bluuebunny/crossref_metadata_2025 huggingface dataset:
Acute Metabolic Responses to Glucose and Fructose Supplementation in Healthy Individuals: A Double-Blind Randomized Crossover Placebo-Controlled Trial
Authors: Eckstein Max L. , Brockfeld Antonia , Haupt Sandra , Schierbauer Janis R. , Zimmer Rebecca T. , Wachsmuth Nadine , Zunner Beate , Zimmermann Paul , Obermayer-Pietsch Barbara , Moser Othmar
Published: November 2021

The aim of this study was to investigate the impact of glucose (Glu), fructose (Fru), glucose and fructose (GluFru) and sucralose on blood glucose response in healthy individuals. Fifteen healthy individuals (five females, age of 25.4 ± 2.5 years, BMI of 23.7 ± 1.7 kg/m2 with a body mass (BM) of 76.3 ± 12.3 kg) participated in this double-blind randomized crossover placebo-controlled trial. Participants received a mixture of 300 mL of water with 1 g/kg BM of Glu, 1 g/kg BM of Fru, 0.5 g/kg BM of GluFru (each), and 0.2 g sucralose as a placebo. Peak BG values Glu were reached after 40 ± 13 min (peak BG: 141 ± 20 mg/dL), for Fru after 36 ± 22 min (peak BG: 98 ± 7 mg/dL), for GluFru after 29 ± 8 min (BG 128 ± 18 mg/dL), and sucralose after 34 ± 27 min (peak BG: 83 ± 5 mg/dL). Significant differences regarding the time until peak BG were found only between Glu and GluFru supplementation (p = 0.02). Peak blood glucose levels were significantly lower following the ingestion of Fru compared to the supplementation of Glu and GluFru (p < 0.0001) while Glu and GluFru supplementation showed no difference in peak values (p = 0.23). All conditions led to a significantly higher peak BG value compared to sucralose (p < 0.0001). Blood lactate increased in Glu (p = 0.002), Fru and GluFru (both p < 0.0001), whereas sucralose did not increase compared to the baseline (p = 0.051). Insulin levels were significantly higher in all conditions at peak compared to sucralose (p < 0.0001). The findings of this study prove the feasibility of combined carbohydrate supplementations for many applications in diabetic or healthy exercise cohorts.
Protein Appetite at the Interface between Nutrient Sensing and Physiological Homeostasis
Authors: Khan Md Shahjalal , Spann Redin A. , Münzberg Heike , Yu Sangho , Albaugh Vance L. , He Yanlin , Berthoud Hans-Rudolf , Morrison Christopher D.
Published: November 2021

Feeding behavior is guided by multiple competing physiological needs, as animals must sense their internal nutritional state and then identify and consume foods that meet nutritional needs. Dietary protein intake is necessary to provide essential amino acids and represents a specific, distinct nutritional need. Consistent with this importance, there is a relatively strong body of literature indicating that protein intake is defended, such that animals sense the restriction of protein and adaptively alter feeding behavior to increase protein intake. Here, we argue that this matching of food consumption with physiological need requires at least two concurrent mechanisms: the first being the detection of internal nutritional need (a protein need state) and the second being the discrimination between foods with differing nutritional compositions. In this review, we outline various mechanisms that could mediate the sensing of need state and the discrimination between protein-rich and protein-poor foods. Finally, we briefly describe how the interaction of these mechanisms might allow an animal to self-select between a complex array of foods to meet nutritional needs and adaptively respond to changes in either the external environment or internal physiological state.
Type of Milk Feeding and Introduction to Complementary Foods in Relation to Infant Sleep: A Systematic Review
Authors: Fu Xiaoxi , Lovell Amy L. , Braakhuis Andrea J. , Mithen Richard F. , Wall Clare R.
Published: November 2021

Inconsistent conclusions from infant sleep and feeding studies may influence parents feeding-related decisions. This study aimed to systematically review the existing literature on infant sleep and its relation to the timing of introduction to complementary foods and type of milk feeding to better understand their role(s) in infant sleep. Cohort, longitudinal, cross-sectional studies, and controlled trials were identified using online searches of five databases up to April 2020. Twenty-one articles with a total of 6225 infants under 12 months-of-age were eligible. Exclusively breastfed infants (≤6 months-of-age) had a greater number of night wakings, but most studies (67%) reported no difference in night-time and 24 h sleep duration compared to formula-fed infants. However, after 6 months-of-age, most studies (>65%) reported breastfed infants to sleep less in the night-time and over 24 h compared to formula-fed infants. Furthermore, studies reported no association between the timing of introduction to complementary foods and infant sleep duration (<12 months-of-age). Future studies using standardized methodologies and definitions, transdisciplinary expertise, and longitudinal design are required to better understand the complex role of feeding on sleep.
Role of Epigallocatechin Gallate in Glucose, Lipid, and Protein Metabolism and L-Theanine in the Metabolism-Regulatory Effects of Epigallocatechin Gallate
Authors: Lin Ling , Zeng Li , Liu An , Yuan Dongyin , Peng Yingqi , Zhang Sheng , Li Yinhua , Chen Jinhua , Xiao Wenjun , Gong Zhihua
Published: November 2021

Epigallocatechin gallate (EGCG) and L-theanine (LTA) are important bioactive components in tea that have shown promising effects on nutrient metabolism. However, whether EGCG alone or combined with LTA can regulate the glucose, lipid, and protein metabolism of healthy rats remains unclear. Therefore, we treated healthy rats with EGCG or the combination of EGCG and LTA (EGCG+LTA) to investigate the effects of EGCG on nutrient metabolism and the role of LTA in the metabolism-regulatory effects of EGCG. The results showed that compared with the control group, EGCG activated insulin and AMP-activated protein kinase (AMPK) signals, thus regulating glucose, lipid, and protein metabolism. Compared with EGCG, EGCG+LTA enhanced hepatic and muscle glycogen levels and suppressed phosphorylation of AMPK, glycogen synthase 2, mammalian target of rapamycin, and ribosomal protein S6 kinase. In addition, EGCG+LTA inhibited the expression of liver kinase B1, insulin receptor and insulin receptor substrate, and promoted the phosphorylation level of acetyl-CoA carboxylase. Furthermore, both EGCG and EGCG+LTA were harmless for young rats. In conclusion, EGCG activated AMPK and insulin pathways, thereby promoting glycolysis, glycogen, and protein synthesis and inhibiting fatty acid (FA) and cholesterol synthesis. However, LTA cooperated with EGCG to promote glycogen metabolism and suppressed the effect EGCG on FA and protein synthesis via AMPK signals.
High-Salt Diet Impairs the Neurons Plasticity and the Neurotransmitters-Related Biological Processes
Authors: Du Xiaoyue , Yu Lingqi , Ling Shengan , Xie Jiayu , Chen Wenfeng
Published: November 2021

Salt, commonly known as sodium chloride, is an important ingredient that the body requires in relatively minute quantities. However, consuming too much salt can lead to high blood pressure, heart disease and even disruption of circadian rhythms. The biological process of the circadian rhythm was first studied in Drosophila melanogaster and is well understood. Their locomotor activity gradually increases before the light is switched on and off, a phenomenon called anticipation. In a previous study, we showed that a high-salt diet (HSD) impairs morning anticipation behavior in Drosophila. Here, we found that HSD did not significantly disrupt clock gene oscillation in the heads of flies, nor did it disrupt PERIOD protein oscillation in clock neurons or peripheral tissues. Remarkably, we found that HSD impairs neuronal plasticity in the axonal projections of circadian pacemaker neurons. Interestingly, we showed that increased excitability in PDF neurons mimics HSD, which causes morning anticipation impairment. Moreover, we found that HSD significantly disrupts neurotransmitter-related biological processes in the brain. Taken together, our data show that an HSD affects the multiple functions of neurons and impairs physiological behaviors.
Immunomodulatory Actions of Mesenchymal Stromal Cells (MSCs) in Osteoarthritis of the Knee
Authors: Babu Gangireddi Suresh , Badrish Yelisetty , Oswal Vinit M , Jeyaraman Naveen , Prajwal Gollahalli Shivashankar , Jeyaraman Madhan , Muthu Sathish , Khanna Manish
Published: November 2021

Cellular therapy offers regeneration which curbs osteoarthritis of the knee. Among cellular therapies, mesenchymal stromal cells (MSCs) are readily isolated from various sources as culture expanded and unexpanded cellular population which are used as therapeutic products. Though MSCs possess a unique immunological and regulatory profile through cross-talk between MSCs and immunoregulatory cells (T cells, NK cells, dendritic cells, B cells, neutrophils, monocytes, and macrophages), they provide an immunotolerant environment when transplanted to the site of action. Immunophenotypic profile allows MSCs to escape immune surveillance and promotes their hypoimmunogenic or immune-privileged status. MSCs do not elicit a proliferative response when co-cultured with allogeneic T cells in vitro. MSCs secrete a wide range of anti-inflammatory mediators such as PGE-2, IDO, IL-1Ra, and IL-10. They also stimulate the resilient chondrogenic progenitors and enhance the chondrocyte differentiation by secretion of BMPs and TGFβ1. We highlight the various mechanisms of MSCs during tissue healing signals, their interaction with the immune system, and the impact of their lifespan in the management of osteoarthritis of the knee. A better understanding of the immunobiology of MSC renders them as an efficient therapeutic product for the management of osteoarthritis of the knee.
Novel DYRK1A Inhibitor Rescues Learning and Memory Deficits in a Mouse Model of Down Syndrome
Authors: Stensen Wenche , Rothweiler Ulli , Engh Richard Alan , Stasko Melissa R. , Bederman Ilya , Costa Alberto C. S. , Fugelli Anders , Svendsen John S. Mjøen
Published: November 2021

Down syndrome (DS) is a complex genetic disorder associated with substantial physical, cognitive, and behavioral challenges. Due to better treatment options for the physical co-morbidities of DS, the life expectancy of individuals with DS is beginning to approach that of the general population. However, the cognitive deficits seen in individuals with DS still cannot be addressed pharmacologically. In young individuals with DS, the level of intellectual disability varies from mild to severe, but cognitive ability generally decreases with increasing age, and all individuals with DS have early onset Alzheimer’s disease (AD) pathology by the age of 40. The present study introduces a novel inhibitor for the protein kinase DYRK1A, a key controlling kinase whose encoding gene is located on chromosome 21. The novel inhibitor is well characterized for use in mouse models and thus represents a valuable tool compound for further DYRK1A research.
CRISPR-Cas Technology: Emerging Applications in Clinical Microbiology and Infectious Diseases
Authors: Serajian Sahar , Ahmadpour Ehsan , Oliveira Sonia M. Rodrigues , Pereira Maria de Lourdes , Heidarzadeh Siamak
Published: November 2021

Through the years, many promising tools for gene editing have been developed including zinc-finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs), CRISPR-associated protein 9 (Cas9), and homing endonucleases (HEs). These novel technologies are now leading new scientific advancements and practical applications at an inimitable speed. While most work has been performed in eukaryotes, CRISPR systems also enable tools to understand and engineer bacteria. The increase in the number of multi-drug resistant strains highlights a necessity for more innovative approaches to the diagnosis and treatment of infections. CRISPR has given scientists a glimmer of hope in this area that can provide a novel tool to fight against antimicrobial resistance. This system can provide useful information about the functions of genes and aid us to find potential targets for antimicrobials. This paper discusses the emerging use of CRISPR-Cas systems in the fields of clinical microbiology and infectious diseases with a particular emphasis on future prospects.
3D-Printed Products for Topical Skin Applications: From Personalized Dressings to Drug Delivery
Authors: de Oliveira Rafaela Santos , Fantaus Stephani Silva , Guillot Antonio José , Melero Ana , Beck Ruy Carlos Ruver
Published: November 2021

3D printing has been widely used for the personalization of therapies and on-demand production of complex pharmaceutical forms. Recently, 3D printing has been explored as a tool for the development of topical dosage forms and wound dressings. Thus, this review aims to present advances related to the use of 3D printing for the development of pharmaceutical and biomedical products for topical skin applications, covering plain dressing and products for the delivery of active ingredients to the skin. Based on the data acquired, the important growth in the number of publications over the last years confirms its interest. The semisolid extrusion technique has been the most reported one, probably because it allows the use of a broad range of polymers, creating the most diverse therapeutic approaches. 3D printing has been an excellent field for customizing dressings, according to individual needs. Studies discussed here imply the use of metals, nanoparticles, drugs, natural compounds and proteins and peptides for the treatment of wound healing, acne, pain relief, and anti-wrinkle, among others. The confluence of 3D printing and topical applications has undeniable advantages, and we would like to encourage the research groups to explore this field to improve the patient’s life quality, adherence and treatment efficacy.
Comparison of Surface Modification Methods for Improving the Compatibility of Recycled Plastic Film-Based Aggregates
Authors: Lee Jea Uk , Hong Jin-Yong
Published: November 2021

The surface modification of recycled plastic film-based aggregates was investigated to improve the compatibility between the aggregates and a cement paste. Surface modification was performed using ultraviolet–ozone treatment (UV-O3), a silane coupling agent, O2 atmospheric pressure plasma, and acrylic binder coating methods. The surface properties of the modified aggregates were analyzed using a contact angle measuring instrument. The results revealed that for all surface modification methods, the contact angle decreased with an increase in the treatment time. According to the comparative evaluation results of the changes in the surface characteristics of the aggregates through various surface modification methods, the contact angle reduction rates were 58.9%, 51.4%, 25.5%, and 24.5% for the O2 atmospheric pressure plasma, the acrylic binder coating, the silane coupling agent, and the UV-O3 method, respectively. After 48 h, the contact angle had increased by 110.9%, 29.9%, 16.4%, and 5.9% for the O2 atmospheric pressure plasma, UV-O3, the silane coupling agent, and the acrylic binder coating, respectively. Namely, the surface modification using the acrylic binder coating method was found to be the most effective method in terms of the wettability increase effect and the long-term storage stability.
Chitin Nerve Conduits with Three-Dimensional Spheroids of Mesenchymal Stem Cells from SD Rats Promote Peripheral Nerve Regeneration
Authors: Li Ci , Zhang Meng , Liu Song-Yang , Zhang Feng-Shi , Wan Teng , Ding Zhen-Tao , Zhang Pei-Xun
Published: November 2021

Peripheral nerve injury (PNI) is an unresolved medical problem with limited therapeutic effects. Epineurium neurorrhaphy is an important method for treating PNI in clinical application, but it is accompanied by inevitable complications such as the misconnection of nerve fibers and neuroma formation. Conduits small gap tubulization has been proved to be an effective suture method to replace the epineurium neurorrhaphy. In this study, we demonstrated a method for constructing peripheral nerve conduits based on the principle of chitosan acetylation. In addition, the micromorphology, mechanical properties and biocompatibility of the chitin nerve conduits formed by chitosan acetylation were further tested. The results showed chitin was a high-quality biological material for constructing nerve conduits. Previous reports have demonstrated that mesenchymal stem cells culture as spheroids can improve the therapeutic potential. In the present study, we used a hanging drop protocol to prepare bone marrow mesenchymal stem cell (BMSCs) spheroids. Meanwhile, spherical stem cells could express higher stemness-related genes. In the PNI rat model with small gap tubulization, BMSCs spheres exhibited a higher ability to improve sciatic nerve regeneration than BMSCs suspension. Chitin nerve conduits with BMSCs spheroids provide a promising therapy option for peripheral nerve regeneration.
The Influence of Various Photoinitiators on the Properties of Commercial Dental Composites
Authors: Kowalska Andrea , Sokolowski Jerzy , Gozdek Tomasz , Krasowski Michał , Kopacz Karolina , Bociong Kinga
Published: November 2021

The aim of this article was to compare the biomechanical properties of commercial composites containing different photoinitiators: Filtek Ultimate (3M ESPE) containing camphorquinone (CQ); Estelite Σ Quick (Tokuyama Dental) with CQ in RAP Technology®; Tetric EvoCeram Bleach BLXL (Ivoclar Vivadent AG) with CQ and Lucirin TPO; and Tetric Evoceram Powerfill IVB (Ivoclar Vivadent AG) with CQ and Ivocerin TPO. All samples were cured with a polywave Valo Lamp (Ultradent Products Inc.) with 1450 mW/cm2. The microhardness, hardness by Vicker’s method, diametral tensile strength, flexural strength and contraction stress with photoelastic analysis were tested. The highest hardness and microhardness were observed for Filtek Ultimate (93.82 ± 17.44 HV), but other composites also displayed sufficient values (from 52 ± 3.92 to 58,82 ± 7.33 HV). Filtek Ultimate not only demonstrated the highest DTS (48.03 ± 5.97 MPa) and FS (87.32 ± 19.03 MPa) but also the highest contraction stress (13.7 ± 0.4 MPa) during polymerization. The TetricEvoCeram Powerfill has optimal microhardness (54.27 ± 4.1 HV), DTS (32.5 ± 5.29 MPa) and FS (79.3 ± 14.37 MPa) and the lowest contraction stress (7.4 ± 1 MPa) during photopolymerization. To summarize, Filtek Ultimate demonstrated the highest microhardness, FS and DTS values; however, composites with additional photoinitiators such as Lucirin TPO and Ivocerin have the lowest polymerization shrinkage. These composites also have higher FS and DTS and microhardness than material containing CQ in Rap Technology.
Preparation and Characterisation of Polyurethane Acrylate-Based Titanium Dioxide Pigment for Blue Light-Curable Ink
Authors: Wang Chenglong , Qiao Luyang , Zhai Qiang , Yan Kai , Wang Lili , Zheng Jinhuan
Published: November 2021

Herein, a polyurethane acrylate-based TiO2 (PU-TiO2) was fabricated using a two-step method. First, a polyurethane prepolymer was prepared. Second, PU-TiO2 was prepared using amino-modified TiO2 (A-TiO2). The best synthesis process of the polyurethane prepolymer was when the reaction temperature was 80 °C, the reaction time was 3 h and the R-value of the polyurethane acrylate was 2. Next, the influence of the A-TiO2 content on the structure and performance of PU-TiO2 was examined. The analysis of the rheological properties of the PU-TiO2 ink indicated that its viscosity gradually increased as the A-TiO2 content increased. The tensile performance of film improved because of the presence of A-TiO2. The photo-polymerisation and photo-rheological performance indicated that the PU-TiO2 structure changed from a hyperbranched structure with TiO2 as the core to a segmented structure, as the A-TiO2 content was 3%.
Recent Advances in the GPR Detection of Grouting Defects behind Shield Tunnel Segments
Authors: Peng Ming , Wang Dengyi , Liu Liu , Shi Zhenming , Shen Jian , Ma Fuan
Published: November 2021

Injecting grout into the gaps between tunnel shield segments and surrounding rocks can reduce ground subsidence and prevent ground water penetration. However, insufficient grouting and grouting defects may cause serious geological disasters. Ground penetrating radar (GPR) is widely used as a nondestructive testing (NDT) method to evaluate grouting quality and determine the existence of defects. This paper provides an overview of GPR applications for grouting defect detection behind tunnel shield segments. State-of-the-art methodologies, field cases, experimental tests and signal processing methods are discussed. The reported field cases and model test results show that GPR can detect grouting defects behind shield tunnel segments by identifying reflected waves. However, some subsequent problems still exist, including the interference of steel bars and small differences in the dielectric constants among media. Recent studies have focused on enhancing the signal-to-noise ratio and imaging methods. Advanced GPR signal processing methods, including full waveform inversion and machine learning methods, are promising for detecting imaging defects. Additionally, we conduct a preliminary experiment to investigate environmental noise, antenna configuration and coupling condition influences. Some promising topics, including multichannel configuration, rapid evaluation methods, elastic wave method scanning equipment for evaluating grout quality and comprehensive NDT methods, are recommended for future studies.
Estimation of Vertical Fuel Layers in Tree Crowns Using High Density LiDAR Data
Authors: Arkin Jeremy , Coops Nicholas C. , Daniels Lori D. , Plowright Andrew
Published: November 2021

The accurate prediction and mitigation of wildfire behaviour relies on accurate estimations of forest canopy fuels. New techniques to collect LiDAR point clouds from remotely piloted aerial systems (RPAS) allow for the prediction of forest fuels at extremely fine scales. This study uses a new method to examine the ability of such point clouds to characterize the vertical arrangement and volume of crown fuels from within individual trees. This method uses the density and vertical arrangement of LiDAR points to automatically extract and measure the dimensions of each cluster of vertical fuel. The amount and dimensions of these extracted clusters were compared against manually measured clusters that were collected through the manual measurement of over 100 trees. This validation dataset was composed of manual point cloud measurements for all portions of living crown fuel for each tree. The point clouds used for this were ground-based LiDAR point clouds that were ~80 times denser than the RPAS LiDAR point clouds. Over 96% of the extracted clusters were successfully matched to a manually measured cluster, representing ~97% of the extracted volume. A smaller percentage of the manually measured clusters (~79%) were matched to an extracted cluster, although these represented ~99% of the total measured volume. The vertical arrangement and dimensions of the matched clusters corresponded strongly to one another, although the automated method generally overpredicted each cluster’s lower boundary. Tree-level volumes and crown width were, respectively, predicted with R-squared values of 0.9111 and 0.7984 and RMSE values of 44.36 m2 and 0.53 m. Weaker relationships were observed for tree-level metrics that relied on the extraction of lower crown features (live crown length, live crown base height, lowest live branch height). These metrics were predicted with R-squared values of 0.5568, 0.3120, and 0.2011 and RMSE values of 3.53 m, 3.55 m, and 3.66 m. Overall, this study highlights strengths and weaknesses of the developed method and the utility of RPAS LiDAR point clouds relative to ground-based point clouds.
The Salinity Pilot-Mission Exploitation Platform (Pi-MEP): A Hub for Validation and Exploitation of Satellite Sea Surface Salinity Data
Authors: Guimbard Sébastien , Reul Nicolas , Sabia Roberto , Herlédan Sylvain , Khoury Hanna Ziad El , Piollé Jean-Francois , Paul Frédéric , Lee Tong , Schanze Julian J. , Bingham Frederick M. , Le Vine David , Vinogradova-Shiffer Nadya , Mecklenburg Susanne , Scipal Klaus , Laur Henri
Published: November 2021

The Pilot-Mission Exploitation Platform (Pi-MEP) for salinity is an ESA initiative originally meant to support and widen the uptake of Soil Moisture and Ocean Salinity (SMOS) mission data over the ocean. Starting in 2017, the project aims at setting up a computational web-based platform focusing on satellite sea surface salinity data, supporting studies on enhanced validation and scientific process over the ocean. It has been designed in close collaboration with a dedicated science advisory group in order to achieve three main objectives: gathering all the data required to exploit satellite sea surface salinity data, systematically producing a wide range of metrics for comparing and monitoring sea surface salinity products’ quality, and providing user-friendly tools to explore, visualize and exploit both the collected products and the results of the automated analyses. The Salinity Pi-MEP is becoming a reference hub for the validation of satellite sea surface salinity missions by providing valuable information on satellite products (SMOS, Aquarius, SMAP), an extensive in situ database (e.g., Argo, thermosalinographs, moorings, drifters) and additional thematic datasets (precipitation, evaporation, currents, sea level anomalies, sea surface temperature, etc.). Co-localized databases between satellite products and in situ datasets are systematically generated together with validation analysis reports for 30 predefined regions. The data and reports are made fully accessible through the web interface of the platform. The datasets, validation metrics and tools (automatic, user-driven) of the platform are described in detail in this paper. Several dedicated scienctific case studies involving satellite SSS data are also systematically monitored by the platform, including major river plumes, mesoscale signatures in boundary currents, high latitudes, semi-enclosed seas, and the high-precipitation region of the eastern tropical Pacific. Since 2019, a partnership in the Salinity Pi-MEP project has been agreed between ESA and NASA to enlarge focus to encompass the entire set of satellite salinity sensors. The two agencies are now working together to widen the platform features on several technical aspects, such as triple-collocation software implementation, additional match-up collocation criteria and sustained exploitation of data from the SPURS campaigns.
Remote Sensing Imagery Segmentation: A Hybrid Approach
Authors: Pare Shreya , Mittal Himanshu , Sajid Mohammad , Bansal Jagdish Chand , Saxena Amit , Jan Tony , Pedrycz Witold , Prasad Mukesh
Published: November 2021

In remote sensing imagery, segmentation techniques fail to encounter multiple regions of interest due to challenges such as dense features, low illumination, uncertainties, and noise. Consequently, exploiting vast and redundant information makes segmentation a difficult task. Existing multilevel thresholding techniques achieve low segmentation accuracy with high temporal difficulty due to the absence of spatial information. To mitigate this issue, this paper presents a new Rényi’s entropy and modified cuckoo search-based robust automatic multi-thresholding algorithm for remote sensing image analysis. In the proposed method, the modified cuckoo search algorithm is combined with Rényi’s entropy thresholding criteria to determine optimal thresholds. In the modified cuckoo search algorithm, the Lévy flight step size was modified to improve the convergence rate. An experimental analysis was conducted to validate the proposed method, both qualitatively and quantitatively against existing metaheuristic-based thresholding methods. To do this, the performance of the proposed method was intensively examined on high-dimensional remote sensing imageries. Moreover, numerical parameter analysis is presented to compare the segmented results against the gray-level co-occurrence matrix, Otsu energy curve, minimum cross entropy, and Rényi’s entropy-based thresholding. Experiments demonstrated that the proposed approach is effective and successful in attaining accurate segmentation with low time complexity.
Subtidal Natural Hard Substrate Quantitative Habitat Mapping: Interlinking Underwater Acoustics and Optical Imagery with Machine Learning
Authors: Montereale Gavazzi Giacomo , Kapasakali Danae Athena , Kerchof Francis , Deleu Samuel , Degraer Steven , Van Lancker Vera
Published: November 2021

Subtidal natural hard substrates (SNHS) promote occupancy by rich benthic communities that provide irreplaceable and fundamental ecosystem functions, representing a global priority target for nature conservation and recognised in most European environmental legislation. However, scientifically validated methodologies for their quantitative spatial demarcation, including information on species occupancy and fine-scale environmental drivers (e.g., the effect of stone size on colonisation) are rare. This is, however, crucial information for sound ecological management. In this investigation, high-resolution (1 m) multibeam echosounder (MBES) depth and backscatter data and derivates, underwater imagery (UI) by video drop-frame, and grab sediment samples, all acquired within 32 km2 of seafloor in offshore Belgian waters, were integrated to produce a random forest (RF) spatial model, predicting the continuous distribution of the seafloor areal cover/m2 of the stones’ grain sizes promoting colonisation by sessile epilithic organisms. A semi-automated UI acquisition, processing, and analytical workflow was set up to quantitatively study the colonisation proportion of different grain sizes, identifying the colonisation potential to begin at stones with grain sizes Ø ≥ 2 cm. This parameter (i.e., % areal cover of stones Ø ≥ 2 cm/m2) was selected as the response variable for spatial predictive modelling. The model output is presented along with a protocol of error and uncertainty estimation. RF is confirmed as an accurate, versatile, and transferable mapping methodology, applicable to area-wide mapping of SNHS. UI is confirmed as an essential aid to acoustic seafloor classification, providing spatially representative numerical observations needed to carry out quantitative seafloor modelling of ecologically relevant parameters. This contribution sheds innovative insights into the ecologically relevant delineation of subtidal natural reef habitat, exploiting state-of-the-art underwater remote sensing and acoustic seafloor classification approaches.
Global Satellite-Based Coastal Bathymetry from Waves
Authors: Almar Rafael , Bergsma Erwin W. J. , Thoumyre Gregoire , Baba Mohamed Wassim , Cesbron Guillaume , Daly Christopher , Garlan Thierry , Lifermann Anne
Published: November 2021

The seafloor—or bathymetry—of the world’s coastal waters remains largely unknown despite its primary importance to human activities and ecosystems. Here we present S2Shores (Satellite to Shores), the first sub-kilometer global atlas of coastal bathymetry based on depth inversion from wave kinematics captured by the Sentinel-2 constellation. The methodology reveals coastal seafloors up to a hundred meters in depth which allows covering most continental shelves and represents 4.9 million km2 along the world coastline. Although the vertical accuracy (RMSE 6–9 m) is currently coarser than that of traditional surveying techniques, S2Shores is of particular interest to countries that do not have the means to carry out in situ surveys and to unexplored regions such as polar areas. S2Shores is a major step forward in mitigating the effects of global changes on coastal communities and ecosystems by providing scientists, engineers, and policy makers with new science-based decision tools.
Uncertainty Estimation of Dense Optical Flow for Robust Visual Navigation
Authors: Ng Yonhon , Li Hongdong , Kim Jonghyuk
Published: November 2021

This paper presents a novel dense optical-flow algorithm to solve the monocular simultaneous localisation and mapping (SLAM) problem for ground or aerial robots. Dense optical flow can effectively provide the ego-motion of the vehicle while enabling collision avoidance with the potential obstacles. Existing research has not fully utilised the uncertainty of the optical flow—at most, an isotropic Gaussian density model has been used. We estimate the full uncertainty of the optical flow and propose a new eight-point algorithm based on the statistical Mahalanobis distance. Combined with the pose-graph optimisation, the proposed method demonstrates enhanced robustness and accuracy for the public autonomous car dataset (KITTI) and aerial monocular dataset.
Real-Time Closed-Loop Detection Method of vSLAM Based on a Dynamic Siamese Network
Authors: Yuan Quande , Zhang Zhenming , Pi Yuzhen , Kou Lei , Zhang Fangfang
Published: November 2021

As visual simultaneous localization and mapping (vSLAM) is easy disturbed by the changes of camera viewpoint and scene appearance when building a globally consistent map, the robustness and real-time performance of key frame image selections cannot meet the requirements. To solve this problem, a real-time closed-loop detection method based on a dynamic Siamese networks is proposed in this paper. First, a dynamic Siamese network-based fast conversion learning model is constructed to handle the impact of external changes on key frame judgments, and an elementwise convergence strategy is adopted to ensure the accurate positioning of key frames in the closed-loop judgment process. Second, a joint training strategy is designed to ensure the model parameters can be learned offline in parallel from tagged video sequences, which can effectively improve the speed of closed-loop detection. Finally, the proposed method is applied experimentally to three typical closed-loop detection scenario datasets and the experimental results demonstrate the effectiveness and robustness of the proposed method under the interference of complex scenes.
Residual Energy Estimation-Based MAC Protocol for Wireless Powered Sensor Networks
Authors: Lee Sol-Bee , Kwon Jung-Hyok , Kim Eui-Jik
Published: November 2021

This paper presents a residual energy estimation-based medium access control (REE-MAC) protocol for wireless powered sensor networks (WPSNs) composed of a central coordinator and multiple sensor devices. REE-MAC aims to reduce overhead due to control messages for scheduling the energy harvesting operation of sensor devices and provide fairness for data transmission opportunities to sensor devices. REE-MAC uses two types of superframes that operate simultaneously in different frequency bands: the wireless energy transfer (WET) superframe and wireless information transfer (WIT) superframe. At the beginning of each superframe, the coordinator estimates the change in the residual energy of individual sensor devices caused by their energy consumption and energy harvesting during the previous superframe. It then determines the devices’ charging priorities, based on which it allocates dedicated power slots (DPSs) within the WET superframe. The simulation results demonstrated that REE-MAC exhibits superior performance for the harvested energy, average freezing time, and fairness to existing representative WPSN MAC protocols.
Distributed Two-Dimensional MUSIC for Joint Range and Angle Estimation with Distributed FMCW MIMO Radars
Authors: Seo Jiho , Lee Jonghyeok , Park Jaehyun , Kim Hyungju , You Sungjin
Published: November 2021

To estimate range and angle information of multiple targets, FMCW MIMO radars have been exploited with 2D MUSIC algorithms. To improve estimation accuracy, received signals from multiple FMCW MIMO radars are collected at the data fusion center and processed coherently, which increases data communication overhead and implementation complexity. To resolve them, we propose the distributed 2D MUSIC algorithm with coordinate transformation, in which 2D MUSIC algorithm is operated with respect to the reference radar’s coordinate at each radar in a distributed way. Rather than forwarding the raw data of received signal to the fusion center, each radar performs 2D MUSIC with its own received signal in the transformed coordinates. Accordingly, the distributed radars do not need to report all their measured signals to the data fusion center, but they forward their local cost function values of 2D MUSIC for the radar image region of interest. The data fusion center can then estimate the range and angle information of targets jointly from the aggregated cost function. By applying the proposed scheme to the experimentally measured data, its performance is verified in the real environment test.
Quasi-Simultaneous Sensitive Detection of Two Gas Species by Cavity-Ringdown Spectroscopy with Two Lasers
Authors: Ma Guosheng , He Yabai , Chen Bing , Deng Hao , Liu Ying , Wang Xingping , Zhao Zhihao , Kan Ruifeng
Published: November 2021

We developed a cavity ringdown spectrometer by utilizing a step-scanning and dithering method for matching laser wavelengths to optical resonances of an optical cavity. Our approach is capable of working with two and more lasers for quasi-simultaneous measurements of multiple gas species. The developed system was tested with two lasers operating around 1654 nm and 1658 nm for spectral detections of 12CH4 and its isotope 13CH4 in air, respectively. The ringdown time of the empty cavity was about 340 µs. The achieved high detection sensitivity of a noise-equivalent absorption coefficient was 2.8 × 10−11 cm−1 Hz−1/2 or 1 × 10−11 cm−1 by averaging for 30 s. The uncertainty of the high precision determination of δ13CH4 in air is about 1.3‰. Such a system will be useful for future applications such as environmental monitoring.
Wearable IMU-Based Human Activity Recognition Algorithm for Clinical Balance Assessment Using 1D-CNN and GRU Ensemble Model
Authors: Kim Yeon-Wook , Joa Kyung-Lim , Jeong Han-Young , Lee Sangmin
Published: November 2021

In this study, a wearable inertial measurement unit system was introduced to assess patients via the Berg balance scale (BBS), a clinical test for balance assessment. For this purpose, an automatic scoring algorithm was developed. The principal aim of this study is to improve the performance of the machine-learning-based method by introducing a deep-learning algorithm. A one-dimensional (1D) convolutional neural network (CNN) and a gated recurrent unit (GRU) that shows good performance in multivariate time-series data were used as model components to find the optimal ensemble model. Various structures were tested, and a stacking ensemble model with a simple meta-learner after two 1D-CNN heads and one GRU head showed the best performance. Additionally, model performance was enhanced by improving the dataset via preprocessing. The data were down sampled, an appropriate sampling rate was found, and the training and evaluation times of the model were improved. Using an augmentation process, the data imbalance problem was solved, and model accuracy was improved. The maximum accuracy of 14 BBS tasks using the model was 98.4%, which is superior to the results of previous studies.
Multiple Cylinder Extraction from Organized Point Clouds
Authors: Moradi Saed , Laurendeau Denis , Gosselin Clement
Published: November 2021

Most man-made objects are composed of a few basic geometric primitives (GPs) such as spheres, cylinders, planes, ellipsoids, or cones. Thus, the object recognition problem can be considered as one of geometric primitives extraction. Among the different geometric primitives, cylinders are the most frequently used GPs in real-world scenes. Therefore, cylinder detection and extraction are of great importance in 3D computer vision. Despite the rapid progress of cylinder detection algorithms, there are still two open problems in this area. First, a robust strategy is needed for the initial sample selection component of the cylinder extraction module. Second, detecting multiple cylinders simultaneously has not yet been investigated in depth. In this paper, a robust solution is provided to address these problems. The proposed solution is divided into three sub-modules. The first sub-module is a fast and accurate normal vector estimation algorithm from raw depth images. With the estimation method, a closed-form solution is provided for computing the normal vector at each point. The second sub-module benefits from the maximally stable extremal regions (MSER) feature detector to simultaneously detect cylinders present in the scene. Finally, the detected cylinders are extracted using the proposed cylinder extraction algorithm. Quantitative and qualitative results show that the proposed algorithm outperforms the baseline algorithms in each of the following areas: normal estimation, cylinder detection, and cylinder extraction.
Simply Fabricated Inexpensive Dual-Polymer-Coated Fabry-Perot Interferometer-Based Temperature Sensors with High Sensitivity
Authors: Salunkhe Tejaswi Tanaji , Lee Ho Kyung , Choi Hyung Wook , Park Sang Joon , Kim Il Tae
Published: November 2021

We designed simply fabricated, highly sensitive, and cost-effective dual-polymer-coated Fabry–Perot interferometer (DFPI)-based temperature sensors by employing thermosensitive polymers and non-thermosensitive polymers, as well as different two successive dip-coating techniques (stepwise dip coating and polymer mixture coating). Seven sensors were fabricated using different polymer combinations for performance optimization. The experiments demonstrated that the stepwise dip-coated dual thermosensitive polymer sensors exhibited the highest sensitivity (2142.5 pm °C−1 for poly(methyl methacrylate)-polycarbonate (PMMA_PC) and 785.5 pm °C−1 for poly(methyl methacrylate)- polystyrene (PMMA_PS)). Conversely, the polymer-mixture-coated sensors yielded low sensitivities (339.5 pm °C−1 for the poly(methyl methacrylate)-polycarbonate mixture (PMMA_PC mixture) and 233.5 pm °C−1 for the poly(methyl methacrylate)-polystyrene mixture (PMMA_PS mixture). Thus, the coating method, polymer selection, and thin air-bubble-free coating are crucial for high-sensitivity DFPI-based sensors. Furthermore, the DFPI-based sensors yielded stable readouts, based on three measurements. Our comprehensive results confirm the effectiveness, reproducibility, stability, fast response, feasibility, and accuracy of temperature measurements using the proposed sensors. The excellent performance and simplicity of our proposed sensors are promising for biomedical, biochemical, and physical applications.
Diagnosis of Alzheimer’s Disease with Ensemble Learning Classifier and 3D Convolutional Neural Network
Authors: Zhang Peng , Lin Shukuan , Qiao Jianzhong , Tu Yue
Published: November 2021

Alzheimer’s disease (AD), the most common type of dementia, is a progressive disease beginning with mild memory loss, possibly leading to loss of the ability to carry on a conversation and respond to environments. It can seriously affect a person’s ability to carry out daily activities. Therefore, early diagnosis of AD is conducive to better treatment and avoiding further deterioration of the disease. Magnetic resonance imaging (MRI) has become the main tool for humans to study brain tissues. It can clearly reflect the internal structure of a brain and plays an important role in the diagnosis of Alzheimer’s disease. MRI data is widely used for disease diagnosis. In this paper, based on MRI data, a method combining a 3D convolutional neural network and ensemble learning is proposed to improve the diagnosis accuracy. Then, a data denoising module is proposed to reduce boundary noise. The experimental results on ADNI dataset demonstrate that the model proposed in this paper improves the training speed of the neural network and achieves 95.2% accuracy in AD vs. NC (normal control) task and 77.8% accuracy in sMCI (stable mild cognitive impairment) vs. pMCI (progressive mild cognitive impairment) task in the diagnosis of Alzheimer’s disease.
Controlled Measurement Setup for Ultra-Wideband Dielectric Modeling of Muscle Tissue in 20–45 °C Temperature Range
Authors: Maenhout Gertjan , Markovic Tomislav , Nauwelaers Bart
Published: November 2021

In order to design electromagnetic applicators for diagnostic and therapeutic applications, an adequate dielectric tissue model is required. In addition, tissue temperature will heavily influence the dielectric properties and the dielectric model should, thus, be extended to incorporate this temperature dependence. Thus, this work has a dual purpose. Given the influence of temperature, dehydration, and probe-to-tissue contact pressure on dielectric measurements, this work will initially present the first setup to actively control and monitor the temperature of the sample, the dehydration rate of the investigated sample, and the applied probe-to-tissue contact pressure. Secondly, this work measured the dielectric properties of porcine muscle in the 0.5–40 GHz frequency range for temperatures from 20 °C to 45 °C. Following measurements, a single-pole Cole–Cole model is presented, in which the five Cole–Cole parameters (ϵ∞, σs, Δϵ, τ, and α) are given by a first order polynomial as function of tissue temperature. The dielectric model closely agrees with the limited dielectric models known in literature for muscle tissue at 37 °C, which makes it suited for the design of in vivo applicators. Furthermore, the dielectric data at 41–45 °C is of great importance for the design of hyperthermia applicators.
Supportive Business Environments to Develop Grass Bioeconomy in Europe
Authors: Orozco Richard , Mosquera-Losada María Rosa , Rodriguez Javier , Adamseged Muluken Elias , Grundmann Philipp
Published: November 2021

Grasslands cover almost half of the total European agricultural area and are the source of a wide range of public goods and services. Yet, their potential to produce innovative bio-based products, such as paper and plastic, remains widely untapped. We employ a multiple case study approach and implement the Business Environment Framework by Adamseged and Grundmann (2020) on eighteen alternative grass-based businesses to investigate the interdependencies between these successful business models and their business environments. The subsequent analysis reveals that the deployment of funds and policies to support alternative grass-based products remains low in most regions of Europe. Our findings highlight that aligned funding mechanisms that incorporate and promote the specific benefits generated by grass-producing and grass-processing businesses are key to overcoming the barriers related to the competition of bio-based products with the established fossil-fuels-based economic system. To make alternative grass-based markets more dynamic, increasing consumer awareness through adequate marketing is perceived as an important aspect. Capacity building and alignment efforts need to be strengthened and coordinated at local and higher levels to enable the replication and scale-up of novel grass-based businesses in Europe and beyond.