bluuebunny/crossref_metadata_2025 huggingface dataset:
Are Circulating Mg2+ Levels Associated with Glucose Tolerance Profiles and Incident Type 2 Diabetes?
Authors: Spiga Rosangela , Mannino Gaia Chiara , Mancuso Elettra , Averta Carolina , Paone Claudia , Rubino Mariangela , Sciacqua Angela , Succurro Elena , Perticone Francesco , Andreozzi Francesco , Sesti Giorgio
Published: October 2019

Magnesium (Mg2+) is an enzyme co-factor that plays a key role in many biochemical reactions, as well as in glucose metabolism. Clinical evidences have demonstrated that depletion of serum Mg2+ increases exponentially with the duration of type 2 diabetes mellitus (T2DM). Diabetes is associated with low Mg2+, and hypomagnesemia is associated with insulin resistance, inflammation, and increased risk for cardiovascular disease. In subjects at high risk of inflammation and insulin resistance, supplementation of Mg2+ alone ameliorates both phenotypes, slowing the development and progression of hepatic steatosis. We analyze the relationship between serum Mg2+ levels and the onset of T2DM in a large cohort of well-characterized adult white individuals participating in the CATAMERI study, who were reexamined after a mean follow-up of 5.6 ± 0.9 years. In our analysis we acquired a significant negative correlation between Mg2+ levels, fasting glucose, and 2h-post load glucose in subjects who underwent an OGTT. Moreover, Mg2+ levels correlated negatively with fasting insulin levels, and positively with the lipid profile. As for the detrimental effect of lower circulating Mg2+ levels, our data revealed a significant reduction of T2DM risk of about 20% for each 1 mg/dL increase of circulating Mg2+. The present results are consistent with the theory that Mg2+ supplementation could ameliorate insulin sensitivity reducing the risk to develop T2DM.
Enhanced Treatment Effects of Tilmicosin Against Staphylococcus aureus Cow Mastitis by Self-Assembly Sodium Alginate-Chitosan Nanogel
Authors: Zhou Kaixiang , Wang Xiaofang , Chen Dongmei , Yuan Yuanyuan , Wang Shuge , Li Chao , Yan Yuanyuan , Liu Qianying , Shao Liwei , Huang Lingli , Yuan Zonghui , Xie Shuyu
Published: October 2019

The Staphylococcus aureus (S. aureus) cow mastitis causes great losses to the cow industry. In order to improve the treatment effect of tilmicosin against cow mastitis, the combination of solid lipid nanoparticle (SLN) technology with in situ hydrogel technology was used to prepare the self-assembly tilmicosin nanogel (TIL-nanogel). The physicochemical characteristics, in vitro release, antibacterial activity and in vivo treatment efficacy of TIL-SLNs and TIL-nanogel were studied, respectively. The results showed the loading capacity (LC), encapsulation efficiency (EE), size, zeta potential and poly dispersion index (PDI) of TIL-nanogel were 23.33 ± 0.77%, 67.89 ± 3.01%, 431.57 ± 12.87 nm, 8.3 ± 0.06 mv and, 0.424 ± 0.032, respectively. The TIL-nanogel showed stronger sustained release in vitro than TIL-SLNs and commercial injection. The cure rate of half dosage and normal dosage of TIL-nanogel was 58.3% and 75.0%, which was higher than that of commercial injection (50.0%) at normal dosage. The results suggest that the treatment dosage of tilmicosin for cow mastitis could be reduced by TIL-nanogel. The novel TIL-nanogel will be beneficial by decreasing the usage of tilmicosin and the treatment costs of cow mastitis.
Pharmacokinetics and Novel Metabolite Identification of Tartary Buckwheat Extracts in Beagle Dogs Following Co-Administration with Ethanol
Authors: Liu Yuancai , Gan Jun , Liu Wanyu , Zhang Xin , Xu Jian , Wu Yue , Yang Yuejun , Si Luqin , Li Gao , Huang Jiangeng
Published: October 2019

Alcoholic liver disease (ALD) has become a critical global public health issue worldwide. Tartary buckwheat extracts exhibit potential therapeutic effects against ALD due to its antioxidant and anti-inflammatory activities. However, in vivo pharmacokinetics and metabolite identification of tartary buckwheat extracts have not been clearly elucidated. Accordingly, the current manuscript aimed to investigate pharmacokinetics and to identify novel metabolites in beagle dogs following oral co-administration of tartary buckwheat extracts and ethanol. To support pharmacokinetic study, a simple LC-MS/MS method was developed and validated for simultaneous determination of quercetin and kaempferol in beagle dog plasma. The conjugated forms of both analytes were hydrolyzed by β-glucuronidase and sulfatase followed by liquid-liquid extraction using methyl tert-butyl ether. In addition, another effective approach was established using advanced ultrafast liquid chromatography coupled with a Q-Exactive hybrid quadrupole orbitrap high resolution mass spectrometer to identify the metabolites in beagle dog biological samples including urine, feces, and plasma. The pharmacokinetic study demonstrated that the absolute oral bioavailability for quercetin and kaempferol was determined to be 4.6% and 1.6%, respectively. Oral bioavailability of quercetin and kaempferol was limited in dogs probably due to poor absorption, significant first pass effect, and biliary elimination, etc. Using high resolution mass spectrometric analysis, a total of nine novel metabolites were identified for the first time and metabolic pathways included methylation, glucuronidation, and sulfation. In vivo pharmacokinetics and metabolite identification results provided preclinical support of co-administration of tartary buckwheat extracts and ethanol in humans.
Poly(ε-caprolactone) (PCL) Hollow Nanoparticles with Surface Sealability and On-Demand Pore Generability for Easy Loading and NIR Light-Triggered Release of Drug
Authors: Park Ju Hyang , Kim Da In , Hong Sang Gi , Seo Hojun , Kim Jongbok , Moon Geon Dae , Hyun Dong Choon
Published: October 2019

A new system for the easy loading and NIR light-triggered release of drugs is introduced. It consists of poly(ε-caprolactone) (PCL) hollow nanoparticles with surface openings containing a biodegradable fatty acid with phase-change ability and a biocompatible photothermal agent. These openings, which can enhance the connectivity between the interior and the exterior, enable the easy loading of drug molecules into the interior voids, and their successive sealing ensures a stable encapsulation of the drug. Upon exposure to an external NIR light irradiation, the photothermal agent generates heat that raises the local temperature of the hollow particles above the melting point of the fatty acid, leading to the formation of nanopores on their shells, and consequently, the instant release of the encapsulated drug molecules through the pores. The synergistic activity of the hyperthermia effect from the photothermal agent and the NIR-triggered release of the drug molecules results in noticeable anticancer efficacy.
Myth or Truth: The Glass Forming Ability Class III Drugs Will Always Form Single-Phase Homogenous Amorphous Solid Dispersion Formulations
Authors: Panini Piyush , Rampazzo Massimiliano , Singh Abhishek , Vanhoutte Filip , Van den Mooter Guy
Published: October 2019

The physical stability of amorphous solid dispersions (ASD) of active pharmaceutical ingredients (APIs) of high glass forming ability (GFA class III) is generally expected to be high among the scientific community. In this study, the ASD of ten-selected class III APIs with the two polymers, PVPVA 64 and HPMC-E5, have been prepared by spray-drying, film-casting, and their amorphicity at T0 was investigated by modulated differential scanning calorimetry and powder X-ray diffraction. It was witnessed that only five out of ten APIs form good quality amorphous solid dispersions with no phase separation and zero crystalline content, immediately after the preparation and drying process. Hence, it was further established that the classification of an API as GFA class III does not guarantee the formulation of single phase amorphous solid dispersions.
Intracellular Delivery of Natural Antioxidants via Hyaluronan Nanohydrogels
Authors: Montanari Elita , Di Meo Chiara , Coviello Tommasina , Gueguen Virginie , Pavon-Djavid Graciela , Matricardi Pietro
Published: October 2019

Natural antioxidants, such as astaxanthin (AX), resveratrol (RV) and curcumin (CU), are bioactive molecules that show a number of therapeutic effects. However, their applications are remarkably limited by their poor water solubility, physico-chemical instability and low bioavailability. In the present work, it is shown that self-assembled hyaluronan (HA)-based nanohydrogels (NHs) are taken up by endothelial cells (Human Umbilical Vein Endothelial Cells, HUVECs), preferentially accumulating in the perinuclear area of oxidatively stressed HUVECs, as evidenced by flow cytometry and confocal microscopy analyses. Furthermore, NHs are able to physically entrap and to significantly enhance the apparent water solubility of AX, RV and CU in aqueous media. AX/NHs, RV/NHs and CU/NHs systems showed good hydrodynamic diameters (287, 214 and 267 nm, respectively), suitable ζ-potential values (−45, −43 and −37 mV, respectively) and the capability to neutralise reactive oxygen species (ROS) in tube. AX/NHs system was also able to neutralise ROS in vitro and did not show any toxicity against HUVECs. This research suggests that HA-based NHs can represent a kind of nano-carrier suitable for the intracellular delivery of antioxidant agents, for the treatment of oxidative stress in endothelial cells.
Recent Advances in Nanovaccines Using Biomimetic Immunomodulatory Materials
Authors: Vijayan Veena , Mohapatra Adityanarayan , Uthaman Saji , Park In-Kyu
Published: October 2019

The development of vaccines plays a vital role in the effective control of several fatal diseases. However, effective prophylactic and therapeutic vaccines have yet to be developed for completely curing deadly diseases, such as cancer, malaria, HIV, and serious microbial infections. Thus, suitable vaccine candidates need to be designed to elicit appropriate immune responses. Nanotechnology has been found to play a unique role in the design of vaccines, providing them with enhanced specificity and potency. Nano-scaled materials, such as virus-like particles, liposomes, polymeric nanoparticles (NPs), and protein NPs, have received considerable attention over the past decade as potential carriers for the delivery of vaccine antigens and adjuvants, due to their beneficial advantages, like improved antigen stability, targeted delivery, and long-time release, for which antigens/adjuvants are either encapsulated within, or decorated on, the NP surface. Flexibility in the design of nanomedicine allows for the programming of immune responses, thereby addressing the many challenges encountered in vaccine development. Biomimetic NPs have emerged as innovative natural mimicking biosystems that can be used for a wide range of biomedical applications. In this review, we discuss the recent advances in biomimetic nanovaccines, and their use in anti-bacterial therapy, anti-HIV therapy, anti-malarial therapy, anti-melittin therapy, and anti-tumor immunity.
Development of Chitosan/Silver Sulfadiazine/Zeolite Composite Films for Wound Dressing
Authors: Hissae Yassue-Cordeiro Patricia , Henrique Zandonai Cássio , Pereira Genesi Bianca , Santos Lopes Patrícia , Sanchez-Lopez Elena , Luisa Garcia Maria , Regina Camargo Fernandes-Machado Nádia , Severino Patrícia , B. Souto Eliana , Ferreira da Silva Classius
Published: October 2019

Biopolymeric films with silver sulfadiazine (AgSD) are proposed as an alternative to the occlusive AgSD-containing creams and gauzes, which are commonly used in the treatment of conventional burns. While the recognized cytotoxicity of AgSD has been reported to compromise its use as an antimicrobial drug in pharmaceuticals, this limitation can be overcome by developing sustained-release formulations. Microporous materials as zeolites can be used as drug delivery systems for sustained release of AgSD. The purpose of this work was the development and characterization of chitosan/zeolite composite films to be used as wound dressings. Zeolite was impregnated with AgSD before the production of the composite films. The physicochemical properties of zeolites and the films were evaluated, as well as the antimicrobial activity of the polymeric films and the cytotoxicity of the films in fibroblasts Balb 3T3/c. Impregnated zeolite exhibited changes in FTIR spectra and XRD diffraction patterns, in comparison to non-impregnated composites, which corroborate the results obtained with EDX-SEM. The pure chitosan film was compact and without noticeable defects and macropores, while the film with zeolite was opaquer, more rigid, and efficient against Candida albicans and some gram-negative bacteria. The safety evaluation showed that although the AgSD films present cytotoxicity, they could be used in a concentration-dependent fashion.
Multilayer Nanofiber Composite Separator for Lithium-Ion Batteries with High Safety
Authors: Yang Wenxiu , Liu Yanbo , Hu Xuemin , Yao Jinbo , Chen Zhijun , Hao Ming , Tian Wenjun , Huang Zheng , Li Fangying
Published: October 2019

An original Von Koch curve-shaped tipped electrospinneret was used to prepare a polyimide (PI)-based nanofiber membrane. A multilayer Al2O3@polyimide/polyethylene/Al2O3@polyimide (APEAP) composite membrane was tactfully designed with an Al2O3@ polyimide (AP) membrane as outer shell, imparting high temperature to the thermal run-away separator performance and a core polyethylene (PE) layer imparts the separator with a thermal shut-down property at low temperature (123 °C). An AP electrospun nanofiber was obtained by doping Al2O3 nanoparticles in PI solution. The core polyethylene layer was prepared using polyethylene powder and polyterafluoroethylene (PTFE) miniemulsion through a coating process. The addition of PTFE not only bonds PE power, but also increases the adhesion force between the PE and AP membranes. As a result, the multilayer composite separator has high safety, outstanding electrochemical properties, and better cycling performance as a lithium-ion battery separator.
Synthesis, Thermal Properties and Decomposition Mechanism of Poly(Ethylene Vanillate) Polyester
Authors: Zamboulis Alexandra , Papadopoulos Lazaros , Terzopoulou Zoi , Bikiaris Dimitrios N. , Patsiaoura Dimitra , Chrissafis Konstantinos , Gazzano Massimo , Lotti Nadia , Papageorgiou George Z.
Published: October 2019

Plastics are perceived as modern and versatile materials, but their use is linked to numerous environmental issues as their production is based on finite raw materials (petroleum or natural gas). Additionally, their low biodegradability results in the accumulation of microplastics. As a result, there is extensive interest in the production of new, environmentally friendly, bio-based and biodegradable polymers. In this context, poly(ethylene vanillate) (PEV) has a great potential as a potentially bio-based alternative to poly(ethylene terephthalate); however, it has not yet been extensively studied. In the present work, the preparation of PEV is reported. The enthalpy and the entropy of fusion of the pure crystalline PEV have been estimated for the first time. Additionally, the equilibrium melting temperature has also been calculated. Furthermore, the isothermal and non-isothermal crystallization behavior are reported in detail, and new insights on the thermal stability and degradation mechanism of PEV are given.
Characteristics of Self-Healable Copolymers of Styrene and Eugenol Terminated Polyurethane Prepolymer
Authors: Liang Jing-Yu , Shin Se-Ra , Lee Soo-Hyoung , Lee Dai-Soo
Published: October 2019

With limited biomass that can be currently utilized as a renewable resource, it is important to develop a method to convert biomass into materials that can replace fossil fuel product. In this paper, eugenol, a bio-based allyl chain-substituted guaiacol, was used to synthesize self-healable copolymers. Eugenol terminated polyurethane prepolymer (ETPU) was synthesized from eugenol and polyurethane prepolymers terminated with isocyanate groups. ETPU contained two allyl groups. Self-healing copolymer networks were obtained by copolymerization of ETPU and styrene monomer via free radical polymerization. Effects of ETPU content on the properties of copolymers were then studied. These copolymers containing ETPU exhibited good thermal stability and mechanical properties. These copolymers showed higher tensile strength and elongation at break than PS. Their maximum tensile strength reached 19 MPa. In addition, these copolymers showed self-healing property at elevated temperature due to the reversible nature of urethane units in ETPU.
Synthesis of a Carrageenan–Iron Complex and Its Effect on Flame Retardancy and Smoke Suppression for Waterborne Epoxy
Authors: Wang Na , Teng Haiwei , Zhang Xinyu , Zhang Jing , Li Long , Zhang Jing , Fang Qinghong
Published: October 2019

A k-carrageenan–iron complex (KC–Fe) was synthesized by complexation between degraded KC and FeCl3. Furthermore, KC–Fe and ammonium polyphosphate (APP) were simultaneously added into waterborne epoxy (EP) to improve its flame retardancy and smoke suppression performance. The structure and properties of KC–Fe were assessed using Fourier transform infrared spectroscopy (FTIR), ultraviolet (UV) spectroscopy, thermo gravimetric analysis (TGA), and X-ray powder diffraction analysis (XRD). The analysis showed that KC–Fe was successfully synthesized and exhibited good thermal properties with a 49% char residue at 800 °C. The enhanced flame retardancy and smoke suppression performance of waterborne epoxy were evaluated using a limiting oxygen index (LOI) and UL-94. Moreover, the flame retardancy of waterborne epoxy coated on a steel plate was also investigated using cone calorimetry. The results showed that the flame-retardant waterborne epoxy blend exhibited the best flame retardancy when the mass ratio of APP and KC–Fe was 2:1. The total heat release (THR) and total smoke production (TSP) was decreased by 44% and 45%, respectively, which indicated good fire safety performance and smoke suppression properties. Analysis of the residual char using FTIR, SEM, and elemental analysis (EDS) indicated that the action of KC–Fe was promoted by the presence of APP. The formation of a dense thermal stable char layer from an intumescent coating was essential to protect the underlying materials.
Comparison of the Economy and Controllability of Pressure Swing Distillation with Two Energy-Saving Modes for Separating a Binary Azeotrope Containing Lower Alcohols
Authors: Lv Liping , Li Hang , Zhang Zheng , Huang Huisheng
Published: October 2019

The pressure swing distillation (PSD) with two different energy-saving modes are put forward to separate a binary azeotrope containing lower alcohols: benzene/methanol. A comparison of the economy and controllability for the partial and fully heat integrated pressure swing distillation (HIPSD) is made by detailed simulation analysis. The optimal operating parameters of partial and fully HIPSD processes are obtained by minimizing total annual cost (TAC). These results show that the fully HIPSD mode saves 5.88% TAC compared with the partial HIPSD mode. Meanwhile, this paper proposes that the composition slope profile can help to select the temperature control stage (TCS), when the temperature profile in the column is rising rapidly near the bottom and the maximum of temperature slope value occurs in the bottom of the column. Several control structures are developed to check the rationality of the selection of the TCS and evaluate the industrial application. These results illustrate the composition/temperature cascade control structure for the PSD with two energy-saving modes can both get good control performances, and the purities of benzene and methanol can be brought close back to the initial value. However, the fully HIPSD mode can only handle much smaller composition disturbances (<10%) compared with the partial HIPSD mode. Therefore, the selection of energy-saving modes for the separation process should weigh economy against controllability.
Mistaken Identities: The Media and Parental Ethno-Religious Socialization in a Midwestern Sikh Community
Authors: Rana Meenal , Qin Desiree B. , Vital-Gonzalez Carmina
Published: October 2019

Strong anti-Islamic sentiments increased dramatically after the 9/11 terror attacks on the United States, leading to an uptick in prejudice and the perpetration of hate crimes targeting Muslims. Sikh men and boys, often mistaken for Muslims, suffered as collateral damage. The overall health of both communities has been adversely affected by these experiences. Faced with such realities, communities and parents often adopt adaptive behaviors to foster healthy development in their children. In this paper, drawing on interviews with 23 Sikh parents from 12 families, we examine Sikh parents’ ethno-religious socialization of their children. The confluence of media stereotyping and mistaken identities has shaped Sikh parents’ beliefs regarding their children’s retention/relinquishment of outward identity markers. Sikh parents, in general, are concerned about the safety of their boys, due to the distinctive appearance of their religious markers, such as the turban. They are engaged in a constant struggle to ensure that their children are not identified as Muslims and to protect them from potential harm. In most of the families in our study, boys were raised to give up wearing the indicators of their ethno-religious group. In addition, many parents took responsibility for educating the wider community about their ethno-religious practices through direct communication, participation in cultural events, and support of other ethno-religious minorities. Policy implications are discussed.
Structures of Organisation and Loci of Authority in a Glocal Islamic Movement: The Tablighi Jama’at in Britain
Authors: Timol Riyaz
Published: October 2019

The Tablighi Jama’at (TJ) is widely regarded as the largest grassroots Islamic revival movement in the world, but it remains significantly under-researched. This paper, based on sustained ethnographic fieldwork conducted between 2013 and 2015, provides a comprehensive overview of the movement’s organisational structures and loci of authority in Britain. It describes how different levels of the movement interact, from the local and regional to the national and international, to constitute a truly glocal movement. TJ’s European headquarters, located in Dewsbury, West Yorkshire, is identified as a centralised hub that for several decades has co-ordinated the movement’s activities in the West through the devoted leadership of Hafiz Muhammad Patel (1926–2016) and ongoing contact with the global spiritual centre in Nizamuddin, New Delhi. TJ’s simultaneous links with hundreds of mosques across the country, largely—though not exclusively—of Deobandi orientation, are also described. The functioning of its regional centres of operation in Birmingham, Blackburn, Glasgow, Leicester and London is elaborated with reference to key weekly meetings convened on-site and the “routing” of numerous TJ groups to various British mosques each weekend. Although TJ’s leadership has recently become embroiled in schism, the paper argues for the successful establishment of a robust institutional infrastructure in Britain which has facilitated the movement’s transmission to a generation of British-born activists.
The Cult of Old Believers’ Domestic Icons and the Beginning of Old Belief in Russia in the 17th-18th Centuries
Authors: Sulikowska-Bełczowska Aleksandra
Published: October 2019

The aim of this paper is to present the cult of icons in the Old Believer communities from the perspective of private devotion. For the Old Believers, from the beginning of the movement, in the middle of the 17th century, icons were at the center of their religious life. They were also at the center of religious conflict between Muscovite Patriarch Nikon, who initiated the reforms of the Russian Orthodox Church, and the Old Believers and their proponent, archpriest Avvakum Petrov. Some sources and documents from the 16th and 18th centuries make it possible to analyze the reasons for the popularity of small-sized icons among priested (popovtsy) and priestless (bespopovtsy) Old Believers, not only in their private houses but also in their prayer houses (molennas). The article also shows the role of domestic icons from the middle of the 17th century as a material foundation of the identity of the Old Believers movement.
A Spectral-Spatial Cascaded 3D Convolutional Neural Network with a Convolutional Long Short-Term Memory Network for Hyperspectral Image Classification
Authors: Qi Wenchao , Zhang Xia , Wang Nan , Zhang Mao , Cen Yi
Published: October 2019

Deep learning methods used for hyperspectral image (HSI) classification often achieve greater accuracy than traditional algorithms but require large numbers of training epochs. To simplify model structures and reduce their training epochs, an end-to-end deep learning framework incorporating a spectral-spatial cascaded 3D convolutional neural network (CNN) with a convolutional long short-term memory (CLSTM) network, called SSCC, is proposed herein for HSI classification. The SSCC framework employs cascaded 3D CNN to learn the spectral-spatial features of HSIs and uses the CLSTM network to extract sequence features. Residual connections are used in SSCC to accelerate model convergence, with the outputs of previous convolutional layers concatenated as inputs for subsequent layers. Moreover, the data augmentation, parametric rectified linear unit, dynamic learning rate, batch normalization, and regularization (including dropout and L2) methods are used to increase classification accuracy and prevent overfitting. These attributes allow the SSCC framework to achieve good performance for HSI classification within 20 epochs. Three well-known datasets including Indiana Pines, University of Pavia, and Pavia Center were employed to evaluate the classification performance of the proposed algorithm. The GF-5 dataset of Anxin County, obtained from China’s recently launched spaceborne Advanced Hyperspectral Imager, was also used for classification experiments. The experimental results demonstrate that the proposed SSCC framework achieves state-of-the-art performance with better training efficiency than other deep learning methods.
Sub-Daily Temperature Heterogeneity in a Side Channel and the Influence on Habitat Suitability of Freshwater Fish
Authors: Collas Frank P.L. , van Iersel Wimala K. , Straatsma Menno W. , Buijse Anthonie D. , Leuven Rob S.E.W.
Published: October 2019

Rising surface water temperatures in fluvial systems increasingly affect biodiversity negatively in riverine ecosystems, and a more frequent exceedance of thermal tolerance levels of species is expected to impoverish local species assemblages. Reliable prediction of the effect of increasing water temperature on habitat suitability requires detailed temperature measurements over time. We assessed (1) the accuracy of high-resolution images of water temperature of a side channel in a river floodplain acquired using a consumer-grade thermal camera mounted on an unmanned airborne vehicle (UAV), and (2) the associated habitat suitability for native and alien fish assemblages. Water surface temperatures were mapped four times throughout a hot summer day and calibrated with 24 in-situ temperature loggers in the water at 0.1 m below the surface using linear regression. The calibrated thermal imagery was used to calculate the potentially occurring fraction (POF) of freshwater fish using species sensitivity distributions. We found high temperatures (25–30 °C) in the side channel during mid-day resulting in reduced habitat suitability. The accuracy of water temperature estimates based on the RMSE was 0.53 °C over all flights (R2 = 0.94). Average daily POF was 0.51 and 0.64 for native and alien fish species in the side channel. The error of the POF estimates is 76% lower when water temperature is estimated with thermal UAV imagery compared to temperatures measured at an upstream gauging station. Accurately quantifying water temperature and the heterogeneity thereof is a critical step in adaptation of riverine ecosystems to climate change. Our results show that measurements of surface water temperature can be made accurately and easily using thermal imagery from UAVs allowing for an improved habitat management, but coincident collection of long wave radiation is needed for a more physically-based prediction of water temperature. Because of climate change, management of riverine ecosystems should consider thermal pollution control and facilitate cold water refugia and connectivity between waterbodies in floodplains and the cooler main channel for fish migration during extremely hot summer periods.
Statistical Characterization of the Observed Cold Wake Induced by North Atlantic Hurricanes
Authors: Haakman Koen , Sayol Juan-Manuel , van der Boog Carine G. , Katsman Caroline A.
Published: October 2019

This work quantifies the magnitude, spatial structure, and temporal evolution of the cold wake left by North Atlantic hurricanes. To this end we composited the sea surface temperature anomalies (SSTA) induced by hurricane observations from 2002 to 2018 derived from the international best track archive for climate stewardship (IBTrACS). Cold wake characteristics were distinguished by a set of hurricane and oceanic properties: Hurricane translation speed and intensity, and the characteristics of the upper ocean stratification represented by two barrier layer metrics: Barrier layer thickness (BLT) and barrier layer potential energy (BLPE). The contribution of the above properties to the amplitude of the cold wake was analyzed individually and in combination. The mean magnitude of the hurricane-induced cooling was of 1.7 °C when all hurricanes without any distinction were considered, and the largest cooling was found for slow-moving, strong hurricanes passing over thinner barrier layers, with a cooling above 3.5 °C with respect to pre-storm sea surface temperature (SST) conditions. On average the cold wake needed about 60 days to disappear and experienced a strong decay in the first 20 days, when the magnitude of the cold wake had decreased by 80%. Differences between the cold wakes yielded by mostly infrared and merged infrared and microwave remote sensed SST data were also evaluated, with an overall relative underestimation of the hurricane-induced cooling of about 0.4 °C for infrared-mostly data.
A Bidirectional Analysis Method for Extracting Glacier Crevasses from Airborne LiDAR Point Clouds
Authors: Huang Ronggang , Jiang Liming , Wang Hansheng , Yang Bisheng
Published: October 2019

A crevasse is an important surface feature of a glacier. This study aims to detect crevasses from high-density airborne LiDAR point clouds. However, existing methods continue to suffer from the data holes within the crevasse region and the influence of the undulating non-crevasse glacier surfaces. Therefore, a bidirectional analysis method is proposed to robustly extract the crevasses from the point clouds, which utilizes their vertical and horizontal characteristics. First, crevasse points are separated from non-crevasse points using a hybrid-entity method, where the height difference and the nearly vertical characteristic of a crevasse sidewall are considered, to better distinguish the crevasses from the undulating non-crevasse glacier surfaces. Second, the crevasse regions/edges are adaptively delineated by a local statistical analysis method that is based on a novel feature of the Delaunay triangulation mesh of non-crevasse points in the horizontal plane. Last, the pseudo-crevasse points and regions are removed by a cross-analysis method. To test the performance of the proposed method, this study selected airborne LiDAR point clouds from two sites of Alaskan glaciers (i.e., Tyndall Glacier and Seward Glacier) as the experimental datasets. The results were verified by a comparison with the ground truth that was manually delineated. The proposed method achieved acceptable results: the recall, precision, and F 1 scores of both sites exceeded 94.00%. Moreover, a comparative experiment was carried out and the results confirmed that the proposed method achieved superior performance.
Using Neural Network to Identify the Severity of Wheat Fusarium Head Blight in the Field Environment
Authors: Zhang Dongyan , Wang Daoyong , Gu Chunyan , Jin Ning , Zhao Haitao , Chen Gao , Liang Hongyi , Liang Dong
Published: October 2019

Fusarium head blight (FHB), one of the most important diseases of wheat, mainly occurs in the ear. Given that the severity of the disease cannot be accurately identified, the cost of pesticide application increases every year, and the agricultural ecological environment is also polluted. In this study, a neural network (NN) method was proposed based on the red-green-blue (RGB) image to segment wheat ear and disease spot in the field environment, and then to determine the disease grade. Firstly, a segmentation dataset of single wheat ear was constructed to provide a benchmark for the segmentation of the wheat ear. Secondly, a segmentation model of single wheat ear based on the fully convolutional network (FCN) was established to effectively realize the segmentation of the wheat ear in the field environment. An FHB segmentation algorithm was proposed based on a pulse-coupled neural network (PCNN) with K-means clustering of the improved artificial bee colony (IABC) to segment the diseased spot of wheat ear by automatic optimization of PCNN parameters. Finally, the disease grade was calculated using the ratio of the disease spot to the whole wheat ear. The experimental results show that: (1) the accuracy of the segmentation model for single wheat ear constructed in this study is 0.981. The segmentation time is less than 1 s, indicating that the model can quickly and accurately segment wheat ear in the field environment; (2) the segmentation method of the disease spot performed under each evaluation indicator is improved compared with the traditional segmentation methods, and the accuracy is 0.925 in the disease severity identification. These research results can provide important reference value for grading wheat FHB in the field environment, which also can be beneficial for real-time monitoring of other crops’ diseases under near-Earth remote sensing.
DE-Net: Deep Encoding Network for Building Extraction from High-Resolution Remote Sensing Imagery
Authors: Liu Hao , Luo Jiancheng , Huang Bo , Hu Xiaodong , Sun Yingwei , Yang Yingpin , Xu Nan , Zhou Nan
Published: October 2019

Deep convolutional neural networks have promoted significant progress in building extraction from high-resolution remote sensing imagery. Although most of such work focuses on modifying existing image segmentation networks in computer vision, we propose a new network in this paper, Deep Encoding Network (DE-Net), that is designed for the very problem based on many lately introduced techniques in image segmentation. Four modules are used to construct DE-Net: the inception-style downsampling modules combining a striding convolution layer and a max-pooling layer, the encoding modules comprising six linear residual blocks with a scaled exponential linear unit (SELU) activation function, the compressing modules reducing the feature channels, and a densely upsampling module that enables the network to encode spatial information inside feature maps. Thus, DE-Net achieves state-of-the-art performance on the WHU Building Dataset in recall, F1-Score, and intersection over union (IoU) metrics without pre-training. It also outperformed several segmentation networks in our self-built Suzhou Satellite Building Dataset. The experimental results validate the effectiveness of DE-Net on building extraction from aerial imagery and satellite imagery. It also suggests that given enough training data, designing and training a network from scratch may excel fine-tuning models pre-trained on datasets unrelated to building extraction.
Evaluation of Aboveground Nitrogen Content of Winter Wheat Using Digital Imagery of Unmanned Aerial Vehicles
Authors: Yang Baohua , Wang Mengxuan , Sha Zhengxia , Wang Bing , Chen Jianlin , Yao Xia , Cheng Tao , Cao Weixing , Zhu Yan
Published: October 2019

Nitrogen (N) content is an important basis for the precise management of wheat fields. The application of unmanned aerial vehicles (UAVs) in agriculture provides an easier and faster way to monitor nitrogen content. Previous studies have shown that the features acquired from UAVs yield favorable results in monitoring wheat growth. However, since most of them are based on different vegetation indices, it is difficult to meet the requirements of accurate image interpretation. Moreover, resampling also easily ignores the structural features of the image information itself. Therefore, a spectral-spatial feature is proposed combining vegetation indices (VIs) and wavelet features (WFs), especially the acquisition of wavelet features from the UAV image, which was transformed from the spatial domain to the frequency domain with a wavelet transformation. In this way, the complete spatial information of different scales can be obtained to realize good frequency localization, scale transformation, and directional change. The different models based on different features were compared, including partial least squares regression (PLSR), support vector regression (SVR), and particle swarm optimization-SVR (PSO-SVR). The results showed that the accuracy of the model based on the spectral-spatial feature by combining VIs and WFs was higher than that of VIs or WF indices alone. The performance of PSO-SVR was the best (R2 = 0.9025, root mean square error (RMSE) = 0.3287) among the three regression algorithms regardless of the use of all the original features or the combination features. Our results implied that our proposed method could improve the estimation accuracy of aboveground nitrogen content of winter wheat from UAVs with consumer digital cameras, which have greater application potential in predicting other growth parameters.
Terrain Feature Estimation Method for a Lower Limb Exoskeleton Using Kinematic Analysis and Center of Pressure
Authors: Shim Myounghoon , Han Jong In , Choi Ho Seon , Ha Seong Min , Kim Jung-Hoon , Baek Yoon Su
Published: October 2019

While controlling a lower limb exoskeleton providing walking assistance to wearers, the walking terrain is an important factor that should be considered for meeting performance and safety requirements. Therefore, we developed a method to estimate the slope and elevation using the contact points between the limb exoskeleton and ground. We used the center of pressure as a contact point on the ground and calculated the location of the contact points on the walking terrain based on kinematic analysis of the exoskeleton. Then, a set of contact points collected from each step during walking was modeled as the plane that represents the surface of the walking terrain through the least-square method. Finally, by comparing the normal vectors of the modeled planes for each step, features of the walking terrain were estimated. We analyzed the estimation accuracy of the proposed method through experiments on level ground, stairs, and a ramp. Classification using the estimated features showed recognition accuracy higher than 95% for all experimental motions. The proposed method approximately analyzed the movement of the exoskeleton on various terrains even though no prior information on the walking terrain was provided. The method can enable exoskeleton systems to actively assist walking in various environments.
An Innovative Fingerprint Location Algorithm for Indoor Positioning Based on Array Pseudolite
Authors: Huang Lu , Gan Xingli , Yu Baoguo , Zhang Heng , Li Shuang , Cheng Jianqiang , Liang Xiaohu , Wang Boyuan
Published: October 2019

Since the signals of the global navigation satellite system (GNSS) are blocked by buildings, accurate positioning cannot be achieved in an indoor environment. Pseudolite can simulate similar outdoor satellite signals and can be used as a stable and reliable positioning signal source in indoor environments. Therefore, it has been proposed as a good substitute and has become a research hotspot in the field of indoor positioning. There are still some problems in the pseudolite positioning field, such as: Integer ambiguity of carrier phase, initial position determination, and low signal coverage. To avoid the limitation of these factors, an indoor positioning system based on fingerprint database matching of homologous array pseudolite is proposed in this paper, which can achieve higher positioning accuracy. The realization of this positioning system mainly includes the offline phase and the online phase. In the offline phase, the carrier phase data in the indoor environment is first collected, and a fingerprint database is established. Then a variational auto-encoding (VAE) network with location information is used to learn the probability distribution characteristics of the carrier phase difference of pseudolite in the latent space to realize feature clustering. Finally, the deep neural network is constructed by using the hidden features learned to further study the mapping relationship between different carrier phases of pseudolite and different indoor locations. In the online phase, the trained model and real-time carrier phases of pseudolite are used to predict the location of the positioning terminal. In this paper, by a large number of experiments, the performance of the pseudolite positioning system is evaluated under dynamic and static conditions. The effectiveness of the algorithm is evaluated by the comparison experiments, the experimental results show that the average positioning accuracy of the positioning system in a real indoor scene is 0.39 m, and the 95% positioning error is less than 0.85 m, which outperforms the traditional fingerprint positioning algorithms.
A Robust Direction of Arrival Estimation Method for Uniform Circular Array
Authors: Xu Zhengqin , Wu Shiqian , Yu Zhuliang , Guang Xingyu
Published: October 2019

Estimating the Direction of Arrival (DOA) is a basic and crucial problem in array signal processing. The existing DOA methods fail to obtain reliable and accurate results when noise and reverberation occur in real applications. In this paper, an accurate and robust estimation method for estimating the DOA of sources signal is proposed. Incorporating the Estimating Signal Parameters via Rotational Invariance Techniques (ESPRIT) algorithm with the RANdom SAmple Consensus (RANSAC) algorithm gives rise to the RAN-ESPRIT method, which removes outliers automatically in noise-corrupted environments. In this work, a uniform circular array (UCA) is converted into a virtual uniform linear array (ULA) to begin with. Then, the covariance matrix of the received signals of the virtual linear array is reconstructed, and the ESPRIT algorithm is deployed to estimate initial DOA of the source signal. Finally, the modified RANSAC method with automatically selected thresholds is used to fit the source signal to obtain accurate DOA. The proposed method can remove the unreliable DOA feature data and leads to more accuracy of DOA estimation of source signals in reverberation environments. Experimental results demonstrate that the proposed method is more robust and efficient compared to the traditional methods (i.e., ESPRIT, TLS-ESPRIT).
Fast Depth Estimation in a Single Image Using Lightweight Efficient Neural Network
Authors: Kim Sangwon , Nam Jaeyeal , Ko Byoungchul
Published: October 2019

Depth estimation is a crucial and fundamental problem in the computer vision field. Conventional methods re-construct scenes using feature points extracted from multiple images; however, these approaches require multiple images and thus are not easily implemented in various real-time applications. Moreover, the special equipment required by hardware-based approaches using 3D sensors is expensive. Therefore, software-based methods for estimating depth from a single image using machine learning or deep learning are emerging as new alternatives. In this paper, we propose an algorithm that generates a depth map in real time using a single image and an optimized lightweight efficient neural network (L-ENet) algorithm instead of physical equipment, such as an infrared sensor or multi-view camera. Because depth values have a continuous nature and can produce locally ambiguous results, pixel-wise prediction with ordinal depth range classification was applied in this study. In addition, in our method various convolution techniques are applied to extract a dense feature map, and the number of parameters is greatly reduced by reducing the network layer. By using the proposed L-ENet algorithm, an accurate depth map can be generated from a single image quickly and, in a comparison with the ground truth, we can produce depth values closer to those of the ground truth with small errors. Experiments confirmed that the proposed L-ENet can achieve a significantly improved estimation performance over the state-of-the-art algorithms in depth estimation based on a single image.
Wearables and the Quantified Self: Systematic Benchmarking of Physiological Sensors
Authors: Sagl Günther , Resch Bernd , Petutschnig Andreas , Kyriakou Kalliopi , Liedlgruber Michael , Wilhelm Frank H.
Published: October 2019

Wearable sensors are increasingly used in research, as well as for personal and private purposes. A variety of scientific studies are based on physiological measurements from such rather low-cost wearables. That said, how accurate are such measurements compared to measurements from well-calibrated, high-quality laboratory equipment used in psychological and medical research? The answer to this question, undoubtedly impacts the reliability of a study’s results. In this paper, we demonstrate an approach to quantify the accuracy of low-cost wearables in comparison to high-quality laboratory sensors. We therefore developed a benchmark framework for physiological sensors that covers the entire workflow from sensor data acquisition to the computation and interpretation of diverse correlation and similarity metrics. We evaluated this framework based on a study with 18 participants. Each participant was equipped with one high-quality laboratory sensor and two wearables. These three sensors simultaneously measured the physiological parameters such as heart rate and galvanic skin response, while the participant was cycling on an ergometer following a predefined routine. The results of our benchmarking show that cardiovascular parameters (heart rate, inter-beat interval, heart rate variability) yield very high correlations and similarities. Measurement of galvanic skin response, which is a more delicate undertaking, resulted in lower, but still reasonable correlations and similarities. We conclude that the benchmarked wearables provide physiological measurements such as heart rate and inter-beat interval with an accuracy close to that of the professional high-end sensor, but the accuracy varies more for other parameters, such as galvanic skin response.