bluuebunny/crossref_metadata_2025 huggingface dataset:
Profiling of Intestinal Microbiota in Patients Infected with Respiratory Influenza A and B Viruses
Authors: Al Khatib Hebah A. , Mathew Shilu , Smatti Maria K. , Eltai Nahla O. , Pathan Sameer A. , Al Thani Asmaa A. , Coyle Peter V. , Al Maslamani Muna A. , Yassine Hadi M.
Published: June 2021

Little is known about the association between respiratory viral infections and their impact on intestinal microbiota. Here, we compared the effect of influenza types, A and B, and influenza shedding in patients’ stools on the gut microbiota diversity and composition. Deep sequencing analysis was performed for the V4 region of the 16S rRNA gene. Fecal samples were collected from 38 adults with active respiratory influenza infection and 11 age-matched healthy controls. Influenza infection resulted in variations in intestinal bacterial community composition rather than in overall diversity. Overall, infected patients experienced an increased abundance of Bacteroidetes and a corresponding decrease in Firmicutes. Differential abundance testing illustrated that differences in gut microbiota composition were influenza type-dependent, identifying ten differentially abundant operational taxonomic units (OTUs) between influenza A- and influenza B-infected patients. Notably, virus shedding in fecal samples of some patients had significantly reduced gut bacterial diversity (p = 0.023). Further taxonomic analysis revealed that the abundance of Bacteroides fragilis was significantly higher among shedders compared to non-shedders (p = 0.037). These results provide fundamental evidence of the direct effect of influenza infection on gut microbiota diversity, as reported in patients shedding the virus.
Interleukin-17A Contributes to Bacterial Clearance in a Mouse Model of Streptococcal Toxic Shock-Like Syndrome
Authors: Xu Lei , Lu Xi , Xiao Peng , Liu Ran , Xia Kun-Long , Wu Mei-Zhou , Jin Mei-Lin , Zhang An-Ding
Published: June 2021

Streptococcus suis (S. suis), an emerging zoonotic pathogen, can cause streptococcal toxic shock-like syndrome (STSLS) in humans with high mortality. STSLS is characterized by high bacterial burden, an inflammatory cytokine storm, multi-organ dysfunction, and ultimately acute host death. Although it has been found that a significantly high level of IL-17A was induced in an NLRP3-dependent manner during STSLS development, the role of IL-17A on S. suis STSLS remains to be elucidated. In this study, we found that the epidemic strain SC 19 caused a significantly higher level of IL-17A than the non-epidemic strain P1/7. In addition, higher bacterial burden was observed from SC 19-infected il17a−/− mice than il17a+/+ mice, although acute death, tissue injury and inflammatory cytokines storm were observed in both types of mice. Furthermore, compared with il17a+/+ mice, the level of neutrophils recruitment was lower in il17a−/− mice, and the levels of induced antimicrobial proteins, such as CRAMP, S100A8 and lipocalin-2, were also decreased in il17a−/− mice. In conclusion, this study demonstrated that IL-17A does not contribute to the severe inflammation, although it may play a minor role for bacterial clearance by inducing antimicrobial proteins and promoting neutrophil recruitment during STSLS.
Discovery of Selective Inhibitor Leads by Targeting an Allosteric Site in Insulin-Regulated Aminopeptidase
Authors: Temponeras Ioannis , Chiniadis Lykourgos , Papakyriakou Athanasios , Stratikos Efstratios
Published: June 2021

Insulin-Regulated aminopeptidase (IRAP) is a zinc-dependent aminopeptidase with several important biological functions and is an emerging pharmaceutical target for cognitive enhancement and immune system regulation. Aiming to discover lead-like IRAP inhibitors with enhanced selectivity versus homologous enzymes, we targeted an allosteric site at the C-terminal domain pocket of IRAP. We compiled a library of 2.5 million commercially available compounds from the ZINC database, and performed molecular docking at the target pocket of IRAP and the corresponding pocket of the homologous endoplasmic reticulum aminopeptidase 1 (ERAP1). Of the top compounds that showed high selectivity, 305 were further analyzed by molecular dynamics simulations and free energy calculations, leading to the selection of 33 compounds for in vitro evaluation. Two orthogonal functional assays were employed: one using a small fluorogenic substrate and one following the degradation of oxytocin, a natural peptidic substrate of IRAP. In vitro evaluation suggested that several of the compounds tested can inhibit IRAP, but the inhibition profile was dependent on substrate size, consistent with the allosteric nature of the targeted site. Overall, our results describe several novel leads as IRAP inhibitors and suggest that the C-terminal domain pocket of IRAP is a promising target for developing highly selective IRAP inhibitors.
Small Molecule Inhibitors of Influenza Virus Entry
Authors: Chen Zhaoyu , Cui Qinghua , Caffrey Michael , Rong Lijun , Du Ruikun
Published: June 2021

Hemagglutinin (HA) plays a critical role during influenza virus receptor binding and subsequent membrane fusion process, thus HA has become a promising drug target. For the past several decades, we and other researchers have discovered a series of HA inhibitors mainly targeting its fusion machinery. In this review, we summarize the advances in HA-targeted development of small molecule inhibitors. Moreover, we discuss the structural basis and mode of action of these inhibitors, and speculate upon future directions toward more potent inhibitors of membrane fusion and potential anti-influenza drugs.
Potentiating Therapeutic Effects of Epidermal Growth Factor Receptor Inhibition in Triple-Negative Breast Cancer
Authors: You Kyu Sic , Yi Yong Weon , Cho Jeonghee , Park Jeong-Soo , Seong Yeon-Sun
Published: June 2021

Triple-negative breast cancer (TNBC) is a subset of breast cancer with aggressive characteristics and few therapeutic options. The lack of an appropriate therapeutic target is a challenging issue in treating TNBC. Although a high level expression of epidermal growth factor receptor (EGFR) has been associated with a poor prognosis among patients with TNBC, targeted anti-EGFR therapies have demonstrated limited efficacy for TNBC treatment in both clinical and preclinical settings. However, with the advantage of a number of clinically approved EGFR inhibitors (EGFRis), combination strategies have been explored as a promising approach to overcome the intrinsic resistance of TNBC to EGFRis. In this review, we analyzed the literature on the combination of EGFRis with other molecularly targeted therapeutics or conventional chemotherapeutics to understand the current knowledge and to provide potential therapeutic options for TNBC treatment.
Topical Nanoemulgel for the Treatment of Skin Cancer: Proof-of-Technology
Authors: Nagaraja Sreeharsha , Basavarajappa Girish Meravanige , Attimarad Mahesh , Pund Swati
Published: June 2021

The present study is a mechanistic validation of ‘proof-of-technology’ for the effective topical delivery of chrysin nanoemulgel for localized, efficient treatment of melanoma-affected skin. Background: Currently available treatments for skin cancer are inefficient due to systemic side effects and poor transcutaneous permeation, thereby presenting a formidable challenge for the development of novel nanocarriers. Methods: We opted for a novel approach and formulated a nanocomplex system composed of hydrophobic chrysin dissolved in a lipid mix, which was further nanoemulsified in Pluronic® F-127 gel to enhance physicochemical and biopharmaceutic characteristics. Chrysin, a flavone extracted from passion flowers, exhibits potential anti-cancer activities; however, it has limited applicability due to its poor solubility. Pseudo-ternary phase diagrams were constructed to identify the best self-nanoemulsifying region by varying the compositions of oil, Caproyl® 90 surfactant, Tween® 80, and co-solvent Transcutol® HP. Chrysin-loaded nanoemulsifying compositions were characterized for various physicochemical properties. Results: This thermodynamically stable, self-emulsifying drug delivery system showed a mean droplet size of 156.9 nm, polydispersity index of 0.26, and viscosity of 9100 cps after dispersion in gel. Mechanical characterization using Texture Analyzer exhibited that the gel had a hardness of 487 g and adhesiveness of 500 g. Ex vivo permeation through rat abdominal skin revealed significant improvement in percutaneous absorption measured as flux, the apparent permeability coefficient, the steady-state diffusion coefficient, and drug deposition. In vitro cytotoxicity on A375 and SK-MEL-2 cell lines showed a significantly improved therapeutic effect, thus ensuring reduction in dose. The safety of the product was established through biocompatibility testing on the L929 cell line. Conclusion: Aqueous, gel-based, topical, nanoemulsified chrysin is a promising technology approach for effective localized transcutaneous delivery that will help reduce the frequency and overall dose usage and ultimately improve the therapeutic index.
Development of In Situ Self-Assembly Nanoparticles to Encapsulate Lopinavir and Ritonavir for Long-Acting Subcutaneous Injection
Authors: Tanaudommongkon Irin , Tanaudommongkon Asama , Dong Xiaowei
Published: June 2021

Most antiretroviral medications for human immunodeficiency virus treatment and prevention require high levels of patient adherence, such that medications need to be administered daily without missing doses. Here, a long-acting subcutaneous injection of lopinavir (LPV) in combination with ritonavir (RTV) using in situ self-assembly nanoparticles (ISNPs) was developed to potentially overcome adherence barriers. The ISNP approach can improve the pharmacokinetic profiles of the drugs. The ISNPs were characterized in terms of particle size, drug entrapment efficiency, drug loading, in vitro release study, and in vivo pharmacokinetic study. LPV/RTV ISNPs were 167.8 nm in size, with a polydispersity index of less than 0.35. The entrapment efficiency was over 98% for both LPV and RTV, with drug loadings of 25% LPV and 6.3% RTV. A slow release rate of LPV was observed at about 20% on day 5, followed by a sustained release beyond 14 days. RTV released faster than LPV in the first 5 days and slower than LPV thereafter. LPV trough concentration remained above 160 ng/mL and RTV trough concentration was above 50 ng/mL after 6 days with one subcutaneous injection. Overall, the ISNP-based LPV/RTV injection showed sustained release profiles in both in vitro and in vivo studies.
The Phenomenology of “Pure” Consciousness as Reported by an Experienced Meditator of the Tibetan Buddhist Karma Kagyu Tradition. Analysis of Interview Content Concerning Different Meditative States
Authors: Costines Cyril , Borghardt Tilmann Lhündrup , Wittmann Marc
Published: June 2021

A philosopher and a cognitive neuroscientist conversed with Buddhist lama Tilmann Lhündrup Borghardt (TLB) about the unresolved phenomenological concerns and logical questions surrounding “pure” consciousness or minimal phenomenal experience (MPE), a quasi-contentless, non-dual state whose phenomenology of “emptiness” is often described in terms of the phenomenal quality of luminosity that experienced meditators have reported occurs in deep meditative states. Here, we present the excerpts of the conversation that relate to the question of how it is possible to first have and later retrieve such non-dual states of selflessness and timelessness that are unrelated to sensory input. According to TLB, a “pure” experience of consciousness contains the phenomenal quality of luminous clarity, which is experienced solely in the transitional phase from the non-dual state of absolute emptiness to the state of minimal emptiness, when the person gradually returns to duality. However, this quality of luminous clarity can also be experienced in non-minimal states as in the experiential mode of being awakened. TLB describes this transition as a kind of ephemeral afterglow in the form of a maximally abstract phenomenal quality, i.e., luminosity, which justifies the conclusion of having been in a state of “pure” consciousness.
Sharp Focusing of a Hybrid Vector Beam with a Polarization Singularity
Authors: Kotlyar Victor V. , Stafeev Sergey S. , Nalimov Anton G.
Published: June 2021

The key result of this work is the use of the global characteristics of the polarization singularities of the entire beam as a whole, rather than the analysis of local polarization, Stokes and Poincare–Hopf indices. We extend Berry’s concept of the topological charge of scalar beams to hybrid vector beams. We discuss tightly focusing a new type of nth-order hybrid vector light field comprising n C-lines (circular polarization lines). Using a complex Stokes field, it is shown that the field polarization singularity index equals n/2 and does not preserve in the focal plane. The intensity and Stokes vector components in the focal plane are expressed analytically. It is theoretically and numerically demonstrated that at an even n, the intensity pattern at the focus is symmetrical, and instead of C-lines, there occur C-points around which axes of polarization ellipses are rotated. At n = 4, C-points characterized by singularity indices 1/2 and ‘lemon’-type topology are found at the focus. For an odd source field order n, the intensity pattern at the focus has no symmetry, and the field becomes purely vectorial (with no elliptical polarization) and has n V-points, around which linear polarization vectors are rotating.
Determination of Cellulose Degree of Polymerization in Historical Papers with High Lignin Content
Authors: Malešič Jasna , Kraševec Ida , Kralj Cigić Irena
Published: June 2021

Determination of cellulose degree of polymerization (DP) is one of the most commonly used methods in paper degradation studies, performed either by a standardized method using viscometry (as average degree of polymerization (DPv)) or size-exclusion chromatography (SEC) (as weight average molecular mass (Mw)). Due to the insolubility of papers with high lignin content in cupriethylenediamine (CED), such as groundwood papers, viscometric determination is not possible; therefore, pretreatment is required to allow subsequent dissolution of the papers. In this study, the pretreatment of historical papers containing groundwood with sodium chlorite in acetic acid was investigated, which enables dissolution of the paper samples in CED and determination of the cellulose average degree of polymerization by viscometry (DPv). Kappa number was determined to estimate the lignin content in the papers. The suitability of SEC UV-VIS analysis for determination of Mw in papers with high lignin content had been verified before it was used as a comparative method for viscometry. Using SEC, changes in the weight average molecular mass (Mw) of cellulose tricarbanilate (CTC) derivative during delignification were evaluated. The results indicate that no significant depolymerization occurred in the selected samples under the studied delignification conditions, which was additionally confirmed with determination of monosaccharides by ion chromatography. The results of the Mw determinations by SEC and DPv by viscometry are in good correlation, justifying the use of viscometry after chlorite/acetic acid pretreatment to determine the cellulose average degree of polymerization in historical papers with high lignin content.
Amphiphilic Polymer Nanoreactors for Multiple Step, One-Pot Reactions and Spontaneous Product Separation
Authors: Harrison Andrew , Tang Christina
Published: June 2021

Performing multiple reaction steps in “one pot” to avoid the need to isolate intermediates is a promising approach for reducing solvent waste associated with liquid phase chemical processing. In this work, we incorporated gold nanoparticle catalysts into polymer nanoreactors via amphiphilic block copolymer directed self-assembly. With the polymer nanoreactors dispersed in water as the bulk solvent, we demonstrated the ability to facilitate two reaction steps in one pot with spontaneous precipitation of the product from the reaction mixture. Specifically, we achieved imide synthesis from 4-nitrophenol and benzaldehyde as a model reaction. The reaction occured in water at ambient conditions; the desired 4-benzylideneaminophenol product spontaneously precipitated from the reaction mixture while the nanoreactors remained stable in dispersion. A 65% isolated yield was achieved. In contrast, PEGylated gold nanoparticles and citrate stabilized gold nanoparticles precipitated with the reaction product, which would complicate both the isolation of the product as well as reuse of the catalyst. Thus, amphiphilic nanoreactors dispersed in water are a promising approach for reducing solvent waste associated with liquid phase chemical processing by using water as the bulk solvent, eliminating the need to isolate intermediates, achieving spontaneous product separation to facilitate the recycling of the reaction mixture, and simplifying the isolation of the desired product.
Two-Photon Polymerization: Functionalized Microstructures, Micro-Resonators, and Bio-Scaffolds
Authors: Otuka Adriano J. G. , Tomazio Nathália B. , Paula Kelly T. , Mendonça Cleber R.
Published: June 2021

The direct laser writing technique based on two-photon polymerization (TPP) has evolved considerably over the past two decades. Its remarkable characteristics, such as 3D capability, sub-diffraction resolution, material flexibility, and gentle processing conditions, have made it suitable for several applications in photonics and biosciences. In this review, we present an overview of the progress of TPP towards the fabrication of functionalized microstructures, whispering gallery mode (WGM) microresonators, and microenvironments for culturing microorganisms. We also describe the key physical-chemical fundamentals underlying the technique, the typical experimental setups, and the different materials employed for TPP.
The Effect of Adding Modified Chitosan on the Strength Properties of Bacterial Cellulose for Clinical Applications
Authors: Lipovka Anna , Kharchenko Alexey , Dubovoy Andrey , Filipenko Maxim , Stupak Vyacheslav , Mayorov Alexander , Fomenko Vladislav , Geydt Pavel , Parshin Daniil
Published: June 2021

Currently, several materials for the closure of the dura mater (DM) defects are known. However, the long-term results of their usage reveal a number of disadvantages. The use of antibiotics and chitosan is one of the major trends in solving the problems associated with infectious after-operational complications. This work compares the mechanical properties of samples of bacterial nanocellulose (BNC) impregnated with Novochizol™ and vancomycin with native BNC and preserved and native human DM. An assessment of the possibility of controling the mechanical properties of these materials by changing their thickness has been performed by statistical analysis methods. A total of 80 specimens of comparable samples were investigated. During the analysis, the results obtained, the factor of Novochizol™ addition has provided a statistically significant impact on the strength properties (Fisher Criteria p-value 0.00509 for stress and 0.00112 for deformation). Moreover, a stronger relationship between the thickness of the samples and their ultimate load was shown: R2=0.236 for BNC + Novochizol™ + vancomycin, compared to R2=0.0405 for native BNC. Using factor analysis, it was possible to show a significant effect of modified chitosan (Novochizol™) on the ultimate stress (p-value = 0.005).
A Systematic Study on the Degradation Products Generated from Artificially Aged Microplastics
Authors: Biale Greta , La Nasa Jacopo , Mattonai Marco , Corti Andrea , Vinciguerra Virginia , Castelvetro Valter , Modugno Francesca
Published: June 2021

Most of the analytical studies focused on microplastics (MPs) are based on the detection and identification of the polymers constituting the particles. On the other hand, plastic debris in the environment undergoes chemical and physical degradation processes leading not only to mechanical but also to molecular fragmentation quickly resulting in the formation of leachable, soluble and/or volatile degradation products that are released in the environment. We performed the analysis of reference MPs–polymer micropowders obtained by grinding a set of five polymer types down to final size in the 857–509 μm range, namely high- and low-density polyethylene, polystyrene (PS), polypropylene (PP), and polyethylene terephthalate (PET). The reference MPs were artificially aged in a solar-box to investigate their degradation processes by characterizing the aged (photo-oxidized) MPs and their low molecular weight and/or highly oxidized fraction. For this purpose, the artificially aged MPs were subjected to extraction in polar organic solvents, targeting selective recovery of the low molecular weight fractions generated during the artificial aging. Analysis of the extractable fractions and of the residues was carried out by a multi-technique approach combining evolved gas analysis–mass spectrometry (EGA–MS), pyrolysis–gas chromatography–mass spectrometry (Py–GC–MS), and size exclusion chromatography (SEC). The results provided information on the degradation products formed during accelerated aging. Up to 18 wt% of extractable, low molecular weight fraction was recovered from the photo-aged MPs, depending on the polymer type. The photo-degradation products of polyolefins (PE and PP) included a wide range of long chain alcohols, aldehydes, ketones, carboxylic acids, and hydroxy acids, as detected in the soluble fractions of aged samples. SEC analyses also showed a marked decrease in the average molecular weight of PP polymer chains, whereas cross-linking was observed in the case of PS. The most abundant low molecular weight photo-degradation products of PS were benzoic acid and 1,4-benzenedicarboxylic acid, while PET had the highest stability towards aging, as indicated by the modest generation of low molecular weight species.
Advanced Research and Development of Face Masks and Respirators Pre and Post the Coronavirus Disease 2019 (COVID-19) Pandemic: A Critical Review
Authors: Ogbuoji Ebuka A. , Zaky Amr M. , Escobar Isabel C.
Published: June 2021

The outbreak of the COVID-19 pandemic, in 2020, has accelerated the need for personal protective equipment (PPE) masks as one of the methods to reduce and/or eliminate transmission of the coronavirus across communities. Despite the availability of different coronavirus vaccines, it is still recommended by the Center of Disease Control and Prevention (CDC), World Health Organization (WHO), and local authorities to apply public safety measures including maintaining social distancing and wearing face masks. This includes individuals who have been fully vaccinated. Remarkable increase in scientific studies, along with manufacturing-related research and development investigations, have been performed in an attempt to provide better PPE solutions during the pandemic. Recent literature has estimated the filtration efficiency (FE) of face masks and respirators shedding the light on specific targeted parameters that investigators can measure, detect, evaluate, and provide reliable data with consistent results. This review showed the variability in testing protocols and FE evaluation methods of different face mask materials and/or brands. In addition to the safety requirements needed to perform aerosol viral filtration tests, one of the main challenges researchers currently face is the inability to simulate or mimic true aerosol filtration scenarios via laboratory experiments, field tests, and in vitro/in vivo investigations. Moreover, the FE through the mask can be influenced by different filtration mechanisms, environmental parameters, filtration material properties, number of layers used, packing density, fiber charge density, fiber diameter, aerosol type and particle size, aerosol face velocity and concentration loadings, and infectious concentrations generated due to different human activities. These parameters are not fully understood and constrain the design, production, efficacy, and efficiency of face masks.
Group Acceptance Sampling Plan Using Marshall–Olkin Kumaraswamy Exponential (MOKw-E) Distribution
Authors: Almarashi Abdullah M. , Khan Khushnoor , Chesneau Christophe , Jamal Farrukh
Published: June 2021

The current research concerns the group acceptance sampling plan in the case where (i) the lifetime of the items follows the Marshall–Olkin Kumaraswamy exponential distribution (MOKw-E) and (ii) a large number of items, considered as a group, can be tested at the same time. When the consumer’s risk and the test terminsation period are defined, the key design parameters are extracted. The values of the operating characteristic function are determined for different quality levels. At the specified producer’s risk, the minimum ratios of the true average life to the specified average life are also calculated. The results of the present study will set the platform for future research on various nano quality level topics when the items follow different probability distributions under the Marshall–Olkin Kumaraswamy scheme. Real-world data are used to explain the technique.
Study on the Removal of Fluorescent Whitening Agent from Paper-Mill Wastewater Using the Submerged Membrane Bioreactor (SMBR) with Ozone Oxidation Process
Authors: Ryu Seunghan , Lee Sanghun , Oh Hannah , Oh Sanghwa , Park Minsoo , Kim Jinho , Heo Jaeeun
Published: June 2021

In this study, paper-mill wastewater was treated using the Submerged Membrane Bioreactor (SMBR) process. In particular, the ozone oxidation treatment process is applied after SMBR to remove the fluorescent whitening agent, which is a trace pollutant and non-biodegradable. Fluorescent whitening agent concentration was indirectly measured by UV scanning and COD concentration. The concentration of COD before SMBR and ozone oxidation was 449.3 mg/L, and the concentration of treated water was 100.3 mg/ℓ. The COD removal efficiency of paper-mill wastewater through SMBR and the ozone oxidation process was about 77.68%. The optimized amount of ozone was required for the removal of the fluorescent whitening agent after SMBR was 95 mg·O3/ℓ calculated by UV scan results. Additionally, the optimized amount of required ozone to remove COD was calculated to 0.126 mg·COD/mg·O3.
Oil Spill Detection with Multiscale Conditional Adversarial Networks with Small-Data Training
Authors: Li Yongqing , Lyu Xinrong , Frery Alejandro C. , Ren Peng
Published: June 2021

We investigate the problem of training an oil spill detection model with small data. Most existing machine-learning-based oil spill detection models rely heavily on big training data. However, big amounts of oil spill observation data are difficult to access in practice. To address this limitation, we developed a multiscale conditional adversarial network (MCAN) consisting of a series of adversarial networks at multiple scales. The adversarial network at each scale consists of a generator and a discriminator. The generator aims at producing an oil spill detection map as authentically as possible. The discriminator tries its best to distinguish the generated detection map from the reference data. The training procedure of MCAN commences at the coarsest scale and operates in a coarse-to-fine fashion. The multiscale architecture comprehensively captures both global and local oil spill characteristics, and the adversarial training enhances the model’s representational power via the generated data. These properties empower the MCAN with the capability of learning with small oil spill observation data. Empirical evaluations validate that our MCAN trained with four oil spill observation images accurately detects oil spills in new images.
Remote Sensing Based Yield Estimation of Rice (Oryza Sativa L.) Using Gradient Boosted Regression in India
Authors: Arumugam Ponraj , Chemura Abel , Schauberger Bernhard , Gornott Christoph
Published: June 2021

Accurate and spatially explicit yield information is required to ensure farmers’ income and food security at local and national levels. Current approaches based on crop cutting experiments are expensive and usually too late for timely income stabilization measures like crop insurances. We, therefore, utilized a Gradient Boosted Regression (GBR), a machine learning technique, to estimate rice yields at ~500 m spatial resolution for rice-producing areas in India with potential application for near real-time estimates. We used resampled intermediate resolution (~5 km) images of the Moderate Resolution Imaging Spectroradiometer (MODIS) Leaf Area Index (LAI) and observed yields at the district level in India for calibrating GBR models. These GBRs were then used to downscale district yields to 500 m resolution. Downscaled yields were re-aggregated for validation against out-of-sample district yields not used for model training and an additional independent data set of block-level (below district-level) yields. Our downscaled and re-aggregated yields agree well with reported district-level observations from 2003 to 2015 (r = 0.85 & MAE = 0.15 t/ha). The model performance improved further when estimating separate models for different rice cropping densities (up to r = 0.93). An additional out-of-sample validation for the years 2016 and 2017, proved successful with r = 0.84 and r = 0.77, respectively. Simulated yield accuracy was higher in water-limited, rainfed agricultural systems. We conclude that this downscaling approach of rice yield estimation using GBR is feasible across India and may complement current approaches for timely rice yield estimation required by insurance companies and government agencies.
Estimation of the Monthly Dynamics of Surface Water in Wetlands from Satellite and Secondary Hydro-Climatological Data
Authors: Collados-Lara Antonio-Juan , Pardo-Igúzquiza Eulogio , Pulido-Velazquez David , Baena-Ruiz Leticia
Published: June 2021

Satellites produce valuable information for studying the surface water in wetlands, but in many cases the period covered, the spatial resolution and/or the revisit frequency is not enough to produce long historical series. In this paper we propose a novel method which uses regression models that include climatic and hydrological variables to complete the satellite information. We used this method in the Lagunas de Ruidera wetland (Spain). We approached the monthly dynamic of the surface water for a long period (1984–2015). Information from LANDSAT (30-m resolution) and MODIS (250-m resolution) satellites were tested but, due to the size of some lagoons, only the LANDSAT approach produced satisfactory results. An ensemble of regression models based on hydro-climatological explanatory variables was defined to complete the gaps in the monthly surface water. It showed a root mean squared error of around 476 pixels (0.4 Km2) in the cross-validation analysis. Our analysis showed that the explanatory variables with a more significant participation in the regression ensemble are the aquifer discharge, the effective precipitation and the surface water from the previous month. From January to June, the mean surface water in Lagunas de Ruidera is around 4.3 Km2. In summer a reduction of around 13% of the surface water can be observed, which is recovered during the autumn.
Hyperspectral Image Super-Resolution under the Guidance of Deep Gradient Information
Authors: Zhao Minghua , Ning Jiawei , Hu Jing , Li Tingting
Published: June 2021

Hyperspectral image (HSI) super-resolution has gained great attention in remote sensing, due to its effectiveness in enhancing the spatial information of the HSI while preserving the high spectral discriminative ability, without modifying the imagery hardware. In this paper, we proposed a novel HSI super-resolution method via a gradient-guided residual dense network (G-RDN), in which the spatial gradient is exploited to guide the super-resolution process. Specifically, there are three modules in the super-resolving process. Firstly, the spatial mapping between the low-resolution HSI and the desired high-resolution HSI is learned via a residual dense network. The residual dense network is used to fully exploit the hierarchical features learned from all the convolutional layers. Meanwhile, the gradient detail is extracted via a residual network (ResNet), which is further utilized to guide the super-resolution process. Finally, an empirical weight is set between the fully obtained global hierarchical features and the gradient details. Experimental results and the data analysis on three benchmark datasets with different scaling factors demonstrated that our proposed G-RDN achieved favorable performance.
Automated Simulation Framework for Urban Wind Environments Based on Aerial Point Clouds and Deep Learning
Authors: Sun Chujin , Zhang Fan , Zhao Pengju , Zhao Xinyi , Huang Yuli , Lu Xinzheng
Published: June 2021

Computational fluid dynamics (CFD) simulation is a core component of wind engineering assessment for urban planning and architecture. CFD simulations require clean and low-complexity models. Existing modeling methods rely on static data from geographic information systems along with manual efforts. They are extraordinarily time-consuming and have difficulties accurately incorporating the up-to-date information of a target area into the flow model. This paper proposes an automated simulation framework with superior modeling efficiency and accuracy. The framework adopts aerial point clouds and an integrated two-dimensional and three-dimensional (3D) deep learning technique, with four operational modules: data acquisition and preprocessing, point cloud segmentation based on deep learning, geometric 3D reconstruction, and CFD simulation. The advantages of the framework are demonstrated through a case study of a local area in Shenzhen, China.
Integrating Geophysical and Photographic Data to Visualize the Quarried Structures of the Roman Town of Bassianae
Authors: Filzwieser Roland , Ivanišević Vujadin , Verhoeven Geert J. , Gugl Christian , Löcker Klaus , Bugarski Ivan , Schiel Hannes , Wallner Mario , Trinks Immo , Trausmuth Tanja , Hinterleitner Alois , Marković Nemanja , Docter Roald , Daim Falko , Neubauer Wolfgang
Published: June 2021

Large parts of the urban layout of the abandoned Roman town of Bassianae (in present-day Serbia) are still discernible on the surface today due to the deliberate and targeted quarrying of the Roman foundations. In 2014, all of the town’s intramural (and some extramural) areas were surveyed using aerial photography, ground-penetrating radar, and magnetometry to analyze the site’s topography and to map remaining buried structures. The surveys showed a strong agreement between the digital surface model derived from the aerial photographs and the geophysical prospection data. However, many structures could only be detected by one method, underlining the benefits of a complementary archaeological prospection approach using multiple methods. This article presents the results of the extensive surveys and their comprehensive integrative interpretation, discussing Bassianae’s ground plan and urban infrastructure. Starting with an overview of this Roman town’s research history, we present the details of the triple prospection approach, followed by the processing, integrative analysis, and interpretation of the acquired data sets. Finally, this newly gained information is contrasted with a plan of Roman Bassianae compiled in 1935.
Shallow Landslides Physically Based Susceptibility Assessment Improvement Using InSAR. Case Study: Carpathian and Subcarpathian Prahova Valley, Romania
Authors: Armaș Iuliana , Gheorghe Mihaela , Silvaș George Cătălin
Published: June 2021

A multi-temporal satellite radar interferometry technique is used for deriving the actual surface displacement patterns in a slope environment in Romania, in order to validate and improve a landslide susceptibility map. The probability the occurrence of future events is established using a deterministic approach based on a classical one-dimension infinite slope stability model. The most important geotechnical parameters for slope failure in the proposed study area are cohesion, unit weight and friction angle, and the triggering factor is a rapid rise in groundwater table under wetting conditions. Employing a susceptibility analysis using the physically based model under completely saturated conditions proved to be the most suitable scenario for identifying unstable areas. The kinematic characteristics are assessed by the Small BAseline Subsets (SBAS) interferometry technique applied to C-band synthetic aperture radar (SAR) Sentinel-1 imagery. The analysis was carried out mainly for inhabited areas which present a better backscatter return. The validation revealed that more than 22% of the active landslides identified by InSAR were predicted as unstable areas by the infinite slope model. We propose a refinement of the susceptibility map using the InSAR results for unravelling the danger of the worst-case scenario.
Deep Neural Networks with Transfer Learning for Forest Variable Estimation Using Sentinel-2 Imagery in Boreal Forest
Authors: Astola Heikki , Seitsonen Lauri , Halme Eelis , Molinier Matthieu , Lönnqvist Anne
Published: June 2021

Estimation of forest structural variables is essential to provide relevant insights for public and private stakeholders in forestry and environmental sectors. Airborne light detection and ranging (LiDAR) enables accurate forest inventory, but it is expensive for large area analyses. Continuously increasing volume of open Earth Observation (EO) imagery from high-resolution (<30 m) satellites together with modern machine learning algorithms provide new prospects for spaceborne large area forest inventory. In this study, we investigated the capability of Sentinel-2 (S2) image and metadata, topography data, and canopy height model (CHM), as well as their combinations, to predict growing stock volume with deep neural networks (DNN) in four forestry districts in Central Finland. We focused on investigating the relevance of different input features, the effect of DNN depth, the amount of training data, and the size of image data sampling window to model prediction performance. We also studied model transfer between different silvicultural districts in Finland, with the objective to minimize the amount of new field data needed. We used forest inventory data provided by the Finnish Forest Centre for model training and performance evaluation. Leaving out CHM features, the model using RGB and NIR bands, the imaging and sun angles, and topography features as additional predictive variables obtained the best plot level accuracy (RMSE% = 42.6%, |BIAS%| = 0.8%). We found 3×3 pixels to be the optimal size for the sampling window, and two to three hidden layer DNNs to produce the best results with relatively small improvement to single hidden layer networks. Including CHM features with S2 data and additional features led to reduced relative RMSE (RMSE% = 28.6–30.7%) but increased the absolute value of relative bias (|BIAS%| = 0.9–4.0%). Transfer learning was found to be beneficial mainly with training data sets containing less than 250 field plots. The performance differences of DNN and random forest models were marginal. Our results contribute to improved structural variable estimation performance in boreal forests with the proposed image sampling and input feature concept.
Uplink vs. Downlink: Machine Learning-Based Quality Prediction for HTTP Adaptive Video Streaming
Authors: Loh Frank , Poignée Fabian , Wamser Florian , Leidinger Ferdinand , Hoßfeld Tobias
Published: June 2021

Streaming video is responsible for the bulk of Internet traffic these days. For this reason, Internet providers and network operators try to make predictions and assessments about the streaming quality for an end user. Current monitoring solutions are based on a variety of different machine learning approaches. The challenge for providers and operators nowadays is that existing approaches require large amounts of data. In this work, the most relevant quality of experience metrics, i.e., the initial playback delay, the video streaming quality, video quality changes, and video rebuffering events, are examined using a voluminous data set of more than 13,000 YouTube video streaming runs that were collected with the native YouTube mobile app. Three Machine Learning models are developed and compared to estimate playback behavior based on uplink request information. The main focus has been on developing a lightweight approach using as few features and as little data as possible, while maintaining state-of-the-art performance.
An Experimental Comparison of IoT-Based and Traditional Irrigation Scheduling on a Flood-Irrigated Subtropical Lemon Farm
Authors: Zia Huma , Rehman Ahsan , Harris Nick R. , Fatima Sundus , Khurram Muhammad
Published: June 2021

Over recent years, the demand for supplies of freshwater is escalating with the increasing food demand of a fast-growing population. The agriculture sector of Pakistan contributes to 26% of its GDP and employs 43% of the entire labor force. However, the currently used traditional farming methods such as flood irrigation and rotating water allocation system (Warabandi) results in excess and untimely water usage, as well as low crop yield. Internet of things (IoT) solutions based on real-time farm sensor data and intelligent decision support systems have led to many smart farming solutions, thus improving water utilization. The objective of this study was to compare and optimize water usage in a 2-acre lemon farm test site in Gadap, Karachi, for a 9-month duration, by deploying an indigenously developed IoT device and an agriculture-based decision support system (DSS). The sensor data are wirelessly collected over the cloud and a mobile application, as well as a web-based information visualization, and a DSS system makes irrigation recommendations. The DSS system is based on weather data (temperature and humidity), real time in situ sensor data from the IoT device deployed in the farm, and crop data (Kc and crop type). These data are supplied to the Penman–Monteith and crop coefficient model to make recommendations for irrigation schedules in the test site. The results show impressive water savings (~50%) combined with increased yield (35%) when compared with water usage and crop yields in a neighboring 2-acre lemon farm where traditional irrigation scheduling was employed and where harsh conditions sometimes resulted in temperatures in excess of 50 °C.
Exploring Misclassification Information for Fine-Grained Image Classification
Authors: Wang Da-Han , Zhou Wei , Li Jianmin , Wu Yun , Zhu Shunzhi
Published: June 2021

Fine-grained image classification is a hot topic that has been widely studied recently. Many fine-grained image classification methods ignore misclassification information, which is important to improve classification accuracy. To make use of misclassification information, in this paper, we propose a novel fine-grained image classification method by exploring the misclassification information (FGMI) of prelearned models. For each class, we harvest the confusion information from several prelearned fine-grained image classification models. For one particular class, we select a number of classes which are likely to be misclassified with this class. The images of selected classes are then used to train classifiers. In this way, we can reduce the influence of irrelevant images to some extent. We use the misclassification information for all the classes by training a number of confusion classifiers. The outputs of these trained classifiers are combined to represent images and produce classifications. To evaluate the effectiveness of the proposed FGMI method, we conduct fine-grained classification experiments on several public image datasets. Experimental results prove the usefulness of the proposed method.
Photoacoustic Laser System for Food Fraud Detection
Authors: Fiorani Luca , Artuso Florinda , Giardina Isabella , Lai Antonia , Mannori Simone , Puiu Adriana
Published: June 2021

Economically motivated adulterations of food, in general, and spices, in particular, are an emerging threat to world health. Reliable techniques for the rapid screening of counterfeited ingredients in the supply chain need further development. Building on the experience gained with CO2 lasers, the Diagnostic and Metrology Laboratory of ENEA realized a compact and user-friendly photoacoustic laser system for food fraud detection, based on a quantum cascade laser. The sensor has been challenged with saffron adulteration. Multivariate data analysis tools indicated that the photoacoustic laser system was able to detect adulterants at mass ratios of 2% in less than two minutes.
Activity-Aware Vital Sign Monitoring Based on a Multi-Agent Architecture
Authors: Ivașcu Todor , Negru Viorel
Published: June 2021

Vital sign monitoring outside the clinical environment based on wearable sensors ensures better support in assessing a patient’s health condition, and in case of health deterioration, automatic alerts can be sent to the care providers. In everyday life, the users can perform different physical activities, and considering that vital sign measurements depend on the intensity of the activity, we proposed an architecture based on the multi-agent paradigm to handle this issue dynamically. Different types of agents were proposed that processed different sensor signals and recognized simple activities of daily living. The system was validated using a real-life dataset where subjects wore accelerometer sensors on the chest, wrist, and ankle. The system relied on ontology-based models to address the data heterogeneity and combined different wearable sensor sources in order to achieve better performance. The results showed an accuracy of 95.25% on intersubject activity classification. Moreover, the proposed method, which automatically extracted vital sign threshold ranges for each physical activity recognized by the system, showed promising results for remote health status evaluation.
An Improved Deep Residual Network Prediction Model for the Early Diagnosis of Alzheimer’s Disease
Authors: Sun Haijing , Wang Anna , Wang Wenhui , Liu Chen
Published: June 2021

The early diagnosis of Alzheimer’s disease (AD) can allow patients to take preventive measures before irreversible brain damage occurs. It can be seen from cross-sectional imaging studies of AD that the features of the lesion areas in AD patients, as observed by magnetic resonance imaging (MRI), show significant variation, and these features are distributed throughout the image space. Since the convolutional layer of the general convolutional neural network (CNN) cannot satisfactorily extract long-distance correlation in the feature space, a deep residual network (ResNet) model, based on spatial transformer networks (STN) and the non-local attention mechanism, is proposed in this study for the early diagnosis of AD. In this ResNet model, a new Mish activation function is selected in the ResNet-50 backbone to replace the Relu function, STN is introduced between the input layer and the improved ResNet-50 backbone, and a non-local attention mechanism is introduced between the fourth and the fifth stages of the improved ResNet-50 backbone. This ResNet model can extract more information from the layers by deepening the network structure through deep ResNet. The introduced STN can transform the spatial information in MRI images of Alzheimer’s patients into another space and retain the key information. The introduced non-local attention mechanism can find the relationship between the lesion areas and normal areas in the feature space. This model can solve the problem of local information loss in traditional CNN and can extract the long-distance correlation in feature space. The proposed method was validated using the ADNI (Alzheimer’s disease neuroimaging initiative) experimental dataset, and compared with several models. The experimental results show that the classification accuracy of the algorithm proposed in this study can reach 97.1%, the macro precision can reach 95.5%, the macro recall can reach 95.3%, and the macro F1 value can reach 95.4%. The proposed model is more effective than other algorithms.