bluuebunny/crossref_metadata_2025 huggingface dataset:
Different Contribution of Monocyte- and Platelet-Derived Microvesicles to Endothelial Behavior
Authors: Brambilla Marta , Talmon Maria , Canzano Paola , Fresu Luigia G. , Brunelleschi Sandra , Tremoli Elena , Camera Marina
Published: April 2022

Several contributions of circulating microvesicles (MVs) to the endothelial dysfunction have been reported in the past; a head-to-head comparison of platelet- and monocyte–derived MVs has however never been performed. To this aim, we assessed the involvement of these MVs in vessel damage related processes, i.e., oxidative stress, inflammation, and leukocyte-endothelial adhesion. Platelets and monocytes isolated from healthy subjects (HS, n = 15) were stimulated with TRAP-6 and LPS to release MVs that were added to human vascular endothelial cell (hECV) culture to evaluate superoxide anion production, inflammatory markers (IL-6, TNFα, NF-κB mRNA expression), and hECV adhesiveness. The effects of the MVs-induced from HS were compared to those induced by MVs spontaneously released from cells of patients with ST-segment elevation myocardial infarction (STEMI, n = 7). MVs released by HS-activated cells triggered a threefold increase in oxidative burst in a concentration-dependent manner. Only MVs released from monocytes doubled IL-6, TNFα, and NF-κB mRNA expression and monocyte-endothelial adhesion. Interestingly, the effects of the MVs isolated from STEMI-monocytes were not superimposable to previous ones except for adhesion to hECV. Conversely, MVs released from STEMI-platelets sustained both redox state and inflammatory phenotype. These data provide evidence that MVs released from activated and/or pathologic platelets and monocytes differently affect endothelial behavior, highlighting platelet-MVs as causative factors of impaired endothelial function in the acute phase of STEMI.
Antagonizing RARγ Drives Necroptosis of Cancer Stem Cells
Authors: Brown Geoffrey
Published: April 2022

There is a need for agents that eliminate cancer stem cells, which sustain cancer and are also largely responsible for disease relapse and metastasis. Conventional chemotherapeutics and radiotherapy are often highly effective against the bulk of cancer cells, which are proliferating, but spare cancer stem cells. Therapeutics that target cancer stem cells may also provide a bona fide cure for cancer. There are two rationales for targeting the retinoic acid receptor (RAR)γ. First, RARγ is expressed selectively within primitive cells. Second, RARγ is a putative oncogene for a number of human cancers, including cases of acute myeloid leukemia, cholangiocarcinoma, and colorectal, renal and hepatocellular carcinomas. Prostate cancer cells depend on active RARγ for their survival. Antagonizing all RARs caused necroptosis of prostate and breast cancer stem cell-like cells, and the cancer stem cells that gave rise to neurospheres from pediatric patients’ primitive neuroectodermal tumors and an astrocytoma. As tested for prostate cancer, antagonizing RARγ was sufficient to drive necroptosis. Achieving cancer-selectively is a longstanding paradigm for developing new treatments. The normal prostate epithelium was less sensitive to the RARγ antagonist and pan-RAR antagonist than prostate cancer cells, and fibroblasts and blood mononuclear cells were insensitive. The RARγ antagonist and pan-RAR antagonist are promising new cancer therapeutics.
Etonogestrel Administration Reduces the Expression of PHOX2B and Its Target Genes in the Solitary Tract Nucleus
Authors: Cardani Silvia , Janes Tara A. , Saini Jasmeen K. , Di Lascio Simona , Benfante Roberta , Fornasari Diego , Pagliardini Silvia
Published: April 2022

Heterozygous mutations of the transcription factor PHOX2B are responsible for Congenital Central Hypoventilation Syndrome, a neurological disorder characterized by inadequate respiratory response to hypercapnia and life-threatening hypoventilation during sleep. Although no cure is currently available, it was suggested that a potent progestin drug provides partial recovery of chemoreflex response. Previous in vitro data show a direct molecular link between progestins and PHOX2B expression. However, the mechanism through which these drugs ameliorate breathing in vivo remains unknown. Here, we investigated the effects of chronic administration of the potent progestin drug Etonogestrel (ETO) on respiratory function and transcriptional activity in adult female rats. We assessed respiratory function with whole-body plethysmography and measured genomic changes in brain regions important for respiratory control. Our results show that ETO reduced metabolic activity, leading to an enhanced chemoreflex response and concurrent increased breathing cycle variability at rest. Furthermore, ETO-treated brains showed reduced mRNA and protein expression of PHOX2B and its target genes selectively in the dorsal vagal complex, while other areas were unaffected. Histological analysis suggests that changes occurred in the solitary tract nucleus (NTS). Thus, we propose that the NTS, rich in both progesterone receptors and PHOX2B, is a good candidate for ETO-induced respiratory modulation.
Recovery of Post-Stroke Spatial Memory and Thalamocortical Connectivity Following Novel Glycomimetic and rhBDNF Treatment
Authors: Houlton Josh , Zubkova Olga V. , Clarkson Andrew N.
Published: April 2022

Stroke-induced cognitive impairments remain of significant concern, with very few treatment options available. The involvement of glycosaminoglycans in neuroregenerative processes is becoming better understood and recent advancements in technology have allowed for cost-effective synthesis of novel glycomimetics. The current study evaluated the therapeutic potential of two novel glycomimetics, compound A and G, when administered systemically five-days post-photothrombotic stroke to the PFC. As glycosaminoglycans are thought to facilitate growth factor function, we also investigated the combination of our glycomimetics with intracerebral, recombinant human brain-derived neurotrophic factor (rhBDNF). C56BL/6J mice received sham or stroke surgery and experimental treatment (day-5), before undergoing the object location recognition task (OLRT). Four-weeks post-surgery, animals received prelimbic injections of the retrograde tracer cholera toxin B (CTB), before tissue was collected for quantification of thalamo-PFC connectivity and reactive astrogliosis. Compound A or G treatment alone modulated a degree of reactive astrogliosis yet did not influence spatial memory performance. Contrastingly, compound G+rhBDNF treatment significantly improved spatial memory, dampened reactive astrogliosis and limited stroke-induced loss of connectivity between the PFC and midline thalamus. As rhBDNF treatment had negligible effects, these findings support compound A acted synergistically to enhance rhBDNF to restrict secondary degeneration and facilitate functional recovery after PFC stroke.
Intestinal ELF4 Deletion Exacerbates Alcoholic Liver Disease by Disrupting Gut Homeostasis
Authors: Liu Tongtong , Yu Haitao , Zhang Zeming , Xie Yunfei , Yang Long , You Fuping
Published: April 2022

Alcohol liver disease (ALD) is characterized by intestinal barrier disruption and gut dysbiosis. Dysfunction of E74-like ETS transcription factor 4 (ELF4) leads to colitis. We aimed to test the hypothesis that intestinal ELF4 plays a critical role in maintaining the normal function of intestinal barrier and gut homeostasis in a mouse model of ALD. Intestinal ELF4 deficiency resulted in dysfunction of the intestinal barrier. Elf4−/− mice exhibited gut microbiota (GM) dysbiosis with the characteristic of a larger proportion of Proteobacteria. The LPS increased in Elf4−/− mice and was the most important differential metabolite between Elf4−/− mice and WT mice. Alcohol exposure increased liver-to-body weight ratio, and hepatic inflammation response and steatosis in WT mice. These deleterious effects were exaggerated in Elf4−/− mice. Alcohol exposure significantly increased serum levels of TG, ALT, and AST in Elf4−/− mice but not in WT mice. In addition, alcohol exposure resulted in enriched expression of genes associated with cholesterol metabolism and lipid metabolism in livers from Elf4−/− mice. 16S rRNA sequencing showed a decrease abundance of Akkermansia and Bilophila in Elf4−/− mice. In conclusion, intestinal ELF4 is an important host protective factor in maintaining gut homeostasis and alleviating alcohol exposure-induced hepatic steatosis and injury.
Divalent Yb-Doped Silica Glass and Fiber with High Quantum Efficiency for White Light Source
Authors: Xia Changming , Liu Jiantao , Hou Zhiyun , Zhou Guiyao
Published: April 2022

The 4f135d–4f14 energy transition of Yb2+ ions can cover the whole white light wavelength, Yb2+-doped materials have thus been a hot research field. In order to obtain a white light source, many kinds of Yb2+-doped materials have been prepared. In this study, divalent Yb2+-doped silica fiber was fabricated using rod-in-tube technology. The fiber core of Yb2+-doped silica glass was prepared with high-temperature melting technology under vacuum conditions. The spectroscopic properties of the Yb2+-doped glass and fiber were studied. The experiments indicate that divalent Yb2+-doped glass has a high quantum efficiency and super-broadband fluorescence in the visible region with an excitation wavelength of 405 nm. In addition, the results suggest that Yb2+-doped fiber has a potential for application in visible fiber lasers and fiber amplification.
Equivalent Identification of Distributed Random Dynamic Load by Using K–L Decomposition and Sparse Representation
Authors: Li Kun , Zhao Yue , Fu Zhuo , Tan Chenghao , Man Xianfeng , Liu Chi
Published: April 2022

By aiming at the common distributed random dynamic loads in engineering practice, an equivalent identification method that is based on K–L decomposition and sparse representation is proposed. Considering that the establishment of a probability model of the distributed random dynamic load is usually unfeasible because of the requirement of a large number of samples, this method describes it by using an interval process model. Through K–L series expansion, the interval process model of the distributed random dynamic load is recast as the sum of the load median function and the load uncertainty. Then, the original load identification problem is transformed into two deterministic ones: the identification of the load median function and the reconstruction of the load covariance matrix, which reveals the load uncertainty characteristics. By integrating the structural modal parameters, and by adopting the Green’s kernel function method and sparse representation, the continuously distributed load median function is equivalently identified as several concentrated dynamic loads that act on the appropriate positions. On the basis of the realization of the first inverse problem, the forward model of the load covariance matrix reconstruction is derived by using K–L series expansion and spectral decomposition. The resolutions to both inverse problems are assisted by the regularization operation so as to overcome the inherent ill-posedness. At the end, a numerical example is presented to show the effectiveness of the proposed method.
An Unscented Kalman Filter Online Identification Approach for a Nonlinear Ship Motion Model Using a Self-Navigation Test
Authors: Zheng Jian , Yan Duowen , Yan Ming , Li Yun , Zhao Yabing
Published: April 2022

This paper proposes a method for the online parameter identification of nonlinear ship motion systems. First, the motion system of a ship is nonlinear, and in the course of sailing, the motion parameters of the ship will change with the change of the motion state of the ship and the sailing environment. To achieve the effect of real-time identification, we adopted an online receding horizon identification method. Second, identification parameters are the essential elements in the navigation control of intelligent merchant ships, and high-precision identification results can achieve better control effects. Therefore, we used an unscented Kalman filter (UKF) that has simpler mathematical structure and higher feedback efficiency than other identification algorithms listed in this paper, such as extended the Kalman filter, Kalman filtering and Ordinary Least Squares, as the identification scheme design algorithm, which is applied to ship motion system identification. Then, to solve the problem of significant identification errors in complex environments, we design a navigation identification framework combining a UKF and rolling wavelet denoising to realize the effect of the online identification of ships. Finally, a Korea Research Institute of Ships and Ocean Engineering (KRISO) Container Ship (KCS) was used for a self-navigation model experiment and data collection. The collected data and identification data were compared and analyzed. By comparing different identification algorithms before and after denoising, it was verified that the UKF algorithm proposed in this paper is superior relative to other traditional algorithms in identifying ship motion systems.
Phosphorus Fertilizers from Sewage Sludge Ash and Animal Blood as an Example of Biobased Environment-Friendly Agrochemicals: Findings from Field Experiments
Authors: Jastrzębska Magdalena , Kostrzewska Marta K. , Saeid Agnieszka
Published: April 2022

Wastes of biological origin from wastewater treatment systems and slaughterhouses contain substantial amounts of phosphorus (P) with high recovery potential and can contribute to alleviating the global P supply problem. This paper presents the performance of fertilizer (AF) and biofertilizer (BF) from sewage sludge ash and animal blood under field conditions. BF is AF incorporated with lyophilized cells of P-solubilizing bacteria, Bacillus megaterium. In the experiments with spring or winter wheat, the biobased fertilizers were compared to commercial P fertilizer, superphosphate (SP). No P fertilization provided an additional reference. Fertilizer effects on wheat productivity and on selected properties of soil were studied. BF showed the same yield-forming efficiency as SP, and under poorer habitat conditions, performed slightly better than AF in increasing yield and soil available P. Biobased fertilizers applied at the P rate up to 35.2 kg ha–1 did not affect the soil pH, did not increase As, Cd, Cr, Ni, and Pb content, and did not alter the abundance of heterotrophic bacteria and fungi in the soil. The findings indicate that biobased fertilizers could at least partially replace conventional P fertilizers. Research into strain selection and the proportion of P-solubilizing microorganisms introduced into fertilizers should be continued.
A Murine Model of Waning Scrub Typhus Cross-Protection between Heterologous Strains of Orientia tsutsugamushi
Authors: Mendell Nicole L. , Xu Guang , Shelite Thomas R. , Bouyer Donald H. , Walker David H.
Published: April 2022

Orientia tsutsugamushi, the etiologic agent of the life-threatening febrile disease scrub typhus, is an obligately intracellular small coccobacillary bacterium belonging to the family Rickettsiaceae and is transmitted by the parasitic larval stage of trombiculid mites. Progress towards a vaccine for protection against scrub typhus has been impeded by characteristics of the pathogen and the infection. There are numerous strains of O. tsutsugamushi in the Asia-Pacific region with geographical overlap. In human cases immunity has been described as poor against heterologous strains of the pathogen, as well as short-lived against the homologous strain, with a mean antibody reversion rate of less than one year. Animal models of cross-protection as well as of deterioration of this cross-protection are needed to enhance understanding of transient immunity to scrub typhus. To build upon current understanding of this ineffective protection we sought to utilize our recently developed models, sublethal intradermal infection followed by challenge via ordinarily lethal hematogenous dissemination. Mice that were initially infected sublethally with O. tsutsugamushi Gilliam strain and were challenged with an ordinarily lethal dose of heterologous Karp strain were protected from death by a robust immune response at one month after the primary infection as evidenced by an abundance of mononuclear cellular infiltrates in target organs such as lung, liver, and kidney; maintenance of body weight; and low bacterial loads in the organs. Waning protection from lethal Karp strain challenge indicated by weight loss mirroring that observed in naïve mice was observed as early as 9 months after primary Gilliam strain infection, and higher bacterial loads, severe disease, and eventual death in some mice was observed after challenge with Karp strain at 14 months post-initial heterologous infection.
Transthoracic Echocardiography-Based Prediction Model of Adverse Event Risk in Patients with COVID-19
Authors: Golukhova Elena Zelikovna , Slivneva Inessa Viktorovna , Mamalyga Maxim Leonidovich , Marapov Damir Ildarovich , Alekhin Mikhail Nikolaevich , Rybka Mikhail Mikhailovich , Volkovskaya Irina Vasilevna
Published: April 2022

Cardiopulmonary disorders cause a significant increase in the risk of adverse events in patients with COVID-19. Therefore, the development of new diagnostic and treatment methods for comorbid disorders in COVID-19 patients is one of the main public health challenges. The aim of the study was to analyze patient survival and to develop a predictive model of survival in adults with COVID-19 infection based on transthoracic echocardiography (TTE) parameters. We conducted a prospective, single-center, temporary hospital-based study of 110 patients with moderate to severe COVID-19. All patients underwent TTE evaluation. The predictors of mortality we identified in univariate and multivariable models and the predictive performance of the model were assessed using receiver operating characteristic (ROC) analysis and area under the curve (AUC). The predictive model included three factors: right ventricle (RV)/left ventricle (LV) area (odds ratio (OR) = 1.048 per 1/100 increase, p = 0.03), systolic pulmonary artery pressure (sPAP) (OR = 1.209 per 1 mm Hg increase, p < 0.001), and right ventricle free wall longitudinal strain (RV FW LS) (OR = 0.873 per 1% increase, p = 0.036). The AUC-ROC of the obtained model was 0.925 ± 0.031 (95% confidence interval (95% CI): 0.863–0.986). The sensitivity (Se) and specificity (Sp) measures of the models at the cut-off point of 0.129 were 93.8% and 81.9%, respectively. A binary logistic regression method resulted in the development of a prognostic model of mortality in patients with moderate and severe COVID-19 based on TTE data. It may also have additional implications for early risk stratification and clinical decision making in patients with COVID-19.
Biopesticide Activity of Guayule Resin
Authors: Latorre Guayente , García-Martínez María Mercedes , Martín-Bejerano María , Julio Luis F. , Zalacain Amaya , Carrión María Engracia , Carmona Manuel
Published: April 2022

The extensive use of synthetic pesticides has created considerable concern for both human health and the environment, which has prompted the search for safer alternatives, such as the resin of guayule (Parthenium argentatum). Thus, in the present study, we aimed to test the biopesticide activity of crude guayule resin and three derived fractions and compare them to reference products that act against four of the most economically significant plant pests: Tetranychus urticae, Bemisia tabaci, Myzus persicae and Frankliniella occidentalis. None of the guayule products caused plant damage. The crude guayule resin and the hexane and ethyl acetate fractions displayed moderate to high contact mortality against T. urticae and B. tabaci, as well as moderate to high antifeedant activity against T. urticae, B. tabaci and M. persicae. No significant activity was observed against F. occidentalis. A correlation analysis of the activity and fraction composition revealed that guayulins C and D, isoargentatins A and B, argentatins A, B and D and an unknown compound C6 were likely responsible for the contact mortality. By contrast, the antifeedant activity appeared to be caused by guayulins A and B against T. urticae and B. tabaci and by guayulins C and D and argentatin B against M. persicae. The feeding reduction in F. occidentalis was associated with an unknown compound C2 and argentatin C. Therefore, guayule appears to be a promising novel biopesticide.
Exogenous Rosmarinic Acid Application Enhances Thermotolerance in Tomatoes
Authors: Zhou Zhiwen , Li Jiajia , Zhu Changan , Jing Beiyu , Shi Kai , Yu Jingquan , Hu Zhangjian
Published: April 2022

Due to global warming, high-temperature stress has become a major threat to plant growth and development, which causes a severe challenge to food security worldwide. Therefore, it is necessary to explore the plant bioactive molecules, which could be a promising approach to strengthening plant thermotolerance. Rosmarinic acid (RA) serves as a plant-derived phenolic compound and has beneficial and health-promoting effects for human beings. However, the involvement of RA in plant stress response and the underlying molecular mechanism was largely unknown. In this study, we found that exogenous RA application conferred improved thermotolerance in tomatoes. The transcript abundance and the enzyme activity of enzymatic antioxidants, such as ascorbate peroxidase (APX), catalase (CAT), glutathione reductase (GR), and dehydroascorbate reductase (DHAR), were further promoted by RA treatment in tomato plants subjected to high-temperature stress. Moreover, RA activated the antioxidant system and modulated the cellular redox homeostasis also associated with the redox status of nonenzymatic glutathione and ascorbic acid. The results of RNA-seq data showed that transcriptional regulation was involved in RA-mediated thermotolerance. Consistently, the gene expression of several high temperature-responsive transcription factors like HsfA2, and WRKY family genes were substantially induced by RA treatment, which potentially contributed to the induction of heat shock proteins (HSPs). Overall, these findings not only gave a direct link between RA and plant thermotolerance but also provided an attractive approach to protecting crop plants from high-temperature damage in a global warming future.
Sorbent Properties of Orange Peel-Based Biochar for Different Pollutants in Water
Authors: Zhang Weichao , Wang Yuwei , Fan Liquan , Liu Xingmei , Cao Weiyan , Ai Honglin , Wang Ziteng , Liu Xijun , Jia Hongge
Published: April 2022

Efficient and reasonable utilization of waste biomass resources can not only avoid serious waste of material resources, but also solve the problem of environmental pollution. Therefore, the development of efficient and environmentally friendly waste biomass carbonization technology has important practical significance. Here, the activated carbon from orange peel (OAC) is prepared by potassium hydroxide (KOH) activation combined with high-temperature carbonization. The adsorption effects of OAC on three different pollutant aqueous solutions, methylene blue (MB), tetracycline (TC), and fluorescein sodium (NaFL), are examined. The OAC absorbent has excellent adsorption capacity for MB, TC, and NaFL pollutants of 10 mg L−1, with adsorption rates of 99.17%, 73.5%, and 94.24%, respectively. This study provides a new idea for turning waste biomass into treasure and eliminating the hidden danger of environmental pollution.
Multilayer Reversible Data Hiding Based on the Difference Expansion Method Using Multilevel Thresholding of Host Images Based on the Slime Mould Algorithm
Authors: Mehbodniya Abolfazl , Douraki Behnaz karimi , Webber Julian L. , Alkhazaleh Hamzah Ali , Elbasi Ersin , Dameshghi Mohammad , Abu Zitar Raed , Abualigah Laith
Published: April 2022

Researchers have scrutinized data hiding schemes in recent years. Data hiding in standard images works well, but does not provide satisfactory results in distortion-sensitive medical, military, or forensic images. This is because placing data in an image can cause permanent distortion after data mining. Therefore, a reversible data hiding (RDH) technique is required. One of the well-known designs of RDH is the difference expansion (DE) method. In the DE-based RDH method, finding spaces that create less distortion in the marked image is a significant challenge, and has a high insertion capacity. Therefore, the smaller the difference between the selected pixels and the more correlation between two consecutive pixels, the less distortion can be achieved in the image after embedding the secret data. This paper proposes a multilayer RDH method using the multilevel thresholding technique to reduce the difference value in pixels and increase the visual quality and the embedding capacity. Optimization algorithms are one of the most popular methods for solving NP-hard problems. The slime mould algorithm (SMA) gives good results in finding the best solutions to optimization problems. In the proposed method, the SMA is applied to the host image for optimal multilevel thresholding of the image pixels. Moreover, the image pixels in different and more similar areas of the image are located next to one another in a group and classified using the specified thresholds. As a result, the embedding capacity in each class can increase by reducing the value of the difference between two consecutive pixels, and the distortion of the marked image can decrease after inserting the personal data using the DE method. Experimental results show that the proposed method is better than comparable methods regarding the degree of distortion, quality of the marked image, and insertion capacity.
Technical and Economical Assessment of CO2 Capture-Based Ammonia Aqueous
Authors: Slavu Nela , Badea Adrian , Dinca Cristian
Published: April 2022

In the context of climate change and the reduction in CO2 emissions from fossil fuel combustion, the integration of CO2 capture technologies in steam power plants is a key solution. The aim of this study was to analyze the use of ammonia, at different mass concentrations, in capturing post-combustion CO2 in a coal-fired power station and comparing it with the reference 30% MEA case. In this regard, a multi-criteria model was developed to establish the optimal solvent used, considering the least impact on technical performance, economic, and environmental indicators. As a result, the lowest CO2 capture cost was obtained for the CO2 capture process based on 7% NH3, with 59.07 €/tCO2. Integration of the CO2 capture process is more economically viable when the CO2 emissions tax is higher than 70 €/tCO2 for 7% NH3 and 15% NH3, 80 €/tCO2 for 5% NH3 and 30% MEA, and 90 €/tCO2 for 2% NH3. Regarding the overall efficiency, the energy penalty associated with the CO2 capture process integration varied between 15 and 35%, and the lowest value was obtained for 15% NH3. The GWP indicator ranged between 113 and 149 kg_CO2_eq/MWh for NH3 compared to MEA 133 kg_CO2_eq/MWh and the case with no CO2 capture was 823 kg_CO2_eq/MWh.
Machine Learning to Estimate the Mass-Diffusion Distance from a Point Source under Turbulent Conditions
Authors: Ishigami Takahiro , Irikura Motoki , Tsukahara Takahiro
Published: April 2022

Technologies that predict the sources of substances diffused in the atmosphere, ocean, and chemical plants are being researched in various fields. The flows transporting such substances are typically in turbulent states, and several problems including the nonlinearity of turbulence must be overcome to enable accurate estimations of diffusion-source location from limited observation data. We studied the feasibility of machine learning, specifically convolutional neural networks (CNNs), to the problem of estimating the diffusion distance from a point source, based on two-dimensional, instantaneous information of diffused-substance distributions downstream of the source. The input image data for the learner are the concentration (or luminance of fluorescent dye) distributions affected by turbulent motions of the transport medium. In order to verify our approach, we employed experimental data of a fully developed turbulent channel flow with a dye nozzle, wherein we attempted to estimate the distances between the dye nozzle and downstream observation windows. The inference accuracy of four different CNN architectures were investigated, and some achieved an accuracy of more than 90%. We confirmed the independence of the inference accuracy on the anisotropy (or rotation) of the image. The trained CNN can recognize the turbulent characteristics for estimating the diffusion source distance without statistical processing. The learners have a strong dependency on the condition of learning images, such as window size and image noise, implying that learning images should be carefully handled for obtaining higher generalization performance.
Parental Attitudes and Beliefs toward Attention Deficit Hyperactivity Disorder in Prince Sultan Military Medical City, Riyadh City
Authors: Alrahili Nader , Al Harthi Mansour Abdulaziz , Ababtain Sarah Jassim , Al Sharif Salma Saad , Alnuwaysir Mohammed Abdulaziz , Al Huzaimi Roaa Fahad
Published: April 2022

The present study was designed to investigate attitudes and beliefs among parents of patients with attention deficit hyperactivity disorder (ADHD), with regard to symptoms, causes, prognosis, and management. The study was of a cross-sectional descriptive design including 283 parents of patients with ADHD (preschool, primary school, and adolescent). It was conducted at child and adolescent outpatient clinics in Prince Sultan Military Medical City, Riyadh to assess parents’ knowledge, attitudes, treatments, and beliefs regarding ADHD. Among 283 parents, 59.8% believed that diet could improve ADHD symptoms. Approximately 75% of parents believed that ADHD is a form of misbehavior rather than a neurodevelopmental disorder, whereas 55.6% believed that ADHD is a genetic disease. In total, 89% of parents wanted to treat their children by receiving specialist advice and recommendations. A significant and nonsignificant correlation between gender and preschool and primary school was recorded, respectively. However, a positive correlation was recorded between age and caregiver’s education in adolescents. Since parents’ attitudes and perceptions were poor overall, the mismanagement of ADHD was noticed. Furthermore, government-level media campaigns should be implemented to enhance parents’ knowledge and debunk myths in order to improve ADHD management.
A General Spline-Based Method for Centerline Extraction from Different Segmented Road Maps in Remote Sensing Imagery
Authors: Xiao Fanghong , Tong Ling , Li Yuxia , Luo Shiyu , Benediktsson Jón Atli
Published: April 2022

Road centerline extraction is the foundation for integrating the segmented road map from a remote sensing image into a geographic information system (GIS) database. Considering that existing approaches tend to have a decline in performance for centerline and junction extraction when segmented road structures are irregular, this paper proposes a novel method which models the road network as a sequence of connected spline curves. Based on this motivation, the ratio of cross operators is firstly proposed to detect direction and width features of roads. Then, road pixels are divided into different clusters by local features using three perceptual grouping principles (i.e., direction grouping, proximity grouping, and continuity grouping). After applying a polynomial curve fitting on each cluster using pixel coordinates as observation data, the internal control points are determined according to the adjacency relation between clusters. Finally, road centerlines are generated based on spline fitting with constraints. We test our approach on segmented road maps which were obtained previously by machine recognition, or manual extraction from real optical (WorldView-2) and synthetic aperture radar (TerraSAR-X, Radarsat-2) images. Depending on the accuracy of the input segmented road maps, experimental results from our test data show that both the completeness and correctness of extracted centerlines are over 84% and 68% for optical and radar images, respectively. Furthermore, experiments also demonstrate the advantages of our proposed method, in contrast to existing methods for gaining smooth centerlines and precise junctions.
Integration of Satellite-Based Optical and Synthetic Aperture Radar Imagery to Estimate Winter Cover Crop Performance in Cereal Grasses
Authors: Jennewein Jyoti S. , Lamb Brian T. , Hively W. Dean , Thieme Alison , Thapa Resham , Goldsmith Avi , Mirsky Steven B.
Published: April 2022

The magnitude of ecosystem services provided by winter cover crops is linked to their performance (i.e., biomass and associated nitrogen content, forage quality, and fractional ground cover), although few studies quantify these characteristics across the landscape. Remote sensing can produce landscape-level assessments of cover crop performance. However, commonly employed optical vegetation indices (VI) saturate, limiting their ability to measure high-biomass cover crops. Contemporary VIs that employ red-edge bands have been shown to be more robust to saturation issues. Additionally, synthetic aperture radar (SAR) data have been effective at estimating crop biophysical characteristics, although this has not been demonstrated on winter cover crops. We assessed the integration of optical (Sentinel-2) and SAR (Sentinel-1) imagery to estimate winter cover crops biomass across 27 fields over three winter–spring seasons (2018–2021) in Maryland. We used log-linear models to predict cover crop biomass as a function of 27 VIs and eight SAR metrics. Our results suggest that the integration of the normalized difference red-edge vegetation index (NDVI_RE1; employing Sentinel-2 bands 5 and 8A), combined with SAR interferometric (InSAR) coherence, best estimated the biomass of cereal grass cover crops. However, these results were season- and species-specific (R2 = 0.74, 0.81, and 0.34; RMSE = 1227, 793, and 776 kg ha−1, for wheat (Triticum aestivum L.), triticale (Triticale hexaploide L.), and cereal rye (Secale cereale), respectively, in spring (March–May)). Compared to the optical-only model, InSAR coherence improved biomass estimations by 4% in wheat, 5% in triticale, and by 11% in cereal rye. Both optical-only and optical-SAR biomass prediction models exhibited saturation occurring at ~1900 kg ha−1; thus, more work is needed to enable accurate biomass estimations past the point of saturation. To address this continued concern, future work could consider the use of weather and climate variables, machine learning models, the integration of proximal sensing and satellite observations, and/or the integration of process-based crop-soil simulation models and remote sensing observations.
Improving Heterogeneous Forest Height Maps by Integrating GEDI-Based Forest Height Information in a Multi-Sensor Mapping Process
Authors: Morin David , Planells Milena , Baghdadi Nicolas , Bouvet Alexandre , Fayad Ibrahim , Le Toan Thuy , Mermoz Stéphane , Villard Ludovic
Published: April 2022

Forests are one of the key elements in ecological transition policies in Europe. Sustainable forest management is needed in order to optimise wood harvesting, while preserving carbon storage, biodiversity and other ecological functions. Forest managers and public bodies need improved and cost-effective forest monitoring tools. Research studies have been carried out to assess the use of optical and radar images for producing forest height or biomass maps. The main limitations are the quantity, quality and representativeness of the reference data for model training. The Global Ecosystem Dynamics Investigation (GEDI) mission (full waveform LiDAR on board the International Space Station) has provided an unprecedented number of forest canopy height samples from 2019. These samples could be used to improve reference datasets. This paper aims to present and validate a method for estimating forest dominant height from open access optical and radar satellite images (Sentinel-1, Sentinel-2 and ALOS-2 PALSAR-2), and then to assess the use of GEDI samples to replace field height measurements in model calibration. Our approach combines satellite image features and dominant height measurements, or GEDI metrics, in a Support Vector Machine regression algorithm, with a feature selection process. The method is tested on mixed uneven-aged broadleaved and coniferous forests in France. Using dominant height measurements for model training, the cross-validation shows 7.3 to 11.6% relative Root Mean Square Error (RMSE) depending on the forest class. When using GEDI height metrics instead of field measurements for model training, errors increase to 12.8–16.7% relative RMSE. This level of error remains satisfactory; the use of GEDI could allow the production of dominant height maps on large areas with better sample representativeness. Future work will focus on confirming these results on new study sites, improving the filtering and processing of GEDI data, and producing height maps at regional or national scale. The resulting maps will help forest managers and public bodies to optimise forest resource inventories, as well as allow scientists to integrate these cartographic data into climate models.
Remote Sensing Survey of Altiplano-Puna Volcanic Complex Rocks and Minerals for Planetary Analog Use
Authors: Ito Gen , Flahaut Jessica , González-Maurel Osvaldo , Godoy Benigno , Payet Vincent , Barthez Marie
Published: April 2022

The Altiplano-Puna Volcanic Complex (APVC) of the Central Andes is an arid region with extensive volcanism, possessing various geological features comparable to those of other solar system objects. The unique features of the APVC, e.g., hydrothermal fields and evaporite salars, have been used as planetary analogs before, but the complexity of the APVC presents a wealth of opportunities for more analog studies that have not been exploited previously. Motivated by the potential of using the APVC as an analog of the volcanic terrains of solar system objects, we mapped the mineralogy and silica content of the APVC up to ~100,000 km2 in northern Chile based on a combination of remote sensing data resembling those of the Moon and Mars. The band ratio indices of Landsat 8 Operational Land Imager multispectral images and mineral classifications based on spectral hourglass approach using Earth Observing-1 Hyperion hyperspectral images (both in the visible to shortwave infrared wavelengths) were used to map iron-bearing and alteration minerals. We also used Hyperion imagery to detect feldspar spectral signatures and demonstrated that feldspar minerals can be detected on non-anorthosites, which may influence interpretations of feldspar spectral signatures on Mars. From the Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Emissivity Dataset, we derived the silica percentage of non-evaporite rocks within errors of approximately 2–3 wt.% SiO2 for those in the 60–70 wt.% range (about 8 wt.% errors for the 50–60 wt.% range). Based on an integrated assessment of the three datasets, we highlighted three regions of particular interest worthy of further field investigation. We also evaluated the benefits and limitations of all three remote sensing methods for mapping key minerals and capturing rock diversity, based on available samples and existing geological maps.
Hyperspectral Monitoring Driven by Machine Learning Methods for Grassland Above-Ground Biomass
Authors: Huang Weiye , Li Wenlong , Xu Jing , Ma Xuanlong , Li Changhui , Liu Chenli
Published: April 2022

Above-ground biomass (AGB) is a key indicator for studying grassland productivity and evaluating carbon sequestration capacity; it is also a key area of interest in hyperspectral ecological remote sensing. In this study, we use data from a typical alpine meadow in the Qinghai–Tibet Plateau during the main growing season (July–September), compare the results of various feature selection algorithms to extract an optimal subset of spectral variables, and use machine learning methods and data mining techniques to build an AGB prediction model and realize the optimal inversion of above-ground grassland biomass. The results show that the Lasso and RFE_SVM band filtering machine learning models can effectively select the global optimal feature and improve the prediction effect of the model. The analysis also compares the support vector machine (SVM), least squares regression boosting (LSB), and Gaussian process regression (GPR) AGB inversion models; our findings show that the results of the three models are similar, with the GPR machine learning model achieving the best outcomes. In addition, through the analysis of different data combinations, it is found that the accuracy of AGB inversion can be significantly improved by combining the spectral characteristics with the growing season. Finally, by constructing a machine learning interpretable model to analyze the specific role of features, it was found that the same band plays different roles in different records, and the related results can provide a scientific basis for the research of grassland resource monitoring and estimation.
Wearable Immersive Virtual Reality Device for Promoting Physical Activity in Parkinson’s Disease Patients
Authors: Campo-Prieto Pablo , Cancela-Carral José Mª , Rodríguez-Fuentes Gustavo
Published: April 2022

Parkinson’s disease (PD) is a neurological disorder that usually appears in the 6th decade of life and affects up to 2% of older people (65 years and older). Its therapeutic management is complex and includes not only pharmacological therapies but also physiotherapy. Exercise therapies have shown good results in disease management in terms of rehabilitation and/or maintenance of physical and functional capacities, which is important in PD. Virtual reality (VR) could promote physical activity in this population. We explore whether a commercial wearable head-mounted display (HMD) and the selected VR exergame could be suitable for people with mild–moderate PD. In all, 32 patients (78.1% men; 71.50 ± 11.80 years) were a part of the study. Outcomes were evaluated using the Simulator Sickness Questionnaire (SSQ), the System Usability Scale (SUS), the Game Experience Questionnaire (GEQ post-game module), an ad hoc satisfaction questionnaire, and perceived effort. A total of 60 sessions were completed safely (without adverse effects (no SSQ symptoms) and with low scores in the negative experiences of the GEQ (0.01–0.09/4)), satisfaction opinions were positive (88% considered the training “good” or “very good”), and the average usability of the wearable HMD was good (75.16/100). Our outcomes support the feasibility of a boxing exergame combined with a wearable commercial HMD as a suitable physical activity for PD and its applicability in different environments due to its safety, usability, low cost, and small size. Future research is needed focusing on postural instability, because it seems to be a symptom that could have an impact on the success of exergaming programs aimed at PD.
Mixing Rules for an Exact Determination of the Dielectric Properties of Engine Soot Using the Microwave Cavity Perturbation Method and Its Application in Gasoline Particulate Filters
Authors: Walter Stefanie , Schwanzer Peter , Steiner Carsten , Hagen Gunter , Rabl Hans-Peter , Dietrich Markus , Moos Ralf
Published: April 2022

In recent years, particulate filters have become mandatory in almost all gasoline-powered vehicles to comply with emission standards regarding particulate number. In contrast to diesel applications, monitoring gasoline particulate filters (GPFs) by differential pressure sensors is challenging due to lower soot masses to be deposited in the GPFs. A different approach to determine the soot loading of GPFs is a radio frequency-based sensor (RF sensor). To facilitate sensor development, in previous work, a simulation model was created to determine the RF signal at arbitrary engine operating points. To ensure accuracy, the exact dielectric properties of the soot need to be known. This work has shown how small samples of soot-loaded filter are sufficient to determine the dielectric properties of soot itself using the microwave cavity perturbation method. For this purpose, mixing rules were determined through simulation and measurement, allowing the air and substrate fraction of the sample to be considered. Due to the different geometry of filter substrates compared to crushed soot samples, a different mixing rule had to be derived to calculate the effective filter properties required for the simulation model. The accuracy of the determined mixing rules and the underlying simulation model could be verified by comparative measurements on an engine test bench.
A Novel Coupling Mechanism for CSRRs as Near-Field Dielectric Sensors
Authors: Albishi Ali M.
Published: April 2022

This work proposes a novel coupling mechanism for a complementary split-ring resonator as a planar near-field microwave sensor for dielectric materials. The resonator is etched into the ground plane of a microstrip line. This mechanism is based on the inductive coupling synthesized by utilizing a via that connects the power plane of the microstrip line to the central island of the resonator. The proposed coupling makes the coupling capacitance between the transmission line and the resonator relatively small and insignificant compared to the capacitance of the resonator, making it more sensitive to changes in the dielectric constant of the materials under test. In addition, the coupling is no longer dependent solely on the capacitive coupling, which significantly reduces the coupling degradation caused by loading the resonator with dielectric materials, so the inductive coupling plays an important role in the proposed design. Therefore, the proposed coupling mechanism improves the sensitivity and enhances the coupling between the transmission line and the resonator. The sensor is evaluated for sensitivity, normalized resonance shift, and coupling factor using a full-wave numerical simulation. The sensitivity of the proposed sensor is 12% and 5.6% when detecting dielectric constants of 2 and 10, respectively. Compared to recent studies, the sensitivity improvement when detecting similar permittivity is 20% (1.32 times) and 9.8% (1.1 times). For verification, the proposed sensor is manufactured using PCB technology and is used to detect the presence of two dielectric laminates.
Evaluation of Different Bearing Fault Classifiers in Utilizing CNN Feature Extraction Ability
Authors: Xie Wenlang , Li Zhixiong , Xu Yang , Gardoni Paolo , Li Weihua
Published: April 2022

In aerospace, marine, and other heavy industries, bearing fault diagnosis has been an essential part of improving machine life, reducing economic losses, and avoiding safety problems caused by machine bearing failures. Most existing bearing fault diagnosis methods face challenges in extracting the fault features from raw bearing fault data. Compared with traditional methods for bearing fault characteristics extraction, deep neural networks can automatically extract intrinsic features without expert knowledge. The convolutional neural network (CNN) was utilized most widely in extracting representative features of bearing faults. Fundamental to this, the hybrid models based on the CNN and individual classifiers were proposed to diagnose bearing faults. However, CNN may not be suitable for all bearing fault classifiers. It is crucial to identify the classifiers which can maximize the CNN feature extraction ability. In this paper, four hybrid models based on CNN were built, and their fault detection accuracy and efficiency were compared. The comparative analysis showed that the random forest (RF) and support vector machine (SVM) could make full use of the CNN feature extraction ability.