bluuebunny/crossref_metadata_2025 huggingface dataset:
Cannabinoid CB1 Receptors in the Intestinal Epithelium Are Required for Acute Western-Diet Preferences in Mice
Authors: Avalos Bryant , Argueta Donovan A. , Perez Pedro A. , Wiley Mark , Wood Courtney , DiPatrizio Nicholas V.
Published: September 2020

The endocannabinoid system plays an important role in the intake of palatable food. For example, endocannabinoid signaling in the upper small-intestinal epithelium is increased (i) in rats after tasting dietary fats, which promotes intake of fats, and (ii) in a mouse model of diet-induced obesity, which promotes overeating via impaired nutrient-induced gut–brain satiation signaling. We now utilized a combination of genetic, pharmacological, and behavioral approaches to identify roles for cannabinoid CB1Rs in upper small-intestinal epithelium in preferences for a western-style diet (WD, high-fat/sucrose) versus a standard rodent diet (SD, low-fat/no sucrose). Mice were maintained on SD in automated feeding chambers. During testing, mice were given simultaneous access to SD and WD, and intakes were recorded. Mice displayed large preferences for the WD, which were inhibited by systemic pretreatment with the cannabinoid CB1R antagonist/inverse agonist, AM251, for up to 3 h. We next used our novel intestinal epithelium-specific conditional cannabinoid CB1R-deficient mice (IntCB1−/−) to investigate if intestinal CB1Rs are necessary for WD preferences. Similar to AM251 treatment, preferences for WD were largely absent in IntCB1−/− mice when compared to control mice for up to 6 h. Together, these data suggest that CB1Rs in the murine intestinal epithelium are required for acute WD preferences.
Supermarket Circulars Promoting the Sales of ‘Healthy’ Foods: Analysis Based on Degree of Processing
Authors: Botelho Alyne Michelle , Milbratz de Camargo Anice , Medeiros Kharla Janinny , Irmão Gabriella Beatriz , Dean Moira , Fiates Giovanna Medeiros Rataichesck
Published: September 2020

The health and wellness food sector grew 98% from 2009 to 2014 in Brazil, the world’s fourth-biggest market. The trend has reached supermarket circulars, which recently started to feature whole sections advertising health and wellness-enhancing foods. This study identified food items advertised in circulars’ specific sections of two Brazilian supermarket chains (one regional, one national) during a 10-week period. Foods were classified according to degree of food processing and presence/type of claims on their front-of-pack (FoP) labels. Comparison between groups of Unprocessed/Minimally Processed foods vs. Ultra-processed foods and presence/type of claims employed Pearson chi-square test. From the 434 alleged health and wellness-enhancing foods advertised, around half (51.4%) were classified as Ultra-processed. Presence of reduced and increased nutrient-content claims was significantly higher in labels of Ultra-processed foods. Most frequent claims addressed sugar and fibre content. Brazilian supermarket circulars were found to be promoting the sale of Ultra-processed foods in their health and wellness sections, leading to a situation that can mislead the consumer and bring negative health outcomes.
Time Course of Salivary Protein Responses to Cranberry-Derived Polyphenol Exposure as a Function of PROP Taster Status
Authors: Yousaf Neeta Y. , Melis Melania , Mastinu Mariano , Contini Cristina , Cabras Tiziana , Tomassini Barbarossa Iole , Tepper Beverly J.
Published: September 2020

Astringency is a complex oral sensation, commonly experienced when dietary polyphenols interact with salivary proteins. Most astringent stimuli alter protein levels, which then require time to be replenished. Although it is standard practice in astringency research to provide breaks in between stimuli, there is limited consensus over the amount of time needed to restore the oral environment to baseline levels. Here we examined salivary protein levels after exposure to 20 mL of a model stimulus (cranberry polyphenol extract, 0.75 g/L CPE) or unsweetened cranberry juice (CJ), over a 10 min period. Whole saliva from healthy subjects (n = 60) was collected at baseline and after 5 and 10 min following either stimulus. Five families of proteins: basic proline-rich proteins (bPRPs); acidic proline-rich proteins (aPRPs); histatins; statherin; and S-type cystatins, were analyzed in whole saliva via HPLC-low resolution-ESI-IT-MS, using the area of the extracted ion current (XIC) peaks. Amylase was quantified via immunoblotting. In comparison to baseline (resting), both stimuli led to a rise in levels of aPRPs (p < 0.000) at 5 min which remained elevated at 10 min after stimulation. Additionally, an interaction of PROP taster status and time was observed, wherein super-tasters had higher levels of amylase in comparison to non-tasters after stimulation with CJ at both timepoints (p = 0.014–0.000). Further, male super-tasters had higher levels of bPRPs at 5 min after stimulation with both CJ and CPE (p = 0.015–0.007) in comparison to baseline. These data provide novel findings of interindividual differences in the salivary proteome that may influence the development of astringency and that help inform the design of sensory experiments of astringency.
Late Eating Is Associated with Obesity, Inflammatory Markers and Circadian-Related Disturbances in School-Aged Children
Authors: Martínez-Lozano Nuria , Tvarijonaviciute Asta , Ríos Rafael , Barón Isabel , Scheer Frank A. J. L. , Garaulet Marta
Published: September 2020

Late eating has been shown to promote metabolic dysregulation and to be associated with obesity in adults. However, few studies have explored this association in children. We compared the presence of obesity, metabolic alterations and circadian-related disturbances between school-aged children who were early dinner eaters (EDE) or late dinner eaters (LDE). School-age children (n = 397; 8–12 years; mean BMI (range): 19.4 kg/m2 (11.6–35.1); 30.5% overweight/obesity) from Spain were classified into EDE and LDE, according to dinner timing (Median: 21:07). Seven-day-dietary-records were used to assess food-timing and composition. Non-invasive tools were used to collect metabolic biomarkers (saliva), sleep and circadian-related variables (body-temperature and actigraphy). Compared to EDE, LDE were more likely to be overweight/obese [OR: 2.1 (CI: 1.33, 3.31); p = 0.002], and had higher waist-circumference and inflammatory markers, such as IL-6 (1.6-fold) (p = 0.036)) and CRP (1.4-fold) than EDE (p = 0.009). LDE had alterations in the daily patterns of: (a) body-temperature, with a phase delay of 26 min (p = 0.002), and a reduced amplitude (LDE = 0.028 (0.001) and EDE = 0.030 (0.001) (Mean (SEM); p = 0.039); (b) cortisol, with a reduced amplitude (LDE = 0.94 (0.02) and EDE = 1.00 (0.02); p = 0.035). This study represents a significant step towards the understanding of novel aspects in the timing of food intake in children.
Molecular Features of Non-Selective Small Molecule Antagonists of the Bradykinin Receptors
Authors: Rasaeifar Bahareh , Gomez-Gutierrez Patricia , Perez Juan J.
Published: September 2020

Angiotensin converting enzyme 2 (ACE2) downregulation is a key negative factor for the severity of lung edema and acute lung failure observed in patients infected with SARS-CoV-2. ACE2 downregulation affects the levels of diverse peptide mediators of the renin-agiotensin-aldestosterone and kallikrein-kinin systems, compromising vascular hemostasis. Increasing evidence suggests that the inflammatory response observed in covid-19 patients is initiated by the action of kinins on the bradykinin receptors. Accordingly, the use of bradykinin antagonists should be considered as a strategy for therapeutic intervention against covid-19 illness progression. Presently, icatibant is the only bradykinin antagonist drug approved. In the present report, we investigated the molecular features characterizing non-selective antagonists targeting the bradykinin receptors and carried out a in silico screening of approved drugs, aimed at the identification of compounds with a non-selective bradykinin antagonist profile that can be evaluated for drug repurposing. The study permitted to identify eight compounds as prospective non-selective antagonists of the bradykinin receptors, including raloxifene; sildenafil; cefepime; cefpirome; imatinib; ponatinib; abemaciclib and entrectinib.
Development and Optimization of Naringenin-Loaded Chitosan-Coated Nanoemulsion for Topical Therapy in Wound Healing
Authors: Akrawi Sabah H. , Gorain Bapi , Nair Anroop B. , Choudhury Hira , Pandey Manisha , Shah Jigar N. , Venugopala Katharigatta N.
Published: September 2020

The potential role of naringenin (NAR), a natural flavonoid, in the treatment of chronic wound has prompted the present research to deliver the drug in nanoemulsion (NE) form, where synergistic role of chitosan was achieved through development of chitosan-coated NAR NE (CNNE). The NE consisted of Capryol 90, Tween 20 and Transcutol P, which was fabricated by low-energy emulsification method to encapsulate NAR within the oil core. The optimization of the formulated NEs was performed using Box–Behnken statistical design to obtain crucial variable parameters that influence globule size, size distribution and surface charge. Finally, the optimized formulation was coated with different concentrations of chitosan and subsequently characterized in vitro. The size of the CNNE was found to be increased when the drug-loaded formulation was coated with chitosan. Controlled release characteristics depicted 67–81% release of NAR from the CNNE, compared to 89% from the NE formulation. Cytotoxicity study of the formulation was performed in vitro using fibroblast cell line (NIH-3T3), where no inhibition in proliferation of the cells was observed with CNNE. Finally, the wound healing potential of the CNNE was evaluated in an abrasion-created wound model in experimental animals where the animals were treated and compared histologically at 0 and 14 days. Significant improvement in construction of the abrasion wound was observed when the animals were treated with formulated CNNE, whereas stimulation of skin regeneration was depicted in the histological examination. Therefore, it could be summarized that the chitosan coating of the developed NAR NE is a potential platform to accelerate healing of wounds.
Can Implementation of Genetics and Pharmacogenomics Improve Treatment of Chronic Low Back Pain?
Authors: Suntsov Vladislav , Jovanovic Filip , Knezevic Emilija , Candido Kenneth D. , Knezevic Nebojsa Nick
Published: September 2020

Etiology of back pain is multifactorial and not completely understood, and for the majority of people who suffer from chronic low back pain (cLBP), the precise cause cannot be determined. We know that back pain is somewhat heritable, chronic pain more so than acute. The aim of this review is to compile the genes identified by numerous genetic association studies of chronic pain conditions, focusing on cLBP specifically. Higher-order neurologic processes involved in pain maintenance and generation may explain genetic contributions and functional predisposition to formation of cLBP that does not involve spine pathology. Several genes have been identified in genetic association studies of cLBP and roughly, these genes could be grouped into several categories, coding for: receptors, enzymes, cytokines and related molecules, and transcription factors. Treatment of cLBP should be multimodal. In this review, we discuss how an individual’s genotype could affect their response to therapy, as well as how genetic polymorphisms in CYP450 and other enzymes are crucial for affecting the metabolic profile of drugs used for the treatment of cLBP. Implementation of gene-focused pharmacotherapy has the potential to deliver select, more efficacious drugs and avoid unnecessary, polypharmacy-related adverse events in many painful conditions, including cLBP.
Bioactive Compounds from Polygala tenuifolia and Their Inhibitory Effects on Lipopolysaccharide-Stimulated Pro-inflammatory Cytokine Production in Bone Marrow-Derived Dendritic Cells
Authors: Vinh Le Ba , Heo Myungsook , Phong Nguyen Viet , Ali Irshad , Koh Young Sang , Kim Young Ho , Yang Seo Young
Published: September 2020

The roots of Polygala tenuifolia Wild (Polygalaceae), which is among the most important components of traditional Chinese herbal medicine, have been widely used for over 1000 years to treat a variety of diseases. In the current investigation of secondary metabolites with anti-inflammatory properties from Korean medicinal plants, a phytochemical constituent study led to the isolation of 15 compounds (1–15) from the roots of P. tenuifolia via a combination of chromatographic methods. Their structures were determined by means of spectroscopic data such as nuclear magnetic resonance (NMR), 1D- and 2D-NMR, and liquid chromatography-mass spectrometry (LC-MS). As the obtained results, the isolated compounds were divided into two groups—phenolic glycosides (1–9) and triterpenoid saponins (10–15). The anti-inflammatory effects of crude extracts, fractions, and isolated compounds were investigated on the production of the pro-inflammatory cytokines interleukin (IL)-12 p40, IL-6, and tumour necrosis factor-α in lipopolysaccharide-stimulated bone marrow-derived dendritic cells. The IC50 values, ranging from 0.08 ± 0.01 to 21.05 ± 0.40 μM, indicated potent inhibitory effects of the isolated compounds on the production of all three pro-inflammatory cytokines. In particular, compounds 3–12, 14, and 15 showed promising anti-inflammatory activity. These results suggest that phenolic and triterpenoid saponins from P. tenuifolia may be excellent anti-inflammatory agents.
Bio-Guided Fractionation Driven by In Vitro α-Amylase Inhibition Assays of Essential Oils Bearing Specialized Metabolites with Potential Hypoglycemic Activity
Authors: Capetti Francesca , Cagliero Cecilia , Marengo Arianna , Bicchi Carlo , Rubiolo Patrizia , Sgorbini Barbara
Published: September 2020

Type 2 diabetes mellitus (T2DM) is a metabolic disorder characterized by unpaired blood glycaemia maintenance. T2DM can be treated by inhibiting carbohydrate hydrolyzing enzymes (α-amylases and α-glucosidases) to decrease postprandial hyperglycemia. Acarbose and voglibose are inhibitors used in clinical practice. However, these drugs are associated with unpleasant gastrointestinal side effects. This study explores new α-amylase inhibitors deriving from plant volatile specialized metabolites. Sixty-two essential oils (EOs) from different plant species and botanical families were subjected to α-amylase in vitro enzymatic assay and chemically characterized using gas chromatography coupled to mass spectrometry. Several EOs were found to be potential α-amylase inhibitors, and Eucalyptus radiata, Laurus nobilis, and Myristicafragrans EOs displayed inhibitory capacities comparable to that of the positive control (i.e., acarbose). A bio-guided fractionation approach was adopted to isolate and identify the active fractions/compounds of Eucalyptus radiata and Myristica fragrans EOs. The bio-guided fractionation revealed that EOs α-amylase inhibitory activity is often the result of antagonist, additive, or synergistic interactions among their bioactive constituents and led to the identification of 1,8-cineole, 4-terpineol, α-terpineol, α-pinene, and β-pinene as bioactive compounds, also confirmed when they were tested singularly. These results demonstrate that EO oils are a promising source of potential α-amylase inhibitors.
Increasing Air Temperatures and Its Effects on Growth and Productivity of Tomato in South Florida
Authors: Ayankojo Ibukun T. , Morgan Kelly T.
Published: September 2020

Florida ranks first among US states in fresh-market tomato production with annual production exceeding one-third of the total annual production in the country. Although tomato is a signature crop in Florida, current and future ambient temperatures could impose a major production challenge, especially during the fall growing season. This problem is increasingly becoming an important concern among tomato growers in south Florida, but studies addressing these concerns have not been conducted until now. Therefore, this study was conducted to determine the impacts of the present ambient temperature conditions and planting dates on tomato productivity in south Florida. The study was conducted using crop simulation model CROPGRO-Tomato of DSSAT (Decision Support System for Agricultural Transfer) version 4.7. Five treatments were evaluated, and included AT (simulated treatment using 14 years of actual daily weather conditions at the study location) while other treatments were conducted based on a percentage (−20%, −10%, +10%, +20%) of AT to simulate cooler and warmer temperature regimes. The results suggested that under the current temperature conditions during the fall growing season in south Florida, average tomato yield was up to 29% lower compared to the cooler temperature regimes. Tomato yield further decreased by 52% to 85% at air temperatures above the current condition. Yield reduction under high temperature was primarily due to lower fruit production. Contrary to yield, both tomato biomass accumulation and leaf area index increased with increase in temperature. Results also indicated that due to changes in air temperature pattern, tomato yield increased as planting date increased from July to December. Therefore, planting date modification during the fall season from the current July–September to dates between November and December will reduce the impacts of heat stress and increase tomato productivity in south Florida.
Synthesis, Physical Properties and Enzymatic Degradation of Biodegradable Nanocomposites Fabricated Using Poly(Butylene Carbonate-Co-Terephthalate) and Organically Modified Layered Zinc Phenylphosphonate
Authors: Tseng Li-Ying , Chen Erh-Chiang , Wang Jie-Mao , Wu Tzong-Ming
Published: September 2020

A new biodegradable aliphatic-aromatic poly (butylene carbonate-co-terephthalate) (PBCT-85) with the molar ratio [BC]/[BT] = 85/15, successfully synthesized through transesterification and polycondensation processes, was identified using 1H-NMR spectra. Various weight ratios of PBCT/organically modified layered zinc phenylphosphonate (m-PPZn) nanocomposites were manufactured using the solution mixing process. Wide-angle X-ray diffraction and transmission electron microscopy were used to examine the morphology of PBCT-85/m-PPZn nanocomposites. Both results exhibited that the stacking layers of m-PPZn were intercalated into the PBCT-85 polymer matrix. The additional m-PPZn into PBCT-85 copolymer matrix significantly enhanced the storage modulus at −70 °C, as compared to that of neat PBCT-85. The lipase from Pseudomonas sp. was used to investigate the enzymatic degradation of PBCT-85/m-PPZn nanocomposites. The weight loss decreased as the loading of m-PPZn increased, indicating that the existence of m-PPZn inhibits the degradation of the PBCT-85 copolymers. This result might be attributed to the higher degree of contact angle for PBCT-85/m-PPZn nanocomposites. The PBCT-85/m-PPZn composites approved by MTT assay are appropriate for cell growth and might have potential in the application of biomedical materials.
Different Strategies for the Preparation of Galactose-Functionalized Thermo-Responsive Nanogels with Potential as Smart Drug Delivery Systems
Authors: González-Ayón Mirian A. , Licea-Claverie Angel , Sañudo-Barajas J. Adriana
Published: September 2020

Different synthetic strategies were tested for the incorporation of galactose molecules on thermoresponsive nanogels owing to their affinity for receptors expressed in cancer cells. Three families of galactose-functionalized poly(N-vinylcaprolactam) nanogels were prepared with the aim to control the introduction of galactose-moieties into the core, the core-shell interface and the shell. First and second of the above mentioned, were prepared via surfactant free emulsion polymerization (SFEP) by a free-radical mechanism and the third one, via SFEP/reversible addition-fragmentation chain transfer (RAFT) polymerization. Synthetic recipes for the SFEP/free radical method included besides N-vinylcaprolactam (NVCL), a shell forming poly(ethylene glycol) methyl ether methacrylate (PEGMA), while the galactose (GAL) moiety was introduced via 6-O-acryloyl-1,2,:3,4-bis-O-(1-methyl-ethylidene)-α-D-galactopiranose (6-ABG, protected GAL-monomer): nanogels I, or 2-lactobionamidoethyl methacrylate (LAMA, GAL-monomer): nanogels II. For the SFEP/RAFT methodology poly(2-lactobionamidoethyl methacrylate) as GAL macro-chain transfer agent (PLAMA macro-CTA) was first prepared and on a following stage, the macro-CTA was copolymerized with PEGMA and NVCL, nanogels III. The crosslinker ethylene glycol dimethacrylate (EGDMA) was added in both methodologies for the polymer network construction. Nanogel’s sizes obtained resulted between 90 and 370 nm. With higher content of PLAMA macro-CTA or GAL monomer in nanogels, a higher the phase-transition temperature (TVPT) was observed with values ranging from 28 to 46 °C. The ρ-parameter, calculated by the ratio of gyration and hydrodynamic radii from static (SLS) and dynamic (DLS) light scattering measurements, and transmission electron microscopy (TEM) micrographs suggest that core-shell nanogels of flexible chains were obtained; in either spherical (nanogels II and III) or hyperbranched (nanogels I) form.
The Science of Cathedral Studies: Exploring Demographic Profile, Motivational Intentions, and Perceived Impact among those Attending the Holly Bough Service in Liverpool Cathedral
Authors: Francis Leslie J. , Jones Susan H. , McKenna Ursula , Pike Nelson , Williams Emma
Published: September 2020

The Holly Bough service is a unique pre-Christmas event, combining musical excellence and theological depth, crafted by the founding dean of Liverpool Cathedral in the early twentieth century for the Fourth Sunday of Advent. Located within the developing science of cathedral studies, this paper analyses the demographic profile, motivational intention (drawing on religious orientation theory) and perceived impact on spiritual wellbeing (drawing on Fisher’s four dimensional model) among 564 participants who completed a detailed survey at the service held in 2019. The data demonstrated a mix of ages, a sense of Anglican commitment to this form of event-belonging by those who return year-on-year and invite friends to join them, and a perceived beneficial impact on all four dimensions of spiritual wellbeing.
Comparison of Empirical and Physical Modelling for Estimation of Biochemical and Biophysical Vegetation Properties: Field Scale Analysis across an Arctic Bioclimatic Gradient
Authors: Kennedy Blair E. , King Douglas J. , Duffe Jason
Published: September 2020

To evaluate the potential of multi-angle hyperspectral sensors for monitoring vegetation variables in Arctic environments, empirical and physical modelling using field data was implemented for the retrieval of leaf and canopy chlorophyll content (LCC, CCC) and plant area index (PAI) measured at four sites situated across a bioclimatic gradient in the Western Canadian Arctic. Field reflectance data were acquired with an ASD FieldSpec (305–1075 nm) and used to simulate CHRIS Mode1 spectra (411–997 nm). Multi-angle measurements were taken corresponding to CHRIS view zenith angles (VZA) (−55°, −36°, 0°, +36°, +55°). Empirical modelling compared parametric regression based on vegetation indices (VIs) to non-parametric Gaussian Processes Regression (GPR). In physical modelling, PROSAIL was inverted using numerical optimization and look-up table (LUT) approaches. Cross-validation of the empirical models ranked GPR as best, followed by simple ratio (SR) with optimally selected NIR and red wavelengths, and then ROSAVI using its published wavelengths (mean r2cv = 0.62, 0.58, and 0.54, respectively across all sites, variables, and VZAs). However, the best predictive performance was achieved by SR followed by GPR and ROSAVI (NRMSEcv = 0.12, 0.16, 0.16, respectively). PROSAIL simulated the multi-angle top-of-canopy reflectance well with numerical optimization (r2 = ~0.99, RMSE = 0.004 ± 0.002), but best performing LUT models of LCC, CCC and PAI were poorer than the empirical approaches (mean r2 = 0.48, mean NRMSE = 0.22). PROSAIL performed best at the high Arctic sparsely vegetated site (r2 = 0.57–0.86 for all parameters). Overall, the best performing VZA was −55° for empirical modelling and 0° and ±55° for physical modelling; however, these were not significantly better than the other VZAs. Overall, this study demonstrates that, for Arctic vegetation, nadir narrowband reflectance data used to derive simple empirical VIs with optimally selected bands is a more efficient approach for modelling chlorophyll and PAI than more complex empirical and physical approaches.
Comparison of Field and Laboratory Wet Soil Spectra in the Vis-NIR Range for Soil Organic Carbon Prediction in the Absence of Laboratory Dry Measurements
Authors: Biney James Kobina Mensah , Borůvka Luboš , Chapman Agyeman Prince , Němeček Karel , Klement Aleš
Published: September 2020

Spectroscopy has demonstrated the ability to predict specific soil properties. Consequently, it is a promising avenue to complement the traditional methods that are costly and time-consuming. In the visible-near infrared (Vis-NIR) region, spectroscopy has been widely used for the rapid determination of organic components, especially soil organic carbon (SOC) using laboratory dry (lab-dry) measurement. However, steps such as collecting, grinding, sieving and soil drying at ambient (room) temperature and humidity for several days, which is a vital process, make the lab-dry preparation a bit slow compared to the field or laboratory wet (lab-wet) measurement. The use of soil spectra measured directly in the field or on a wet sample remains challenging due to uncontrolled soil moisture variations and other environmental conditions. However, for direct and timely prediction and mapping of soil properties, especially SOC, the field or lab-wet measurement could be an option in place of the lab-dry measurement. This study focuses on comparison of field and naturally acquired laboratory measurement of wet samples in Visible (VIS), Near-Infrared (NIR) and Vis-NIR range using several pretreatment approaches including orthogonal signal correction (OSC). The comparison was concluded with the development of validation models for SOC prediction based on partial least squares regression (PLSR) and support vector machine (SVMR). Nonetheless, for the OSC implementation, we use principal component regression (PCR) together with PLSR as SVMR is not appropriate under OSC. For SOC prediction, the field measurement was better in the VIS range with R2CV = 0.47 and RMSEPcv = 0.24, while in Vis-NIR range the lab-wet measurement was better with R2CV = 0.44 and RMSEPcv = 0.25, both using the SVMR algorithm. However, the prediction accuracy improves with the introduction of OSC on both samples. The highest prediction was obtained with the lab-wet dataset (using PLSR) in the NIR and Vis-NIR range with R2CV = 0.54/0.55 and RMSEPcv = 0.24. This result indicates that the field and, in particular, lab-wet measurements, which are not commonly used, can also be useful for SOC prediction, just as the lab-dry method, with some adjustments.
Automatic Detection and Segmentation on Gas Plumes from Multibeam Water Column Images
Authors: Zhao Jianhu , Mai Dongxin , Zhang Hongmei , Wang Shiqi
Published: September 2020

The detection of gas plumes from multibeam water column (MWC) data is the most direct way to discover gas hydrate reservoirs, but current methods often have low reliability, leading to inefficient detections. Therefore, this paper proposes an automatic method for gas plume detection and segmentation by analyzing the characteristics of gas plumes in MWC images. This method is based on the AdaBoost cascade classifier, combining the Haar-like feature and Local Binary Patterns (LBP) feature. After obtaining the detected result from the above algorithm, a target localization algorithm, based on a histogram similarity calculation, is given to exactly localize the detected target boxes, by considering the differences in gas plume and background noise in the backscatter strength. On this basis, a real-time segmentation method is put forward to get the size of the detected gas plumes, by integration of the image intersection and subtraction operation. Through the shallow-water and deep-water experiment verification, the detection accuracy of this method reaches 95.8%, the precision reaches 99.35% and the recall rate reaches 82.7%. Integrated with principles and experiments, the performance of the proposed method is analyzed and discussed, and finally some conclusions are drawn.
AdQSM: A New Method for Estimating Above-Ground Biomass from TLS Point Clouds
Authors: Fan Guangpeng , Nan Liangliang , Dong Yanqi , Su Xiaohui , Chen Feixiang
Published: September 2020

Forest above-ground biomass (AGB) can be estimated based on light detection and ranging (LiDAR) point clouds. This paper introduces an accurate and detailed quantitative structure model (AdQSM), which can estimate the AGB of large tropical trees. AdQSM is based on the reconstruction of 3D tree models from terrestrial laser scanning (TLS) point clouds. It represents a tree as a set of closed and complete convex polyhedra. We use AdQSM to model 29 trees of various species (total 18 species) scanned by TLS from three study sites (the dense tropical forests of Peru, Indonesia, and Guyana). The destructively sampled tree geometry measurement data is used as reference values to evaluate the accuracy of diameter at breast height (DBH), tree height, tree volume, branch volume, and AGB estimated from AdQSM. After AdQSM reconstructs the structure and volume of each tree, AGB is derived by combining the wood density of the specific tree species from destructive sampling. The AGB estimation from AdQSM and the post-harvest reference measurement data show a satisfying agreement. The coefficient of variation of root mean square error (CV-RMSE) and the concordance correlation coefficient (CCC) are 20.37% and 0.97, respectively. AdQSM provides accurate tree volume estimation, regardless of the characteristics of the tree structure, without major systematic deviations. We compared the accuracy of AdQSM and TreeQSM in modeling the volume of 29 trees. The tree volume from AdQSM is compared with the reference value, and the determination coefficient (R2), relative bias (rBias), and CV-RMSE of tree volume are 0.96, 6.98%, and 22.62%, respectively. The tree volume from TreeQSM is compared with the reference value, and the R2, relative Bias (rBias), and CV-RMSE of tree volume are 0.94, −9.69%, and 23.20%, respectively. The CCCs between the volume estimates based on AdQSM, TreeQSM, and the reference values are 0.97 and 0.96. AdQSM also models the branches in detail. The volume of branches from AdQSM is compared with the destructive measurement reference data. The R2, rBias, and CV-RMSE of the branches volume are 0.97, 12.38%, and 36.86%, respectively. The DBH and height of the harvested trees were used as reference values to test the accuracy of AdQSM’s estimation of DBH and tree height. The R2, rBias, and CV-RMSE of DBH are 0.94, −5.01%, and 9.06%, respectively. The R2, rBias, and CV-RMSE of the tree height were 0.95, 1.88%, and 5.79%, respectively. This paper provides not only a new QSM method for estimating AGB based on TLS point clouds but also the potential for further development and testing of allometric equations.
A Usability Study of Physiological Measurement in School Using Wearable Sensors
Authors: Thammasan Nattapong , Stuldreher Ivo V. , Schreuders Elisabeth , Giletta Matteo , Brouwer Anne-Marie
Published: September 2020

Measuring psychophysiological signals of adolescents using unobtrusive wearable sensors may contribute to understanding the development of emotional disorders. This study investigated the feasibility of measuring high quality physiological data and examined the validity of signal processing in a school setting. Among 86 adolescents, a total of more than 410 h of electrodermal activity (EDA) data were recorded using a wrist-worn sensor with gelled electrodes and over 370 h of heart rate data were recorded using a chest-strap sensor. The results support the feasibility of monitoring physiological signals at school. We describe specific challenges and provide recommendations for signal analysis, including dealing with invalid signals due to loose sensors, and quantization noise that can be caused by limitations in analog-to-digital conversion in wearable devices and be mistaken as physiological responses. Importantly, our results show that using toolboxes for automatic signal preprocessing, decomposition, and artifact detection with default parameters while neglecting differences between devices and measurement contexts yield misleading results. Time courses of students’ physiological signals throughout the course of a class were found to be clearer after applying our proposed preprocessing steps.
A Method for Detecting the Randomness of Barkhausen Noise in a Material Fatigue Test Using Sensitivity and Uncertainty Analysis
Authors: Hou Yuting , Li Xiang , Zheng Yang , Zhou Jinjie , Tan Jidong , Chen Xiaoping
Published: September 2020

The magnetic Barkhausen noise (MBN) signal provides interesting clues about the evolution of microstructure of the magnetic material (internal stresses, level of degradation, etc.). This makes it widely used in non-destructive evaluation of ferromagnetic materials. Although researchers have made great effort to explore the intrinsic random characteristics and stable features of MBN signals, they have failed to provide a deterministic definition of the stochastic quality of the MBN signals. Because many features are not reproducible, there is no quantitative description for the stochastic nature of MBN, and no uniform standards to evaluate performance of features. We aim to make further study on the stochastic characteristics of MBN signal and transform it into the quantification of signal uncertainty and sensitivity, to solve the above problems for fatigue state prediction. In the case of parameter uncertainty in the prediction model, a prior approximation method was proposed. Thus, there are two distinct sources of uncertainty: feature(observation) uncertainty and model uncertainty were discussed. We define feature uncertainty from the perspective of a probability distribution using a confidence interval sensitivity analysis, and uniformly quantize and re-parameterize the feature matrix from the feature probability distribution space. We also incorporate informed priors into the estimation process by optimizing the Kullback–Leibler divergence between prior and posterior distribution, approximating the prior to the posterior. Thus, in an insufficient data situation, informed priors can improve prediction accuracy. Experiments prove that our proposed confidence interval sensitivity analysis to capture feature uncertainty has the potential to determine the instability in MBN signals quantitatively and reduce the dispersion of features, so that all features can produce positive additive effects. The false prediction rate can be reduced to almost 0. The proposed priors can not only measure model parameter uncertainties but also show superior performance similar to that of maximum likelihood estimation (MLE). The results also show that improvements in parameter uncertainties cannot be directly propagated to improve prediction uncertainties.
Unsupervised Monocular Depth Estimation Method Based on Uncertainty Analysis and Retinex Algorithm
Authors: Song Chuanxue , Qi Chunyang , Song Shixin , Xiao Feng
Published: September 2020

Depth estimation of a single image presents a classic problem for computer vision, and is important for the 3D reconstruction of scenes, augmented reality, and object detection. At present, most researchers are beginning to focus on unsupervised monocular depth estimation. This paper proposes solutions to the current depth estimation problem. These solutions include a monocular depth estimation method based on uncertainty analysis, which solves the problem in which a neural network has strong expressive ability but cannot evaluate the reliability of an output result. In addition, this paper proposes a photometric loss function based on the Retinex algorithm, which solves the problem of pulling around pixels due to the presence of moving objects. We objectively compare our method to current mainstream monocular depth estimation methods and obtain satisfactory results.
Development of a Robust Multi-Scale Featured Local Binary Pattern for Improved Facial Expression Recognition
Authors: Yasmin Suraiya , Pathan Refat Khan , Biswas Munmun , Khandaker Mayeen Uddin , Faruque Mohammad Rashed Iqbal
Published: September 2020

Compelling facial expression recognition (FER) processes have been utilized in very successful fields like computer vision, robotics, artificial intelligence, and dynamic texture recognition. However, the FER’s critical problem with traditional local binary pattern (LBP) is the loss of neighboring pixels related to different scales that can affect the texture of facial images. To overcome such limitations, this study describes a new extended LBP method to extract feature vectors from images, detecting each image from facial expressions. The proposed method is based on the bitwise AND operation of two rotational kernels applied on LBP(8,1) and LBP(8,2) and utilizes two accessible datasets. Firstly, the facial parts are detected and the essential components of a face are observed, such as eyes, nose, and lips. The portion of the face is then cropped to reduce the dimensions and an unsharp masking kernel is applied to sharpen the image. The filtered images then go through the feature extraction method and wait for the classification process. Four machine learning classifiers were used to verify the proposed method. This study shows that the proposed multi-scale featured local binary pattern (MSFLBP), together with Support Vector Machine (SVM), outperformed the recent LBP-based state-of-the-art approaches resulting in an accuracy of 99.12% for the Extended Cohn–Kanade (CK+) dataset and 89.08% for the Karolinska Directed Emotional Faces (KDEF) dataset.
A Systematic Review on Cloud Storage Mechanisms Concerning e-Healthcare Systems
Authors: Tahir Adnan , Chen Fei , Khan Habib Ullah , Ming Zhong , Ahmad Arshad , Nazir Shah , Shafiq Muhammad
Published: September 2020

As the expenses of medical care administrations rise and medical services experts are becoming rare, it is up to medical services organizations and institutes to consider the implementation of medical Health Information Technology (HIT) innovation frameworks. HIT permits health associations to smooth out their considerable cycles and offer types of assistance in a more productive and financially savvy way. With the rise of Cloud Storage Computing (CSC), an enormous number of associations and undertakings have moved their healthcare data sources to distributed storage. As the information can be mentioned whenever universally, the accessibility of information becomes an urgent need. Nonetheless, outages in cloud storage essentially influence the accessibility level. Like the other basic variables of cloud storage (e.g., reliability quality, performance, security, and protection), availability also directly impacts the data in cloud storage for e-Healthcare systems. In this paper, we systematically review cloud storage mechanisms concerning the healthcare environment. Additionally, in this paper, the state-of-the-art cloud storage mechanisms are critically reviewed for e-Healthcare systems based on their characteristics. In short, this paper summarizes existing literature based on cloud storage and its impact on healthcare, and it likewise helps researchers, medical specialists, and organizations with a solid foundation for future studies in the healthcare environment.
Data-Driven Methods for the Estimation of Leaf Water and Dry Matter Content: Performances, Potential and Limitations
Authors: Yang Bin , Lin Hui , He Yuhao
Published: September 2020

Leaf equivalent water thickness (EWT) and dry matter content (expressed as leaf mass per area (LMA)) are two critical traits for vegetation function monitoring, crop yield estimation, and precise agriculture management. Data-driven methods are widely used for remote sensing of leaf EWT and LMA because of their simplicity, satisfactory accuracy, and computation efficiency, such as the vegetation indices (VI)-based and machine learning (ML)-based methods. However, most of the data-driven methods are utilized at the canopy level, comparison of the performances of the data-driven methods at the leaf level has not been well documented. Moreover, the ML-based data-driven methods generally adopt leaf optical properties directly as their inputs, which may subsequently decrease their ability in remote sensing of leaf biochemical constituents. Performances of the ML-based methods cooperating with VI are rarely evaluated. Using the independent LOPEX and ANGERS datasets, we compared the performances of three data-driven methods: VI-based, ML-reflectance-based, and ML-VI-based methods, for the estimation of leaf EWT and LMA. Three sampling strategies were also utilized for evaluation of the generalization of these data-driven methods. Our results evidenced that ML-VI-based methods were the most accurate among these data-driven methods. Compared to the ML-reflectance-based and VI-based methods, the ML-VI-based model with support vector regression overall reduced errors by 5.7% (41.5%) and 1.8% (12.4%) for the estimation of leaf EWT (LMA), respectively. The ML-VI-based model inherits advantages of vegetation indices and ML techniques, which made it sensitive to changes of leaf biochemical constituents and capable of solving nonlinear tasks. It is thus recommended for the estimation of EWT and LMA at the leaf level. Moreover, its performance can further be enhanced by improving its generalization ability, such as adopting techniques on the selection of better wavelengths and definition of new vegetation indices. These results thus provided a prior knowledge of the data-driven methods and can be helpful for future studies on the remote sensing of leaf biochemical constituents.
An Underwater Acoustic Target Recognition Method Based on Restricted Boltzmann Machine
Authors: Luo Xinwei , Feng Yulin
Published: September 2020

This article focuses on an underwater acoustic target recognition method based on target radiated noise. The difficulty of underwater acoustic target recognition is mainly the extraction of effective classification features and pattern classification. Traditional feature extraction methods based on Low Frequency Analysis Recording (LOFAR), Mel-Frequency Cepstral Coefficients (MFCC), Gammatone-Frequency Cepstral Coefficients (GFCC), etc. essentially compress data according to a certain pre-set model, artificially discarding part of the information in the data, and often losing information helpful for classification. This paper presents a target recognition method based on feature auto-encoding. This method takes the normalized frequency spectrum of the signal as input, uses a restricted Boltzmann machine to perform unsupervised automatic encoding of the data, extracts the deep data structure layer by layer, and classifies the acquired features through the BP neural network. This method was tested using actual ship radiated noise database, and the results show that proposed classification system has better recognition accuracy and adaptability than the hand-crafted feature extraction based method.
Mo-Based Layered Nanostructures for the Electrochemical Sensing of Biomolecules
Authors: Zribi Rayhane , Neri Giovanni
Published: September 2020

Mo-based layered nanostructures are two-dimensional (2D) nanomaterials with outstanding characteristics and very promising electrochemical properties. These materials comprise nanosheets of molybdenum (Mo) oxides (MoO2 and MoO3), dichalcogenides (MoS2, MoSe2, MoTe2), and carbides (MoC2), which find application in electrochemical devices for energy storage and generation. In this feature paper, we present the most relevant characteristics of such Mo-based layered compounds and their use as electrode materials in electrochemical sensors. In particular, the aspects related to synthesis methods, structural and electronic characteristics, and the relevant electrochemical properties, together with applications in the specific field of electrochemical biomolecule sensing, are reviewed. The main features, along with the current status, trends, and potentialities for biomedical sensing applications, are described, highlighting the peculiar properties of Mo-based 2D-nanomaterials in this field.
Bend-Direction and Rotation Plastic Optical Fiber Sensor
Authors: Sartiano Demetrio , Geernaert Thomas , Torres Roca Elena , Sales Salvador
Published: September 2020

A plastic filament of poly (methyl methacrylate) (PMMA) was fabricated by extrusion. The mode confinement was simulated using numerical software. The idea is to study how the light intensity changes inside the plastic optical fiber (POF) when a bending in multiple directions is applied. The results obtained from the simulation were compared to the experimental observations. The non-circular shape of the POF allows sensing a rotation applied as well. The angle of rotation was obtained processing two images of the end facet of the fiber (one with the fiber in a reference position and one with the rotated fiber), using an intensity-based automatic image registration. The accuracy in the rotation calculation was of 0.01°.
International Students’ Experiences at a Saudi University and Academic Leaders’ Perceptions Regarding Them
Authors: Almutairi Yousef Mubrik N.
Published: September 2020

This qualitative study addresses international students’ experiences at a Saudi university to gain insight into the challenges these students encounter during their studies. The study also explores academic leaders’ perceptions in supporting international students. The guiding theories behind this study include culture shock and socialization. I conducted interviews with 16 international students and 10 academic leaders at a university in the Kingdom of Saudi Arabia to gain an understanding of their perspectives. The findings were then analyzed for common trends. The interviews showed that some students experienced greater culture shock than others, including language barriers, when coming from non-Arab countries, and women faced more challenges than men. Meanwhile, the academic leaders and faculty in this study appeared to understand international students’ experiences. Despite the rise in international students attending Saudi universities on full scholarships, limited research has considered this unique student population. This study addresses this gap and discusses future directions. This paper discusses implications for higher-education personnel and international students. The paper recommends providing sufficient material to allow students to prepare for culture shock before coming to Saudi Arabia and making professors and personnel more available to students for support.