bluuebunny/crossref_metadata_2025 huggingface dataset:
α-Galactosidase A Augmentation by Non-Viral Gene Therapy: Evaluation in Fabry Disease Mice
Authors: Rodríguez-Castejón Julen , Alarcia-Lacalle Ana , Gómez-Aguado Itziar , Vicente-Pascual Mónica , Solinís Aspiazu María Ángeles , del Pozo-Rodríguez Ana , Rodríguez-Gascón Alicia
Published: May 2021

Fabry disease (FD) is a monogenic X-linked lysosomal storage disorder caused by a deficiency in the lysosomal enzyme α-Galactosidase A (α-Gal A). It is a good candidate to be treated with gene therapy, in which moderately low levels of enzyme activity should be sufficient for clinical efficacy. In the present work we have evaluated the efficacy of a non-viral vector based on solid lipid nanoparticles (SLN) to increase α-Gal A activity in an FD mouse model after intravenous administration. The SLN-based vector incremented α-Gal A activity to about 10%, 15%, 20% and 14% of the levels of the wild-type in liver, spleen, heart and kidney, respectively. In addition, the SLN-based vector significantly increased α-Gal A activity with respect to the naked pDNA used as a control in plasma, heart and kidney. The administration of a dose per week for three weeks was more effective than a single-dose administration. Administration of the SLN-based vector did not increase liver transaminases, indicative of a lack of toxicity. Additional studies are necessary to optimize the efficacy of the system; however, these results reinforce the potential of lipid-based nanocarriers to treat FD by gene therapy.
Coupling Additive Manufacturing with Hot Melt Extrusion Technologies to Validate a Ventilator-Associated Pneumonia Mouse Model
Authors: Shaqour Bahaa , Aizawa Juliana , Guarch-Pérez Clara , Górecka Żaneta , Christophersen Lars , Martinet Wim , Choińska Emilia , Riool Martijn , Verleije Bart , Beyers Koen , Moser Claus , Święszkowski Wojciech , Zaat Sebastian A. J. , Cos Paul
Published: May 2021

Additive manufacturing is widely used to produce highly complex structures. Moreover, this technology has proven its superiority in producing tools which can be used in different applications. We designed and produced an extrusion nozzle that allowed us to hot melt extrude drug-loaded tubes. The tubes were an essential part of a new mouse ventilator-associated pneumonia (VAP) model. Ciprofloxacin (CPX) was selected for its expected activity against the pathogen Staphylococcus aureus and ease of incorporation into thermoplastic polyurethane (TPU). TPU was selected as the carrier polymer for its biocompatibility and use in a variety of medical devices such as tubing and catheters. The effect of loading CPX within the TPU polymeric matrix and the physicochemical properties of the produced tubes were investigated. CPX showed good thermal stability and in vitro activity in preventing S. aureus biofilm formation after loading within the tube’s polymeric matrix. Moreover, the produced tubes showed anti-infective efficacy in vivo. The produced tubes, which were extruded via our novel nozzle, were vital for the validation of our mouse VAP model. This model can be adopted to investigate other antibacterial and antibiofilm compounds incorporated in polymeric tubes using hot melt extrusion.
Assessment of Antibody Stability in a Novel Protein-Free Serum Model
Authors: Schuster Joachim , Kamuju Vinay , Mathaes Roman
Published: May 2021

Therapeutic proteins can degrade upon administration as they are subjected to a variety of stresses in human body compartments. In vivo degradation may cause undesirable pharmacokinetic/pharmacodynamic profiles. Pre-clinical in vitro models have gained scientific interest as they enable one to evaluate the in vivo stability of monoclonal antibodies (mAbs) and ultimately can improve patient safety. We used a novel approach by stripping serum of endogenous proteins, which interfere with analytical test methods. This enabled the direct analysis of the target protein without laborious sample work-up procedures. The developed model retained the osmolality, conductivity, temperature, and pH of serum. We compared the impact of human, bovine, and artificial serum to accelerated stability conditions in histidine buffer. Target mAbs were assessed in regard to visible and sub-visible particles, as well as protein aggregation and fragmentation. Both mAbs degraded to a higher extent under physiological conditions compared to accelerated stability conditions. No relevant stability differences between the tested mAbs were observed. Our results reinforced the importance of monitoring protein stability in biological fluids or fluids emulating these conditions closely. Models enabling analysis in fluids directly allow high throughput testing in early pre-clinical stages and help in selecting molecules with increased in vivo stability.
Physiologically Based Pharmacokinetic Modelling of Cabozantinib to Simulate Enterohepatic Recirculation, Drug–Drug Interaction with Rifampin and Liver Impairment
Authors: Gerner Bettina , Scherf-Clavel Oliver
Published: May 2021

Cabozantinib (CAB) is a receptor tyrosine kinase inhibitor approved for the treatment of several cancer types. Enterohepatic recirculation (EHC) of the substance is assumed but has not been further investigated yet. CAB is mainly metabolized via CYP3A4 and is susceptible for drug–drug interactions (DDI). The goal of this work was to develop a physiologically based pharmacokinetic (PBPK) model to investigate EHC, to simulate DDI with Rifampin and to simulate subjects with hepatic impairment. The model was established using PK-Sim® and six human clinical studies. The inclusion of an EHC process into the model led to the most accurate description of the pharmacokinetic behavior of CAB. The model was able to predict plasma concentrations with low bias and good precision. Ninety-seven percent of all simulated plasma concentrations fell within 2-fold of the corresponding concentration observed. Maximum plasma concentration (Cmax) and area under the curve (AUC) were predicted correctly (predicted/observed ratio of 0.9–1.2 for AUC and 0.8–1.1 for Cmax). DDI with Rifampin led to a reduction in predicted AUC by 77%. Several physiological parameters were adapted to simulate hepatic impairment correctly. This is the first CAB model used to simulate DDI with Rifampin and hepatic impairment including EHC, which can serve as a starting point for further simulations with regard to special populations.
Translational Studies on the Potential of a VEGF Nanoparticle-Loaded Hyaluronic Acid Hydrogel
Authors: O’Dwyer Joanne , Murphy Robert , González-Vázquez Arlyng , Kovarova Lenka , Pravda Martin , Velebny Vladimir , Heise Andreas , Duffy Garry P. , Cryan Sally Ann
Published: May 2021

Heart failure has a five-year mortality rate approaching 50%. Inducing angiogenesis following a myocardial infarction is hypothesized to reduce cardiomyocyte death and tissue damage, thereby preventing heart failure. Herein, a novel nano-in-gel delivery system for vascular endothelial growth factor (VEGF), composed of star-shaped polyglutamic acid-VEGF nanoparticles in a tyramine-modified hyaluronic acid hydrogel (nano-VEGF-HA-TA), is investigated. The ability of the nano-VEGF-HA-TA system to induce angiogenesis is assessed in vivo using a chick chorioallantoic membrane model (CAM). The formulation is then integrated with a custom-made, clinically relevant catheter suitable for minimally invasive endocardial delivery and the effect of injection on hydrogel properties is examined. Nano-VEGF-HA-TA is biocompatible on a CAM assay and significantly improves blood vessel branching (p < 0.05) and number (p < 0.05) compared to a HA-TA hydrogel without VEGF. Nano-VEGF-HA-TA is successfully injected through a 1.2 m catheter, without blocking or breaking the catheter and releases VEGF for 42 days following injection in vitro. The released VEGF retains its bioactivity, significantly improving total tubule length on a Matrigel® assay and human umbilical vein endothelial cell migration on a Transwell® migration assay. This VEGF-nano in a HA-TA hydrogel delivery system is successfully integrated with an appropriate device for clinical use, demonstrates promising angiogenic properties in vivo and is suitable for further clinical translation.
Induced Tissue-Specific Stem Cells (iTSCs): Their Generation and Possible Use in Regenerative Medicine
Authors: Saitoh Issei , Sato Masahiro , Kiyokawa Yuki , Inada Emi , Iwase Yoko , Ibano Natsumi , Noguchi Hirofumi
Published: May 2021

Induced tissue-specific stem cells (iTSCs) are partially reprogrammed cells which have an intermediate state, such as progenitors or stem cells. They originate from the de-differentiation of differentiated somatic cells into pluripotent stem cells, such as induced pluripotent stem cells (iPSCs) and embryonic stem cells (ESCs), or from the differentiation of undifferentiated cells. They show a limited capacity to differentiate and a morphology similar to that of somatic cell stem cells present in tissues, but distinct from that of iPSCs and ESCs. iTSCs can be generally obtained 7 to 10 days after reprogramming of somatic cells with Yamanaka’s factors, and their fibroblast-like morphology remains unaltered. iTSCs can also be obtained directly from iPSCs cultured under conditions allowing cellular differentiation. In this case, to effectively induce iTSCs, additional treatment is required, as exemplified by the conversion of iPSCs into naïve iPSCs. iTSCs can proliferate continuously in vitro, but when transplanted into immunocompromised mice, they fail to generate solid tumors (teratomas), implying loss of tumorigenic potential. The low tendency of iTSCs to elicit tumors is beneficial, especially considering applications for regenerative medicine in humans. Several iTSC types have been identified, including iTS-L, iTS-P, and iTS-D, obtained by reprogramming hepatocytes, pancreatic cells, and deciduous tooth-derived dental pulp cells, respectively. This review provides a brief overview of iPSCs and discusses recent advances in the establishment of iTSCs and their possible applications in regenerative medicine.
Lipophilicity, Pharmacokinetic Properties, and Molecular Docking Study on SARS-CoV-2 Target for Betulin Triazole Derivatives with Attached 1,4-Quinone
Authors: Kadela-Tomanek Monika , Jastrzębska Maria , Marciniec Krzysztof , Chrobak Elwira , Bębenek Ewa , Boryczka Stanisław
Published: May 2021

A key parameter in the design of new active compounds is lipophilicity, which influences the solubility and permeability through membranes. Lipophilicity affects the pharmacodynamic and toxicological profiles of compounds. These parameters can be determined experimentally or by using different calculation methods. The aim of the research was to determine the lipophilicity of betulin triazole derivatives with attached 1,4-quinone using thin layer chromatography in a reverse phase system and a computer program to calculate its theoretical model. The physiochemical and pharmacokinetic properties were also determined by computer programs. For all obtained parameters, the similarity analysis and multilinear regression were determined. The analyses showed that there is a relationship between structure and properties under study. The molecular docking study showed that betulin triazole derivatives with attached 1,4-quinone could inhibit selected SARS-CoV-2 proteins. The MLR regression showed that there is a correlation between affinity scoring values (ΔG) and the physicochemical properties of the tested compounds.
Biocomposites of Epoxidized Natural Rubber/Poly(Lactic Acid) Modified with Natural Substances: Influence of Biomolecules on the Aging Properties (Part II)
Authors: Masek Anna , Cichosz Stefan
Published: May 2021

The aim of this study is to present the possible influence of natural substances on the aging properties of epoxidized natural rubber (ENR) and poly(lactic acid) (PLA) eco-friendly elastic blends. Therefore, the ENR/PLA blends were filled with natural pro-health substances of potentially antioxidative behavior, namely, δ-tocopherol (vitamin E), curcumin, β-carotene and quercetin. In this way, the material biodeterioration potential was maintained and the material’s lifespan was prolonged while subjected to increased temperatures or high-energy UVA irradiation (340 nm). The investigation of the samples’ properties indicated that curcumin and quercetin are the most promising natural additives that may contribute to the delay of ENR/PLA degradation under the above-mentioned conditions. The efficiency of the proposed new natural anti-aging additives was proven with static mechanical analysis, color change investigation, as well as mass loss during a certain aging. The aging coefficient, which compares the mechanical properties before and after the aging process, indicated that the ENR/PLA performance after 200 h of accelerated aging might decrease only by approximately 30% with the blend loaded with quercetin. This finding paves new opportunities for bio-based and green anti-aging systems employed in polymer technology.
Microwave Discharges in Liquid Hydrocarbons: Physical and Chemical Characterization
Authors: Lebedev Yuri A.
Published: May 2021

Microwave discharges in dielectric liquids are a relatively new area of plasma physics and plasma application. This review cumulates results on microwave discharges in wide classes of liquid hydrocarbons (alkanes, cyclic and aromatic hydrocarbons). Methods of microwave plasma generation, composition of gas products and characteristics of solid carbonaceous products are described. Physical and chemical characteristics of discharge are analyzed on the basis of plasma diagnostics and 0D, 1D and 2D simulation.
The Potential Cytotoxic Activity Enhancement of α-Mangostin in Chitosan-Kappa Carrageenan-Loaded Nanoparticle against MCF-7 Cell Line
Authors: Wathoni Nasrul , Meylina Lisna , Rusdin Agus , Mohammed Ahmed Fouad Abdelwahab , Tirtamie Dorandani , Herdiana Yedi , Motoyama Keiichi , Panatarani Camelia , Joni I Made , Lesmana Ronny , Muchtaridi Muchtaridi
Published: May 2021

α-mangostin (αM), a xanthone derivative compound isolated from the extract of mangosteen pericarp (Garcinia mangostana L), has potential anticancer properties for breast cancer. However, it has poor solubility in water and low selectivity towards cancer cells. The polymeric nanoparticle formulation approach can be used to overcome these problems. In this study, a chitosan biopolymer-based αM polymeric nanoparticle formulation was encapsulated using kappa carrageenan (αM-Ch/Cr) as a novel carrier for breast cancer therapy and evaluated for their physicochemical properties, drug release profile, and in vitro cytotoxicity against breast cancer cells (MCF-7). Polymeric nanoparticles formulated with varying concentrations of kappa carrageenan were successfully prepared by ionic gelation and spray pyrolysis techniques. αM-Ch/Cr nanoparticles formed perfectly round particles with a size of 200–400 nm and entrapment efficiency ≥ 98%. In vitro release studies confirmed that αM-Ch/Cr nanoparticles had a sustained release system profile. Interestingly, the formulation of polymeric nanoparticles significantly (p < 0.05) increased the cytotoxicity of αM against MCF-7 cell with IC50 value of 4.7 μg/mL compared to the non-nanoparticle with IC50 of 8.2 μg/mL. These results indicate that αM-Ch/Cr nanoparticles have the potential to improve the physicochemical properties and cytotoxicity effects of αM compounds as breast cancer therapy agents.
Interfacial Binding Energy between Calcium-Silicate-Hydrates and Epoxy Resin: A Molecular Dynamics Study
Authors: Wang Xianfeng , Xie Wei , Ren Jun , Zhu Jihua , Li Long-Yuan , Xing Feng
Published: May 2021

Microcapsules encapsulated within epoxy as a curing agent have been successfully applied in self-healing materials, in which the healing performance significantly depends on the binding behaviour of the epoxy curing agent with the cement matrix. In this paper, the binding energy was investigated by molecular dynamics simulation, which could overcome the shortcomings of traditional microscopic experimental methods. In addition to the construction of different molecular models of epoxy, curing agents, and dilutants, seven models were established to investigate the effects of chain length, curing agent, and epoxy resin chain direction on the interfacial binding energy. The results showed that an increase of chain length exhibited had limited effect on the binding energy, while the curing agent and the direction of the epoxy significantly affected the interfacial binding energy. Among different factors, the curing agent tetrethylenepentamine exhibited the highest value of interfacial binding energy by an increment of 31.03 kcal/mol, indicating a better binding ability of the microcapsule core and the cement matrix. This study provides a microscopic insight into the interface behaviour between the microcapsule core and the cement matrix.
Cyclodextrin Monomers and Polymers for Drug Activity Enhancement
Authors: Matencio Adrián , Hoti Gjylije , Monfared Yousef , Rezayat Azam , Pedrazzo Alberto , Caldera Fabrizio , Trotta Francesco
Published: May 2021

Cyclodextrins (CDs) and cyclodextrin (CD)-based polymers are well-known complexing agents. One of their distinctive features is to increase the quantity of a drug in a solution or improve its delivery. However, in certain instances, the activity of the solutions is increased not only due to the increase of the drug dose but also due to the drug complexation. Based on numerous studies reviewed, the drug appeared more active in a complex form. This review aims to summarize the performance of CDs and CD-based polymers as activity enhancers. Accordingly, the review is divided into two parts, i.e., the effect of CDs as active drugs and as enhancers in antimicrobials, antivirals, cardiovascular diseases, cancer, neuroprotective agents, and antioxidants.
Development and Characterization of Environmentally Friendly Wood Plastic Composites from Biobased Polyethylene and Short Natural Fibers Processed by Injection Moulding
Authors: Dolza Celia , Fages Eduardo , Gonga Eloi , Gomez-Caturla Jaume , Balart Rafael , Quiles-Carrillo Luis
Published: May 2021

Environmentally friendly wood plastic composites (WPC) with biobased high density polyethylene (BioHDPE) as the polymer matrix and hemp, flax and jute short fibers as natural reinforcements, were melt-compounded using twin-screw extrusion and shaped into pieces by injection molding. Polyethylene-graft-maleic anhydride (PE-g-MA) was added at two parts per hundred resin to the WPC during the extrusion process in order to reduce the lack in compatibility between the lignocellulosic fibers and the non-polar polymer matrix. The results revealed a remarkable improvement of the mechanical properties with the combination of natural fibers, along with PE-g-MA, highly improved stiffness and mechanical properties of neat BioHDPE. Particularly, hemp fiber drastically increased the Young’s modulus and impact strength of BioHDPE. Thermal analysis revealed a slight improvement in thermal stability with the addition of the three lignocellulosic fibers, increasing both melting and degradation temperatures. The incorporation of the fibers also increased water absorption due to their lignocellulosic nature, which drastically improved the polarity of the composite. Finally, fire behavior properties were also improved in terms of flame duration, thanks to the ability of the fibers to form char protective barriers that isolate the material from oxygen and volatiles.
New Approach for Preparing In Vitro Bioactive Scaffold Consisted of Ag-Doped Hydroxyapatite + Polyvinyltrimethoxysilane
Authors: Rabiei Marzieh , Palevicius Arvydas , Ebrahimi-Kahrizsangi Reza , Nasiri Sohrab , Vilkauskas Andrius , Janusas Giedrius
Published: May 2021

Recently, researchers have focused on the biocompatibility and mechanical properties of highly porous structures of biomaterials products. Porous composites are a new category of bioengineering that possess excellent functional and structural properties. In this study, the physical and mechanical properties of prepared doped silver (Ag)-hydroxyapatite (HA) by the mechanochemical and spark plasma sintering (SPS) methods were investigated. The influence of dopant on phase formation, structural properties, mechanical properties and morphological characteristics was investigated. Furthermore, in this case, as a new approach to produce a porous scaffold with an average size of >100 µm, the hair band was used as a mold. According to the Monshi–Scherrer method, the crystal size of scaffold was calculated 38 ± 2 nm and this value was in the good agreement with average value from transmission electron microscopy (TEM) analysis. In addition, the stress–strain compression test of scaffold was considered, and the maximum value of compressive strength was recorded ~15.71 MPa. Taking into account the XRD, TEM, Fourier-transform infrared (FTIR), scanning electron microscope (SEM) and energy dispersive X-Ray analysis (EDAX) analysis, the prepared scaffold was bioactive and the effects of doped Ag-HA and the use of polyvinyltrimethoxysilane (PVTMS) as an additive were desirable. The results showed that the effect of thermal treatment on composed of Ag and HA were impressive while no change in transformation was observed at 850 °C. In addition, PVTMS plays an important role as an additive for preventing the decomposition and creating open-microporous in the scaffold that these porosities can be helpful for increasing bioactivity.
Medieval Monasticism in Iceland and Norse Greenland
Authors: Kristjánsdóttir Steinunn
Published: May 2021

The aim of this paper is to provide an overview of the monastic houses operated on the northernmost periphery of Roman Catholic Europe during the Middle Ages. The intention is to debunk the long-held theory of Iceland and Norse Greenland’s supposed isolation from the rest of the world, as it is clear that medieval monasticism reached both of these societies, just as it reached their counterparts elsewhere in the North Atlantic. During the Middle Ages, fourteen monastic houses were opened in Iceland and two in Norse Greenland, all following the Benedictine or Augustinian Orders.
Data Fusion of XRF and Vis-NIR Using Outer Product Analysis, Granger–Ramanathan, and Least Squares for Prediction of Key Soil Attributes
Authors: Javadi S. Hamed , Mouazen Abdul M.
Published: May 2021

Visible-near-infrared (vis-NIR) and X-ray fluorescence (XRF) are key technologies becoming pervasive in proximal soil sensing (PSS), whose fusion holds promising potential to improve the estimation accuracy of soil attributes. In this paper, we examine different data fusion methods for the prediction of key soil fertility attributes including pH, organic carbon (OC), magnesium (Mg), and calcium (Ca). To this end, the vis-NIR and XRF spectra of 267 soil samples were collected from nine fields in Belgium, from which the soil samples of six fields were used for calibration of the single-sensor and data fusion models while the validation was performed on the remaining three fields. The first fusion method was the outer product analysis (OPA), for which the outer product (OP) of the two spectra is computed, flattened, and then subjected to partial least squares (PLS) regression model. Two versions of OPA were evaluated: (i) OPA-FS in which the full spectra were used as input; and (ii) OPA-SS in which selected spectral ranges were used as input. In addition, we examined the potential of least squares (LS) and Granger–Ramanathan (GR) analyses for the fusion of the predictions provided by the single-sensor PLS models. Results demonstrate that the prediction performance of the single-sensor PLS models is improved by GR in addition to the LS fusion method for all soil attributes since it accounts for residuals. Resorting to LS, the largest improvements compared to the single-sensor models were obtained, respectively, for Mg (residual prediction deviation (RPD) = 4.08, coefficient of determination (R2) = 0.94, ratio of performance of inter-quantile (RPIQ) = 1.64, root mean square error (RMSE) = 4.57 mg/100 g), OC (RPD = 1.79, R2 = 0.69, RPIQ = 2.82, RMSE = 0.16%), pH (RPD = 1.61, R2 = 0.61, RPIQ = 3.06, RMSE = 0.29), and Ca (RPD = 3.33, R2 = 0.91, RPIQ = 1, RMSE = 207.48 mg/100 g). OPA-FS and OPA-SS outperformed the individual, GR, and LS models for pH only, while OPA-FS was effective in improving the individual sensor models for Mg as well. The results of this study suggest LS as a robust fusion method in improving the prediction accuracy for all the studied soil attributes.
Adaptive Determination of the Flow Accumulation Threshold for Extracting Drainage Networks from DEMs
Authors: Zhang Wei , Li Wenkai , Loaiciga Hugo A. , Liu Xiuguo , Liu Shuya , Zheng Shengjie , Zhang Han
Published: May 2021

Selecting the flow accumulation threshold (FAT) plays a central role in extracting drainage networks from Digital Elevation Models (DEMs). This work presents the MR-AP (Multiple Regression and Adaptive Power) method for choosing suitable FAT when extracting drainage from DEMs. This work employs 36 sample sub-basins in Hubei (China) province. Firstly, topography, the normalized difference vegetation index (NDVI), and water storage change are used in building multiple regression models to calculate the drainage length. Power functions are fit to calculate the FAT of each sub-basin. Nine randomly chosen regions served as test sub-basins. The results show that: (1) water storage change and NDVI have high correlation with the drainage length, and the coefficient of determination (R2) ranges between 0.85 and 0.87; (2) the drainage length obtained from the Multiple Regression model using water storage change, NDVI, and topography as influence factors is similar to the actual drainage length, featuring a coefficient of determination (R2) equal to 0.714; (3) the MR-AP method calculates suitable FATs for each sub-basin in Hubei province, with a drainage length error equal to 5.13%. Moreover, drainage network extraction by the MR-AP method mainly depends on the water storage change and the NDVI, thus being consistent with the regional water-resources change.
A Through-the-Wall Imaging Approach Based on a TSVD/Variable-Exponent Lebesgue-Space Method
Authors: Randazzo Andrea , Ponti Cristina , Fedeli Alessandro , Estatico Claudio , D’Atanasio Paolo , Pastorino Matteo , Schettini Giuseppe
Published: May 2021

A hybrid inversion scheme for through-the-wall imaging is proposed in this paper. The approach is based on a linearized model of the inverse-scattering problem, employing the Green’s function developed for a layered background. The reconstruction is obtained by means of a Landweber-like iterative method performing a regularization in the framework of variable-exponent Lebesgue spaces. Thanks to the non-conventional geometrical properties of such spaces, it is possible to enhance the reconstruction capabilities, e.g., by promoting sparseness and reducing over-smoothing. The exponent function defining the specific space adopted in the inversion procedure is adaptively obtained directly from the measured data, through a truncated-singular value decomposition method. In this way, it is possible to precompute and reuse in both steps, for a given scenario, all the matrices necessary in the inversion process, thus leading to a computationally efficient solving strategy. The effectiveness of the approach is evaluated by using experimental data obtained with a commercial GPR apparatus employing a pulsed source field. A fast Fourier transform is applied to the time-domain measurements to extract frequency-domain data at a set of frequencies in the source spectrum, which are fed in input to the imaging scheme. Very good reconstruction capabilities are obtained both with a single metallic target, as well as in a challenging two targets layout including both a metallic object and a low-permittivity target.
Mapping Urban Green Spaces at the Metropolitan Level Using Very High Resolution Satellite Imagery and Deep Learning Techniques for Semantic Segmentation
Authors: Huerta Roberto E. , Yépez Fabiola D. , Lozano-García Diego F. , Guerra Cobián Víctor H. , Ferriño Fierro Adrián L. , de León Gómez Héctor , Cavazos González Ricardo A. , Vargas-Martínez Adriana
Published: May 2021

Urban green spaces (UGSs) provide essential environmental services for the well-being of ecosystems and society. Due to the constant environmental, social, and economic transformations of cities, UGSs pose new challenges for management, particularly in fast-growing metropolitan areas. With technological advancement and the evolution of deep learning, it is possible to optimize the acquisition of UGS inventories through the detection of geometric patterns present in satellite imagery. This research evaluates two deep learning model techniques for semantic segmentation of UGS polygons with the use of different convolutional neural network encoders on the U-Net architecture and very high resolution (VHR) imagery to obtain updated information on UGS polygons at the metropolitan area level. The best model yielded a Dice coefficient of 0.57, IoU of 0.75, recall of 0.80, and kappa coefficient of 0.94 with an overall accuracy of 0.97, which reflects a reliable performance of the network in detecting patterns that make up the varied geometry of UGSs. A complete database of UGS polygons was quantified and categorized by types with location and delimited by municipality, allowing for the standardization of the information at the metropolitan level, which will be useful for comparative analysis with a homogenized and updated database. This is of particular interest to urban planners and UGS decision-makers.
How BFAST Trend and Seasonal Model Components Affect Disturbance Detection in Tropical Dry Forest and Temperate Forest
Authors: Gao Yan , Solórzano Jonathan V. , Quevedo Alexander , Loya-Carrillo Jaime Octavio
Published: May 2021

Time series analysis has gained popularity in forest disturbance monitoring thanks to the availability of satellite and airborne remote sensing images and the development of different time series methods for change detection. Previous research has focused on time series data noise reduction, the magnitude of breakpoints, and accuracy assessment; however, few have looked in detail at how the trend and seasonal model components contribute to disturbance detection in different forest types. Here, we use Landsat time series images spanning 1994–2018 to map forest disturbance in a western Pacific area of Mexico, where both temperate and tropical dry forests have been subject to severe deforestation and forest degradation processes. Since these two forest types have distinct seasonal characteristics, we investigate how trend and seasonal model components, such as the goodness-of-fit (R2), magnitude of change, amplitude, and model length in a stable historical period, affect forest disturbance detection. We applied the Breaks For Additive Season and Trend Monitor (BFAST) algorithm and after accuracy assessment by stratified random sample points, and we obtained 68% and 86% of user accuracy and 75.6% and 86% of producer’s accuracy in disturbance detection, in tropical dry forests and temperate forests, respectively. We extracted the noncorrelated trend and seasonal model components R2, magnitude, amplitude, length of the stable historical period, and percentage of pixels with NA and tested their effects on disturbance detection employing forest-type specific logistic regression. Our results showed that, for all forests combined, the amplitude and stable historical period length contributed to disturbance detection. While for tropical dry forest alone, amplitude was the main predictor, and for the temperate forest alone, the stable historical period length contributed most to the prediction, although it was not statistically significant. These findings provide insights for improving the results of forest disturbance detection in different forest types.
Comparison of the Segmentation Results of Two Carrier Tracking Loop Types and Analysis of Theoretical Influencing Factors
Authors: Wang Qian , Han Mengyue , Wen Yuanlan , He Min , He Xiufeng
Published: May 2021

This paper proposes an accurate quantitative segmentation method by analyzing the probability distribution of tracking variance and strict derivation based on the tracking loop theory. The segmentation points are taken as characteristics of phase lock loop (PLL) and frequency lock loop (FLL) performances, and the two factors that cause the performance difference are discriminator gain and filtering coefficient, which denote proportional and integration coefficients, respectively. The filtering coefficients lead to a difference of 2.5 dB-Hz between the FLL and PLL. Moreover, through the analysis of the normalized bandwidth and phase margin, it is found that the integration time and bandwidth need a dynamic balance to achieve the best performance. Finally, the simulation results and real data are in good agreement with the theoretical analysis results. The minimum mean error rate of the deviation between the real data and the theoretical data is only 1.8%. In the proposed method, the influence of external hardware factors on the tracking loop is removed, and the loop design factors are modeled directly. Instead of testing the denoising performance based on the ranging and angle measuring error after location calculation, the filter coefficient is proposed to evaluate the processing performance of the tracking loop objectively and directly at the theoretical level, which proposes a new performance evaluation method at the theoretical level. The results presented in this study provide theoretical support for the design of a new-type tracking loop with enhanced performances.
Methodology for the Definition of Durum Wheat Yield Homogeneous Zones by Using Satellite Spectral Indices
Authors: Romano Elio , Bergonzoli Simone , Pecorella Ivano , Bisaglia Carlo , De Vita Pasquale
Published: May 2021

One of the main questions facing precision agriculture is the evaluation of different algorithms for the delineation of homogeneous management zones. In the present study, a new approach based on the use of time series of satellite imagery, collected during two consecutive growing seasons, was proposed. Texture analysis performed using the Gray-Level Co-Occurrence Matrix (GLCM) was used to integrate and correct the sum of the vegetation indices maps (NDVI and MCARI2) and define the homogenous productivity zones on ten durum wheat fields in southern Italy. The homogenous zones identified through the method that integrates the GLCM indices with the spectral indices studied showed a greater accuracy (0.18–0.22 Mg ha−1 for ∑NDVIs + GLCM and 0.05–0.49 Mg ha−1 for ∑MCARI2s + GLCM) with respect to the methods that considered only the sum of the indices. Best results were also obtained with respect to the homogeneous zones derived by using yield maps of the previous year or vegetation indices acquired in a single day. Therefore, the survey methods based on the data collected over the entire study period provided the best results in terms of estimated yield; the addition of clustering analysis performed with the GLCM method allowed to further improve the accuracy of the estimate and better define homogeneous productivity zones of durum wheat fields.
SAR Oil Spill Detection System through Random Forest Classifiers
Authors: Conceição Marcos Reinan Assis , de Mendonça Luis Felipe Ferreira , Lentini Carlos Alessandre Domingos , da Cunha Lima André Telles , Lopes José Marques , de Vasconcelos Rodrigo Nogueira , Gouveia Mainara Biazati , Porsani Milton José
Published: May 2021

A set of open-source routines capable of identifying possible oil-like spills based on two random forest classifiers were developed and tested with a Sentinel-1 SAR image dataset. The first random forest model is an ocean SAR image classifier where the labeling inputs were oil spills, biological films, rain cells, low wind regions, clean sea surface, ships, and terrain. The second one was a SAR image oil detector named “Radar Image Oil Spill Seeker (RIOSS)”, which classified oil-like targets. An optimized feature space to serve as input to such classification models, both in terms of variance and computational efficiency, was developed. It involved an extensive search from 42 image attribute definitions based on their correlations and classifier-based importance estimative. This number included statistics, shape, fractal geometry, texture, and gradient-based attributes. Mixed adaptive thresholding was performed to calculate some of the features studied, returning consistent dark spot segmentation results. The selected attributes were also related to the imaged phenomena’s physical aspects. This process helped us apply the attributes to a random forest, increasing our algorithm’s accuracy up to 90% and its ability to generate even more reliable results.
Estimating Urban Evapotranspiration at 10m Resolution Using Vegetation Information from Sentinel-2: A Case Study for the Beijing Sponge City
Authors: Zhang Xuanze , Song Peilin
Published: May 2021

Estimating accurately evapotranspiration (ET) in urban ecosystems is difficult due to the complex surface conditions and a lack of fine measurement of vegetation dynamics. To overcome such difficulties using recent developments of remote sensing technology, we estimate leaf area index (LAI) from Sentinel-2-based Normalized Difference Vegetation Index (NDVI) using the NDVI–LAI nonlinear relationship. By applying Sentinel-2-based LAI and land cover classification (LCC) to a carbon-water coupling model (PML-V2.1) with surface meteorological forcing data as input, we, for the first time, estimate monthly ET at 10m × 10m resolution for the Beijing Sponge City. Results show that for the whole sponge city during June 2018, the LAI, ET and gross primary productivity (GPP) are 0.83 m2 m−2, 1.6 mm d−1 and 2.8 gC m−2 d−1, respectively. For different LCCs, lakes and rivers have the highest ET (≥8 mm d−1), followed by mixed forests and croplands (ET is 4–6 mm d−1 and LAI is 2–3 m2 m−2) with dominant contribution (>80%) from plant transpiration, while grasslands (2–4 mm d−1) have 50–70% from transpiration due to smaller LAI (1~2 m2 m−2). The impervious surfaces occupying ~60% of the sponge city area, have the smallest ET (<2.0 mm d−1) in which interception evaporation by impervious surface contributes 20–30%, and transpiration from greenbelts (0.5–1.0 m2 m−2 of LAI) contributes 40–50%. These findings can provide a valuable scientific basis for policymaking and urban water use planning. This study proposes a Sentinel-2-based technology for estimating ET as a feasible framework to evaluate city-level hydrological dynamics in urban ecosystems.
Using Satellite Data to Optimize Wheat Yield and Quality under Climate Change
Authors: Shiff Shilo , Lensky Itamar M. , Bonfil David J.
Published: May 2021

Climatic conditions during the grain-filling period are a major factor affecting wheat grain yield and quality. Wheat in many semi-arid and arid areas faces high-temperature stress during this period. Remote sensing can be used to monitor both crops and environmental temperature. The objective of this study was to develop a tool to optimize field management (cultivar and sowing time). Analysis of 155 cultivar experiments (from 10 growth seasons) representing different environmental conditions revealed the required degree-days for each Israeli spring wheat cultivar to reach heading (from emergence). We developed a Google Earth Engine (GEE) app to analyze time series of gap-filled 1 km MODIS land surface temperature (LSTcont). By changing the cultivar and/or emergence date in the GEE app, the farmer can “expose” each wheat field to different climatic conditions during the grain-filling period, thereafter enabling him to choose the best cultivar to be sown in the field with the right timing. This approach is expected to reduce the number of fields that suffer from heat stress during the grain-filling period. The app can be also used to assess the effects of different global warming scenarios and to plan adaptation strategies in other regions too.
Characterization of the 2014 Indus River Flood Using Hydraulic Simulations and Satellite Images
Authors: Tariq Aqil , Shu Hong , Kuriqi Alban , Siddiqui Saima , Gagnon Alexandre S. , Lu Linlin , Linh Nguyen Thi Thuy , Pham Quoc Bao
Published: May 2021

Rivers play an essential role to humans and ecosystems, but they also burst their banks during floods, often causing extensive damage to crop, property, and loss of lives. This paper characterizes the 2014 flood of the Indus River in Pakistan using the US Army Corps of Engineers Hydrologic Engineering Centre River Analysis System (HEC-RAS) model, integrated into a geographic information system (GIS) and satellite images from Landsat-8. The model is used to estimate the spatial extent of the flood and assess the damage that it caused by examining changes to the different land-use/land-cover (LULC) types of the river basin. Extreme flows for different return periods were estimated using a flood frequency analysis using a log-Pearson III distribution, which the Kolmogorov–Smirnov (KS) test identified as the best distribution to characterize the flow regime of the Indus River at Taunsa Barrage. The output of the flood frequency analysis was then incorporated into the HEC-RAS model to determine the spatial extent of the 2014 flood, with the accuracy of this modelling approach assessed using images from the Moderate Resolution Imaging Spectroradiometer (MODIS). The results show that a supervised classification of the Landsat images was able to identify the LULC types of the study region with a high degree of accuracy, and that the most affected LULC was crop/agricultural land, of which 50% was affected by the 2014 flood. Finally, the hydraulic simulation of extent of the 2014 flood was found to visually compare very well with the MODIS image, and the surface area of floods of different return periods was calculated. This paper provides further evidence of the benefit of using a hydrological model and satellite images for flood mapping and for flood damage assessment to inform the development of risk mitigation strategies.
Special Issue “Remote Sensing of Greenhouse Gases and Air Pollution”
Authors: Xiong Xiaozhen , Liu Jane , Chen Liangfu , Ju Weimin , Moshary Fred
Published: May 2021

Continuous increases in the human population and human activities have resulted in remarkable changes in the composition of the atmosphere since the industrial revolution [...]
Using High-Frequency Information and RH to Estimate AQI Based on SVR
Authors: Liaw Jiun-Jian , Chen Kuan-Yu
Published: May 2021

The Environmental Protection Administration of Taiwan’s Executive Yuan has set up many air quality monitoring stations to monitor air pollution in the environment. The current weather forecast also includes information used to predict air pollution. Since air quality indicators have a considerable impact on people, the development of a simple, fast, and low-cost method to measure the AQI value is a worthy topic of research. In this study, a method was proposed to estimate AQI. Visibility had a clear positive relationship with AQI. When images and AQI were compared, it was easy to see that visibility decreased with the AQI value increase. Distance is the main factor affecting visibility, so measuring visibility with images has also become a research topic. Images with high and low PM2.5 concentrations were used to obtain regions of interest (RoI). The pixels in the RoI were calculated to obtain high-frequency information. The high-frequency information of RoI, RH, and true AQI was used for training via SVR, which was used to generate the model for AQI estimation. One year of experimental samples was collected for the experiment. Two indices were used to evaluate the performance of the proposed method. The results showed that the proposed method could be used to estimate AQI with acceptable performance in a simple, fast, and low-cost way.
Application of Nematode Community Analyses-Based Models towards Identifying Sustainable Soil Health Management Outcomes: A Review of the Concepts
Authors: Melakeberhan Haddish , Bonito Gregory , Kravchenko Alexandra N.
Published: May 2021

Soil health connotes the balance of biological, physicochemical, nutritional, structural, and water-holding components necessary to sustain plant productivity. Despite a substantial knowledge base, achieving sustainable soil health remains a goal because it is difficult to simultaneously: (i) improve soil structure, physicochemistry, water-holding capacity, and nutrient cycling; (ii) suppress pests and diseases while increasing beneficial organisms; and (iii) improve biological functioning leading to improved biomass/crop yield. The objectives of this review are (a) to identify agricultural practices (APs) driving soil health degradations and barriers to developing sustainable soil health, and (b) to describe how the nematode community analyses-based soil food web (SFW) and fertilizer use efficiency (FUE) data visualization models can be used towards developing sustainable soil health. The SFW model considers changes in beneficial nematode population dynamics relative to food and reproduction (enrichment index, EI; y-axis) and resistance to disturbance (structure index, SI; x-axis) in order to identify best-to-worst case scenarios for nutrient cycling and agroecosystem suitability of AP-driven outcomes. The FUE model visualizes associations between beneficial and plant-parasitic nematodes (x-axis) and ecosystem services (e.g., yield or nutrients, y-axis). The x-y relationship identifies best-to-worst case scenarios of the outcomes for sustainability. Both models can serve as platforms towards developing integrated and sustainable soil health management strategies on a location-specific or a one-size-fits-all basis. Future improvements for increased implementation of these models are discussed.
A Novel Machine Learning-Based Framework for Optimal and Secure Operation of Static VAR Compensators in EAFs
Authors: Zeng Li , Xia Tian , Elsayed Salah K. , Ahmed Mahrous , Rezaei Mostafa , Jermsittiparsert Kittisak , Dampage Udaya , Mohamed Mohamed A.
Published: May 2021

A static VAR compensator (SVC) is a critical component for reactive power compensation in electric arc furnaces (EAFs) that is used to relieve the flicker impacts and maintain the voltage level. A weak voltage profile can not only reduce the power-quality services, but can also result in system instability in severe cases. The cybersecurity of EAFs is becoming a significant concern due to their cyber-physical structure. The reliance of SVC controllers on reactive power measurement and network communications has resulted in a cyber-vulnerability point for unauthorized access to the EAF, which can affect its normal operation. This paper addresses concerns about cyber attacks on EAFs, which can cause network communication issues in measurement data for SVCs. Three significant and different types of cyber attacks that are launched on SVC controllers—a replay attack, delay attack, and false data injection attack (FDIA)—were simulated and investigated. In order to stop the activities of cyber attacks, a secured anomaly detection model (ADM) based on a prediction interval is proposed. The proposed model is dependent on a support vector regression and a new smooth cost function for constructing the optimal and symmetrical intervals. A modified algorithm based on teaching–learning-based optimization was developed to adapt the ADM’s parameters during training. The simulation’s outcomes on a genuine dataset showed the strong capability of the proposed model against cyber attacks in EAFs.