bluuebunny/crossref_metadata_2025 huggingface dataset:
Prevalence of Vitamin D Deficiency in a Large Newborn Cohort from Northern United States and Effect of Intrauterine Drug Exposure
Authors: Kanike Neelakanta , Hospattankar Krupa Gowri , Sharma Amit , Worley Sarah , Groh-Wargo Sharon
Published: July 2020

Vitamin D is not only a vital element in bone health but is also a prohormone. Data regarding distribution of vitamin D status among preterm and term neonates in the United States are limited. There are no data on the effect of intrauterine drug exposure on vitamin D status. Our objective was to determine the distribution of vitamin D levels among preterm and term neonates and the effect of intrauterine illicit drug exposure. We did a retrospective chart review of neonates admitted from 2009 to 2016 to our neonatal intensive care unit with serum 25-hydroxycholecalciferol (25[OH]D) levels measured during the hospital stay. Of 1517 neonates, the median 25[OH]D level was 19 ng/mL with 31% deficient and 49% insufficient, even though 75% of mothers took prenatal vitamins. In pregnant women, 38% were vitamin-D-deficient and 44% were vitamin-D-insufficient. Four hundred seventy-one neonates had intrauterine drug exposure, with a median 25[OH]D level of 22.9 ng/mL versus 17.8 ng/mL in nonexposed neonates (p = 0.001). Despite maternal prenatal vitamin intake, neonates are at risk of vitamin D deficiency. Maternal illicit drug use was not related to lower 25[OH]D levels in neonates.
Identification of A Putative T6SS Immunity Islet in Salmonella Typhi
Authors: Barretto Luke , Fowler Casey
Published: July 2020

Typhoid fever is a major global health problem and is the result of systemic infections caused by the human-adapted bacterial pathogen Salmonella enterica serovar Typhi (S. Typhi). The pathology underlying S. Typhi infections significantly differ from infections caused by broad host range serovars of the same species, which are a common cause of gastroenteritis. Accordingly, identifying S. Typhi genetic factors that impart functionality absent from broad host range serovars offers insights into its unique biology. Here, we used an in-silico approach to explore the function of an uncharacterized 14-gene S. Typhi genomic islet. Our results indicated that this islet was specific to the S. enterica species, where it was encoded by the Typhi and Paratyphi A serovars, but was generally absent from non-typhoidal serovars. Evidence was gathered using comparative genomics and sequence analysis tools, and indicated that this islet was comprised of Type VI secretion system (T6SS) and contact-dependent growth inhibition (CDI) genes, the majority of which appeared to encode orphan immunity proteins that protected against the activities of effectors and toxins absent from the S. Typhi genome. We herein propose that this islet represents an immune system that protects S. Typhi against competing bacteria within the human gut.
Parasitic Infections in African Humans and Non-Human Primates
Authors: Medkour Hacène , Amona Inestin , Laidoudi Younes , Davoust Bernard , Bitam Idir , Levasseur Anthony , Akiana Jean , Diatta Georges , Pacheco Liliana , Gorsane Slim , Sokhna Cheikh , Hernandez-Aguilar Raquel Adriana , Barciela Amanda , Fenollar Florence , Raoult Didier , Mediannikov Oleg
Published: July 2020

Different protozoa and metazoa have been detected in great apes, monkeys and humans with possible interspecies exchanges. Some are either nonpathogenic or their detrimental effects on the host are not yet known. Others lead to serious diseases that can even be fatal. Their survey remains of great importance for public health and animal conservation. Fecal samples from gorillas (Gorilla gorilla) and humans living in same area in the Republic of Congo, chimpanzees (Pan troglodytes) from Senegal and one other from the Republic of Congo, Guinea baboons (Papio papio) from Senegal, hamadryas baboons (Papio hamadryas) from Djibouti and Barbary macaques (Macaca sylvanus) from Algeria, were collected. DNA was extracted and screened using specific qPCR assays for the presence of a large number of helminths and protozoa. Positive samples were then amplified in standard PCRs and sequenced when possible. Overall, infection rate was 36.5% in all non-human primates (NHPs) and 31.6% in humans. Great apes were more often infected (63.6%) than monkeys (7.3%). At least twelve parasite species, including ten nematodes and two protozoa were discovered in NHPs and five species, including four nematodes and a protozoan in humans. The prevalences of Giarida lamblia, Necator americanus, Enterobius vermicularis, Strongyloides stercoralis were similar between gorillas and human community co-habiting the same forest ecosystem in the Republic of Congo. In addition, human specific Mansonella perstans (5.1%) and other Mansonella spp. (5.1%) detected in these gorillas suggest a possible cross-species exchange. Low prevalence (2%) of Ascaris lumbricoides, Enterobius vermicularis, Strongyloides stercoralis were observed in chimpanzees, as well as a high prevalence of Abbreviata caucasica (57.1%), which should be considered carefully as this parasite can affect other NHPs, animals and humans. The Barbary macaques were less infected (7.2%) and Oesophagostomum muntiacum was the main parasite detected (5.8%). Finally, we report the presence of Pelodera sp. and an environmental Nematoda DNAs in chimpanzee feces, Nematoda sp. and Bodo sp. in gorillas, as well as DNA of uncharacterized Nematoda in apes and humans, but with a relatively lower prevalence in humans. Prevalence of extraintestinal parasites remains underestimated since feces are not the suitable sampling methods. Using non-invasive sampling (feces) we provide important information on helminths and protozoa that can infect African NHPs and human communities living around them. Public health and animal conservation authorities need to be aware of these infections, as parasites detected in African NHPs could affect both human and other animals’ health.
Molecular Characterization of Echinococcus granulosus sensu lato from Humans in Slovenia
Authors: Šoba Barbara , Gašperšič Špela , Keše Darja , Kotar Tadeja
Published: July 2020

The larval form of tapeworms of the Echinococcus granulosus sensu lato species cluster cause an important zoonotic infection, cystic echinococcosis (CE). Molecular characterization of the cluster’s isolates from different hosts greatly contributes to a better understanding of its transmission dynamics. To date, no genetic information is available on CE in Slovenia. In this work, we characterized isolates from human CE cases. Parasite samples from 18 patients were collected, together with the patients’ demographic and clinical data. Genomic DNA was analyzed by conventional PCR and sequencing at four mitochondrial loci (cytochrome c oxidase subunit 1, cox1; NADH dehydrogenase subunit 1, nad1; NADH dehydrogenase subunit 5, nad5; and small ribosomal RNA, rrnS). Thirteen isolates were successfully amplified and sequenced. Seven (58.8%) patients were infected with E. granulosus sensu stricto (s.s.) G1, five (38.5%) with E. canadensis G7 and one (7.7%) with E. granulosus s.s. G3. Echinococcus canadensis G7, the pig genotype, was identified exclusively in autochthonous Slovenes, while the patients originating from the Western Balkans were all infected with E. granulosus s.s. Our findings suggest that pigs are important intermediate hosts for human CE in Slovenia.
Biotic Factors Influence Microbiota of Nymph Ticks from Vegetation in Sydney, Australia
Authors: Chandra Shona , Šlapeta Jan
Published: July 2020

Ticks are haematophagous ectoparasites of medical and veterinary significance due to their excellent vector capacity. Modern sequencing techniques enabled the rapid sequencing of bacterial pathogens and symbionts. This study’s aims were two-fold; to determine the nymph diversity in Sydney, and to determine whether external biotic factors affect the microbiota. Tick DNA was isolated, and the molecular identity was determined for nymphs at the cox1 level. The tick DNA was subjected to high throughput DNA sequencing to determine the bacterial profile and the impact of biotic factors on the microbiota. Four nymph tick species were recovered from Sydney, NSW: Haemaphysalis bancrofti, Ixodes holocyclus, Ixodes trichosuri and Ixodes tasmani. Biotic factors, notably tick species and geography, were found to have a significance influence on the microbiota. The microbial analyses revealed that Sydney ticks display a core microbiota. The dominating endosymbionts among all tick species were Candidatus Midichloria sp. Ixholo1 and Candidatus Midichloria sp. Ixholo2. A novel Candidatus Midichloria sp. OTU_2090 was only found in I. holocyclus ticks (nymph: 96.3%, adult: 75.6%). Candidatus Neoehrlichia australis and Candidatus Neoehrlichia arcana was recovered from I. holocyclus and one I. trichosuri nymph ticks. Borrelia spp. was absent from all ticks. This study has shown that nymph and adult ticks carry different bacteria, and a tick bite in Sydney, Australia will result in different bacterial transfer depending on tick life stage, tick species and geography.
Seroprevalence of Hepatitis E Virus in Forestry Workers from Trentino-Alto Adige Region (Northern Italy)
Authors: Monini Marina , Ostanello Fabio , Dominicis Alessandra , Tagliapietra Valentina , Vaccari Gabriele , Rizzoli Annapaola , Trombetta Claudia M. , Montomoli Emanuele , Di Bartolo Ilaria
Published: July 2020

People with some occupational or recreational activities, such as hunters and veterinarians, may have increased risk to be infected by the hepatitis E virus (HEV). The aim of the present study was to establish whether forestry workers could be considered at a higher risk of HEV infection than a control group. One hundred and fifty sera from forestry workers and a control group of 85 sera were analysed by anti-HEV IgG antibodies detection using a commercial ELISA kit. The anti-HEV IgG seroprevalence was 14% for forestry workers and 9.4% for the control group. Comparing the risk of HEV infection in the two groups, there was no difference in the odds ratio. However, the seroprevalence in older subjects was higher in the forestry workers than in the control group. Two sera from forestry workers were also positive for anti-HEV IgM, and, in one of them, HEV-RNA was detected. Our findings showed an increase of seroprevalence with age, which is likely to reflect cumulative exposure to HEV over time. The occupation of forestry workers did not seem to be associated with a higher risk of HEV infection. The study provided new insights into the risk of acquiring HEV in occupational exposure workers with open-air activities.
Microbial Etiology and Prevention of Dental Caries: Exploiting Natural Products to Inhibit Cariogenic Biofilms
Authors: Chen Xiuqin , Daliri Eric Banan-Mwine , Kim Namhyeon , Kim Jong-Rae , Yoo Daesang , Oh Deog-Hwan
Published: July 2020

Dental caries is one of the most common microbe-mediated oral diseases in human beings. At present, the accepted etiology of caries is based on a four-factor theory that includes oral microorganisms, oral environment, host, and time. Excessive exposure to dietary carbohydrates leads to the accumulation of acid-producing and acid-resistant microorganisms in the mouth. Dental caries is driven by dysbiosis of the dental biofilm adherent to the enamel surface. Effective preventive methods include inhibiting the cariogenic microorganisms, treatment with an anti-biofilm agent, and sugar intake control. The goal is to reduce the total amount of biofilm or the levels of specific pathogens. Natural products could be recommended for preventing dental caries, since they may possess fewer side effects in comparison with synthetic antimicrobials. Herein, the mechanisms of oral microbial community development and functional specialization are discussed. We highlight the application of widely explored natural products in the last five years for their ability to inhibit cariogenic microorganisms.
Phylogenetic Analyses of Rotavirus A from Cattle in Uruguay Reveal the Circulation of Common and Uncommon Genotypes and Suggest Interspecies Transmission
Authors: Castells Matías , Caffarena Rubén Darío , Casaux María Laura , Schild Carlos , Miño Samuel , Castells Felipe , Castells Daniel , Victoria Matías , Riet-Correa Franklin , Giannitti Federico , Parreño Viviana , Colina Rodney
Published: July 2020

Uruguay is one of the main exporters of beef and dairy products, and cattle production is one of the main economic sectors in this country. Rotavirus A (RVA) is the main pathogen associated with neonatal calf diarrhea (NCD), a syndrome that leads to significant economic losses to the livestock industry. The aims of this study are to determine the frequency of RVA infections, and to analyze the genetic diversity of RVA strains in calves in Uruguay. A total of 833 samples from dairy and beef calves were analyzed through RT-qPCR and sequencing. RVA was detected in 57.0% of the samples. The frequency of detection was significantly higher in dairy (59.5%) than beef (28.4%) calves (p < 0.001), while it did not differ significantly among calves born in herds that were vaccinated (64.0%) or not vaccinated (66.7%) against NCD. The frequency of RVA detection and the viral load were significantly higher in samples from diarrheic (72.1%, 7.99 log10 genome copies/mL of feces) than non-diarrheic (59.9%, 7.35 log10 genome copies/mL of feces) calves (p < 0.005 and p = 0.007, respectively). The observed G-types (VP7) were G6 (77.6%), G10 (20.7%), and G24 (1.7%), while the P-types were P[5] (28.4%), P[11] (70.7%), and P[33] (0.9%). The G-type and P-type combinations were G6P[11] (40.4%), G6P[5] (38.6%), G10P[11] (19.3%), and the uncommon genotype G24P[33] (1.8%). VP6 and NSP1-5 genotyping were performed to better characterize some strains. The phylogenetic analyses suggested interspecies transmission, including transmission between animals and humans.
Investigation of Cytotoxicity and Cell Uptake of Cationic Beta-Cyclodextrins as Valid Tools in Nasal Delivery
Authors: Rassu Giovanna , Fancello Silvia , Roldo Marta , Malanga Milo , Szente Lajos , Migheli Rossana , Gavini Elisabetta , Giunchedi Paolo
Published: July 2020

Cyclodextrin polymers have high applicability in pharmaceutical formulations due to better biocompatibility, solubility enhancement, loading capacity and controlled drug release than their parent, cyclodextrins. The cytotoxicity and cell uptake of new cationic beta-cyclodextrin monomers and polymers were evaluated as suitable materials for nasal formulations and their protective effects on cells exposed to hydrogen peroxide were studied. PC12 and CACO-2 cells were selected as the neuronal- and epithelial-type cells, respectively, to mimic the structure of respiratory and olfactory epithelia of the nasal cavity. All cationic beta-cyclodextrin polymers tested showed dose- and time-dependent toxicity; nevertheless, at 5 µM concentration and 60 min of exposure, the quaternary-ammonium-beta-cyclodextrin soluble polymer could be recognized as nontoxic. Based on these results, a fluorescently labelled quaternary-ammonium-beta-cyclodextrin monomer and polymer were selected for uptake studies in CACO-2 cells. The monomeric and polymeric beta-cyclodextrins were internalized in the cytoplasm of CACO-2 cells; the cationic monomer showed higher permeability than the hydroxypropyl-beta-cyclodextrin, employed as comparison. Therefore, these cationic beta-cyclodextrins showed potential as excipients able to improve the nasal absorption of drugs. Furthermore, amino-beta-cyclodextrin and beta-cyclodextrin soluble polymers were able to reduce oxidative damage in PC12 and CACO-2 cells and thus could be studied as bioactive carriers or potential drugs for cell protection against oxidative stress.
Conjugation of Therapeutic PSD-95 Inhibitors to the Cell-Penetrating Peptide Tat Affects Blood–Brain Barrier Adherence, Uptake, and Permeation
Authors: Kristensen Mie , Kucharz Krzysztof , Felipe Alves Fernandes Eduardo , Strømgaard Kristian , Schallburg Nielsen Morten , Cederberg Helms Hans Christian , Bach Anders , Ulrikkaholm Tofte-Hansen Malte , Irene Aldana Garcia Blanca , Lauritzen Martin , Brodin Birger
Published: July 2020

Novel stroke therapies are needed. Inhibition of the interaction between the postsynaptic density-95 (PSD-95)/disc large/ZO-1 (PDZ) domains of PSD-95 and the N-methyl-D-aspartate (NMDA) receptor has been suggested as a strategy for relieving neuronal damage. The peptides NR2B9c and N-dimer have been designed to hinder this interaction; they are conjugated to the cell-penetrating peptide Tat to facilitate blood–brain barrier (BBB) permeation and neuronal uptake. Tat-N-dimer exhibits 1000-fold better target affinity than Tat-NR2B9c, but the same magnitude of improvement is not observed in terms of therapeutic effect. Differences in BBB permeation by Tat-NR2B9c and Tat-N-dimer may explain this difference, but studies providing a direct comparison of Tat-NR2B9c and Tat-N-dimer are lacking. The aim of the present study was therefore to compare the BBB uptake and permeation of Tat-NR2B9c and Tat-N-dimer. The peptides were conjugated to the fluorophore TAMRA and their chemical stability assessed. Endothelial membrane association and cell uptake, and transendothelial permeation were estimated using co-cultures of primary bovine brain capillary endothelial cells and rat astrocytes. In vivo BBB permeation was demonstrated in mice using two-photon microscopy imaging. Tissue distribution was evaluated in mice demonstrating brain accumulation of TAMRA-Tat (0.4% ID/g), TAMRA-Tat-NR2B9c (0.3% ID/g), and TAMRA-Tat-N-dimer (0.25% ID/g). In conclusion, we demonstrate that attachment of NR2B9c or N-dimer to Tat affects both the chemical stability and the ability of the resulting construct to interact with and permeate the BBB.
Harnessing the Complete Repertoire of Conventional Dendritic Cell Functions for Cancer Immunotherapy
Authors: Amon Lukas , Hatscher Lukas , Heger Lukas , Dudziak Diana , Lehmann Christian H. K.
Published: July 2020

The onset of checkpoint inhibition revolutionized the treatment of cancer. However, studies from the last decade suggested that the sole enhancement of T cell functionality might not suffice to fight malignancies in all individuals. Dendritic cells (DCs) are not only part of the innate immune system, but also generals of adaptive immunity and they orchestrate the de novo induction of tolerogenic and immunogenic T cell responses. Thus, combinatorial approaches addressing DCs and T cells in parallel represent an attractive strategy to achieve higher response rates across patients. However, this requires profound knowledge about the dynamic interplay of DCs, T cells, other immune and tumor cells. Here, we summarize the DC subsets present in mice and men and highlight conserved and divergent characteristics between different subsets and species. Thereby, we supply a resource of the molecular players involved in key functional features of DCs ranging from their sentinel function, the translation of the sensed environment at the DC:T cell interface to the resulting specialized T cell effector modules, as well as the influence of the tumor microenvironment on the DC function. As of today, mostly monocyte derived dendritic cells (moDCs) are used in autologous cell therapies after tumor antigen loading. While showing encouraging results in a fraction of patients, the overall clinical response rate is still not optimal. By disentangling the general aspects of DC biology, we provide rationales for the design of next generation DC vaccines enabling to exploit and manipulate the described pathways for the purpose of cancer immunotherapy in vivo. Finally, we discuss how DC-based vaccines might synergize with checkpoint inhibition in the treatment of malignant diseases.
White Christian Privilege in 2020: A Review Essay
Authors: Clark Emily Suzanne
Published: July 2020

This review essay examines Khyati Y. Joshi’s new book, White Christian Privilege: The Illusion of Religious Equality in America. With attention to relevant historiography on religion and race, as well as reflection on contemporary white Christian nationalism, this review essay argues that Joshi’s book is an important contribution to the field and will be particularly instructive in classrooms and other educational settings.
Results from Verification of Reference Irradiance and Radiance Sources Laboratory Calibration Experiment Campaign
Authors: Białek Agnieszka , Goodman Teresa , Woolliams Emma , Brachmann Johannes F. S. , Schwarzmaier Thomas , Kuusk Joel , Ansko Ilmar , Vabson Viktor , Lau Ian C. , MacLellan Christopher , Marty Sabine , Ondrusek Michael , Servantes William , Taylor Sarah , Van Dommelen Ronnie , Barnard Andrew , Vellucci Vincenzo , Banks Andrew C. , Fox Nigel , Vendt Riho , Donlon Craig , Casal Tânia
Published: July 2020

We present the results from Verification of Reference Irradiance and Radiance Sources Laboratory Calibration Experiment Campaign. Ten international laboratories took part in the measurements. The spectral irradiance comparison included the measurements of the 1000 W tungsten halogen filament lamps in the spectral range of 350 nm–900 nm in the pilot laboratory. The radiance comparison took a form of round robin where each participant in turn received two transfer radiometers and did the radiance calibration in their own laboratory. The transfer radiometers have seven spectral bands covering the wavelength range from 400 nm–700 nm. The irradiance comparison results showed an agreement between all lamps within ±1.5%. The radiance comparison results presented higher than expected discrepancies at the level of ±4%. Additional investigation to determine the causes for these discrepancies identified them as a combination of the size-of-source effect and instrument effective field of view that affected some of the results.
Classification of Atlantic Coastal Sand Dune Vegetation Using In Situ, UAV, and Airborne Hyperspectral Data
Authors: Laporte-Fauret Quentin , Lubac Bertrand , Castelle Bruno , Michalet Richard , Marieu Vincent , Bombrun Lionel , Launeau Patrick , Giraud Manuel , Normandin Cassandra , Rosebery David
Published: July 2020

Mapping coastal dune vegetation is critical to understand dune mobility and resilience in the context of climate change, sea level rise, and increased anthropogenic pressure. However, the identification of plant species from remotely sensed data is tedious and limited to broad vegetation communities, while such environments are dominated by fragmented and small-scale landscape patterns. In June 2019, a comprehensive multi-scale survey including unmanned aerial vehicle (UAV), hyperspectral ground, and airborne data was conducted along approximately 20 km of a coastal dune system in southwest France. The objective was to generate an accurate mapping of the main sediment and plant species ground cover types in order to characterize the spatial distribution of coastal dune stability patterns. Field and UAV data were used to assess the quality of airborne data and generate a robust end-member spectral library. Next, a two-step classification approach, based on the normalized difference vegetation index and Random Forest classifier, was developed. Results show high performances with an overall accuracy of 100% and 92.5% for sand and vegetation ground cover types, respectively. Finally, a coastal dune stability index was computed across the entire study site. Different stability patterns were clearly identified along the coast, highlighting for the first time the high potential of this methodology to support coastal dune management.
Automated SAR Image Thresholds for Water Mask Production in Alberta’s Boreal Region
Authors: Mahoney Craig , Merchant Michael , Boychuk Lyle , Hopkinson Chris , Brisco Brian
Published: July 2020

Mapping and monitoring surface water features is important for sustainably managing this critical natural resource that is in decline due to numerous natural and anthropogenic pressures. Satellite Synthetic Aperture Radar is a popular and inexpensive solution for such exercises over large scales through the application of thresholds to distinguish water from non-water. Despite improvements to threshold methods, threshold selection is traditionally manual, which introduces subjectivity and inconsistency over large scales. This study presents a novel method for objectively determining and applying a threshold to determine water masks from Synthetic Aperture Radar (SAR) imagery on a scene-by-scene basis. The method was applied to Radarsat-2 and simulated Radarsat Constellation Mission scenes, and validated against two independent validation sources with high accuracy (Kappa ranging from 0.85 to 0.93). Expectedly, greatest misclassification occurs near shorelines, which are often ecologically important zones. Comparisons between Radarsat-2 and Radarsat Constellation Mission thresholds and outputs suggest that the latter is a capable successor for surface water applications. This work represents a foundational step toward objectivity and consistency in large-scale water mapping and monitoring.
Spaceborne L-Band Synthetic Aperture Radar Data for Geoscientific Analyses in Coastal Land Applications: A Review
Authors: Ottinger Marco , Kuenzer Claudia
Published: July 2020

The coastal zone offers among the world’s most productive and valuable ecosystems and is experiencing increasing pressure from anthropogenic impacts: human settlements, agriculture, aquaculture, trade, industrial activities, oil and gas exploitation and tourism. Earth observation has great capability to deliver valuable data at the local, regional and global scales and can support the assessment and monitoring of land- and water-related applications in coastal zones. Compared to optical satellites, cloud-cover does not limit the timeliness of data acquisition with spaceborne Synthetic Aperture Radar (SAR) sensors, which have all-weather, day and night capabilities. Hence, active radar systems demonstrate great potential for continuous mapping and monitoring of coastal regions, particularly in cloud-prone tropical and sub-tropical climates. The canopy penetration capability with long radar wavelength enables L-band SAR data to be used for coastal terrestrial environments and has been widely applied and investigated for the following geoscientific topics: mapping and monitoring of flooded vegetation and inundated areas; the retrieval of aboveground biomass; and the estimation of soil moisture. Human activities, global population growth, urban sprawl and climate change-induced impacts are leading to increased pressure on coastal ecosystems causing land degradation, deforestation and land use change. This review presents a comprehensive overview of existing research articles that apply spaceborne L-band SAR data for geoscientific analyses that are relevant for coastal land applications.
Robust Single-Image Haze Removal Using Optimal Transmission Map and Adaptive Atmospheric Light
Authors: Ngo Dat , Lee Seungmin , Kang Bongsoon
Published: July 2020

Haze removal is an ill-posed problem that has attracted much scientific interest due to its various practical applications. Existing methods are usually founded upon various priors; consequently, they demonstrate poor performance in circumstances in which the priors do not hold. By examining hazy and haze-free images, we determined that haze density is highly correlated with image features such as contrast energy, entropy, and sharpness. Then, we proposed an iterative algorithm to accurately estimate the extinction coefficient of the transmission medium via direct optimization of the objective function taking into account all of the features. Furthermore, to address the heterogeneity of the lightness, we devised adaptive atmospheric light to replace the homogeneous light generally used in haze removal. A comparative evaluation against other state-of-the-art approaches demonstrated the superiority of the proposed method. The source code and data sets used in this paper are made publicly available to facilitate further research.
Predicting and Mapping of Soil Organic Carbon Using Machine Learning Algorithms in Northern Iran
Authors: Emadi Mostafa , Taghizadeh-Mehrjardi Ruhollah , Cherati Ali , Danesh Majid , Mosavi Amir , Scholten Thomas
Published: July 2020

Estimation of the soil organic carbon (SOC) content is of utmost importance in understanding the chemical, physical, and biological functions of the soil. This study proposes machine learning algorithms of support vector machines (SVM), artificial neural networks (ANN), regression tree, random forest (RF), extreme gradient boosting (XGBoost), and conventional deep neural network (DNN) for advancing prediction models of SOC. Models are trained with 1879 composite surface soil samples, and 105 auxiliary data as predictors. The genetic algorithm is used as a feature selection approach to identify effective variables. The results indicate that precipitation is the most important predictor driving 14.9% of SOC spatial variability followed by the normalized difference vegetation index (12.5%), day temperature index of moderate resolution imaging spectroradiometer (10.6%), multiresolution valley bottom flatness (8.7%) and land use (8.2%), respectively. Based on 10-fold cross-validation, the DNN model reported as a superior algorithm with the lowest prediction error and uncertainty. In terms of accuracy, DNN yielded a mean absolute error of 0.59%, a root mean squared error of 0.75%, a coefficient of determination of 0.65, and Lin’s concordance correlation coefficient of 0.83. The SOC content was the highest in udic soil moisture regime class with mean values of 3.71%, followed by the aquic (2.45%) and xeric (2.10%) classes, respectively. Soils in dense forestlands had the highest SOC contents, whereas soils of younger geological age and alluvial fans had lower SOC. The proposed DNN (hidden layers = 7, and size = 50) is a promising algorithm for handling large numbers of auxiliary data at a province-scale, and due to its flexible structure and the ability to extract more information from the auxiliary data surrounding the sampled observations, it had high accuracy for the prediction of the SOC base-line map and minimal uncertainty.
Combining Satellite Multispectral Imagery and Topographic Data for the Detection and Mapping of Fluvial Avulsion Processes in Lowland Areas
Authors: Iacobucci Giulia , Troiani Francesco , Milli Salvatore , Mazzanti Paolo , Piacentini Daniela , Zocchi Marta , Nadali Davide
Published: July 2020

Fluvial avulsion is an important process in the dynamics of the riverscapes and plays a key role in the drainage network evolution in lowland areas, also influencing past and present social processes and economic activities. Crevasse splays represent significant geomorphological features for understanding the fluvial morphodynamics in lowland areas dominated by avulsion processes. Within wide floodplains characterized by very low elevation ranges, the detection and accurate mapping of crevasse splay morphology and features, such as crevasse channels, levees, and deposit, can be very challenging considering floodplain extension, anthropic impact on the natural channels network, logistic difficulties, and in some cases, climate conditions that prevent field work. This research aims at improving the detection and mapping of crevasse splays in lowland areas through the combination of different remote sensing techniques based on optical multispectral imagery and topographic data derived from satellite earth observation missions. The Lower Mesopotamia Plain (LMP) offers a unique opportunity to study the avulsion processes because it presents numerous examples of crevasse splays, characterized by different sizes and states of activity. Furthermore, in this area, a strong correlation exists between the formation and development of crevasse splays and the expansion of agriculture and early societies since the Early Holocene. Different supervised classification (SC) methods of Landsat 8 satellite images have been tested together with topographic analysis of the microrelief, carried out based on two different 1-arcsec DEMs (AW3D30 and GDEM2). The results of this study demonstrate that the combination of multispectral imagery analysis and topographic analysis of the microrelief is useful for discerning different crevasse elements, distinguishing between active and relict landforms. The methodological approach proved helpful for improving the mapping of erosional and depositional landforms generated by the avulsion process and, in the study area, provided the best results for the active landforms.
Learning from the 2018 Western Japan Heavy Rains to Detect Floods during the 2019 Hagibis Typhoon
Authors: Moya Luis , Mas Erick , Koshimura Shunichi
Published: July 2020

Applications of machine learning on remote sensing data appear to be endless. Its use in damage identification for early response in the aftermath of a large-scale disaster has a specific issue. The collection of training data right after a disaster is costly, time-consuming, and many times impossible. This study analyzes a possible solution to the referred issue: the collection of training data from past disaster events to calibrate a discriminant function. Then the identification of affected areas in a current disaster can be performed in near real-time. The performance of a supervised machine learning classifier to learn from training data collected from the 2018 heavy rainfall at Okayama Prefecture, Japan, and to identify floods due to the typhoon Hagibis on 12 October 2019 at eastern Japan is reported in this paper. The results show a moderate agreement with flood maps provided by local governments and public institutions, and support the assumption that previous disaster information can be used to identify a current disaster in near-real time.
Macadamia Orchard Planting Year and Area Estimation at a National Scale
Authors: Brinkhoff James , Robson Andrew J.
Published: July 2020

Accurate estimates of tree crop orchard age and historical crop area are important to develop yield prediction algorithms, and facilitate improving accuracy in ongoing crop forecasts. This is particularly relevant for the increasingly productive macadamia industry in Australia, where knowledge of tree age, as well as total planted area, are important predictors of productivity, and the area devoted to macadamia orchards is rapidly increasing. We developed a technique to aggregate more than 30 years of historical imagery, generate summary tables from the data, and search multiple combinations of parameters to find the most accurate planting year prediction algorithm. This made use of known planting dates of more than 90 macadamia blocks spread across multiple growing regions. The selected algorithm achieved a planting year mean absolute error of 1.7 years. The algorithm was then applied to all macadamia features in east Australia, as defined in an recent Australian tree crops map, to determine the area planted per year and the total cumulative area of macadamia orchards in Australia. The area estimates were refined by improving the resolution of the mapped macadamia features, by removing non-productive areas based on an optimal vegetation index threshold.
Global Wildfire Outlook Forecast with Neural Networks
Authors: Song Yongjia , Wang Yuhang
Published: July 2020

Wildfire occurrence and spread are affected by atmospheric and land-cover conditions, and therefore meteorological and land-cover parameters can be used in area burned prediction. We apply three forecast methods, a generalized linear model, regression trees, and neural networks (Levenberg–Marquardt backpropagation) to produce monthly wildfire predictions 1 year in advance. The models are trained using the Global Fire Emissions Database version 4 with small fires (GFEDv4s). Continuous 1-year monthly fire predictions from 2011 to 2015 are evaluated with GFEDs data for 10 major fire regions around the globe. The predictions by the neural network method are superior. The 1-year moving predictions have good prediction skills over these regions, especially over the tropics and the southern hemisphere. The temporal refined index of agreement (IOA) between predictions and GFEDv4s regional burned areas are 0.82, 0.82, 0.8, 0.75, and 0.56 for northern and southern Africa, South America, equatorial Asia and Australia, respectively. The spatial refined IOA for 5-year averaged monthly burned area range from 0.69 in low-fire months to 0.86 in high-fire months over South America, 0.3–0.93 over northern Africa, 0.69–0.93 over southern Africa, 0.47–0.85 over equatorial Asia, and 0.53–0.8 over Australia. For fire regions in the northern temperate and boreal regions, the temporal and spatial IOA between predictions and GFEDv4s data in fire seasons are 0.7–0.79 and 0.24–0.83, respectively. The predictions in high-fire months are better than low-fire months. This study illustrates the feasibility of global fire activity outlook forecasts using a neural network model and the method can be applied to quickly assess the potential effects of climate change on wildfires.
Physical Parameterization of Hyperspectral Reflectance in the Oxygen A-Band for Single-Layer Water Clouds
Authors: Yang Jie , Li Siwei , Mao Feiyue , Min Qilong , Gong Wei , Zhang Lei , Liu Sheng
Published: July 2020

Previous studies have shown that it is feasible to retrieve multiple cloud properties simultaneously based on the space-borne hyperspectral observation in the oxygen A-band, such as cloud optical depth, cloud-top height, and cloud geometrical thickness. However, hyperspectral remote sensing is time-consuming if based on the precise radiative transfer solution that counts multiple scatterings of light. To speed up the radiation transfer solution in cloud scenarios for nadir space-borne observations, we developed a physical parameterization of hyperspectral reflectance in the oxygen A-band for single-layer water clouds. The parameterization takes into account the influences of cloud droplet forward-scattering and nonlinear oxygen absorption on hyperspectral reflectance, which are improvements over the previous studies. The performance of the parameterization is estimated through comparison with DISORT (Discrete Ordinates Radiative Transfer Program Multi-Layered Plane-Parallel Medium) on the cases with solar zenith angle θ, the cloud optical depth τc, and the single-scattering albedo ω in the range of 0 ≤ θ ≤ 75, 5 ≤ τc ≤ 50, 0.5 ≤ ω ≤ 1. The relative error of the cloud reflectance is within 5% for most cases, even for clouds with optical depths around five or at strong absorption wavelengths. We integrate the parameterization with a slit function and a simplified atmosphere to evaluate its performance in simulating the observed cloud reflection at the top of the atmosphere by OCO-2 (Orbiting Carbon Observatory-2). To better visualize the possible errors from the new parameterization, gas molecular scattering, aerosol scattering, and reflection from the underlying surface are ignored. The relative error of the out-of-band radiance is less than 4% and the relative error of the intra-band radiance ratio is less than 4%. The radiance ratio is the ratio of simulated observations with and without in-cloud absorption and is used to assess the accuracy of the parameterization in quantifying the in-cloud absorption. The parameterization is a preparation for rapid hyperspectral remote sensing in the oxygen A-band. It would help to improve retrieval efficiency and provide cloud geometric thickness products.
The Problem of Mismatch between the CORINE Land Cover Data Classification and the Development of Settlement in Poland
Authors: Śleszyński Przemysław , Gibas Piotr , Sudra Paweł
Published: July 2020

The main goal of the article is the evaluation of usefulness of CORINE Land Cover (CLC) data—acquired predominantly by visual interpretation of Landsat satellite imagery—for monitoring of changes in settlement development and land use. This has been done by comparison of occurrence of buildings (and address points) in Poland with delimitations of land use belonging to particular classes in the CLC 2018 dataset. Large discrepancies have been identified, which reach on average approx. 34% of addresses and 35% of buildings located outside class 1 (artificial surfaces), mainly on terrains of class 2 (agricultural areas). Among single-family buildings it was 37% and among new addresses (forecasted or “under construction” buildings)—as much as 50%. This puts a question mark over the possibility of using CLC data with a resolution of 25 ha for monitoring of spatial planning and development in Poland for purposes of the diagnosis and assessment of the scale of dispersion of built-up areas. It is worth carrying out similar analyses in other countries, known for the deconcentration processes and a relatively large share of dispersed settlement, e.g., other CEE countries, Spain, Portugal, Italy.
VIS-NIR, Red-Edge and NIR-Shoulder Based Normalized Vegetation Indices Response to Co-Varying Leaf and Canopy Structural Traits in Heterogeneous Grasslands
Authors: Imran Hafiz Ali , Gianelle Damiano , Rocchini Duccio , Dalponte Michele , Martín M. Pilar , Sakowska Karolina , Wohlfahrt Georg , Vescovo Loris
Published: July 2020

Red-edge (RE) spectral vegetation indices (SVIs)—combining bands on the sharp change region between near infrared (NIR) and visible (VIS) bands—alongside with SVIs solely based on NIR-shoulder bands (wavelengths 750–900 nm) have been shown to perform well in estimating leaf area index (LAI) from proximal and remote sensors. In this work, we used RE and NIR-shoulder SVIs to assess the full potential of bands provided by Sentinel-2 (S-2) and Sentinel-3 (S-3) sensors at both temporal and spatial scales for grassland LAI estimations. Ground temporal and spatial observations of hyperspectral reflectance and LAI were carried out at two grassland sites (Monte Bondone, Italy, and Neustift, Austria). A strong correlation (R2 > 0.8) was observed between grassland LAI and both RE and NIR-shoulder SVIs on a temporal basis, but not on a spatial basis. Using the PROSAIL Radiative Transfer Model (RTM), we demonstrated that grassland structural heterogeneity strongly affects the ability to retrieve LAI, with high uncertainties due to structural and biochemical PTs co-variation. The RENDVI783.740 SVI was the least affected by traits co-variation, and more studies are needed to confirm its potential for heterogeneous grasslands LAI monitoring using S-2, S-3, or Gaofen-5 (GF-5) and PRISMA bands.
Inspecting the Food–Water Nexus in the Ogallala Aquifer Region Using Satellite Remote Sensing Time Series
Authors: Zhou Yuting , Gholizadeh Hamed , LaVanchy G. Thomas , Hasan Emad
Published: July 2020

Agricultural production in the Great Plains provides a significant amount of food for the United States while contributing greatly to farm income in the region. However, recurrent droughts and expansion of crop production are increasing irrigation demand, leading to extensive pumping and attendant depletion of the Ogallala aquifer. In order to optimize water use, increase the sustainability of agricultural production, and identify best management practices, identification of food–water conflict hotspots in the Ogallala Aquifer Region (OAR) is necessary. We used satellite remote sensing time series of agricultural production (net primary production, NPP) and total water storage (TWS) to identify hotspots of food–water conflicts within the OAR and possible reasons behind these conflicts. Mean annual NPP (2001–2018) maps clearly showed intrusion of high NPP, aided by irrigation, into regions of historically low NPP (due to precipitation and temperature). Intrusion is particularly acute in the northern portion of OAR, where mean annual TWS (2002–2020) is high. The Oklahoma panhandle and Texas showed large decreasing TWS trends, which indicate the negative effects of current water demand for crop production on TWS. Nebraska demonstrated an increasing TWS trend even with a significant increase of NPP. A regional analysis of NPP and TWS can convey important information on current and potential conflicts in the food–water nexus and facilitate sustainable solutions. Methods developed in this study are relevant to other water-constrained agricultural production regions.
Large-Scale Detection and Categorization of Oil Spills from SAR Images with Deep Learning
Authors: Bianchi Filippo Maria , Espeseth Martine M. , Borch Njål
Published: July 2020

We propose a deep-learning framework to detect and categorize oil spills in synthetic aperture radar (SAR) images at a large scale. Through a carefully designed neural network model for image segmentation trained on an extensive dataset, we obtain state-of-the-art performance in oil spill detection, achieving results that are comparable to results produced by human operators. We also introduce a classification task, which is novel in the context of oil spill detection in SAR. Specifically, after being detected, each oil spill is also classified according to different categories of its shape and texture characteristics. The classification results provide valuable insights for improving the design of services for oil spill monitoring by world-leading providers. Finally, we present our operational pipeline and a visualization tool for large-scale data, which allows detection and analysis of the historical occurrence of oil spills worldwide.
A Novel Feature Extension Method for the Forest Disaster Monitoring Using Multispectral Data
Authors: Quan Yinghui , Zhong Xian , Feng Wei , Dauphin Gabriel , Gao Lianru , Xing Mengdao
Published: July 2020

Remote sensing images classification is the key technology for monitoring forest changes. Texture features have been demonstrated to have better effectiveness than spectral features in the improvement of the classification accuracy. The accuracy of extracting texture information by window-based method depends on the choice of the window size. Moreover, the size should ideally match the spatial scale of the object or class under consideration. However, most of the existing texture feature extraction methods are all based on a single window and do not adequately consider the scale of different objects. Our first proposition is to use a composite window for extracting texture features, which is a small window surrounded by a larger window. Our second proposition is to reinforce the performance of the trained ensemble classifier by training it using only the most important features. Considering the advantages of random forest classifier, such as fast training speed and few parameters, these features feed this classifier. Measures of feature importance are estimated along with the growth of the base classifiers, here decision trees. We aim to classify each pixel of the forest images disturbed by hurricanes and fires in three classes, damaged, not damaged, or unknown, as this could be used to compute time-dependent aggregates. In this study, two research areas—Nezer Forest in France and Blue Mountain Forest in Australia—are utilized to validating the effectiveness of the proposed method. Numerical simulations show increased performance and improved monitoring ability of forest disturbance when using these two propositions. When compared with the reference methods, the best increase of the overall accuracy obtained by the proposed algorithm is 4.77% and 2.96% on the Nezer forest data and Blue Mountain forest data, respectively.