bluuebunny/crossref_metadata_2025 huggingface dataset:
New Rare Ent-Clerodane Diterpene Peroxides from Egyptian Mountain Tea (Qourtom) and Its Chemosystem as Herbal Remedies and Phytonutrients Agents
Authors: Hussien Taha A. , Mahmoud Ahmed A. , Mohamed Naglaa S. , Shahat Abdelaaty A. , El-Seedi Hesham R. , Hegazy Mohamed-Elamir F.
Published: May 2020

Genus Stachys, the largest genera of the family Lamiaceae, and its species are frequently used as herbal teas due to their essential oils. Tubers of some Stachys species are also consumed as important nutrients for humans and animals due to their carbohydrate contents. Three new neo-clerodane diterpene peroxides, named stachaegyptin F-H (1, 2, and 4), together with two known compounds, stachysperoxide (3) and stachaegyptin A (5), were isolated from Stachys aegyptiaca aerial parts. Their structures were determined using a combination of spectroscopic techniques, including HR-FAB-MS and extensive 1D and 2D NMR (1H, 13C NMR, DEPT, 1H-1H COSY, HMQC, HMBC and NOESY) analyses. Additionally, a biosynthetic pathway for the isolated compounds (1–5) was discussed. The chemotaxonomic significance of the isolated diterpenoids of S. aegyptiaca in comparison to the previous reported ones from other Stachys species was also studied.
Multi-Targeting Bioactive Compounds Extracted from Essential Oils as Kinase Inhibitors
Authors: Maruca Annalisa , Lanzillotta Delia , Rocca Roberta , Lupia Antonio , Costa Giosuè , Catalano Raffaella , Moraca Federica , Gaudio Eugenio , Ortuso Francesco , Artese Anna , Trapasso Francesco , Alcaro Stefano
Published: May 2020

Essential oils (EOs) are popular in aromatherapy, a branch of alternative medicine that claims their curative effects. Moreover, several studies reported EOs as potential anti-cancer agents by inducing apoptosis in different cancer cell models. In this study, we have considered EOs as a potential resource of new kinase inhibitors with a polypharmacological profile. On the other hand, computational methods offer the possibility to predict the theoretical activity profile of ligands, discovering dangerous off-targets and/or synergistic effects due to the potential multi-target action. With this aim, we performed a Structure-Based Virtual Screening (SBVS) against X-ray models of several protein kinases selected from the Protein Data Bank (PDB) by using a chemoinformatics database of EOs. By evaluating theoretical binding affinity, 13 molecules were detected among EOs as new potential kinase inhibitors with a multi-target profile. The two compounds with higher percentages in the EOs were studied more in depth by means Induced Fit Docking (IFD) protocol, in order to better predict their binding modes taking into account also structural changes in the receptor. Finally, given its good binding affinity towards five different kinases, cinnamyl cinnamate was biologically tested on different cell lines with the aim to verify the antiproliferative activity. Thus, this work represents a starting point for the optimization of the most promising EOs structure as kinase inhibitors with multi-target features.
Effective Enantiodiscrimination in Electroanalysis Based on a New Inherently Chiral 1,1′-Binaphthyl Selector Directly Synthesizable in Enantiopure Form
Authors: Bonetti Giorgia , Arnaboldi Serena , Grecchi Sara , Appoloni Giulio , Massolo Elisabetta , Rossi Sergio , Martinazzo Rocco , Orsini Francesco , Mussini Patrizia R. , Benincori Tiziana
Published: May 2020

Enantioselective electroanalysis, which aims to discriminate the enantiomers of electroactive chiral probes in terms of potential difference, is a very attractive goal. To achieve this, its implementation is being studied for various "inherently chiral" selectors, either at the electrode surface or in the medium, yielding outstanding performance. In this context, the new inherently chiral monomer Naph2T4 is introduced, based on a biaromatic atropisomeric core, which is advantageously obtainable in enantiopure form without HPLC separation steps by a synthetic route hinging on enantiopure 2,2’-dibromo-1,1’-binaphthalenes. The antipodes of the new inherently chiral monomer can be easily electrooligomerized, yielding inherently chiral electrode surfaces that perform well in both cyclic voltammetry (CV) enantiodiscrimination tests with pharmaceutically interesting molecules and in magnetoelectrochemistry experiments.
Synthesis and Biological Evaluation of 2-Substituted Benzyl-/Phenylethylamino-4-amino-5-aroylthiazoles as Apoptosis-Inducing Anticancer Agents
Authors: Oliva Paola , Onnis Valentina , Balboni Elisa , Hamel Ernest , Estévez-Sarmiento Francisco , Quintana José , Estévez Francisco , Brancale Andrea , Ferla Salvatore , Manfredini Stefano , Romagnoli Romeo
Published: May 2020

Induction of apoptosis is a common chemotherapeutic mechanism to kill cancer cells The thiazole system has been reported over the past decades as a building block for the preparation of anticancer agents. A novel series of 2-arylalkylamino-4-amino-5-(3′,4′,5′-trimethoxybenzoyl)-thiazole derivatives designed as dual inhibitors of tubulin and cyclin-dependent kinases (CDKs) were synthesized and evaluated for their antiproliferative activity in vitro against two cancer cell lines and, for selected highly active compounds, for interactions with tubulin and cyclin-dependent kinases and for cell cycle and apoptosis effects. Structure-activity relationships were elucidated for various substituents at the 2-position of the thiazole skeleton. Among the synthesized compounds, the most active analogues were found to be the p-chlorobenzylamino derivative 8e as well as the p-chloro and p-methoxyphenethylamino analogues 8f and 8k, respectively, which inhibited the growth of U-937 and SK-MEL-1 cancer cell lines with IC50 values ranging from 5.7 to 12.2 μM. On U-937 cells, the tested compounds 8f and 8k induced apoptosis in a time and concentration dependent manner. These two latter molecules did not affect tubulin polymerization (IC50 > 20 μM) nor CDK activity at a single concentration of 10 μM, suggesting alternative targets than tubulin and CDK for the compounds.
Innovative Bio-Based Organic UV-A and Blue Light Filters from Meldrum’s Acid
Authors: Peyrot Cédric , Mention Matthieu M. , Brunissen Fanny , Balaguer Patrick , Allais Florent
Published: May 2020

Faced with the ban of some organic UV filters such as octinoxate or avobenzone, especially in Hawaii, it became essential to offer new alternatives that are both renewable and safe for humans and the environment. In this context, a class of bio-based molecules displaying interesting UV filter properties and great (photo)stability has been developed from Meldrum’s acid and bio-based and synthetic p-hydroxycinnamic acids, furans and pyrroles. Moreover, p-hydroxycinnamic acid-based Meldrum’s derivatives possess valuable secondary activities sought by the cosmetic industry such as antioxidant and anti-tyrosinase properties. The evaluation of the properties of mixture of judiciously chosen Meldrum’s acid derivatives highlighted the possibility to modulate secondary activity while maintaining excellent UV protection. Meldrum’s acid derivatives are not only competitive when benchmarked against organic filters currently on the market (i.e., avobenzone), but they also do not exhibit any endocrine disruption activity.
Elemental Content in Pleurotus ostreatus and Cyclocybe cylindracea Mushrooms: Correlations with Concentrations in Cultivation Substrates and Effects on the Production Process
Authors: Koutrotsios Georgios , Danezis Georgios , Georgiou Constantinos , Zervakis Georgios
Published: May 2020

Few data exist about the effect of substrates’ elemental content on the respective concentrations in cultivated mushrooms, on the degradation of lignocellulosics or on production parameters. Sixteen elements (14 metals and 2 metalloids) were measured by inductively coupled plasma mass spectrometry (ICP-MS) in Pleurotus ostreatus and Cyclocybe cylindracea mushrooms, and in their seven cultivation substrates composed of various plant-based residues. Results revealed a high variability in elemental concentration among substrates which generally led to significant differences in the respective mushroom contents. High bioconcentration factors (BCFs) were noted for Cd, Cu, Mg and Zn for both species in all substrates. BCF of each element was variously affected by substrates’ pH, crude composition, and P and K content. Significant positive correlations were demonstrated for Cu, Fe, Mn and Li concentrations vs. a decrease of cellulose and hemicellulose in P. ostreatus substrates, and vs. mushrooms’ biological efficiency. In the case of C. cylindracea, Be, Mg and Mn concentrations were positively correlated with the decrease of hemicellulose in substrates, while a significant positive correlation was also recorded vs. mushroom productivity. Finally, it was found that 15% to 35% of the daily dietary needs in Mg, Se and Zn could be covered by mushroom consumption.
Evaluation of Anti-Cytotoxic and Anti-Genotoxic Effects of Nigella sativa through a Micronucleus Test in BALB/c Mice
Authors: Franco-Ramos Raúl S. , López-Romero Carlos A. , Torres-Ortega Hugo , Oseguera-Herrera Darío , Lamoreaux-Aguayo Jose P. , Molina-Noyola Daniel , Juárez-Vázquez Clara I. , Torres-Bugarín Olivia
Published: May 2020

Nigella sativa (N. sativa) is a medicinal plant used for its therapeutic pharmacological effects such as anti-inflammatory, antioxidant, anticancer, antidiabetic, and immunomodulation. This study explored the anti-cytotoxic and anti-genotoxic effect of N. sativa through a micronucleus test (MNT) of BALB/c mice peripheral blood. Using 6-to-8-week-old healthy male BALB/c mice, four groups were formed: (1) Control (sterile water), single-dose 2 mg/kg/intraperitoneal (i.p); (2) N. sativa oil, 500 mg/kg/24 h/7 days/i.p; (3) Cisplatin (CP), single-dose 2 mg/kg/subcutaneous (s.c); (4) N. sativa + CP with their respective dosage. When evaluating polychromatic erythrocytes (PCE), a biomarker of cytotoxicity, the group treated with N. sativa + CP experienced an increase in the frequency of PCE, which demonstrated the recovery of bone marrow and modulation of cell proliferation. The analysis of micronucleated polychromatic erythrocytes (MNPCE), an acute genotoxicity biomarker, showed similar frequency of MNPCE within the groups except in CP, but, in the N. sativa + CP group, the frequency of MNPCE decreased and then regulated. Finally, the frequency of micronucleated erythrocytes (MNE), a biomarker of genotoxicity, the supplementation of N. sativa oil did not induce genotoxic damage in this model. Thus, we conclude that N. sativa has both cytoprotective, genoprotective effects and modulates cell proliferation in BALB/c mice.
Nutrient Intake during Pregnancy and Post-Partum: ECLIPSES Study
Authors: Aparicio Estefania , Jardí Cristina , Bedmar Cristina , Pallejà Meritxell , Basora Josep , Arija Victoria ,   the ECLIPSES Study Group
Published: May 2020

Pregnancy and post-partum are critical periods in which nutritional intake is essential to maternal and child health. Our aim was to describe dietary intake during pregnancy and post-partum and assess its adequacy. A longitudinal study was conducted on 793 pregnant women. Data about maternal characteristics, health, diet and lifestyle were assessed. Energy and nutritional intake were compared to the Recommended Dietary Allowances (RDA). The results showed that the intake of energy (82.6%), protein (80.6%) and carbohydrate (99.5%) was adequate (above 80% of RDA) during pregnancy, as were vitamins C, B2 and B12; but vitamin D, iron and folate intake were a long way from RDA (below 35%). Similar results were observed for the post-partum period although fiber, and vitamins E and C decreased compared to intake during pregnancy. In conclusion, although nutritional requirements increase during gestation, pregnant women did not increase their energy and nutritional intake during pregnancy and postpartum and they had a high risk of deficient intake of vitamin D, iron and folates during pregnancy, and therefore, of developing an unfavorable nutritional status, contrary to health recommendations. These findings underscore the necessity of intensive nutrition programs during and after pregnancy.
In Vitro Intracellular Hyperthermia of Iron Oxide Magnetic Nanoparticles, Synthesized at High Temperature by a Polyol Process
Authors: Iacovita Cristian , Fizeșan Ionel , Pop Anca , Scorus Lavinia , Dudric Roxana , Stiufiuc Gabriela , Vedeanu Nicoleta , Tetean Romulus , Loghin Felicia , Stiufiuc Rares , Lucaciu Constantin Mihai
Published: May 2020

We report the synthesis of magnetite nanoparticles (IOMNPs) using the polyol method performed at elevated temperature (300 °C) and high pressure. The ferromagnetic polyhedral IOMNPs exhibited high saturation magnetizations at room temperature (83 emu/g) and a maximum specific absorption rate (SAR) of 2400 W/gFe in water. The uniform dispersion of IOMNPs in solid matrix led to a monotonous increase of SAR maximum (3600 W/gFe) as the concentration decreased. Cytotoxicity studies on two cell lines (cancer and normal) using Alamar Blues and Neutral Red assays revealed insignificant toxicity of the IOMNPs on the cells up to a concentration of 1000 μg/mL. The cells internalized the IOMNPs inside lysosomes in a dose-dependent manner, with higher amounts of IOMNPs in cancer cells. Intracellular hyperthermia experiments revealed a significant increase in the macroscopic temperatures of the IOMNPs loaded cell suspensions, which depend on the amount of internalized IOMNPs and the alternating magnetic field amplitude. The cancer cells were found to be more sensitive to the intracellular hyperthermia compared to the normal ones. For both cell lines, cells heated at the same macroscopic temperature presented lower viability at higher amplitudes of the alternating magnetic field, indicating the occurrence of mechanical or nanoscale heating effects.
4D Micro-Computed X-ray Tomography as a Tool to Determine Critical Process and Product Information of Spin Freeze-Dried Unit Doses
Authors: Vanbillemont Brecht , Lammens Joris , Goethals Wannes , Vervaet Chris , Boone Matthieu N. , De Beer Thomas
Published: May 2020

Maintaining chemical and physical stability of the product during freeze-drying is important but challenging. In addition, freeze-drying is typically associated with long process times. Therefore, mechanistic models have been developed to maximize drying efficiency without altering the chemical or physical stability of the product. Dried product mass transfer resistance ( R p ) is a critical input for these mechanistic models. Currently available techniques to determine R p only provide an estimation of the mean R p and do not allow measuring and determining essential local (i.e., intra-vial) R p differences. In this study, we present an analytical method, based on four-dimensional micro-computed tomography (4D- μ CT), which enables the possibility to determine intra-vial R p differences. Subsequently, these obtained R p values are used in a mechanistic model to predict the drying time distribution of a spin-frozen vial. Finally, this predicted primary drying time distribution is experimentally verified via thermal imaging during drying. It was further found during this study that 4D- μ CT uniquely allows measuring and determining other essential freeze-drying process parameters such as the moving direction(s) of the sublimation front and frozen product layer thickness, which allows gaining accurate process knowledge. To conclude, the study reveals that the variation in the end of primary drying time of a single vial could be predicted accurately using 4D- μ CT as similar results were found during the verification using thermal imaging.
Interface Characteristics of Neat Melts and Binary Mixtures of Polyethylenes from Atomistic Molecular Dynamics Simulations
Authors: Lee Sanghun , Frank Curtis W. , Yoon Do Y.
Published: May 2020

Molecular dynamics simulations of free-standing thin films of neat melts of polyethylene (PE) chains up to C150H302 and their binary mixtures with n-C13H28 are performed employing a united atom model. We estimate the surface tension values of PE melts from the atomic virial tensor over a range of temperatures, which are in good agreement with experimental results. Compared with short n-alkane systems, there is an enhanced surface segregation of methyl chain ends in longer PE chains. Moreover, the methyl groups become more segregated in the surface region with decreasing temperature, leading to the conclusion that the surface-segregation of methyl chain ends mainly arises from the enthalpic origin attributed to the lower cohesive energy density of terminal methyl groups. In the mixtures of two different chain lengths, the shorter chains are more likely to be found in the surface region, and this molecular segregation in moderately asymmetric mixtures in the chain length (C13H28 + C44H90) is dominated by the enthalpic effect of methyl chain ends. Such molecular segregation is further enhanced and dominated by the entropic effect of conformational constraints in the surface for the highly asymmetric mixtures containing long polymer chains (C13H28 + C150H3020). The estimated surface tension values of the mixtures are consistent with the observed molecular segregation characteristics. Despite this molecular segregation, the normalized density of methyl chain ends of the longer chain is more strongly enhanced, as compared with the all-segment density of the longer chain itself, in the surface region of melt mixtures. In addition, the molecular segregation results in higher order parameter of the shorter-chain segments at the surface and deeper persistence of surface-induced segmental order into the film for the longer chains, as compared with those in neat melt films.
On the Tensile Behaviour of Bio-Sourced 3D-Printed Structures from a Microstructural Perspective
Authors: Guessasma Sofiane , Belhabib Sofiane , Altin Abdullah
Published: May 2020

The influence of the microstructural arrangement of 3D-printed polylactic acid (PLA) on its mechanical properties is studied using both numerical and experimental approaches. Thermal cycling during the laying down of PLA filament is investigated through infra-red measurements for different printing conditions. The microstructure induced by 3D printing is determined using X-ray micro-tomography. The mechanical properties are measured under tensile testing conditions. Finite element computation is considered to predict the mechanical performance of 3D-printed PLA by converting the acquired 3D images into structural meshes. The results confirm the leading role of the printing temperature on thermal cycling during the laying down process. In addition, the weak influence of the printing temperature on the stiffness of 3D-printed PLA is explained by the relatively small change in porosity content. However, the influence of the printing temperature on the ultimate properties is found to be substantial. This major influence is explained from finite element predictions as an effect of pore connectivity which is found to be the control factor for tensile strength.
Temperature and Time Dependence of the Solvent-Induced Crystallization of Poly(l-lactide)
Authors: Udayakumar Mahitha , Kollár Mariann , Kristály Ferenc , Leskó Máté , Szabó Tamás , Marossy Kálmán , Tasnádi Ildikó , Németh Zoltán
Published: May 2020

The role of organic solvents in governing the crystallization and morphology of semi-crystalline poly-l-lactide (PLLA) sheets was systematically investigated. Three different organic solvents; ethyl acetate (EA), o-dichlorobenzene (ODCB), and nitrobenzene (NB), with a solubility parameter analogous to PLLA and with a high capability of swelling, were chosen. It has been witnessed that the degree of crystallization and crystal morphology depends highly on the degree of swelling and evaporation rate of the solvent. Besides, the temperature and time of treatment played a significant role in the crystallization of polymers. The effect of different solvents and curing times are reflected by the measured X-ray diffraction (XRD) peaks and the differences are best shown by the unit cell size. The largest variation is observed along the c-axis, indicating shorter bonds, thus, showing better conformation after NB and ODCB treatment. The percentage of crystallinity calculated using the classical relative crystallinity index of XRD shows closer values to those calculated with differential scanning calorimetry (DSC) data, but a huge variation is observed while using the LeBail deconvolution method. The strong birefringence of polarised optical micrograph (POM) and the crystal morphology of scanning electron micrograph (SEM) also evidenced the orientation of polymer crystallites and increased crystallinity after solvent-supported heat treatment.
Directed Polymers and Interfaces in Disordered Media
Authors: Acosta Diaz Róbinson J. , Rodríguez-Camargo Christian D. , Svaiter Nami F.
Published: May 2020

We consider field theory formulation for directed polymers and interfaces in the presence of quenched disorder. We write a series representation for the averaged free energy, where all the integer moments of the partition function of the model contribute. The structure of field space is analysed for polymers and interfaces at finite temperature using the saddle-point equations derived from each integer moments of the partition function. For the case of an interface we obtain the wandering exponent ξ = ( 4 − d ) / 2 , also obtained by the conventional replica method for the replica symmetric scenario.
Isolating Anthropogenic Wetland Loss by Concurrently Tracking Inundation and Land Cover Disturbance across the Mid-Atlantic Region, U.S.
Authors: Vanderhoof Melanie K. , Christensen Jay , Beal Yen-Ju G. , DeVries Ben , Lang Megan W. , Hwang Nora , Mazzarella Christine , Jones John W.
Published: May 2020

Global trends in wetland degradation and loss have created an urgency to monitor wetland extent, as well as track the distribution and causes of wetland loss. Satellite imagery can be used to monitor wetlands over time, but few efforts have attempted to distinguish anthropogenic wetland loss from climate-driven variability in wetland extent. We present an approach to concurrently track land cover disturbance and inundation extent across the Mid-Atlantic region, United States, using the Landsat archive in Google Earth Engine. Disturbance was identified as a change in greenness, using a harmonic linear regression approach, or as a change in growing season brightness. Inundation extent was mapped using a modified version of the U.S. Geological Survey’s Dynamic Surface Water Extent (DSWE) algorithm. Annual (2015–2018) disturbance averaged 0.32% (1095 km2 year-1) of the study area per year and was most common in forested areas. While inundation extent showed substantial interannual variability, the co-occurrence of disturbance and declines in inundation extent represented a minority of both change types, totaling 109 km2 over the four-year period, and 186 km2, using the National Wetland Inventory dataset in place of the Landsat-derived inundation extent. When the annual products were evaluated with permitted wetland and stream fill points, 95% of the fill points were detected, with most found by the disturbance product (89%) and fewer found by the inundation decline product (25%). The results suggest that mapping inundation alone is unlikely to be adequate to find and track anthropogenic wetland loss. Alternatively, remotely tracking both disturbance and inundation can potentially focus efforts to protect, manage, and restore wetlands.
Multi-Component and Multi-Source Approach for Studying Land Subsidence in Deltas
Authors: Vitagliano Eleonora , Riccardi Umberto , Piegari Ester , Boy Jean-Paul , Di Maio Rosa
Published: May 2020

The coupled effects of climate change and land sinking make deltas and coastal areas prone to inundation and flooding, meaning that reliable estimation of land subsidence is becoming crucial. Commonly, land subsidence is monitored by accurate continuous and discrete measurements collected by terrestrial and space geodetic techniques, such as Global Navigation Satellite System (GNSS), Interferometry Synthetic Aperture Radar (InSAR), and high precision leveling. In particular, GNSS, which includes the Global Positioning System (GPS), provides geospatial positioning with global coverage, then used for deriving local displacements through time. These site-positioning time series usually exhibit a linear trend plus seasonal oscillations of annual and semi-annual periods. Although the periodic components observed in the geodetic signal affect the velocity estimate, studies dealing with the prediction and prevention of risks associated with subsidence focus mainly on the permanent component. Periodic components are simply removed from the original dataset by statistical analyses not based on the underlying physical mechanisms. Here, we propose a systematic approach for detecting the physical mechanisms that better explain the permanent and periodic components of subsidence observed in the geodetic time series. It consists of three steps involving a component recognition phase, based on statistical and spectral analyses of geodetic time series, a source selection phase, based on their comparison with data of different nature (e.g., geological, hydro-meteorological, hydrogeological records), and a source validation step, where the selected sources are validated through physically-based models. The application of the proposed procedure to the Codigoro area (Po River Delta, Northern Italy), historically affected by land subsidence, allowed for an accurate estimation of the subsidence rate over the period 2009–2017. Significant differences turn out in the retrieved subsidence velocities by using or not periodic trends obtained by physically based models.
Fully Convolutional Networks and a Manifold Graph Embedding-Based Algorithm for PolSAR Image Classification
Authors: He Chu , He Bokun , Tu Mingxia , Wang Yan , Qu Tao , Wang Dingwen , Liao Mingsheng
Published: May 2020

With the rapid development of artificial intelligence, how to take advantage of deep learning and big data to classify polarimetric synthetic aperture radar (PolSAR) imagery is a hot topic in the field of remote sensing. As a key step for PolSAR image classification, feature extraction technology based on target decomposition is relatively mature, and how to extract discriminative spatial features and integrate these features with polarized information to maximize the classification accuracy is the core issue. In this context, this paper proposes a PolSAR image classification algorithm based on fully convolutional networks (FCNs) and a manifold graph embedding model. First, to describe different types of land objects more comprehensively, various polarized features of PolSAR images are extracted through seven kinds of traditional decomposition methods. Afterwards, drawing on transfer learning, the decomposed features are fed into multiple parallel and pre-trained FCN-8s models to learn deep multi-scale spatial features. Feature maps from the last layer of each FCN model are concatenated to obtain spatial polarization features with high dimensions. Then, a manifold graph embedding model is adopted to seek an effective and compact representation for spatially polarized features in a manifold subspace, simultaneously removing redundant information. Finally, a support vector machine (SVM) is selected as the classifier for pixel-level classification in a manifold subspace. Extensive experiments on three PolSAR datasets demonstrate that the proposed algorithm achieves a superior classification performance.
NDVI as a Proxy for Estimating Sedimentation and Vegetation Spread in Artificial Lakes—Monitoring of Spatial and Temporal Changes by Using Satellite Images Overarching Three Decades
Authors: Szabó Loránd , Deák Balázs , Bíró Tibor , Dyke Gareth J. , Szabó Szilárd
Published: May 2020

Observing wetland areas and monitoring changes are crucial to understand hydrological and ecological processes. Sedimentation-induced vegetation spread is a typical process in the succession of lakes endangering these habitats. We aimed to survey the tendencies of vegetation spread of a Hungarian lake using satellite images, and to develop a method to identify the areas of risk. Accordingly, we performed a 33-year long vegetation spread monitoring survey. We used the Normalized Difference Vegetation Index (NDVI) and the Modified Normalized Difference Water Index (MNDWI) to assess vegetation and open water characteristics of the basins. We used these spectral indices to evaluate sedimentation risk of water basins combined with the fact that the most abundant plant species of the basins was the water caltrop (Trapa natans) indicating shallow water. We proposed a 12-scale Level of Sedimentation Risk Index (LoSRI) composed from vegetation cover data derived from satellite images to determine sedimentation risk within any given water basin. We validated our results with average water basin water depth values, which showed an r = 0.6 (p < 0.05) correlation. We also pointed on the most endangered locations of these sedimentation-threatened areas, which can provide crucial information for management planning of water directorates and management organizations.
Detection, Segmentation, and Model Fitting of Individual Tree Stems from Airborne Laser Scanning of Forests Using Deep Learning
Authors: Windrim Lloyd , Bryson Mitch
Published: May 2020

Accurate measurements of the structural characteristics of trees such as height, diameter, sweep and taper are an important part of forest inventories in managed forests and commercial plantations. Both terrestrial and aerial LiDAR are currently employed to produce pointcloud data from which inventory metrics can be determined. Terrestrial/ground-based scanning typically provides pointclouds resolutions of many thousands of points per m 2 from which tree stems can be observed and inventory measurements made directly, whereas typical resolutions from aerial scanning (tens of points per m 2 ) require inventory metrics to be regressed from LiDAR variables using inventory reference data collected from the ground. Recent developments in miniaturised LiDAR sensors are enabling aerial capture of pointclouds from low-flying aircraft at high-resolutions (hundreds of points per m 2 ) from which tree stem information starts to become directly visible, enabling the possibility for plot-scale inventories that do not require access to the ground. In this paper, we develop new approaches to automated tree detection, segmentation and stem reconstruction using algorithms based on deep supervised machine learning which are designed for use with aerially acquired high-resolution LiDAR pointclouds. Our approach is able to isolate individual trees, determine tree stem points and further build a segmented model of the main tree stem that encompasses tree height, diameter, taper, and sweep. Through the use of deep learning models, our approach is able to adapt to variations in pointcloud densities and partial occlusions that are particularly prevalent when data is captured from the air. We present results of our algorithms using high-resolution LiDAR pointclouds captured from a helicopter over two Radiata pine forests in NSW, Australia.
Google Earth Engine Open-Source Code for Land Surface Temperature Estimation from the Landsat Series
Authors: Ermida Sofia L. , Soares Patrícia , Mantas Vasco , Göttsche Frank-M. , Trigo Isabel F.
Published: May 2020

Land Surface Temperature (LST) is increasingly important for various studies assessing land surface conditions, e.g., studies of urban climate, evapotranspiration, and vegetation stress. The Landsat series of satellites have the potential to provide LST estimates at a high spatial resolution, which is particularly appropriate for local or small-scale studies. Numerous studies have proposed LST retrieval algorithms for the Landsat series, and some datasets are available online. However, those datasets generally require the users to be able to handle large volumes of data. Google Earth Engine (GEE) is an online platform created to allow remote sensing users to easily perform big data analyses without increasing the demand for local computing resources. However, high spatial resolution LST datasets are currently not available in GEE. Here we provide a code repository that allows computing LSTs from Landsat 4, 5, 7, and 8 within GEE. The code may be used freely by users for computing Landsat LST as part of any analysis within GEE.
Basal Channel Extraction and Variation Analysis of Nioghalvfjerdsfjorden Ice Shelf in Greenland
Authors: Wang Zemin , Song Xiangyu , Zhang Baojun , Liu Tingting , Geng Hong
Published: May 2020

The ice shelf controls the ice flow and affects the rates of sea level rise. Its stability is affected by the basal channel to some extent. However, despite its importance, high spatiotemporal variation in the length of the basal channels and influencing factors remain poorly characterized. Here, we present evidence from satellite and airborne remote-sensing for the basal channel beneath the floating Nioghalvfjerdsfjorden (79 North Glacier) ice shelf in Northeast Greenland. We observe the surface depression of the ice shelf using IceBridge, which is an ongoing NASA mission to monitor changes in polar ice. We find that the basal channel corresponds with the depression. Temporal and spatial changes of the basal channels from 2000 to 2018 are obtained annually. The results show that the main influencing factor affecting the basal channel is the sea surface temperature (SST), and the major area of the channel length change is found in the midstream area of the ice shelf.
Evaluating Simulated RADARSAT Constellation Mission (RCM) Compact Polarimetry for Open-Water and Flooded-Vegetation Wetland Mapping
Authors: Olthof Ian , Rainville Thomas
Published: May 2020

When severe flooding occurs in Canada, the Emergency Geomatics Service (EGS) is tasked with creating and disseminating maps that depict flood extents in near real time. EGS flood mapping methods were created with efficiency and robustness in mind, to allow maps to be published quickly, and therefore have the potential to generate high-repeat water products that can enhance frequent wetland monitoring. The predominant imagery currently used is synthetic aperture radar (SAR) from RADARSAT-2 (R2). With the commissioning phase of the RADARSAT Constellation Mission (RCM) complete, the EGS is adapting its methods for use with this new source of SAR data. The introduction of RCM’s circular-transmit linear-receive (CTLR) beam mode provides the option to exploit compact polarimetric (CP) information not previously available with R2. The aim of this study was to determine the most effective CP parameters for use in mapping open water and flooded vegetation, using current EGS methodologies, and compare these products to those created by using R2 data. Nineteen quad-polarization R2 scenes selected from three regions containing wetlands prone to springtime flooding were used to create reference flood maps, using existing EGS tools. These scenes were then used to simulate 22 RCM CP parameters at different noise floors and spatial resolutions representative of the three RCM beam modes. Using multiple criteria, CP parameters were ranked in order of importance and entered into a stepwise classification procedure, for evaluation against reference R2 products. The top four CP parameters —m-chi-volume or m-delta-volume, RR intensity, Shannon Entropy intensity (SEi), and RV intensity—achieved a maximum agreement with baseline R2 products of upward of 98% across all 19 scenes and three beam modes. Separability analyses between flooded vegetation and other land-cover classes identified four candidate CP parameters—RH intensity, RR intensity, SEi, and the first Stokes parameter (SV0)—suitable for flooded-vegetation-region growing. Flooded-vegetation-region-growing CP thresholds were found to be dependent on incidence angle for each of these four parameters. After region growing using each of the four candidate CP parameters, RH intensity was deemed best to map flooded vegetation, based on our evaluations. The results of the study suggest a set of suitable CP parameters to generate flood maps from RCM data, using current EGS methodologies that must be validated further as real RCM data become available.
Evaluation of Radar-Rainfall Products over Coastal Louisiana
Authors: Sharif Ridwana Binte , Habib Emad H. , ElSaadani Mohamed
Published: May 2020

Radar-rainfall products provide valuable information for hydro-ecological modeling and ecosystem applications, especially over coastal regions that lack adequate in-situ rainfall observations. This study evaluates two radar-based rainfall products, the Multi-Sensor Stage IV and the Multi-Radar Multi-Sensor (MRMS), over the Louisiana coastal region in the United States. Surface reference rainfall observations from two independent rain gage networks were used in the analysis. The evaluation included distribution-based comparisons between radar and gage observations at different time scales (hourly to monthly), bias decomposition to quantify the contribution of different error sources, and conditional evaluation of systematic and random components of the estimation errors. Both products report large levels of random errors at the hourly scale; however, the performance of the radar-rainfall products improves significantly with the increase in time scales. After decomposing the total bias, the results show that the largest contributor to the overall bias in radar-rainfall products is false rainfall detection, followed by missed rainfall. The results also reveal that the Stage IV product experienced a significant improvement over the area in the past few years (post 2015) compared to earlier periods. The results have implications for ongoing and future coastal ecosystem modeling and planning studies.
Development and Testing of a Clear-Sky Data Selection Algorithm for FY-3C/D Microwave Temperature Sounder-2
Authors: Niu Zeyi , Zou Xiaolei , Ray Peter Sawin
Published: May 2020

The Fengyun (FY)-3C/D microwave temperature sounder-2 (MWTS-2) is similar to the Advanced Microwave Sounding Unit-A (AMSU-A), except it lacks two window channels located at 23.8 GHz and 31.4 GHz. This makes a clear-sky data determination challenging for the MWTS-2 due to the unavailability of cloud liquid water path (LWP) retrievable from the two window channels. The purpose of this study is to develop a clear-sky data selection algorithm for the FY-3C/D MWTS-2 based on the bias-removed differences between observations and model simulations of the MWTS-2 50.3-GHz channel 1 (or equivalently AMSU-A channel 3). First, a point is defined as a temporal clear-sky (cloudy) point if the bias-removed difference between observed and simulated brightness temperatures is smaller than or equal to (greater than) 2 K. Then, a temporal clear-sky (cloudy) point is defined as a final clear-sky (cloudy) point if all points within its 60-km (100-km) radial distance are temporal clear-sky (cloudy) points. Finally, if the mean value of the bias-removed differences between observations and simulations in the 100-km circle from a temporal cloudy point are smaller than or equal to (greater than) 2 K, all temporal clear-sky points within this circle are (not) taken as the final clear-sky points. Applications of this algorithm to FY-3C MWTS-2 and MetOp-B AMSU-A lead to the following conclusions: (i) more than 70% (95%) of the clear-sky (cloudy) data points are successfully identified from both AMSU-A and MWTS-2 observations; (ii) the algorithm-selected clear-sky data points were located in clear-sky areas in the GOES-15 imager, and (iii) the bias-removed differences between observations and model simulations of MWTS-2 channel 1 well reveals the eye, the eyewall, and the spiral rainband structure of Super Typhoon Halong (2014).
Electrochemical Assessment of Anticancer Compounds on the Human Tongue Squamous Carcinoma Cells
Authors: Huang Chun-Chung , Tung Tse-Hua , Huang Chien-Chu , Lin Shao-Yi , Chao Shih-Chi , Chiu Sheng-Po , Lee Shiao-Pieng , Lo Chun-Min
Published: May 2020

The most common oral cancer is squamous cell carcinoma (SCC) and its highest occurrence is in the tongue. Almost 30% of patients with one primary head and neck tumor will have a second primary malignancy. In recent studies, two novel plant extracts, andrographolide and cannabidiol (CBD), have been exploited for their anticancer effects. Here, we investigated the cytotoxic effects of these two compounds on SCC-25 cells, a human tongue squamous carcinoma cell line, and compared the outcomes with two chemotherapeutic drugs, cisplatin and fluorouracil. Electric cell substrate impedance sensing (ECIS) system was applied to measure frequency- and time-dependent impedance of SCC-25 cell-covered electrodes and to further assess subtle changes in cell morphology and micromotion in response to different concentrations (0, 10, 30, 100, and 300 µM) of these compounds. AlamarBlue and Annexin V/7-AAD binding assays were used to measure the concentration dependent changes in viability and apoptosis of SCC-25 cells. Our results demonstrate that 24 hours after exposure to 30 µM CBD can significantly decrease the micromotion rate, damage the integrity of cell morphology, reduce cell viability, and induce higher apoptosis in treated SCC-25 cells, while the other three drugs attain similar effects at the concentration of 100 µM or higher. The apoptosis-induced changes in cell morphology and micromotion monitored by ECIS correlate well with biochemical assays. Thus, both frequency- and time-dependent impedance measurements using ECIS can be used to real-time follow cancer cell activities in response to anticancer drugs with different temporal cytotoxicity profiles.
Energy-Efficient and Fast MAC Protocol in UAV-Aided Wireless Sensor Networks for Time-Critical Applications
Authors: Poudel Sabitri , Moh Sangman
Published: May 2020

Unmanned aerial vehicle (UAV)-aided wireless sensor networks (UWSNs) can be effectively used for time-critical sensing applications. UAVs can be used to collect the sensed data from sensors and transfer them to a base station. The real-time transfer of data is highly desired in the time-critical applications. However, the medium access control (MAC) protocols designed for UWSNs so far are primarily focused on the efficient use of UAVs to collect data in the sensing areas. In this paper, we propose an energy-efficient and fast MAC (EF-MAC) protocol in UWSNs for time-critical sensing applications. EF-MAC adopts carrier sense multiple access (CSMA) for the registration of sensor nodes with a UAV and time division multiple access (TDMA) with variable slot time for the transmission of collected data. The UAV is equipped with two transceivers to minimize both energy consumption and delay in air-to-ground communication. The energy consumption and delay are formally analyzed and the performance of EF-MAC is evaluated via extensive simulation. The simulation results show that the proposed EF-MAC outperforms the conventional MAC protocols in terms of energy efficiency and communication delay.
Sensory Interactive Table (SIT)—Development of a Measurement Instrument to Support Healthy Eating in a Social Dining Setting
Authors: Haarman Juliet A. M. , Vries Roelof A. J. de , Harmsen Emiel C. , Hermens Hermie J. , Heylen Dirk K. J.
Published: May 2020

This paper presents the Sensory Interactive Table (SIT): an instrumented, interactive dining table. Through the use of load cells and LEDs that are embedded in the table surface, SIT allows us to study: (1) the eating behaviors of people in a social setting, (2) the social interactions around the eating behaviors of people in a social setting, and (3) the continuous cycle of feedback through LEDs on people’s eating behavior and their response to this feedback in real time, to ultimately create an effective dietary support system. This paper presents the hard- and software specifications of the system, and it shows the potential of the system to capture mass-related dimensions in real time and with high accuracy and spatial resolution.
Facial Expression Recognition Based on Weighted-Cluster Loss and Deep Transfer Learning Using a Highly Imbalanced Dataset
Authors: Ngo Quan T. , Yoon Seokhoon
Published: May 2020

Facial expression recognition (FER) is a challenging problem in the fields of pattern recognition and computer vision. The recent success of convolutional neural networks (CNNs) in object detection and object segmentation tasks has shown promise in building an automatic deep CNN-based FER model. However, in real-world scenarios, performance degrades dramatically owing to the great diversity of factors unrelated to facial expressions, and due to a lack of training data and an intrinsic imbalance in the existing facial emotion datasets. To tackle these problems, this paper not only applies deep transfer learning techniques, but also proposes a novel loss function called weighted-cluster loss, which is used during the fine-tuning phase. Specifically, the weighted-cluster loss function simultaneously improves the intra-class compactness and the inter-class separability by learning a class center for each emotion class. It also takes the imbalance in a facial expression dataset into account by giving each emotion class a weight based on its proportion of the total number of images. In addition, a recent, successful deep CNN architecture, pre-trained in the task of face identification with the VGGFace2 database from the Visual Geometry Group at Oxford University, is employed and fine-tuned using the proposed loss function to recognize eight basic facial emotions from the AffectNet database of facial expression, valence, and arousal computing in the wild. Experiments on an AffectNet real-world facial dataset demonstrate that our method outperforms the baseline CNN models that use either weighted-softmax loss or center loss.
A Comprehensive Survey of Indoor Localization Methods Based on Computer Vision
Authors: Morar Anca , Moldoveanu Alin , Mocanu Irina , Moldoveanu Florica , Radoi Ion Emilian , Asavei Victor , Gradinaru Alexandru , Butean Alex
Published: May 2020

Computer vision based indoor localization methods use either an infrastructure of static cameras to track mobile entities (e.g., people, robots) or cameras attached to the mobile entities. Methods in the first category employ object tracking, while the others map images from mobile cameras with images acquired during a configuration stage or extracted from 3D reconstructed models of the space. This paper offers an overview of the computer vision based indoor localization domain, presenting application areas, commercial tools, existing benchmarks, and other reviews. It provides a survey of indoor localization research solutions, proposing a new classification based on the configuration stage (use of known environment data), sensing devices, type of detected elements, and localization method. It groups 70 of the most recent and relevant image based indoor localization methods according to the proposed classification and discusses their advantages and drawbacks. It highlights localization methods that also offer orientation information, as this is required by an increasing number of applications of indoor localization (e.g., augmented reality).