bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparative Analysis of Cytokine Expression in Oral Keratinocytes and THP-1 Macrophages in Response to the Most Prevalent Serotypes of Aggregatibacter actinomycetemcomitans
Authors: Betancur Daniel , Muñoz Grez Camila , Oñate Angel
Published: March 2021

Background: Periodontitis is a chronic inflammatory disease associated with a dysbiotic biofilm. Many pathogens have been related with its progression and severity, one of which is Aggregatibacter actinomycetemcomitans, a Gram-negative bacteria with seven serotypes (a–g) according with the structure of its LPS, with serotype b defined as the most virulent compared with the other serotypes. The aim of this study was to evaluate the response of oral keratinocytes and macrophages to A. actinomycetemcomitans. Methods: Oral keratinocytes (OKF6/TERT2) and macrophages (THP-1) were infected with A. actinomycetemcomitans serotypes a, b and c. The expression of IL-1β, IL-6, IL-8, IL-18, TNF-α, MMP-9, RANKL, TLR-2, TLR-4, TLR-6, thymic stromal lymphopoietin (TSLP), and ICAM-1 was evaluated by qPCR at 2 and 24 h after infection. Results: An increase in the expression of these molecules was induced by all serotypes at both times of infection, with macrophages showing higher levels of expression at 24 h compared to epithelial cells in which the highest levels were observed in the first hours after infection. Conclusions: Keratinocytes and macrophages contribute to the inflammation in periodontitis from the early stages of infection, producing the first waves of cytokines, acting as the first signal for professional immune cell recruitment and modulation of more specific immune responses.
Identification of Phytoplasmas Representing Multiple New Genetic Lineages from Phloem-Feeding Leafhoppers Highlights the Diversity of Phytoplasmas and Their Potential Vectors
Authors: Wei Wei , Trivellone Valeria , Dietrich Christopher H. , Zhao Yan , Bottner-Parker Kristi D. , Ivanauskas Algirdas
Published: March 2021

Phytoplasmas are obligate transkingdom bacterial parasites that infect a variety of plant species and replicate in phloem-feeding insects in the order Hemiptera, mainly leafhoppers (Cicadellidae). The insect capacity in acquisition, transmission, survival, and host range directly determines the epidemiology of phytoplasmas. However, due to the difficulty of insect sampling and the lack of follow-up transmission trials, the confirmed phytoplasma insect hosts are still limited compared with the identified plant hosts. Recently, quantitative polymerase chain reaction (qPCR)-based quick screening of 227 leafhoppers collected in natural habitats unveiled the presence of previously unknown phytoplasmas in six samples. In the present study, 76 leafhoppers, including the six prescreened positive samples, were further examined to identify and characterize the phytoplasma strains by semi-nested PCR. A total of ten phytoplasma strains were identified in leafhoppers from four countries including South Africa, Kyrgyzstan, Australia, and China. Based on virtual restriction fragment length polymorphism (RFLP) analysis, these ten phytoplasma strains were classified into four distinct ribosomal (16Sr) groups (16SrI, 16SrIII, 16SrXIV, and 16SrXV), representing five new subgroups (16SrI-AO, 16SrXIV-D, 16SrXIV-E, 16SrXIV-F, and 16SrXV-C). The results strongly suggest that the newly identified phytoplasma strains not only represent new genetic subgroup lineages, but also extend previously undiscovered geographical distributions. In addition, ten phytoplasma-harboring leafhoppers belonged to seven known leafhopper species, none of which were previously reported insect vectors of phytoplasmas. The findings from this study provide fresh insight into genetic diversity, geographical distribution, and insect host range of phytoplasmas. Further transmission trials and screening of new potential host plants and weed reservoirs in areas adjacent to collection sites of phytoplasma harboring leafhoppers will contribute to a better understanding of phytoplasma transmission and epidemiology.
Squalene-Based Influenza Vaccine Adjuvants and Their Impact on the Hemagglutinin-Specific B Cell Response
Authors: Nguyen-Contant Phuong , Sangster Mark Y. , Topham David J.
Published: March 2021

Influenza infections continue to cause significant annual morbidity and mortality despite ongoing influenza vaccine research. Adjuvants are administered in conjunction with influenza vaccines to enhance the immune response and strengthen protection against disease. Squalene-based emulsion adjuvants including MF59, AS03, and AF03, are registered for administration with influenza vaccines and are widely used in many countries. Squalene-based emulsion adjuvants induce a strong innate immune response, enhancing antigen presentation both quantitively and qualitatively to generate strong B cell responses and antibody production. They also diversify the reactivity profiles and strengthen the affinities of antibodies against the influenza hemagglutinin, increasing protection across virus clades. In this review, we consider the mechanisms of the enhancement of innate and adaptive immune responses by squalene-based emulsionSE adjuvants and the resulting increase in magnitude and breadth of hemagglutinin-specific B cell responses. We relate observed effects of SE adjuvants and current mechanistic understandings to events in responding lymph nodes. These insights will guide the rational design and optimization of influenza vaccines to provide broad and effective protection.
Genotype Diversity before and after the Introduction of a Rotavirus Vaccine into the National Immunisation Program in Fiji
Authors: Thomas Sarah , Donato Celeste M. , Covea Sokoveti , Ratu Felisita T. , Jenney Adam W. J. , Reyburn Rita , Sahu Khan Aalisha , Rafai Eric , Grabovac Varja , Serhan Fatima , Bines Julie E. , Russell Fiona M.
Published: March 2021

The introduction of the rotavirus vaccine, Rotarix, into the Fiji National Immunisation Program in 2012 has reduced the burden of rotavirus disease and hospitalisations in children less than 5 years of age. The aim of this study was to describe the pattern of rotavirus genotype diversity from 2005 to 2018; to investigate changes following the introduction of the rotavirus vaccine in Fiji. Faecal samples from children less than 5 years with acute diarrhoea between 2005 to 2018 were analysed at the WHO Rotavirus Regional Reference Laboratory at the Murdoch Children’s Research Institute, Melbourne, Australia, and positive samples were serotyped by EIA (2005–2006) or genotyped by heminested RT-PCR (2007 onwards). We observed a transient increase in the zoonotic strain equine-like G3P[8] in the initial period following vaccine introduction. G1P[8] and G2P[4], dominant genotypes prior to vaccine introduction, have not been detected since 2015 and 2014, respectively. A decrease in rotavirus genotypes G2P[8], G3P[6], G8P[8] and G9P[8] was also observed following vaccine introduction. Monitoring the rotavirus genotypes that cause diarrhoeal disease in children in Fiji is important to ensure that the rotavirus vaccine will continue to be protective and to enable early detection of new vaccine escape strains if this occurs.
Looking Ahead to 2030: Survey of Evolving Needs in Pharmacy Education
Authors: Papadopoulos Vassilios , Goldman Dana , Wang Clay , Keller Michele , Chen Steven
Published: March 2021

In order to keep pharmacy education relevant to a rapidly-evolving future, this study sought to identify key insights from leaders from a broad array of pharmacy and non-pharmacy industries on the future of the pharmacy profession, pharmaceutical sciences, and pharmacy education. Thought leaders representing a variety of industries were surveyed regarding their perspectives on the future of pharmacy practice, pharmaceutical science disciplines, and pharmacy education in seven domains. From 46 completed surveys, top challenges/threats were barriers that limit clinical practice opportunities, excessive supply of pharmacists, and high drug costs. Major changes in the drug distribution system, automation/robotics, and new therapeutic approaches were identified as the top technological disrupters. Key drivers of pharmacy education included the primary care provider shortage, growing use of technology and data, and rising drug costs. The most significant sources of job growth outside of retail and hospital settings were managed care organizations, technology/biotech/pharmaceutical companies, and ambulatory care practices. Needs in the industry included clinical management of complex patients, leadership and management, pharmaceutical scientists, and implementation science. Knowledge gaps were pharmacists not recognizing their value on the health care team, preparation to embrace and lead change, and expertise in data science and analytics. Pharmacy schools will need to address several disruptive trends to future-proof their curricula, including expanding patient management skills, leadership and management training, technology, and data analytics.
Novel Low-Loss Fiber-Chip Edge Coupler for Coupling Standard Single Mode Fibers to Silicon Photonic Wire Waveguides
Authors: Sun Siwei , Chen Ying , Sun Yu , Liu Fengman , Cao Liqiang
Published: March 2021

Fiber-to-chip optical interconnects is a big challenge in silicon photonics application scenarios such as data centers and optical transmission systems. An edge coupler, compared to optical grating, is appealing to in the application of silicon photonics due to the high coupling efficiency between standard optical fibers (SMF-28) and the sub-micron silicon wire waveguides. In this work, we proposed a novel fiber–chip edge coupler approach with a large mode size for silicon photonic wire waveguides. The edge coupler consists of a multiple structure which was fulfilled by multiple silicon nitride layers embedded in SiO2 upper cladding, curved waveguides and two adiabatic spot size converter (SSC) sections. The multiple structure can allow light directly coupling from large mode size fiber-to-chip coupler, and then the curved waveguides and SSCs transmit the evanescent field to a 220 nm-thick silicon wire waveguide based on the silicon-on-insulator (SOI) platform. The edge coupler, designed for a standard SMF-28 fiber with 8.2 μm mode field diameter (MFD) at a wavelength of 1550 nm, exhibits a mode overlap efficiency exceeding 95% at the chip facet and the overall coupling exceeding 90%. The proposed edge coupler is fully compatible with standard microfabrication processes.
Seasonal Variations of the Nutritive Value and Phytotherapeutic Potential of Cladium mariscus L. (Pohl.) Targeting Ruminant’s Production
Authors: Oliveira Marta , João Rodrigues Maria , Neng Nuno R. , Nogueira José Manuel Florêncio , Bessa Rui J. B. , Custódio Luísa
Published: March 2021

In our endeavor to identify salt-tolerant plants with potential veterinary uses in ruminants’ production strategies, we focused on Cladium mariscus L. Pohl (sawgrass), due to its high total phenolic and tannin content, anti-radical properties, and ethnomedicinal uses. Aerial parts were collected along the year in Southern Portugal and evaluated for the nutritional profile and in vitro organic matter digestibility (IVOMD), aiming for its use as feed. Acetone extracts were appraised for total contents in phenolics (TPC), flavonoids (TFC), and tannins (CTC), as well as the chemical composition by HPLC-DAD and in vitro antioxidant and anti-inflammatory properties, targeting its exploitation as phytotherapeutic products. Sawgrass biomass has a limited nutritive value, due to its high neutral detergent fiber (NDF; 596–690 g kg−1 dry matter (DM)) and acid detergent fiber (ADF; 330–418 g kg−1 DM) contents, low crude protein (51.8–87.3 g kg−1 DM) and IVOMD (172–317 g kg−1 organic matter (OM)). Despite differences among seasons, the mineral profile was adequate. The extracts were rich in TPC (88–112 mg g−1), CTC (115–169 mg g−1), and TFC (18.5–20.2 mg g−1), and displayed significant antioxidant capacity, particularly in summer and autumn, whilst no seasonal influence was detected for anti-inflammatory properties (30% reduction of nitric oxide production). Eleven phenolics were quantified: chlorogenic, ferulic, and syringic acids were the most abundant, especially in the autumn sample. Overall, despite the low nutritional interest, sawgrass extracts hold the potential as a source of antioxidant and anti-inflammatory phenolic compounds.
Characteristics of Fragaria vesca Yield Parameters and Anthocyanin Accumulation under Water Deficit Stress
Authors: Rugienius Rytis , Bendokas Vidmantas , Siksnianas Tadeusas , Stanys Vidmantas , Sasnauskas Audrius , Kazanaviciute Vaiva
Published: March 2021

Plants exposed to drought stress conditions often increase the synthesis of anthocyanins—natural plant pigments and antioxidants. However, water deficit (WD) often causes significant yield loss. The aim of our study was to evaluate the productivity as well as the anthocyanin content and composition of berries from cultivated Fragaria vesca “Rojan” and hybrid No. 17 plants (seedlings) grown under WD. The plants were grown in an unheated greenhouse and fully irrigated (control) or irrigated at 50% and 25%. The number of berries per plant and the berry weight were evaluated every 4 days. The anthocyanin content and composition of berries were evaluated with the same periodicity using HPLC. The effect of WD on the yield parameters of two evaluated F. vesca genotypes differed depending on the harvest time. The cumulative yield of plants under WD was not less than that of the control plants for 20–24 days after the start of the experiment. Additionally, berries accumulated 36–56% (1.5–2.3 times, depending on the harvest time) more anthocyanins compared with fully irrigated plants. Our data show that slight or moderate WD at a stable air temperature of about 20 °C positively affected the biosynthesis of anthocyanins and the yield of F. vesca berries.
Comparison of Genomic Prediction Methods for Yellow, Stem, and Leaf Rust Resistance in Wheat Landraces from Afghanistan
Authors: Tehseen Muhammad Massub , Kehel Zakaria , Sansaloni Carolina P. , Lopes Marta da Silva , Amri Ahmed , Kurtulus Ezgi , Nazari Kumarse
Published: March 2021

Wheat rust diseases, including yellow rust (Yr; also known as stripe rust) caused by Puccinia striiformis Westend. f. sp. tritici, leaf rust (Lr) caused by Puccinia triticina Eriks. and stem rust (Sr) caused by Puccinia graminis Pres f. sp. tritici are major threats to wheat production all around the globe. Durable resistance to wheat rust diseases can be achieved through genomic-assisted prediction of resistant accessions to increase genetic gain per unit time. Genomic prediction (GP) is a promising technology that uses genomic markers to estimate genomic-assisted breeding values (GBEVs) for selecting resistant plant genotypes and accumulating favorable alleles for adult plant resistance (APR) to wheat rust diseases. To evaluate GP we compared the predictive ability of nine different parametric, semi-parametric and Bayesian models including Genomic Unbiased Linear Prediction (GBLUP), Ridge Regression (RR), Least Absolute Shrinkage and Selection Operator (LASSO), Elastic Net (EN), Bayesian Ridge Regression (BRR), Bayesian A (BA), Bayesian B (BB), Bayesian C (BC) and Reproducing Kernel Hilbert Spacing model (RKHS) to estimate GEBV’s for APR to yellow, leaf and stem rust of wheat in a panel of 363 bread wheat landraces of Afghanistan origin. Based on five-fold cross validation the mean predictive abilities were 0.33, 0.30, 0.38, and 0.33 for Yr (2016), Yr (2017), Lr, and Sr, respectively. No single model outperformed the rest of the models for all traits. LASSO and EN showed the lowest predictive ability in four of the five traits. GBLUP and RR gave similar predictive abilities, whereas Bayesian models were not significantly different from each other as well. We also investigated the effect of the number of genotypes and the markers used in the analysis on the predictive ability of the GP model. The predictive ability was highest with 1000 markers and there was a linear trend in the predictive ability and the size of the training population. The results of the study are encouraging, confirming the feasibility of GP to be effectively applied in breeding programs for resistance to all three wheat rust diseases.
A Comparison of the Transcriptomes of Cowpeas in Response to Two Different Ionizing Radiations
Authors: Kang Ryulyi , Seo Eunju , Park Aron , Kim Woon Ji , Kang Byeong Hee , Lee Jeong-Hee , Kim Sang Hoon , Kang Si-Yong , Ha Bo-Keun
Published: March 2021

In this study, gene expression changes in cowpea plants irradiated by two different types of radiation: proton-beams and gamma-rays were investigated. Seeds of the Okdang cultivar were exposed to 100, 200, and 300 Gy of gamma-rays and proton-beams. In transcriptome analysis, the 32, 75, and 69 differentially expressed genes (DEGs) at each dose of gamma-ray irradiation compared with that of the control were identified. A total of eight genes were commonly up-regulated for all gamma-ray doses. However, there were no down-regulated genes. In contrast, 168, 434, and 387 DEGs were identified for each dose of proton-beam irradiation compared with that of the control. A total of 61 DEGs were commonly up-regulated for all proton-beam doses. As a result of GO and KEGG analysis, the ranks of functional categories according to the number of DEGs were not the same in both treatments and were more diverse in terms of pathways in the proton-beam treatments than gamma-ray treatments. The number of genes related to defense, photosynthesis, reactive oxygen species (ROS), plant hormones, and transcription factors (TF) that were up-/down-regulated was higher in the proton beam treatment than that in gamma ray treatment. Proton-beam treatment had a distinct mutation spectrum and gene expression pattern compared to that of gamma-ray treatment. These results provide important information on the mechanism for gene regulation in response to two ionizing radiations in cowpeas.
A Review of Wet Compounding of Cellulose Nanocomposites
Authors: Clemons Craig , Sabo Ronald
Published: March 2021

Cellulose nanomaterials (CNs) are an emerging class of materials with numerous potential applications, including as additives or reinforcements for thermoplastics. Unfortunately, the preparation of CNs typically results in dilute, aqueous suspensions, and the lack of efficient water removal methods has hindered commercialization. However, water may also present opportunities for improving overall efficiencies if its potential is better understood and if it is better managed through the various stages of CN and composite production. Wet compounding represents one such possible opportunity by leveraging water’s ability to aid in CN dispersion, act as a transport medium for metering and feeding of CNs, plasticize some polymers, or potentially facilitate the preparation of CNs during compounding. However, there are also considerable challenges and much investigation remains. Here, we review various wet compounding approaches used in the preparation of cellulose nanocomposites as well as the related concepts of wet feeding and wet extrusion fibrillation of cellulose. We also discuss potential opportunities, remaining challenges, and research and development needs with the ultimate goal of developing a more integrated approach to cellulose nanocomposite preparation and a more sophisticated understanding of water’s role in the compounding process.
Cropmarks in Aerial Archaeology: New Lessons from an Old Story
Authors: Czajlik Zoltán , Árvai Mátyás , Mészáros János , Nagy Balázs , Rupnik László , Pásztor László
Published: March 2021

Cropmarks are a major factor in the effectiveness of traditional aerial archaeology. Identified almost 100 years ago, the positive and negative features shown by cropmarks are now well understood, as are the role of the different cultivated plants and the importance of precipitation and other elements of the physical environment. Generations of aerial archaeologists are in possession of empirical knowledge, allowing them to find as many cropmarks as possible every year. However, the essential analyses belong mostly to the predigital period, while the significant growth of datasets in the last 30 years could open a new chapter. This is especially true in the case of Hungary, as scholars believe it to be one of the most promising cropmark areas in Europe. The characteristics of soil formed of Late Quaternary alluvial sediments are intimately connected to the young geological/geomorphological background. The predictive soil maps elaborated within the framework of renewed data on Hungarian soil spatial infrastructure use legacy, together with recent remote sensing imagery. Based on the results from three study areas investigated, analyses using statistical methods (the Kolmogorov–Smirnov and Random Forest tests) showed a different relative predominance of pedological variables in each study area. The geomorphological differences between the study areas explain these variations satisfactorily.
Detecting Forest Degradation in the Three-North Forest Shelterbelt in China from Multi-Scale Satellite Images
Authors: Yu Tao , Liu Pengju , Zhang Qiang , Ren Yi , Yao Jingning
Published: March 2021

Detecting forest degradation from satellite observation data is of great significance in revealing the process of decreasing forest quality and giving a better understanding of regional or global carbon emissions and their feedbacks with climate changes. In this paper, a quick and applicable approach was developed for monitoring forest degradation in the Three-North Forest Shelterbelt in China from multi-scale remote sensing data. Firstly, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Ratio Vegetation Index (RVI), Leaf Area Index (LAI), Fraction of Photosynthetically Active Radiation (FPAR) and Net Primary Production (NPP) from remote sensing data were selected as the indicators to describe forest degradation. Then multi-scale forest degradation maps were obtained by adopting a new classification method using time series MODerate Resolution Imaging Spectroradiometer (MODIS) and Landsat Enhanced Thematic Mapper Plus (ETM+) images, and were validated with ground survey data. At last, the criteria and indicators for monitoring forest degradation from remote sensing data were discussed, and the uncertainly of the method was analyzed. Results of this paper indicated that multi-scale remote sensing data have great potential in detecting regional forest degradation.
The Role of Remote Sensing Data in Habitat Suitability and Connectivity Modeling: Insights from the Cantabrian Brown Bear
Authors: Cisneros-Araujo Pablo , Goicolea Teresa , Mateo-Sánchez María Cruz , García-Viñás Juan Ignacio , Marchamalo Miguel , Mercier Audrey , Gastón Aitor
Published: March 2021

Ecological modeling requires sufficient spatial resolution and a careful selection of environmental variables to achieve good predictive performance. Although national and international administrations offer fine-scale environmental data, they usually have limited spatial coverage (country or continent). Alternatively, optical and radar satellite imagery is available with high resolutions, global coverage and frequent revisit intervals. Here, we compared the performance of ecological models trained with free satellite data with models fitted using regionally restricted spatial datasets. We developed brown bear habitat suitability and connectivity models from three datasets with different spatial coverage and accessibility. These datasets comprised (1) a Sentinel-1 and 2 land cover map (global coverage); (2) pan-European vegetation and land cover layers (continental coverage); and (3) LiDAR data and the Forest Map of Spain (national coverage). Results show that Sentinel imagery and pan-European datasets are powerful sources to estimate vegetation variables for habitat and connectivity modeling. However, Sentinel data could be limited for understanding precise habitat–species associations if the derived discrete variables do not distinguish a wide range of vegetation types. Therefore, more effort should be taken to improving the thematic resolution of satellite-derived vegetation variables. Our findings support the application of ecological modeling worldwide and can help select spatial datasets according to their coverage and resolution for habitat suitability and connectivity modeling.
Relative Total Variation Structure Analysis-Based Fusion Method for Hyperspectral and LiDAR Data Classification
Authors: Quan Yinghui , Tong Yingping , Feng Wei , Dauphin Gabriel , Huang Wenjiang , Zhu Wentao , Xing Mengdao
Published: March 2021

The fusion of the hyperspectral image (HSI) and the light detecting and ranging (LiDAR) data has a wide range of applications. This paper proposes a novel feature fusion method for urban area classification, namely the relative total variation structure analysis (RTVSA), to combine various features derived from HSI and LiDAR data. In the feature extraction stage, a variety of high-performance methods including the extended multi-attribute profile, Gabor filter, and local binary pattern are used to extract the features of the input data. The relative total variation is then applied to remove useless texture information of the processed data. Finally, nonparametric weighted feature extraction is adopted to reduce the dimensions. Random forest and convolutional neural networks are utilized to evaluate the fusion images. Experiments conducted on two urban Houston University datasets (including Houston 2012 and the training portion of Houston 2017) demonstrate that the proposed method can extract the structural correlation from heterogeneous data, withstand a noise well, and improve the land cover classification accuracy.
CDOM Optical Properties and DOC Content in the Largest Mixing Zones of the Siberian Shelf Seas
Authors: Drozdova Anastasia N. , Nedospasov Andrey A. , Lobus Nikolay V. , Patsaeva Svetlana V. , Shchuka Sergey A.
Published: March 2021

Notable changes in the Arctic ecosystem driven by increased atmospheric temperature and ice cover reduction were observed in the last decades. Ongoing environmental shifts affect freshwater discharge to the Arctic Ocean, and alter Arctic land-ocean fluxes. The monitoring of DOC distribution and CDOM optical properties is of great interest both from the point of view of validation of remote sensing models, and for studying organic carbon transformation and dynamics. In this study we report the DOC concentrations and CDOM optical characteristics in the mixing zones of the Ob, Yenisei, Khatanga, Lena, Kolyma, and Indigirka rivers. Water sampling was performed in August–October 2015 and 2017. The DOC was determined by high-temperature combustion, and absorption coefficients and spectroscopic indices were calculated using the seawater absorbance obtained with spectrophotometric measurements. Kara and Laptev mixing zones were characterized by conservative DOC behavior, while the East Siberian sea waters showed nonconservative DOC distribution. Dominant DOM sources are discussed. The absorption coefficient aCDOM (350) in the East Siberian Sea was two-fold lower compared to Kara and Laptev seawaters. For the first time we report the DOC content in the Khatanga River of 802.6 µM based on the DOC in the Khatanga estuary.
High-Resolution Rice Mapping Based on SNIC Segmentation and Multi-Source Remote Sensing Images
Authors: Yang Lingbo , Wang Limin , Abubakar Ghali Abdullahi , Huang Jingfeng
Published: March 2021

High-resolution crop mapping is of great significance in agricultural monitoring, precision agriculture, and providing critical information for crop yield or disaster monitoring. Meanwhile, medium resolution time-series optical and synthetic aperture radar (SAR) images can provide useful phenological information. Combining high-resolution satellite data and medium resolution time-series images provides a great opportunity for fine crop mapping. Simple Non-Iterative Clustering (SNIC) is a state-of-the-art image segmentation algorithm that shows the advantages of efficiency and high accuracy. However, the application of SNIC in crop mapping based on the combination of high-resolution and medium-resolution images is unknown. Besides, there is still little research on the influence of the superpixel size (one of the key user-defined parameters of the SNIC method) on classification accuracy. In this study, we employed a 2 m high-resolution GF-1 pan-sharpened image and 10 m medium resolution time-series Sentinel-1 C-band Synthetic Aperture Radar Instrument (C-SAR) and Sentinel-2 Multispectral Instrument (MSI) images to carry out rice mapping based on the SNIC method. The results show that with the increase of the superpixel size, the classification accuracy increased at first and then decreased rapidly after reaching the summit when the superpixel size is 27. The classification accuracy of the combined use of optical and SAR data is higher than that using only Sentinel-2 MSI or Sentinel-1 C-SAR vertical transmitted and vertical received (VV) or vertical transmitted and horizontal received (VH) data, with overall accuracies of 0.8335, 0.8282, 0.7862, and 0.7886, respectively. Meanwhile, the results also indicate that classification based on superpixels obtained by SNIC significantly outperforms classification based on original pixels. The overall accuracy, producer accuracy, and user accuracy of SNIC superpixel-based classification increased by 9.14%, 17.16%, 27.35% and 1.36%, respectively, when compared with the pixel-based classification, based on the combination of optical and SAR data (using the random forest as the classifier). The results show that SNIC superpixel segmentation is a feasible method for high-resolution crop mapping based on multi-source remote sensing data. The automatic selection of the optimal superpixel size of SNIC will be focused on in future research.
An Approach to Improving GNSS Positioning Accuracy Using Several GNSS Devices
Authors: Jiménez-Martínez María Jesús , Farjas-Abadia Mercedes , Quesada-Olmo Nieves
Published: March 2021

Single point positioning (SPP) mode, related to pseudorange measurements, limits the level of accuracy to several meters in open sky and to several dozens of meters in urban canyons. This paper explores the effect of using a large number of SPP observations from low-cost global navigation system (GNSS) receivers, smartphones, and handheld GNSS units. Data segmentation and bootstrapping statistical methods were used to obtain the deviation, which can describe the accuracy of the large sample. The empirical test recording data showed that the error may achieve a sub-meter horizontal accuracy by the simple process of increasing the measurements of smartphones and handheld GNSS units. However, the drawback is the long period of time required. To reduce the satellite tracking time, a least squares solution network was applied over all the recorded data, assisted by the external geometric conditions. The final goal was to obtain the absolute positioning and associated deviations of one vertex from three or five GNSS receivers positioned on a network. The process was tested in three geodetic network examples. The results indicated that the enhanced SPP mode was able to improve its accuracy. Errors of several meters were reduced to values close to 50 cm in 25–37 min periods.
A Method to Compensate for the Errors Caused by Temperature in Structured-Light 3D Cameras
Authors: Vila Oriol , Boada Imma , Raba David , Farres Esteve
Published: March 2021

Although low cost red-green-blue-depth (RGB-D) cameras are factory calibrated, to meet the accuracy requirements needed in many industrial applications proper calibration strategies have to be applied. Generally, these strategies do not consider the effect of temperature on the camera measurements. The aim of this paper is to evaluate this effect considering an Orbbec Astra camera. To analyze this camera performance, an experimental study in a thermal chamber has been carried out. From this experiment, it has been seen that produced errors can be modeled as an hyperbolic paraboloid function. To compensate for this error, a two-step method that first computes the error and then corrects it has been proposed. To compute the error two possible strategies are proposed, one based on the infrared distortion map and the other on the depth map. The proposed method has been tested in an experimental scenario with different Orbbec Astra cameras and also in a real environment. In both cases, its good performance has been demonstrated. In addition, the method has been compared with the Kinect v1 achieving similar results. Therefore, the proposed method corrects the error due to temperature, is simple, requires a low computational cost and might be applicable to other similar cameras.
ANTIGONE: A Programmable Energy-Efficient Current Digitizer for an ISFET Wearable Sweat Sensing System
Authors: Voulgari Evgenia , Krummenacher François , Kayal Maher
Published: March 2021

This article describes the design and the characterization of the ANTIGONE (ANalog To dIGital cONvErter) ASIC (Application Specific Integrated Circuit) built in AMS 0.35 m technology for low dc-current sensing. This energy-efficient ASIC was specifically designed to interface with multiple Ion-Sensitive Field-Effect Transistors (ISFETs) and detect biomarkers like pH, Na+, K+ and Ca2+ in human sweat. The ISFET-ASIC system can allow real-time noninvasive and continuous health monitoring. The ANTIGONE ASIC architecture is based on the current-to-frequency converter through the charge balancing principle. The same front-end can digitize multiple currents produced by four sweat ISFET sensors in time multiplexing. The front-end demonstrates good linearity over a dynamic range that spans from 1 pA up to 500 nA. The consumed energy per conversion is less than 1 J. The chip is programmable and works in eight different modes of operation. The system uses a standard Serial Peripheral Interface (SPI) to configure, control and read the digitally converted sensor data. The chip is controlled by a portable device over Bluetooth Low Energy (BLE) through a Microcontroller Unit (MCU). The sweat sensing system is part of a bigger wearable platform that exploits the convergence of multiparameter biosensors and environmental sensors for personalized and preventive healthcare.
Design and Fabrication of a New Wearable Pressure Sensor for Blood Pressure Monitoring
Authors: Ion Marian , Dinulescu Silviu , Firtat Bogdan , Savin Mihaela , Ionescu Octavian N. , Moldovan Carmen
Published: March 2021

In recent years, research into the field of materials for flexible sensors and fabrication techniques directed towards wearable devices has helped to raise awareness of the need for new sensors with healthcare applicability. Our goal was to create a wearable flexible pressure sensor that could be integrated into a clinically approved blood pressure monitoring device. The sensor is built from a microfluidic channel encapsulated between two polymer layers, one layer being covered by metal transducers and the other being a flexible membrane containing the microfluidic channel, which also acts as a sealant for the structure. The applied external pressure deforms the channel, causing changes in resistance to the microfluidic layer. Electrical characterization has been performed in 5 different configurations, using alternating current (AC) and (DC) direct current measurements. The AC measurements for the fabricated pressure sensor resulted in impedance values at tens of hundreds of kOhm. Our sensor proved to have a high sensitivity for pressure values between 0 and 150 mm Hg, being subjected to repeatable external forces. The novelty presented in our work consists in the unique technological flow for the fabrication of the flexible wearable pressure sensor. The proposed miniaturized pressure sensor will ensure flexibility, low production cost and ease of use. It is made of very sensitive microfluidic elements and biocompatible materials and can be integrated into a wearable cuffless device for continuous blood pressure monitoring.
Specular Detection on Glossy Surface Using Geometric Characteristics of Specularity in Top-View Images
Authors: Kim Seunghyun , Ra Moonsoo , Kim Whoi-Yul
Published: March 2021

In an autonomous driving assistance system (ADAS), top views effectively represent objects around the vehicle on a 2D plane. Top-view images are therefore widely used to detect lines in ADAS applications such as lane-keeping assistance and parking assistance. Because line detection is a crucial step for these applications, the false positive detection of lines can lead to failure of the system. Specular reflections from a glossy surface are often the cause of false positives, and since certain specular patterns resemble actual lines in the top-view image, their presence induces false positive lines. Incorrect positions of the lines or parking stalls can thus be obtained. To alleviate this problem, we propose two methods to estimate specular pixels in the top-view image. The methods use a geometric property of the specular region: the shape of the specular region is stretched long in the direction of the camera as the distance between the camera and the light source becomes distant, resulting in a straight line. This property can be used to distinguish the specular region in images. One estimates the pixel-wise probability of the specularity using gradient vectors obtained from an edge detector and the other estimates specularity using the line equation of each line segment obtained by line detection. To evaluate the performance of the proposed method, we added our methods as a pre-processing step to existing parking stall detection methods and investigated changes in their performance. The proposed methods improved line detection performance by accurately estimating specular components in the top-view images.
Low Power Contactless Bioimpedance Sensor for Monitoring Breathing Activity
Authors: Pavlin Marko , Novak Franc , Papa Gregor
Published: March 2021

An electronic circuit for contactless detection of impedance changes in a tissue is presented. It operates on the principle of resonant frequency change of the resonator having the observed tissue as a dielectric. The operating frequency reflects the tissue dielectric properties (i.e., the tissue composition and on the tissue physiological changes). The sensor operation was tested within a medical application by measuring the breathing of a patient, which was an easy detectable physiological process. The advantage over conventional contact bioimpedance measurement methods is that no direct contact between the resonator and the body is required. Furthermore, the sensor’s wide operating range, ability to adapt to a broad range of measured materials, fast response, low power consumption, and small outline dimensions enables applications not only in the medical sector, but also in other domains. This can be extended, for example, to food industry or production maintenance, where the observed phenomena are reflected in dynamic dielectric properties of the observed object or material.
Design, Implementation, and Configuration of Laser Systems for Vehicle Detection and Classification in Real Time
Authors: Ripoll Nieves Gallego , Aguilera Luís Enrique Gómez , Belenguer Ferran Mocholí , Salcedo Antonio Mocholí , Ballester Merelo Francisco José
Published: March 2021

The use of real-time vehicle detection and classification systems is essential for the accurate management of traffic and road infrastructure. Over time, diverse systems have been proposed for it, such as the widely known magnetic loops or microwave radars. However, these types of sensors do not offer all the information currently required for exhaustive and comprehensive traffic control. Thus, this paper presents the design, implementation, and configuration of laser systems to obtain 3D profiles of vehicles, which collect more precise information about the state of the roads. Nevertheless, to obtain reliable information on vehicle traffic by means of these systems, it is fundamental to correctly carry out a series of preliminary steps: choose the most suitable type of laser, select its configuration properly, determine the optimal location, and process the information provided accurately. Therefore, this paper details a series of criteria to help make these crucial and difficult decisions. Furthermore, following these guidelines, a complete laser system implemented for vehicle detection and classification is presented as result, which is characterized by its versatility and the ability to control up to four lanes in real time.
Rubber Material Property Prediction Using Electron Microscope Images of Internal Structures Taken under Multiple Conditions
Authors: Togo Ren , Saito Naoki , Maeda Keisuke , Ogawa Takahiro , Haseyama Miki
Published: March 2021

A method for prediction of properties of rubber materials utilizing electron microscope images of internal structures taken under multiple conditions is presented in this paper. Electron microscope images of rubber materials are taken under several conditions, and effective conditions for the prediction of properties are different for each rubber material. Novel approaches for the selection and integration of reliable prediction results are used in the proposed method. The proposed method enables selection of reliable results based on prediction intervals that can be derived by the predictors that are each constructed from electron microscope images taken under each condition. By monitoring the relationship between prediction results and prediction intervals derived from the corresponding predictors, it can be determined whether the target prediction results are reliable. Furthermore, the proposed method integrates the selected reliable results based on Dempster–Shafer (DS) evidence theory, and this integration result is regarded as a final prediction result. The DS evidence theory enables integration of multiple prediction results, even if the results are obtained from different imaging conditions. This means that integration can even be realized if electron microscope images of each material are taken under different conditions and even if these conditions are different for target materials. This nonconventional approach is suitable for our application, i.e., property prediction. Experiments on rubber material data showed that the evaluation index mean absolute percent error (MAPE) was under 10% by the proposed method. The performance of the proposed method outperformed conventional comparative property estimation methods. Consequently, the proposed method can realize accurate prediction of the properties with consideration of the characteristic of electron microscope images described above.
A Multi-Level Output-Based DBN Model for Fine Classification of Complex Geo-Environments Area Using Ziyuan-3 TMS Imagery
Authors: Li Meng , Tang Zhuang , Tong Wei , Li Xianju , Chen Weitao , Wang Lizhe
Published: March 2021

Fine-scale land use and land cover (LULC) data in a mining area are helpful for the smart supervision of mining activities. However, the complex landscape of open-pit mining areas severely restricts the classification accuracy. Although deep learning (DL) algorithms have the ability to extract informative features, they require large amounts of sample data. As a result, the design of more interpretable DL models with lower sample demand is highly important. In this study, a novel multi-level output-based deep belief network (DBN-ML) model was developed based on Ziyuan-3 imagery, which was applied for fine classification in an open-pit mine area of Wuhan City. First, the last DBN layer was used to output fine-scale land cover types. Then, one of the front DBN layers outputted the first-level land cover types. The coarse classification was easier and fewer DBN layers were sufficient. Finally, these two losses were weighted to optimize the DBN-ML model. As the first-level class provided a larger amount of additional sample data with no extra cost, the multi-level output strategy enhanced the robustness of the DBN-ML model. The proposed model produces an overall accuracy of 95.10% and an F1-score of 95.07%, outperforming some other models.
Colour-Balanced Edge-Guided Digital Inpainting: Applications on Artworks
Authors: Ciortan Irina-Mihaela , George Sony , Hardeberg Jon Yngve
Published: March 2021

The virtual inpainting of artworks provides a nondestructive mode of hypothesis visualization, and it is especially attractive when physical restoration raises too many methodological and ethical concerns. At the same time, in Cultural Heritage applications, the level of details in virtual reconstruction and their accuracy are crucial. We propose an inpainting algorithm that is based on generative adversarial network, with two generators: one for edges and another one for colors. The color generator rebalances chromatically the result by enforcing a loss in the discretized gamut space of the dataset. This way, our method follows the modus operandi of an artist: edges first, then color palette, and, at last, color tones. Moreover, we simulate the stochasticity of the lacunae in artworks with morphological variations of a random walk mask that recreate various degradations, including craquelure. We showcase the performance of our model on a dataset of digital images of wall paintings from the Dunhuang UNESCO heritage site. Our proposals of restored images are visually satisfactory and they are quantitatively comparable to state-of-the-art approaches.
Fast Quantification of Air Pollutants by Mid-Infrared Hyperspectral Imaging and Principal Component Analysis
Authors: Meléndez Juan , Guarnizo Guillermo
Published: March 2021

An imaging Fourier-transform spectrometer in the mid-infrared (1850–6667 cm−1) has been used to acquire transmittance spectra at a resolution of 1 cm−1 of three atmospheric pollutants with known column densities (Q): methane (258 ppm·m), nitrous oxide (107.5 ppm·m) and propane (215 ppm·m). Values of Q and T have been retrieved by fitting them with theoretical spectra generated with parameters from the HITRAN database, based on a radiometric model that takes into account gas absorption and emission, and the instrument lineshape function. A principal component analysis (PCA) of experimental data has found that two principal components are enough to reconstruct gas spectra with high fidelity. PCA-processed spectra have better signal-to-noise ratio without loss of spatial resolution, improving the uniformity of retrieval. PCA has been used also to speed up retrieval, by pre-calculating simulated spectra for a range of expected Q and T values, applying PCA to them and then comparing the principal components of experimental spectra with those of the simulated ones to find the gas Q and T values. A reduction in calculation time by a factor larger than one thousand is achieved with improved accuracy. Retrieval can be further simplified by obtaining T and Q as quadratic functions of the two first principal components.
Identification of IoT Actors
Authors: Hadzovic Suada , Mrdovic Sasa , Radonjic Milutin
Published: March 2021

The Internet of Things (IoT) is a leading trend with numerous opportunities accompanied by advantages as well as disadvantages. Parallel with IoT development, significant privacy and personal data protection challenges are also growing. In this regard, the General Data Protection Regulation (GDPR) is often considered the world’s strongest set of data protection rules and has proven to be a catalyst for many countries around the world. The concepts and interaction of the data controller, the joint controllers, and the data processor play a key role in the implementation of the GDPR. Therefore, clarifying the blurred IoT actors’ relationships to determine corresponding responsibilities is necessary. Given the IoT transformation reflected in shifting computing power from cloud to the edge, in this research we have considered how these computing paradigms are affecting IoT actors. In this regard, we have introduced identification of IoT actors according to a new five-computing layer IoT model based on the cloud, fog, edge, mist, and dew computing. Our conclusion is that identifying IoT actors in the light of the corresponding IoT data manager roles could be useful in determining the responsibilities of IoT actors for their compliance with data protection and privacy rules.