bluuebunny/crossref_metadata_2025 huggingface dataset:
Cilia Stimulatory and Antibacterial Activities of T2R Bitter Taste Receptor Agonist Diphenhydramine: Insights into Repurposing Bitter Drugs for Nasal Infections
Authors: Kuek Li , McMahon Derek , Ma Ray , Miller Zoey , Jolivert Jennifer , Adappa Nithin , Palmer James , Lee Robert
Published: April 2022

T2R bitter taste receptors in airway motile cilia increase ciliary beat frequency (CBF) and nitric oxide (NO) production. Polymorphisms in some T2Rs are linked to disease outcomes in chronic rhinosinusitis (CRS) and cystic fibrosis (CF). We examined the expression of cilia T2Rs during the differentiation of human nasal epithelial cells grown at air–liquid interface (ALI). The T2R expression increased with differentiation but did not vary between CF and non-CF cultures. Treatment with Pseudomonas aeruginosa flagellin decreased the expression of diphenhydramine-responsive T2R14 and 40, among others. Diphenhydramine increased both NO production, measured by fluorescent dye DAF-FM, and CBF, measured via high-speed imaging. Increases in CBF were disrupted after flagellin treatment. Diphenhydramine impaired the growth of lab and clinical strains of P. aeruginosa, a major pathogen in CF and CF-related CRS. Diphenhydramine impaired biofilm formation of P. aeruginosa, measured via crystal violet staining, as well as the surface attachment of P. aeruginosa to CF airway epithelial cells, measured using colony-forming unit counting. Because the T2R agonist diphenhydramine increases NO production and CBF while also decreasing bacterial growth and biofilm production, diphenhydramine-derived compounds may have potential clinical usefulness in CF-related CRS as a topical therapy. However, utilizing T2R agonists as therapeutics within the context of P. aeruginosa infection may require co-treatment with anti-inflammatories to enhance T2R expression.
Circumsporozoite Protein of Plasmodium berghei- and George Baker Virus A-Derived Peptides Trigger Efficient Cell Internalization of Bioconjugates and Functionalized Poly(ethylene glycol)-b-poly(benzyl malate)-Based Nanoparticles in Human Hepatoma Cells
Authors: Vène Elise , Jarnouen Kathleen , Ribault Catherine , Vlach Manuel , Verres Yann , Bourgeois Mickaël , Lepareur Nicolas , Cammas-Marion Sandrine , Loyer Pascal
Published: April 2022

In order to identify the peptides, selected from the literature, that exhibit the strongest tropism towards human hepatoma cells, cell uptake assays were performed using biotinylated synthetic peptides bound to fluorescent streptavidin or engrafted onto nanoparticles (NPs), prepared from biotin-poly(ethylene glycol)-block-poly(benzyl malate) (Biot-PEG-b-PMLABe) via streptavidin bridging. Two peptides, derived from the circumsporozoite protein of Plasmodium berghei- (CPB) and George Baker (GB) Virus A (GBVA10-9), strongly enhanced the endocytosis of both streptavidin conjugates and NPs in hepatoma cells, compared to primary human hepatocytes and non-hepatic cells. Unexpectedly, the uptake of CPB- and GBVA10-9 functionalized PEG-b-PMLABe-based NPs by hepatoma cells involved, at least in part, the peptide binding to apolipoproteins, which would promote NP’s interactions with cell membrane receptors of HDL particles. In addition, CPB and GBVA10-9 peptide–streptavidin conjugates favored the uptake by hepatoma cells over that of the human macrophages, known to strongly internalize nanoparticles by phagocytosis. These two peptides are promising candidate ligands for targeting hepatocellular carcinomas.
Development of an Organ-Directed Exosome-Based siRNA-Carrier Derived from Autologous Serum for Lung Metastases and Testing in the B16/BL6 Spontaneous Lung Metastasis Model
Authors: Hazekawa Mai , Nishinakagawa Takuya , Hosokawa Masato , Ishibashi Daisuke
Published: April 2022

Exosomes are nano-sized extracellular vesicles that are known to carry various messages to distant cells. It was recently reported that cancer-derived exosomes are orientated to metastatic organs. However, there are no reports on drug carrier development using autologous serum-derived exosomes in vivo. The purpose of this study was to deliver therapeutic siRNAs for melanoma lung metastases using autologous serum-derived exosomes. Primary tumors were induced by subcutaneously injecting melanoma cells into the hindlimbs of female C57BL/6 mice. Primary tumors were surgically removed on day 14. On day 21 after tumor removal, lung metastases were evaluated. Exosomes were isolated from serum collected from mice on days 0, 3, 7, 10, and 14 after primary tumor inoculation. After isolating serum exosomes, siRNA-loaded exosomes were prepared. siRNA-loaded exosomes were intravenously injected into the B16/BL6 spontaneous lung metastasis model mice on days 0, 3, 7, and 10 after tumor removal. siRNA-loaded exosomes prepared with autologous serum-derived exosomes significantly decreased the number of metastatic lung colonies. Autologous serum-derived exosomes, which have high organ accumulation, could potentially be used as efficient carriers of therapeutic siRNAs for melanoma patients with lung metastases.
Non-Effective Improvement of Absorption for Some Nanoparticle Formulations Explained by Permeability under Non-Sink Conditions
Authors: Sugita Kazuya , Takata Noriyuki , Yonemochi Etsuo
Published: April 2022

We evaluated the in vitro permeability of nanoparticle formulations of high and low lipophilic compounds under non-sink conditions, wherein compounds are not completely dissolved. The permeability of the highly lipophilic compound, griseofulvin, was improved by about 30% due to nanonization under non-sink conditions. Moreover, this permeability was about 50% higher than that under sink conditions. On the other hand, for the low lipophilic compound, hydrocortisone, there was no difference in permeability between micro-and nano-sized compounds under non-sink conditions. The nanonization of highly lipophilic compounds improves the permeability of the unstirred water layer (UWL), which in turn improves overall permeability. On the other hand, because the rate-limiting step in permeation for the low lipophilic compounds is the diffusion of the compounds in the membrane, the improvement of UWL permeability by nanonization does not improve the overall permeability. Based on this mechanism, nanoparticle formulations are not effective for low lipophilic compounds. To accurately predict the absorption of nanoparticle formulations, it is necessary to consider their permeability under non-sink conditions which reflect in vivo conditions.
Performance Optimization of Holmium Doped Fiber Amplifiers for Optical Communication Applications in 2–2.15 μm Wavelength Range
Authors: Alharbi Abdullah G. , Kanwal Firdous , Ghafoor Salman , Habib Nazish , Kanwal Benish , Atieh Ahmad , Kousar Tasleem , Mirza Jawad
Published: April 2022

In this paper, we address the performance optimization of Holmium doped fiber amplifier (HDFA) for optical communications in 2–2.15 μm wavelength range based on a single in-band forward pump source. The performance of the HDFA is analyzed with the help of theoretical simulations by considering an optimized length of Holmium doped fiber (HDF), doping concentration of Ho3+, and pump power. The impact of signal wavelength and power on gain, amplified spontaneous emission (ASE) noise, and noise figure (NF) of the amplifier is investigated. Furthermore, we investigate the variations in the gain of the amplifier, its output power, and NF by varying the power and wavelength of the pump source. After optimizing the parameters of the amplifier, the peak gain observed is around 56.5 dB, the 3 dB saturated output power obtained is 33.3 dBm, and the output power is 3 W at signal wavelength of 2.0321 μm for HDF having an optimized length of 12 m and pump power of 3.5 W. Minimum NF of around 8.2 dB is observed at 2.0321 μm for signal power of −5 dBm. The impact of ion-ion interaction on the performance of HDFA is also investigated. A reduction of 24.2 dB and 0.051 W is observed in peak gain and output power of HDFA, respectively by considering the ion-ion interaction.
Biocompatible Films of Calcium Alginate Inactivate Enveloped Viruses Such as SARS-CoV-2
Authors: Cano-Vicent Alba , Hashimoto Rina , Takayama Kazuo , Serrano-Aroca Ángel
Published: April 2022

The current pandemic is urgently demanding the development of alternative materials capable of inactivating the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that causes the coronavirus 2019 (COVID-19) disease. Calcium alginate is a crosslinked hydrophilic biopolymer with an immense range of biomedical applications due to its excellent chemical, physical, and biological properties. In this study, the cytotoxicity and antiviral activity of calcium alginate in the form of films were studied. The results showed that these films, prepared by solvent casting and subsequent crosslinking with calcium cations, are biocompatible in human keratinocytes and are capable of inactivating enveloped viruses such as bacteriophage phi 6 with a 1.43-log reduction (94.92% viral inactivation) and SARS-CoV-2 Delta variant with a 1.64-log reduction (96.94% viral inactivation) in virus titers. The antiviral activity of these calcium alginate films can be attributed to its compacted negative charges that may bind to viral envelopes inactivating membrane receptors.
Fire Resistance Performance of Steel–Polymer Prefabricated Composite Floors Using Standard Fire Tests
Authors: Park Min Jae , Alemayehu Robel Wondimu , Ju Young K.
Published: April 2022

In this study, the fire resistance performance of steel–polymer prefabricated composite floors, which have a sandwich-type structure, was assessed via standard fire tests and analyzed using finite element analysis. This form of analysis should consider two aspects, namely the thermal and structural fields, so as to simulate complicated material properties and large deformations. As previous studies have already conducted analysis in the thermal field, this study entailed only the structural analysis based on the temperature distributions obtained from the thermal analysis. The variables of the specimens were the thicknesses of the top and bottom steel plates and polymers. According to the analysis results, the top steel plate thickness had no impact on the stability ratings, a criterion for fire resistance performance, whereas the bottom steel plate showed a linear correlation with the stability rating. An equation for the stability rating of composite floors was proposed, and an equation for fire resistance performance was devised based on the insulation ratings, which were obtained from the thermal analysis results.
Ternary Electrical Memory Devices Based on Polycarbazole: SnO2 Nanoparticles Composite Material
Authors: Zhang Yingna , Dou Feng , Zhou Yijia , Zhao Xiaofeng , Chen Jiangshan , Wang Cheng , Wang Shuhong
Published: April 2022

In this paper, a D–A polymer (PIB) containing carbazole as the donor group in the main chain and benzimidazole benzisoindolinone as the acceptor group was synthesized by Suzuki reaction. The Suzuki reaction, also known as the Suzuki coupling reaction, is a relatively new organic coupling reaction in which aryl or alkenyl boronic acids or boronic acid esters react with chlorine, bromine, iodoaromatic hydrocarbons or alkenes under the catalysis of zerovalent palladium complexes cross-coupling. A series of devices were fabricated by a spin-coating approach, and the devices all exhibited ternary resistance switching storage behavior. Among them, the composite device with the mass fraction of SnO2 NPs of 5 wt% has the best storage performance, with a threshold voltage of −0.4 V and a switching current ratio of 1:101.5:104.5. At the same time, the current of the device remained stable after a 3-h test. Furthermore, after 103 cycles, the current has no obvious attenuation. The device has good stability and continuity. Moreover, the conduction mechanism is further revealed. Inorganic nanoparticle composite devices have splendid memory performances and exhibit underlying application significance in storing data.
Study on the Incorporation of Chitosan Flakes in Electrospun Polycaprolactone Scaffolds
Authors: Querido Diana , Vieira Tânia , Ferreira José Luís , Henriques Célia , Borges João Paulo , Silva Jorge Carvalho
Published: April 2022

Hybrid scaffolds obtained by combining two or more biopolymers are studied in the context of tissue regeneration due to the possibility of achieving new functional properties or structural features. The aim of this work was to produce a new type of hybrid polycaprolactone (PCL)/chitosan (CS) electrospun mat through the controlled deposition of CS flakes interspaced between the PCL fibers. A poly(ethylene oxide) (PEO) solution was used to transport CS flakes with controlled size. This, and the PCL solution, were simultaneously electrospun onto a rotatory mandrel in a perpendicular setup. Different PCL/CS mass ratios were also studied. The morphology of the resulting fibers, evaluated by SEM, confirmed the presence of the CS flakes between the PCL fibers. The addition of PEO/CS fibers resulted in hydrophilic mats with lower Young’s modulus relatively to PCL mats. In vitro cell culture results indicated that the addition of CS lowers both the adhesion and the proliferation of human dermal fibroblasts. The present work demonstrates the feasibility of achieving a controlled deposition of a polymeric component in granular form onto a collector where electrospun nanofibers are being deposited, thereby producing a hybrid scaffold.
Preparation of a Novel Nanocomposite and Its Antibacterial Effectiveness against Enterococcus faecalis—An In Vitro Evaluation
Authors: Jose Jerry , Teja Kavalipurapu Venkata , Janani Krishnamachari , Alam Mohammad Khursheed , Khattak Osama , Salloum Mahmoud Gamal , Magar Shilpa S. , Magar Shaliputra P. , Rajeshkumar Shanmugam , Palanivelu Ajitha , Srivastava Kumar Chandan , Shrivastava Deepti
Published: April 2022

The interest in the use of green-mediated synthesis of nanoparticles (NPs) is shown to have increased due to their biocompatibility and reduction of overall production costs. The current study aimed to evaluate a novel nanocomposite (NC) prepared by using a combination of zinc oxide, silver and chitosan with lemon extract as a cross-linking agent and assessed its antimicrobial effectiveness against Enterococcus faecalis (E. faecalis). The NPs and NC were prepared individually using a modification of previously established methods. Ananalys is of the physiochemical properties of the NC was conducted using ultraviolet-visible spectroscopy (UV-Vis) (Shimadzu Corporation, Kyoto, Japan). and transmission electron microscopy (TEM) imaging(HR-TEM; JEOL Ltd., Akishima-shi, Japan. The microbial reduction with this novel NC was evaluated by measuring the minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) using a tube assay analytic technique. A time-kill assay analysis was conducted to evaluate the kinetic potential against E. faecalis at different time intervals. The novel NC showed a homogenous nanoparticle size under TEM imaging and under UV-Vis established an absorption range of 350–420 nm making it similar to its individual counterparts. The MIC and MIB were measured at 62.5 ± 20 mg/L (p < 0.05) and 250 ± 72 mg/L (p < 0.05), respectively. A time-kill assay analysis for the NC showed 5 h was required to eradicate E. faecalis. Based on the achieved results, it was seen that the novel NC using a combination of silver, zinc oxide and chitosan showed improved antimicrobial action against E. faecalis compared with its individual components under laboratory conditions. A complete eradication of 108 log units of E. faecalis at 250 mg/L occurred after a total of 5 h. These preliminary results establish the use of lemon extract-mediated silver, zinc and chitosan-based NC had an antibacterial effectiveness against E. faecalis similar to the individual counterparts used for its production under laboratory conditions.
Pyrolysis Evaluation of Tennis String Polyurethane and Water-Borne Polyurethane Wastes through Isoconversional Kinetic Analysis
Authors: Wan Haibo , Huang Zhen
Published: April 2022

A detailed kinetic analysis of pyrolysis processes of Tennis string polyurethane (TSPU) waste and waterborne polyurethane (WPU) waste was carried out in the present paper. Non-isothermal pyrolysis characterizations of two wastes were acquired through thermogravimetric determinations under the constant heating rates of 5, 10, 15 and 20 K/min. Experimental results showed that the pyrolysis processes of TSPU and WPU were mainly characteristic of three stages and two stages, respectively. Two pyrolysis performance indices, the devolatilization index (DI) and heat-resistance index (HRI), were used to interpret the heating rate effect on the pyrolysis features and different thermal dependences of TSPU and WPU. Isoconversional kinetic analysis was thoroughly performed with model-free and model-fitting methods. By using Starink, Vyazovkin–Dollimore and Coats–Redfern methods, the activation energies thus obtained were in the range of 103.3~148.3 kJ/mol and 92.5~204.3 kJ/mol, respectively, for TSPU and WPU, over the entire pyrolysis process. Their respective pre-exponential factor lnA values were determined to be within 17.94~33.42 min−1 and 16.56~20.82 min−1. Thermodynamic parameters in terms of ΔG#, ΔH# and ΔS# throughout the entire pyrolysis process were also calculated. Finally, by means of the model-fitting Coats–Redfern method, the most appropriate mechanism functions were ascertained for, respectively, describing multi-stage pyrolysis degradations of TSPU and WPU waste. These results may offer meaningful support for designing any industrial pyrolysis reactor to dispose of polyurethane wastes.
Cuprous Oxide Nanoparticles: Synthesis, Characterization, and Their Application for Enhancing the Humidity-Sensing Properties of Poly(dioctylfluorene)
Authors: Tahir Muhammad , Zeb Muhammad , Alamgeer  , Hussain Shahid , Sarker Mahidur R. , Khan Dil Nawaz , Wahab Fazal , Ali Sawal Hamid Md
Published: April 2022

In this paper, we report on the synthesis—via the wet chemical precipitation route method—and thin film characteristics of inorganic semiconductor, cuprous oxide (Cu2O) nanoparticles, for their potential application in enhancing the humidity-sensing properties of semiconducting polymer poly(9,9-dioctylfluorene) (F8). For morphological analysis of the synthesized Cu2O nanoparticles, transmission electron microscope (TEM) and scanning electron microscope (SEM) micrographs are studied to investigate the texture, distribution, shape, and sizes of Cu2O crystallites. The TEM image of the Cu2O nanoparticles exhibits somewhat non-uniform distribution with almost uniform shape and size having an average particle size of ≈24 ± 2 nm. Fourier transformed infrared (FTIR) and X-ray diffraction (XRD) spectra are studied to validate the formation of Cu2O nanoparticles. Additionally, atomic force microscopy (AFM) is performed to analyze the surface morphology of polymer-inorganic (F8-Cu2O) nanocomposites thin film to see the grain sizes, mosaics, and average surface roughness. In order to study the enhancement in sensing properties of F8, a hybrid organic–inorganic (F8-Cu2O) surface-type humidity sensor Ag/F8-Cu2O/Ag is fabricated by employing F8 polymer as an active matrix layer and Cu2O nanoparticles as a dopant. The Ag/F8-Cu2O/Ag device is prepared by spin coating a 10:1 wt% solution of F8-Cu2O nanocomposite on pre-patterned silver (Ag) electrodes on glass. The inter-electrode gap (≈5 μm) between Ag is developed by photolithography. To study humidity sensing, the Ag/F8-Cu2O/Ag device is characterized by measuring its capacitance (C) as a function of relative humidity (%RH) at two different frequencies (120 Hz and 1 kHz). The device exhibits a broad humidity sensing range (27–86%RH) with shorter response time and recovery time, i.e., 9 s and 8 s, respectively. The present results show significant enhancement in the humidity-sensing properties as compared to our previously reported results of Ag/F8/Ag sensor wherein the humidity sensing range was 45–78%RH with 15 s and 7 s response and recovery times, respectively. The improvement in the humidity-sensing properties is attributed to the potential use of Cu2O nanoparticles, which change the hydrophobicity, surface to volume ratio of Cu2O nanoparticles, as well as modification in electron polarizability and polarity of the F8 matrix layer.
Synthetic Biology: A New Era in Hydrocarbon Bioremediation
Authors: Jiménez-Díaz Valentina , Pedroza-Rodríguez Aura Marina , Ramos-Monroy Oswaldo , Castillo-Carvajal Laura C.
Published: April 2022

Crude oil is a viscous dark liquid resource composed by a mix of hydrocarbons which, after refining, is used for the elaboration of distinct products. A major concern is that many petroleum components are highly toxic due to their teratogenic, hemotoxic, and carcinogenic effects, becoming an environmental concern on a global scale, which must be solved through innovative, efficient, and sustainable techniques. One of the most widely used procedures to totally degrade contaminants are biological methods such as bioremediation. Synthetic biology is a scientific field based on biology and engineering principles, with the purpose of redesigning and restructuring microorganisms to optimize or create new biological systems with enhanced features. The use of this discipline offers improvement of bioremediation processes. This article will review some of the techniques that use synthetic biology as a platform to be used in the area of hydrocarbon bioremediation.
Read the Signs: Detecting Early Warning Signals of Interreligious Conflict
Authors: Ochs Peter , Fahim Essam , Pinzon Paola
Published: April 2022

Building on recent directions in religion-related social and political science, our essay addresses the need for location-specific and religion-specific scientific research that might contribute directly to local and regional interreligious peacemaking. Over the past 11 years, our US–Pakistani research team has conducted research of this kind. We have developed a social scientific method for diagnosing the probable near-future behavior of religious stakeholder groups toward other groups. By integrating features of ethnography, linguistics, and semiotics, the method enables researchers to read a range of ethno-linguistic signals that appear uniquely in the discourses of religious groups. Examining the results, we observe, firstly, that our religion and location-specific science identifies features of religious group behavior that are not evident in broader social scientific studies of religion and conflict. We observe, secondly, that our science integrates constative and performative elements: it seeks facts, and it serves a purpose. We conclude that strictly constative fact-driven sciences may fail to detect certain crucial features of religious stakeholder group behavior.
Long-Term Sustainability of Copper and Iron Based on a System Dynamics Model
Authors: Teseletso Larona S. , Adachi Tsuyoshi
Published: April 2022

Copper and iron are critical to the economic growth of modern society. Nations depend on these metals for the development of infrastructure, transportation, and other industries. However, concerns regarding future availability of “peak minerals” with a “limit to growth” have been extensively debated. The purpose of this study was to investigate the amount of potential resources and the recycling rate from secondary metal scrap recycling for the sustainable development of mineral resources. The long-term mineral supply and demand balance with respect to recycling for copper and iron were developed for the next 50 years at the regional and global levels. The results indicate that the supply of copper would increase four-fold by 2070 compared to 1991, with primary copper remaining the main contributing source. For iron, the total supply would increase by nine times from 2000 to 2070, with secondary recycling surpassing the primary iron supply by 2033 and becoming the main contributor by 2070. Even though there is no future resource constraint, further promotion of scrap recycling, especially for copper, is necessary to address environmental concerns through reduction in material extraction. Emphasizing the importance of metals in society is essential for stock accountability through resource efficiency and resource conservation.
A Scheme for Quickly Simulating Extraterrestrial Solar Radiation over Complex Terrain on a Large Spatial-Temporal Span—A Case Study over the Entirety of China
Authors: Lin Siwei , Chen Nan , Zhou Qianqian , Lin Tinmin , Li Huange
Published: April 2022

Extraterrestrial solar radiation (ESR) is the essential basic background for solar radiation, which determines the occurrence of the weather and atmospheric phenomena. Since the influence of ESR variation on actual rugged terrain is a diverse, complex, and dynamic process, simulating ESR over a large spatial-temporal span, especially with a high-resolution digital elevation model (DEM), is a significant challenge. In this paper, we developed a new scheme for simulating ESR over the entirety of China using a DEM with a resolution of 30 m. To fully consider regional terrain status, the feature variables used were elevation, slope, and aspects of the located grid and the surrounding four grids to reveal the topography. In addition, latitude was used as a feature variable to consider the geographical location, and the month number was used to consider the duration. On the basis of different geographical locations, the training dataset was established from 20,000 grids. With the feature variable composition and training dataset, a backpropagation artificial neural network (BP ANN) was found to have the best performance compared with the other three machine learning methods in simulating ESR for a DEM. In terms of the proposed scheme and BP ANN, we drew an ESR map of China with a resolution of 30 m. The determination coefficient of the simulation result achieved 0.99 and the root-mean-square error was less than 50 MJ/m2 in all sample areas, confirming its remarkable accuracy. In terms of efficiency, the time consumption of ESR simulated using the proposed scheme shrinks over 150 times in all sample areas compared to that simulated via the theoretical model. Simultaneously, the developed scheme was also used to simulate an ESR for a DEM with a resolution of 90 m to verify the universality and robustness of the developed scheme. In addition, we used the proposed scheme to derive the direct solar radiation and global solar radiation, thereby further proving the reliability and applicability of our study. Overall, our work convincingly proved that the proposed scheme is a potential and effective approach for quickly simulating ESR with high accuracy. This study provides the basis for different solar radiation inversions of long time series and large spatial scales, offering additional insights for simulating ESR on a large spatial-temporal span.
Optimal Band Selection for Airborne Hyperspectral Imagery to Retrieve a Wide Range of Cyanobacterial Pigment Concentration Using a Data-Driven Approach
Authors: Jang Wonjin , Park Yongeun , Pyo JongCheol , Park Sanghyun , Kim Jinuk , Kim Jin Hwi , Cho Kyung Hwa , Shin Jae-Ki , Kim Seongjoon
Published: April 2022

Understanding the concentration and distribution of cyanobacteria blooms is an important aspect of managing water quality problems and protecting aquatic ecosystems. Airborne hyperspectral imagery (HSI)—which has high temporal, spatial, and spectral resolutions—is widely used to remotely sense cyanobacteria bloom, and it provides the distribution of the bloom over a wide area. In this study, we determined the input spectral bands that were relevant in effectively estimating the main two pigments (PC, Phycocyanin; Chl-a, Chlorophyll-a) of cyanobacteria by applying data-driven algorithms to HSI and then evaluating the change in the spatio-temporal distribution of cyanobacteria. The input variables for the algorithms consisted of reflectance band ratios associated with the optical properties of PC and Chl-a, which were calculated by the selected hyperspectral bands using a feature selection method. The selected input variable was composed of six reflectance bands (465.7–589.6, 603.6–631.8, 641.2–655.35, 664.8–679.0, 698.0–712.3, and 731.4–784.1 nm). The artificial neural network showed the best results for the estimation of the two pigments with average coefficients of determination 0.80 and 0.74. This study proposes relevant input spectral information and an algorithm that can effectively detect the occurrence of cyanobacteria in the weir pool along the Geum river, South Korea. The algorithm is expected to help establish a preemptive response to the formation of cyanobacterial blooms, and to contribute to the preparation of suitable water quality management plans for freshwater environments.
A Review of Remote Sensing for Water Quality Retrieval: Progress and Challenges
Authors: Yang Haibo , Kong Jialin , Hu Huihui , Du Yao , Gao Meiyan , Chen Fei
Published: April 2022

Water pollution has become one of the most serious issues threatening water environments, water as a resource and human health. The most urgent and effective measures rely on dynamic and accurate water quality monitoring on a large scale. Due to their temporal and spatial advantages, remote sensing technologies have been widely used to retrieve water quality data. With the development of hyper-spectral sensors, unmanned aerial vehicles (UAV) and artificial intelligence, there has been significant advancement in remotely sensed water quality retrieval owing to various data availabilities and retrieval methodologies. This article presents the application of remote sensing for water quality retrieval, and mainly discusses the research progress in terms of data sources and retrieval modes. In particular, we summarize some retrieval algorithms for several specific water quality variables, including total suspended matter (TSM), chlorophyll-a (Chl–a), colored dissolved organic matter (CDOM), chemical oxygen demand (COD), total nitrogen (TN) and total phosphorus (TP). We also discuss the significant challenges to atmospheric correction, remotely sensed data resolution, and retrieval model applicability in the domains of spatial, temporal and water complexity. Finally, we propose possible solutions to these challenges. The review can provide detailed references for future development and research in water quality retrieval.
Improved Daily Evapotranspiration Estimation Using Remotely Sensed Data in a Data Fusion System
Authors: Yang Yun , Anderson Martha , Gao Feng , Xue Jie , Knipper Kyle , Hain Christopher
Published: April 2022

Evapotranspiration (ET) represents crop water use and is a key indicator of crop health. Accurate estimation of ET is critical for agricultural irrigation and water resource management. ET retrieval using energy balance methods with remotely sensed thermal infrared data as the key input has been widely applied for irrigation scheduling, yield prediction, drought monitoring and so on. However, limitations on the spatial and temporal resolution of available thermal satellite data combined with the effects of cloud contamination constrain the amount of detail that a single satellite can provide. Fusing satellite data from different satellites with varying spatial and temporal resolutions can provide a more continuous estimation of daily ET at field scale. In this study, we applied an ET fusion modeling system, which uses a surface energy balance model to retrieve ET using both Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) data and then fuses the Landsat and MODIS ET retrieval timeseries using the Spatial-Temporal Adaptive Reflectance Fusion Model (STARFM). In this paper, we compared different STARFM ET fusion implementation strategies over various crop lands in the central California. In particular, the use of single versus two Landsat-MODIS pair images to constrain the fusion is explored in cases of rapidly changing crop conditions, as in frequently harvested alfalfa fields, as well as an improved dual-pair method. The daily 30 m ET retrievals are evaluated with flux tower observations and analyzed based on land cover type. This study demonstrates improvement using the new dual-pair STARFM method compared with the standard one-pair STARFM method in estimating daily field scale ET for all the major crop types in the study area.
Intensity/Inertial Integration-Aided Feature Tracking on Event Cameras
Authors: Li Zeyu , Liu Yong , Zhou Feng , Li Xiaowan
Published: April 2022

Achieving efficient and accurate feature tracking on event cameras is a fundamental step for practical high-level applications, such as simultaneous localization and mapping (SLAM) and structure from motion (SfM) and visual odometry (VO) in GNSS (Global Navigation Satellite System)-denied environments. Although many asynchronous tracking methods purely using event flow have been proposed, they suffer from high computation demand and drift problems. In this paper, event information is still processed in the form of synthetic event frames to better adapt to the practical demands. Weighted fusion of multiple hypothesis testing with batch processing (WF-MHT-BP) is proposed based on loose integration of event, intensity, and inertial information. More specifically, with inertial information acting as priors, multiple hypothesis testing with batch processing (MHT-BP) produces coarse feature-tracking solutions on event frames in a batch processing way. With a time-related stochastic model, a weighted fusion mechanism fuses feature-tracking solutions from event and intensity frames compared with other state-of-the-art feature-tracking methods on event cameras. Evaluation on public datasets shows significant improvements on accuracy and efficiency and comparable performances in terms of feature-tracking length.
Thermal Responses of the Largest Freshwater Lake in the Tibetan Plateau and Its Nearby Saline Lake to Climate Change
Authors: Wen Lijuan , Wang Chan , Li Zhaoguo , Zhao Lin , Lyu Shihua , Leppäranta Matti , Kirillin Georgiy , Chen Shiqiang
Published: April 2022

There are thousands of lakes in the Tibetan Plateau (TP), and most are saline. However, little is known about the responses of TP lakes to climate change, especially saline ones. We investigated the thermal responses of the largest freshwater lake (Ngoring Lake) in the TP and its nearby small saline lake (Hajiang Salt Pond) to climate change using the improved lake scheme in the Community Land model (CLM4-LISSS), in which we primarily developed the salinity parameterizations previously evaluated in the Great Salt Lake in USA and further considered the effect of salinity on the temperature of the maximum density of saline water in the present study. The improved lake model with salinity parameterizations was first applied to a saline lake in the TP, where saline lakes make up the majority of water bodies. The CLM4-LISSS model could effectively simulate lake surface water temperature (LSWT), lake water temperature (LT) and ice thickness in Ngoring Lake. Additionally, the model including our salinity parameterizations significantly improved simulations of LSWT and LT in Hajiang Salt Pond, especially in winter. The LSWT of the two completely opposite lakes were warming in the simulations at a rate above 0.6 °C/decade. Meteorological forces were the main driving factor, with increasing downward longwave radiation, air temperature and air humidity, as well as weakening winds contributing to LSWT increase. Compared to a hypothetical shallow freshwater lake, the greater depth of Ngoring Lake made its surface warm faster, and salinity slightly accelerated the warming of Hajiang Salt Pond. Monthly mean LSWT differences between the two lakes were induced by salinity effects in cold periods and lake depth in the unfrozen period. In response to a warming climate, the LSWT in the ice-free Hajiang Salt Pond rapidly increased from January to April due to the warming climate, whereas the LSWT of Ngoring Lake increased faster in the first and last month of the ice-cover period due to later ice-on and earlier ice-off. This study will provide a useful tool for saline lakes in the TP and help deepen our knowledge about the responses of TP lakes, especially the saline lakes, to climate change, as well as response differences between freshwater and saline lakes and the reasons for these differences.
Radargrammetric DSM Generation by Semi-Global Matching and Evaluation of Penalty Functions
Authors: Wang Jinghui , Gong Ke , Balz Timo , Haala Norbert , Soergel Uwe , Zhang Lu , Liao Mingsheng
Published: April 2022

Radargrammetry is a useful approach to generate Digital Surface Models (DSMs) and an alternative to InSAR techniques that are subject to temporal or atmospheric decorrelation. Stereo image matching in radargrammetry refers to the process of determining homologous points in two images. The performance of image matching influences the final quality of DSM used for spatial-temporal analysis of landscapes and terrain. In SAR image matching, local matching methods are commonly used but usually produce sparse and inaccurate homologous points adding ambiguity to final products; global or semi-global matching methods are seldom applied even though more accurate and dense homologous points can be yielded. To fill this gap, we propose a hierarchical semi-global matching (SGM) pipeline to reconstruct DSMs in forested and mountainous regions using stereo TerraSAR-X images. In addition, three penalty functions were implemented in the pipeline and evaluated for effectiveness. To make accuracy and efficiency comparisons between our SGM dense matching method and the local matching method, the normalized cross-correlation (NCC) local matching method was also applied to generate DSMs using the same test data. The accuracy of radargrammetric DSMs was validated against an airborne photogrammetric reference DSM and compared with the accuracy of NASA’s 30 m SRTM DEM. The results show the SGM pipeline produces DSMs with height accuracy and computing efficiency that exceeds the SRTM DEM and NCC-derived DSMs. The penalty function adopting the Canny edge detector yields a higher vertical precision than the other two evaluated penalty functions. SGM is a powerful and efficient tool to produce high-quality DSMs using stereo Spaceborne SAR images.
A Shrink-Branch-Bound Algorithm for eLoran Pseudorange Positioning Initialization
Authors: Liu Kaiqi , Yuan Jiangbin , Yan Wenhe , Yang Chaozhong , Guo Wei , Li Shifeng , Hua Yu
Published: April 2022

Currently, eLoran is the ideal backup and supplement for global navigation satellite systems. The time synchronization accuracy between stations in the eLoran system has improved, providing conditions for eLoran pseudorange positioning. The pseudorange positioning of eLoran is a nonlinear least-squares problem and the location of the eLoran transmitting stations may cause the above problem to be non-convex. This makes the conventional pseudorange positioning al-gorithm strongly depend on the initial value when solving the eLoran pseudorange positioning. We propose a shrink-branch-bound (SBB) algorithm to solve the eLoran pseudorange positioning initialization problem. The algorithm first uses a shrink method to reduce the search space of the position estimator. Then, optimization is performed using a branch and bound algorithm within the shrunk region, where a trust region reflective algorithm is used for the lower bound process. The algorithm can help the receiver to complete the initial positioning without any initial value information. Simulation experiments verify that the algorithm has a success rate of more than 99.5% in solving the initialization problem of eLoran pseudorange positioning, and can be used as an initialization algorithm for pseudorange positioning problems for eLoran or other long-range terrestrial-based radio navigation system.
Effect of Supplementing Grass Silage-Based Diets with Concentrate Carbohydrate Sources with Different Fermentation Profiles on N Metabolism of Beef Heifers Fed to Maintenance
Authors: Kirwan Stuart F. , Pierce Karina M. , Serra Eleonora , Gath Vivian , Rajauria Gaurav , Boland Tommy M.
Published: April 2022

The synchronous supply of energy and nitrogen (N) substrates to the rumen microbes on grass silage (GS)-based diets can potentially lead to reduced levels of N excreted in the urine. The objective of this study was to evaluate the effect of supplementing GS-based diet with carbohydrate sources differing in rumen fermentation profile on N metabolism of beef heifers. Six Belgian Blue × Holstein Friesian cross beef heifers (487 ± 29 kg BW) were used in a 3 × 3 Latin Square design (n = 6). Dietary treatments were: (RB) GS supplemented with rolled barley; (MM) GS supplemented with maize meal and; (SH) GS supplemented with soya hulls offered at 40:60 forage to concentrate ratio on a dry matter (DM) basis, at maintenance feeding (40 g DM/kg BW0.75). Carbohydrate source had no effect on DM, organic matter, or N intake or total N excretion and the amount of N excreted in the urine (p > 0.05). Animals offered MM excreted a higher percentage of N in the faeces and a lower percentage of N in the urine compared to animals offered RB (p < 0.05). There was a time by interaction for ruminal ammonia (NH3) concentrations (p < 0.01). Ruminal NH3 concentrations peaked at 2 h post-feeding for all treatments. At 3 h post-feeding, ruminal NH3 concentrations for the RB treatment remained higher compared to MM and SH treatments. Molar proportions and total ruminal volatile fatty acids were similar among dietary treatments (p > 0.05). Supplementing GS-based diets with different carbohydrate sources had no impact on the total level of N excreted or the amount of N excreted in the urine. However, there was a higher percentage of N excreted in the faeces and a lower percentage of N excreted in the urine when animals were offered MM compared to those offered RB (p < 0.05).
Deep Feature Fusion and Optimization-Based Approach for Stomach Disease Classification
Authors: Mohammad Farah , Al-Razgan Muna
Published: April 2022

Cancer is the deadliest disease among all the diseases and the main cause of human mortality. Several types of cancer sicken the human body and affect organs. Among all the types of cancer, stomach cancer is the most dangerous disease that spreads rapidly and needs to be diagnosed at an early stage. The early diagnosis of stomach cancer is essential to reduce the mortality rate. The manual diagnosis process is time-consuming, requires many tests, and the availability of an expert doctor. Therefore, automated techniques are required to diagnose stomach infections from endoscopic images. Many computerized techniques have been introduced in the literature but due to a few challenges (i.e., high similarity among the healthy and infected regions, irrelevant features extraction, and so on), there is much room to improve the accuracy and reduce the computational time. In this paper, a deep-learning-based stomach disease classification method employing deep feature extraction, fusion, and optimization using WCE images is proposed. The proposed method comprises several phases: data augmentation performed to increase the dataset images, deep transfer learning adopted for deep features extraction, feature fusion performed on deep extracted features, fused feature matrix optimized with a modified dragonfly optimization method, and final classification of the stomach disease was performed. The features extraction phase employed two pre-trained deep CNN models (Inception v3 and DenseNet-201) performing activation on feature derivation layers. Later, the parallel concatenation was performed on deep-derived features and optimized using the meta-heuristic method named the dragonfly algorithm. The optimized feature matrix was classified by employing machine-learning algorithms and achieved an accuracy of 99.8% on the combined stomach disease dataset. A comparison has been conducted with state-of-the-art techniques and shows improved accuracy.
Detecting Trivariate Associations in High-Dimensional Datasets
Authors: Liu Chuanlu , Wang Shuliang , Yuan Hanning , Dang Yingxu , Liu Xiaojia
Published: April 2022

Detecting correlations in high-dimensional datasets plays an important role in data mining and knowledge discovery. While recent works achieve promising results, detecting multivariable correlations especially trivariate associations still remains a challenge. For example, maximal information coefficient (MIC) introduces generality and equitability to detect bivariate correlations but fails to detect multivariable correlation. To solve the problem mentioned above, we proposed quadratic optimized trivariate information coefficient (QOTIC). Specifically, QOTIC equitably measures dependence among three variables. Our contributions are three-fold: (1) we present a novel quadratic optimization procedure to approach the correlation with high accuracy; (2) QOTIC exceeds existing methods in generality and equitability as QOTIC has general test functions and is applicable in detecting multivariable correlation in datasets of various sample sizes and noise levels; (3) QOTIC achieved both higher accuracy and higher time-efficiency than previous methods. Extensive experiments demonstrate the excellent performance of QOTIC.
Motorway Bottleneck Probability Estimation in Connected Vehicles Environment Using Speed Transition Matrices
Authors: Tišljarić Leo , Vrbanić Filip , Ivanjko Edouard , Carić Tonči
Published: April 2022

Increased development of the urban areas leads to intensive transport service demand, especially on urban motorways. To increase traffic flow and reduce congestion, motorway traffic bottlenecks caused by high traffic demand need to be efficiently resolved using Intelligent Transport Systems services. Communication technology development that supports Connected Vehicles (CVs), which act as an active mobile sensor for collecting traffic data, provides an opportunity to harness the large datasets to develop novel methods regarding traffic bottlenecks detection. This paper presents a speed transition matrix based model for bottleneck probability estimation on motorways. The method is based on the computation of the speed at the vehicle transition point between consecutive motorway segments, which forms a traffic pattern that is represented using transition matrices. The main feature extracted from the traffic patterns was the center of mass, whose position is used as an input to the fuzzy-based system for bottleneck probability estimation. The proposed method is evaluated on four different simulated motorway traffic scenarios: (i) traffic collision site, (ii) short recurring bottleneck, (iii) long recurring bottleneck, and (iv) moving bottleneck. The method achieves comparable bottleneck detection results on every scenario, with a total accuracy of 92% on the validation dataset. The results indicate possible implementation of the method in the motorway traffic environment with a high CVs penetration rate using them as the sensory input data for the control systems based on the machine learning algorithms.