bluuebunny/crossref_metadata_2025 huggingface dataset:
Independent Dose–Response Associations between Fetuin-A and Lean Nonalcoholic Fatty Liver Disease
Authors: Lu Chia-Wen , Lee Yi-Chen , Chiang Chien-Hsieh , Chang Hao-Hsiang , Yang Wei-Shiung , Huang Kuo-Chin
Published: August 2021

Patients with lean NAFLD make up an increasing subset of liver disease patients. The association between lean NAFLD and feutin-A, which serves as a hepatokine and adipokine, has never been examined. Our study aimed to explore the association of serum fetuin-A among lean and non-lean patients. The study comprised 606 adults from the community, stratified into lean or non-lean (BMI </≥ 24 kg/m2) and NAFLD or non-NAFLD (scoring of ultrasonographic fatty liver indicator, US-FLI ≥ 2/< 2). Multivariate logistic regression analyses were performed to estimate the odds ratio of having NAFLD among the tertiles of fetuin-A after adjustment. The least square means were computed by general linear models to estimate marginal means of the serum fetuin-A concentrations in relation to the NAFLD groups. The odds ratio (OR) of having NAFLD for the highest versus the lowest tertile of fetuin-A was 2.62 (95% CI: 1.72–3.98; p for trend < 0.001). Stratifying by BMI, the OR of having lean NAFLD for the highest versus the lowest tertile of fetuin-A was 2.09 (95% CI: 1.09–3.98; p for trend 0.026), while non-lean NAFLD had no significant association with the fetuin-A gradient after adjustments. Fetuin-A was positively associated with lean NAFLD after adjusting for central obesity and insulin resistance.
Supplementation with a Natural Source of Amino Acids, Sil-Q1 (Silk Peptide), Enhances Natural Killer Cell Activity: A Redesigned Clinical Trial with a Reduced Supplementation Dose and Minimized Seasonal Effects in a Larger Population
Authors: Cho Jung Min , Yoo Dokyeong , Lee Jeong-Yong , Oh Mi-Sun , Ha Ki-Chan , Baek Hyang-Im , Lee Seung-Min , Lee Jong Ho , Yoo Hye Jin
Published: August 2021

The aim of this study was to re-validate the changes in natural killer (NK) cell cytotoxicity and cytokines related to T cells after Sil-Q1 (SQ; silk peptide) supplementation in a larger pool of Korean adults with minimized daily dose of SQ and controlling seasonal influence compared to the previous study. A total of 130 subjects were randomly assigned (1:1) to consume either 7.5 g of SQ or placebo for 8 weeks. NK cell cytotoxicity and cytokines were measured at T0 (baseline) and T8 (follow-up). Comparing the NK cell cytotoxicity values at T0 and T8 within each group, the cytotoxicity at all effector cell (E) to target cell (T) ratios of 10:1, 5:1, 2.5:1, and 1.25:1 was significantly increased in the SQ group at T8. Additionally, significant differences in the changed value (Δ, subtract baseline values from follow-up values) comparison between the groups at E:T = 10:1, 5:1, and 2.5:1 were found. As a secondary endpoint, the interleukin (IL)-12 level in the SQ group was significantly increased for 8 weeks, and Δ IL-12 in the SQ group was greater than in the placebo group. In conclusion, the present study showed considerable practical implications of SQ supplementation. Thus, SQ is an effective and safe functional food supplement for enhancing immune function.
Consumer Choices in the Pasta Market: The Importance of Fiber in Consumer Decisions
Authors: Sajdakowska Marta , Gębski Jerzy , Jeżewska-Zychowicz Marzena , Jeznach Maria , Kosicka-Gębska Małgorzata
Published: August 2021

The aim of the current study was two-fold: (1) to identify consumer segments based on pasta selection motives and (2) to examine the differences between the identified segments in terms of perception of pasta and pasta with added fiber and information on the food label. The data were collected using a CAPI (computer-assisted personal interview) survey on a sample of 1013 consumers. The k-means clustering method was used to identify four clusters of consumers, namely, quality-oriented, sensory-oriented, convenience-oriented, and neutral consumers. The quality-oriented group was the group that expressed the most positive opinions about the pasta and about the addition of fiber to pasta. Moreover, they appreciated the information placed on the pasta label the most. Consumers in the sensory-oriented segment were the least likely to agree that the addition of fiber to pasta deteriorated its taste and to agree that it looked worse compared to pasta without fiber. These findings are of significance for those involved in the public nutrition sector as well as for those responsible for preparing well-targeted marketing messages. The conclusions may constitute invaluable insights for those devising educational initiatives and campaigns.
Ring-Opening of Cyclodextrins: An Efficient Route to Pure Maltohexa-, Hepta-, and Octaoses
Authors: Pélingre Matthieu , Koffi Teki Dindet Steve-Evanes , El-Abid Jamal , Chagnault Vincent , Kovensky José , Bonnet Véronique
Published: August 2021

Many preparations of maltooligosaccharides have been described in literature, essentially using enzymatic or biotechnological processes. These compounds, derived from starch, are well-known as prebiotic agents. The use of maltohexa-, hepta-, and octaoses as synthons in organic synthesis was also well documented in literature. They can indeed be obtained as single compounds by the cyclodextrins’ ring-opening. This reaction has been studied for many years, varying the protecting and functional groups and the reaction conditions, leading to functionalized oligomaltoses. These compounds are of wide interest in various fields. They have a strong potential as scaffolds for multivalence in chemobiology, as building blocks for the production of biomimetic pseudo-glycopeptides, as well as monomers for the preparation of materials. In view of the importance of these oligomaltoses, this review focuses on the different methodologies allowing access to them via chemical and enzymatic ring-opening of cyclodextrins.
Loop-Mediated Isothermal Amplification (LAMP) Assay for Detecting Burkholderia cepacia Complex in Non-Sterile Pharmaceutical Products
Authors: Daddy Gaoh Soumana , Kweon Ohgew , Lee Yong-Jin , LiPuma John J. , Hussong David , Marasa Bernard , Ahn Youngbeom
Published: August 2021

Simple and rapid detection of Burkholderia cepacia complex (BCC) bacteria, a common cause of pharmaceutical product recalls, is essential for consumer safety. In this study, we developed and evaluated a ribB-based colorimetric loop-mediated isothermal amplification (LAMP) assay for the detection of BCC in (i) nuclease-free water after 361 days, (ii) 10 μg/mL chlorhexidine gluconate (CHX) solutions, and (iii) 50 μg/mL benzalkonium chloride (BZK) solutions after 184 days. The RibB 5 primer specifically detected 20 strains of BCC but not 36 non-BCC strains. The limit of detection of the LAMP assay was 1 pg/μL for Burkholderia cenocepacia strain J2315. Comparison of LAMP with a qPCR assay using 1440 test sets showed higher sensitivity: 60.6% in nuclease-free water and 42.4% in CHX solution with LAMP vs. 51.3% and 31.1%, respectively, with qPCR. These results demonstrate the potential of the ribB-based LAMP assay for the rapid and sensitive detection of BCC in pharmaceutical manufacturing.
Dermal Drug Delivery of Phytochemicals with Phenolic Structure via Lipid-Based Nanotechnologies
Authors: Gugleva Viliana , Ivanova Nadezhda , Sotirova Yoana , Andonova Velichka
Published: August 2021

Phenolic compounds are a large, heterogeneous group of secondary metabolites found in various plants and herbal substances. From the perspective of dermatology, the most important benefits for human health are their pharmacological effects on oxidation processes, inflammation, vascular pathology, immune response, precancerous and oncological lesions or formations, and microbial growth. Because the nature of phenolic compounds is designed to fit the phytochemical needs of plants and not the biopharmaceutical requirements for a specific route of delivery (dermal or other), their utilization in cutaneous formulations sets challenges to drug development. These are encountered often due to insufficient water solubility, high molecular weight and low permeation and/or high reactivity (inherent for the set of representatives) and subsequent chemical/photochemical instability and ionizability. The inclusion of phenolic phytochemicals in lipid-based nanocarriers (such as nanoemulsions, liposomes and solid lipid nanoparticles) is so far recognized as a strategic physico-chemical approach to improve their in situ stability and introduction to the skin barriers, with a view to enhance bioavailability and therapeutic potency. This current review is focused on recent advances and achievements in this area.
Synthesis and Study of Antifungal Properties of New Cationic Beta-Glucan Derivatives
Authors: Kaminski Kamil , Skora Magdalena , Krzyściak Paweł , Stączek Sylwia , Zdybicka-Barabas Agnieszka , Cytryńska Małgorzata
Published: August 2021

The interaction of positively charged polymers (polycations) with a biological membrane is considered to be the cause of the frequently observed toxicity of these macromolecules. If it is possible to obtain polymers with a predominantly negative effect on bacterial and fungal cells, such systems would have great potential in the treatment of infectious diseases, especially now when reports indicate the growing risk of fungal co-infections in COVID-19 patients. We describe in this article cationic derivatives of natural beta-glucan polymers obtained by reacting the polysaccharide isolated from Saccharomyces boulardii (SB) and Cetraria islandica (CI) with glycidyl trimethyl ammonium chloride (GTMAC). Two synthesis strategies were applied to optimize the product yield. Fungal diseases particularly affect low-income countries, hence the emphasis on the simplicity of the synthesis of such drugs so they can be produced without outside help. The three structures obtained showed selective anti-mycotic properties (against, i.e., Scopulariopsis brevicaulis, Aspergillus brasiliensis, and Fusarium solani), and their toxicity established using fibroblast 3T3-L1 cell line was negligible in a wide range of concentrations. For one of the polymers (SB derivative), using in vivo model of Aspergillus brasiliensis infection in Galleria mellonella insect model, we confirmed the promising results obtained in the preliminary study.
Mangiferin-Loaded Smart Gels for HSV-1 Treatment
Authors: Sicurella Mariaconcetta , Sguizzato Maddalena , Cortesi Rita , Huang Nicolas , Simelière Fanny , Montesi Leda , Marconi Peggy , Esposito Elisabetta
Published: August 2021

Infections due to HSV-1 affect many people all over the world. To counteract this pathology, usually characterized by perioral sores or by less frequent serious symptoms including keratitis, synthetic antiviral drugs are employed, such as acyclovir, often resulting in resistant viral strains under long-term use. Many plant-derived compounds, such as mangiferin and quercetin, have demonstrated antiviral potentials. In this study, smart semisolid forms based on phosphatidylcholine and Pluronic were investigated as delivery systems to administer mangiferin on skin and mucosae affected by HSV-1 infection. Particularly, lecithin organogels, Pluronic gel, and Pluronic lecithin organogels were formulated and characterized. After the selection of gel compositions, physical aspects, such as rheological behavior, spreadability, leakage, and adhesion were evaluated, suggesting a scarce suitability of the lecithin organogel for topical administration. Mangiferin was efficiently included in all type of gels. An in vitro study based on the Franz cell enabled us to find evidence of the gel capability to control drug diffusion, especially in the case of Pluronic organogel, while an in vivo study conducted on human volunteers demonstrated the safeness of all of the gels after cutaneous administration. Furthermore, a plaque reduction assay demonstrated the virucidal effect of mangiferin loaded in a Pluronic gel and a Pluronic lecithin organogel against the HSV-1 KOS strain.
The Water Polymorphism and the Liquid–Liquid Transition from Transport Data
Authors: Mallamace Francesco , Mallamace Domenico , Mensitieri Giuseppe , Chen Sow-Hsin , Lanzafame Paola , Papanikolaou Georgia
Published: August 2021

NMR spectroscopic literature data are used, in a wide temperature-pressure range (180–350 K and 0.1–400 MPa), to study the water polymorphism and the validity of the liquid–liquid transition (LLT) hypothesis. We have considered the self-diffusion coefficient DS and the reorientational correlation time τθ (obtained from spin-lattice T1 relaxation times), measured, respectively, in bulk and emulsion liquid water from the stable to well inside the metastable supercooled region. As an effect of the hydrogen bond (HB) networking, the isobars of both these transport functions evolve with T by changing by several orders of magnitude, whereas their pressure dependence become more and more pronounced at lower temperatures. Both these transport functions were then studied according to the Adam–Gibbs model, typical of glass forming liquids, obtaining the water configurational entropy and the corresponding specific heat contribution. The comparison of the evaluated CP,conf isobars with the experimentally measured water specific heat reveals the full consistency of this analysis. In particular, the observed CP,conf maxima and its diverging behaviors clearly reveals the presence of the LLT and with a reasonable approximation the liquid–liquid critical point (LLCP) locus in the phase diagram.
Succinylation of Polyallylamine: Influence on Biological Efficacy and the Formation of Electrospun Fibers
Authors: Jurko Lucija , Bračič Matej , Hribernik Silvo , Makuc Damjan , Plavec Janez , Jerenec Filip , Žabkar Sonja , Gubeljak Nenad , Štern Alja , Kargl Rupert
Published: August 2021

Succinylation of proteins is a commonly encountered reaction in biology and introduces negatively charged carboxylates on previously basic primary amine groups of amino acid residues. In analogy, this work investigates the succinylation of primary amines of the synthetic polyelectrolyte polyallylamine (PAA). It investigates the influence of the degree of succinylation on the cytotoxicity and antibacterial activity of the resulting polymers. Succinylation was performed in water with varying amounts of succinic anhydride and at different pH values. The PAA derivatives were analyzed in detail with respect to molecular structure using nuclear magnetic resonance and infrared absorbance spectroscopy. Polyelectrolyte and potentiometric charge titrations were used to elucidate charge ratios between primary amines and carboxylates in the polymers. The obtained materials were then evaluated with respect to their minimum inhibitory concentration against Staphylococcus aureus and Pseudomonas aeruginosa. The biocompatibility was assessed using mouse L929 fibroblasts. The degree of succinylation decreased cytotoxicity but more significantly reduced antibacterial efficacy, demonstrating the sensitivity of the fibroblast cells against this type of ampholytic polyelectrolytes. The obtained polymers were finally electrospun into microfiber webs in combination with neutral water-soluble polyvinyl alcohol. The resulting non-woven could have the potential to be used as wound dressing materials or coatings.
The Use of Computational Methods for the Development of Molecularly Imprinted Polymers
Authors: Nicholls Ian A. , Golker Kerstin , Olsson Gustaf D. , Suriyanarayanan Subramanian , Wiklander Jesper G.
Published: August 2021

Recent years have witnessed a dramatic increase in the use of theoretical and computational approaches in the study and development of molecular imprinting systems. These tools are being used to either improve understanding of the mechanisms underlying the function of molecular imprinting systems or for the design of new systems. Here, we present an overview of the literature describing the application of theoretical and computational techniques to the different stages of the molecular imprinting process (pre-polymerization mixture, polymerization process and ligand–molecularly imprinted polymer rebinding), along with an analysis of trends within and the current status of this aspect of the molecular imprinting field.
Radio-Oxidation Ageing of XLPE Containing Different Additives and Filler: Effect on the Gases Emission and Consumption
Authors: Ferry Muriel , Carpentier Floriane , Cornaton Manon
Published: August 2021

In the lifetime extension of nuclear power plants (NPPs) context, aging of electric cables has to be very well understood in order to predict their end-of-life and thus to replace them on time. Therefore, evaluation and understanding of the ageing mechanism of the cable insulating material is mandatory under conditions as close as possible of those encountered in NPPs. In this context, different formulated crosslinked polyethylenes (XLPE)—one of the polymers used nowadays to manufacture the insulator layer—have been irradiated under oxidative conditions, at two different dose rates and at different aging doses. Gases emitted and consumed from the irradiated polymers were quantified to identify the primary processes happening in the materials and thus the interactions involved between the different molecules composing the formulated polymers.
A New Method for Determination of Pectin Content Using Spectrophotometry
Authors: Wang Furong , Du Chao , Chen Junjun , Shi Lisheng , Li Hailong
Published: August 2021

The study aimed at developing a new spectrophotometric method for determining the pectin content. Take commercial pectin as an example, and the method is based on the reaction of copper ions with pectin to produce copper pectate. The spectrophotometer was used to measure the remaining content of copper ions so as to calculate the pectin content. This method eliminated the weight step and avoided the error associated with it. Effects of reaction time, temperature, and pH on absorbance were also studied. Additionally, the accuracy of this method was verified. It indicated excellent repeatability and accuracy with the relative standard deviation of 2.09%. In addition, three different plant types were used to demonstrate the reliability of the method. To summarize, this method can be widely used for the determination of pectin content in many materials.
Experimentally Calculated Study of the Effectiveness on the Process of Non-Catalytic Synthesis of Biodiesel in Reactors of Various Type
Authors: Meshalkin Valeriy , Sapunov Valentin , Kozlovskiy Roman , Kozlovskiy Ivan , Staroverov Dmitry , Luganskiy Artur , Voronov Mikhail
Published: August 2021

It was experimentally established that the dependence of the partial pressure of methanol on the molar fraction of methanol in oil shows a pronounced negative deviation from Raoul’s law, which significantly changes the idea of the influence of a large excess of methanol during non-catalytic synthesis of biodiesel. The efficiency of use of a molar excess of methanol is reduced as it grows, and with a more than 10-fold molar excess of the amount of reacted methanol, is practically constant. The comparison of biodiesel production processes in the range 220–235 °C showed that a slight change in the process temperature more effectively affects the biodiesel yield than an increase in the molar excess of methanol. A mathematical model of the process of transesterification of rapeseed oil in reactors of various types (batch and tubular reactors) is developed. A satisfactory correlation between the experimental and calculated data was observed. The calculation showed that the rate constants of the reverse reactions at 230 °C were not significant.
Hierarchical Nanoflowers of Colloidal WS2 and Their Potential Gas Sensing Properties for Room Temperature Detection of Ammonia
Authors: Gqoba Siziwe S. , Rodrigues Rafael , Mphahlele Sharon Lerato , Ndala Zakhele , Airo Mildred , Fadojutimi Paul Olawale , Hümmelgen Ivo A. , Linganiso Ella C. , Moloto Makwena J. , Moloto Nosipho
Published: August 2021

A one-step colloidal synthesis of hierarchical nanoflowers of WS2 is reported. The nanoflowers were used to fabricate a chemical sensor for the detection of ammonia vapors at room temperature. The gas sensing performance of the WS2 nanoflowers was measured using an in-house custom-made gas chamber. SEM analysis revealed that the nanoflowers were made up of petals and that the nanoflowers self-assembled to form hierarchical structures. Meanwhile, TEM showed the exposed edges of the petals that make up the nanoflower. A band gap of 1.98 eV confirmed a transition from indirect-to-direct band gap as well as a reduction in the number of layers of the WS2 nanoflowers. The formation of WS2 was confirmed by XPS and XRD with traces of the oxide phase, WO3. XPS analysis also confirmed the successful capping of the nanoflowers. The WS2 nanoflowers exhibited a good response and selectivity for ammonia.
On the Non-Uniqueness of Statistical Ensembles Defining a Density Operator and a Class of Mixed Quantum States with Integrable Wigner Distribution
Authors: de Gosson Charlyne , de Gosson Maurice
Published: August 2021

It is standard to assume that the Wigner distribution of a mixed quantum state consisting of square-integrable functions is a quasi-probability distribution, i.e., that its integral is one and that the marginal properties are satisfied. However, this is generally not true. We introduced a class of quantum states for which this property is satisfied; these states are dubbed “Feichtinger states” because they are defined in terms of a class of functional spaces (modulation spaces) introduced in the 1980s by H. Feichtinger. The properties of these states were studied, giving us the opportunity to prove an extension to the general case of a result due to Jaynes on the non-uniqueness of the statistical ensemble, generating a density operator.
Transcending the Technocratic Mentality through the Humanities
Authors: Sullivan John
Published: August 2021

Catholic education has a long tradition of engagement with the liberal arts and especially the humanities. The place of the humanities today in the curriculum is under threat for several reasons, one being the predominance of the technocratic mentality. This paper revisits (in three steps) the contested issue of the role of the humanities in education. First, I review arguments about the role of the humanities within education. Second, some of the defects of the technocratic mentality are pointed out. Third, a Christian lens for viewing the humanities is deployed. Here I propose that the humanities play a valuable role in nurturing the imagination, thereby contributing both to a capacity to transcend the technocratic outlook and to the development of the holistic and humanising education that is central to a Catholic worldview.
One-Year and Ultimate Reserve Risk in Mack Chain Ladder Model
Authors: Szatkowski Marcin , Delong Łukasz
Published: August 2021

We investigate the relation between one-year reserve risk and ultimate reserve risk in Mack Chain Ladder model in a simulation study. The first goal is to validate the so-called linear emergence pattern formula, which maps the ultimate loss to the one-year loss, in case when we measure the risks with Value-at-Risk. The second goal is to estimate the true emergence pattern of the ultimate loss, i.e., the conditional distribution of the one-year loss given the ultimate loss, from which we can properly derive a risk measure for the one-year horizon from the simulations of ultimate losses. Finally, our third goal is to test if classical actuarial distributions can be used for modelling of the outstanding loss from the ultimate and the one-year perspective. In our simulation study, we investigate several synthetic loss triangles with various duration of the claims development process, volatility, skewness, and distributional assumptions of the individual development factors. We quantify the reserve risks without and with the estimation error of the claims development factors.
Persistent Scatterer Interferometry and Statistical Analysis of Time-Series for Landslide Monitoring: Application to Santo Stefano d’Aveto (Liguria, NW Italy)
Authors: Balbi Evandro , Terrone Martino , Faccini Francesco , Scafidi Davide , Barani Simone , Tosi Silvano , Crispini Laura , Cianfarra Paola , Poggi Flavio , Ferretti Gabriele
Published: August 2021

Landslides are a major threat for population and urban areas. Persistent Scatterer Interferometry (PSI) is a powerful tool for identifying landslides and monitoring their evolution over long periods and has proven to be very useful especially in urban areas, where a sufficient number of PS can be generated. In this study, we applied PS interferometry to investigate the landslide affecting Santo Stefano d’Aveto (Liguria, NW Italy) by integrating classic interferometric techniques with cross-correlation analysis of PS time-series and with geological and geotechnical field information. We used open-source software and packages to process Synthetic Aperture Radar (SAR) images from the Copernicus Sentinel-1A satellite for both ascending and descending orbits over the period 2015–2021 and calculate both the vertical motion and the E-W horizontal displacement. By computing the cross-correlation of the PS time-series, we identified three families of PS with a similarity greater than 0.70. The cross-correlation analysis allowed subdividing the landslide in different sectors, each of which is characterized by a specific type of movement. The geological meaning of this subdivision is still a matter of discussion but it is presumably driven by the geomorphological setting of the area and by the regional tectonics.
Consistent Multi-Mission Measures of Inland Water Algal Bloom Spatial Extent Using MERIS, MODIS and OLCI
Authors: Zeng Chuiqing , Binding Caren E.
Published: August 2021

Envisat’s MERIS and its successor Sentinel OLCI have proven invaluable for documenting algal bloom conditions in coastal and inland waters. Observations over turbid eutrophic waters, in particular, have benefited from the band at 708 nm, which captures the reflectance peak associated with intense algal blooms and is key to line-height algorithms such as the Maximum Chlorophyll Index (MCI). With the MERIS mission ending in early 2012 and OLCI launched in 2016, however, time-series studies relying on these two sensors have to contend with an observation gap spanning four years. Alternate sensors, such as MODIS Aqua, offering neither the same spectral band configuration nor consistent spatial resolution, present challenges in ensuring continuity in derived bloom products. This study explores a neural network (NN) solution to fill the observation gap between MERIS and OLCI with MODIS Aqua data, delivering consistent algal bloom spatial extent products from 2002 to 2020 using these three sensors. With 14 bands of MODIS level 2 partially atmospherically corrected spectral reflectance as the NN input, the missing MERIS/OLCI band at 708 nm required for the MCI is simulated. The resulting NN-derived MODIS MCI (NNMCI) is shown to be in good agreement with MERIS and OLCI MCI in 2011 and 2017 respectively over the western basin of Lake Erie (R2 = 0.84, RMSE = 0.0032). To overcome the impact of MODIS sensor saturation over bright water targets, which otherwise renders pixels unusable for bloom detection using R-NIR wavebands, a variant NN model is employed which uses the 9 MODIS bands with the lowest probability of saturation to simulate the MCI. This variant NN predicts MCI with only a small increase in uncertainty (R2 = 0.73, RMSE = 0.005) allowing reliable estimates of bloom conditions in those previously unreported pixels. The NNMCI is shown to be robust when applied beyond the initial training dataset on Lake Erie, and when re-trained on different geographic areas (Lake Winnipeg and Lake of the Woods). Despite differences in spatial, temporal, and spectral resolution, MODIS algal bloom presence/absence was correctly classified in >92% of cases and bloom spatial extent derived within 25% uncertainty, allowing the application to the 2012–2015 time period to form a continuous and consistent multi-mission monitoring dataset from 2002 to 2020.
A Quick Band-to-Band Mis-Registration Detection Method for Sentinel-2 MSI Images
Authors: Chen Tianxin , Liu Yongxue
Published: August 2021

A band-to-band mis-registration (BBMR) error often occurs in remote sensing (RS) images acquired by multi-spectral push broom spectrometers such as the Sentinel-2 Multi-spectral Instrument (MSI), leading to adverse impacts on the reliability of further RS applications. Although the systematic band-to-band registration conducted during the image production process corrects most BBMR errors, there are still quite a few images being observed with discernible BBMR. Thus, a quick BBMR detection method is needed to assess the quality of online RS products. We here propose a hybrid framework for detecting BBMR between the visible bands in MSI images. This framework comprises three main steps: first, candidate chips are captured based on Google Earth Engine (GEE) spatial analysis functions to shrink the valid areas inside image scenes as potential target chips. The redundant data pertaining to the local operation process are thus narrowed down. Second, spectral abnormal areas are precisely extracted from inside every single chip, excluding the influences of clouds and water surfaces. Finally, the abnormal areas are matched pixel by pixel between bands, and the best-fit coordinates are then determined to compare with tolerance. Here, the proposed method was applied to 71,493 scenes of MSI Level-1C images covering China and its surrounding areas on the GEE platform. From these images, 4356 chips from 442 scenes were detected with inter-band offsets among the visible bands. Further manual visual inspection revealed that the proposed method had an accuracy of 98.07% at the chip scale and 88.46% at the scene scale.
Mapping Atmospheric Exposure of the Intertidal Zone with Sentinel-1 CSAR in Northern Norway
Authors: Haarpaintner Jörg , Davids Corine
Published: August 2021

The intertidal zone (ITZ) is a highly dynamic and diverse coastal ecosystem under pressure that provides important eco-services. Being periodically under water makes it challenging to monitor, and the only possibility to map it in all tidal stages is by using dense time series of observations. At high latitudes, the Sentinel-1 (S1) constellation of the European Copernicus Program consistently provides radar imagery at fixed times on a near-daily basis, independently of cloud cover and sunlight. As tides have a period of 12 h 25.2 min, 1–2 year long S1 time series are therefore able to sample the whole tidal range and, thus, map the percentage of atmospheric exposure of the ITZ, which is an important environmental parameter. Tidal reference levels of mean high/low water at spring, mean and neap tide correspond each to specific percentiles of tidal heights and inversely correspond to atmospheric exposure. The presented method maps atmospheric exposure on the basis of purely statistical analyses of Sentinel-1 time series without the need for any tidal gauge data, by extracting water lines via simple thresholding of radar backscatter percentiles images. The individual thresholds for the second, fifth, 25th, 50th, 75th, 95th, and 98th percentile image were determined by fitting the threshold contour lines to in situ water line GPS tracks collected at corresponding tidal reference levels at five locations around Tromsø in Northern Norway. They inversely correspond to atmospheric exposures of 98%, 95%, 75%, 50%, 25%, 5%, and 2%, respectively. The method was applied to the whole Tromsø Municipality resulting in an ITZ atmospheric exposure map. The validation shows that the mean low water lines at neap, mid, and spring tide were mapped with accuracies of 93%, 84%, and 64%, respectively. The overall approach should be applicable worldwide.
Inverted Algorithm of Terrestrial Water-Storage Anomalies Based on Machine Learning Combined with Load Model and Its Application in Southwest China
Authors: Shen Yifan , Zheng Wei , Yin Wenjie , Xu Aigong , Zhu Huizhong , Yang Shuai , Su Kai
Published: August 2021

Dense Global Position System (GPS) arrays can be used to invert the terrestrial water-storage anomaly (TWSA) with higher accuracy. However, the uneven distribution of GPS stations greatly limits the application of GPS to derive the TWSA. Aiming to solve this problem, we grid the GPS array using regression to raise the reliability of TWSA inversion. First, the study uses the random forest (RF) model to simulate crustal deformation in unobserved grids. Meanwhile, the new Machine-Learning Loading-Inverted Method (MLLIM) is constructed based on the traditional GPS derived method to raise the truthfulness of TWSA inversion. Second, this research selects southwest China as the study region, the MLLIM and traditional GPS inversion methods are used to derive the TWSA, and the inverted results are contrasted with datasets of the Gravity Recovery and Climate Experiment (GRACE) Mascon and the Global Land Data Assimilation System (GLDAS) model. The comparison shows that values of Pearson Correlation Coefficient (PCC) between the MLLIM and GRACE and GRACE Follow-On (GRACE-FO) are equal to 0.91 and 0.88, respectively; and the values of R-squared (R2) are equal to 0.76 and 0.65, respectively; the values of PCC and R2 between MLLIM and GLDAS solutions are equal to 0.79 and 0.65. Compared with the traditional GPS inversion, the MLLIM improves PCC and R2 by 8.85% and 7.99% on average, which indicates that the MLLIM can improve the accuracy of TWSA inversion more than the traditional GPS method. Third, this study applies the MLLIM to invert the TWSA in each province of southwest China and combines the precipitation to analyze the change of TWSA in each province. The results are as follows: (1) The spatial distribution of TWSA and precipitation is coincident, which is highlighted in southwest Yunnan and southeast Guangxi; (2) this study compares TWSA of MLLIM with GRACE and GLDAS solutions in each province, which indicates that the maximum value of PCC is as high as 0.86 and 0.94, respectively, which indicates the MLLIM can be used to invert the TWSA in the regions with sparse GPS stations. The TWSA based on the MLLIM can be used to fill the vacancy between GRACE and GRACE-FO.
An Improved Adaptive Spatial Preprocessing Method for Remote Sensing Images
Authors: Zheng Liangliang , Xu Wei
Published: August 2021

Since remote sensing images are one of the main sources for people to obtain required information, the quality of the image becomes particularly important. Nevertheless, noise often inevitably exists in the image, and the targets are usually blurred by the acquisition of the imaging system, resulting in the degradation of quality of the images. In this paper, a novel preprocessing algorithm is proposed to simultaneously smooth noise and to enhance the edges, which can improve the visual quality of remote sensing images. It consists of an improved adaptive spatial filter, which is a weighted filter integrating functions of both noise removal and edge sharpness. Its processing parameters are flexible and adjustable relative to different images. The experimental results confirm that the proposed method outperforms the existing spatial algorithms both visually and quantitatively. It can play an important role in the remote sensing field in order to achieve more information of interested targets.
An RSS Transform—Based WKNN for Indoor Positioning
Authors: Zhou Rong , Yang Yexi , Chen Puchun
Published: August 2021

An RSS transform–based weighted k-nearest neighbor (WKNN) indoor positioning algorithm, Q-WKNN, is proposed to improve the positioning accuracy and real-time performance of Wi-Fi fingerprint–based indoor positioning. To smooth the RSS fluctuation difference caused by acquisition equipment, time, and environment changes, base Q is introduced in Q-WKNN to transform RSS to Q-based RSS, based on the relationship between the received signal strength (RSS) and physical distance. Analysis of the effective range of base Q indicates that Q-WKNN is more suitable for regions with noticeable environmental changes and fixed access points (APs). To reduce the positioning time, APs are selected to form a Q-WKNN similarity matrix. Adaptive K is applied to estimate the test point (TP) position. Commonly used indoor positioning algorithms are compared to Q-WKNN on Zenodo and underground parking databases. Results show that Q-WKNN has better positioning accuracy and real-time performance than WKNN, modified-WKNN (M-WKNN), Gaussian kernel (GK), and least squares-support vector machine (LS-SVM) algorithms.
CAPformer: Pedestrian Crossing Action Prediction Using Transformer
Authors: Lorenzo Javier , Alonso Ignacio Parra , Izquierdo Rubén , Ballardini Augusto Luis , Saz Álvaro Hernández , Llorca David Fernández , Sotelo Miguel Ángel
Published: August 2021

Anticipating pedestrian crossing behavior in urban scenarios is a challenging task for autonomous vehicles. Early this year, a benchmark comprising JAAD and PIE datasets have been released. In the benchmark, several state-of-the-art methods have been ranked. However, most of the ranked temporal models rely on recurrent architectures. In our case, we propose, as far as we are concerned, the first self-attention alternative, based on transformer architecture, which has had enormous success in natural language processing (NLP) and recently in computer vision. Our architecture is composed of various branches which fuse video and kinematic data. The video branch is based on two possible architectures: RubiksNet and TimeSformer. The kinematic branch is based on different configurations of transformer encoder. Several experiments have been performed mainly focusing on pre-processing input data, highlighting problems with two kinematic data sources: pose keypoints and ego-vehicle speed. Our proposed model results are comparable to PCPA, the best performing model in the benchmark reaching an F1 Score of nearly 0.78 against 0.77. Furthermore, by using only bounding box coordinates and image data, our model surpasses PCPA by a larger margin (F1=0.75 vs. F1=0.72). Our model has proven to be a valid alternative to recurrent architectures, providing advantages such as parallelization and whole sequence processing, learning relationships between samples not possible with recurrent architectures.
Neural Reduction of Image Data in Order to Determine the Quality of Malting Barley
Authors: Boniecki Piotr , Raba Barbara , A. Pilarska Agnieszka , Sujak Agnieszka , Zaborowicz Maciej , Pilarski Krzysztof , Wojcieszak Dawid
Published: August 2021

Image analysis using neural modeling is one of the most dynamically developing methods employing artificial intelligence. The feature that caused such widespread use of this technique is mostly the ability of automatic generalization of scientific knowledge as well as the possibility of parallel analysis of the empirical data. A properly conducted learning process of artificial neural network (ANN) allows the classification of new, unknown data, which helps to increase the efficiency of the generated models in practice. Neural image analysis is a method that allows extracting information carried in the form of digital images. The paper focuses on the determination of imperfections such as contaminations and damages in the malting barley grains on the basis of information encoded in the graphic form represented by the digital photographs of kernels. This choice was dictated by the current state of knowledge regarding the classification of contamination that uses undesirable features of kernels to exclude them from use in the malting industry. Currently, a qualitative assessment of kernels is carried by malthouse-certified employees acting as experts. Contaminants are separated from a sample of malting barley manually, and the percentages of previously defined groups of contaminations are calculated. The analysis of the problem indicates a lack of effective methods of identifying the quality of barley kernels, such as the use of information technology. There are new possibilities of using modern methods of artificial intelligence (such as neural image analysis) for the determination of impurities in malting barley. However, there is the problem of effective compression of graphic data to a form acceptable for ANN simulators. The aim of the work is to develop an effective procedure of graphical data compression supporting the qualitative assessment of malting barley with the use of modern information technologies. Image analysis can be implemented into dedicated software.
Measurement and Analysis of the Parameters of Modern Long-Range Thermal Imaging Cameras
Authors: Barela Jaroslaw , Firmanty Krzysztof , Kastek Mariusz
Published: August 2021

Today’s long-range infrared cameras (LRIRC) are used in many systems for the protection of critical infrastructure or national borders. The basic technical parameters of such systems are noise equivalent temperature difference (NETD); minimum resolvable temperature difference (MRTD); and the range of detection, recognition and identification of selected objects (DRI). This paper presents a methodology of the theoretical determination of these parameters on the basis of technical data of LRIRCs. The first part of the paper presents the methods used for the determination of the detection, recognition and identification ranges based on the well-known Johnson criteria. The theoretical backgrounds for both approaches are given, and the laboratory test stand is described together with a brief description of the methodology adopted for the measurements of the selected necessary characteristics of a tested observation system. The measurements were performed in the Accredited Testing Laboratory of the Institute of Optoelectronics of the Military University of Technology (AL IOE MUT), whose activity is based on the ISO/IEC 17025 standard. The measurement results are presented, and the calculated ranges for a selected set of IR cameras are given, obtained on the basis of the Johnson criteria. In the final part of the article, the obtained measurement results are presented together with an analysis of the measurement uncertainty for 10 LRIRCs. The obtained measurement results were compared to the technical parameters presented by the manufacturers.
On the Use of Deep Learning for Imaging-Based COVID-19 Detection Using Chest X-rays
Authors: Okolo Gabriel Iluebe , Katsigiannis Stamos , Althobaiti Turke , Ramzan Naeem
Published: August 2021

The global COVID-19 pandemic that started in 2019 and created major disruptions around the world demonstrated the imperative need for quick, inexpensive, accessible and reliable diagnostic methods that would allow the detection of infected individuals with minimal resources. Radiography, and more specifically, chest radiography, is a relatively inexpensive medical imaging modality that can potentially offer a solution for the diagnosis of COVID-19 cases. In this work, we examined eleven deep convolutional neural network architectures for the task of classifying chest X-ray images as belonging to healthy individuals, individuals with COVID-19 or individuals with viral pneumonia. All the examined networks are established architectures that have been proven to be efficient in image classification tasks, and we evaluated three different adjustments to modify the architectures for the task at hand by expanding them with additional layers. The proposed approaches were evaluated for all the examined architectures on a dataset with real chest X-ray images, reaching the highest classification accuracy of 98.04% and the highest F1-score of 98.22% for the best-performing setting.
Classification of Tennis Shots with a Neural Network Approach
Authors: Ganser Andreas , Hollaus Bernhard , Stabinger Sebastian
Published: August 2021

Data analysis plays an increasingly valuable role in sports. The better the data that is analysed, the more concise training methods that can be chosen. Several solutions already exist for this purpose in the tennis industry; however, none of them combine data generation with a wristband and classification with a deep convolutional neural network (CNN). In this article, we demonstrate the development of a reliable shot detection trigger and a deep neural network that classifies tennis shots into three and five shot types. We generate a dataset for the training of neural networks with the help of a sensor wristband, which recorded 11 signals, including an inertial measurement unit (IMU). The final dataset included 5682 labelled shots of 16 players of age 13–70 years, predominantly at an amateur level. Two state-of-the-art architectures for time series classification (TSC) are compared, namely a fully convolutional network (FCN) and a residual network (ResNet). Recent advances in the field of machine learning, like the Mish activation function and the Ranger optimizer, are utilized. Training with the rather inhomogeneous dataset led to an F1 score of 96% in classification of the main shots and 94% for the expansion. Consequently, the study yielded a solid base for more complex tennis analysis tools, such as the indication of success rates per shot type.
Automatic Detection of Colorectal Polyps Using Transfer Learning
Authors: Dulf Eva-H. , Bledea Marius , Mocan Teodora , Mocan Lucian
Published: August 2021

Colorectal cancer is the second leading cause of cancer death and ranks third worldwide in diagnosed malignant pathologies (1.36 million new cases annually). An increase in the diversity of treatment options as well as a rising population require novel diagnostic tools. Current diagnostics involve critical human thinking, but the decisional process loses accuracy due to the increased number of modulatory factors involved. The proposed computer-aided diagnosis system analyses each colonoscopy and provides predictions that will help the clinician to make the right decisions. Artificial intelligence is included in the system both offline and online image processing tools. Aiming to improve the diagnostic process of colon cancer patients, an application was built that allows the easiest and most intuitive interaction between medical staff and the proposed diagnosis system. The developed tool uses two networks. The first, a convolutional neural network, is capable of classifying eight classes of tissue with a sensitivity of 98.13% and an F1 score of 98.14%, while the second network, based on semantic segmentation, can identify the malignant areas with a Jaccard index of 75.18%. The results could have a direct impact on personalised medicine combining clinical knowledge with the computing power of intelligent algorithms.
Application of High-Speed Quantum Cascade Detectors for Mid-Infrared, Broadband, High-Resolution Spectroscopy
Authors: Dougakiuchi Tatsuo , Akikusa Naota
Published: August 2021

Broadband, high-resolution, heterodyne, mid-infrared absorption spectroscopy was performed with a high-speed quantum cascade (QC) detector. By strictly reducing the device capacitance and inductance via air-bridge wiring and a small mesa structure, a 3-dB frequency response over 20 GHz was obtained for the QC detector, which had a 4.6-μm peak wavelength response. In addition to the high-speed, it exhibited low noise characteristics limited only by Johnson–Nyquist noise, bias-free operation without cooling, and photoresponse linearity over a wide dynamic range. In the detector characterization, the noise-equivalent power was 7.7 × 10−11 W/Hz1/2 at 4.6 μm, and it had good photoresponse linearity up to 250 mW, with respect to the input light power. Broadband and high-accuracy molecular spectroscopy based on heterodyne detection was demonstrated by means of two distributed-feedback 4.5-μm QC lasers. Specifically, several nitrous oxide absorption lines were acquired over a wavelength range of 0.8 cm−1 with the wide-band QC detector.