bluuebunny/crossref_metadata_2025 huggingface dataset:
Colombian Contributions Fighting Leishmaniasis: A Systematic Review on Antileishmanials Combined with Chemoinformatics Analysis
Authors: Sánchez-Suárez Jeysson , Bernal Freddy A. , Coy-Barrera Ericsson
Published: December 2020

Leishmaniasis is a parasitic morbid/fatal disease caused by Leishmania protozoa. Twelve million people worldwide are appraised to be currently infected, including ca. two million infections each year, and 350 million people in 88 countries are at risk of becoming infected. In Colombia, cutaneous leishmaniasis (CL) is a public health problem in some tropical areas. Therapeutics is based on traditional antileishmanial drugs, but this practice has several drawbacks for patients. Thus, the search for new antileishmanial agents is a serious need, but the lack of adequately funded research programs on drug discovery has hampered its progress. Some Colombian researchers have conducted different research projects focused on the assessment of the antileishmanial activity of naturally occurring and synthetic compounds against promastigotes and/or amastigotes. Results of such studies have separately demonstrated important hits and reasonable potential, but a holistic view of them is lacking. Hence, we present the outcome from a systematic review of the literature (under PRISMA guidelines) on those Colombian studies investigating antileishmanials during the last thirty-two years. In order to combine the general efforts aiming at finding a lead against Leishmania panamensis (one of the most studied and incident parasites in Colombia causing CL) and to recognize structural features of representative compounds, fingerprint-based analyses using conventional machine learning algorithms and clustering methods are shown. Abstraction from such a meta-description led to describe some function-determining molecular features and simplify the clustering of plausible isofunctional hits. This systematic review indicated that the Colombian efforts for the antileishmanials discovery are increasingly intensified, though improvements in the followed pathways must be definitively pursued. In this context, a brief discussion about scope, strengths and limitations of such advances and relationships is addressed.
Photobiology and Endogenous Fluorophore Based Applications, from Natural Environment to Biomedicine to Improve Human Life
Authors: Croce Anna C.
Published: December 2020

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Lignans and Gut Microbiota: An Interplay Revealing Potential Health Implications
Authors: Senizza Alice , Rocchetti Gabriele , Mosele Juana I. , Patrone Vania , Callegari Maria Luisa , Morelli Lorenzo , Lucini Luigi
Published: December 2020

Plant polyphenols are a broad group of bioactive compounds characterized by different chemical and structural properties, low bioavailability, and several in vitro biological activities. Among these compounds, lignans (a non-flavonoid polyphenolic class found in plant foods for human nutrition) have been recently studied as potential modulators of the gut–brain axis. In particular, gut bacterial metabolism is able to convert dietary lignans into therapeutically relevant polyphenols (i.e., enterolignans), such as enterolactone and enterodiol. Enterolignans are characterized by various biologic activities, including tissue-specific estrogen receptor activation, together with anti-inflammatory and apoptotic effects. However, variation in enterolignans production by the gut microbiota is strictly related to both bioaccessibility and bioavailability of lignans through the entire gastrointestinal tract. Therefore, in this review, we summarized the most important dietary source of lignans, exploring the interesting interplay between gut metabolites, gut microbiota, and the so-called gut–brain axis.
Bipolymeric Pectin Millibeads Doped with Functional Polymers as Matrices for the Controlled and Targeted Release of Mesalazine
Authors: Wójcik-Pastuszka Dorota , Potempa Aleksandra , Musiał Witold
Published: December 2020

Targeted drug delivery systems are a very convenient method of treating inflammatory bowel disease. The properties of pectin make this biopolymer a suitable drug carrier. These properties allow pectin to overcome the diverse environment of the digestive tract and deliver the drug to the large intestine. This investigation proposed bipolymeric formulations consisting of the natural polymer pectin and a synthetic polymer containing the drug 5-aminosalicylic acid. Pectin beads were prepared via ionotropic gelation involving the interaction between the hydrophilic gel and calcium ions. The obtained formulations consisted of natural polymer, 5-aminosalicylic acid (5-ASA) and one of the synthetic polymers, such as polyacrylic acid, polyvinylpyrrolidone, polyethylene glycol or aristoflex. The release of the drug was carried out employing a basket apparatus (USP 1). The acceptor fluid was pH = 7.4 buffer with added enzyme pectinase to reflect the colon environment. The amount of the released drug was determined using UV-Vis spectrophotometry at a wavelength of λ = 330 nm. The kinetics of the drug dissolution revealed that none of the employed models was appropriate to describe the release process. A kinetic analysis of the release profile during two release stages was carried out. The fastest drug release occurred during the first stage from a formulation containing pectin and polyethylene glycol. However, according to the applied kinetic models, the dissolution of 5-ASA was rather high in the formulation without the synthetic polymer during the second stage. Depending on the formulation, 68–77% of 5-ASA was released in an 8-hour time period. The FTIR and DSC results showed that there was no interaction between the drug and the polymers, but interactions between pectin and synthetic polymers were found.
Bioactive Agent Discovery from the Natural Compounds for the Treatment of Type 2 Diabetes Rat Model
Authors: Yang Shih-Chun , Hsu Ching-Yun , Chou Wei-Ling , Fang Jia-You , Chuang Shih-Yi
Published: December 2020

Diabetes mellitus is a well-known chronic metabolic disease that poses a long-term threat to human health and is characterized by a relative or absolute lack of insulin, resulting in hyperglycemia. Type 2 diabetes mellitus (T2DM) typically affects many metabolic pathways, resulting in β-cell dysfunction, insulin resistance, abnormal blood glucose levels, inflammatory processes, excessive oxidative reactions, and impaired lipid metabolism. It also leads to diabetes-related complications in many organ systems. Antidiabetic drugs have been approved for the treatment of hyperglycemia in T2DM; these are beneficial for glucose metabolism and promote weight loss, but have the risk of side effects, such as nausea or an upset stomach. A wide range of active components, derived from medicinal plants, such as alkaloids, flavonoids, polyphenol, quinones, and terpenoids may act as alternative sources of antidiabetic agents. They are usually attributed to improvements in pancreatic function by increasing insulin secretions or by reducing the intestinal absorption of glucose. Ease of availability, low cost, least undesirable side effects, and powerful pharmacological actions make plant-based preparations the key player of all available treatments. Based on the study of therapeutic reagents in the pathogenesis of humans, we use the appropriate animal models of T2DM to evaluate medicinal plant treatments. Many of the rat models have characteristics similar to those in humans and have the advantages of ease of genetic manipulation, a short breeding span, and access to physiological and invasive testing. In this review, we summarize the pathophysiological status of T2DM rat models and focus on several bioactive compounds from herbal medicine with different functional groups that exhibit therapeutic potential in the T2DM rat models, in turn, may guide future approach in treating diabetes with natural drugs.
Detection of the agr System and Resistance to Antimicrobials in Biofilm-Producing S. epidermidis
Authors: Cataneli Pereira Valéria , Pinheiro-Hubinger Luiza , de Oliveira Adilson , Moraes Riboli Danilo Flávio , Benini Martins Katheryne , Calixto Romero Letícia , Ribeiro de Souza da Cunha Maria de Lourdes
Published: December 2020

The ability of Staphylococcus epidermidis to produce virulence factors, such as biofilm, added to its increased resistance to antimicrobials can cause infections that are difficult to treat. Many staphylococcal virulence factors are under the control of the accessory gene regulator (agr). The objective of this study was to establish the agr locus and susceptibility of biofilm-producing S. epidermidis specimens to antimicrobial agents, through PCR reactions, reverse transcription polymerase chain reaction (RT-PCR), and the determination of minimum inhibitory concentration (MIC), and to analyze the clonal profile of 300 strains isolated from blood culture specimens from inpatients at a University Hospital in Brazil, over a 20-year period by pulsed-field gel electrophoresis (PFGE) and multilocus sequence typing (MLST) techniques. The ica operon expression was shown in 83.6% strains, bhp gene in 11.5%, and aap gene in 32.8%. Oxacillin resistance was detected in 90.1%, while 4.9% showed tigecycline resistance, and intermediate resistance to quinupristin/dalfopristin was identified in 0.4%. Clonal profile determination showed 11 clusters, with the ST2 type determined as the major cluster. The S. epidermidis biofilm producer demonstrated a predominance of agr I locus, oxacillin resistance, and SCCmec III as well as the potential dissemination of pathogenic clones in hospital settings over long periods.
Development and Validation of a Bioanalytical Method for 3′- and 6′-Sialyllactose in Minipig Liver and Kidney Using Liquid Chromatography-Tandem Mass Spectrometry and Its Application to Analysis of Tissue Distribution
Authors: Eom Han Young , Jang Seok-In , Lee Jong-Hwa
Published: December 2020

Breast milk contains human milk oligosaccharides (HMOs), including sialyllactose (SL). SL is composed of sialic acid and lactose, and is divided into 3′-SL and 6′-SL according to the binding position. SL has immunoprotective effects against bacteria and viruses, and acts as a probiotic in the gastrointestinal tract. In this study, we developed a bioanalytical method for simultaneous analysis of 3′-SL and 6′-SL in liver and kidney tissues of Yucatan minipigs using liquid chromatography–tandem mass spectrometry (LC-MS/MS) under conditions optimized in our previous study. LC-MS/MS was performed using a hydrophilic interaction liquid chromatography (HILIC) column (50 mm × 2.1 mm, 3 μm) with a mobile phase consisting of 10 mM ammonium acetate in water (pH 4.5) and acetonitrile with gradient elution at a flow rate of 0.3 mL/min. A surrogate matrix method using water was applied for analysis of endogenous SL. The developed method was validated with regard to selectivity, linearity, precision, accuracy, the matrix effect, recovery, parallelism, dilution integrity, carryover, and stability according to the US Food and Drug Administration guidelines. We performed a tissue distribution study of minipigs, and analyzed liver and kidney tissues using the developed method to determine the tissue distribution of 3′-SL and 6′-SL. The tissue concentrations of 3′-SL and 6′-SL were readily measurable, suggesting that the method would be useful for evaluating the tissue distributions of these compounds in minipigs.
Effects of Omega-3 Fatty Acids on Muscle Mass, Muscle Strength and Muscle Performance among the Elderly: A Meta-Analysis
Authors: Huang Ya-Hui , Chiu Wan-Chun , Hsu Yuan-Pin , Lo Yen-Li , Wang Yuan-Hung
Published: December 2020

There is increasing evidence showing the role of fatty acids and their derived lipid intermediates in the regulation of skeletal muscle mass synthesis and function. However, the role of omega-3 fatty acids remains unclear. Therefore, we conducted a meta-analysis to evaluate the potential effects of omega-3 fatty acids on sarcopenia-related performances among the elderly. Eligible literature and reports of randomized controlled trials were comprehensively searched from the PubMed, Cochrane Library, ClinicalTrials.gov, and Cumulative Index to Nursing and Allied Health Literature (CINAHL) databases until July 2018. A total of 10 articles were available for the meta-analysis. There were minor benefits for muscle mass gain (0.33 kg; 95% CI: 0.05, 0.62) and timed up and go performance (−0.30 s; 95% CI: −0.43, −0.17). Subgroup analyses regarding muscle mass and walk speed indicated that omega-3 fatty acid supplements at more than 2 g/day may contribute to muscle mass gain (0.67 kg; 95% CI: 0.16, 1.18) and improve walking speed, especially for those receiving more than 6 months of intervention (1.78 m/sec; 95% CI: 1.38, 2.17). Our findings provide some insight into the effects of omega-3 fatty acids on muscle mass, especially for those taking supplements at more than 2 g/day. We also observed that a long period of omega-3 fatty acids supplementation may improve walking speed.
Hemolysin-Producing Strains among Diarrheagenic Escherichia coli Isolated from Children under 2 Years Old with Diarrheal Disease
Authors: Mare Anca , Man Adrian , Toma Felicia , Ciurea Cristina Nicoleta , Coșeriu Răzvan Lucian , Vintilă Camelia , Maier Adrian Cornel
Published: December 2020

Even if serotyping based on O antigens is still routinely used by most laboratories for the detection of diarrheagenic Escherichia coli, this method can provide false-positive reactions, due to the high diversity of O antigens. Molecular methods represent a valuable tool that clarifies these situations. In the Bacteriology Laboratory of Mureș County Hospital, between May 2016 and July 2019, 160 diarrheagenic E. coli strains were isolated from children under 2 years old with diarrheic disease. The strains were identified as Shiga toxin-producing E. coli (STEC)/enteropathogenic Escherichia coli (EPEC) via agglutination with polyvalent sera. STEC strains were serotyped using monovalent sera for serogroup O157. Simplex PCR was performed on the strains to determine the presence of the hlyA gene, and, for the positive ones, the hemolytic activity was tested. Antibiotic susceptibility of the identified diarrheagenic E. coli strains was also investigated. STEC strains were the most frequently identified (49.1%), followed by EPEC (40.2%). The hlyA gene was identified in 12 cases, representing 18.2% of the STEC strains. Even if the extended-spectrum β-lactamase (ESBL)-producing strains represented only 10%, a relevant percentage of multidrug-resistant (MDR) strains (24%) was identified.
FDA-Approved Drugs with Potent In Vitro Antiviral Activity against Severe Acute Respiratory Syndrome Coronavirus 2
Authors: Mostafa Ahmed , Kandeil Ahmed , A. M. M. Elshaier Yaseen , Kutkat Omnia , Moatasim Yassmin , Rashad Adel A. , Shehata Mahmoud , Gomaa Mokhtar R. , Mahrous Noura , Mahmoud Sara H. , GabAllah Mohamed , Abbas Hisham , Taweel Ahmed El , Kayed Ahmed E. , Kamel Mina Nabil , Sayes Mohamed El , Mahmoud Dina B. , El-Shesheny Rabeh , Kayali Ghazi , Ali Mohamed A.
Published: December 2020

(1) Background: Drug repositioning is an unconventional drug discovery approach to explore new therapeutic benefits of existing drugs. Currently, it emerges as a rapid avenue to alleviate the COVID-19 pandemic disease. (2) Methods: Herein, we tested the antiviral activity of anti-microbial and anti-inflammatory Food and Drug Administration (FDA)-approved drugs, commonly prescribed to relieve respiratory symptoms, against Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the viral causative agent of the COVID-19 pandemic. (3) Results: Of these FDA-approved antimicrobial drugs, Azithromycin, Niclosamide, and Nitazoxanide showed a promising ability to hinder the replication of a SARS-CoV-2 isolate, with IC50 of 0.32, 0.16, and 1.29 µM, respectively. We provided evidence that several antihistamine and anti-inflammatory drugs could partially reduce SARS-CoV-2 replication in vitro. Furthermore, this study showed that Azithromycin can selectively impair SARS-CoV-2 replication, but not the Middle East Respiratory Syndrome Coronavirus (MERS-CoV). A virtual screening study illustrated that Azithromycin, Niclosamide, and Nitazoxanide bind to the main protease of SARS-CoV-2 (Protein data bank (PDB) ID: 6lu7) in binding mode similar to the reported co-crystalized ligand. Also, Niclosamide displayed hydrogen bond (HB) interaction with the key peptide moiety GLN: 493A of the spike glycoprotein active site. (4) Conclusions: The results suggest that Piroxicam should be prescribed in combination with Azithromycin for COVID-19 patients.
PEG Graft Polymer Carriers of Antioxidants: In Vitro Evaluation for Transdermal Delivery
Authors: Odrobińska Justyna , Skonieczna Magdalena , Neugebauer Dorota
Published: December 2020

The in vitro biochemical evaluation of the applicability of polymers carrying active substances (micelles and conjugates) was carried out. Previously designed amphiphilic graft copolymers with retinol or 4-n-butylresorcinol functionalized polymethacrylate backbone and poly(ethylene glycol) (PEG) side chains that included Janus-type heterografted copolymers containing both PEG and poly(ε-caprolactone) (PCL) side chains were applied as micellar carriers. The polymer self-assemblies were convenient to encapsulate arbutin (ARB) as the selected active substances. Moreover, the conjugates of PEG graft copolymers with ferulic acid (FA) or lipoic acid (LA) were also investigated. The permeability of released active substances through a membrane mimicking skin was evaluated by conducting transdermal tests in Franz diffusion cells. The biological response to new carriers with active substances was tested across cell lines, including normal human dermal fibroblasts (NHDF), human epidermal keratinocyte (HaCaT), as well as cancer melanoma (Me45) and metastatic human melanoma (451-Lu), for comparison. These polymer systems were safe and non-cytotoxic at the tested concentrations for healthy skin cell lines according to the MTT test. Cytometric evaluation of cell cycles as well as cell death defined by Annexin-V apoptosis assays and senescence tests showed no significant changes under action of the delivery systems, as compared to the control cells. In vitro tests confirmed the biochemical potential of these antioxidant carriers as beneficial components in cosmetic products, especially applied in the form of masks and eye pads.
Pharmacokinetics, Pharmacodynamics and Drug–Drug Interactions of New Anti-Migraine Drugs—Lasmiditan, Gepants, and Calcitonin-Gene-Related Peptide (CGRP) Receptor Monoclonal Antibodies
Authors: Szkutnik-Fiedler Danuta
Published: December 2020

In the last few years, there have been significant advances in migraine management and prevention. Lasmiditan, ubrogepant, rimegepant and monoclonal antibodies (erenumab, fremanezumab, galcanezumab, and eptinezumab) are new drugs that were launched on the US pharmaceutical market; some of them also in Europe. This publication reviews the available worldwide references on the safety of these anti-migraine drugs with a focus on the possible drug–drug (DDI) or drug–food interactions. As is known, bioavailability of a drug and, hence, its pharmacological efficacy depend on its pharmacokinetics and pharmacodynamics, which may be altered by drug interactions. This paper discusses the interactions of gepants and lasmiditan with, i.a., serotonergic drugs, CYP3A4 inhibitors, and inducers or breast cancer resistant protein (BCRP) and P-glycoprotein (P-gp) inhibitors. In the case of monoclonal antibodies, the issue of pharmacodynamic interactions related to the modulation of the immune system functions was addressed. It also focuses on the effect of monoclonal antibodies on expression of class Fc gamma receptors (FcγR).
Abnormal Fano Profile in Graphene-Wrapped Dielectric Particle Dimer
Authors: Huang Yang , Ma Pujuan , Wu Ya Min
Published: December 2020

We give a theoretical study on the near field enhancement and far field spectrum of an adjacent graphene-wrapped sphere dimer with different radii. The Fano profile is found in the near field enhancement spectrum of such a symmetry-broken dimer system, which is, however, hidden in the far field spectrum. We demonstrate that this kind of Fano profile is rising from the coupling of dimer’s plasmon hybridization modes by analyzing the dipole moments of each sphere. Moreover, different orientation of incident wave polarization will lead to the different plasmon hybridization coupling, thus giving rise to a different Fano profile. By changing the Fermi energy level, we could achieve tunable Fano profile in near field enhancement.
Ultra-High Early Strength Cementitious Grout Suitable for Additive Manufacturing Applications Fabricated by Using Graphene Oxide and Viscosity Modifying Agents
Authors: Mohammed Alyaa , Al-Saadi Nihad Tareq Khshain
Published: December 2020

One of the considerable challenges in the design of cementitious mixtures for additive manufacturing/three-dimensional (3D) printing applications is achieving both suitable fresh properties and significant mechanical strengths. This paper presents the use of graphene oxide (GO) as a promising nano reinforcement material with the potential to improve the printing feasibility and quality of a 3D printed cementitious matrix. Additionally, in this study, a viscosity modifying agent (VMA) was employed as a chemical additive to attain the required consistency and flow. The printed mixture was fabricated using various cementitious materials and waste materials. This study investigated the impact of GO and VMA on the enhancement of the 3D printing of cementitious composites through several tests. A flow test was conducted using the flow table test. The results showed a high fluidity and practical consistency, which are essential for nozzle pumping and accurateness in printed shapes. Furthermore, the bleeding test showed minimal bleeding up to hardening, and a considerable self-cleaning ability was noted during handling when conducting examinations of fresh properties. For hardened properties, the mechanical strengths were exceptionally high, especially at early ages, which is crucial for the stability of sequence layers of printed composites. The tensile strengths were 3.77, 10.5, 13.35, and 18.83 MPa at 1, 3, 7, and 28 days, respectively, and the compressive strengths were 25.1, 68.4, 85.6, and 125.4 MPa at 1, 3, 7, and 28 days, respectively. The test results showed the effectiveness of the fabricated cementitious mixture design method for meeting the requirements for 3D concrete printing applications.
Polyethylene-Carbon Composite (Velostat®) Based Tactile Sensor
Authors: Dzedzickis Andrius , Sutinys Ernestas , Bucinskas Vytautas , Samukaite-Bubniene Urte , Jakstys Baltramiejus , Ramanavicius Arunas , Morkvenaite-Vilkonciene Inga
Published: December 2020

The progress observed in ‘soft robotics’ brought some promising research in flexible tactile, pressure and force sensors, which can be based on polymeric composite materials. Therefore, in this paper, we intend to evaluate the characteristics of a force-sensitive material—polyethylene-carbon composite (Velostat®) by implementing this material into the design of the flexible tactile sensor. We have explored several possibilities to measure the electrical signal and assessed the mechanical and time-dependent properties of this tactile sensor. The response of the sensor was evaluated by performing tests in static, long-term load and cyclic modes. Experimental results of loading cycle measurements revealed the hysteresis and nonlinear properties of the sensor. The transverse resolution of the sensor was defined by measuring the response of the sensor at different distances from the loaded point. Obtained dependencies of the sensor’s sensitivity, hysteresis, response time, transversal resolution and deformation on applied compressive force promise a practical possibility to use the polyethylene-carbon composite as a sensitive material for sensors with a single electrode pair or its matrix. The results received from experimental research have defined the area of the possible implementation of the sensor based on a composite material—Velostat®.
Influence of Diamond-Like Carbon Coating on the Channel Deformation of Injection-Molded Microfluidic Chips during the Demolding Process
Authors: Wang Yilei , Jiang Bingyan , Zhou Mingyong , Chen Jiachen , Weng Can
Published: December 2020

Injection molding is one of the main techniques for manufacturing microfluidic chips. As an important stage, the demolding process in injection molding will directly affect the quality of the functional unit of microfluidic chips (polymer microchannels), thus limiting the realization of its functions. In this study, molecular dynamics (MD) simulations on the demolding process were carried out to investigate the influence of diamond-like carbon (DLC) coating on the channel deformation. The channel qualities of polystyrene (PS), polymethyl methacrylate (PMMA), cyclic olefin copolymer (COC) and polycarbonate (PC) were analyzed after demolding with nickel (Ni) and DLC-coated mold inserts, respectively. In particular, the non-bonded interfacial interaction energy, elastic recovery and gyration radius of polymer molecular chains were further studied. The results showed that the non-bonded interfacial interaction energies could be significantly reduced by DLC-coating treatment on the mold insert. Moreover, common channel defects such as molecular chain separation, surface burrs and necking did not occur. The treatment of DLC coating could also significantly reduce the change in the gyration radius of polymer molecular chains, so the morphology of the polymer channel could be maintained well. However, the change in the elastic recovery of the polymer channel was increased, and the opening width became larger. In a word, DLC-coating treatment on the mold insert has great application potential for improving the demolding quality of injection-molded microfluidic chips.
Hyperspectral Anomaly Detection via Graph Dictionary-Based Low Rank Decomposition with Texture Feature Extraction
Authors: Song Shangzhen , Yang Yixin , Zhou Huixin , Chan Jonathan Cheung-Wai
Published: December 2020

The accuracy of anomaly detection in hyperspectral images (HSIs) faces great challenges due to the high dimensionality, redundancy of data, and correlation of spectral bands. In this paper, to further improve the detection accuracy, we propose a novel anomaly detection method based on texture feature extraction and a graph dictionary-based low rank decomposition (LRD). First, instead of using traditional clustering methods for the dictionary, the proposed method employs the graph theory and designs a graph Laplacian matrix-based dictionary for LRD. The robust information of the background matrix in the LRD model is retained, and both the low rank matrix and the sparse matrix are well separated while preserving the correlation of background pixels. To further improve the detection performance, we explore and extract texture features from HSIs and integrate with the low-rank model to obtain the sparse components by decomposition. The detection results from feature maps are generated in order to suppress background components similar to anomalies in the sparse matrix and increase the strength of real anomalies. Experiments were run on one synthetic dataset and three real datasets to evaluate the performance. The results show that the performance of the proposed method yields competitive results in terms of average area under the curve (AUC) for receiver operating characteristic (ROC), i.e., 0.9845, 0.9962, 0.9699, and 0.9900 for different datasets, respectively. Compared with seven other state-of-the-art algorithms, our method yielded the highest average AUC for ROC in all datasets.
An Efficient Spectral Feature Extraction Framework for Hyperspectral Images
Authors: Li Zhen , Zhao Baojun , Wang Wenzheng
Published: December 2020

Extracting diverse spectral features from hyperspectral images has become a hot topic in recent years. However, these models are time consuming for training and test and suffer from a poor discriminative ability, resulting in low classification accuracy. In this paper, we design an effective feature extracting framework for the spectra of hyperspectral data. We construct a structured dictionary to encode spectral information and apply learning machine to map coding coefficients. To reduce training and testing time, the sparsity constraint is replaced by a block-diagonal constraint to accelerate the iteration, and an efficient extreme learning machine is employed to fit the spectral characteristics. To optimize the discriminative ability of our model, we first add spectral convolution to extract abundant spectral information. Then, we design shared constraints for subdictionaries so that the common features of subdictionaries can be expressed more effectively, and the discriminative and reconstructive ability of dictionary will be improved. The experimental results on diverse databases show that the proposed feature extraction framework can not only greatly reduce the training and testing time, but also lead to very competitive accuracy performance compared with deep learning models.
Consistency of the Single Calculus Chain Optical Products with Archived Measurements from an EARLINET Lidar Station
Authors: Voudouri Kalliopi Artemis , Siomos Nikolaos , Michailidis Konstantinos , D’Amico Giuseppe , Mattis Ina , Balis Dimitris
Published: December 2020

A long-term analysis and climatology of aerosol backscatter and extinction coefficients profiles using a five-year study period lidar dataset derived from a multiwavelenth Raman lidar at Thessaloniki station is presented. All measurements have been processed with the latest version of the Single Calculus Chain (SCCv5.1.6) fully automated algorithm, which has been developed to provide a common lidar processing tool, within EARLINET (European Aerosol Research Lidar NETwork) stations. The optical products delivered by the SCC tool have already been compared with the optical products derived from the operational algorithm of Thessaloniki (THessaloniki Aerosol LIdar Algorithm-THALIA) and discussed in terms of inhomogeneities. In this contribution, we analyze these products for climatological purposes, in order to investigate the aerosol columnar properties over Thessaloniki lidar station, drawing conclusions about the issues to be considered when switching from the current operational algorithm to the SCCv5. The SCCv5 algorithm is evaluated for the AOD both for 355 and 532 nm. The agreement with THALIA algorithm seems promising with correlations of 0.89 and 0.84, respectively, and absolute deviations within the range of the EARLINET quality requirements. Time series of the AOD at 355 nm denote a decrease of 0.017 per year in the free troposphere, a trend that is also shown in the AOD values derived from the operational algorithm (0.014). A decrease of 0.01 per year in the lower troposphere is also noted from the SCC, whereas the corresponding AOD values derived from the operational algorithm denote a decrease of 0.017.
Spatio-Temporal Variability in Bio-Optical Properties of the Southern Caspian Sea: A Historic Analysis of Ocean Color Data
Authors: Ahmadi Bonyad , Gholamalifard Mehdi , Kutser Tiit , Vignudelli Stefano , Kostianoy Andrey
Published: December 2020

Currently, satellite ocean color imageries play an important role in monitoring of water properties in various oceanic, coastal, and inland ecosystems. Although there is a long-time and global archive of such valuable data, no study has comprehensively used these data to assess the changes in the Caspian Sea. Hence, this study assessed the variability of bio-optical properties of the upper-water column in the Southern Caspian Sea (SCS) using the archive of the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) and the Moderate Resolution Imaging Spectroradiometer (MODIS). The images acquired from SeaWiFS (January 1998 to December 2002) and MODIS Aqua (January 2003 to December 2015) satellites were used to investigate the spatial–temporal variability of bio-optical properties including Chlorophyll-a (Chl-a), attenuation coefficient, and remote sensing reflectance, and environmental parameters such as sea surface temperature (SST), wind stress and the El Nino-southern oscillation (ENSO) phenomena at different time lags in the study area. The trend analysis demonstrated an overall increase of 0.3358 mg m−3 in the Chl-a concentration during 1998–2015 (annual increase rate of 0.018 mg m−3 year−1) and four algal blooms and in turn an abnormal increase in Chl-a concentration were occurred in August 2001, September 2005, 2009, and August 2010. The linear model revealed that Chl-a in the northern and middle part of the study area had been influenced by the attenuation coefficient after a one-month lag time. The analysis revealed a sharp decline in Chl-a concentration during 2011–2015 and showed a high correlation with the turbidity and attenuation coefficient in the southern region, while Kd_490nm and remote sensing reflectance did a low one. Generally, Chl-a concentration exhibited a positive correlation with the attenuation coefficient (r = 0.63) and with remote sensing reflectance at the 555 nm (r = 0.111). This study can be used as the basis for predictive modeling to evaluate the changes of water quality and bio-optical indices in the Southern Caspian Sea (SCS).
A Grid Feature-Point Selection Method for Large-Scale Street View Image Retrieval Based on Deep Local Features
Authors: Chu Tianyou , Chen Yumin , Huang Liheng , Xu Zhiqiang , Tan Huangyuan
Published: December 2020

Street view image retrieval aims to estimate the image locations by querying the nearest neighbor images with the same scene from a large-scale reference dataset. Query images usually have no location information and are represented by features to search for similar results. The deep local features (DELF) method shows great performance in the landmark retrieval task, but the method extracts many features so that the feature file is too large to load into memory when training the features index. The memory size is limited, and removing the part of features simply causes a great retrieval precision loss. Therefore, this paper proposes a grid feature-point selection method (GFS) to reduce the number of feature points in each image and minimize the precision loss. Convolutional Neural Networks (CNNs) are constructed to extract dense features, and an attention module is embedded into the network to score features. GFS divides the image into a grid and selects features with local region high scores. Product quantization and an inverted index are used to index the image features to improve retrieval efficiency. The retrieval performance of the method is tested on a large-scale Hong Kong street view dataset, and the results show that the GFS reduces feature points by 32.27–77.09% compared with the raw feature. In addition, GFS has a 5.27–23.59% higher precision than other methods.
Biosensors for the Detection of Bacterial and Viral Clinical Pathogens
Authors: Castillo-Henríquez Luis , Brenes-Acuña Mariana , Castro-Rojas Arianna , Cordero-Salmerón Rolando , Lopretti-Correa Mary , Vega-Baudrit José Roberto
Published: December 2020

Biosensors are measurement devices that can sense several biomolecules, and are widely used for the detection of relevant clinical pathogens such as bacteria and viruses, showing outstanding results. Because of the latent existing risk of facing another pandemic like the one we are living through due to COVID-19, researchers are constantly looking forward to developing new technologies for diagnosis and treatment of infections caused by different bacteria and viruses. Regarding that, nanotechnology has improved biosensors’ design and performance through the development of materials and nanoparticles that enhance their affinity, selectivity, and efficacy in detecting these pathogens, such as employing nanoparticles, graphene quantum dots, and electrospun nanofibers. Therefore, this work aims to present a comprehensive review that exposes how biosensors work in terms of bacterial and viral detection, and the nanotechnological features that are contributing to achieving a faster yet still efficient COVID-19 diagnosis at the point-of-care.
Temporal Aggregation Effects in Spatiotemporal Traffic Modelling
Authors: Pavlyuk Dmitry
Published: December 2020

Spatiotemporal models are a popular tool for urban traffic forecasting, and their correct specification is a challenging task. Temporal aggregation of traffic sensor data series is a critical component of model specification, which determines the spatial structure and affects models’ forecasting accuracy. Through extensive experiments with real-world data, we investigated the effects of the selected temporal aggregation level for forecasting performance of different spatiotemporal model specifications. A set of analysed models include travel-time-based and correlation-based spatially restricted vector autoregressive models, compared to classical univariate and multivariate time series models. Research experiments are executed in several dimensions: temporal aggregation levels, forecasting horizons (one-step and multi-step forecasts), spatial complexity (sequential and complex spatial structures), the spatial restriction approach (unrestricted, travel-time-based and correlation-based), and series transformation (original and detrended traffic volumes). The obtained results demonstrate the crucial role of the temporal aggregation level for identification of the spatiotemporal traffic flow structure and selection of the best model specification. We conclude that the common research practice of an arbitrary selection of the temporal aggregation level could lead to incorrect conclusions on optimal model specification. Thus, we recommend extending the traffic forecasting methodology by validation of existing and newly developed model specifications for different temporal aggregation levels. Additionally, we provide empirical results on the selection of the optimal temporal aggregation level for the discussed spatiotemporal models for different forecasting horizons.
Methodology for the Accurate Measurement of the Power Dissipated by Braking Rheostats
Authors: Giordano Domenico , Signorino Davide , Gallo Daniele , van den Brom Helko E. , Sira Martin
Published: December 2020

The energy efficiency of transportation is a crucial point for the rail and metro system today. The optimized recovery of the energy provided by the electrical braking can lead to savings of about 10% to 30%. Such figures can be reached by infrastructure measures which allow the recovery of the breaking energy that is not directly consumed by the rail system and dissipated in rheostat resistors. A methodology for the accurate estimate of such energy is valuable for a reliable evaluation of the cost–benefit ratio associated with the infrastructural investment. The energy can be estimated by measuring a braking current flowing in the rheostats. The varying duty-cycle associated with the high dynamic variation, from zero to thousands of amperes, makes the current measurement very challenging. Moreover, the digitization of such waveforms introduces systematic errors that affect the energy estimation. To overcome these issues, this paper proposes a technique to measure the power and energy dissipated by the rheostat of a DC operated train with high accuracy. By means of an accurate model of the electrical braking circuit (chopper and rheostat) and the frequency characterization of the current transducer, a correction coefficient as a function of the duty-cycle is estimated. The method is then applied to data recorded during a measurement campaign performed on-board a 1.5 kV train of Metro de Madrid during normal operation. Using the proposed technique, the estimation of the dissipated braking energy is improved by 20%.
Comparison of a Deep Learning-Based Pose Estimation System to Marker-Based and Kinect Systems in Exergaming for Balance Training
Authors: Vonstad Elise Klæbo , Su Xiaomeng , Vereijken Beatrix , Bach Kerstin , Nilsen Jan Harald
Published: December 2020

Using standard digital cameras in combination with deep learning (DL) for pose estimation is promising for the in-home and independent use of exercise games (exergames). We need to investigate to what extent such DL-based systems can provide satisfying accuracy on exergame relevant measures. Our study assesses temporal variation (i.e., variability) in body segment lengths, while using a Deep Learning image processing tool (DeepLabCut, DLC) on two-dimensional (2D) video. This variability is then compared with a gold-standard, marker-based three-dimensional Motion Capturing system (3DMoCap, Qualisys AB), and a 3D RGB-depth camera system (Kinect V2, Microsoft Inc). Simultaneous data were collected from all three systems, while participants (N = 12) played a custom balance training exergame. The pose estimation DLC-model is pre-trained on a large-scale dataset (ImageNet) and optimized with context-specific pose annotated images. Wilcoxon’s signed-rank test was performed in order to assess the statistical significance of the differences in variability between systems. The results showed that the DLC method performs comparably to the Kinect and, in some segments, even to the 3DMoCap gold standard system with regard to variability. These results are promising for making exergames more accessible and easier to use, thereby increasing their availability for in-home exercise.
Visual Robot Relocalization Based on Multi-Task CNN and Image-Similarity Strategy
Authors: Xie Tao , Wang Ke , Li Ruifeng , Tang Xinyue
Published: December 2020

The traditional CNN for 6D robot relocalization which outputs pose estimations does not interpret whether the model is making sensible predictions or just guessing at random. We found that convnet representations trained on classification problems generalize well to other tasks. Thus, we propose a multi-task CNN for robot relocalization, which can simultaneously perform pose regression and scene recognition. Scene recognition determines whether the input image belongs to the current scene in which the robot is located, not only reducing the error of relocalization but also making us understand with what confidence we can trust the prediction. Meanwhile, we found that when there is a large visual difference between testing images and training images, the pose precision becomes low. Based on this, we present the dual-level image-similarity strategy (DLISS), which consists of two levels: initial level and iteration-level. The initial level performs feature vector clustering in the training set and feature vector acquisition in testing images. The iteration level, namely, the PSO-based image-block selection algorithm, can select the testing images which are the most similar to training images based on the initial level, enabling us to gain higher pose accuracy in testing set. Our method considers both the accuracy and the robustness of relocalization, and it can operate indoors and outdoors in real time, taking at most 27 ms per frame to compute. Finally, we used the Microsoft 7Scenes dataset and the Cambridge Landmarks dataset to evaluate our method. It can obtain approximately 0.33 m and 7.51∘ accuracy on 7Scenes dataset, and get approximately 1.44 m and 4.83∘ accuracy on the Cambridge Landmarks dataset. Compared with PoseNet, our CNN reduced the average positional error by 25% and the average angular error by 27.79% on 7Scenes dataset, and reduced the average positional error by 40% and the average angular error by 28.55% on the Cambridge Landmarks dataset. We show that our multi-task CNN can localize from high-level features and is robust to images which are not in the current scene. Furthermore, we show that our multi-task CNN gets higher accuracy of relocalization by using testing images obtained by DLISS.
Comparison of Thermal and Flow-Based Modulation in Comprehensive Two-Dimensional Gas Chromatography—Time-of-Flight Mass Spectrometry (GC × GC-TOFMS) for the Analysis of Base Oils
Authors: Boswell Haleigh A. , Edwards Matthew , Górecki Tadeusz
Published: December 2020

Base oils are produced by refining crude oil or through chemical synthesis. They are a key component of engine oils. With an immense range of carbon numbers and boiling points, analyzing such complex mixtures is very difficult. The need to monitor industrial petroleum processing steps, as well as to identify petrochemical environmental pollutants, drives the search for improved characterization methods. Comprehensive two-dimensional gas chromatography (GC × GC) is one of the best tools for that. The modulator used in GC × GC is responsible for trapping/sampling the first dimension (1D) column analytes, then reinjecting them in the form of narrow bands onto the second dimension (2D) column for further separation. Modulators used today generally fall into two categories, thermal and flow ones. Heater-based thermal modulators trap the 1D column effluent at or above ambient temperatures. Flow-based modulators utilize storage loop(s) to collect the 1D effluent, which is subsequently flushed into the second-dimension column for further separation. A single-stage, consumable-free thermal modulator and a reverse fill/flush flow modulator were compared for the characterization of base oils. Both were evaluated on their ability to achieve separation of several conventional and synthetic engine oils components. A reverse column set, polar 1D and nonpolar 2D, allowed group-type analysis of all classes, including linear, branched, and aromatic species. The results show the ability to achieve a comprehensive separation of specific compound classes and the differentiation of engine oil types and manufacturers. Soft ionization assisted in tentative identification of two alkylated diphenylamines in each sample. The advantages and limitations of both thermal and flow modulation are presented.