bluuebunny/crossref_metadata_2025 huggingface dataset:
Identification of ε-Poly-L-lysine as an Antimicrobial Product from an Epichloë Endophyte and Isolation of Fungal ε-PL Synthetase Gene
Authors: Purev Enkhee , Kondo Tatsuhiko , Takemoto Daigo , Niones Jennifer T. , Ojika Makoto
Published: February 2020

The endophytic fungus Epichloë festucae is known to produce bioactive metabolites, which consequently protect the host plants from biotic and abiotic stresses. We previously found that the overexpression of vibA (a gene for transcription factor) in E. festucae strain E437 resulted in the secretion of an unknown fungicide. In the present study, the active substance was purified and chemically identified as ε-poly-L-lysine (ε-PL), which consisted of 28–34 lysine units. The productivity was 3.7-fold compared with that of the wild type strain E437. The isolated ε-PL showed inhibitory activity against the spore germination of the plant pathogens Drechslera erythrospila, Botrytis cinerea, and Phytophthora infestans at 1–10 μg/mL. We also isolated the fungal gene “epls” encoding ε-PL synthetase Epls. Overexpression of epls in the wild type strain E437 resulted in the enhanced production of ε-PL by 6.7-fold. Interestingly, overexpression of epls in the different strain E. festucae Fl1 resulted in the production of shorter ε-PL with 8–20 lysine, which exhibited a comparable antifungal activity to the longer one. The results demonstrate the first example of ε-PL synthetase gene from the eukaryotic genomes and suggest the potential of enhanced expression of vibA or/and epls genes in the Epichloë endophyte for constructing pest-tolerant plants.
Glycosaminoglycans as Tools to Decipher the Platelet Tumor Cell Interaction: A Focus on P-Selectin
Authors: Schwarz Svenja , Gockel Lukas Maria , Naggi Annamaria , Barash Uri , Gobec Martina , Bendas Gerd , Schlesinger Martin
Published: February 2020

Tumor cell–platelet interactions are regarded as an initial crucial step in hematogenous metastasis. Platelets protect tumor cells from immune surveillance in the blood, mediate vascular arrest, facilitate tumor extravasation, growth, and finally angiogenesis in the metastatic foci. Tumor cells aggregate platelets in the bloodstream by activation of the plasmatic coagulation cascade and by direct contact formation. Antimetastatic activities of unfractionated or low molecular weight heparin (UFH/LMWH) can undoubtedly be related to attenuated platelet activation, but molecular mechanisms and contribution of contact formation vs. coagulation remain to be elucidated. Using a set of non-anticoagulant heparin derivatives varying in size or degree of sulfation as compared with UFH, we provide insight into the relevance of contact formation for platelet activation. Light transmission aggregometry and ATP release assays confirmed that only those heparin derivatives with P-selectin blocking capacities were able to attenuate breast cancer cell-induced platelet activation, while pentasaccharide fondaparinux was without effects. Furthermore, a role of P-selectin in platelet activation and signaling could be confirmed by proteome profiler arrays detecting platelet kinases. In this study, we demonstrate that heparin blocks tumor cell-induced coagulation. Moreover, we identify platelet P-selectin, which obviously acts as molecular switch and controls aggregation and secretion of procoagulant platelets.
Solvent-Free Iron(III) Chloride-Catalyzed Direct Amidation of Esters
Authors: Mkhonazi Blessing D. , Shandu Malibongwe , Tshinavhe Ronewa , Simelane Sandile B. , Moshapo Paseka T.
Published: February 2020

Amide functional groups are prominent in a broad range of organic compounds with diverse beneficial applications. In this work, we report the synthesis of these functional groups via an iron(iii) chloride-catalyzed direct amidation of esters. The reactions are conducted under solvent-free conditions and found to be compatible with a range of amine and ester substrates generating the desired amides in short reaction times and good to excellent yields at a catalyst loading of 15 mol%.
Acid Properties of Hierarchical Zeolites Y
Authors: Gackowski Mariusz , Datka Jerzy
Published: February 2020

The article reviews different strategies towards obtaining mesoporous zeolites Y: desilication; surfactant templating and assembly of zeolite crystals. The impact of those methods on physicochemical properties is covered, with a special focus on the acidity of the samples measured with infrared (IR) spectroscopy. The methods of characterization of acidity are presented. Quaternary ammonium cations used for desilication lead to obtaining crystalline; mesoporous and highly acidic zeolites. Si-OH-Al groups of extremely high acidity can be produced by calcination in a humid atmosphere. When the conditions are optimized, post-synthetic surfactant templating allows crystalline mesoporous zeolite to be obtained with no loss of material. All mesoporous zeolites Y proved to be active catalysts in liquid phase isomerization, catalytic cracking, and other reactions.
Chemometrics-Assisted Identification of Anti-Inflammatory Compounds from the Green Alga Klebsormidium flaccidum var. zivo
Authors: Qiu Shi , Khan Shabana I. , Wang Mei , Zhao Jianping , Ren Siyu , Khan Ikhlas A. , Steffek Amy , Pfund William P. , Li Xing-Cong
Published: February 2020

The green alga Klebsormidium flaccidum var. zivo is a rich source of proteins, polyphenols, and bioactive small-molecule compounds. An approach involving chromatographic fractionation, anti-inflammatory activity testing, ultrahigh performance liquid chromatography-mass spectrometry profiling, chemometric analysis, and subsequent MS-oriented isolation was employed to rapidly identify its small-molecule anti-inflammatory compounds including hydroxylated fatty acids, chlorophyll-derived pheophorbides, carotenoids, and glycoglycerolipids. Pheophorbide a, which decreased intracellular nitric oxide production by inhibiting inducible nitric oxide synthase, was the most potent compound identified with an IC50 value of 0.24 µM in lipopolysaccharides-induced macrophages. It also inhibited nuclear factor kappaB activation with an IC50 value of 32.1 µM in phorbol 12-myristate 13-acetate-induced chondrocytes. Compared to conventional bioassay-guided fractionation, this approach is more efficient for rapid identification of multiple chemical classes of bioactive compounds from a complex natural product mixture.
Protective Effect of Diphlorethohydroxycarmalol Isolated from Ishige okamurae Against Particulate Matter-Induced Skin Damage by Regulation of NF-κB, AP-1, and MAPKs Signaling Pathways In Vitro in Human Dermal Fibroblasts
Authors: Wang Lei , Kim Hyun Soo , Je Jun-Geon , Oh Jae Young , Kim Young-Sang , Cha Seon-Heui , Jeon You-Jin
Published: February 2020

Particulate matters (PM), the main contributor to air pollution, have become a serious issue that threatens human’s health. Skin is the largest organ in humans, as well as the primary organ exposed to PM. Overexposure of PM induces skin damage. Diphlorethohydroxycarmalol (DPHC), an algal polyphenol with the potential of skin protection, has been isolated from the edible brown seaweed Ishige okamurae. The purpose of the present study is to investigate the protective effect of DPHC against PM (ERM-CZ100)-induced skin damage in human dermal fibroblasts (HDF) cells. The results indicated that DPHC significantly and dose-dependently reduced intracellular reactive oxygen species generation in HDF cells. In addition, DPHC significantly induced collagen synthesis and inhibited collagenase activity in ERM-CZ100-stimulated HDF cells. Further study demonstrated that DPHC remarkably reduced the expression of human matrix metalloproteinases through regulation of nuclear factor kappa B, activator protein 1, and mitogen-activated protein kinases signaling pathways in ERM-CZ100-stimulated HDF cells. This study suggested that DPHC is a potential candidate to protect skins against PM-induced damage, and it could be used as an ingredient in pharmaceutical and cosmeceutical industries.
Laser Fragmentation Synthesis of Colloidal Bismuth Ferrite Particles
Authors: Siebeneicher Simon , Waag Friedrich , Escobar Castillo Marianela , Shvartsman Vladimir V. , Lupascu Doru C. , Gökce Bilal
Published: February 2020

Laser fragmentation of colloidal submicron-sized bismuth ferrite particles was performed by irradiating a liquid jet to synthesize bismuth ferrite nanoparticles. This treatment achieved a size reduction from 450 nm to below 10 nm. A circular and an elliptical fluid jet were compared to control the energy distribution within the fluid jet and thereby the product size distribution and educt decomposition. The resulting colloids were analysed via UV-VIS, XRD and TEM. All methods were used to gain information on size distribution, material morphology and composition. It was found that using an elliptical liquid jet during the laser fragmentation leads to a slightly smaller and narrower size distribution of the resulting product compared to the circular jet.
Surface/Interface Engineering for Constructing Advanced Nanostructured Photodetectors with Improved Performance: A Brief Review
Authors: Ding Meng , Guo Zhen , Chen Xuehang , Ma Xiaoran , Zhou Lianqun
Published: February 2020

Semiconductor-based photodetectors (PDs) convert light signals into electrical signals via a photon–matter interaction process, which involves surface/interface carrier generation, separation, and transportation of the photo-induced charge media in the active media, as well as the extraction of these charge carriers to external circuits of the constructed nanostructured photodetector devices. Because of the specific electronic and optoelectronic properties in the low-dimensional devices built with nanomaterial, surface/interface engineering is broadly studied with widespread research on constructing advanced devices with excellent performance. However, there still exist some challenges for the researchers to explore corresponding mechanisms in depth, and the detection sensitivity, response speed, spectral selectivity, signal-to-noise ratio, and stability are much more important factors to judge the performance of PDs. Hence, researchers have proposed several strategies, including modification of light absorption, design of novel PD heterostructures, construction of specific geometries, and adoption of specific electrode configurations to modulate the charge-carrier behaviors and improve the photoelectric performance of related PDs. Here, in this brief review, we would like to introduce and summarize the latest research on enhancing the photoelectric performance of PDs based on the designed structures by considering their surface/interface engineering and how to obtain advanced nanostructured photo-detectors with improved performance, which could be applied to design and fabricate novel low-dimensional PDs with ideal properties in the near future.
Increasing the Price of Alcohol as an Obesity Prevention Measure: The Potential Cost-Effectiveness of Introducing a Uniform Volumetric Tax and a Minimum Floor Price on Alcohol in Australia
Authors: Robinson Ella , Nguyen Phuong , Jiang Heng , Livingston Michael , Ananthapavan Jaithri , Lal Anita , Sacks Gary
Published: February 2020

The objective of this study was to estimate, from an obesity prevention perspective, the cost-effectiveness of two potential policies that increase the price of alcohol in Australia: a volumetric tax applied to all alcohol (Intervention 1) and a minimum unit floor price (Intervention 2). Estimated changes in alcoholic drink consumption and corresponding changes in energy intake were calculated using the 2011–12 Australian Health Survey data, published price elasticities, and nutrition information. The incremental changes in body mass index (BMI), BMI-related disease outcomes, healthcare costs, and Health Adjusted Life Years (HALYs) were estimated using a validated model. Costs associated with each intervention were estimated for government and industry. Both interventions were estimated to lead to reductions in mean alcohol consumption (Intervention 1: 20.7% (95% Uncertainty Interval (UI): 20.2% to 21.1%); Intervention 2: 9.2% (95% UI: 8.9% to 9.6%)); reductions in mean population body weight (Intervention 1: 0.9 kg (95% UI: 0.84 to 0.96); Intervention 2: 0.45 kg (95% UI: 0.42 to 0.48)); HALYs gained (Intervention 1: 566,648 (95% UI: 497,431 to 647,262); Intervention 2: 317,653 (95% UI: 276,334 to 361,573)); and healthcare cost savings (Intervention 1: $5.8 billion (B) (95% UI: $5.1B to $6.6B); Intervention 2: $3.3B (95% UI: $2.9B to $3.7B)). Intervention costs were estimated as $24M for Intervention 1 and $30M for Intervention 2. Both interventions were dominant, resulting in health gains and cost savings. Increasing the price of alcohol is likely to be cost-effective from an obesity prevention perspective in the Australian context, provided consumers substitute alcoholic beverages with low or no kilojoule alternatives.
Effects of Preconception Care and Periconception Interventions on Maternal Nutritional Status and Birth Outcomes in Low- and Middle-Income Countries: A Systematic Review
Authors: Lassi Zohra S. , Kedzior Sophie G. E. , Tariq Wajeeha , Jadoon Yamna , Das Jai K. , Bhutta Zulfiqar A.
Published: February 2020

Pregnancy in adolescence and malnutrition are common challenges in low- and middle-income countries (LMICs), and are associated with many complications and comorbidities. The preconception period is an ideal period for intervention as a preventative tactic for teenage pregnancy, and to increase micronutrient supplementation prior to conception. Over twenty databases and websites were searched and 45 randomized controlled trials (RCTs) or quasi-experimental interventions with intent to delay the age at first pregnancy (n = 26), to optimize inter-pregnancy intervals (n = 4), and supplementation of folic acid (n = 5) or a combination of iron and folic acid (n = 10) during the periconception period were included. The review found that educational interventions to delay the age at first pregnancy and optimizing inter-pregnancy intervals significantly improved the uptake of contraception use (RR = 1.71, 95% CI = 1.42–2.05; two studies, n = 911; I2 = 0%) and (RR = 2.25, 95% CI = 1.29–3.93; one study, n = 338), respectively. For periconceptional folic acid supplementation, the incidence of neural tube defects were reduced (RR = 0.53; 95% CI = 0.41–0.77; two studies, n = 248,056; I2 = 0%), and iron-folic acid supplementation improved the rates of anemia (RR = 0.66, 95% CI = 0.53–0.81; six studies; n = 3430, I2 = 88%), particularly when supplemented weekly and in a school setting. Notwithstanding the findings, more robust RCTs are required from LMICs to further support the evidence.
Nutritional Quality of Fast Food Kids Meals and Their Contribution to the Diets of School-Aged Children
Authors: Palos Lucio Ana Gabriela , Sansores Martínez Diana Nicte-Há , Olvera Miranda Corina , Quezada Méndez Laura , Tolentino-Mayo Lizbeth
Published: February 2020

International data indicate that children and adolescents obtain around one third of their daily food intake from products consumed outside their home. Food products from restaurants are usually higher in calories and lower in nutritional value than those prepared home. We evaluated the nutritional quality in kids’ meals from three fast food chains and two movie theaters and compared them with nutritional recommendations for Mexican school-age population. Results showed that the menu options marketed for school-age children have higher caloric contributions than those recommended, in addition to a deficient nutritional quality. The contribution of caloric and of almost all macronutrients for all mealtimes is not only high but even above 100% or 200% of the mean recommended daily intake (reaching to more than 400% of the recommendations of carbohydrates and lipids of preschool age group). In particular, the snack main dish (popcorn), provides over 100% of the mean energy intake recommendations for the three school age groups and for preschool age group, this contribution could reach to 270%. Therefore, regulations regarding nutritional recommendations should exist for these types of commercialized food products for school-age children, along with mandatory and clear labeling that allows consumers to be able to make better choices for their kids.
Efficacy of Bacteriophages Against Staphylococcus aureus Isolates from Bovine Mastitis
Authors: Titze Isabel , Lehnherr Tatiana , Lehnherr Hansjörg , Krömker Volker
Published: February 2020

The lytic efficacy of bacteriophages against Staphylococcus aureus isolates from bovine milk was investigated in vitro, regarding possible applications in the therapy of udder inflammation caused by bacterial infections (mastitis). The host range of sequenced, lytic bacteriophages was determined against a collection of 92 Staphylococcus (S.) aureus isolates. The isolates originated from quarter foremilk samples of clinical and subclinical mastitis cases. A spot test and a subsequent plaque assay were used to determine the phage host range. According to their host range, propagation and storage properties, three phages, STA1.ST29, EB1.ST11, and EB1.ST27, were selected for preparing a bacteriophage mixture (1:1:1), which was examined for its lytic activity against S. aureus in pasteurized and raw milk. It was found that almost two thirds of the isolates could be lysed by at least one of the tested phages. The bacteriophage mixture was able to reduce the S. aureus germ density in pasteurized milk and its reduction ability was maintained in raw milk, with only a moderate decrease compared to the results in pasteurized milk. The significant reduction ability of the phage mixture in raw milk promotes further in vivo investigation.
Citrus Flavones: An Update on Sources, Biological Functions, and Health Promoting Properties
Authors: Barreca Davide , Mandalari Giuseppina , Calderaro Antonella , Smeriglio Antonella , Trombetta Domenico , Felice Maria Rosa , Gattuso Giuseppe
Published: February 2020

Citrus spp. are among the most widespread plants cultivated worldwide and every year millions of tons of fruit, juices, or processed compounds are produced and consumed, representing one of the main sources of nutrients in human diet. Among these, the flavonoids play a key role in providing a wide range of health beneficial effects. Apigenin, diosmetin, luteolin, acacetin, chrysoeriol, and their respective glycosides, that occur in concentrations up to 60 mg/L, are the most common flavones found in Citrus fruits and juices. The unique characteristics of their basic skeleton and the nature and position of the substituents have attracted and stimulated vigorous investigations as a consequence of an enormous biological potential, that manifests itself as (among other properties) antioxidant, anti-inflammatory, antiviral, antimicrobial, and anticancer activities. This review analyzes the biochemical, pharmacological, and biological properties of Citrus flavones, emphasizing their occurrence in Citrus spp. fruits and juices, on their bioavailability, and their ability to modulate signal cascades and key metabolic enzymes both in vitro and in vivo. Electronic databases including PubMed, Scopus, Web of Science, and SciFinder were used to investigate recent published articles on Citrus spp. in terms of components and bioactivity potentials.
A TMT-Based Quantitative Proteome Analysis to Elucidate the TSWV Induced Signaling Cascade in Susceptible and Resistant Cultivars of Solanum lycopersicum
Authors: Gupta Ravi , Min Cheol Woo , Kim So Wun , Yoo Ju Soon , Moon Ah-Ram , Shin Ah-Young , Kwon Suk-Yoon , Kim Sun Tae
Published: February 2020

Tomato spotted wilt virus (TSWV), transmitted by small insects known as thrips, is one of the major threats to tomato productivity across the globe. In addition to tomato, this virus infects more than 1000 other plants belonging to 85 families and is a cause of serious concern. Very little, however, is known about the molecular mechanism of TSWV induced signaling in plants. Here, we used a tandem mass tags (TMT)-based quantitative proteome approach to investigate the protein profiles of tomato leaves of two cultivars (cv 2621 and 2689; susceptible and resistant to TSWV infection, respectively) following TSWV inoculation. This approach resulted in the identification of 5112 proteins of which 1022 showed significant changes in response to TSWV. While the proteome of resistant cultivar majorly remains unaltered, the proteome of susceptible cultivar showed distinct differences following TSWV inoculation. TSWV modulated proteins in tomato included those with functions previously implicated in plant defense including secondary metabolism, reactive oxygen species (ROS) detoxification, mitogen-activated protein (MAP) kinase signaling, calcium signaling and jasmonate biosynthesis, among others. Taken together, results reported here provide new insights into the TSWV induced signaling in tomato leaves and may be useful in the future to manage this deadly disease of plants.
In-Situ Synthesis of Hydrophobic Polyurethane Ternary Composite Induced by Hydroxyethyl Cellulose through a Green Method for Efficient Oil Removal
Authors: Chen Junyong , Yue Xian , Xiao Zhou , Li Huaxin , Yu Xianbo , Xiang Junhui
Published: February 2020

Hydroxyethyl cellulose (HEC) was introduced to activate the surface of polyurethane (PU) sponge to successfully prepare a hydrophobic ternary composite PU/HEC/SiO2. The hydrophobic layer of the composite was realized by in-situ polymerization of methyltriethoxysilane (MTES) onto the surface of PU sponge. The formation of a stable hydrophobic SiO2 layer solved successfully the problem of ease of SiO2 particles shedding from the composite. Moreover, the amphiphilic molecules produced by the hydrolysis of MTES monomers facilitated the preparation of hydrophobic materials by aqueous dispersion polymerization. Aqueous synthesis made the reaction process environmentally-friendly and pollution-free. The as-prepared composite PU/HEC/SiO2 not only retains high porosity and low density of the PU sponge, but also considerably reduced the surface free energy and increased the surface roughness of the PU sponge. Therefore, outstanding hydrophobicity and high porosity endow the composite with excellent oil removal capability as a high-efficiency absorbent. Moreover, the hydrophobic composite that had absorbed oil could be regenerated easily by squeezing and recycling.
Steel Wool for Water Treatment: Intrinsic Reactivity and Defluoridation Efficiency
Authors: Hildebrant Benjamin , Ndé-Tchoupé Arnaud Igor , Lufingo Mesia , Licha Tobias , Noubactep Chicgoua
Published: February 2020

Studies were undertaken to characterize the intrinsic reactivity of Fe0-bearing steel wool (Fe0 SW) materials using the ethylenediaminetetraacetate method (EDTA test). A 2 mM Na2-EDTA solution was used in batch and column leaching experiments. A total of 15 Fe0 SW specimens and one granular iron (GI) were tested in batch experiments. Column experiments were performed with four Fe0 SW of the same grade but from various suppliers and the GI. The conventional EDTA test (0.100 g Fe0, 50 mL EDTA, 96 h) protocol was modified in two manners: (i) Decreasing the experimental duration (down to 24 h) and (ii) decreasing the Fe0 mass (down to 0.01 g). Column leaching studies involved glass columns filled to 1/4 with sand, on top of which 0.50 g of Fe0 was placed. Columns were daily gravity fed with EDTA and effluent analyzed for Fe concentration. Selected reactive Fe0 SW specimens were additionally investigated for discoloration efficiency of methylene blue (MB) in shaken batch experiments (75 rpm) for two and eight weeks. The last series of experiments tested six selected Fe0 SW for water defluoridation in Fe0/sand columns. Results showed that (i) the modifications of the conventional EDTA test enabled a better characterization of Fe0 SW; (ii) after 53 leaching events the Fe0 SW showing the best kEDTA value released the lowest amount of iron; (iii) all Fe0 specimens were efficient at discoloring cationic MB after eight weeks; (iv) limited water defluoridation by all six Fe0 SW was documented. Fluoride removal in the column systems appears to be a viable tool to characterize the Fe0 long-term corrosion kinetics. Further research should include correlation of the intrinsic reactivity of SW specimens with their efficiency at removing different contaminants in water.
Simulation of Solid Oxide Fuel Cell Anode in Aspen HYSYS—A Study on the Effect of Reforming Activity on Distributed Performance Profiles, Carbon Formation, and Anode Oxidation Risk
Authors: Ahmed Khaliq , Amiri Amirpiran , O. Tadé Moses
Published: February 2020

A distributed variable model for solid oxide fuel cell (SOFC), with internal fuel reforming on the anode, has been developed in Aspen HYSYS. The proposed model accounts for the complex and interactive mechanisms involved in the SOFC operation through a mathematically viable and numerically fast modeling framework. The internal fuel reforming reaction calculations have been carried out in a plug flow reactor (PFR) module integrated with a spreadsheet module to interactively calculate the electrochemical process details. By interlinking the two modules within Aspen HYSYS flowsheeting environment, the highly nonlinear SOFC distributed profiles have been readily captured using empirical correlations and without the necessity of using an external coding platform, such as MATLAB or FORTRAN. Distributed variables including temperature, current density, and concentration profiles along the cell length, have been discussed for various reforming activity rates. Moreover, parametric estimation of anode oxidation risk and carbon formation potential against fuel reformation intensity have been demonstrated that contributes to the SOFC lifetime evaluation. Incrementally progressive catalyst activity has been proposed as a technically viable approach for attaining smooth profiles within the SOFC anode. The proposed modeling platform paves the way for SOFC system flowsheeting and optimization, particularly where the study of systems with stack distributed variables is of interest.
Mobilizing Benefit-Sharing Through Transportation Infrastructure: Informal Roads, Extractive Industries and Benefit-Sharing in the Irkutsk Oil and Gas Region, Russia
Authors: Kuklina Vera , Petrov Andrey N , Krasnoshtanova Natalia , Bogdanov Viktor
Published: February 2020

Road infrastructure development is an existing, but not a frequent element of extractive industry benefit-sharing frameworks in remote northern regions. However, it is often at the center of extractive activity and inflicts major impact on environment and communities. This paper examines the benefits and impacts derived from development of informal roads, i.e., vehicular roadways beyond the current publicly-governed road networks constructed, maintained and/or used by various entities and individuals based on private, special purpose and/or informal practices and regulations. Based on several field studies, GIS analysis of road networks and examination of secondary sources, the article investigates the use of informal roads as a form of benefit-sharing and details their impact on mobilities, environment and livelihoods of local and indigenous communities in the Irkutsk Oil and Gas region, Russia. We argue that construction, maintenance and use of the industry-built roads can be a part of benefit-sharing agreements, albeit mostly semi-formal and negotiated. The gains and problems stemming from ‘trickle-down’ (i.e., unintended) effects of the road networks are the most significant. The community-relevant implications of informal roads go far beyond immediate impacts on surrounding environment, but deeply affect subsistence activities, mobility, food security, personal safety and even consumer preferences of the indigenous residents.
Time Series Remote Sensing Data-Based Identification of the Dominant Factor for Inland Lake Surface Area Change: Anthropogenic Activities or Natural Events?
Authors: Liu Xiaolong , Shi Zhengtao , Huang Guangcai , Bo Yanchen , Chen Guangjie
Published: February 2020

Inland lake variations are considered sensitive indicators of global climate change. However, human activity is playing as a more and more important role in inland lake area variations. Therefore, it is critical to identify whether anthropogenic activity or natural events is the dominant factor in inland lake surface area change. In this study, we proposed a method that combines the Douglas-Peucker simplification algorithm and the bend simplification algorithm to locate major lake surface area disturbances. These disturbances were used to extract the features that been used to classify disturbances into anthropogenic or natural. We took the nine lakes in Yunnan Province as test sites, a 31-year long (from 1987 to 2017) time series Landsat TM/OLI images and HJ-1A/1B used as data sources, the official records were used as references to aid the feature extraction and disturbance identification accuracy assessment. Results of our method for disturbance location and disturbance identification could be concluded as follows: (1) The method can accurately locate the main lake changing events based on the time series lake surface area curve. The accuracy of this model for segmenting the time series of lake surface area in our study area was 94.73%. (2) Our proposed method achieved an overall accuracy of 87.75%, with an F-score of 85.71 for anthropogenic disturbances and an F-score of 88.89 for natural disturbances. (3) According to our results, lakes in Yunnan Province of China have undergone intensive disturbances. Human-induced disturbances occurred almost twice as much as natural disturbances, indicating intensified disturbances caused by human activities. This inland lake area disturbance identification method is expected to uncover whether a disturbance to inland lake area is human activity-induced or a natural event, and to monitor whether disturbances of lake surface area are intensified for a region.
A Novel Approach for Estimation of Above-Ground Biomass of Sugar Beet Based on Wavelength Selection and Optimized Support Vector Machine
Authors: Zhang Jing , Tian Haiqing , Wang Di , Li Haijun , Mouazen Abdul Mounem
Published: February 2020

Timely diagnosis of sugar beet above-ground biomass (AGB) is critical for the prediction of yield and optimal precision crop management. This study established an optimal quantitative prediction model of AGB of sugar beet by using hyperspectral data. Three experiment campaigns in 2014, 2015 and 2018 were conducted to collect ground-based hyperspectral data at three different growth stages, across different sites, for different cultivars and nitrogen (N) application rates. A competitive adaptive reweighted sampling (CARS) algorithm was applied to select the most sensitive wavelengths to AGB. This was followed by developing a novel modified differential evolution grey wolf optimization algorithm (MDE–GWO) by introducing differential evolution algorithm (DE) and dynamic non-linear convergence factor to grey wolf optimization algorithm (GWO) to optimize the parameters c and γ of a support vector machine (SVM) model for the prediction of AGB. The prediction performance of SVM models under the three GWO, DE–GWO and MDE–GWO optimization methods for CARS selected wavelengths and whole spectral data was examined. Results showed that CARS resulted in a huge wavelength reduction of 97.4% for the rapid growth stage of leaf cluster, 97.2% for the sugar growth stage and 97.4% for the sugar accumulation stage. Models resulted after CARS wavelength selection were found to be more accurate than models developed using the entire spectral data. The best prediction accuracy was achieved after the MDE–GWO optimization of SVM model parameters for the prediction of AGB in sugar beet, independent of growing stage, years, sites and cultivars. The best coefficient of determination (R2), root mean square error (RMSE) and residual prediction deviation (RPD) ranged, respectively, from 0.74 to 0.80, 46.17 to 65.68 g/m2 and 1.42 to 1.97 for the rapid growth stage of leaf cluster, 0.78 to 0.80, 30.16 to 37.03 g/m2 and 1.69 to 2.03 for the sugar growth stage, and 0.69 to 0.74, 40.17 to 104.08 g/m2 and 1.61 to 1.95 for the sugar accumulation stage. It can be concluded that the methodology proposed can be implemented for the prediction of AGB of sugar beet using proximal hyperspectral sensors under a wide range of environmental conditions.
The Determination of Aerosol Distribution by a No-Blind-Zone Scanning Lidar
Authors: Wang Jie , Liu Wenqing , Liu Cheng , Zhang Tianshu , Liu Jianguo , Chen Zhenyi , Xiang Yan , Meng Xiaoyan
Published: February 2020

A homemade portable no-blind zone laser detection and ranging (lidar) system was designed to map the three-dimensional (3D) distribution of aerosols based on a dual-field-of-view (FOV) receiver system. This innovative lidar prototype has a space resolution of 7.5 m and a time resolution of 30 s. A blind zone of zero meters, and a transition zone of approximately 60 m were realized with careful optical alignments, and were rather meaningful to the lower atmosphere observation. With a scanning platform, the lidar system was used to locate the industrial pollution sources at ground level. The primary parameters of the transmitter, receivers, and detectors are described in this paper. Acquiring a whole return signal of this lidar system represents the key step to the retrieval of aerosol distribution with applying a linear joining method to the two FOV signals. The vertical profiles of aerosols were retrieved by the traditional Fernald method and verified by real-time observations. To effectively and reliably retrieve the horizontal distributions of aerosols, a composition of the Fernald method and the slope method were applied. In this way, a priori assumptions of even atmospheric conditions and the already-known reference point in the lidar equation were avoided. No-blind-zone vertical in-situ observation of aerosol illustrated a detailed evolution from almost 0 m to higher altitudes. No-blind-zone detection provided tiny structures of pollution distribution in lower atmosphere, which is closely related to human health. Horizontal field scanning experiments were also conducted in the Shandong Province. The results showed a high accuracy of aerosol mass movement by this lidar system. An effective quantitative way to locate pollution sources distribution was paved with the portable lidar system after validation by the mass concentration of suspended particulate matter from a ground air quality station.
Monitoring of Fine-Scale Warm Drain-Off Water from Nuclear Power Stations in the Daya Bay Based on Landsat 8 Data
Authors: Liu Mengdi , Yin Xiaobin , Xu Qing , Chen Yuxiang , Wang Bowen
Published: February 2020

Monitoring the drain-off water from nuclear power stations by high-resolution remote sensing satellites is of great significance for ensuring the safe operation of nuclear power stations and monitoring environmental changes. In order to select the optimal algorithm for Landsat 8 Thermal Infrared Sensor (TIRS) data to monitor warm drain-off water from the Daya Bay Nuclear Power Station (DNPS) and the Ling Ao Nuclear Power Station (LNPS) located on the southern coast of China, this study applies the edge detection method to remove stripes and produces estimates of four Sea Surface Temperature (SST) inversion methods, the Radiation Transfer Equation Method (RTM), the Single Channel algorithm (SC), the Mono Window algorithm (MW) and the Split Window algorithm (SW), using the buoy and Minimum Orbit Intersection Distances (MOIDS) SST data. Among the four algorithms, the SST from the SW algorithm is the most consistent with the buoy, the MODIS SST, the ERA-Interim and the Optimum Interpolation Sea Surface Temperature (OISST). Based on the SST retrieved from the SW algorithm, the tidal currents calculated by the Finite-Volume Coastal Ocean Model (FVCOM) and winds from ERA-Interim, the distribution of the warm drain-off from the two nuclear power stations is analyzed. First, warm drain-off water is mainly distributed in a fan-shaped area from the two nuclear power stations to the center of the Daya Bay. The SST of the warm drain-off is about 1–4 °C higher than the surrounding water and exceeds 6 °C at the drain-off outfall. Second, the tide determines the shape and distribution characteristics of the warm drain-off area. The warm drain-off water flows to the northeast during the flood tide. During the ebb tide, the warm drain-off water flows toward the southwest direction as the tide flows toward the bay mouth, forming a fan-shaped area. Moreover, the temperature increase intensity in the combined discharge channel during the flood tide is lower than that during the ebb tide, and the low temperature rising area during the flood tide is smaller than that during the ebb tide.
Properties Analysis of Lunar Regolith at Chang’E-4 Landing Site Based on 3D Velocity Spectrum of Lunar Penetrating Radar
Authors: Dong Zejun , Feng Xuan , Zhou Haoqiu , Liu Cai , Zeng Zhaofa , Li Jing , Liang Wenjing
Published: February 2020

The Chinese Chang’E-4 mission for moon exploration has been successfully completed. The Chang’E-4 probe achieved the first-ever soft landing on the floor of Von Kármán crater (177.59°E, 45.46°S) of the South Pole-Aitken (SPA) basin on January 3, 2019. Yutu-2 rover is mounted with several scientific instruments including a lunar penetrating radar (LPR), which is an effective instrument to detect the lunar subsurface structure. During the interpretation of LPR data, subsurface velocity of electromagnetic waves is a vital parameter necessary for stratigraphic division and computing other properties. However, the methods in previous research on Chang’E-3 cannot perform velocity analysis automatically and objectively. In this paper, the 3D velocity spectrum is applied to property analysis of LPR data from Chang’E-4. The result shows that 3D velocity spectrum can automatically search for hyperbolas; the maximum value at velocity axis with a soft threshold function can provide the horizontal position, two-way reflected time and velocity of each hyperbola; the average maximum relative error of velocity is estimated to be 7.99%. Based on the estimated velocities of 30 hyperbolas, the structures of subsurface properties are obtained, including velocity, relative permittivity, density, and content of FeO and TiO2.
Automated Plantation Mapping in Southeast Asia Using MODIS Data and Imperfect Visual Annotations
Authors: Jia Xiaowei , Khandelwal Ankush , Carlson Kimberly M. , Gerber James S. , West Paul C. , Samberg Leah H. , Kumar Vipin
Published: February 2020

Expansion of large-scale tree plantations for commodity crop and timber production is a leading cause of tropical deforestation. While automated detection of plantations across large spatial scales and with high temporal resolution is critical to inform policies to reduce deforestation, such mapping is technically challenging. Thus, most available plantation maps rely on visual inspection of imagery, and many of them are limited to small areas for specific years. Here, we present an automated approach, which we call Plantation Analysis by Learning from Multiple Classes (PALM), for mapping plantations on an annual basis using satellite remote sensing data. Due to the heterogeneity of land cover classes, PALM utilizes ensemble learning to simultaneously incorporate training samples from multiple land cover classes over different years. After the ensemble learning, we further improve the performance by post-processing using a Hidden Markov Model. We implement the proposed automated approach using MODIS data in Sumatra and Indonesian Borneo (Kalimantan). To validate the classification, we compare plantations detected using our approach with existing datasets developed through visual interpretation. Based on random sampling and comparison with high-resolution images, the user’s accuracy and producer’s accuracy of our generated map are around 85% and 80% in our study region.
Mapping Forested Wetland Inundation in the Delmarva Peninsula, USA Using Deep Convolutional Neural Networks
Authors: Du Ling , McCarty Gregory W. , Zhang Xin , Lang Megan W. , Vanderhoof Melanie K. , Li Xia , Huang Chengquan , Lee Sangchul , Zou Zhenhua
Published: February 2020

The Delmarva Peninsula in the eastern United States is partially characterized by thousands of small, forested, depressional wetlands that are highly sensitive to weather variability and climate change, but provide critical ecosystem services. Due to the relatively small size of these depressional wetlands and their occurrence under forest canopy cover, it is very challenging to map their inundation status based on existing remote sensing data and traditional classification approaches. In this study, we applied a state-of-the-art U-Net semantic segmentation network to map forested wetland inundation in the Delmarva area by integrating leaf-off WorldView-3 (WV3) multispectral data with fine spatial resolution light detection and ranging (lidar) intensity and topographic data, including a digital elevation model (DEM) and topographic wetness index (TWI). Wetland inundation labels generated from lidar intensity were used for model training and validation. The wetland inundation map results were also validated using field data, and compared to the U.S. Fish and Wildlife Service National Wetlands Inventory (NWI) geospatial dataset and a random forest output from a previous study. Our results demonstrate that our deep learning model can accurately determine inundation status with an overall accuracy of 95% (Kappa = 0.90) compared to field data and high overlap (IoU = 70%) with lidar intensity-derived inundation labels. The integration of topographic metrics in deep learning models can improve the classification accuracy for depressional wetlands. This study highlights the great potential of deep learning models to improve the accuracy of wetland inundation maps through use of high-resolution optical and lidar remote sensing datasets.
Nonlinear Manifold Learning Integrated with Fully Convolutional Networks for PolSAR Image Classification
Authors: He Chu , Tu Mingxia , Xiong Dehui , Liao Mingsheng
Published: February 2020

Synthetic Aperture Rradar (SAR) provides rich ground information for remote sensing survey and can be used all time and in all weather conditions. Polarimetric SAR (PolSAR) can further reveal surface scattering difference and improve radar’s application ability. Most existing classification methods for PolSAR imagery are based on manual features, such methods with fixed pattern has poor data adaptability and low feature utilization, if directly input to the classifier. Therefore, combining PolSAR data characteristics and deep network with auto-feature learning ability forms a new breakthrough direction. In fact, feature learning of deep network is to realize function approximation from data to label, through multi-layer accumulation, but finite layers limit the network’s mapping ability. According to manifold hypothesis, high-dimensional data exists in potential low-dimensional manifold and different types of data locates in different manifolds. Manifold learning can model core variables of the target, and separate different data’s manifold as much as possible, so as to complete data classification better. Therefore, taking manifold hypothesis as a starting point, nonlinear manifold learning integrated with fully convolutional networks for PolSAR image classification method is proposed in this paper. Firstly, high-dimensional polarized features are extracted based on scattering matrix and coherence matrix of original PolSAR data, whose compact representation is mined by manifold learning. Meanwhile, drawing on transfer learning, pre-trained Fully Convolutional Networks (FCN) model is utilized to learn deep spatial features of PolSAR imagery. Considering complementary advantages, weighted strategy is adopted to embed manifold representation into deep spatial features, which are input into support vector machine (SVM) classifier for final classification. A series of experiments on three PolSAR datasets have verified effectiveness and superiority of the proposed classification algorithm.
GF-5 Hyperspectral Data for Species Mapping of Mangrove in Mai Po, Hong Kong
Authors: Wan Luoma , Lin Yinyi , Zhang Hongsheng , Wang Feng , Liu Mingfeng , Lin Hui
Published: February 2020

Hyperspectral data has been widely used in species discrimination of plants with rich spectral information in hundreds of spectral bands, while the availability of hyperspectral data has hindered its applications in many specific cases. The successful operation of the Chinese satellite, Gaofen-5 (GF-5), provides potentially promising new hyperspectral dataset with 330 spectral bands in visible and near infrared range. Therefore, there is much demand for assessing the effectiveness and superiority of GF-5 hyperspectral data in plants species mapping, particularly mangrove species mapping, to better support the efficient mangrove management. In this study, mangrove forest in Mai Po Nature Reserve (MPNR), Hong Kong was selected as the study area. Four dominant native mangrove species were investigated in this study according to the field surveys. Two machine learning methods, Random Forests and Support Vector Machines, were employed to classify mangrove species with Landsat 8, Simulated Hyperion and GF-5 data sets. The results showed that 97 more bands of GF-5 over Hyperion brought a higher over accuracy of 87.12%, in comparison with 86.82% from Hyperion and 73.89% from Landsat 8. The higher spectral resolution of 5 nm in GF-5 was identified as making the major contribution, especially for the mapping of Aegiceras corniculatum. Therefore, GF-5 is likely to improve the classification accuracy of mangrove species mapping via enhancing spectral resolution and thus has promising potential to improve mangrove monitoring at species level to support mangrove management.
Investigating Banksia Coastal Woodland Decline Using Multi-Temporal Remote Sensing and Field-Based Monitoring Techniques
Authors: Bell Rose-Anne , Callow J. Nikolaus
Published: February 2020

Coastal woodlands, notable for their floristic diversity and ecosystem service values, are increasingly under threat from a range of interacting biotic and abiotic stressors. Monitoring these complex ecosystems has traditionally been confined to field-scale vegetation surveys; however, remote sensing applications are increasingly becoming more viable. This study reports on the application of field-based monitoring and remote sensing/(Geographic Information System) GIS to interrogate trends in Banksia coastal woodland decline (Kings Park, Perth and Western Australia) and documents the patterns, and potential drivers, of tree mortality over the period 2012–2016. Application of geographic object-based image analysis (GEOBIA) at a park scale was of limited benefit within the closed-canopy ecosystem, with manual digitisation methods feasible only at the smaller transect scale. Analysis of field-based identification of tree mortality, crown-specific spectral characteristics and park-scale change detection imagery identified climate-driven stressors as the likely primary driver of tree mortality in the woodland, with vegetation decline exacerbated by secondary factors, including water stress and low system resilience occasioned by the inability to access the water table and competition between tree species. The results from this paper provide a platform to inform monitoring efforts using airborne remote sensing within coastal woodlands.