bluuebunny/crossref_metadata_2025 huggingface dataset:
Inhibition of the Formation In Vitro of Putatively Carcinogenic Metabolites Derived from S. haematobium and O. viverrini by Combination of Drugs with Antioxidants
Authors: Gouveia Maria João , Nogueira Verónica , Araújo Bruno , Gärtner Fátima , Vale Nuno
Published: October 2019

Infections caused by Schistosoma haematobium and Opisthorchis viverrini are classified as carcinogenic. Although carcinogenesis might be a multifactorial process, it has been postulated that these helminth produce/excrete oxysterols and estrogen-like metabolites that might act as initiators of their infection-associated carcinogenesis. Current treatment and control of these infections rely on a single drug, praziquantel, that mainly targets the parasites and not the pathologies related to the infection including cancer. Thus, there is a need to search for novel therapeutic alternatives that might include combinations of drugs and drug repurposing. Based on these concepts, we propose a novel therapeutic strategy that combines drugs with molecule antioxidants. We evaluate the efficacy of a novel therapeutic strategy to prevent the formation of putative carcinogenic metabolites precursors and DNA adducts. Firstly, we used a methodology previously established to synthesize metabolites precursors and DNA adducts in the presence of CYP450. Then, we evaluated the inhibition of their formation induced by drugs and antioxidants alone or in combination. Drugs and resveratrol alone did not show a significant inhibitory effect while N-acetylcysteine inhibited the formation of most metabolite precursors and DNA adducts. Moreover, the combinations of classical drugs with antioxidants were more effective rather than compounds alone. This strategy might be a valuable tool to prevent the initiation of helminth infection-associated carcinogenesis.
Medical-Grade Silicone Coated with Rhamnolipid R89 Is Effective against Staphylococcus spp. Biofilms
Authors: Ceresa Chiara , Tessarolo Francesco , Maniglio Devid , Tambone Erica , Carmagnola Irene , Fedeli Emanuele , Caola Iole , Nollo Giandomenico , Chiono Valeria , Allegrone Gianna , Rinaldi Maurizio , Fracchia Letizia
Published: October 2019

Staphylococcus aureus and Staphylococcus epidermidis are considered two of the most important pathogens, and their biofilms frequently cause device-associated infections. Microbial biosurfactants recently emerged as a new generation of anti-adhesive and anti-biofilm agents for coating implantable devices to preserve biocompatibility. In this study, R89 biosurfactant (R89BS) was evaluated as an anti-biofilm coating on medical-grade silicone. R89BS is composed of homologues of the mono- (75%) and di-rhamnolipid (25%) families, as evidenced by mass spectrometry analysis. The antimicrobial activity against Staphylococcus spp. planktonic and sessile cells was evaluated by microdilution and metabolic activity assays. R89BS inhibited S. aureus and S. epidermidis growth with minimal inhibitory concentrations (MIC99) of 0.06 and 0.12 mg/mL, respectively and dispersed their pre-formed biofilms up to 93%. Silicone elastomeric discs (SEDs) coated by R89BS simple adsorption significantly counteracted Staphylococcus spp. biofilm formation, in terms of both built-up biomass (up to 60% inhibition at 72 h) and cell metabolic activity (up to 68% inhibition at 72 h). SEM analysis revealed significant inhibition of the amount of biofilm-covered surface. No cytotoxic effect on eukaryotic cells was detected at concentrations up to 0.2 mg/mL. R89BS-coated SEDs satisfy biocompatibility requirements for leaching products. Results indicate that rhamnolipid coatings are effective anti-biofilm treatments and represent a promising strategy for the prevention of infection associated with implantable devices.
Electrochemical Properties of Carbon Aerogel Electrodes: Dependence on Synthesis Temperature
Authors: Malkova Alena N. , Sipyagina Nataliya A. , Gozhikova Inna O. , Dobrovolsky Yury A. , Konev Dmitry V. , Baranchikov Alexander E. , Ivanova Olga S. , Ukshe Alexander E. , Lermontov Sergey A.
Published: October 2019

A series of carbon aerogels (C-AGs) were prepared by the pyrolysis of resorcinol-formaldehyde aerogels at 700–1100 °C as potential supercapacitor electrodes, and their texture and electrochemical properties were determined. The specific surface area of all C-AGs was in the range of 700–760 m2/g, their electron conductivity increased linearly from 0.4 to 4.46 S/cm with an increase of the pyrolysis temperature. The specific capacitance of electrode material based on C-AGs reached 100 F/g in sulfuric acid and could be realized at a 2 A/g charge-discharge current, which makes it possible to use carbon aerogels as electrode materials.
A New Hyaluronan Modified with β-Cyclodextrin on Hydroxymethyl Groups Forms a Dynamic Supramolecular Network
Authors: Kovačević Jelica , Prucková Zdeňka , Pospíšil Tomáš , Kašpárková Věra , Rouchal Michal , Vícha Robert
Published: October 2019

A new hyaluronan derivative modified with β-cyclodextrin units (CD-HA) was prepared via the click reaction between propargylated hyaluronan and monoazido-cyclodextrin (CD) to achieve a degree of substitution of 4%. The modified hyaluronan was characterized by 1H-nuclear magnetic resonance spectroscopy (NMR) and size exclusion chromatography. Subsequent 1H-NMR and isothermal calorimetric titration experiments revealed that the CD units on CD-HA can form virtual 1:1, 1:2, and 1:3 complexes with one-, two-, and three-site adamantane-based guests, respectively. These results imply that the CD-HA chains used the multitopic guests to form a supramolecular cross-linked network. The free CD-HA polymer was readily restored by the addition of a competing macrocycle, which entrapped the cross-linking guests. Thus, we demonstrated that the new CD-HA polymer is a promising component for the construction of chemical stimuli-responsive supramolecular architectures.
Fruit Seeds as Sources of Bioactive Compounds: Sustainable Production of High Value-Added Ingredients from By-Products within Circular Economy
Authors: Fidelis Marina , de Moura Cristiane , Kabbas Junior Tufy , Pap Nora , Mattila Pirjo , Mäkinen Sari , Putnik Predrag , Bursać Kovačević Danijela , Tian Ye , Yang Baoru , Granato Daniel
Published: October 2019

The circular economy is an umbrella concept that applies different mechanisms aiming to minimize waste generation, thus decoupling economic growth from natural resources. Each year, an estimated one-third of all food produced is wasted; this is equivalent to 1.3 billion tons of food, which is worth around US$1 trillion or even $2.6 trillion when social and economic costs are included. In the fruit and vegetable sector, 45% of the total produced amount is lost in the production (post-harvest, processing, and distribution) and consumption chains. Therefore, it is necessary to find new technological and environmentally friendly solutions to utilize fruit wastes as new raw materials to develop and scale up the production of high value-added products and ingredients. Considering that the production and consumption of fruits has increased in the last years and following the need to find the sustainable use of different fruit side streams, this work aimed to describe the chemical composition and bioactivity of different fruit seeds consumed worldwide. A comprehensive focus is given on the extraction techniques of water-soluble and lipophilic compounds and in vitro/in vivo functionalities, and the link between chemical composition and observed activity is holistically explained.
On the Mechanical Properties of N-Functionalised Dipeptide Gels
Authors: Fuentes-Caparrós Ana M. , McAulay Kate , Rogers Sarah E. , Dalgliesh Robert M. , Adams Dave J.
Published: October 2019

The properties of a hydrogel are controlled by the underlying network that immobilizes the solvent. For gels formed by the self-assembly of a small molecule, it is common to show the primary fibres that entangle to form the network by microscopy, but it is difficult to access information about the network. One approach to understand the network is to examine the effect of the concentration on the rheological properties, such that G′∝ cx, where G′ is the storage modulus and c is the concentration. A number of reports link the exponent x to a specific type of network. Here, we discuss a small library of gels formed using functionalized dipeptides, and describe the underlying networks of these gels, using microscopy, small angle scattering and rheology. We show that apparently different networks can give very similar values of x.
Improving the Stability of Oil Body Emulsions from Diverse Plant Seeds Using Sodium Alginate
Authors: Zhang Yuemei , Yang Nan , Xu Yao , Wang Qian , Huang Ping , Nishinari Katsuyoshi , Fang Yapeng
Published: October 2019

In this study, peanut, sesame, and rapeseed oil bodies (OBs) were extracted by the aqueous medium method. The surface protein composition, microstructure, average particle size d 4 ,   3 , ζ-potential of the extracted OBs in aqueous emulsion were characterized. The stability of the OB emulsions was investigated. It was found that different OB emulsions contained different types and contents of endogenous and exogenous proteins. Aggregation at low pHs (<6) and creaming at high pHs (7 and 8) both occurred for all of three OB emulsions. Sodium alginate (ALG) was used to solve the instability of OB emulsions under different conditions—low concentration of ALG improved the stability of OB emulsions below and near the isoelectric point of the OBs, through electrostatic interaction. While a high concentration of ALG improved the OB emulsion stability through the viscosity effect at pH 7. The OB emulsions stabilized by ALG were salt-tolerant and freeze–thaw resistant.
Gold Nanopost-Shell Arrays Fabricated by Nanoimprint Lithography as a Flexible Plasmonic Sensing Platform
Authors: Farcau Cosmin , Marconi Daniel , Colniță Alia , Brezeștean Ioana , Barbu-Tudoran Lucian
Published: October 2019

Plasmonic noble metal nanostructured films have a huge potential for the development of efficient, tunable, miniaturized optical sensors. Herein, we report on the fabrication and characterization of gold-coated nanopost arrays, their use as refractometric sensors, and their optimization through photonics simulations. Monolithic square nanopost arrays having different period and nanopost size are fabricated by nanoimprint lithography on polymer foils, and sputter-coated by gold films. The reflectivity of these gold nanopost-shell arrays present dips in the visible range, which are efficient for refractometric sensing. By finite-difference time-domain (FDTD) simulations we reproduce the experimental spectra, describe the electric fields distribution around the nanopost-shells, and then explain their good sensitivity, around 450 nm/RIU. Furthermore, we determine by simulations the influence of several geometrical parameters, such as array period, nanopost width, gold film thickness, and nanopost side coverage on both reflectivity spectra and sensing capabilities. Fully coated nanoposts provide an extremely deep reflectivity minimum, approaching zero, which makes the relative reflectivity change extremely high, more than two orders of magnitude higher than for partially coated nanoposts. These results contribute to the understanding of the plasmonic properties of metal coated nanopost arrays, and to the development of efficient platforms for sensing and other surface plasmon based applications.
Synthesis of CaF2 Nanoparticles Coated by SiO2 for Improved Al2O3/TiC Self-Lubricating Ceramic Composites
Authors: Chen Zhaoqiang , Guo Niansheng , Ji Lianggang , Xu Chonghai
Published: October 2019

In order to reduce the influence of CaF2 addition on the mechanical properties of self-lubricating ceramic tools, we applied a silicon dioxide (SiO2) coating on calcium fluoride (CaF2) nanoparticles through hydrolysis and condensation reactions using the tetraethoxysilane (TEOS) method. The powder was dried by the azeotropic method, so that it acquired a better dispersibility. The resulting composite powders were characterized using XRD (X-ray diffraction) and TEM (transmission electron microscopy), showing that the surface of CaF2 was coated with a layer of uniform and compact SiO2. SiO2 shells with different thicknesses could be obtained by changing the amount of TEOS added, and the thickness of the SiO2 shells could be controlled between 1.5 and 15 nm. At the same time, a ceramic material containing CaF2 nanoparticles and CaF2@SiO2-coated nanoparticles was prepared. It had the best mechanical properties when CaF2@SiO2-coated nanoparticles were added; its flexural strength, fracture toughness, and hardness were 562 ± 28 MPa, 5.51 ± 0.26 MPa·m1/2, and 15.26 ± 0.16 GPa, respectively. Compared with the ceramic tool containing CaF2 nanoparticles, these mechanical properties were increased by 17.57%, 12.67%, and 4.88%, respectively. The addition of CaF2@SiO2-coated nanoparticles greatly improved the antifriction and wear resistance of the ceramic material, and the antifriction and wear resistance were balanced.
Bouncing Dynamics of Impact Droplets on the Biomimetic Plane and Convex Superhydrophobic Surfaces with Dual-Level and Three-Level Structures
Authors: Lian Zhongxu , Xu Jinkai , Ren Wanfei , Wang Zuobin , Yu Huadong
Published: October 2019

Reducing the contact time of a water droplet on non-wetting surfaces has great potential in the areas of self-cleaning and anti-icing, and gradually develops into a hot issue in the field of wettability surfaces. However, the existing literature on dynamic behavior of water drops impacting on superhydrophobic surfaces with various structural shapes is insufficient. Inspired by the microstructure of lotus leaf and rice leaf, dual-level and three-level structures on plane and convex surfaces were successfully fabricated by wire electrical discharge machining on aluminum alloy. After spraying hydrophobic nanoparticles on the surfaces, the plane and convex surfaces with dual-level and three-level structures showed good superhydrophobic property. Bouncing dynamics of impact droplets on the superhydrophobic surfaces wereinvestigated, and the results indicated that the contact time of plane superhydrophobic surface with a three-level structure was minimal, which is 60.4% less than the plane superhydrophobic surface with dual-level structure. The effect of the interval S, width D, and height H of the structure on the plane superhydrophobic surface with three-level structure on contact time was evaluated to obtain the best structural parameters for reducing contact time. This research is believed to guide the direction of the structural design of the droplet impinging on solid surfaces.
Patients Discharged with Home Enteral Nutrition from a Third-Level Hospital in 2018
Authors: Campos-Martín Cristina , García-Torres María Dolores , Castillo-Martín Cristina , Domínguez-Rabadán Rocío , Rabat-Restrepo Juana María
Published: October 2019

Patients who, during admission, begin to use enteral nutrition (EN) and do not recover adequate oral intake need proper planning prior to discharge. The present study is a descriptive analysis of patients discharged with EN from our hospital in 2018. In all, the study included 141 patients (50.3% male) with an average age of 76.18 ± 14 years with the most frequent reasons for enteral support being neurological disease (71.3%) and ear, nose, and throat (ENT) and maxillofacial surgery (17.02%) (others accounted for 11.68%). In these two groups, differences were observed in both the average age (77 vs. 70.5 years) and sex of patients—mostly women (58%) in the first group and men (70%) in the second. Overall, the access routes used were nasogastric tube (76.4%), and percutaneous endoscopic gastrostomy (18.4%); 67.1% of the episodes ended by 30 June, 60.6% of patients died (47% of neurological patients), and 39.3% patients recovered function of the oral passage (85% of surgical/head and neck tumor). The duration of support was as follows: 1–3 months, 32%; 6–12 months, 26.9%; more than 12 months, 18.5%. This indicated some frequent and clearly differentiated profiles in the patients studied, which may contribute to better care and support in order to maintain long-term treatment.
Silicon and Plant Natural Defenses against Insect Pests: Impact on Plant Volatile Organic Compounds and Cascade Effects on Multitrophic Interactions
Authors: Leroy Nicolas , de Tombeur Félix , Walgraffe Yseult , Cornélis Jean-Thomas , Verheggen François J.
Published: October 2019

Environmental factors controlling silicon (Si) accumulation in terrestrial plant are key drivers to alleviate plant biotic stresses, including insect herbivory. While there is a general agreement on the ability of Si-enriched plant to better resist insect feeding, recent studies suggest that Si also primes biochemical defense pathways in various plant families. In this review, we first summarize how soil parameters and climate variables influence Si assimilation in plants. Then, we describe recent evidences on the ability of Si to modulate plant volatile emissions, with potential cascade effects on phytophagous insects and higher trophic levels. Even though the mechanisms still need to be elucidated, Si accumulation in plants leads to contrasting effects on the levels of the three major phytohormones, namely jasmonic acid, salicylic acid and ethylene, resulting in modified emissions of plant volatile organic compounds. Herbivore-induced plant volatiles would be particularly impacted by Si concentration in plant tissues, resulting in a cascade effect on the attraction of natural enemies of pests, known to locate their prey or hosts based on plant volatile cues. Since seven of the top 10 most important crops in the world are Si-accumulating Poaceae species, it is important to discuss the potential of Si mobility in soil-plant systems as a novel component of an integrated pest management.
HWA1- and HWA2-Mediated Hybrid Weakness in Rice Involves Cell Death, Reactive Oxygen Species Accumulation, and Disease Resistance-Related Gene Upregulation
Authors: Shiragaki Kumpei , Iizuka Takahiro , Ichitani Katsuyuki , Kuboyama Tsutomu , Morikawa Toshinobu , Oda Masayuki , Tezuka Takahiro
Published: October 2019

Hybrid weakness is a type of reproductive isolation in which F1 hybrids of normal parents exhibit weaker growth characteristics than their parents. F1 hybrid of the Oryza sativa Indian cultivars ‘P.T.B.7′ and ‘A.D.T.14′ exhibits hybrid weakness that is associated with the HWA1 and HWA2 loci. Accordingly, the aim of the present study was to analyze the hybrid weakness phenotype of the ‘P.T.B.7′ × ‘A.D.T.14′ hybrids. The height and tiller number of the F1 hybrid were lower than those of either parent, and F1 hybrid also exhibited leaf yellowing that was not observed in either parent. In addition, the present study demonstrates that SPAD values, an index correlated with chlorophyll content, are effective for evaluating the progression of hybrid weakness that is associated with the HWA1 and HWA2 loci because it accurately reflects degree of leaf yellowing. Both cell death and H2O2, a reactive oxygen species, were detected in the yellowing leaves of the F1 hybrid. Furthermore, disease resistance-related genes were upregulated in the yellowing leaves of the F1 hybrids, whereas photosynthesis-related genes tended to be downregulated. These results suggest that the hybrid weakness associated with the HWA1 and HWA2 loci involves hypersensitive response-like mechanisms.
Harnessing Deep-Hole Drilling to Fabricate Air-Structured Polymer Optical Fibres
Authors: Arrospide Eneko , Bikandi Iñaki , Larrañaga Igor , Cearsolo Xabier , Zubia Joseba , Durana Gaizka
Published: October 2019

The performance of a precisely controlled drilling technique is critical in the fabrication process of microstructured polymer optical fibres. For the creation of a holey preform, adequate drilling bits with large length-to-diameter ratios provide the ability of machining preforms with complex structures and large lengths in a relatively short time. In this work, we analysed different drilling bits and techniques that can be employed for the creation of such preforms, and key parameters characterising the quality of the drilled holes, such as surface rugosity, diameter deviation, coaxiality and cylindricity were measured. For this purpose, based on theoretical simulations, four rings of air holes arranged in a hexagonal pattern were drilled in the preforms with different drill bits, and the experimental results for the above mentioned parameters have been presented. Additionally, optical power distribution of the fabricated microstructured polymer optical fibres was theoretically calculated and experimentally measured.
Photothermal Polymer Nanocomposites of Tungsten Bronze Nanorods with Enhanced Tensile Elongation at Low Filler Contents
Authors: Jeon Byoungyun , Kim Taehyung , Lee Dabin , Shin Tae Joo , Oh Kyung Wha , Park Juhyun
Published: October 2019

We present polymer nanocomposites of tungsten bronze nanorods (TBNRs) and ethylene propylene diene monomers (EPDM). The combination of these components allows the simultaneous enhancement in the mechanical and photothermal properties of the composites at low filler contents. The as-synthesized TBNRs had lengths and diameters of 14.0 ± 2.4 nm and 2.5 ± 0.5 nm, respectively, and were capped with oleylamine, which has a chemical structure similar to EPDM, making the TBNRs compatible with the bulk EPDM matrix. The TBNRs absorb a wide range of near-infrared light because of the sub-band transitions induced by alkali metal doping. Thus, the nanocomposites of TBNRs in EPDM showed enhanced photothermal properties owing to the light absorption and subsequent heat emission by the TBNRs. Noticeably, the nanocomposite with only 3 wt% TBNRs presented significantly enhanced tensile strain at break, in comparison with those of pristine EPDM, nanocomposites with 1 and 2 wt % TBNRs, and those with tungsten bronze nanoparticles, because of the alignment of the nanorods during tensile elongation. The photothermal and mechanical properties of these nanocomposites make them promising materials for various applications such as in fibers, foams, clothes with cold weather resistance, patches or mask-like films for efficient transdermal delivery upon heat generation, and photoresponsive surfaces for droplet transport by the thermocapillary effect in microfluidic devices and microengines.
Albumin-Enriched Fibrin Hydrogel Embedded in Active Ferromagnetic Networks Improves Osteoblast Differentiation and Vascular Self-Organisation
Authors: Katarivas Levy Galit , Ong John , Birch Mark A. , Justin Alexander W. , Markaki Athina E.
Published: October 2019

Porous coatings on prosthetic implants encourage implant fixation. Enhanced fixation may be achieved using a magneto-active porous coating that can deform elastically in vivo on the application of an external magnetic field, straining in-growing bone. Such a coating, made of 444 ferritic stainless steel fibres, was previously characterised in terms of its mechanical and cellular responses. In this work, co-cultures of human osteoblasts and endothelial cells were seeded into a novel fibrin-based hydrogel embedded in a 444 ferritic stainless steel fibre network. Albumin was successfully incorporated into fibrin hydrogels improving the specific permeability and the diffusion of fluorescently tagged dextrans without affecting their Young’s modulus. The beneficial effect of albumin was demonstrated by the upregulation of osteogenic and angiogenic gene expression. Furthermore, mineralisation, extracellular matrix production, and formation of vessel-like structures were enhanced in albumin-enriched fibrin hydrogels compared to fibrin hydrogels. Collectively, the results indicate that the albumin-enriched fibrin hydrogel is a promising bio-matrix for bone tissue engineering and orthopaedic applications.
Development of Self-Healable Organic/Inorganic Hybrid Materials Containing a Biobased Copolymer via Diels–Alder Chemistry and Their Application in Electromagnetic Interference Shielding
Authors: Lee Yi-Huan , Ko Wen-Chi , Zhuang Yan-Nian , Wang Lu-Ying , Yu Tao-Wei , Lee Shaio-Yen , Way Tun-Fun , Rwei Syang-Peng
Published: October 2019

In this study, a novel biobased poly(ethylene brassylate)-poly(furfuryl glycidyl ether) copolymer (PEBF) copolymer was synthesized and applied as a structure-directing template to incorporate graphene and 1,1′-(methylenedi-4,1-phenylene)bismaleimide (BMI) to fabricate a series of self-healing organic/inorganic hybrid materials. This ternary material system provided different types of diene/dienophile pairs from the furan/maleimide, graphene/furan, and graphene/maleimide combinations to build a crosslinked network via multiple Diels–Alder (DA) reactions and synergistically co-assembled graphene sheets into the polymeric matrix with a uniform dispersibility. The PEBF/graphene/BMI hybrid system possessed an efficient self-repairability for healing structural defects and an electromagnetic interference shielding ability in the Ku-band frequency range. We believe that the development of the biobased self-healing hybrid system provides a promising direction for the creation of a new class of materials with the advantages of environmental friendliness as well as durability, and shows potential for use in advanced electromagnetic applications.
The Covenants of the Prophet and the Subject of Succession
Authors: Morrow John Andrew
Published: October 2019

In order to advance their argument that Imām ‘Alī was the divinely and prophetically designated spiritual and political successor of the Prophet Muḥammad, Shī‘ite Muslim scholars have primarily drawn evidence from the Qur’ān and Ḥadīth which was supplemented by reason and rational arguments. Oddly enough, in their quest to legitimize the Caliphate and Imāmate of Ahl al-Bayt, the People of the House of the Prophet, Shī‘ite scholars have generally ignored one of the most important bodies of literature, namely, the letters, treaties and covenants of the Prophet Muḥammad. The following study examines the Messenger of Allāh’s Covenants with Christians and Jews in search of previously overlooked material on the subject of succession, shedding light on the state of Islām prior to the definitive rupture of the early Muslim community into Sunnīs and Shī‘ites.
Using Predictive and Differential Methods with K2-Raster Compact Data Structure for Hyperspectral Image Lossless Compression
Authors: Chow Kevin , Tzamarias Dion , Blanes Ian , Serra-Sagristà Joan
Published: October 2019

This paper proposes a lossless coder for real-time processing and compression of hyperspectral images. After applying either a predictor or a differential encoder to reduce the bit rate of an image by exploiting the close similarity in pixels between neighboring bands, it uses a compact data structure called k 2 -raster to further reduce the bit rate. The advantage of using such a data structure is its compactness, with a size that is comparable to that produced by some classical compression algorithms and yet still providing direct access to its content for query without any need for full decompression. Experiments show that using k 2 -raster alone already achieves much lower rates (up to 55% reduction), and with preprocessing, the rates are further reduced up to 64%. Finally, we provide experimental results that show that the predictor is able to produce higher rates reduction than differential encoding.
Deep Learning for Precipitation Estimation from Satellite and Rain Gauges Measurements
Authors: Moraux Arthur , Dewitte Steven , Cornelis Bruno , Munteanu Adrian
Published: October 2019

This paper proposes a multimodal and multi-task deep-learning model for instantaneous precipitation rate estimation. Using both thermal infrared satellite radiometer and automatic rain gauge measurements as input, our encoder–decoder convolutional neural network performs a multiscale analysis of these two modalities to estimate simultaneously the rainfall probability and the precipitation rate value. Precipitating pixels are detected with a Probability Of Detection (POD) of 0.75 and a False Alarm Ratio (FAR) of 0.3. Instantaneous precipitation rate is estimated with a Root Mean Squared Error (RMSE) of 1.6 mm/h.
Effective Planar Cluster Detection in Point Clouds Using Histogram-Driven Kd-Like Partition and Shifted Mahalanobis Distance Based Regression
Authors: Walczak Jakub , Poreda Tadeusz , Wojciechowski Adam
Published: October 2019

Point cloud segmentation for planar surface detection is a valid problem of automatic laser scans analysis. It is widely exploited for many industrial remote sensing tasks, such as LIDAR city scanning, creating inventories of buildings, or object reconstruction. Many current methods rely on robustly calculated covariance and centroid for plane model estimation or global energy optimization. This is coupled with point cloud division strategies, based on uniform or regular space subdivision. These approaches result in many redundant divisions, plane maladjustments caused by outliers, and excessive number of processing iterations. In this paper, a new robust method of point clouds segmentation, based on histogram-driven hierarchical space division, inspired by kd-tree is presented. The proposed partition method produces results with a smaller oversegmentation rate. Moreover, state-of-the-art partitions often lead to nodes of low cardinality, which results in the rejection of many points. In the proposed method, the point rejection rate was reduced. Point cloud subdivision is followed by resilient plane estimation, using Mahalanobis distance with respect to seven cardinal points. These points were established based on eigenvectors of the covariance matrix of the considered point cluster. The proposed method shows high robustness and yields good quality metrics, much faster than a FAST-MCD approach. The overall results indicate improvements in terms of plane precision, plane recall, under-, and the over- segmentation rate with respect to the reference benchmark methods. Plane precision for the S3DIS dataset increased on average by 2.6pp and plane recall- by 3pp. Both over- and under- segmentation rates fell by 3.2pp and 4.3pp.
Online Correction of the Mutual Miscalibration of Multimodal VIS–IR Sensors and 3D Data on a UAV Platform for Surveillance Applications
Authors: Siekański Piotr , Paśko Sławomir , Malowany Krzysztof , Malesa Marcin
Published: October 2019

Unmanned aerial vehicles (UAVs) are widely used to protect critical infrastructure objects, and they are most often equipped with one or more RGB cameras and, sometimes, with a thermal imaging camera as well. To obtain as much information as possible from them, they should be combined or fused. This article presents a situation in which data from RGB (visible, VIS) and thermovision (infrared, IR) cameras and 3D data have been combined in a common coordinate system. A specially designed calibration target was developed to enable the geometric calibration of IR and VIS cameras in the same coordinate system. 3D data are compatible with the VIS coordinate system when the structure from motion (SfM) algorithm is used. The main focus of this article is to provide the spatial coherence between these data in the case of relative camera movement, which usually results in a miscalibration of the system. Therefore, a new algorithm for the detection of sensor system miscalibration, based on phase correlation with automatic calibration correction in real time, is introduced.
Frost Damage Assessment in Wheat Using Spectral Mixture Analysis
Authors: Fitzgerald Glenn J. , Perry Eileen M. , Flower Ken C. , Callow J. Nikolaus , Boruff Bryan , Delahunty Audrey , Wallace Ashley , Nuttall James
Published: October 2019

Frost damage to broadacre crops can cause up to an 85% loss in productivity. Although growers have few options for crop protection from frost, a rapid method for assessing frost-induced sterility would allow for timely management decisions (e.g., cutting for hay and altering marketing strategies). Spectral mixture analysis (SMA) has shown success in mapping landscape components and was used with hyperspectral data collected on the canopy, heads, and leaves of wheat at different sites to determine if this could quantify frost damage. Spectral libraries were assembled from canopy components collected from local field sites to generate spectral libraries for SMA from which a series of fraction sets was derived. The frost (Fr) fraction was then used to estimate final yield as a means of measuring frost damage. The best-fitting Fr fractions to yield were derived from the same data set as the source Fr spectra, and these ranged over R2 = 0.58–0.75 at the canopy scale. It was clear that spectral signatures need to be collected at scale to assess frost damage. While Fr fractions were able to estimate yield there was no “universal” endmember set from which a Fr fraction could be derived. The normalized difference vegetation index (NDVI) was not able to estimate frost damage consistently. Future work requires determining whether there is a “universal” set of endmembers and a minimum set of targeted wavebands that could lead to multispectral instruments for frost assessment for use in ground and aerial sensors.
PPP and PPP-AR Kinematic Post-Processed Performance of GPS-Only, Galileo-Only and Multi-GNSS
Authors: Katsigianni Georgia , Loyer Sylvain , Perosanz Felix
Published: October 2019

Precise point positioning (PPP) has been used for decades not only for general positioning needs but also for geodetic and other scientific applications. The CNES-CLS Analysis Centre (AC) of the International GNSS Service (IGS) is performing PPP with phase ambiguity resolution (PPP-AR) using the zero-difference ambiguity fixing approach also known as “Integer PPP” (IPPP). In this paper we examine the postprocessed kinematic PPP and PPP-AR using Galileo-only, GPS-only and Multi-GNSS (GPS + Galileo) constellations. The interest is to examine the accuracy for each GNSS system individually but also of their combination to measure the current benefits of using Galileo within a Multi-GNSS PPP and PPP-AR. Results show that Galileo-only positioning is nearly at the same level as GPS-only; around 2–4 mm horizontal and aound 10 mm vertical repeatability (example station of BRUX). In addition, the use of Galileo system—even uncompleted—improves the performance of the positioning when combined with GPS giving mm level repeatability (improvement of around 30% in East, North and Up components). Repeatabilities observed for Multi-GNSS (GPS + GAL) PPP-AR, taking into account the global network statistics, are a little larger, with 8 mm in horizontal and 17 mm in vertical directions. This result shows that including Galileo ameliorates the best positioning accuracy achieved until today with GPS PPP-AR.
Projecting Mammal Distributions in Response to Future Alternative Landscapes in a Rapidly Transitioning Region
Authors: Cove Michael V. , Fergus Craig , Lacher Iara , Akre Thomas , McShea William J.
Published: October 2019

Finding balance between the needs of people and wildlife is an essential component of planning sustainable landscapes. Because mammals make up a diverse and ecologically important taxon with varying responses to human disturbance, we used representative mammal species to examine how alternative land-use policies might affect their habitats and distributions in the near future. We used wildlife detections from camera traps at 1591 locations along a large-scale urban to wild gradient in northern Virginia, to create occupancy models which determined land cover relationships and the drivers of contemporary mammal distributions. From the 15 species detected, we classified five representative species into two groups based on their responses to human development; sensitive species (American black bears and bobcats) and synanthropic species (red foxes, domestic cats, and white-tailed deer). We then used the habitat models for the representative species to predict their distributions under four future planning scenarios based on strategic versus reactive planning and high or low human population growth. The distributions of sensitive species did not shrink drastically under any scenario, whereas the distributions of synanthropic species increased in response to anthropogenic development, but the magnitude of the response varied based on the projected rate of human population growth. This is likely because most sensitive species are dependent on large, protected public lands in the region, and the majority of projected habitat losses should occur in non-protected private lands. These findings illustrate the importance of public protected lands in mitigating range loss due to land use changes, and the potential positive impact of strategic planning in further mitigating mammalian diversity loss in private lands.
Depthwise Separable Convolution Neural Network for High-Speed SAR Ship Detection
Authors: Zhang Tianwen , Zhang Xiaoling , Shi Jun , Wei Shunjun
Published: October 2019

As an active microwave imaging sensor for the high-resolution earth observation, synthetic aperture radar (SAR) has been extensively applied in military, agriculture, geology, ecology, oceanography, etc., due to its prominent advantages of all-weather and all-time working capacity. Especially, in the marine field, SAR can provide numerous high-quality services for fishery management, traffic control, sea-ice monitoring, marine environmental protection, etc. Among them, ship detection in SAR images has attracted more and more attention on account of the urgent requirements of maritime rescue and military strategy formulation. Nowadays, most researches are focusing on improving the ship detection accuracy, while the detection speed is frequently neglected, regardless of traditional feature extraction methods or modern deep learning (DL) methods. However, the high-speed SAR ship detection is of great practical value, because it can provide real-time maritime disaster rescue and emergency military planning. Therefore, in order to address this problem, we proposed a novel high-speed SAR ship detection approach by mainly using depthwise separable convolution neural network (DS-CNN). In this approach, we integrated multi-scale detection mechanism, concatenation mechanism and anchor box mechanism to establish a brand-new light-weight network architecture for the high-speed SAR ship detection. We used DS-CNN, which consists of a depthwise convolution (D-Conv2D) and a pointwise convolution (P-Conv2D), to substitute for the conventional convolution neural network (C-CNN). In this way, the number of network parameters gets obviously decreased, and the ship detection speed gets dramatically improved. We experimented on an open SAR ship detection dataset (SSDD) to validate the correctness and feasibility of the proposed method. To verify the strong migration capacity of our method, we also carried out actual ship detection on a wide-region large-size Sentinel-1 SAR image. Ultimately, under the same hardware platform with NVIDIA RTX2080Ti GPU, the experimental results indicated that the ship detection speed of our proposed method is faster than other methods, meanwhile the detection accuracy is only lightly sacrificed compared with the state-of-art object detectors. Our method has great application value in real-time maritime disaster rescue and emergency military planning.
Quantifying Spatiotemporal Patterns and Major Explanatory Factors of Urban Expansion in Miami Metropolitan Area During 1992–2016
Authors: Rifat Shaikh Abdullah Al , Liu Weibo
Published: October 2019

Urban expansion is one of the most dramatic forms of land transformation in the world and it is one of the greatest challenges in achieving sustainable development in the 21st century. Previous studies analyzed urbanization patterns in areas with rapid urban expansion while urban areas with low to moderate expansion have been overlooked, especially in developed countries. In this study, we examined the spatiotemporal dynamics of urban expansion patterns in South Florida, United States (US) over the last 25 years (1992–2016) using Remote Sensing and GIS techniques. The main goal of this paper was to investigate the degree and spatiotemporal patterns of urban expansion at different administrative level in the study area and how spatiotemporal variance in different explanatory factors influence urban expansion in this region. More specifically, this research quantifies the rates, types, intensity, and landscape metrics of urban expansion in Miami-Fort Lauderdale-Palm Beach, Florida Metropolitan Statistical Area (Miami MSA) which is the 7th largest MSA and 4th largest urbanized area in the US using remote sensing (satellite imageries) data from National Land Cover Datasets (NLCD) and Coastal Change Analysis Program (C-CAP) at 30 m spatial resolution. We further investigated the urban growth patterns at the county and city areas that are located within this MSA to portray the local ‘picture’ of urban growth in this region. Urban expansion in this region can be divided into two time periods: pre-2001 and post-2001 where the former experienced rapid urban expansion and the later had comparatively slow urban expansion. Results suggest that infilling was the dominant type of urban expansion followed by edge-expansion and outlying. Results from landscape metrics represent that newly developed urban lands became more aggregated and simplified in form as the time progressed in the study region. Also, new urban lands were generated away from the east coast and historic cities which eventually created new urban cores. We also used correlation analysis and multiple linear stepwise regression to address major explanatory factors of spatiotemporal change in urban expansion during the study period. Although the influence of factors on urban expansion varied temporally, Population and Distance to Coast were the strongest variables followed by Distance to Roads and Median Income that influence overall urban expansion in the study area.
Static and Dynamic Algorithms for Terrain Classification in UAV Aerial Imagery
Authors: Matos-Carvalho J. P. , Moutinho Filipe , Salvado Ana Beatriz , Carrasqueira Tiago , Campos-Rebelo Rogerio , Pedro Dário , Campos Luís Miguel , Fonseca José M. , Mora André
Published: October 2019

The ability to precisely classify different types of terrain is extremely important for Unmanned Aerial Vehicles (UAVs). There are multiple situations in which terrain classification is fundamental for achieving a UAV’s mission success, such as emergency landing, aerial mapping, decision making, and cooperation between UAVs in autonomous navigation. Previous research works describe different terrain classification approaches mainly using static features from RGB images taken onboard UAVs. In these works, the terrain is classified from each image taken as a whole, not divided into blocks; this approach has an obvious drawback when applied to images with multiple terrain types. This paper proposes a robust computer vision system to classify terrain types using three main algorithms, which extract features from UAV’s downwash effect: Static textures- Gray-Level Co-Occurrence Matrix (GLCM), Gray-Level Run Length Matrix (GLRLM) and Dynamic textures- Optical Flow method. This system has been fully implemented using the OpenCV library, and the GLCM algorithm has also been partially specified in a Hardware Description Language (VHDL) and implemented in a Field Programmable Gate Array (FPGA)-based platform. In addition to these feature extraction algorithms, a neural network was designed with the aim of classifying the terrain into one of four classes. Lastly, in order to store and access all the classified terrain information, a dynamic map, with this information was generated. The system was validated using videos acquired onboard a UAV with an RGB camera.
Application of Deep Learning for Delineation of Visible Cadastral Boundaries from Remote Sensing Imagery
Authors: Crommelinck Sophie , Koeva Mila , Yang Michael Ying , Vosselman George
Published: October 2019

Cadastral boundaries are often demarcated by objects that are visible in remote sensing imagery. Indirect surveying relies on the delineation of visible parcel boundaries from such images. Despite advances in automated detection and localization of objects from images, indirect surveying is rarely automated and relies on manual on-screen delineation. We have previously introduced a boundary delineation workflow, comprising image segmentation, boundary classification and interactive delineation that we applied on Unmanned Aerial Vehicle (UAV) data to delineate roads. In this study, we improve each of these steps. For image segmentation, we remove the need to reduce the image resolution and we limit over-segmentation by reducing the number of segment lines by 80% through filtering. For boundary classification, we show how Convolutional Neural Networks (CNN) can be used for boundary line classification, thereby eliminating the previous need for Random Forest (RF) feature generation and thus achieving 71% accuracy. For interactive delineation, we develop additional and more intuitive delineation functionalities that cover more application cases. We test our approach on more varied and larger data sets by applying it to UAV and aerial imagery of 0.02–0.25 m resolution from Kenya, Rwanda and Ethiopia. We show that it is more effective in terms of clicks and time compared to manual delineation for parcels surrounded by visible boundaries. Strongest advantages are obtained for rural scenes delineated from aerial imagery, where the delineation effort per parcel requires 38% less time and 80% fewer clicks compared to manual delineation.
A Radio Channel Model for D2D Communications Blocked by Single Trees in Forest Environments
Authors: Picallo Imanol , Klaina Hicham , Lopez-Iturri Peio , Aguirre Erik , Celaya-Echarri Mikel , Azpilicueta Leyre , Eguizábal Alejandro , Falcone Francisco , Alejos Ana
Published: October 2019

In this paper we consider the D2D (Device-to-Device) communication taking place between Wireless Sensor Networks (WSN) elements operating in vegetation environments in order to achieve the radio channel characterization at 2.4 GHz, focusing on the radio links blocked by oak and pine trees modelled from specimens found in a real recreation area located within forest environments. In order to fit and validate a radio channel model for this type of scenarios, both measurements and simulations by means of an in-house developed 3D Ray Launching algorithm have been performed, offering as outcomes the path loss and multipath information of the scenarios under study for forest immersed isolated trees and non-isolated trees. The specific forests, composed of thick in-leaf trees, are called Orgi Forest and Chandebrito, located respectively in Navarre and Galicia, Spain. A geometrical and dielectric model of the trees were created and introduced in the simulation software. We concluded that the scattering produced by the tree can be divided into two zones with different dominant propagation mechanisms: an obstructed line of sight (OLoS) zone far from the tree fitting a log-distance model, and a diffraction zone around the edge of the tree. 2D planes of delay spread value are also presented which similarly reflects the proposed two-zone model.