bluuebunny/crossref_metadata_2025 huggingface dataset:
Revisiting Steroidogenic Pathways in the Human Placenta and Primary Human Trophoblast Cells
Authors: Karahoda Rona , Kallol Sampada , Groessl Michael , Ontsouka Edgar , Anderle Pascale , Fluck Christa , Staud Frantisek , Albrecht Christiane
Published: February 2021

Steroid hormones play a crucial role in supporting a successful pregnancy and ensuring proper fetal development. The placenta is one of the principal tissues in steroid production and metabolism, expressing a vast range of steroidogenic enzymes. Nevertheless, a comprehensive characterization of steroidogenic pathways in the human placenta and potential developmental changes occurring during gestation are poorly understood. Furthermore, the specific contribution of trophoblast cells in steroid release is largely unknown. Thus, this study aimed to (i) identify gestational age-dependent changes in the gene expression of key steroidogenic enzymes and (ii) explore the role of trophoblast cells in steroid biosynthesis and metabolism. Quantitative and Droplet Digital PCR analysis of 12 selected enzymes was carried out in the first trimester (n = 13) and term (n = 20) human placentas. Primary trophoblast cells (n = 5) isolated from human term placentas and choriocarcinoma-derived cell lines (BeWo, BeWo b30 clone, and JEG-3) were further screened for gene expression of enzymes involved in placental synthesis/metabolism of steroids. Finally, de novo steroid synthesis by primary human trophoblasts was evaluated, highlighting the functional activity of steroidogenic enzymes in these cells. Collectively, we provide insights into the expression patterns of steroidogenic enzymes as a function of gestational age and delineate the cellular origin of steroidogenesis in the human placenta.
Calprotectin in Lung Diseases
Authors: Kotsiou Ourania S. , Papagiannis Dimitrios , Papadopoulou Rodanthi , Gourgoulianis Konstantinos I.
Published: February 2021

Calprotectin (CLP) is a heterodimer formed by two S-100 calcium-binding cytosolic proteins, S100A8 and S100A9. It is a multifunctional protein expressed mainly by neutrophils and released extracellularly by activated or damaged cells mediating a broad range of physiological and pathological responses. It has been more than 20 years since the implication of S100A8/A9 in the inflammatory process was shown; however, the evaluation of its role in the pathogenesis of respiratory diseases or its usefulness as a biomarker for the appropriate diagnosis and prognosis of lung diseases have only gained attention in recent years. This review aimed to provide current knowledge regarding the potential role of CLP in the pathophysiology of lung diseases and describe how this knowledge is, up until now, translated into daily clinical practice. CLP is involved in numerous cellular processes in lung health and disease. In addition to its anti-microbial functions, CLP also serves as a molecule with pro- and anti-tumor properties related to cell survival and growth, angiogenesis, DNA damage response, and the remodeling of the extracellular matrix. The findings of this review potentially introduce CLP in daily clinical practice within the spectrum of respiratory diseases.
A Role for Human Renal Tubular Epithelial Cells in Direct Allo-Recognition by CD4+ T-Cells and the Effect of Ischemia-Reperfusion
Authors: Eleftheriadis Theodoros , Pissas Georgios , Crespo Marta , Nikolaou Evdokia , Liakopoulos Vassilios , Stefanidis Ioannis
Published: February 2021

Direct allorecognition is the earliest and most potent immune response against a kidney allograft. Currently, it is thought that passenger donor professional antigen-presenting cells (APCs) are responsible. Further, many studies support that graft ischemia-reperfusion injury increases the probability of acute rejection. We evaluated the possible role of primary human proximal renal tubular epithelial cells (RPTECs) in direct allorecognition by CD4+ T-cells and the effect of anoxia-reoxygenation. In cell culture, we detected that RPTECs express all the required molecules for CD4+ T-cell activation (HLA-DR, CD80, and ICAM-1). Anoxia-reoxygenation decreased HLA-DR and CD80 but increased ICAM-1. Following this, RPTECs were co-cultured with alloreactive CD4+ T-cells. In T-cells, zeta chain phosphorylation and c-Myc increased, indicating activation of T-cell receptor and co-stimulation signal transduction pathways, respectively. T-cell proliferation assessed with bromodeoxyuridine assay and with the marker Ki-67 increased. Previous culture of RPTECs under anoxia raised all the above parameters in T-cells. FOXP3 remained unaffected in all cases, signifying that proliferating T-cells were not differentiated towards a regulatory phenotype. Our results support that direct allorecognition may be mediated by RPTECs even in the absence of donor-derived professional APCs. Also, ischemia-reperfusion injury of the graft may enhance the above capacity of RPTECs, increasing the possibility of acute rejection.
Electron-Induced Repair of 2′-Deoxyribose Sugar Radicals in DNA: A Density Functional Theory (DFT) Study
Authors: Bell Michael , Kumar Anil , Sevilla Michael D.
Published: February 2021

In this work, we used ωB97XD density functional and 6-31++G** basis set to study the structure, electron affinity, populations via Boltzmann distribution, and one-electron reduction potentials (E°) of 2′-deoxyribose sugar radicals in aqueous phase by considering 2′-deoxyguanosine and 2′-deoxythymidine as a model of DNA. The calculation predicted the relative stability of sugar radicals in the order C4′• > C1′• > C5′• > C3′• > C2′•. The Boltzmann distribution populations based on the relative stability of the sugar radicals were not those found for ionizing radiation or OH-radical attack and are good evidence the kinetic mechanisms of the processes drive the products formed. The adiabatic electron affinities of these sugar radicals were in the range 2.6–3.3 eV which is higher than the canonical DNA bases. The sugar radicals reduction potentials (E°) without protonation (−1.8 to −1.2 V) were also significantly higher than the bases. Thus the sugar radicals will be far more readily reduced by solvated electrons than the DNA bases. In the aqueous phase, these one-electron reduced sugar radicals (anions) are protonated from solvent and thus are efficiently repaired via the “electron-induced proton transfer mechanism”. The calculation shows that, in comparison to efficient repair of sugar radicals by the electron-induced proton transfer mechanism, the repair of the cyclopurine lesion, 5′,8-cyclo-2′-dG, would involve a substantial barrier.
Homogentisic Acid and Gentisic Acid Biosynthesized Pyomelanin Mimics: Structural Characterization and Antioxidant Activity
Authors: Al Khatib Maher , Costa Jessica , Spinelli Daniele , Capecchi Eliana , Saladino Raffaele , Baratto Maria Camilla , Pogni Rebecca
Published: February 2021

Pyomelanin mimics from homogentisic acid (HGA) and gentisic acid (GA) were biosynthesized by the oxidative enzyme T. versicolor laccase at physiological pH to obtain water soluble melanins. The pigments show brown-black color, broad band visible light absorption, a persistent paramagnetism and high antioxidant activity. The EPR approach shows that at least two different radical species are present in both cases, contributing to the paramagnetism of the samples. This achievement can also shed light on the composition of the ochronotic pigment in the Alkaptonuria disease. On the other hand, these soluble pyomelanin mimics, sharing physico-chemical properties with eumelanin, can represent a suitable alternative to replace the insoluble melanin pigment in biotechnological applications.
LncRNAs and Immunity: Coding the Immune System with Noncoding Oligonucleotides
Authors: Bocchetti Marco , Scrima Marianna , Melisi Federica , Luce Amalia , Sperlongano Rossella , Caraglia Michele , Zappavigna Silvia , Cossu Alessia Maria
Published: February 2021

Long noncoding RNAs (lncRNAs) represent key regulators of gene transcription during the inflammatory response. Recent findings showed lncRNAs to be dysregulated in human diseases, such as inflammatory bowel disease, diabetes, allergies, asthma, and cancer. These noncoding RNAs are crucial for immune mechanism, as they are involved in differentiation, cell migration and in the production of inflammatory mediators through regulating protein–protein interactions or their ability to assemble with RNA and DNA. The last interaction can occur in cis or trans and is responsible for all the possible lncRNAs biological effects. Our proposal is to provide an overview on lncRNAs roles and functions related to immunity and immune mediated diseases, since these elucidations could be beneficial to untangle the complex bond between them.
The Role of mTOR Signaling as a Therapeutic Target in Cancer
Authors: Popova Nadezhda V. , Jücker Manfred
Published: February 2021

The aim of this review was to summarize current available information about the role of phosphatidylinositol-3-kinase (PI3K)/AKT/mammalian target of rapamycin (mTOR) signaling in cancer as a potential target for new therapy options. The mTOR and PI3K/AKT/mTORC1 (mTOR complex 1) signaling are critical for the regulation of many fundamental cell processes including protein synthesis, cell growth, metabolism, survival, catabolism, and autophagy, and deregulated mTOR signaling is implicated in cancer, metabolic dysregulation, and the aging process. In this review, we summarize the information about the structure and function of the mTOR pathway and discuss the mechanisms of its deregulation in human cancers including genetic alterations of PI3K/AKT/mTOR pathway components. We also present recent data regarding the PI3K/AKT/mTOR inhibitors in clinical studies and the treatment of cancer, as well the attendant problems of resistance and adverse effects.
Numerical Reconstruction of Hazardous Zones after the Release of Flammable Gases during Industrial Processes
Authors: Salamonowicz Zdzislaw , Krauze Andrzej , Majder-Lopatka Malgorzata , Dmochowska Anna , Piechota-Polanczyk Aleksandra , Polanczyk Andrzej
Published: February 2021

The storage of large numbers of batteries and accumulators is associated with an increased risk of their ignition, which results in the release of significant amounts of hydrogen into the environment. The aim of the study was to reconstruct hazardous zones after hydrogen and liquefied propane–butane (reference gas) release for different industrial processes with the use of numerical methods. Two numerical tools (Fire Dynamics Simulator and Ansys software) were applied for the three-dimensional reconstruction of flammable gas release. Propane–butane was produced from aerosol packages, and hydrogen was produced during battery charging. Emission was analyzed in an industrial building, and both emissions were independent processes. The obtained results indicated that the hazardous zones correspond to the lower explosive level concentrations for both analyzed gasses. Moreover, the high-resolution computational fluid dynamic (CFD) model for flammable gas emissions provided noninvasive and direct quantitative evidence that may influence the safety procedures prepared by regulatory agencies in refining the safety limits in the cost-effective and time-saving manners.
Prediction of New Distillation-Membrane Separation Integrated Process with Potential in Industrial Application
Authors: Ding Xin , Wang Xiaohong , Du Peng , Tian Zenghu , Chen Jingxuan
Published: February 2021

In this paper, a new integrated distillation-membrane separation process solution strategy based on genetic programming (GP) was established for azeotrope separation. Then, a price evaluation method based on the theory of unit membrane area was proposed, so that those membranes which are still in the experimental stage and have no actual industrial cost for reference can also be used in the experimental research. For different characteristics and separation requirements of various azeotropic systems, the solution strategy can be matched with difference pervaporation membranes, and the optimal distillation-membrane separation integrated process can be solved quickly and accurately. Taking methanol-toluene as an example, the separation operation was optimized by using the algorithm. The effects of different feed flows and compositions on the modification of the chitosan membrane were discussed. These results provide a reliable basis for the prospects for development and modification direction of membrane materials which are still in the experimental research stage.
Downscaling Snow Depth Mapping by Fusion of Microwave and Optical Remote-Sensing Data Based on Deep Learning
Authors: Zhu Linglong , Zhang Yonghong , Wang Jiangeng , Tian Wei , Liu Qi , Ma Guangyi , Kan Xi , Chu Ya
Published: February 2021

Accurate high spatial resolution snow depth mapping in arid and semi-arid regions is of great importance for snow disaster assessment and hydrological modeling. However, due to the complex topography and low spatial-resolution microwave remote-sensing data, the existing snow depth datasets have large errors and uncertainty, and actual spatiotemporal heterogeneity of snow depth cannot be effectively detected. This paper proposed a deep learning approach based on downscaling snow depth retrieval by fusion of satellite remote-sensing data with multiple spatial scales and diverse characteristics. The (Fengyun-3 Microwave Radiation Imager) FY-3 MWRI data were downscaled to 500 m resolution to match Moderate-resolution Imaging Spectroradiometer (MODIS) snow cover, meteorological and geographic data. A deep neural network was constructed to capture detailed spectral and radiation signals and trained to retrieve the higher spatial resolution snow depth from the aforementioned input data and ground observation. Verified by in situ measurements, downscaled snow depth has the lowest root mean square error (RMSE) and mean absolute error (MAE) (8.16 cm, 4.73 cm respectively) among Environmental and Ecological Science Data Center for West China Snow Depth (WESTDC_SD, 9.38 cm and 5.36 cm), the Microwave Radiation Imager (MWRI) Ascend Snow Depth (MWRI_A_SD, 9.45 cm and 5.49 cm) and MWRI Descend Snow Depth (MWRI_D_SD, 10.55 cm and 6.13 cm) in the study area. Meanwhile, downscaled snow depth could provide more detailed information in spatial distribution, which has been used to analyze the decrease of retrieval accuracy by various topography factors.
Application of Deep Learning for Speckle Removal in GOCI Chlorophyll-a Concentration Images (2012–2017)
Authors: Park Ji-Eun , Park Kyung-Ae
Published: February 2021

The detection and removal of erroneous pixels is a critical pre-processing step in producing chlorophyll-a (chl-a) concentration values to adequately understand the bio-physical oceanic process using optical satellite data. Geostationary Ocean Color Imager (GOCI) chl-a images revealed that numerous speckle noises with enormously high and low values were randomly scattered throughout the seas around the Korean Peninsula as well as in the Northwest Pacific. Most of the previous methods used to remove abnormal chl-a concentrations have focused on inhomogeneity in spatial features, which still frequently produce problematic values. Herein, a scheme was developed to detect and eliminate chl-a speckles as well as erroneous pixels near the boundary of clouds; for the purpose, a deep neural network (DNN) algorithm was applied to a large-sized GOCI database from the 6-year period of 2012–2017. The input data of the proposed DNN model were composed of the GOCI level-2 remote-sensing reflectance of each band, chl-a concentration image, median filtered, and monthly climatology chl-a image. The quality of the individual images as well as the monthly composites of chl-a data was improved remarkably after the DNN speckle-removal procedure. The quantitative analyses showed that the DNN algorithm achieved high classification accuracy with regard to the detection of error pixels with both very high and very low chl-a values, and better performance compared to the general arithmetic algorithms of the median filter and threshold scheme. This implies that the implemented method can be useful for investigating not only the short-term variations based on hourly chl-a data but also long-term variabilities with composite products of the GOCI chl-a concentration over the span of a decade.
Data Assimilation for Rainfall-Runoff Prediction Based on Coupled Atmospheric-Hydrologic Systems with Variable Complexity
Authors: Wang Wei , Liu Jia , Li Chuanzhe , Liu Yuchen , Yu Fuliang
Published: February 2021

The data assimilation technique is an effective method for reducing initial condition errors in numerical weather prediction (NWP) models. This paper evaluated the potential of the weather research and forecasting (WRF) model and its three-dimensional data assimilation (3DVar) module in improving the accuracy of rainfall-runoff prediction through coupled atmospheric-hydrologic systems. The WRF model with the assimilation of radar reflectivity and conventional surface and upper-air observations provided the improved initial and boundary conditions for the hydrological process; subsequently, three atmospheric-hydrological systems with variable complexity were established by coupling WRF with a lumped, a grid-based Hebei model, and the WRF-Hydro modeling system. Four storm events with different spatial and temporal rainfall distribution from mountainous catchments of northern China were chosen as the study objects. The assimilation results showed a general improvement in the accuracy of rainfall accumulation, with low root mean square error and high correlation coefficients compared to the results without assimilation. The coupled atmospheric-hydrologic systems also provide more accurate flood forecasts, which depend upon the complexity of the coupled hydrological models. The grid-based Hebei system provided the most stable forecasts regardless of whether homogeneous or inhomogeneous rainfall was considered. Flood peaks before assimilation were underestimated more in the lumped Hebei model relative to the other coupling systems considered, and the model seems more applicable for homogeneous temporal and spatial events. WRF-Hydro did not exhibit desirable predictions of rapid flood process recession. This may reflect increasing infiltration due to the interaction of atmospheric and land surface hydrology at each integration, resulting in mismatched solutions for local runoff generation and confluence.
Automatic Target Recognition for Low Resolution Foliage Penetrating SAR Images Using CNNs and GANs
Authors: Vint David , Anderson Matthew , Yang Yuhao , Ilioudis Christos , Di Caterina Gaetano , Clemente Carmine
Published: February 2021

In recent years, the technological advances leading to the production of high-resolution Synthetic Aperture Radar (SAR) images has enabled more and more effective target recognition capabilities. However, high spatial resolution is not always achievable, and, for some particular sensing modes, such as Foliage Penetrating Radars, low resolution imaging is often the only option. In this paper, the problem of automatic target recognition in Low Resolution Foliage Penetrating (FOPEN) SAR is addressed through the use of Convolutional Neural Networks (CNNs) able to extract both low and high level features of the imaged targets. Additionally, to address the issue of limited dataset size, Generative Adversarial Networks are used to enlarge the training set. Finally, a Receiver Operating Characteristic (ROC)-based post-classification decision approach is used to reduce classification errors and measure the capability of the classifier to provide a reliable output. The effectiveness of the proposed framework is demonstrated through the use of real SAR FOPEN data.
Editorial Summary, Remote Sensing Special Issue “Advances in Remote Sensing for Global Forest Monitoring”
Authors: Tomppo Erkki , Wang Guangxing , Praks Jaan , McRoberts Ronald E. , Waser Lars T.
Published: February 2021

The need for timely, spatially, and thematically accurate information regarding forests is increasing because of the key role of forests in the global carbon balance and sustainable social, economic, ecological, and cultural development [...]
Estimating Pasture Biomass Using Sentinel-2 Imagery and Machine Learning
Authors: Chen Yun , Guerschman Juan , Shendryk Yuri , Henry Dave , Harrison Matthew Tom
Published: February 2021

Effective dairy farm management requires the regular estimation and prediction of pasture biomass. This study explored the suitability of high spatio-temporal resolution Sentinel-2 imagery and the applicability of advanced machine learning techniques for estimating aboveground biomass at the paddock level in five dairy farms across northern Tasmania, Australia. A sequential neural network model was developed by integrating Sentinel-2 time-series data, weekly field biomass observations and daily climate variables from 2017 to 2018. Linear least-squares regression was employed for evaluating the results for model calibration and validation. Optimal model performance was realised with an R2 of ≈0.6, a root-mean-square error (RMSE) of ≈356 kg dry matter (DM)/ha and a mean absolute error (MAE) of 262 kg DM/ha. These performance markers indicated the results were within the variability of the pasture biomass measured in the field, and therefore represent a relatively high prediction accuracy. Sensitivity analysis further revealed what impact each farm’s in situ measurement, pasture management and grazing practices have on the model’s predictions. The study demonstrated the potential benefits and feasibility of improving biomass estimation in a cheap and rapid manner over traditional field measurement and commonly used remote-sensing methods. The proposed approach will help farmers and policymakers to estimate the amount of pasture present for optimising grazing management and improving decision-making regarding dairy farming.
Refining Altimeter-Derived Gravity Anomaly Model from Shipborne Gravity by Multi-Layer Perceptron Neural Network: A Case in the South China Sea
Authors: Zhu Chengcheng , Guo Jinyun , Yuan Jiajia , Jin Xin , Gao Jinyao , Li Chengming
Published: February 2021

Shipborne gravity can be used to refine altimeter-derived gravity whose accuracy is low in shallow waters and areas with complex submarine topography. As altimeter-derived gravity only within a small radius around the shipborne data can be corrected by traditional methods, a new method based on multi-layer perceptron (MLP) neural network is proposed to refine the altimeter-derived gravity. Input variables of MLP include the positional information at observation points and geophysical information (from our own South China Sea gravity anomaly model (SCSGA) V1.0 and bathymetry model ETOPO1) at grid points around observation points. Output variables of MLP are the refined residual gravity anomalies at observation points. Training shipborne data are classified into four cases to train four MLP models, which are used to predict the refined gravity anomaly model SCSGA V1.1. Then all of the training shipborne data are used for training an MLP model to predict the refined gravity anomaly model SCSGA V1.2. Assessed by testing shipborne data, the accuracy of SCSGA V1.2 is 0.14 mGal higher than that of SCSGA V1.0, and similar to that of SCSGA V1.1. Compared with the original gravity anomaly model (SCSGA V1.0), the accuracy of the refined gravity anomaly model (SCSGA V1.2) by MLP is improved by 4.4% in areas where the training data are concentrated, and also improved by 2.2% in other areas. Therefore, the method of MLP can be used to refine the altimeter-derived gravity model by shipborne gravity, overcoming the problem of limited correction radius for traditional methods.
Co-Seismic Inversion and Post-Seismic Deformation Mechanism Analysis of 2019 California Earthquake
Authors: Yang Chengsheng , Wang Ting , Zhu Sainan , Han Bingquan , Dong Jihong , Zhao Chaoying
Published: February 2021

In July 2019, a series of seismic events, including a magnitude (Mw) 7.1 mainshock and Mw 6.4 foreshock, occurred in Eastern California. Studying these seismic events can significantly improve our understanding of the Eastern California tectonic environment. Sentinel-1A and ALOS-2 PALSAR images were utilized to obtain co-seismic deformation fields, including mainshock and foreshock deformation. The Okada elastic dislocation model and ascending and descending orbit results were used to invert the co-seismic slip distribution and obtain a co-seismic focal mechanism solution. Using ascending Sentinel-1A images, a time-series deformation was obtained for 402 d after the earthquake, and the deformation evolution mechanism was analyzed. The maximum uplift caused by the co-seismic mechanism reached 1.5 m in the line of sight (LOS), and the maximum subsidence reached 1 m in the LOS. For 402 d after the earthquake, the area remained active, and its deformation was dominated by after-slip. The co-seismic inversion results illustrated that California earthquakes were mainly strike-slip. The co-seismic inversion magnitude was approximately Mw 7.08. The Coulomb stress change illustrated that the seismic moment caused by the co-seismic slip was 4.24 × 1026 N × m, which is approximately Mw 7.06. This finding is consistent with the co-seismic slip distribution inversion results.
On the Potential Optical Signature of Convective Turbulence over the West Florida Shelf
Authors: Jolliff Jason K. , Ladner Sherwin , Smith Travis A. , Anderson Stephanie , Lewis Mark David , McCarthy Sean C. , Crout Richard L. , Jarosz Ewa , Lawson Adam
Published: February 2021

Atmospheric cold front propagation across the northern Gulf of Mexico is characterized by elevated surface wind velocities and a ~10–15 °C drop in surface air temperatures. These meteorological conditions result in significant heat energy losses from the surface ocean to the overlying atmosphere. These seasonally recurring cold-air outbreak events may penetrate the southern portion of the West Florida continental shelf and initiate turbulent and convective overturn of the water column. Examination of true color images derived from ocean-viewing, satellite-based radiometer data reveals coincident and substantial surface water discolorations that are optically similar to smaller-scale “whiting events,” despite the regional-scale extent of the observed phenomenon (>25,000 km2). Coupled air–sea numerical simulations suggest the surface water discoloration occurs and is sustained where the entire water column is dynamically unstable. The simulation results indicate significant density (σt) inversions between the surface and bottom waters. Thus, the combined numerical model and remote sensing analysis suggest that convective turbulence may be contributing to the sustained ventilation of bottom waters containing a high concentration of suspended particulates. High-temporal resolution true color images rendered from the GOES-R Advanced Baseline Imager (ABI) data appear to support the surface water discoloration’s turbulent-driven nature.
A Review of Remote Sensing of Submerged Aquatic Vegetation for Non-Specialists
Authors: Rowan Gillian S. L. , Kalacska Margaret
Published: February 2021

Submerged aquatic vegetation (SAV) is a critical component of aquatic ecosystems. It is however understudied and rapidly changing due to global climate change and anthropogenic disturbances. Remote sensing (RS) can provide the efficient, accurate and large-scale monitoring needed for proper SAV management and has been shown to produce accurate results when properly implemented. Our objective is to introduce RS to researchers in the field of aquatic ecology. Applying RS to underwater ecosystems is complicated by the water column as water, and dissolved or suspended particulate matter, interacts with the same energy that is reflected or emitted by the target. This is addressed using theoretical or empiric models to remove the water column effect, though no model is appropriate for all aquatic conditions. The suitability of various sensors and platforms to aquatic research is discussed in relation to both SAV as the subject and to project aims and resources. An overview of the required corrections, processing and analysis methods for passive optical imagery is presented and discussed. Previous applications of remote sensing to identify and detect SAV are briefly presented and notable results and lessons are discussed. The success of previous work generally depended on the variability in, and suitability of, the available training data, the data’s spatial and spectral resolutions, the quality of the water column corrections and the level to which the SAV was being investigated (i.e., community versus species.)
Selection of EMG Sensors Based on Motion Coordinated Analysis
Authors: Chen Lingling , Liu Xiaotian , Xuan Bokai , Zhang Jie , Liu Zuojun , Zhang Yan
Published: February 2021

The intelligent prosthesis driven by electromyography (EMG) signal provides a solution for the movement of the disabled. The proper position of EMG sensors can improve the prosthesis’s motion recognition ability. To exert the amputee’s action-oriented ability and the prosthesis’ control ability, the EMG spatial distribution and internal connection of the prosthetic wearer is analyzed in three kinds of movement conditions: appropriate angle, excessive angle, and angle too small. Firstly, the correlation characteristics between the EMG channels are analyzed by mutual information to construct a muscle functional network. Secondly, the network’s features of different movement conditions are analyzed by calculating the characteristic of nodes and evaluating the importance of nodes. Finally, the convergent cross-mapping method is applied to construct a directed network, and the critical muscle groups which can reflect the user’s movement intention are determined. Experiment shows that this method can accurately determine the EMG location and simplify the distribution of EMG sensors inside the prosthetic socket. The network characteristics of key muscle groups can distinguish different movements effectively and provide a new strategy for decoding the relationship between limb nerve control and body movement.
Altitude Measurement-Based Optimization of the Landing Process of UAVs
Authors: Horla Dariusz , Giernacki Wojciech , Cieślak Jacek , Campoy Pascual
Published: February 2021

The paper addresses the loop shaping problem in the altitude control of an unmanned aerial vehicle to land the flying robot with a specific landing scenario adopted. The proposed solution is optimal, in the sense of the selected performance indices, namely minimum-time, minimum-energy, and velocity-penalized related functions, achieving their minimal values, with numerous experiments conducted throughout the development and preparation to the Mohamed Bin Zayed International Robotics Challenge (MBZIRC 2020). A novel approach to generation of a reference altitude trajectory is presented, which is then tracked in a standard, though optimized, control loop. Three landing scenarios are considered, namely: minimum-time, minimum-energy, and velocity-penalized landing scenarios. The experimental results obtained with the use of the Simulink Support Package for Parrot Minidrones, and the OptiTrack motion capture system proved the effectiveness of the proposed approach.
3D Point-of-Intention Determination Using a Multimodal Fusion of Hand Pointing and Eye Gaze for a 3D Display
Authors: Yeamkuan Suparat , Chamnongthai Kosin
Published: February 2021

This paper proposes a three-dimensional (3D) point-of-intention (POI) determination method using multimodal fusion between hand pointing and eye gaze for a 3D virtual display. In the method, the finger joint forms of the pointing hand sensed by a Leap Motion sensor are first detected as pointing intention candidates. Subsequently, differences with neighboring frames, which should be during hand pointing period, are checked by AND logic with the hand-pointing intention candidates. A crossing point between the eye gaze and hand pointing lines is finally decided by the closest distance concept. In order to evaluate the performance of the proposed method, experiments with ten participants, in which they looked at and pointed at nine test points for approximately five second each, were performed. The experimental results show the proposed method measures 3D POIs at 75 cm, 85 cm, and 95 cm with average distance errors of 4.67%, 5.38%, and 5.71%, respectively.
Managing Vibration Training Safety by Using Knee Flexion Angle and Rating Perceived Exertion
Authors: Chuang Long-Ren , Yang Wen-Wen , Chang Po-Ling , Chen Vincent Chiun-Fan , Liu Chiang , Shiang Tzyy-Yuang
Published: February 2021

Whole-body vibration (WBV) is commonly applied in exercise and rehabilitation and its safety issues have been a major concern. Vibration measured using accelerometers can be used to further analyze the vibration transmissibility. Optimal bending angles and rating of perceived exertion (RPE) evaluations have not been sufficiently explored to mitigate the adverse effect. Therefore, the aims of this study were to investigate the effect of various knee flexion angles on the transmissibility to the head and knee, the RPE during WBV exposure, and the link between the transmissibility to the head and the RPE. Sixteen participants randomly performed static squats with knee flexion angles of 90, 110, 130, and 150 degrees on a WBV platform. Three accelerometers were fixed on the head, knee, and center of the vibration platform to provide data of platform-to-head and platform-to-knee transmissibilities. The results showed that the flexion angle of 110 degrees induced the lowest platform-to-head transmissibility and the lowest RPE (p < 0.01). A positive correlation between RPE and the platform-to-head transmissibility was observed. This study concluded that a knee flexion of about 110 degrees is most appropriate for reducing vibration transmissibility. The reported RPE could be used to reflect the vibration impact to the head.
Predictive Modeling of Surface Wear in Mechanical Contacts under Lubricated and Non-Lubricated Conditions
Authors: Rahman Ali , Khan Muhammad , Mushtaq Aleem
Published: February 2021

The surface wear in mechanical contacts under running conditions is always a challenge to quantify. However, the inevitable relationship between the airborne noise and the surface wear can be used to predict the latter with good accuracy. In this paper, a predictive model has been derived to quantify surface wear by using airborne noise signals collected at a microphone. The noise was generated from a pin on disc setup on different dry and lubricated conditions. The collected signals were analyzed, and spectral features estimated from the measurements and regression models implemented in order to achieve an average wear prediction accuracy of within 1mm3.
Reliability of Bi-Axial Ankle Stiffness Measurement in Older Adults
Authors: Kim Hogene , Cho Sangwoo , Lee Hwiyoung
Published: February 2021

This study involves measurements of bi-axial ankle stiffness in older adults, where the ankle joint is passively moved along the talocrural and subtalar joints using a custom ankle movement trainer. A total of 15 elderly individuals participated in test–retest reliability measurements of bi-axial ankle stiffness at exactly one-week intervals for validation of the angular displacement in the device. The ankle’s range of motion was also compared, along with its stiffness. The kinematic measurements significantly corresponded to results from a marker-based motion capture system (dorsi-/plantar flexion: r = 0.996; inversion/eversion: r = 0.985). Bi-axial ankle stiffness measurements showed significant intra-class correlations (ICCs) between the two visits for all ankle movements at slower (2.14°/s, ICC = 0.712) and faster (9.77°/s, ICC = 0.879) speeds. Stiffness measurements along the talocrural joint were thus shown to have significant negative correlation with active ankle range of motion (r = −0.631, p = 0.012). The ankle movement trainer, based on anatomical characteristics, was thus used to demonstrate valid and reliable bi-axial ankle stiffness measurements for movements along the talocrural and subtalar joint axes. Reliable measurements of ankle stiffness may help clinicians and researchers when designing and fabricating ankle-foot orthosis for people with upper-motor neuron disorders, such as stroke.
Reflectometers for Absolute and Relative Reflectance Measurements in the Mid-IR Region at Vacuum
Authors: Gene Jinhwa , Jeon Min Yong , Lim Sun Do
Published: February 2021

We demonstrated spectral reflectometers for two types of reflectances, absolute and relative, of diffusely reflecting surfaces in directional-hemispherical geometry. Both are built based on the integrating sphere method with a Fourier-transform infrared spectrometer operating in a vacuum. The third Taylor method is dedicated to the reflectometer for absolute reflectance, by which absolute spectral diffuse reflectance scales of homemade reference plates are realized. With the reflectometer for relative reflectance, we achieved spectral diffuse reflectance scales of various samples including concrete, polystyrene, and salt plates by comparing against the reference standards. We conducted ray-tracing simulations to quantify systematic uncertainties and evaluated the overall standard uncertainty to be 2.18% (k = 1) and 2.99% (k = 1) for the absolute and relative reflectance measurements, respectively.
Deep-Subwavelength-Optimized Holey-Structured Metamaterial Lens for Nonlinear Air-Coupled Ultrasonic Imaging
Authors: Boccaccio Marco , Rachiglia Pasquale , Malfense Fierro Gian Piero , Pio Pucillo Giovanni , Meo Michele
Published: February 2021

Ultrasound non-destructive testing (NDT) is a common technique used for defect detection in different materials, from aluminium to carbon-fiber-reinforced polymers (CFRPs). In most cases, a liquid coupling medium/immersion of the inspected component is required to maximize impedance matching, limiting the size of the structure and materials. Air-coupled inspection methods have recently been developed for noncontact inspections to reduce contact issues in standard ultrasonic inspections. However, transmission of ultrasound in air is very inefficient because of the enormous impedance mismatch between solids and air, thus requiring a signal amplification system of high-sensitivity transducers. Hence, the captured signal amplitude may not be high enough to reveal any wave distortion due to defects or damage. This work presents a design of a holey-structured metamaterial lens with a feature size of λ/14 aiming at improvement of acousto-ultrasonic imaging using air-coupled transducers. The required effect is obtained by matching geometrical parameters of the proposed holey-structured metamaterials and the Fabry–Perot resonance modes of the structure. Transmission tests have been conducted on different fabricated metamaterial-based structures, to assess the frequency component filtering of the proposed method in both acoustic (f = 5 kHz, 20 kHz) and ultrasonic range (f = 30 kHz, 40 kHz). Results showed an improved sensitivity of damage imaging, with an increase in amplitude of the design frequencies of the lens by 11 dB. Air-coupled inspections were conducted on a stress-corrosion cracked aluminum plate and impacted CFRP plate using the holey-structured lens. Results showed an improvement in the damage-imaging resolution due to a wave-amplitude increase across the defective features, thus demonstrating its potential as an efficient and sensitive inspection tool for damage-detection improvement in geometrically complex components of different materials.
An Overview of Optical and Electrochemical Sensors and Biosensors for Analysis of Antioxidants in Food during the Last 5 Years
Authors: Nejadmansouri Maryam , Majdinasab Marjan , Nunes Gilvanda S. , Marty Jean Louis
Published: February 2021

Antioxidants are a group of healthy substances which are useful to human health because of their antihistaminic, anticancer, anti-inflammatory activity and inhibitory effect on the formation and the actions of reactive oxygen species. Generally, they are phenolic complexes present in plant-derived foods. Due to the valuable nutritional role of these mixtures, analysis and determining their amount in food is of particular importance. In recent years, many attempts have been made to supply uncomplicated, rapid, economical and user-friendly analytical approaches for the on-site detection and antioxidant capacity (AOC) determination of food antioxidants. In this regards, sensors and biosensors are regarded as favorable tools for antioxidant analysis because of their special features like high sensitivity, rapid detection time, ease of use, and ease of miniaturization. In this review, current five-year progresses in different types of optical and electrochemical sensors/biosensors for the analysis of antioxidants in foods are discussed and evaluated well. Moreover, advantages, limitations, and the potential for practical applications of each type of sensors/biosensors have been discussed. This review aims to prove how sensors/biosensors represent reliable alternatives to conventional methods for antioxidant analysis.
Feasibility of Mobile Laser Scanning towards Operational Accurate Road Rut Depth Measurements
Authors: El Issaoui Aimad , Feng Ziyi , Lehtomäki Matti , Hyyppä Eric , Hyyppä Hannu , Kaartinen Harri , Kukko Antero , Hyyppä Juha
Published: February 2021

This paper studied the applicability of the Roamer-R4DW mobile laser scanning (MLS) system for road rut depth measurement. The MLS system was developed by the Finnish Geospatial Research Institute (FGI), and consists of two mobile laser scanners and a Global Navigation Satellite System (GNSS)-inertial measurement unit (IMU) positioning system. In the study, a fully automatic algorithm was developed to calculate and analyze the rut depths, and verified in 64 reference pavement plots (1.0 m × 3.5 m). We showed that terrestrial laser scanning (TLS) data is an adequate reference for MLS-based rutting studies. The MLS-derived rut depths based on 64 plots resulted in 1.4 mm random error, which can be considered adequate precision for operational rutting depth measurements. Such data, also covering the area outside the pavement, would be ideal for multiple road environment applications since the same data can also be used in applications, from high-definition maps to autonomous car navigation and digitalization of street environments over time and in space.
A Combined Method for MEMS Gyroscope Error Compensation Using a Long Short-Term Memory Network and Kalman Filter in Random Vibration Environments
Authors: Zhu Chenhao , Cai Sheng , Yang Yifan , Xu Wei , Shen Honghai , Chu Hairong
Published: February 2021

In applications such as carrier attitude control and mobile device navigation, a micro-electro-mechanical-system (MEMS) gyroscope will inevitably be affected by random vibration, which significantly affects the performance of the MEMS gyroscope. In order to solve the degradation of MEMS gyroscope performance in random vibration environments, in this paper, a combined method of a long short-term memory (LSTM) network and Kalman filter (KF) is proposed for error compensation, where Kalman filter parameters are iteratively optimized using the Kalman smoother and expectation-maximization (EM) algorithm. In order to verify the effectiveness of the proposed method, we performed a linear random vibration test to acquire MEMS gyroscope data. Subsequently, an analysis of the effects of input data step size and network topology on gyroscope error compensation performance is presented. Furthermore, the autoregressive moving average-Kalman filter (ARMA-KF) model, which is commonly used in gyroscope error compensation, was also combined with the LSTM network as a comparison method. The results show that, for the x-axis data, the proposed combined method reduces the standard deviation (STD) by 51.58% and 31.92% compared to the bidirectional LSTM (BiLSTM) network, and EM-KF method, respectively. For the z-axis data, the proposed combined method reduces the standard deviation by 29.19% and 12.75% compared to the BiLSTM network and EM-KF method, respectively. Furthermore, for x-axis data and z-axis data, the proposed combined method reduces the standard deviation by 46.54% and 22.30% compared to the BiLSTM-ARMA-KF method, respectively, and the output is smoother, proving the effectiveness of the proposed method.
An Optimal Footprint Based Coverage Planning for Hydro Blasting Robots
Authors: Pathmakumar Thejus , Rayguru Madan Mohan , Ghanta Sriharsha , Kalimuthu Manivannan , Elara Mohan Rajesh
Published: February 2021

The hydro blasting of metallic surfaces is an essential maintenance task in various industrial sites. Its requirement of a considerable labour force and time, calls for automating the hydro blasting jobs through mobile robots. A hydro blasting robot should be able to cover the required area for a successful implementation. If a conventional robot footprint is chosen, the blasting may become inefficient, even though the concerned area is completely covered. In this work, the blasting arm’s sweeping angle is chosen as the robot’s footprint for hydro blasting task, and a multi-objective optimization-based framework is proposed to compute the optimal sweeping arc. The genetic algorithm (GA) methodology is exploited to compute the optimal footprint, which minimizes the blasting time and energy simultaneously. Multiple numerical simulations are performed to show the effectiveness of the proposed approach. Moreover, the strategy is successfully implemented on our hydro blasting robot named Hornbill, and the efficacy of the proposed approach is validated through experimental trials.
Toward the Use of Temporary Tattoo Electrodes for Impedancemetric Respiration Monitoring and Other Electrophysiological Recordings on Skin
Authors: Taccola Silvia , Poliziani Aliria , Santonocito Daniele , Mondini Alessio , Denk Christian , Ide Alessandro Noriaki , Oberparleiter Markus , Greco Francesco , Mattoli Virgilio
Published: February 2021

The development of dry, ultra-conformable and unperceivable temporary tattoo electrodes (TTEs), based on the ink-jet printing of poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) on top of commercially available temporary tattoo paper, has gained increasing attention as a new and promising technology for electrophysiological recordings on skin. In this work, we present a TTEs epidermal sensor for real time monitoring of respiration through transthoracic impedance measurements, exploiting a new design, based on the application of soft screen printed Ag ink and magnetic interlink, that guarantees a repositionable, long-term stable and robust interconnection of TTEs with external “docking” devices. The efficiency of the TTE and the proposed interconnection strategy under stretching (up to 10%) and over time (up to 96 h) has been verified on a dedicated experimental setup and on humans, fulfilling the proposed specific application of transthoracic impedance measurements. The proposed approach makes this technology suitable for large-scale production and suitable not only for the specific use case presented, but also for real time monitoring of different bio-electric signals, as demonstrated through specific proof of concept demonstrators.