRs were detected in the altimetry and their three-dimensional thermohaline structure was reconstructed, as well as a number of dynamically relevant variables (geostrophic and cyclogeostrophic velocity, relative vorticity, potential vorticity, available potential energy and kinetic energy density etc.). The structure of a typical LCR was computed by fitting an analytical stream function to the LCRs dynamic height signature and reconstructing its vertical structure with the GEM. The total heat and salt contents and energy of each LCR were computed and their cumulative effect on the Gulf of Mexico’s heat, salt and energy balance is discussed. We show that LCRs have a dramatic impact on these balances and estimate that residual surface heat fluxes of -13 Wm−2 are necessary to compensate their heat input, while the fresh water outflow of the Mississippi river approximately compensates for their salt excess input. An average energy dissipation of O[10−10−10−9] Wkg−1 would be necessary to balance their energy input.

Satellite altimetry is successfully developing during the past three decades for the sea level, ocean dynamics, coastal oceanography, planetary waves, ocean tides, wind and wave, ice cover, Earth’s gravity field, and climatology research. We propose a new essential add-on of satellite altimetry related to the peculiarities of the orbits of the Topex/Poseidon and Jasons’ satellite missions which were not mentioned before in the scientific publications. Derived subsets of “self-crossover” and “inter-crossover” points in sub-polar latitudes are discussed in detail in the context of water exchange, and wind-wave dynamics, and potential challenges to be solved. The relatively short time lags between measurements at these crossovers provide additional information on anomalies of magnitudes and directions of ocean currents, and characteristics of wind-driven waves. Resulting data snapshots with constant space and time intervals can be regarded as time series of virtual buoys, an analog of continuous buoy measurements of the sea level, wind speed, and wave height. Areas of the World Ocean where these specific crossovers occur are described in the context of water exchange, wind wave studies, and potential challenges to be solved. The value of these special crossovers for studies and monitoring of the sub-polar seas is illustrated by a case study.

Accurate and consistent rainfall observations are vital for climatological studies in support of better planning and decision making. However, estimation of accurate spatial rainfall is limited by sparse rain gauge distributions. Satellite rainfall products can thus potentially play a role in spatial rainfall estimation but their skill and uncertainties need to be under-stood across spatial-time scales. This study aimed at assessing the temporal and spatial performance of seven satellite products (TARCAT (Tropical Applications of Meteorology using SATellite and ground-based observations (TAMSAT) African Rainfall Climatology And Time series), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), Tropical Rainfall Measuring Mission (TRMM-3B43), Climate Prediction Center (CPC) Morphing (CMORPH), the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks- Climate Data Record (PERSIANN-CDR), CPC Merged Analysis of Precipitation (CMAP) and Global Precipitation Climatology Project (GPCP) using gridded (0.05o) rainfall data over East Africa for 15 years(1998-2012). The products’ error distributions were qualitatively compared with large scale horizontal winds (850 mb) and elevation patterns with respect to corresponding rain gauge data for each month during the ‘long’ (March-May) and ‘short’ (October-December) rainfall seasons. For validation only rainfall means extracted from 284 rain gauge stations were used, from which qualitative analysis using continuous statistics of Root Mean Squared Difference, Standard deviations, Correlations, coefficient of determinations (from scatter plots) were used to evaluate the products’ performance. Results revealed rainfall variability dependence on wind flows and modulated by topographic influences. The products’ errors showed seasonality and dependent on rainfall intensity and topography. Single sensor and coarse resolution products showed lowest performance on high ground areas. All the products showed low skills in retrieving rainfall during ‘short’ rainfall season when orographic processes were dominant. CHIRPS, CMORPH and TRMM performed well, with TRMM showing the best performance in both seasons. There is need to reduce products’ errors before applications.

Earth observation satellites can capture images of specific areas in Earth's orbit based on user requirements. Despite the high demand for satellite imagery, satellites remain a scarce resource. When there are many observation tasks that are large and dense, they may interfere with each other due to satellite mobility. To address this issue, a multi-satellite scheduling algorithm based on a task merging mechanism has been proposed. This paper proposes a two-stage approach to the problem: task merging phase and merging task scheduling phase. The task merging phase introduces two methods based on Mean Shift and Complete Graph to generate the merging task set. In the merging task scheduling phase, a multi-satellite scheduling algorithm based on merging tasks is employed to solve the problem. Finally, certain measurement experiments suggest the effectiveness of the algorithm.

This study extends the CGAN-based MODIS cloud vertical-profile (64×64-scene, about 70km width×15km height) retrieval technique developed by Leinonen et al. (2019) [1] to construct seamless 3D cloud fields for the MODIS granules [2]. Firstly, the accuracy and spatial continuity of the Leinonen et al. [1] retrievals are statistically evaluated. Then, according to the characteristics of the retrieval error, a spatially overlapping-scene ensemble generation method and a bi-directional Ensemble Binning Probability Fusion (CGAN-BEBPF) technique are developed, which improves the CGAN retrieval accuracy and support to construct seamless 3D clouds. The CGAN-BEBPF technique involves three steps: cloud masking, intensity scaling, and optimal value selection. It ensures adequate coverage of the low reflectivity areas while preserving the high reflectivity cloud cores. The technique is applied to retrieve the 3D cloud fields of Typhoon Chaba and a multi-cell convective system. The cloud structures of the CGAN-BEBPF results are highly consistent with the ground-based radar observations. It can retrieve weak clouds at the top levels of convective storms and hurricanes that were missed by ground-based radars and fill the gaps of the ground-based radar lower-level PPI scan. Overall, CGAN-BEBPF can be automated to retrieve the rich informative 3D structures of typhoons and severe convective systems over the oceans along the MODIS swath, which certainly presents great value for improving maritime and near-shore typhoons and convection prediction and other cloud-sensitive applications.

In this paper, we studied the problem of early detection of natural phenomena associated with the formation of severe floods and the possibility of constant monitoring of their occurrence. The test object for the study was severe floods on the Crimean Peninsula, which occurred due to heavy rainfall in the summer of 2021. Based on the selected satellite and other data related to this period, an analysis carried out and key atmospheric objects identified, according to the proposed algorithm for their identification and grouping. Based on the results of the work, a positive conclusion made on the issue of their use to identify the likelihood of natural disasters that could lead to floods. In addition, the authors proposed to develop a portal for collecting and processing a large amount of satellite data related to flood prevention and monitoring the development of such processes, and stressed the need to create it.

Irrigation is one of key agricultural management practices of crop cultivation in the world. Irrigation practice is traceable on satellite image. Most irrigated area mapping methods were developed based on time series of NDVI or back scatter coefficient within the growing season. However, it found the winter irrigation out of growing season is also dominating in north China. This kind of irrigation aims to increase the soil moisture for coping with spring drought, and also reduce the wind erosion with the wet soil in the field surface as the strong wind happens in spring. This study developed a remote sensing-based classification approach to identify irrigated fields with Radom Forest algorithm out of growing season. The results showed that the mean of the highest accuracies of 7 RF models was 94.9% and the mean of the averaged accuracies of 7 RF models was 94.1%; the overall accuracy for all 7 outputs was in the range of 86.8-92.5%, Kappa in the range of 84.0-91.0% and F-1 score in the range of 82.1-90.1%. These results showed that the classification was acceptable and not over performed as the accuracies of all classified images were lower than the models. This study also found that irrigation started to apply in early November and irrigated fields were increased and suspended in December and January due to freeze. The newly irrigated fields were found again in March and April when the temperature goes up above zero degree. The area of irrigated fields in the study area were increasing over time with sizes of 98.6, 166.9, 208.0, 292.8, 538.0, 623.1, 653.8 km2 from December to April, accounting for 6.1%, 10.4%, 12.9%, 18.2%, 33.4%, 38.7%, and 40.6% of the total irrigatable land in the study area, respectively.

In recent decades, as eutrophication in inland and coastal waters (ICWs) has increased due to anthropogenic activities and global warming, so has the need for timely monitoring. Compared with traditional sampling and laboratory analysis methods, satellite remote sensing technology can provide macro-scale, low-cost, and near real-time water quality monitoring services. The Geostationary Ocean Color Imager (GOCI), integrated onboard the Communication Ocean and Meteorological Satellite (COMS) from the Republic of Korea, was the first geostationary ocean color observation satellite and was operational from April 1, 2011 to March 31, 2021. Over ten years, GOCI has observed oceans, coastal waters, and inland waters within its 2,500 km×2,500 km target area centered on the Korean Peninsula. The most attractive feature of GOCI, compared with other commonly used watercolor sensors, was its high temporal resolution (1h, eight times daily from 0 UTC to 7 UTC), providing the opportunity to monitor the quality of ICWs, where optical properties can change rapidly throughout the day. This systematic review aims to comprehensively review GOCI features and applications in ICWs, analyzing progress in atmospheric correction algorithms and water quality monitoring. Analyzing 123 articles from the Web of Science and China National Knowledge Infrastructure (CNKI) through a bibliometric quantitative approach, we examined GOCI’s strength and performance with different processing methods. These articles reveal that GOCI played an essential role in monitoring the ecological health of ICWs in its observation area in East Asia. GOCI has led the way to a new era of geostationary ocean satellites, providing new technical means for monitoring water quality in oceans, coastal zones, and inland lakes. We also discuss the challenges encountered by geostationary satellites in monitoring water quality and provide suggestions for improvements.

This study addresses the limited coverage of regulatory monitoring for particulate matter 2.5 microns or less in diameter (PM2.5) in Thailand due to the lack of ground station data by developing a model to estimate daily PM2.5 concentrations in small regions of Thailand using satellite data at a 1-kilometer resolution. The study employs multiple linear regression and three machine learning models and finds that the random forest model performs the best for PM2.5 estimation over the period of 2011-2020. The model incorporates several factors such as Aerosol Optical Depth (AOD), Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), Elevation (EV), Week of the year (WOY), and year and applies them to the entire region of Thailand without relying on monitoring station data. Model performance is evaluated using the coefficient of determination (R2) and root mean square error (RMSE), and the results indicate high accuracy for training (R2: 0.95, RMSE: 5.58 μg/m3), validation (R2: 0.78, RMSE: 11.18 μg/m3), and testing (R2: 0.71, RMSE: 8.79 μg/m3) data. These PM2.5 data can be used to analyze the short- and long-term effects of PM2.5 on population health and inform government policy decisions and effective mitigation strategies.

Background: Amyloid beta peptides (Aβ) have been identified as the main pathogenic agents in Alzheimer’s disease (AD). Soluble Aβ oligomers, rather than monomer or insoluble amyloid fibrils, show membrane-binding capacity to red blood cells (RBCs) and trigger several morphological and functional alterations in RBCs that can result in impaired oxygen transport and delivery. Since bioactive lipids have been recently proposed as potent protective agents against Aβ toxicity, we investigated the role of sphingosine-1-phosphate (S1P) in signaling pathways involved in the mechanism underlying ATP release in Aβ-treated RBCs. Methods: In RBCs following different treatments, ATP, 2,3 DPG, cAMP levels, and caspase 3 activity were determined by spectrophotometric and immunoassay. Results: S1P rescued the inhibition of ATP release from RBCs triggered by Aβ through a mechanism involving caspase-3 and restoring 2,3 DPG and cAMP levels within the cell. Conclusions: These findings reveal the molecular basis of S1P protection against Aβ in RBCs and suggest new therapeutic avenues in AD.

Distributed/semi-distributed models are considered to be sensitive to the spatial resolution of the data input. In this paper, we take a small catchment in high urbanized Yangtze River Delta, Qinhuai catchment as study area, to analyze the impact of spatial resolution of precipitation and the potential evapotranspiration (PET) on the long-term runoff and flood runoff process. The data source includes the TRMM precipitation data, FEWS download PET data, and the interpolated metrological station data. GIS/RS technique was used to collect and pre-process the geographical, precipitation and PET series, which were then served as the input of CREST (Coupled Routing and Excess Storage) model to simulate the runoff process. The results clearly showed that, the CREST model is applicable to the Qinhuai catchment; the spatial resolution of precipitation had strong influence on the modelled runoff results and the metrological precipitation data cannot be substituted by the TRMM data in small catchment; the CREST model was not sensitive to the spatial resolution of the PET data, while the estimation fourmula of the PET data was correlated with the model quality. This paper focused on the small urbanized catchment, suggesting the influential explanatory variables for the model performance, and providing reliable reference for the study in similar area.

The objectives of the study were to determine the spatial rate of CO₂ flux (Net Ecosystem Exchange) and soil moisture in a wetland ecosystem applying Sentinel-1 IW (Interferometric Wide) data of VH (Vertical Transmit/Horizontal Receive—cross polarization) and VV (Vertical Transmit/Vertical Receive—like polarization) polarization. In-situ measurements of carbon flux, soil moisture, and LAI (Leaf Area Index) were carried out over the Biebrza Wetland in north-eastern Poland. The impact of soil moisture and LAI on backscattering coefficient (σ°) calculated from Sentinel-1 data showed that LAI dominates the influence on σ° when soil moisture is low. The models for soil moisture have been derived for wetland vegetation habitat types applying VH polarization (R² = 0.70 to 0.76). The vegetation habitats: reeds, sedge-moss, sedges, grass-herbs, and grass were classified using combined one Landsat 8 OLI (Operational Land Imager) and three TerraSAR-X (TSX) ScanSAR VV data. The model for the assessment of Net Ecosystem Exchange (NEE) has been developed based on the assumption that soil moisture and biomass represented by LAI have an influence on it. The σ° VH and σ° VV describe soil moisture and LAI, and have been the input to the NEE model. The model, created for classified habitats, is as follows: NEE = f (σ° Sentinel-1 VH, σ° Sentinel-1 VV). Reasonably good predictions of NEE have been achieved for classified habitats (R² = 0.51 to 0.58). The developed model has been used for mapping spatial and temporal distribution of NEE over Biebrza wetland habitat types. Eventually, emissions of CO₂ to the atmosphere (NEE positive) has been noted when soil moisture (SM) and biomass were low. This study demonstrates the importance of the capability of Sentinel-1 microwave data to calculate soil moisture and estimate NEE with all-weather acquisition conditions, offering an important advantage for frequent wetlands monitoring.

The study of the concentrations and effects of fine particulate matter in urban areas have been of great interest to researchers in recent times. This is due to the acknowledgment of the far-reaching impacts of fine particulate matter on public health. Remote sensing data have been used to monitor the trend of concentrations of particulate matter by deriving aerosol optical depth (AOD) from satellite images. The Center for International Earth Science Information Network (CIESIN) has released the second version of its global PM_2.5 data with improvement in spatial resolution. This paper revisits the study of spatial and temporal variations in particulate matter in Saudi Arabia by exploring the cluster analysis of the new data. Cluster analysis of the PM_2.5 values of Saudi cities is performed by using Anselin local Moran’s I statistic. Also, the analysis is carried out at the regional level by using self-organizing map (SOM). The results show an increasing trend in the concentrations of particulate matter in Saudi Arabia, especially in some selected urban areas. The eastern and south-western parts of the Kingdom have significantly clustering high values. Some of the PM2.5 values have passed the threshold indicated by the World Health Organization (WHO) standard and targets posing health risks to Saudi urban population.

This paper presents a system for identification of wind features, such as gusts and wind shear. These are of particular interest in the context of energy-efficient navigation of Small Unmanned Aerial Systems (UAS). The proposed system generates real-time wind vector estimates and a novel algorithm to generate wind field predictions. Estimations are based on the integration of an off-the-shelf navigation system and airspeed readings in a so-called direct approach. Wind predictions use atmospheric models to characterize the wind field with different statistical analyses. During the prediction stage, the system is able to incorporate, in a big-data approach, wind measurements from previous flights in order to enhance the approximations. Wind estimates are classified and fitted into a Weibull probability density function. A Genetic Algorithm (GA) is utilized to determine the shaping and scale parameters of the distribution, which are employed to determine the most probable wind speed at a certain position. The system uses this information to characterize a wind shear or a discrete gust and also utilizes a Gaussian Process regression to characterize continuous gusts. The knowledge of the wind features is crucial for computing energy-efficient trajectories with low cost and payload. Therefore, the system provides a solution that does not require any additional sensors. The system architecture presents a modular decentralized approach, in which the main parts of the system are separated in modules and the exchange of information is managed by a communication handler to enhance upgradeability and maintainability. Validation is done providing preliminary results of both simulations and Software-In-The-Loop testing. Telemetry data collected from real flights, performed in the Seville Metropolitan Area in Andalusia (Spain), was used for testing. Results show that wind estimation and predictions can be calculated at 1 Hz and a wind map can be updated at 0.4 Hz. Predictions show a convergence time with a 95% confidence interval of approximately 30 s.

A large diversity of epigenetic factors, such as microRNAs and histones modifications, are known to be capable of regulating gene expression without altering DNA sequence itself. In particular, miR-1 is considered the first essential microRNA in cardiac development. In this study, miR-1 potential role in early cardiac chamber differentiation is analyzed through specific signaling pathways. For this, we performed in chick embryos functional experiments by means of miR-1 microinjections into the posterior cardiac precursors -of both primitive endocardial tubes- committed to sinoatrial region fates. Subsequently, embryos were subjected to whole mount in situ hybridization, immunohistochemistry and RT-qPCR analysis. As a relevant nov-elty, our results revealed that miR-1 increases Tbx5, Gata4 and AMHC1, while this microRNA diminishes Mef2c expression, during early differentiation of cardiac sinoatrial region. Further-more, we observed in this developmental context that miR-1 upregulates CRABPII and RARß, and downregulates CRABPI, which are three crucial factors in retinoic acid signaling pathway. Interestingly, we also noticed that miR-1 directly interacts with HDAC4 and Calmodulin, as well as with Erk2/MAPK1, which are three key factors actively involved in Mef2c regulation. Our study shows, for the first time, a key role of miR-1 as an epigenetic regulator in early differenti-ation of cardiac sinoatrial region through orchestrating opposite actions between retinoic acid and Mef2c, fundamental to properly assign cardiac cells to their respective heart chambers. A better understanding of those molecular mechanisms modulated by miR-1 will definitely help in fields applied to therapy and cardiac regeneration and repair.

This paper is aimed at studying the temporal evolution of the surface displacements occurred over the past few years in the ocean-reclaimed platforms of the Shanghai megacity (China), which are mainly ascribable to consolidation processes of large dredger fills and alluvial deposits. With respect to previous analyses carried out over the same area, this work provides a joint multi-platform differential interferometry synthetic aperture radar (DInSAR) analysis, based on the application of the advanced Small BAseline Subset (SBAS) algorithm. This led us to retrieve long-term deformation time-series that are helpful for a better understanding of the on-going deformation phenomena. To this aim, we have exploited two sequences of SAR data collected by the ASAR/ENVISAT and by the COSMO-SkyMed (CSK) sensors, respectively, spanning the whole time period from 2007 to 2016. Unfortunately, the large time gap (of about three years) existing between the available ASAR/ENVISAT and CSK datasets gave rise to additional difficulties for their combination. Nevertheless, this problem has been faced by benefiting from the knowledge of a time-dependent model describing the temporal evolution of the expected deformations affecting the Shanghai ocean-reclaimed platforms.

In this study, we present comprehensive climatologies of effective ultraviolet (UV) quantities and photosynthetically active radiation (PAR) over Cyprus for the period 2004 to 2023, leveraging the synergy of earth observation (EO) data and radiative transfer model simulations. The EO dataset, encompassing satellite and re-analysis data for aerosols, total ozone column, and water vapor, alongside cloud modification factors, to capture the nuanced dynamics of Cyprus's atmospheric conditions. With a temporal resolution of 15 minutes and a spatial of 0.05°x0.05°, these climatologies undergo rigorous validation against established satellite datasets and are further evaluated through comparisons with ground-based global horizontal irradiance measurements provided by the Meteorological Office of Cyprus. This dual-method validation approach not only underscores the models' accuracy but also highlights their proficiency in capturing intra-daily cloud coverage variations. Our analysis extends to investigating the long-term trends of these solar radiation quantities, examining their interplay with changes in cloud attenuation, aerosol optical depth (AOD), and total ozone column (TOC). Significant decreasing trends in the noon ultraviolet index (UVI), of -2 to -4% per decade have been found in Autumn, especially marked on the island’s Northeastern part, mainly originating from the (significant) positive trends in TOC. The significant decreasing trends in TOC, of -2 to -3% per decade that were found in Spring do not result in correspondingly significant positive trends in the noon UVI since variations in cloudiness and aerosols also have a strong impact on the UVI in this season. The seasonal trends in the Daylight Integral (DLI) were generally not significant. These insights provide a valuable foundation for further studies aimed at developing public health strategies and enhancing agricultural productivity, highlighting the critical importance of accurate and high-resolution climatological data.

This study explores optimizing Synthetic Aperture Radar (SAR) satellite constellation scheduling for multi-imaging missions in densely targeted areas using an in-house developed Modified Dynamic Programming (MDP) algorithm. By employing Mixed-Integer Linear Programming (MILP) to define the mission planning problem, the research aims to maximize observation of high-value targets within restricted planning horizons. Numerical simulations, covering a wide range of target numbers and satellite configurations, reveal the MDP algorithm’s superior mission allocation efficiency, enhanced success rates, and reduced revisit times, compared to the Greedy algorithm. The findings underscore the MDP algorithm’s improved operational efficiency and planning robustness for complex imaging tasks, demonstrating significant advancements over traditional approaches.

This paper describes the use of the Chinese Gaofen-1 (GF-1) satellite imagery to automatically extract tertiary Linear Archaeological Traces of Tuntian Irrigation Canals (LATTICs) located in the Miran site. The site is adjacent to the ancient Loulan Kingdom at the eastern margin of the Taklimakan Desert in western China. GF-1 data was processed following atmospheric and geometric correction, and spectral analyses were carried out for multispectral data. The low values produced by SSI indicate that it is difficult to distinguish buried tertiary LATTICs from similar backgrounds using spectral signatures. Thus, based on the textual characteristics of high-resolutionGF-1 panchromatic data, this paper proposes an automatic approach that combines joint morphological bottom and hat transformation with a Canny edge operator. The operator was improved by adding stages of geometric filtering and gradient vector direction analysis. Finally, the detected edges of tertiary LATTICs were extracted using the GIS-based draw tool and converted into shapefiles for archaeological mapping within a GIS environment. The proposed automatic approach was verified with an average accuracy of 95.76% for 754 tertiary LATTICs in the entire Miran site and compared with previous manual interpretation results. The results indicate that GF-1 VHR PAN imagery can successfully uncover the ancient tuntian agricultural landscape. Moreover, the proposed method can be generalized and applied to extract linear archaeological traces such as soil and crop marks in other geographic locations.

Lake ice thickness (LIT) is a sensitive indicator of climate change identified as a thematic variable of Lakes as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS). Here, we present a novel and efficient analytically based retracking approach for estimating LIT from high-resolution Ku-band (13.6 GHz) synthetic aperture radar (SAR) altimetry data. The retracker method is based on the analytical modeling of the SAR radar echoes over ice-covered lakes that show a characteristic double-peak feature attributed to the reflection of the Ku-band radar waves at the snow-ice and ice-water interfaces. The method is applied to Sentinel-6 Unfocused SAR (UFSAR) and Fully Focused SAR (FFSAR) data, with their corresponding tailored waveform model, referred to as the SAR_LITand FFSAR_LITretracker, respectively. LIT retrievals from Sentinel-6 SAR data at different posting rates are evaluated against those obtained from thermodynamic lake ice model simulations and Low Resolution Mode (LRM) Sentinel-6 and Jason-3 data over two ice seasons during the tandem phase of the two satellites, allowing precise assessment of the continuity between LRM and SAR LIT retrievals. Consistency checks of the Sentinel-6 SAR LIT estimates are also performed using optical/radar images that provide information on the snow/ice conditions on the same dates. The analysis is performed on Great Slave Lake and Baker Lake (Canada) that differ in terms of lake size, bathymetry, snow/ice properties, and seasonal evolution of LIT. The accuracy of the LIT estimates with the SAR LIT retrackers is on the order of 5 cm once the ice is well established on the lakes, meeting the GCOS requirements of LIT measurement uncertainty, which is a factor of 2 to 3 times better than that of LIT obtained with LRM data, bringing a further improvement compared to previous analyses and methods. The SAR LIT retrackers presented are promising tools for monitoring the inter-annual variability and trends in LIT from current and future SAR altimetry missions.

In this paper, the Hafele‐Keating experiment and the geostationary orbit satellite of related physics problems of are analyzed and discussed in detail. If researchers read the paper and think about it, It will be found that the Hafele‐Keating experiment and the geostationary satellite problem prove that some of the core ideas and theories of relativity are seriously wrong. Isnʹt that a very serious problem in physics? Canʹt this cause the academic circles to pay great attention to it? Isnʹt this worthy of in‐depth analysis and research? The core of the paper is to point out that there are problems with the space concept and the reference system theory of classical physics and relativity, And that would bring up a whole host of physics problems. It takes a concerted effort from the academic community, Do furtherin‐depth analysis and research. Strive forthe further discovery and perfection of physics theory.

Background and Objectives Neurofibromatosis 1 (NF1 MIM # 162200), also known as von Recklinghausen disease, is among the most common autosomal dominantly transmitted disorders characterized mainly by neurocutaneous manifestations such as café au lait spots, intertriginous freckling, various types of neurofibromas (cutaneous neurofibromas or plexiform tumors), neurological manifestations, and bone abnormalities. It occurs with an incidence of 1:3000–1:4000 cases. Cone-rod dystrophies belong to the large group of retinal dystrophies, a highly heterogeneous group of disorders clinically manifested mainly by damage to cone and rod cells and have a broad spectrum of clinical manifestations. Many genes are involved in their etiology, with more than 100 currently identified, including the CRX gene responsible for the autosomal dominant form of cone-rod dystrophy (ad CORD). Materials and Methods: The authors present an intriguing case of a patient diagnosed with Neurofibromatosis type 1 (NF1) as an infant and ocular involvement (myopia and astigmatism onset at age 15), which yielded a surprising molecular testing result. Results: Molecular DNA analysis identified a rare occurrence of two mutational variants, including a pathogenic variant in the NF1 gene, c.3871-2A, and a likely pathogenic variant in the CRX gene c.119G>A. Conclusions: The authors present this case to highlight the surprises offered by molecular testing in a condition where the diagnosis is usually based on clinical criteria, thus providing patients with competent genetic advice.

The reef manta ray (Mobula alfredi) is a highly mobile and plankton-feeding species, classified vulnerable to extinction on the IUCN Red List for Threatened Species. Knowledge on their spatial ecology and the extent of their dispersal remain incomplete, especially within island-fragmented habitats as found in New Caledonia. Satellite telemetry was used to investigate the horizontal movement ecology of reef manta rays in New Caledonia. A total of 21 manta rays were tagged with pop-up satellite archival transmitting tags (21 Fastloc and 2 MiniPAT) that remained deployed for a duration ranging from 3 to 180 days (mean ± SE = 76.7 ± 50.3). Rays presented a strong site fidelity and an important affinity for coastal waters. Long-distance migrations (> 300 km) were also observed, mainly through coastal and shallow water paths. Horizontal movements were compared to a home range area and classified into four distinct patterns: Fidelity, Excursion, Fidelity + Relocation and Relocation. The most dominant pattern was Fidelity where manta rays remained within their home range for the whole duration of the tag deployment. Our findings may assist the design of more appropriate management strategies for the species in New Caledonia and other regions worldwide.

Signal-to-noise ratio (SNR) is an important index to evaluate radiation performance and image quality of optical imaging systems under low illumination background. Under the nighttime lighting condition, the illumination of remote sensing objects is low and varies greatly, usually ranging from several lux to tens of thousands of lux. Nighttime light remote sensing imaging requires high sensitivity and large dynamic range of detectors. Luojia 1-01 is the first professional nighttime light remote sensing satellite in the world. In this paper, we took the nighttime light remote sensing camera carried on the satellite as research object, proposed an in-orbit SNR test method based on time series images to overcome the problem of low spatial resolution. We first analyzed the process of luminous flux transmission between objects and satellite and established a radiative transfer model. By combining the parameters of large relative aperture optical system and high sensitivity CMOS device, we established SNR model and specially analyzed the effect of exposure time and quantization bits on SNR. Finally we used the proposed in-orbit test method to calculate SNR of lighting images acquired by satellite. And the measured result is in good agreement with the model predicted data. Under the condition of 10lx illumination, the SNR of typical objects can reach 27.02dB, which is much better than the requirement of 20dB for engineering application.

The altimeter-derived ocean currents have been sparsely evaluated against in situ measurements in the Arctic, particularly for extended time periods. This study uses the extensive mooring array situated in the Fram Strait, representing one of the longest ocean current records, to investigate the relationship between in situ and satellite-derived currents in the region. The primary objective of this research is to analyze the vertical structure of the West Spitsbergen Current (WSC), its spatio-temporal coherence, and its association with surface geostrophic currents during the period of 1997-2012. To assess the vertical structure of the WSC, we utilized the Empirical Orthogonal Functions (EOF) and cross-correlation analysis between neighboring moorings. Remarkably, both velocity components exhibited robust vertical coherence, particularly within the waters of the North Atlantic Inflow, at depths exceeding 800 m. These findings underscore the potential for surface measurements to effectively complement data gaps and facilitate the reconstruction of deeper currents with a monthly temporal resolution. Notably, the surface measurements explain approximately 67% of the variance in the top 800 m of the water column.This comprehensive investigation advances our understanding of the West Spitsbergen Current and its vertical characteristics in the Arctic. Moreover, our findings highlight the valuable synergy between in situ and satellite-derived data, providing critical insights for future ocean current studies in this ecologically sensitive region.

This study investigates the vertical structure of the dynamical properties of a warm-core ring in the Gulf of Mexico (Loop Current ring) using glider observations. We introduce a new method to correct the glider’s along-track coordinate which is, in general, biased by the unsteady relative movements of the glider and the eddy, yielding large errors on horizontal derivatives. Here, we take advantage of the synopticity of satellite along-track altimetry to apply corrections on the glider’s position, by matching in situ steric height with satellite-measured sea surface height. This relocation method allows to recover the eddy’s azimuthal symmetry, to precisely estimate the rotation axis position, and to compute reliable horizontal derivatives. It is shown to be particularly appropriate to compute the eddy’s cyclo-geostrophic velocity, relative vorticity, and shear strain, which are otherwise out of reach when using the glider’s raw traveled distance as an horizontal coordinate. The Ertel potential vorticity (PV) structure of the warm core ring is studied in details, and we show that the PV anomaly is entirely controlled by vortex stretching. Sign reversal of the PV gradient across the water column suggests that the ring might be baroclinically unstable. The PV gradient is also largely controlled by gradients of the vortex stretching term. We also show that the ring’s total energy partition is strongly skewed, with available potential energy being 3 times larger than kinetic energy. The possible impact of this energy distribution on the Loop Current rings longevity is also discussed.

Euler’s product formula over the primes and Euler’s zeta function equate to enshrine the Fundamental Theorem of Arithmetic that every integer > 1 is the product of a unique set of primes. The product formula has no zero, and with a domain ≤1 Euler’s zeta diverges. Dirichlet’s eta function η(s), negates alternate terms of zeta, permitting convergence when s∈C and Re(s) < 1, and its non-trivial zeros {ρ}, have a deep relationship with the distribution of the primes. The Riemann Hypothesis is that all the non-trivial zeros have Re(ρ) = 1/2. This work examines the symmetries in a partial Euler’s zeta series with a complex domain equating it to the difference between two finite vector series whose matched terms have mirror-image arguments, but whose magnitudes differ when Re(s) ≠ 1/2. Analytical continuation generates a modified eta series η_l(s), in which every l^th term is multiplied by (1-l). If the integer l is appropriately determined by the Im(s), similar paired finite vector series have a difference that closely follows η_l (s) and their terminal vectors intersect in a unique way permitting zeros only when Re(s) = 1/2. Furthermore, those vectors tracking the derivatives of the series, have a special relationship permitting zeros of the differential only when Re(s) > 1/2.

Dipeptidyl peptidase 9 (DPP9) is a multifunctional intracellular protease with roles in tumour growth, inflammation and mitochondrial function. We developed a hepatocyte-specific DPP9 knockout mouse (DPP9-KO) to explore DPP9 in a mouse model of hepatocellular carcinoma (HCC). DPP9-KO mice were generated by crossing Albumin-Cre mice (Wt/Wt Cre+/+) with DPP9 floxed mice (Fl/Fl Cre-/-). Mice were treated with Diethylnitrosamine and Thioacetamide then an atherogenic High Fat Diet until 28 weeks of age. DPP9-KO mice had reduced liver and subcuta-neous adipose tissue mass and lower fasting plasma glucose, fewer small macroscopic liver nod-ules compared to DPP9-WT mice. However, there were no differences in the total number of mac-roscopic liver nodules, tumour burden, inflammation score and steatosis score. Consistent with the known ability of DPP9 to suppress NLRP1 activation, activated caspase-1 protein was ele-vated in DPP9-KO mouse liver. Additionally, Nfkbib, Cxcl10 and Ccl5 mRNA and protein levels of autophagy marker beclin1 and tumour suppressor p53 were increased. In conclusion, DPP9 de-pletion in hepatocytes may reduce liver cancer initiation, via mechanisms that may include in-creased autophagy and innate tumour suppression in this experimental model. Finally, the data supports DPP9 having a role in glucose regulation by the liver.

Several studies have shown that changes in incoming solar radiation and variations of the diffuse fraction can significantly modify the vegetation carbon uptake. Hence, monitoring the incoming solar radiation at large scale and with high temporal frequency is crucial for this reason along with many others. The EUMETSAT Satellite Application Facility for Land Surface Analysis (LSA SAF) operationally disseminates in near real time estimates of the downwelling shortwave radiation at the surface since 2005. This product is derived from observations provided by the SEVIRI instrument onboard the Meteosat Second Generation series of geostationary satellites, which covers Europe, Africa, the Middle East, and part of South America. However, near real time generation of the diffuse fraction at the surface level has only recently been initiated. The main difficulty towards achieving this goal was the general lack of accurate information on the aerosol particles in the atmosphere. This limitation is nowadays less important thanks to the improvements in atmospheric numerical models. This study presents an upgrade of the LSA-SAF operational retrieval method, which provides the simultaneous estimation of the incoming solar radiation and its diffuse fraction from satellite every 15 minutes. The upgrade includes a comprehensive representation of the influence of aerosols based on physical approximations of the radiative transfer within an atmosphere-surface associated medium. This article explains the retrieval method, discusses its limitations and differences with the previous method, and details the characteristics of the output products. A companion article will focus on the evaluation of the products against independent measurements of solar radiation. Finally, the access to the source code is provided through an open access platform in order to share with the community the expertise on the satellite retrieval of this variable.

The use of Light Amplification by Stimulated Emission of Radiation (i.e., LASERs or lasers) by the U.S. Department of Defense is not new and includes laser weapons guidance, laser-aided measurements, even lasers as weapons (e.g., Airborne Laser). Lasers in support of telecommunications is also not new. The use of laser light in fiber optics shattered thoughts on communications bandwidth and throughput. Even the use of lasers in space is no longer new. Lasers are being used for satellite-to-satellite crosslinking. Laser communication can transmit orders-of-magnitude more data using orders-of-magnitude less power and can do so with minimal risk of exposure to the sending and receiving terminals. What is new is using lasers as the uplink and downlink between the terrestrial segment and the space segment of satellite systems. More so, the use of lasers to transmit and receive data between moving terrestrial segments (e.g., ships at sea, airplanes in flight) and geosynchronous satellites is burgeoning. This manuscript examines the technological maturation of employing lasers as the signal carrier for satellite communications linking terrestrial and space systems. The purpose of the manuscript is to develop key performance parameters (KPPs) to inform U.S. Department of Defense initial capabilities documents (ICDs) for near-future satellite acquisition and development. By appreciating the history and technological challenges of employing lasers rather than traditional radio frequency sources for satellite uplink and downlink signal carrier, this manuscript recommends ways for the U.S. Department of Defense to employ lasers to transmit and receive high bandwidth, large-throughput data from moving platforms that need to retain low probabilities of detection, intercept, and exploitation (e.g., carrier battle group transiting to a hostile area of operations, unmanned aerial vehicle collecting over adversary areas). The manuscript also intends to identify commercial sector early-adopter fields and those fields likely to adapt to laser employment for transmission and receipt.

We report neutralization titer data against contemporary SARS-CoV-2 sublineages from an ongoing, open-label, phase 2/3 trial of a single 30-μg dose of monovalent Omicron XBB.1.5-adapted BNT162b2 vaccine. Healthy participants had previously received ≥3 doses of a US-authorized mRNA vaccine, with the most recent being a bivalent Omicron BA.4/BA.5-adapted vaccine administered ≥150 days before study vaccination. In this analysis, Omicron XBB.1.5, BA.2.86, and JN.1 serum neutralizing titers were assessed at baseline and 1 month after vaccination in a subset of ≥18-year-olds (N = 40) with evidence of previous SARS-CoV-2 infection. Immunogenicity was also evaluated in a demographically matched group of participants who received bivalent BA.4/BA.5-adapted BNT162b2 in another study (ClinicalTrials.gov Identifier: NCT05472038). The XBB.1.5-adapted BNT162b2 vaccine elicited higher XBB.1.5, BA.2.86, and JN.1 neutralizing titers than those elicited by bivalent BA.4/BA.5-adapted BNT162b2. Overall geometric mean fold rises in neutralizing titers from baseline to 1 month after vaccination were higher among participants who received XBB.1.5-adapted BNT162b2 than those who received bivalent BA.4/BA.5-adapted BNT162b2 for XBB.1.5 (7.6 vs 5.6), slightly higher for JN.1 (3.9 vs 3.5), and similar for BA.2.86 (4.8 vs 4.9). ClinicalTrials.gov Identifier: NCT05997290.

There are many modern and traditional methods in valuing the performance of the company among them EVA, CVA are modern methods where as EPS, ROI, ROE etc. are some of the traditional methods. modern and traditional methods are not only used to check the performance of a company but it also used in investment decisions by the investors. Stern Stewart, managing partner of M/s Stern Stewart & Co. introduced a modified concept of economic profit in 1990 in the name of Economic Value Added (EVA) as measure of business performance and CVA is only a cash consideration in EVA. In this paper an attempt is made to study which method is used by the investors while taking the investment decisions i.e. traditional methods are modern methods. 100 respondents (Investors) are questioned and collected their views, we are assuming that there is no impact of traditional or modern methods while taking investment decisions by the investors.

Validation of satellite sea surface salinity (SSS) products is typically based on comparisons with in-situ measurements at a few meters depth, that are mostly done at a single location and time. The difference in term of spatio-temporal resolution between the in-situ near-surface salinity and the two-dimensional satellite SSS results in a sampling mismatch uncertainty. The Climate Change Initiative (CCI) project has merged SSS from three satellite missions. Using an optimal interpolation, weekly and monthly SSS and their uncertainties are estimated at a 50 km spatial resolution over the global ocean. Over the 2016-2018 period the mean uncertainty on weekly CCI SSS is 0.13, whereas the standard deviation of weekly CCI minus in-situ Argo salinities is 0.24. Using high resolution SSS simulations, we estimate the expected uncertainty due to the CCI versus Argo sampling mismatch. Most of the largest spatial variability of the satellite minus Argo salinity are observed in regions with large mismatch. A quantitative validation is performed by considering the statistical distribution of the CCI minus Argo salinity normalized by the sampling and retrieval uncertainties. This quantity should follow a Gaussian distribution with a standard deviation of 1, if all uncertainty contributions are properly considered. We find that 1) the sampling mismatch can explain most of the observed differences between Argo and CCI data, especially for monthly products and in dynamical regions (river plumes, fronts), 2) overall, the uncertainties are well estimated in CCI version 3, much better compared to CCI version 2. There are a few dynamical regions where discrepancies remain, and where the satellite SSS, their associated uncertainties and the sampling mismatch estimates should be further validated.

To keep the global navigation satellite system functional during extreme conditions, it is a trend to employ autonomous navigation technology with inter-satellite link. As in the newly built BeiDou system (BDS-3) equipped with Ka-band inter-satellite links, every individual satellite has the ability of communicating and measuring distances among each other. The system also has less dependence on the ground stations and improved navigation performance. Because of the huge amount of measurement data, centralized data processing algorithm for orbit determination is suggested to be replaced by a distributed one in which each satellite in the constellation is required to finish a partial computation task. In current paper, the balanced extended Kalman filter algorithm for distributed orbit determination is proposed and compared with whole-constellation centralized extended Kalman filter, iterative cascade extended Kalman filter, and increasing measurement covariance extended Kalman filter. The proposed method demands a lower computation power however yields results with a relatively good accuracy.

Centromeres, the chromosomal loci where spindle fibers attach during cell division to segregate chromosomes, are typically found within satellite arrays in plants and animals. Satellite arrays have been difficult to analyze because they comprise megabases of tandem head-to-tail highly repeated DNA sequences. Much evidence suggests that centromeres are epigenetically defined by the location of nucleosomes containing the centromere-specific histone H3 variant cenH3, independently of the DNA sequences where they are located; however, the reason that cenH3 nucleosomes are generally found on rapidly evolving satellite arrays has remained unclear. Recently, long read sequencing technology has clarified the structures of satellite arrays and sparked rethinking of how they evolve, while new experiments and analyses have helped bring both understanding and further speculation about the role these highly repeated sequences play in centromere identification.

Here, satellite images from 2006 to 2022 were collected to assess changes in the sandbar topography of the Nakdong River Estuary region, southeast of South Korea. The employed method of satellite image acquisition involves capturing images at the same time each year to facilitate a comparison of topographical changes over time. The shorelines of the islands in the study area, including Jinwoo Island, Shinja Island, and Doyo Sandbar, were analyzed. Shoreline analysis was performed using the digital shoreline analysis system (DSAS) package in ArcGIS. The results indicate that Jinwoo Island is expanding at 6.46 m/year in the direction of Nulcha Island. The western side of Shinja Island is expanding at 4.76 m/year, whereas the eastern side is retreating at a rate of 24.41 m/year. In the case of the Doyo Sandbar, the southern shoreline is retreating at a rate of 14.69 m/year; however, the speed is gradually decreasing. Marked topographical changes are evident in the Nakdong River Estuary, and the interlinked barrier islands undergo deposition and erosion, leading to alterations in the shoreline. These findings serve as foundational data for predicting and addressing environmental and social issues arising in the Nakdong River Estuary region due to climate change.

The impact of large atmospheric attenuation events on data quality and availability is a critical aspect for future altimetry missions based on Ka-band altimetry. The SARAL/AltiKa mission and its Ka-band nadir altimeter offer a unique opportunity to assess this impact. Previous publications (Tournadre et al. 2009, 2015) already analyzed the impact of rain on the waveforms at Ka-band and proposed a definition of an elaborate rain flag. This notion tends to give a simpler black and white view of the atmospheric attenuation when the effect on the altimeter measurement is intense. But in practice, there is continuum of measurements that may be partially distorted or corrupted by rain events. The present study proposes a wider point of view , the ACECAL approach providing statistics on rain cells occurrences as well as their amplitude and their size, as guidelines for future Ka-band missions concerning the impact of the atmosphere. At global scale, 1 % of the measurements are affected by an attenuation larger than 23 dB and 10 % of the atmospheric attenuation events have a size larger than 40 km. This study demonstrates that the data quality and availability over some regions of particular interest for oceanography as Gulf Stream, North Pacific and Brazil currents could be affected compared to global statistics. It also opens some perspectives on the benefits that the community could be drawn from the systematic distribution of the rain cells parameters as secondary products of altimetry missions.

Neuronal cells are believed to rely on autophagy to maintain cellular homeostasis. Nevertheless, autophagy may be a therapeutic target for neurodegenerative diseases because of the role it plays in neuronal cell death. Despite Beclin 1 association with autophagy and apoptosis, the molecular mechanisms that underlie Tau-induced vesicular pathology and Tau protein aggregates in humans remain poorly understood. Here, we set out to examine the role of three genes-ATG14L, Beclin 1 and VPS34- in autophagy nucleation failure-associated Tau disorders in fruit fly models. We found that either reduced ATG14L levels improve neuronal outcomes, or transgenic expression of ATG6/Beclin 1 greatly reduced age-dependent neurodegeneration and Tau pathology; whereas, the loss of Beclin 1 exacerbated these effects. Prior to p-Tau accumulation, the neuronal Beclin 1 network is critical for reducing p-Tau accumulation, indicating that the loss of Beclin 1 causes Tau pathology and neurodegeneration. As neurodegeneration progressed, glial cells showed an upregulation of vesicular Beclin 1 immunoreactivities. The reduced VPS34 also resulted in p-Tau accumulation, suggesting that neurons require both Beclin 1 and VPS34 to reduce p-Tau accumulation. Our results provide mechanistic insight into the molecular pathways that cause age-dependent Tau pathology and neurodegeneration due to an excess of autophagy nucleation processes and autophagy activity, and we derive novel inferences on the therapeutic possibilities of rebalanced Beclin 1 network by reducing autophagic specific nucleation component of ATG14L.

(1) Background: Patients with type 2 diabetes mellitus (T2DM) are at higher risk of cancer but how these two diseases associate is still debated. The goal was the assessment of the overall incidence of cancer among patients with newly diagnosed T2DM in Hungary. Summarizing, incident T2DM is associated with a significantly higher overall risk of incident cancer, with a reverse correlation of age. Newly registered T2DM patients were suggested to be screened for cancer within 6 months. (2) Methods: A nationwide, retrospective, longitudinal study was performed using a Hungarian database. The incident T2DM and diabetes free Hungarian population were included in the study. The primary endpoints were the overall and site-specific incidence and annual percentage change of the incidence of cancer in both populations. (3) Results: The overall incidence of cancer in patients amounted to 29.4/1000 and 6.6/1000 with or without T2DM, respectively, and the OR (95%CI) of cancer of the T2DM group was 4.32 (4.14-4.53), p&lt;0.0001. The risk of having cancer was age-dependent, having the highest odd in the age group 18-39. The incidence of cancer was declining in the non-diabetic but was unchanged in T2DM population. The average lag time of diagnosing a cancer after the detection of T2DM was 3.86 months.; (4) Conclusions: Iincident T2DM is associated with a significantly higher overall risk of incident cancer, with a reverse correlation of age. Newly registered T2DM patients were suggested to be screened for cancer within 6 months.

In this paper, we study the dynamics of following system of nonlinear difference equations x_n+1 = x_n-1y_n - 1, y_n+1 = y_n-1z_n - 1, z_n+1 = z_n-1x_n - 1. Especially we investigate the periodicity, boundedness and stability of related system of difference equations.

Diabetes mellitus (DM) poses a significant challenge to global health, with its prevalence projected to rise dramatically by 2045. This narrative review explores the bidirectional relationship between periodontitis (PD) and type 1 diabetes mellitus (T1DM), focusing on cellular and molecular mechanisms derived by the interplay between oral microbiota and the host immune response. A comprehensive search of literature published between 2008 and 2023 was conducted to elucidate the association between these two diseases. Preclinical and clinical evidence suggests a bidirectional relationship, with individuals with T1DM exhibiting heightened susceptibility to periodontitis, and vice versa. The review includes recent findings from human clinical studies, revealing variations in oral microbiota composition in T1DM patients, including increases in certain pathogenic species such as Porphyromonas gingivalis, Prevotella intermedia, and Aggregatibacter actinomycetemcomitans, along with shifts in microbial diversity and abundance. Molecular mechanisms underlying this association involve oxidative stress and dysregulated host immune responses, mediated by inflammatory cytokines such as IL-6, IL-8, and MMPs. Furthermore, disruptions in bone turnover markers, such as RANKL and OPG, contribute to periodontal complications in T1DM patients. While preventive measures to manage periodontal complications in T1DM patients may improve overall health outcomes, further research is needed to understand the intricate interactions between oral microbiota, host response, periodontal disease, and systemic health in this population.

The lymphocyte function–associated antigen-1 (LFA-1) (also CD11a/CD18 and αLβ2), is just one of many integrins in the human body, but its significance derives from its exclusive presence in leukocytes. In this review, we summarize the studies relating LFA-1 and its major ligand ICAM-1 (CD54) with cancer, through the function of lymphocytes and myeloid cells on tumor cells. We consider how LFA-1 mediates the interaction of leukocytes with tumors and the role of ICAM-1 in tumor dynamics, which can be independent of its interaction with LFA-1. A more detailed examination of LFA’s role within B-cell chronic lymphocytic leukemia is made. Finally, we discuss the role of LFA-1-harboring exosomes in tumor growth and metastasis.

In this article, we investigate two types of double dispersion equations in two dierent dimensions. Double dispersion equation were derived to describe long nonlinear wave evolution in a thin hyperelastic rod. Conservation laws are obtained for these equations by the application of the multiplier method. Finally, travelling waves and line travelling waves are respectively considered for these two equations.

Scene classification plays an important role in the intelligent processing of high-resolution satellite (HRS) remotely sensed image. In HRS image classification, multiple features, e.g. shape, color, and texture features, are employed to represent scenes from different perspectives. Accordingly, effective integration of multiple features always results in better performance compared to methods based on a single feature in the interpretation of HRS image. In this paper, we introduce a multi-task joint sparse and low-rank representation model to combine the strength of multiple features for HRS image interpretation. Specifically, a multi-task learning formulation is applied to simultaneously consider sparse and low-rank structure across multiple tasks. The proposed model is optimized as a non-smooth convex optimization problem using an accelerated proximal gradient method. Experiments on two public scene classification datasets demonstrate that the proposed method achieves remarkable performance and improves upon the state-of-art methods in respective applications.

This paper describes different variants of broadband and simple attenuator modules for beamforming applications, based on Radio Frequency Micro Electro-Mechanical Systems (RF-MEMS), framed within Coplanar Waveguide (CPW) structures. The modules proposed in the first part of this work differ in their actuation voltage, topology, and desired attenuation level. Fabricated samples of basic 1-bit attenuation modules, characterized by a moderate footprint of 690 x 1960 µm2 and aiming at attenuation levels of -2, -3 and -5 dB in the 24.25-27.5 GHz range, are presented in their variants featuring both low actuation voltages (5-7 V) as well as higher values (~ 45V), the latter ones ensuring larger mechanical restoring force (and robustness against stiction). Beyond the fabrication non-idealities that affected the described samples, the substantial agreement between simulations and measurement outcomes proved that the proposed designs could provide precise attenuation levels up to 40 GHz, ranging up to nearly -3 dB and -5 dB for the series and shunt variants, respectively. Moreover, they could be effective building blocks for future wideband and reconfigurable RF-MEMS attenuators. In fact, in the second part of this work, combinations of the discussed cells and other configurations meant for larger attenuation levels will be investigated.

We examine the mass balance of the glaciers in the Novaya Zemlya Archipelago, located in the Russian High Arctic using time series of time-variable gravity from the NASA/DLR Gravity Recovery and Climate Experiment (GRACE) mission, laser altimetry data from the NASA Ice Cloud and land Elevation Satellite (ICESat) mission, and radar altimetry data from the ESA CryoSat-2 mission. We present a new algorithm for detecting changes in glacier elevation from these satellite altimetry data and evaluate its performance in the case Novaya Zemlya by comparing the results with GRACE. We find that the mass loss of Novaya Zemlya increased from 10±5 Gt/yr over 2003-2009 to 14±4 Gt/yr over 2010-2016, with a brief period of near mass balance between 2009 and 2011. The results are consistent across the gravimetric and altimetric methods. Furthermore, the analysis of elevation change from CryoSat-2 indicates that 60\% of the mass loss occurs at low elevation, where thinning rates are highest. We also find that marine-terminating glaciers in Novaya Zemlya are thinning significantly faster than land-terminating glaciers, which indicates an important role of ice dynamics of marine-terminating glaciers. We posit that the glacier changes have been caused by changes in atmospheric and ocean temperatures. We find that the increase in mass loss after 2010 is associated with a warming in air temperatures, which increased the surface melt rates. There is no enough information on the ocean temperature at the front of the glaciers to conclude on the role of the ocean, but we posit that the temperature of subsurface ocean waters must have increased during the observation period.

This research presents the design and characteristics of new wideband polarizer based on the square waveguide with irises. Matching and polarization characteristics of the polarizer have been simulated and optimized. Frequency dependences of the characteristics are presented. Developed polarizer can be applied in satellite information systems.

The NP method was used to elucidate the contributions of vehicles and coal combustion to seasonal and long-term atmospheric PM-bound Pyr and 1-NP concentrations in Kanazawa, Kitakyushu, Shenyang, and Shanghai in the Western Pacific region from 1997 to 2021. Among the four cities, Kanazawa demonstrated the lowest Pyr concentration. The contribution of vehicles to Pyr before and after 2010 were 35% and 5%, respectively. The 1-NP concentration reduced by a factor of more than 1/10. These changes can be attributed to the emission control from vehicles. Kitakyushu revealed the second lowest Pyr and the lowest 1-NP concentrations. Coal combustion was found to be the main contributor to Pyr, while its contribution to 1-NP increased from 9% to 19%. The large contribution of coal combustion is attributed to iron manufactures. Shenyang demonstrated the highest atmospheric Pyr concentration with its largest seasonal change. Vehicles are the largest contributors to 1-NP. However, coal combustion, including winter coal heating, contributed 97% or more to Pyr and more than 14% to 1-NP. Shanghai revealed the second highest Pyr and 1-NP concentrations, but the former was substantially lower than in Shenyang. Coal combustion was the major contributor, but the contribution of vehicles to Pyr was larger before 2010 similar to Kanazawa.

Quaking aspen (Populus tremuloides Michx.) is an important deciduous species in western U.S. forests. Existing maps of aspen distribution are based on Landsat imagery and often miss small stands (&lt;0.09 ha or 30 m²), which rapidly regrow when managed or following disturbance. In this study, we present methods for deriving a new regional map of aspen forests using one year of Sentinel-1 (S1) and Sentinel-2 (S2) imagery in Google Earth Engine. We assessed the annual phenology of aspen in the Southern Rockies and leveraged the frequent temporal resolution of S1 and S2 to create ecologically relevant seasonal composites. Additionally, we derived spectral indices and radar textural features targeting the canopy structure, moisture, and chlorophyll content. Using spatial block cross-validation and Random Forests, we assessed the accuracy of different scenarios and selected the best performing set of features for classification. Comparisons were then made with existing landcover products across the study region. The resulting map improves on existing landcover products in both accuracy (average F1-score = 93.1%) and detection of smaller forest patches. These methods enable accurate mapping at spatial and temporal scales relevant to forest management for one of the most widely distributed tree species in North America.

A relevant aspect in the epidemiology of Tegumentary Leishmaniasis (TL) is the Leishmania parasites carrying a viral endosymbiont Leishmania RNA Virus 1 (LRV1), a dsRNA virus. Leishmania parasites carrying LRV1 are prone to causing more severe TL symptoms, increasing the likelihood of unfavorable clinical outcomes. LRV1 has been observed in cultured strains of five L. (Viannia) species, and host specificity was suggested when studying LRV1 from L. braziliensis and L. guyanensis strains. The coevolution hypothesis of LRV1 and Leishmania was based on phylogenetic analyses, implying an association between LRV1 genotypes, Leishmania species, and their geographic origins. This study aimed to investigate LRV1 specificity to Leishmania (Viannia) species hosts by analyzing LRV1 from L. (Viannia) species. To this end, LRV1 was screened in L. (Viannia) species other than L. braziliensis or L. guyanensis, and it was detected in 11 out of 15 L. naiffi and in 2 out of 4 L. shawi. Phylogenetic analyses based on partial LRV1 genomic sequencing supported the hypothesis of host specificity, as LRV1 clustered according to their respective Leishmania species hosts. These findings underscore the importance of investigating Leishmania and LRV1 coevolution and its impact on Leishmania (Viannia) species dispersion and pathogenesis in the American Continent.

Several recent studies have indicated that miR-30a plays critical roles in various biological processes and diseases. However, the mechanism of miR-30a participation in the regulation of idiopathic pulmonary fibrosis (IPF) is ambiguous. Our previous study demonstrated that miR-30a may function as a novel therapeutic target for lung fibrosis by blocking mitochondrial fission, which is dependent on dynamin-related protein-1 (Drp-1). However, the regulatory mechanism between miR-30a and Drp-1 has yet to be investigated. In addition, whether miR-30a can act as a potential therapeutic has not been verified in vivo. In this study, the miR-30a expression in IPF patients was evaluated. Computational analysis and a dual luciferase reporter system assay were used to identify the target gene of miR-30a, and cell transfection was used to confirm this relationship. Ten-eleven translocation 1 (TET1) was validated as a direct target of miR-30a, and the transfection of miR-30a mimic/inhibitor significantly reduced/increased the expression of TET1 protein. Further experiment verified that the interference on TET1(siRNA) could inhibit the hydroxymethlation of the Drp-1 promoter. Finally, miR-30a agomir was designed and applied to identify and validate the therapeutic effect of miR-30a in vivo. Our study demonstrated that miR-30a could inhibit the TET1 expression by base pairing with complementary sites in the 3′ untranslated region to regulate the hydroxymethlation of the Drp-1 promoter. Furthermore, miR-30a could act as a potential therapeutic target for IPF.

Climate warming is expected to have pronounced effects on Arctic and Subarctic ecosystems, especially regions underlain by discontinuous and relatively warm permafrost. The main goal of this research is to evaluate the vulnerability and dynamics of permafrost under climate warming across the various ecotypes in respect of ecosystem stability, socioeconomic impact, and for better understanding possible future environmental changes. We suggested the new version of the spatially distributed permafrost dynamics model (GIPL2-MPI), which is developed in the Geophysical Institute, University of Alaska Fairbanks. This model is based on the ecosystem approach to simulate the permafrost dynamics, which we are discussing in this paper. We combined ground-based observations and numerical freeze/thaw modeling using climate-ecosystem-permafrost interactions to understand the physical processes and mechanisms controlling permafrost physical state. We predict the changes in permafrost conditions using output from two GCMs (NCAR-CCSM4 and GFDL-CM3) and Five-Model Average Ensemble for the RCP-4.5 and RCP-8.5 scaled down to 1 by 1 km spatial resolution (https://uaf-snap.org/) across entire Alaska. Our result shows that by the end of the current century widespread near-surface permafrost degradation could begin everywhere in Alaska southward of the Brooks Range as well as across some spots at the North Slope Alaska.

I review the solution of QCD in two spacetime dimensions. Following the analysis of Baluni, for a single flavor the model can be analyzed using Abelian bosonization. The theory can be analyzed in strong coupling, when the quarks are much lighter than the gauge coupling. In this limit, the theory is given by a Luttinger liquid.

Caveolin-1 (Cav-1) is 22 kDa caveolae protein, acts as a scaffold within caveolar membranes. It interacts with alpha subunits of G-protein and thereby regulates their activity. Earlier studies reported elevated or up-regulated levels of caveolin-1 in the serum of prostate cancer patients. Secreted Cav-1 promotes angiogenesis, cell proliferation and anti-apoptotic activities in prostate cancer patients. Cav-1 upregulation is mainly related to prostate cancer metastasis. Keeping above facts in view, the present study was designed to explore Cav-1 as a target for prostate cancer therapy using computational approach. Molecular docking, structural base molecular modelling and molecular dynamics simulations were performed to investigate Cav-1 inhibitors. A predictive model was generated and validated to establish a stable structure. ZINC database of biogenic compounds was used for induced fit docking (IFD) and high throughput virtual screening. The H-bond interactions of the compounds with active site residues of Cav-1 were estimated by IFD and 100 ns long molecular dynamic simulations. The reported compounds showed significant binding and thus can be considered as potent therapeutic inhibitors of Cav-1. This study provides a valuable insight into biochemical interactions of Cav-1 for therapeutic applications and warrants for experimental validation of the predicted ‘active(s)’.

Estimating ground surface PM2.5 with fine spatiotemporal resolution is a critical technique for exposure assessments in epidemiological studies of its health risks. Previous studies have utilized monitoring, satellite remote sensing or air quality modeling data to evaluate the spatiotemporal variations of PM2.5 concentrations, but such studies rarely combined these data simultaneously. We develop a three-stage model to fuse PM2.5 monitoring data, satellite-derived aerosol optical depth (AOD) and community multi-scale air quality (CMAQ) simulations together and apply it to estimate daily PM2.5 at a spatial resolution of 0.1˚ over China. Performance of the three-stage model is evaluated using a cross-validation (CV) method step by step. CV results show that the finally fused estimator of PM2.5 is in good agreement with the observational data (RMSE = 23.00 μg/m^3 and R2 = 0.72) and outperforms either AOD-retrieved PM2.5 (R2 = 0.62) or CMAQ simulations (R2 = 0.51). According to step-specific CVs, in data fusion, AOD-retrieved PM2.5 plays a key role to reduce mean bias, whereas CMAQ provides all-spacetime-covered predictions, which avoids sampling bias caused by non-random incompleteness in satellite-derived AOD. Our fused products are more capable than either CMAQ simulations or AOD-based estimates in characterizing the polluting procedure during haze episodes and thus can support both chronic and acute exposure assessments of ambient PM2.5. Based on the products, averaged concentration of annual exposure to PM2.5 was 55.75 μg/m3, while averaged count of polluted days (PM2.5 > 75 μg/m3) was 81, across China during 2014. Fused estimates will be publicly available for future health-related studies.

The article delves into the intricate phase transitions of 1-Octadecanol and n-Nonadecane within a binary system, unveiling dynamic structural changes under varying conditions. Through Fouri-er-transform infrared (FTIR) analysis, specific molecular vibrations were identified, shedding light on the molecular composition and interactions. The study highlights the challenges in detecting subtle phase transitions and emphasizes the individuality of molecular behaviours in closely related compounds. The findings underscore the complexity of phase transitions in binary systems and advocate for a nuanced approach to studying molecular structures and behaviours. Addi-tionally, the research contributes valuable insights into the molecular dynamics of Organic Polymer Functional Adsorption Materials, offering implications for synthetic waxy layers in plant epidermis.

Satellite remote sensing is a powerful tool to map flooded areas. In the last years, the availability of free satellite data sensibly increased in terms of type and frequency, allowing producing flood maps at low cost around the World. In this work, we propose a semi-automatic method for flood mapping, based only on free satellite images and open-source software. As case studies, we selected three flood events recently occurred in Spain and Italy. Multispectral satellite data acquired by MODIS, Proba-V, Landsat, Sentinel-2 and SAR data collected by Sentinel-1 were used to detect flooded areas using different methodologies (e.g., MNDWI; SAR backscattering variation; Supervised classification). Then, we improved and manually refined the automatic mapping using free ancillary data like DEM based water depth model and available ground truth data. For the areas affected by major floods, we also validated and compared the produced flood maps with official maps made by river authorities. We calculated flood detection performance (flood ratio) for the different datasets we used. The results show that it is necessary to take into account different factors for the choice of best satellite data, among these, the time of satellite pass with respect to the flood peak is the most important one. SAR data showed good results only for co-flood acquisitions, whereas multispectral images allowed detecting flooded areas also with the post-flood acquisition. With the support of ancillary data, it was possible to produce reliable geomorphological based flood maps in the study areas.

Data from environmental satellites are underutilized in many branches of operational oceanography. Several challenges exist for potential users of satellite data that may impede or preclude employing satellite products in their work. Users outside of the satellite community often encounter difficulty in discovering the types of satellite measurements that are available, and determining which satellite products are best for operational activities. In addition, the large choice of satellite data providers, each with their own data access protocols and formats, can make data access confusing and inefficient. NOAA’s CoastWatch Program is designed to be a Value Added Provider, whose mission is to help make satellite data easier for users to access. With this objective the West Coast Node of CoastWatch developed the NOAA Ocean Satellite Course, which introduces scientists and resource managers to ocean satellite products, and provides them tools to facilitate data access when using ArcGIS or R software. These tools leverage upon the data services provided by ERDDAP, a data distribution system designed to make data access easier for both humans and machines. The course has been offered annually since 2006, and over 350 participants have taken it. Results of post-course surveys are analyzed to measure course effectiveness. Lessons learned from conducting these courses are summarized.

Treatment of the non-purified mixture of dinitro isomers obtained from the nitration of 1,4-dimethoxybenzene with piperidine, led to the isolation of novel but minor adduct, 1-(2,5-dimethoxy-4-nitrophenyl)piperidine (2b) in 15% yield. Yields of nucleophilic aromatic substitution adducts are high when using purified 1,4-dimethoxy-2,5-dinitrobenzene (1b) with piperidine and pyrrolidine to give 2b and 1-(2,5-dimethoxy-4-nitrophenyl)pyrrolidine (3b) in 76% and 82%, respectively.

Atherosclerosis, a complex metabolic-immune disease characterized by a chronic inflammation driven by the buildup of lipid-rich plaques within arterial walls, has emerged as a pivotal factor in the intricate interplay between cancer and cardiovascular disease. This bidirectional relationship, marked by shared risk factors and pathophysiological mechanisms, underscores the need for a comprehensive understanding of how these two formidable health challenges intersect and influence each other. Cancer and its treatments can contribute to the progression of atherosclerosis while atherosclerosis with its inflammatory microenvironment can exert profound effects on cancer development and outcomes. Both cancer and cardiovascular disease involve intricate interactions between general and personal exposomes. In this review we want to summarize the state-of-the-art of translational data and try to show how oncologic studies on cardiotoxicity can broaden our knowledge of crucial pathways in cardiovascular biology with a positive impact on precision cardiology and cardioncology.

The use of electronic means to support tasks such as pastoralism is a way of intelligently optimizing that activity. As any autonomous system, it requires human intervention in case of failure, and therefore it needs an autonomous mechanism that draws the attention of the human operator whenever the system or the animals evolve to undesired conditions. The present work progresses an existing alarm system, used in the SheepIT gateway, which can monitor the behavior of animals and equipment, warning human supervisors of the occurrence of unwanted events and the need for intervention. Concretely, given the lack of coverage of Internet access in rural areas, the system was integrated with a satellite interface to guarantee communication and the timely delivery of alarm messages. The paper compares the overall networking performance of the satellite link, against a Wi-Fi laboratorial baseline.

The satellite Earth observation (EO) sector is burgeoning with hundreds of commercial satellites being launched each year, delivering a rich source of data that could be exploited for societal benefit. Data streams from the growing constellation of commercial satellites are of variable quality, limiting the potential for their combined use in science applications that need long time-series data from multiple sources. The quality of calibration performed on optical sensors onboard many satellite systems is highly variable due to calibration methods, sensor design, mission objective, budget, or other operational constraints. A small number of currently operating well-characterised satellite systems with onboard calibration, such as Landsat-8/9, Sentinel-2, and planned future missions like the NASA Climate Absolute Radiance and Refractivity Observatory (CLARREO) Pathfinder, the European Space Agency (ESA) Traceable Radiometry Underpinning Terrestrial and Helio Studies (TRUTHS), and LIBRA from China, are considered benchmarks for optical data quality due to their traceability to international measurement standards. This paper describes the concept of a space-based transfer calibration radiometer called the Satellite Cross-Calibration Radiometer (SCR) that would enable the calibration parameters from satellites such as Landsat-8/9, Sentinel-2, or other benchmark systems to be transferred to a range of commercial optical EO satellite systems while in orbit. A description of the key characteristics of the SCR to successfully operate in orbit and transfer calibration from Reference systems to Client systems is presented. A system like the SCR in orbit could complement SI-Traceable satellites (SITSats) to improve data quality and consistency and facilitate the interoperable use of data from multiple optical sensor systems for delivering higher returns on the global investment in EO.

The first diastereoselective synthesis of (–)-1-epi-lentiginosine stars from a common chiral trans-epoxyamide derived from 2-pyridincarbaldehyde is reported. This methodology involves a sequential oxirane ring opening and intramolecular 5-exo-tet cyclization of tosylate tras-epoxyalcohol to afford a diastereomeric mixture of indolizinium salts in a one-pot fashion, followed by regio- and diastereospecific pyridinium ring reduction.

This paper contains a concise overview of the deployment of scattered solar power plants in Poland, mainly from the perspective of their communication networks, and how the recent development of the Polish 4G networks has a very positive impact for the performance of the whole monitoring system (production control and video-surveillance), with a special emphasis on video-analytics, due to its higher bandwidth demand. All the information will be shown from the point of view of the solar photovoltaics developer I+D Energías, and therefore it constitutes a real user’s experience.

An effective on-board cloud detection method in small satellites would greatly improve the downlink data transmission efficiency and reduce the memory cost. In this paper, an ensemble method combining a lightweight U-Net with wavelet image compression is proposed and evaluated. The red, green, blue and infrared waveband images from Landsat-8 dataset are trained and tested to estimate the performance of proposed method. The LeGall-5/3 wavelet transform is applied on the dataset to accelerate the neural network and improve the feasibility of on-board implement. The experiment results illustrate that the overall accuracy of the proposed model achieves 97.45% by utilizing only four bands. Tests on low coefficients of compressed dataset have shown that the overall accuracy of the proposed method is still higher than 95%, while its inference speed is accelerated to 0.055 second per million pixels and maximum memory cost reduces to 2Mb. By taking advantage of mature image compression system in small satellites, the proposed method provides a good possibility of on-board cloud detection based on deep learning.

Actively combating post-harvest food spoilage and waste can dramatically increase the efficiency of food utilization worldwide. In climacteric fruits, chemical treatments such as 1-MCP are an effective way of reducing post-harvest spoilage and waste by inhibiting the fruit’s ability to perceive ethylene. 1-MCP treatment is increasingly being used to explore the complex nature of ripening physiology at a fundamental level, however, differences in application and quantitation methods create difficulties in comparing conclusions across studies. Here we report an effective and reproducible method for 1-MCP application and quantitation for small-medium sized research applications. By use of surrogate alkene standards 1-butene and cis-2-butene, the highly volatile and elusive 1-MCP molecule can be identified and quantified by gas chromatography and subsequent standard curves may be developed. It is hoped that the methodology outlined here can help standardize consistent 1-MCP application for post-harvest research without excessive investment in specialized equipment.

Background: Hepatic erythrophagocytosis is augmented in NASH and amplifies inflammation and fibrosis. Although various pro-phagocytic signals have been identified on erythrocytes of NASH patients, the role of bound thrombospondin-1 (TSP-1), which acts as an “eat-me” signal, arginase-1, which regulates the levels of nitric oxide in erythrocytes, and phosphatidylethano-lamine (PE) which can amplify erythrophagocytosis and hepatic inflammation have not been explored. Hence, we sought to investigate the levels of arginase-1 and TSP-1 in erythrocyte lysate and PE in erythrocyte membranes of NASH patients. Methods: Twenty-four patients and 14 healthy controls participated in our study. The levels of TSP-1 and arginase were quantified by ELISA in erythrocyte lysates, and the levels of PE in erythrocyte membranes by thin layer chro-matography. Results: Erythrocytes of NAFLD patients exhibit lower levels of arginase-1 and TSP-1 (p<0.01). Erythrocyte-bound TSP-1 levels correlated with the levels of erythrocyte surface CD47. Phosphatidylethanolamine was increased in erythrocytes of NASH patients and was accompanied by increased release, indicating exposure. Conclusion: Our results imply reduced TSP-1 binding by erythrocytes which could allow free TSP-1 molecules to act on macrophages, enhancing erythrophagocytosis. Increased PE which could amplify inflammation after efferocytosis, while downregulation of arginase-1 could lead to defective efferocytosis.

Spatial data conflation plays a fundamental role in in many aspects of modern Geographic Information Systems (GIS) research and development such as geospatial data visualization, incremental updating of databases and disaster evaluation. The primary objective of conflation is to derive valuable information based on the comparison of multiple spatial data sources of homogeneous or heterogeneous nature. This paper reviews the state of art of the concept of conflation, feature matching, the most important progresses of conflation between image and road network data, and the complexity of spatial conflation.

Carbon monoxide (CO) concentrations in wildfire plumes are easily measured from satellites. This gas can be used as a proxy for carbon dioxide. Forest fires play an important role in the carbon balance and in particular the CO balance. The most likely causes of mega-fires of 2003, 2012, 2021, and 2023 in the Northern hemisphere are heat waves and severe droughts associated with changes in general circulation. Here we analyze satellite data obtained by two different sounders, AIRS and TROPOMI. Different sensitivity to the lowest troposphere allows obtaining information about anthropogenic or pyrogenic contamination of the boundary layer. Shapes of areas polluted by mega-fires in 2019-2020 (Southeastern Australia) and 2021 (Central Siberia) coincide with the areas occupied by coal deposits. The Siberian Lena and Tunguska coal basins are the two largest coal fields in the world. In 2021, their combined area accounted for 90% of fire CO emission from the entire Russian Federation. So strong fires have not observed in this area before. Under- and sub-ground coal combustion may be included into the list of wildfire fuels, at least their role for ignition should be admitted. Further research is needed to assess the importance of coal fires to global climate.

The emergence of cloud computing, big data analytics, and machine learning has catalysed the use of remote sensing technologies to enable more timely land management of sustainability indicators such as ground cover and grassland biomass, given the uncertainty of future climate and drought conditions. Here, we examine the potential of “regenerative agriculture”, as an adaptive grazing management strategy to minimise bare ground exposure while maximising pasture biomass productivity. High-intensity sheep grazing treatments were conducted in small fields (less than 1 hectare) for short durations (typically less than 1 day). Paddocks were subsequently spelled to allow pasture biomass recovery (treatments comprising 3, 6, 9, 12, and 15 months) with each compared with control treatments with lighter stocking rates for longer periods (2,000 DSE). Pastures were composed of wallaby grass (Austrodanthonia species), kangaroo grass (Themeda triandra), Phalaris (Phalaris aquatica, and cocksfoot (Dactylis glomerata) were destructively sampled to estimate total standing dry matter (TSDM), standing green biomass, standing dry biomass and trampled biomass. We then invoked a machine learning model using Sentinel-2 imagery to quantify TSDM, standing green biomass and standing dry biomass. Faced with La Nina conditions, regenerative grazing did not significantly impact pasture productivity, with all treatments showing similar TSDM and green biomass. However, regenerative treatments significantly impacted litter fall and trampled material, with the high intensity grazing treatments causing more dry matter trampling, increasing litter, enhancing decomposition rates and surface organic matter. Pasture digestibility was greatest for treatments with minimal spelling (3 months), whereas both standing senescent and trampled material were significantly greater for the treatment with 15-month spell periods. Estimates of TSDM using machine learning with Sentinel-2 imagery underestimated TSDM in treatment plots but explained spatiotemporal variability associated within and across treatments. The root mean square error between the measured and modelled TSDM was 903 kg DM/ha, which was less than the variability measured in the field. We conclude that regenerative grazing with short recovery periods (3-6 months) are most conducive to increasing pasture production under high rainfall conditions, and we speculate that high intensity grazing is likely to positively impact on soil organic carbon through increased litterfall and trampling. Our study paves the way forward for using machine learning with satellite imagery to quantify pasture biomass at small scales, enabling management of pastures from afar.

We study the Gaudin models with gl(1|1) symmetry that are twisted by a diagonal matrix and defined on tensor products of polynomial evaluation gl(1|1)[t]-modules, extending all the results of to the twisted case. Namely, we give an explicit description of the algebra of Hamiltonians (Gaudin Hamiltonians) acting on tensor products of polynomial evaluation gl(1|1)[t]-modules and show that there exists a bijection between common eigenvectors (up to proportionality) of the algebra of Hamiltonians and monic divisors of an explicit polynomial written in terms of the highest weights and evaluation parameters. In particular, our result implies that each common eigenspace of the algebra of Hamiltonians has dimension one. We also give dimensions of the generalized eigenspaces.

For the first time VIS/NIR-SWIR (visible and near infrared – shortwave infrared) nighttime spectra of a satellite mission are analyzed, using the EnMAP (Environmental Mapping and Analysis Program) high-resolution imaging spectrometer. The focus of this article is set on the spectral characteristics. First, the spectral calibration of EnMAP is checked based on sodium emissions of lighting. Here, applying a realized novel general method, shifts of +0.3nm for VIS/NIR and −0.2nm for SWIR are identified with uncertainties analyzed to be in the range of [−0.4nm,+0.2nm] for VIS/NIR and [−1.2nm,+1.0nm] for SWIR. These results emphasize the high accuracy of the spectral calibration of EnMAP and illustrate the feasibility of methods based on nighttime Earth observations for the spectral calibration of future nighttime satellite missions. Second, applying a realized simple general method, the dominant lighting types of Las Vegas, Nevada, USA, and thermal emissions are identified per pixel and the consistency of the outcomes is considered. These results illustrate the feasibility of the precise identification of lighting types and thermal emissions based on nighttime high-resolution imaging spectroscopy satellite products and support the specification of, in particular, spectral characteristics of future nighttime missions.

We have studied the stochastic processes A and B concerning fade durations due to rain, by simulating attenuation time series A(t) (dB) in the zenith paths of GeoSurf satellite constellations, at sites located in different climatic regions, with the Synthetic Storm Technique. Process B gives the statistics of outages (occurrences), process A gives the statistics of outage duration (fraction of time), for the same rain attenuation threshold A(t)&gt;S. The two processes are not independent, therefore, we have studied the relationship between their probabilities and defined a uniformity index 0&lt;U(S)≤1. U(S) is useful for comparing real cases – fade durations fragmented in many different intervals, with changing S and site – and the limiting case of all fades lasting the same time. As S increases, U(S) increases, approaching 1 at very large thresholds. These results should guide the designers of future GeoSurf satellite constellations to consider the impact of A(t) on the diverse communications services. Process B (occurrences) impacts on non–real time services, such as data delivery, more disturbed by the number of outages rather than by their duration. Process A (fraction of time) impacts on real–time services such as television, video conference etc., more disturbed by the duration of the outage.

The accurate detection of clouds is an important first-step in the processing of remotely sensed satellite data analyses and subsequent cloud model predictions. While initial cloud retrieval technology began with the exploitation of one or two bands of satellite imagery, it has accelerated rapidly in recent years as sensor and retrieval technology have exploded, creating a new era in space exploration. Additionally, initial emphasis in satellite retrieval technology focused on cloud detection for cloud forecast models, more recently cloud screening in satellite acquired data is playing an increasingly critical role in the investigation of cloud free data for the retrieval of soil moisture, vegetation cover, ocean colour concentration and sea surface temperatures, as well as environmental monitoring of a host of products, e.g. atmospheric aerosol data, to study the Earth’s atmospheric and climatic systems. With about 60% of the Earth covered by clouds, on average, it is necessary to both accurately detect clouds in remote sensing data to screen cloud contaminate data in remote sensing analyses. In this paper, the evolution of cloud detection technology is highlighted with advancement in sensor hardware technology and possible AI algorithmic advances. Additionally, a discussion is presented on methods to obtain the cloud truth data needed to determine the accuracy of these cloud detection approaches.

: This work introduces the instrument design of the medium energy proton detector (MEPD, detection range: 30keV-5MeV) mounted on the Chinese Fengyun-4B (FY-4B) satellite. Compared with the similar detector on the Fengyun-3E (FY-3E) satellite, the instrument has undergone significant changes due to the different orbital radiation environment and solar lighting conditions. Based on the calculation of radiation model AP8, the geometry factor is reduced to 0.002 cm2sr, while the MEPD on FY-3E satellite is 0.005 cm2sr. Another difference is that sensors in some directions is exposed to direct sunlight for 80 minutes every day on this orbit, depending on the attitude angle of the satellite, which is much worse than that on FY-3E satellite. According to the calculation results of permeability of photons through different materials, a 100 nm thickness nickel film is added in front of sensors in order to eliminate light pollution completely. The experiment on the solar simulator shows that the measure is effective and the detector has no error counts when the solar irradiance coefficient is 1.0. In addition, Geant4 software is applied to simulate particle transportation process under complete machine condition so that to check the contamination of electrons on sensors among all directions after magnetic deflection. Data obtained in orbit shows that the instrument works properly, and the data is in good agreement with the AP8 model. The observations of the MEPD on board the FY-4B satellite can provide important support for the safety of the spacecraft and the theoretical research related to space weather.

Background; Programmed death-1 (PD-1) and PD-1 ligand 1 (PD-L1) are target molecules for immunotherapy in non-small cell lung cancer. PD-L1 is expressed not only in cancer cells, but also on macrophages, and has been suggested to contribute to macrophage-mediated immune suppression. Methods; Clinical significance of PD-L1 expression on macrophages in human lung adenocarcinoma was examined. The mechanisms of PD-L1 overexpression on macrophages were investigated by means of cell culture studies. Results; High PD-L1 expression on macrophages was correlated with the presence of EGFR mutation, a lower cancer grade, and a shorter cancer-specific overall survival. In an in vitro study using lung cancer cell lines and human monocyte-derived macrophages, the conditioned medium from cancer cells was found to up-regulate PD-L1 expression on macrophages via STAT3 activation, and a cytokine array revealed that granulocyte-macrophage colony-stimulating factor (GM-CSF) was a candidate factor that induced PD-L1 expression. Culture studies using recombinant GM-CSF, neutralizing antibody, and inhibitors indicated that PD-L1 overexpression was induced via STAT3 activation by GM-CSF derived from cancer cells. Conclusions; PD-L1 overexpression on macrophages via the GM-CSF/STAT3 pathway was suggested to promote cancer progression in lung adenocarcinoma. Cancer cell-derived GM-CSF might be a promising target for anti-cancer therapy.

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.

Preclinical studies have identified glial cells as pivotal players in the genesis and maintenance of neuropathic pain after nerve injury associated with diabetes, chemotherapy, major surgeries, and virus infections. Satellite glial cells (SGCs) in the dorsal root and trigeminal ganglia of the peripheral nervous system (PNS) and astrocytes in the central nervous system (CNS) express similar molecular markers and are protective under physiological conditions. They also serve similar functions in the genesis and maintenance of neuropathic pain, downregulating some of their homeostatic functions and driving pro-inflammatory neuro-glial interactions in the PNS and CNS, i.e. “gliopathy”. However, the role of SGCs in neuropathic pain is not simply as “peripheral astrocytes”. We delineate how these peripheral and central glia participate in neuropathic pain by producing different mediators, engaging different parts of neurons, and becoming active at different stages following nerve injury. Finally, we highlight the recent findings that SGCs are enriched with proteins related to fatty acid metabolism and signaling such as Apo-E, FABP7, and LPAR1. Targeting SGCs and astrocytes may lead to novel therapeutics for the treatment of neuropathic pain.

This paper deals with the use of low earth orbit (LEO) satellite links in long-term monitoring of water, ice and snow levels across wide geographic areas, where no other communication links can be applied. Unlike geostationary satellites, LEO satellites do not maintain the same position relative to the ground station, and transmissions need to be scheduled for satellite overfly periods. For our application, the energy consumption optimization is critical, and we develop a learning approach for scheduling the transmission times from the sensors. Our approach is based on online learning, and is applicable to any LEO satellite transmissions.

The issue of equipment layout in multi-floor satellites consists of two primary parts: allocating the equipment to the satellite's layers and placing the equipment in each layer individually. In reviewing the past literature in this field, firstly, the issue of assigning equipment to layers is observed in a few articles and next in the layout part, the non-overlapping constraint has always been a challenge, particularly for components that do not have a circular cross-section. In addition to presenting a heuristic method for allocating equipment to different layers of the satellite, this article presents a robust flexible programming model (RFPM) for the placement of equipment at different layers, taking into account the inherent flexibility of the equipment in placement and the subject of uncertainty. This model is based on the existing uncertainty between the distances of cuboid equipment which has not been addressed in any of the previous research, and by comparing its outputs with cases from past studies, it demonstrates a significantly higher efficiency in placing the equipment and meeting the limit of non-overlapping constraints between the equipment. Finally, it would be possible to reduce the design time in the conceptual and preparatory stages, as well as the satellite's overall size, while still satisfying other constraints like stability and thermal limitations, moments of inertia and centre of gravity.

During the few years, various algorithms have been developed to extract features from high-resolution satellite imagery. For the classification of these extracted features, several complex algorithms have been developed. But these algorithms do not possess critical refining stages of processing the data at the preliminary phase. Various satellite sensors have been launched such as LISS3, IKONOS, QUICKBIRD, and WORLDVIEW etc. Before classification and extraction of semantic data, imagery of the high resolution must be refined. The whole refinement process involves several steps of interaction with the data. These steps are pre-processing algorithms that are presented in this paper. Pre-processing steps involves Geometric correction, radiometric correction, Noise removal, Image enhancement etc. Due to these pre-processing algorithms, the accuracy of the data is increased. Various applications of these pre-processing of the data are in meteorology, hydrology, soil science, forest, physical planning etc. This paper also provides a brief description of the local maximum likelihood method, fuzzy method, stretch method and pre-processing methods, which are used before classifying and extracting features from the image.

Many applications in agriculture as well as other related fields including natural resources, environment, health, and sustainability, depend on recent and reliable cropland maps. Cropland extent and intensity plays a critical input variable for the study of crop production and food security around the world. However, generating such variables manually is difficult, expensive, and time consuming. In this work, we discuss a cost effective, fast, and simple machine learning based approach to provide reliable cropland mapping model using satellite imagery. The study includes four test regions namely; Iran, Mozambique, Sri-Lanka, and Sudan where Sentinel-2 satellite imagery were obtained with assigned NDVI scores. The solution presented in this paper discusses a complete pipeline including data collection, time series reconstruction , and cropland extent and crop intensity mapping using machine learning models. The approach proposed managed to achieve high accuracy results ranging between 0.92 to 0.98 across the four test regions at hand.

The discovery of water ice on the moon has revolutionized our understanding of lunar science and has profound implications for future space exploration and habitation. Through a comprehensive analysis of remote sensing data, isotopic studies, and neutron detection experiments, this research elucidates the presence, distribution, and origins of lunar water ice. Radar observations from missions like Clementine and Chandrayaan-1 reveal distinct polar regions with elevated circular polarization ratios, indicative of volatile ice deposits. Spectral detections by instruments such as M3 and Diviner further confirm the presence of water ice in lunar-shadowed regions, with estimates suggesting reserves of up to 300 million tons at the poles. Neutron detection experiments on lunar orbiters like LRO provide additional evidence, detecting hydrogen signals consistent with water ice below the lunar surface. Isotopic analyses offer insights into the origin and evolution of lunar water ice, with comparisons to cometary and asteroidal sources. Proposed missions, such as Artemis-3 and Artemis-4, aim to collect samples from the lunar South Pole for detailed isotopic analysis, furthering our understanding of lunar water ice's composition and history. Scientific data, including the lunar bulk water content and estimated water ice reserves, underscore the significance of lunar water ice as a critical resource for future space exploration endeavors. Calculations estimating the total water-ice area and water abundance near specific lunar features provide valuable insights into the potential utilization of lunar water ice for sustained human presence on the Moon and beyond.

This study focuses on examining various approaches for Tensor Decompositions (TDs) and their potential applications in satellite imaging (SI) and deep learning (DL). The research highlights how these decompositions can contribute to the advancement of SI and DL, providing valuable insights for future utilization of TDs in research. It explores how these techniques contribute to the advancement of SI and DL methods, providing insights into their potential applications and suggesting future research directions. The study aims to enhance the use of TDs techniques to further advance research efforts in these fields. More importantly, the current investigation offers a thorough analysis of the potential advantages and reasons for employing TDs techniques in different domains, including satellite imaging and deep learning. The aim is to enhance research outcomes by utilizing TDs. The review also identifies unresolved issues and proposes future directions for further investigation in this field. Fundamentally, these proposed open problems will open new grounds to the research community to articulate, innovate, and provide more real-life applications to improve the current state of the art by delving into a wider vision for a higher-level performance of both satellite imaging (SI) and deep learning (DL). Looking at the bigger scenario, this also suggests that TDs could be potentially employed to revolutionize existing machine learning technologies as well as the current space AI industry.

In recent years, remote sensing technology has been widely used to predict forest stand parameters. In order to compare the effects of different features of remote sensing images and topographic information on the prediction of forest stand parameters, multivariate stepwise regression analysis method was used to build estimation models for important forest stand parameters by using textural and spectral features as well as topographic information of SPOT-5 satellite images in northeastern Heilongjiang Province in China as independent variables. The study results show that the optimal window to predict forest stand parameters using textural features of SPOT-5 satellite image is 9×9; the ability of textural features was better than that of spectral features in terms of predicting forest stand parameters; with the inclusion of topographic information, the accuracy of prediction of all models was improved, of which elevation has the most significant effect. The highest accuracy was achieved when predicting the stand volume (SV) (R²_adj=0.820), followed by basal area (BA) (R²_adj =0.778), accuracy of both above models exceeded 75%. The results show that models combined use of textural, spectral features and topographic information of SPOT-5 images have a good application prospect in predicting forest stand parameters.

Forest fire is a major ecological disaster, which has economic, social and environmental impacts on humans and also causes the loss of biodiversity. Forest officials issue the warnings to the public on the basis of fire danger index classes. There is no fire danger index for the country India due to the sparsely distributed meteorological stations. Previous studies suggest that Static Fire Danger Index (SFDI) as well as Dynamic Fire Danger Index (DFDI) have been derived from the satellite datasets. In this study, we have made an attempt to integrate both the Static and Dynamic fire danger indices and also used the Near Real Time (NRT) data sets that can be available for download through NASA FTP server after one hour of the satellite overpass. In this study, DFDI has been calculated from the MODIS Terra NRT Land Surface Temperature (MOD11_L2) and MODIS TERRA NRT surface reflectance MOD09. Finally, The Forest Fire Danger Index (FFDI) has been developed from the static and dynamic fire danger indices by the additive model and the overall accuracy was around 81.27%. Thus, the FFDI has been useful to predict the fire danger accurately and can be useful anywhere, where the meteorological stations are un-available.

Αbstract: To date, countless satellite image fusions have been made, mainly with panchromatic spatial resolution to a multispectral image ratio of 1/4, fewer fusions with lower ratios, and relatively recently fusions with much higher spatial resolution ratios have been published. Apart from this, there is a small number of publications studying the fusion of aerial photographs with satellite images, with the year of image acquisition varying and the dates of acquisition not mentioned. In addition, in these publications, either no quantitative controls are performed on the composite images produced, or the aerial photographs are recent and colorful and only the RGB bands of the satellite images are used for data fusion purposes. The objective of this paper is the study of the addition of multispectral information from satellite images to black and white aerial photographs of the 80s decade (1980–1990) with small difference (just a few days) in their image acquisition date, the same year and season. Quantitative tests are performed in two case studies and the results are encouraging, as the accuracy of the classification of the features and objects of the Earth’s surface is improved and the automatic digital extraction of their form and shape from the archived aerial photographs is now allowed. This opens up a new field of use for the black and white aerial photographs and archived multispectral satellite images of the same period in a variety of applications, such as the temporal changes of cities, forests and archaeological sites.

Lifestyle interventions can prevent type 2 diabetes (T2DM). However, some in-dividuals do not experience anticipated improvements despite weight loss. Bi-omarkers to identify such individuals at early stages are lacking. IGF-1 and IGFBP-1 were shown to predict T2DM onset in prediabetes. We assessed if these markers also predict the success of lifestyle interventions, thereby possibly guid-ing personalized strategies. We analyzed fasting serum levels of IGF-1, IGFBP-1 and IGFBP-2 in relation to changes in metabolic and anthropometric parameters, including intrahepatic li-pids (IHL) and visceral adipose tissue (VAT) volume measured by MRI, in 345 high-risk prediabetic participants (54% female; aged 36-80 years). Participants were enrolled in three randomized dietary intervention trials and assessed both at baseline and one year post-intervention. Statistical analyses were performed using IBM SPSS Statistics (version 28), significance set at p

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.

Zwitterionic polymers emerge as very useful materials for applications in antifouling surfaces. The properties of these polymers can be tuned by variation of the chemical structure of the corresponding monomer. In this study, two zwitterionic ammonium sulfonate monomers bearing an hydroxyl function and a styrenic polymerizable groups were prepared in two steps. The two monomers were obtained using 4-vinylbenzyl chloride as the key precursor. The zwitterionic monomers were characterized by 1H NMR, 13C NMR, IR spectroscopy and high-resolution mass spectrometry (HRMS). These two monomers enable the preparation of novel zwitterionic polymers with enhanced hydrophilicity due to the presence of a hydroxyl group.

Background and Objectives: In recent years, electric scooters have gained widespread popularity as an easy and affordable mode of transport in urban areas worldwide. Simultaneously, trauma centres have observed an increasing number of associated injuries to users. While injury patterns associated with other vehicles are now well-researched, electric scooter-related injuries are a new topic in the literature. Our study aims to investigate the disparities in injury patterns and other critical accident characteristics among bicycle, electric scooter, and scooter riders. Materials and Methods: In this one-year prospective observational study, we examined patients who sustained injuries while riding bicycles, electric scooters, or scooters between April 2021 and March 2022 at Hungary's largest trauma centre in Budapest. During this one-year period, we identified 1938 patients, 1378 cyclists, 370 electric scooters and 190 scooter users. We collected basic demographic information, recorded injury type and severity, time of day the injury occurred, and alcohol intoxication. Results: While 4.6% of cyclists and 5.8% of scooter riders had consumed alcohol, 26.8% of electric scooter riders were intoxicated at the time of their accident. 45.8% of electric scooter-related injuries occurred at night, compared to only 9.2% and 14.1% of bike and scooter-related injuries, respectively. Sober patients were more likely to suffer mild and severe limb injuries (Odds Ratio (OR): 3.85, P = .0001 and OR: 3.26, P = .0001), but less likely to experience mild and severe head injuries (OR: 0.1, P = .0001 and OR: 0.25, P = .0001) than intoxicated patients. Cyclists had significantly fewer cases of severe head trauma than those injured while using electric scooters (OR: 0.34, P = .0166). Conclusions: Severe craniofacial injuries are more common in intoxicated patients and in patients injured using electric scooters.

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.