bluuebunny/crossref_metadata_2025 huggingface dataset:
A Meta-Methodology for Improving Land Cover and Land Use Classification with SAR Imagery
Authors: Soares Marinalva Dias , Dutra Luciano Vieira , Costa Gilson Alexandre Ostwald Pedro da , Feitosa Raul Queiroz , Negri Rogério Galante , Diaz Pedro M. A.
Published: March 2020

Per-point classification is a traditional method for remote sensing data classification, and for radar data in particular. Compared with optical data, the discriminative power of radar data is quite limited, for most applications. A way of trying to overcome these difficulties is to use Region-Based Classification (RBC), also referred to as Geographical Object-Based Image Analysis (GEOBIA). RBC methods first aggregate pixels into homogeneous objects, or regions, using a segmentation procedure. Moreover, segmentation is known to be an ill-conditioned problem because it admits multiple solutions, and a small change in the input image, or segmentation parameters, may lead to significant changes in the image partitioning. In this context, this paper proposes and evaluates novel approaches for SAR data classification, which rely on specialized segmentations, and on the combination of partial maps produced by classification ensembles. Such approaches comprise a meta-methodology, in the sense that they are independent from segmentation and classification algorithms, and optimization procedures. Results are shown that improve the classification accuracy from Kappa = 0.4 (baseline method) to a Kappa = 0.77 with the presented method. Another test site presented an improvement from Kappa = 0.36 to a maximum of 0.66 also with radar data.
Improving the Estimation of Water Level over Freshwater Ice Cover using Altimetry Satellite Active and Passive Observations
Authors: Ziyad Jawad , Goïta Kalifa , Magagi Ramata , Blarel Fabien , Frappart Frédéric
Published: March 2020

Owing to its temporal resolution of 10-day and its polar orbit allowing several crossings over large lakes, the US National Aeronautics and Space Administration (NASA) and the French Centre National d’Etudes Spatiales (CNES) missions including Topex/Poseidon, Jason-1/2/3 demonstrated strong capabilities for the continuous and long-term monitoring (starting in 1992) of large and medium-sized water bodies. However, the presence of heterogeneous targets in the altimeter footprint, such as ice cover in boreal areas, remains a major issue to obtain estimates of water level over subarctic lakes of similar accuracy as over other inland water bodies using satellite altimetry (i.e., R ≥ 0.9 and RMSE ≤ 10 to 20 cm when compared to in-situ water stages). In this study, we aim to automatically identify the Jason-2 altimetry measurements corresponding to open water, ice and transition (water-ice) to improve the estimations of water level during freeze and thaw periods using only the point measurements of open water. Four Canadian lakes were selected to analyze active (waveform parameters) and passive (brightness temperature) microwave data acquired by the Jason-2 radar altimetry mission: Great Slave Lake, Lake Athabasca, Lake Winnipeg, and Lake of the Woods. To determine lake surface states, backscattering coefficient and peakiness at Ku-band derived from the radar altimeter waveform and brightness temperature at 18.7 and 37 GHz measured by the microwave radiometer contained in the geophysical data records (GDR) of Jason-2 were used in two different unsupervised classification techniques to define the thresholds of discrimination between open water and ice measurements. K-means technique provided better results than hierarchical clustering based upon silhouette criteria and the Calinski-Harabz index. Thresholds of discrimination between ice and water were validated with the Normalized Difference Snow Index (NDSI) snow cover products of the MODIS satellite. The use of open water threshold resulted in improved water level estimation compared to in situ water stages, especially in the presence of ice. For the four lakes, the Pearson coefficient (r) increased on average from about 0.8 without the use of the thresholds to more than 0.90. The unbiased RMSE were generally lower than 20 cm when the threshold of open water was used and more than 22 cm over smaller lakes, without using the thresholds.
Recognizing Eruptions of Mount Etna through Machine Learning Using Multiperspective Infrared Images
Authors: Corradino Claudia , Ganci Gaetana , Cappello Annalisa , Bilotta Giuseppe , Calvari Sonia , Del Negro Ciro
Published: March 2020

Detecting, locating and characterizing volcanic eruptions at an early stage provides the best means to plan and mitigate against potential hazards. Here, we present an automatic system which is able to recognize and classify the main types of eruptive activity occurring at Mount Etna by exploiting infrared images acquired using thermal cameras installed around the volcano. The system employs a machine learning approach based on a Decision Tree tool and a Bag of Words-based classifier. The Decision Tree provides information on the visibility level of the monitored area, while the Bag of Words-based classifier detects the onset of eruptive activity and recognizes the eruption type as either explosion and/or lava flow or plume degassing/ash. Applied in real-time to each image of each of the thermal cameras placed around Etna, the proposed system provides two outputs, namely, visibility level and recognized eruptive activity status. By merging these outcomes, the monitored phenomena can be fully described from different perspectives to acquire more in-depth information in real time and in an automatic way.
Anomaly Detection for Urban Vehicle GNSS Observation with a Hybrid Machine Learning System
Authors: Xia Yan , Pan Shuguo , Meng Xiaolin , Gao Wang , Ye Fei , Zhao Qing , Zhao Xingwang
Published: March 2020

In urban areas, the accuracy and reliability of global navigation satellite systems (GNSS) vehicle positioning decline due to substantial non-line-of-sight (NLOS) signals and multipath effects. Recently, positioning enhancement approaches with supervised GNSS signal type classification based on 3D building model-aided labelling have received widespread attention. Despite the reduced computing costs and improved real-time performance, the strict requirements of 3D building models on accuracy and timeliness limit the popularization of the technology to some extent. Meanwhile, the diversity of anomalous observation sources is beyond the reach of NLOS/multipath detection methods. This paper attempts to construct an alternative framework for quality identification of GNSS observations combining clustering-based anomaly detection and supervised classification, in which the hierarchical density-based spatial clustering of applications with noise (HDBSCAN) algorithm is used to label the offline dataset as normal and anomalous observations without the aid of 3D building models, and the supervised classifier in the online system learns the classification rule for real-time anomaly detection. The experimental results based on the measured vehicle GPS/BeiDou data show that after excluding anomalous observations the single point positioning accuracy of the offline dataset is improved by 87.0%, 45.9%, and 69.6% in the east, north, and up directions, respectively, and the positioning accuracy of two online datasets is improved by 48.4%/45.7%, 39.6%/63.3%, and 49.6%/49.1% in the three directions. Through a large number of comparative experiments and discussion on key issues, it is certified that the proposed method is highly feasible and has great potential in the practical application of urban GNSS vehicle positioning.
Big Earth Observation Data Integration in Remote Sensing Based on a Distributed Spatial Framework
Authors: Cheng Yinyi , Zhou Kefa , Wang Jinlin , Yan Jining
Published: March 2020

The arrival of the era of big data for Earth observation (EO) indicates that traditional data management models have been unable to meet the needs of remote sensing data in big data environments. With the launch of the first remote sensing satellite, the volume of remote sensing data has also been increasing, and traditional data storage methods have been unable to ensure the efficient management of large amounts of remote sensing data. Therefore, a professional remote sensing big data integration method is sorely needed. In recent years, the emergence of some new technical methods has provided effective solutions for multi-source remote sensing data integration. This paper proposes a multi-source remote sensing data integration framework based on a distributed management model. In this framework, the multi-source remote sensing data are partitioned by the proposed spatial segmentation indexing (SSI) model through spatial grid segmentation. The designed complete information description system, based on International Organization for Standardization (ISO) 19115, can explain multi-source remote sensing data in detail. Then, the distributed storage method of data based on MongoDB is used to store multi-source remote sensing data. The distributed storage method is physically based on the sharding mechanism of the MongoDB database, and it can provide advantages for the security and performance of the preservation of remote sensing data. Finally, several experiments have been designed to test the performance of this framework in integrating multi-source remote sensing data. The results show that the storage and retrieval performance of the distributed remote sensing data integration framework proposed in this paper is superior. At the same time, the grid level of the SSI model proposed in this paper also has an important impact on the storage efficiency of remote sensing data. Therefore, the remote storage data integration framework, based on distributed storage, can provide new technical support and development prospects for big EO data.
A Distributed Strategy for Cooperative Autonomous Robots Using Pedestrian Behavior for Multi-Target Search in the Unknown Environment
Authors: Shi Haiyun , Li Jie , Li Zhi
Published: March 2020

Searching multiple targets with swarm robots is a realistic and significant problem. The goal is to search the targets in the minimum time while avoiding collisions with other robots. In this paper, inspired by pedestrian behavior, swarm robotic pedestrian behavior (SRPB) was proposed. It considered many realistic constraints in the multi-target search problem, including limited communication range, limited working time, unknown sources, unknown extrema, the arbitrary initial location of robots, non-oriented search, and no central coordination. The performance of different cooperative strategies was evaluated in terms of average time to find the first, the half, and the last source, the number of located sources and the collision rate. Several experiments with different target signals, fixed initial location, arbitrary initial location, different population sizes, and the different number of targets were implemented. It was demonstrated by numerous experiments that SRPB had excellent stability, quick source seeking, a high number of located sources, and a low collision rate in various search strategies.
Design and Analysis of Electromagnetic-Piezoelectric Hybrid Driven Three-Degree-of-Freedom Motor
Authors: Li Zheng , Guo Peng , Wang Zhe , Zhao Liang , Wang Qunjing
Published: March 2020

Multi-DOF movement actuators are widely used in industry, mainly in the fields of bionics and precision machining. With the non-stop improvement of modern-day industry, the requirements for the precision, integration and flexibility of multi-degree-of-freedom motion actuators in the industrial field have progressively increased. This paper presents a novel electromagnetic–piezoelectric hybrid driven three-degree-of-freedom motor. The driving method of the hybrid drive motor can be divided into electromagnetic driving and piezoelectric driving. The motor structure and working principle are analyzed. The structural parameters are obtained by modal analysis of the stators and rotor. The rationality of the stator structure is proved by using the transient analysis of the piezoelectric stators. The magnetic field characteristics of the motor are analyzed by both analytical method and the finite element method. The contact pressure and displacement between the piezoelectric stator and the rotor are analyzed by the analytical method. A motor drive model is established, which provides the basis for motor optimization design and control. Finally, a motor prototype and its test platform were built, and the experimental results are presented to verify the rationality of the motor design.
An Approximate Estimation Approach of Fault Size for Spalled Ball Bearing in Induction Motor by Tracking Multiple Vibration Frequencies in Current
Authors: Qiu Chidong , Wu Xinbo , Xu Changqing , Qiu Xiang , Xue Zhengyu
Published: March 2020

Fault size estimation is of great importance to bearing performance degradation assessment and life prediction. Until now, fault size estimation has generally been based on acoustic emission signals or vibration signals; an approach based on current signals has not yet been mentioned. In the present research, an approximate estimation approach based on current is introduced. The proposed approach is easy to implement for existing inverter-driven induction motors without complicated calculations and additional sensors, immune to external disturbances, and suitable for harsh conditions. Firstly, a feature transmission route from spall, to Hertzian forces, and then to friction torque is simulated based on a spall model and dynamic model of the bearing. Based on simulated results, the relation between spall size and the multiple characteristic vibration frequencies in friction torque is revealed. Secondly, the multiple characteristic vibration frequencies modulated in the current is investigated. Analysis results show that those frequencies modulated in the current are independent of each other, without spectrum overlap. Thirdly, to address the issue of which fault features modulated in the current are very weak, a fault-feature-highlighting approach based on reduced voltage frequency ratio is proposed. Finally, experimental tests were conducted. The obtained results validate that the proposed approach is feasible and effective for spall size estimation.
Deep Active Learning for Surface Defect Detection
Authors: Lv Xiaoming , Duan Fajie , Jiang Jia-Jia , Fu Xiao , Gan Lin
Published: March 2020

Most of the current object detection approaches deliver competitive results with an assumption that a large number of labeled data are generally available and can be fed into a deep network at once. However, due to expensive labeling efforts, it is difficult to deploy the object detection systems into more complex and challenging real-world environments, especially for defect detection in real industries. In order to reduce the labeling efforts, this study proposes an active learning framework for defect detection. First, an Uncertainty Sampling is proposed to produce the candidate list for annotation. Uncertain images can provide more informative knowledge for the learning process. Then, an Average Margin method is designed to set the sampling scale for each defect category. In addition, an iterative pattern of training and selection is adopted to train an effective detection model. Extensive experiments demonstrate that the proposed method can render the required performance with fewer labeled data.
Spatial–Semantic and Temporal Attention Mechanism-Based Online Multi-Object Tracking
Authors: Meng Fanjie , Wang Xinqing , Wang Dong , Shao Faming , Fu Lei
Published: March 2020

Multi-object tracking (MOT) plays a crucial role in various platforms. Occlusion and insertion among targets, complex backgrounds and higher real-time requirements increase the difficulty of MOT problems. Most state-of-the-art MOT approaches adopt the tracking-by-detection strategy, which relies on compute-intensive sliding windows or anchoring schemes to detect matching targets or candidates in each frame. In this work, we introduce a more efficient and effective spatial–temporal attention scheme to track multiple objects in various scenarios. Using a semantic-feature-based spatial attention mechanism and a novel Motion Model, we address the insertion and location of candidates. Some online-learned target-specific convolutional neural networks (CNNs) were used to estimate target occlusion and classify by adapting the appearance model. A temporal attention mechanism was adopted to update the online module by balancing current and history frames. Extensive experiments were performed on Karlsruhe Institute of Technologyand Toyota Technological Institute (KITTI) benchmarks and an Armored Target Tracking Dataset (ATTD) built for ground-armored targets. Experimental results show that the proposed method achieved outstanding tracking performance and met the actual application requirements.
Quantitative Infrared Thermography to Evaluate the Humidification of Lightweight Concrete
Authors: Barreira Eva , Almeida Ricardo M.S.F. , L. Simões Maria , Rebelo Daniela
Published: March 2020

Moisture is one of the major causes of problems in buildings, and it can compromise their performance. Infrared thermography (IRT) is a non-destructive testing technology that can be used to assess the humidification phenomenon and, thus, prevent some of the problems caused by moisture. The images obtained by IRT reflect the thermal patterns of the surface under study and can be evaluated using a quantitative approach, which allows not only the traditional visualization of the thermal patterns but also quantification of surface temperatures and/or their differences. The relevance of this work is related to the discussion of the strengths and weaknesses of several methods to quantitatively assess the humidification phenomenon using IRT. For that purpose, the partial humidification by the bottom surface of a lightweight concrete specimen was considered as a case study. To evaluate the thermal gradients, the evolution of the thermal imaging throughout the measurement period and the definition of the areas particularly affected by moisture, a methodology that included a pre-processing phase for data reduction, followed by a data processing phase, were implemented. In the data processing, different statistical and numerical methods were tested. The results of the statistical descriptive analysis highlighted the time variation of the surface temperature, both when considering the entire specimen and when considering only specific areas. The variability of the temperatures at certain moments of the experiment could be observed in the box-plot representation. The image subtraction proved to be an interesting technique to quantify the temperature differences if the first image was used as reference. A thermal index, TI, was proposed to assess the cooling rate. The index highlighted the initial instant when the effect of moisture on the surface temperature was detectable.
Timing and Modulation of Activity in the Lower Limb Muscles During Indoor Rowing: What Are the Key Muscles to Target in FES-Rowing Protocols?
Authors: Vieira Taian M. , Cerone Giacinto Luigi , Stocchi Costanza , Lalli Morgana , Andrews Brian , Gazzoni Marco
Published: March 2020

The transcutaneous stimulation of lower limb muscles during indoor rowing (FES Rowing) has led to a new sport and recreation and significantly increased health benefits in paraplegia. Stimulation is often delivered to quadriceps and hamstrings; this muscle selection seems based on intuition and not biomechanics and is likely suboptimal. Here, we sample surface EMGs from 20 elite rowers to assess which, when, and how muscles are activated during indoor rowing. From EMG amplitude we specifically quantified the onset of activation and silencing, the duration of activity and how similarly soleus, gastrocnemius medialis, tibialis anterior, rectus femoris, vastus lateralis and medialis, semitendinosus, and biceps femoris muscles were activated between limbs. Current results revealed that the eight muscles tested were recruited during rowing, at different instants and for different durations. Rectus and biceps femoris were respectively active for the longest and briefest periods. Tibialis anterior was the only muscle recruited within the recovery phase. No side differences in the timing of muscle activity were observed. Regression analysis further revealed similar, bilateral modulation of activity. The relevance of these results in determining which muscles to target during FES Rowing is discussed. Here, we suggest a new strategy based on the stimulation of vasti and soleus during drive and of tibialis anterior during recovery.
Real-Time Prediction of Rheological Properties of Invert Emulsion Mud Using Adaptive Neuro-Fuzzy Inference System
Authors: Alsabaa Ahmed , Gamal Hany , Elkatatny Salaheldin , Abdulraheem Abdulazeez
Published: March 2020

Tracking the rheological properties of the drilling fluid is a key factor for the success of the drilling operation. The main objective of this paper is to relate the most frequent mud measurements (every 15 to 20 min) as mud weight (MWT) and Marsh funnel viscosity (MFV) to the less frequent mud rheological measurements (twice a day) as plastic viscosity (PV), yield point (YP), behavior index (n), and apparent viscosity (AV) for fully automating the process of retrieving rheological properties. The adaptive neuro-fuzzy inference system (ANFIS) was used to develop new models to determine the mud rheological properties using real field measurements of 741 data points. The data were collected from 99 different wells during drilling operations of 12 ¼ inches section. The ANFIS clustering technique was optimized by using training to a testing ratio of 80% to 20% as 591 data points for training and 150 points, cluster radius value of 0.1, and 200 epochs. The results of the prediction models showed a correlation coefficient (R) that exceeded 0.9 between the actual and predicted values with an average absolute percentage error (AAPE) below 5.7% for the training and testing data sets. ANFIS models will help to track in real-time the rheological properties for invert emulsion mud that allows better control for the drilling operation problems.
Real-time Concealed Object Detection from Passive Millimeter Wave Images Based on the YOLOv3 Algorithm
Authors: Pang Lei , Liu Hui , Chen Yang , Miao Jungang
Published: March 2020

The detection of objects concealed under people’s clothing is a very challenging task, which has crucial applications for security. When testing the human body for metal contraband, the concealed targets are usually small in size and are required to be detected within a few seconds. Focusing on weapon detection, this paper proposes using a real-time detection method for detecting concealed metallic weapons on the human body applied to passive millimeter wave (PMMW) imagery based on the You Only Look Once (YOLO) algorithm, YOLOv3, and a small sample dataset. The experimental results from YOLOv3-13, YOLOv3-53, and Single Shot MultiBox Detector (SSD) algorithm, SSD-VGG16, are compared ultimately, using the same PMMW dataset. For the perspective of detection accuracy, detection speed, and computation resource, it shows that the YOLOv3-53 model had a detection speed of 36 frames per second (FPS) and a mean average precision (mAP) of 95% on a GPU-1080Ti computer, more effective and feasible for the real-time detection of weapon contraband on human body for PMMW images, even with small sample data.
Joint Tracking and Classification of Multiple Targets with Scattering Center Model and CBMeMBer Filter
Authors: Zhan Ronghui , Wang Liping , Zhang Jun
Published: March 2020

This paper deals with joint tracking and classification (JTC) of multiple targets based on scattering center model (SCM) and wideband radar observations. We first introduce an SCM-based JTC method, where the SCM is used to generate the predicted high range resolution profile (HRRP) with the information of the target aspect angle, and target classification is implemented through the data correlation of observed HRRP with predicted HRRPs. To solve the problem of multi-target JTC in the presence of clutter and detection uncertainty, we then integrate the SCM-based JTC method into the CBMeMBer filter framework, and derive a novel SCM-JTC-CBMeMBer filter with Bayesian theory. To further tackle the complex integrals’ calculation involved in targets state and class estimation, we finally provide the sequential Monte Carlo (SMC) implementation of the proposed SCM-JTC-CBMeMBer filter. The effectiveness of the presented multi-target JTC method is validated by simulation results under the application scenario of maritime ship surveillance.
Factors Affecting Training and Physical Performance in Recreational Endurance Runners
Authors: Boullosa Daniel , Esteve-Lanao Jonathan , Casado Arturo , Peyré-Tartaruga Leonardo A. , Gomes da Rosa Rodrigo , Del Coso Juan
Published: March 2020

Endurance running has become an immensely popular sporting activity, with millions of recreational runners around the world. Despite the great popularity of endurance running as a recreational activity during leisure time, there is no consensus on the best practice for recreational runners to effectively train to reach their individual objectives and improve physical performance in a healthy manner. Moreover, there are lots of anecdotal data without scientific support, while most scientific evidence on endurance running was developed from studies observing both recreational and professional athletes of different levels. Further, the transference of all this information to only recreational runners is difficult due to differences in the genetic predisposition for endurance running, the time available for training, and physical, psychological, and physiological characteristics. Therefore, the aim of this review is to present a selection of scientific evidence regarding endurance running to provide training guidelines to be used by recreational runners and their coaches. The review will focus on some key aspects of the training process, such as periodization, training methods and monitoring, performance prediction, running technique, and prevention and management of injuries associated with endurance running.
Off-Season Training Habits and BMI, Not Preseason Jump Measures, Are Associated with Time-Loss Injury in Female Collegiate Soccer Players
Authors: Brumitt Jason , Mattocks Alma , Engilis Amy , Sikkema Jill , Loew Jeremy
Published: March 2020

The primary purpose of this study was to determine the effectiveness of the standing long jump (SLJ) and the single-leg hop (SLH) tests to discriminate lower quadrant (low back and lower extremities) injury occurrence in female collegiate soccer players. The secondary purpose of this study was to determine associations between injury and off-season training habits or anthropometric measures. SLJ, SLH, and anthropometric measures were collected during a preseason screening clinic. Each subject completed a questionnaire providing demographic information and off-season training habits. Each athlete performed three SLJ and three SLH per leg. SLJ and SLH scores were not associated with an increased risk of a noncontact time-loss lower quadrant (LQ) injury. Athletes with a higher BMI or who reported less time training during the off-season were two times more likely to sustain an injury. Athletes who had both a higher body mass index (BMI) and lower off-season training habits were three times (relative risk = 3.1 (95% CI: 1.7, 5.5) p-value = 0.0001) more likely to sustain a noncontact time-loss lower quadrant injury. Preseason SLJ and SLH scores do not discriminate injury risk in female collegiate soccer players. Higher BMI and lower off-season training habits are associated with an increased risk of LQ injury.
ExerTrack—Towards Smart Surfaces to Track Exercises
Authors: Fu Biying , Jarms Lennart , Kirchbuchner Florian , Kuijper Arjan
Published: March 2020

The concept of the quantified self has gained popularity in recent years with the hype of miniaturized gadgets to monitor vital fitness levels. Smartwatches or smartphone apps and other fitness trackers are overwhelming the market. Most aerobic exercises such as walking, running, or cycling can be accurately recognized using wearable devices. However whole-body exercises such as push-ups, bridges, and sit-ups are performed on the ground and thus cannot be precisely recognized by wearing only one accelerometer. Thus, a floor-based approach is preferred for recognizing whole-body activities. Computer vision techniques on image data also report high recognition accuracy; however, the presence of a camera tends to raise privacy issues in public areas. Therefore, we focus on combining the advantages of ubiquitous proximity-sensing with non-optical sensors to preserve privacy in public areas and maintain low computation cost with a sparse sensor implementation. Our solution is the ExerTrack, an off-the-shelf sports mat equipped with eight sparsely distributed capacitive proximity sensors to recognize eight whole-body fitness exercises with a user-independent recognition accuracy of 93.5% and a user-dependent recognition accuracy of 95.1% based on a test study with 9 participants each performing 2 full sessions. We adopt a template-based approach to count repetitions and reach a user-independent counting accuracy of 93.6%. The final model can run on a Raspberry Pi 3 in real time. This work includes data-processing of our proposed system and model selection to improve the recognition accuracy and data augmentation technique to regularize the network.
Magnetized Particle Motion around Black Holes in Conformal Gravity: Can Magnetic Interaction Mimic Spin of Black Holes?
Authors: Haydarov Kamoliddin , Abdujabbarov Ahmadjon , Rayimbaev Javlon , Ahmedov Bobomurat
Published: March 2020

Magnetized particle motion around black holes in conformal gravity immersed in asymptotically uniform magnetic field has been studied. We have also analyzed the behavior of magnetic fields near the horizon of the black hole in conformal gravity and shown that with the increase of conformal parameters L and N the value of angular component of magnetic field at the stellar surface decreases. The maximum value of the effective potential corresponding to circular motion of the magnetized particle increases with the increase of conformal parameters. It is shown that in all cases of neutral, charged and magnetized particle collisions in the black hole environment the center-of-mass energy decreases with the increase of conformal parameters L and N. In the case of the magnetized and negatively charged particle collisions, the innermost collision point with the maximum center-of-mass energy comes closer to the central object due to the effects of the parameters of the conformal gravity. We have applied the results to the real astrophysical scenario when a pulsar treated as a magnetized particle is orbiting the super massive black hole (SMBH) Sgr A* in the center of our galaxy in order to obtain the estimation of magnetized compact object’s orbital parameter. The possible detection of pulsar in Sgr A* close environment can provide constraints on black hole parameters. Here we have shown that there is degeneracy between spin of SMBH and ambient magnetic field and consequently the interaction of magnetic field ∼ 10 2 Gauss with magnetic moment of magnetized neutron star can in principle mimic spin of Kerr black holes up to 0.6 .
Systematics, Ecology, and Host Switching: Attributes Affecting Emergence of the Lassa Virus in Rodents across Western Africa
Authors: Olayemi Ayodeji , Fichet-Calvet Elisabeth
Published: March 2020

Ever since it was established that rodents serve as reservoirs of the zoonotic Lassa virus (LASV), scientists have sought to answer the questions: which populations of rodents carry the virus? How do fluctuations in LASV prevalence and rodent abundance influence Lassa fever outbreaks in humans? What does it take for the virus to adopt additional rodent hosts, proliferating what already are devastating cycles of rodent-to-human transmission? In this review, we examine key aspects of research involving the biology of rodents that affect their role as LASV reservoirs, including phylogeography, demography, virus evolution, and host switching. We discuss how this knowledge can help control Lassa fever and suggest further areas for investigation.
Outbreak of Porcine Reproductive and Respiratory Syndrome Virus 1 in Taiwan
Authors: Lin Wei-Hao , Kaewprom Kraijak , Wang Sheng-Yuan , Lin Chuen-Fu , Yang Cheng-Yao , Chiou Ming-Tang , Lin Chao-Nan
Published: March 2020

Porcine reproductive and respiratory syndrome (PRRS) causes significant economic losses in the swine industry worldwide. The PRRS virus (PRRSV) can be divided into two species, PRRSV 1 (European) and PRRSV 2 (North American). In Taiwan, PRRSV 2 isolates are dominant and cause respiratory symptoms in nursing pigs. From October to November 2018, in a pig herd in central Taiwan, pregnant sows had abortions and stillbirths, and piglets suffered from respiratory disorders. Laboratory tests identified the presence of PRRSV 1 in serum from sows and suckling piglets in this scenario. The complete genome of the identified PRRSV 1 strain was genetically closely related to that of a European PRRSV vaccine strain (98.2%). This local European isolate is designated as PRRSV/NPUST-2789-3W-2/TW/2018 (NPUST2789). This report is the first to indicate an outbreak in Taiwan of a PRRSV 1 strain that shares a common evolutionary ancestor with the European PRRSV vaccine strain.
Foodborne Transmission and Clinical Symptoms of Honey Bee Viruses in Ants Lasius spp.
Authors: Schläppi Daniel , Chejanovsky Nor , Yañez Orlando , Neumann Peter
Published: March 2020

Emerging infectious diseases are often the products of host shifts, where a pathogen jumps from its original host to a novel species. Viruses in particular cross species barriers frequently. Acute bee paralysis virus (ABPV) and deformed wing virus (DWV) are viruses described in honey bees (Apis mellifera) with broad host ranges. Ants scavenging on dead honey bees may get infected with these viruses via foodborne transmission. However, the role of black garden ants, Lasius niger and Lasius platythorax, as alternative hosts of ABPV and DWV is not known and potential impacts of these viruses have not been addressed yet. In a laboratory feeding experiment, we show that L. niger can carry DWV and ABPV. However, negative-sense strand RNA, a token of virus replication, was only detected for ABPV. Therefore, additional L. niger colonies were tested for clinical symptoms of ABPV infections. Symptoms were detected at colony (fewer emerging workers) and individual level (impaired locomotion and movement speed). In a field survey, all L. platythorax samples carried ABPV, DWV-A and –B, as well as the negative-sense strand RNA of ABPV. These results show that L. niger and L. platythorax are alternative hosts of ABPV, possibly acting as a biological vector of ABPV and as a mechanical one for DWV. This is the first study showing the impact of honey bee viruses on ants. The common virus infections of ants in the field support possible negative consequences for ecosystem functioning due to host shifts.
Structural and Functional Characterization of the Secondary Mutation N126K Selected by Various HIV-1 Fusion Inhibitors
Authors: Yu Danwei , Su Yang , Ding Xiaohui , Zhu Yuanmei , Qin Bo , Chong Huihui , Cui Sheng , He Yuxian
Published: March 2020

Peptides derived from the C-terminal heptad repeat (CHR) region of HIV-1 gp41 is potent viral membrane fusion inhibitors, such as the first clinically approved peptide drug T20 and a group of newly-designed peptides. The resistance profiles of various HIV-1 fusion inhibitors were previously characterized, and the secondary mutation N126K in the gp41 CHR was routinely identified during the in vitro and in vivo selections. In this study, the functional and structural relevance of the N126K mutation has been characterized from multiple angles. First, we show that a single N126K mutation across several HIV-1 isolates conferred mild to moderate cross-resistances. Second, the N126K mutation exerted different effects on Env-mediated HIV-1 entry and cell-cell fusion. Third, the N126K mutation did not interfere with the expression and processing of viral Env glycoproteins, but it disrupted the Asn126-based glycosylation site in gp41. Fourth, the N126K mutation was verified to enhance the thermal stability of 6-HB conformation. Fifth, we determined the crystal structure of a 6-HB bearing the N126K mutation, which revealed the interhelical and intrahelical interactions underlying the increased thermostability. Therefore, our data provide new information to understand the mechanism of HIV-1 gp41-mediated cell fusion and its resistance mode to viral fusion inhibitors.
Tauopathy Analysis in P301S Mouse Model of Alzheimer Disease Immunized with DNA and MVA Poxvirus-Based Vaccines Expressing Human Full-Length 4R2N or 3RC Tau Proteins
Authors: García-Arriaza Juan , Marín María Q. , Merchán-Rubira Jesús , Mascaraque Sara M. , Medina Miguel , Ávila Jesús , Hernández Félix , Esteban Mariano
Published: March 2020

Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by a progressive memory loss and cognitive decline that has been associated with an accumulation in the brain of intracellular neurofibrillary tangles (NFTs) formed by hyperphosphorylated tau protein, and extracellular senile plaques formed by β-amyloid peptides. Currently, there is no cure for AD and after the failure of anti β-amyloid therapies, active and passive tau immunotherapeutic approaches have been developed in order to prevent, reduce or ideally reverse the disease. Vaccination is one of the most effective approaches to prevent diseases and poxviruses, particularly modified vaccinia virus Ankara (MVA), are one of the most promising viral vectors used as vaccines against several human diseases. Thus, we present here the generation and characterization of the first MVA vectors expressing human tau genes; the full-length 4R2N tau protein or a 3RC tau fragment containing 3 tubulin-binding motifs and the C-terminal region (termed MVA-Tau4R2N and MVA-Tau3RC, respectively). Both MVA-Tau recombinant viruses efficiently expressed the human tau 4R2N or 3RC proteins in cultured cells, being detected in the cytoplasm of infected cells and co-localized with tubulin. These MVA-Tau vaccines impacted the innate immune responses with a differential recruitment of innate immune cells to the peritoneal cavity of infected mice. However, no tau-specific T cell or humoral immune responses were detected in vaccinated mice. Immunization of transgenic P301S mice, a mouse model for tauopathies, with a DNA-Tau prime/MVA-Tau boost approach showed no significant differences in the hyperphosphorylation of tau, motor capacity and survival rate, when compared to non-vaccinated mice. These findings showed that a well-established and potent protocol of T and B cell activation based on DNA/MVA prime/boost regimens using DNA and MVA vectors expressing tau full-length 4R2N or 3RC proteins is not sufficient to trigger tau-specific T and B cell immune responses and to induce a protective effect against tauopathy in this P301S murine model. In the pursuit of AD vaccines, our results highlight the need for novel optimized tau immunogens and additional modes of presentation of tau protein to the immune system.
Better Adjuvants for Better Vaccines: Progress in Adjuvant Delivery Systems, Modifications, and Adjuvant–Antigen Codelivery
Authors: Wang Zhi-Biao , Xu Jing
Published: March 2020

Traditional aluminum adjuvants can trigger strong humoral immunity but weak cellular immunity, limiting their application in some vaccines. Currently, various immunomodulators and delivery carriers are used as adjuvants, and the mechanisms of action of some of these adjuvants are clear. However, customizing targets of adjuvant action (cellular or humoral immunity) and action intensity (enhancement or inhibition) according to different antigens selected is time-consuming. Here, we review the adjuvant effects of some delivery systems and immune stimulants. In addition, to improve the safety, effectiveness, and accessibility of adjuvants, new trends in adjuvant development and their modification strategies are discussed.
An Antibody Persistent and Protective Two rSsCLP-Based Subunit Cocktail Vaccine against Sarcoptes scabiei in a Rabbit Model
Authors: Shen Nengxing , Wei Wenrui , Chen Yuhang , Ren Yongjun , Xiong Lang , Tao Yuanyuan , Gu Xiaobin , Xie Yue , Peng Xuerong , Yang Guangyou
Published: March 2020

Scabies is a highly contagious disease caused by Sarcoptes scabiei which burrows into stratum corneum of host’s skin. In this study, after optimizing vaccination schedule, a vaccination trial is comprised of three test groups of rabbits (n = 10/group) by immunization with (1) rSsCLP5; (2) rSsCLP12; or (3) a mixture of rSsCLP5 and rSsCLP12, three biological replicates groups (n = 10/group) and three control groups (n = 10/group). Levels of specific IgG, total IgE and cytokines in sera were detected and histopathologically analyzed as indicators of vaccine effects. The results showed that 85% (17/20) of rabbits exhibited no detectable skin lesions of S. scabiei infestation in mixed protein groups compared to single protein groups with 75% (15/20) and 70% (14/20), respectively. Moreover, the deworming rates of mixed groups are increased by 10%–20% compared with that of single groups. Each of six groups immunized with rSsCLP displayed significant increases of specific IgG, total IgE, IL-10, and TNF-α. The degree of skin damage in test groups also significantly lower than that of control groups. Thus, purified rSsCLP5 and rSsCLP12 subunit cocktail vaccine induced robust immune protection and could significantly decrease mite populations to reduce the direct transmission between rabbits.
Design of an Epitope-Based Vaccine Ensemble for Animal Trypanosomiasis by Computational Methods
Authors: Michel-Todó Lucas , Bigey Pascal , Reche Pedro A , Pinazo María-Jesus , Gascón Joaquim , Alonso-Padilla Julio
Published: March 2020

African animal trypanosomiasis is caused by vector-transmitted parasites of the genus Trypanosoma. T. congolense and T. brucei brucei are predominant in Africa; T. evansi and T. vivax in America and Asia. They have in common an extracellular lifestyle and livestock tropism, which provokes huge economic losses in regions where vectors are endemic. There are licensed drugs to treat the infections, but adherence to treatment is poor and appearance of resistances common. Therefore, the availability of a prophylactic vaccine would represent a major breakthrough towards the management and control of the disease. Selection of the most appropriate antigens for its development is a bottleneck step, especially considering the limited resources allocated. Herein we propose a vaccine strategy based on multiple epitopes from multiple antigens to counteract the parasites´ biological complexity. Epitopes were identified by computer-assisted genome-wide screenings, considering sequence conservation criteria, antigens annotation and sub-cellular localization, high binding affinity to antigen presenting molecules, and lack of cross-reactivity to proteins in cattle and other breeding species. We ultimately provide 31 B-cell, 8 CD4 T-cell, and 15 CD8 T-cell epitope sequences from 30 distinct antigens for the prospective design of a genetic ensemble vaccine against the four trypanosome species responsible for African animal trypanosomiasis.
Identification and management of frailty in English primary care: a qualitative study of national policy
Authors: Alharbi Khulud , van Marwijk Harm , Reeves David , Blakeman Tom
Published: March 2020

Background

Policymakers are directing attention to addressing the needs of an ageing population. Since 2017, general practices in England have been contractually required to identify and code ‘frailty’ as a new clinical concept and, in doing so, support targeted management for this population with the aim of improving outcomes. However, embedding frailty policies into routine practice is not without challenges and little is currently known about the success of the programme.

Aim

To explore the implementation of a national policy on frailty identification and management in English primary care.

Design & setting

Qualitative study entailing interviews with primary care professionals in the North of England.

Method

Semi-structured interviews were conducted with GPs (n = 10), nurses (n = 6), practice managers (n = 3), and health advisors (n = 3). Normalisation process theory (NPT) and ‘system thinking’ provided sensitising frameworks to support data collection and analysis.

Results

Primary care professionals were starting to use the concept of frailty to structure care within practices and across organisations; however, there was widespread concern about the challenge of providing expanded care for the identified needs with existing resources. Concerns were also expressed around how best to identify the frail subpopulation and the limitations of current tools for this, and there was a professional reticence to use the term ‘frailty’ with patients.

Conclusion

Findings suggests that additional, focused resources and the development of a stronger evidence base are essential to facilitate professional engagement in policies to improve the targeted coding and management of frailty in primary care.
Exploring patients' and clinicians' experiences of video consultations in primary care: a systematic scoping review
Authors: Thiyagarajan Arun , Grant Calum , Griffiths Frances , Atherton Helen
Published: March 2020

Background

Video consultation (VC) is an emerging consultation mode in general practice. The challenges and benefits of implementing it are not necessarily realised until it is in use, and being experienced by patients and clinicians. To date, there has been no review of the evidence about how patients and clinicians experience VC in general practice.

Aim

The study aimed to explore both patients' and clinicians' experiences of VCs in primary care.

Design & setting

A systematic scoping review was carried out of empirical studies.

Method

All major databases were searched for empirical studies of any design, published from 1 January 2010 to 11 October 2018 in the English language. Studies were included where synchronous VCs occurred between a patient and a clinician in a primary care setting. Outcomes of interest related to experience of use. The quality of included studies were assessed. Findings were analysed using narrative synthesis.

Results

Seven studies were included in the review. Patients reported being satisfied with VC, describing reduced waiting times and travel costs as a benefit. For patients and clinicians, VC was not deemed appropriate for all presentations and all situations, and a face-to-face consultation was seen as preferable where this was possible.

Conclusion

The findings of this scoping review show that primary care patients and clinicians report both positive and negative experiences when using VCs, and these experiences are, to a certain extent, context dependent. VC is potentially more convenient for patients, but is not considered superior to a face-to-face consultation. Accounts of experience are useful in the planning and implementation of any VC service.