bluuebunny/crossref_metadata_2025 huggingface dataset:
A Criterion for Deciding the Number of Clusters in a Dataset Based on Data Depth
Authors: Baidari Ishwar , Patil Channamma
Published: July 2020

Clustering is a key method in unsupervised learning with various applications in data mining, pattern recognition and intelligent information processing. However, the number of groups to be formed, usually notated as [Formula: see text] is a vital parameter for most of the existing clustering algorithms as their clustering results depend heavily on this parameter. The problem of finding the optimal [Formula: see text] value is very challenging. This paper proposes a novel idea for finding the correct number of groups in a dataset based on data depth. The idea is to avoid the traditional process of running the clustering algorithm over a dataset for [Formula: see text] times and further, finding the [Formula: see text] value for a dataset without setting any specific search range for [Formula: see text] parameter. We experiment with different indices, namely CH, KL, Silhouette, Gap, CSP and the proposed method on different real and synthetic datasets to estimate the correct number of groups in a dataset. The experimental results on real and synthetic datasets indicate good performance of the proposed method.
Democratizing AI?
Authors: Wolf Christine T.
Published: July 2020

The societal implications of computing are far-reaching. Whether it is building technologies that might save lives in the medical sector, intuitive apps that can transform personal routines at scale, or enterprise systems that ease and enhance the everyday experiences of workers, many are drawn to computing because of its potential to improve the human experience. This article considers the relationship between technology and societal transformation by looking at the topic of accessibility.
Gender, diversity, and inclusion in open source communities
Authors: Fossatti Mariana
Published: July 2020

Hidden social hierarchies keep the doors of open source closed for some. How do we overcome them?
Fundamental Limits of Approximate Gradient Coding
Authors: Wang Sinong , Liu Jiashang , Shroff Ness
Published: July 2020

In the distributed graident coding problem, it has been established that, to exactly recover the gradient under s slow machines, the mmimum computation load (number of stored data partitions) of each worker is at least linear (s + 1), which incurs a large overhead when s is large [13]. In this paper, we focus on approximate gradient coding that aims to recover the gradient with bounded error ε. Theoretically, our main contributions are three-fold: (i) we analyze the structure of optimal gradient codes, and derive the information-theoretical lower bound of minimum computation load: O(log(n)/log(n/s)) for ε = 0 and d ≥ O(log(1/ε)/log(n/s)) for ε > 0, where d is the computation load, and ε is the error in the gradient computation; (ii) we design two approximate gradient coding schemes that exactly match such lower bounds based on random edge removal process; (iii) we implement our schemes and demonstrate the advantage of the approaches over the current fastest gradient coding strategies. The proposed schemes provide order-wise improvement over the state of the art in terms of computation load, and are also optimal in terms of both computation load and latency.
Inferring Streaming Video Quality from Encrypted Traffic
Authors: Bronzino Francesco , Schmitt Paul , Ayoubi Sara , Martins Guilherme , Teixeira Renata , Feamster Nick
Published: July 2020

Inferring the quality of streaming video applications is important for Internet service providers, but the fact that most video streams are encrypted makes it difficult to do so.We develop models that infer quality metrics (i.e., startup delay and resolution) for encrypted streaming video services. Our paper builds on previous work, but extends it in several ways. First, the models work in deployment settings where the video sessions and segments must be identified from a mix of traffic and the time precision of the collected traffic statistics is more coarse (e.g., due to aggregation). Second, we develop a single composite model that works for a range of different services (i.e., Netflix, YouTube, Amazon, and Twitch), as opposed to just a single service. Third, unlike many previous models, our models perform predictions at finer granularity (e.g., the precise startup delay instead of just detecting short versus long delays) allowing to draw better conclusions on the ongoing streaming quality. Fourth, we demonstrate the models are practical through a 16-month deployment in 66 homes and provide new insights about the relationships between Internet "speed" and the quality of the corresponding video streams, for a variety of services; we find that higher speeds provide only minimal improvements to startup delay and resolution.
Privacy-Utility Tradeoffs in Routing Cryptocurrency over Payment Channel Networks
Authors: Tang Weizhao , Wang Weina , Fanti Giulia , Oh Sewoong
Published: July 2020

As the adoption of cryptocurrencies grows to unprecedented levels, the scalability of blockchain technologies has become increasingly important. A major open question is whether cryptocurrencies are fundamentally able to support as much traffic as traditional, centralized solutions. One prominent approach for improving the scalability of blockchains is payment channel networks (PCNs) [1]. Instead of committing every transaction to the blockchain, a separate overlay network (called a PCN) is maintained. Each node represents a user, and each edge (or payment channel) represents pre-allocated funds that can be efficiently transacted between the two endpoints under a mutual agreement. Users can transact with each other by relaying payments over a sequence of channels.
Titanium dioxide nanoparticles exaggerate respiratory syncytial virus-induced airway epithelial barrier dysfunction
Authors: Smallcombe Carrie C. , Harford Terri J. , Linfield Debra T. , Lechuga Susana , Bokun Vladimir , Piedimonte Giovanni , Rezaee Fariba
Published: July 2020

Respiratory syncytial virus (RSV) is the leading cause of lower respiratory tract infections in children worldwide. While most develop a mild, self-limiting illness, some develop severe acute lower respiratory infection and persistent airway disease. Exposure to ambient particulate matter has been linked to asthma, bronchitis, and viral infection in multiple epidemiological studies. We hypothesized that coexposure to nanoparticles worsens RSV-induced airway epithelial barrier dysfunction. Bronchial epithelial cells were incubated with titanium dioxide nanoparticles (TiO2-NP) or a combination of TiO2-NP and RSV. Structure and function of epithelial cell barrier were analyzed. Viral titer and the role of reactive oxygen species (ROS) generation were evaluated. In vivo, mice were intranasally incubated with TiO2-NP, RSV, or a combination. Lungs and bronchoalveolar lavage (BAL) fluid were harvested for analysis of airway inflammation and apical junctional complex (AJC) disruption. RSV-induced AJC disruption was amplified by TiO2-NP. Nanoparticle exposure increased viral infection in epithelial cells. TiO2-NP induced generation of ROS, and pretreatment with antioxidant, N-acetylcysteine, reversed said barrier dysfunction. In vivo, RSV-induced injury and AJC disruption were augmented in the lungs of mice given TiO2-NP. Airway inflammation was exacerbated, as evidenced by increased white blood cell infiltration into the BAL, along with exaggeration of peribronchial inflammation and AJC disruption. These data demonstrate that TiO2-NP exposure exacerbates RSV-induced AJC dysfunction and increases inflammation by mechanisms involving generation of ROS. Further studies are required to determine whether NP exposure plays a role in the health disparities of asthma and other lung diseases, and why some children experience more severe airway disease with RSV infection.
Case Studies in Physiology: Sympathetic neural discharge patterns in a healthy young male during end-expiratory breath hold-induced sinus pause
Authors: Vermeulen Tyler D. , Shafer Brooke M. , Incognito Anthony V. , Nardone Massimo , Teixeira André L. , Millar Philip J. , Shoemaker J. Kevin , Foster Glen E.
Published: July 2020

We characterize the occurrence of a square-wave discharge pattern of efferent muscle sympathetic nerve activity during a sinus pause in a young healthy male. This discharge pattern comprised large recruited action potential clusters undetected at baseline that continuously discharged during the sinus pause. Notably, this discharge pattern was still contained within a single cardiac cycle.
Electronic Nose Technologies in Monitoring Black Tea Manufacturing Process
Authors: Sharmilan Tharaga , Premarathne Iresha , Wanniarachchi Indika , Kumari Sandya , Wanniarachchi Dakshika
Published: July 2020

“Tea” is a beverage which has a unique taste and aroma. The conventional method of tea manufacturing involves several stages. These are plucking, withering, rolling, fermentation, and finally firing. The quality parameters of tea (color, taste, and aroma) are developed during the fermentation stage where polyphenolic compounds are oxidized when exposed to air. Thus, controlling the fermentation stage will result in more consistent production of quality tea. The level of fermentation is often detected by humans as “first” and “second” noses as two distinct smell peaks appear during fermentation. The detection of the “second” aroma peak at the optimum fermentation is less consistent when decided by humans. Thus, an electronic nose is introduced to find the optimum level of fermentation detecting the variation in the aroma level. In this review, it is found that the systems developed are capable of detecting variation of the aroma level using an array of metal oxide semiconductor (MOS) gas sensors using different statistical and neural network techniques (SVD, 2-NM, MDM, PCA, SVM, RBF, SOM, PNN, and Recurrent Elman) successfully.
Dynamic Response of a Gantry Crane’s Beam Subjected to a Two-Axle Moving Trolley
Authors: Chen Qingrong , Cheng Wenming , Gao Lingchong , Du Run
Published: July 2020

In this paper, a novel moving load model for 2D crane systems of which the trolley has two axles is proposed. Based on this model, the dynamics of a 2D gantry crane, which is modelled as a simply supported Euler–Bernoulli beam carrying a two-axle trolley from which a single-pendulum payload is suspended, is studied. The proposed model was verified by comparing with two models in existing papers and can be considered as an extended version of comparative models. Then, the effect of the trolley’s axle base on the dynamic responses of the beam is studied. It can be observed that increasing the length of trolley’s axle base will decrease the deflection of the beam, and a larger initial swing angle will cause a larger deflection of the beam without controlling the swing of the payload.
The Optimization of Feature Selection Based on Chaos Clustering Strategy and Niche Particle Swarm Optimization
Authors: Duan Longzhen , Yang Shuqing , Zhang Dongbo
Published: July 2020

With the rapid increase of the data size, there are increasing demands for feature selection which has been a powerful tool to handle high-dimensional data. In this paper, we propose a novel feature selection of niche particle swarm optimization based on the chaos group, which is used for evaluating the importance of feature selection algorithms. An iterative algorithm is proposed to optimize the new model. It has been proved that solving the new model is equivalent to solving a NP problem with a flexible and adaptable norm regularization. First, the whole population is divided into two groups: NPSO group and chaos group. The two groups are iterated, respectively, and the global optimization is updated. Secondly, the cross-iteration of NPSO group and chaos group avoids the particles falling into the local optimization. Finally, three representative algorithms are selected to be compared with each other in 10 UCI datasets. The experimental results show that the feature selection performance of the algorithm is better than that of the comparison algorithm, and the classification accuracy is significantly improved.
Hydroxy-α-sanshool Possesses Protective Potentials on H2O2-Stimulated PC12 Cells by Suppression of Oxidative Stress-Induced Apoptosis through Regulation of PI3K/Akt Signal Pathway
Authors: Li Ruo-Lan , Zhang Qing , Liu Jia , Sun Jia-yi , He Li-Ying , Duan Hu-Xinyue , Peng Wei , Wu Chun-Jie
Published: July 2020

Zanthoxylum bungeanum pericarp is a commonly used herbal medicine in China with effects of anti-inflammatory and analgesic, improving learning and memory ability, while hydroxy-α-sanshool (HAS) is the most important active ingredient of Z. bungeanum pericarps. The purpose of this study was to investigate the neuroprotective effect of HAS and its related possible mechanisms using a H2O2-stimulated PC12 cell model. CCK-8 assay results showed that HAS had a significant protective effect on H2O2-stimulated PC12 cells without obvious cytotoxicity on normal PC12 cells. Flow cytometry and fluorescence microscope (DAPI staining and DCFH-DA staining) indicated that HAS could reduce the H2O2-induced apoptosis in PC12 cells via reduction of intracellular ROS and increase of mitochondrial membrane potential (MMP). Subsequently, results of malondialdehyde (MDA), superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GSH-Px) determination suggested that HAS could increase the enzyme activities of SOD, CAT, and GSH-Px whereas it could decrease the MDA contents in H2O2-stimulated PC12 cells. Furthermore, the western blotting assays showed that HAS could upregulate the expressions of p-PI3k, Akt, p-Akt, and Bcl-2, while it could downregulate the expressions of cleaved caspase-3 and Bax in H2O2-stimulated PC12 cells. Collectively, it could be concluded according to our results that HAS possesses protective potentials on H2O2-stimulated PC12 cells through suppression of oxidative stress-induced apoptosis via regulation of PI3K/Akt signal pathway.
Agarwood Alcohol Extract Ameliorates Isoproterenol-Induced Myocardial Ischemia by Inhibiting Oxidation and Apoptosis
Authors: Wang Canhong , Peng Deqian , Liu Yangyang , Yu Zhangxin , Guo Peng , Wei Jianhe
Published: July 2020

Agarwood is a traditional medicine used for treating some diseases, including painful and ischemic diseases. This study was carried out to investigate the potential cardioprotective effect of the whole-tree agarwood-inducing technique-produced agarwood alcohol extract (WTAAE) on isoproterenol- (ISO-) induced myocardial ischemia (MI) in rats and explore the underlying molecular mechanisms. Compared to the MI group, WTAAE pretreatment significantly improved ST wave abnormal-elevation, mitigated myocardial histological damage; decreased creatinine kinase (CK), lactate dehydrogenase (LDH), alanine transaminase (ALT), and aspartate transaminase (AST) levels; reduced hydrogen peroxide (H2O2) and lipid peroxide (LPO) production; and increased total antioxidant capacity (T-AOC) and catalase (CAT) activities. Moreover, agarwood alcohol extracts (AAEs) markedly enhanced the mRNA levels of Nrf2-ARE pathway, and Bcl-2 reduced the apoptotic Bax family mRNA expressions. In addition, the effect of WTAAE was greater than that of wild agarwood alcohol extract (WAAE) and burning-chisel-drilling agarwood alcohol extract (FBAAE). All of these data indicate that WTAAE exerted the protective effects of MI, and its mechanism was associated with upregulating Nrf2-ARE and suppressing Bcl-2 pathways.
Potential Anticancer Activity of Crude Ethanol, Ethyl Acetate, and Water Extracts of Ephedra foeminea on Human Osteosarcoma U2OS Cell Viability and Migration
Authors: Mpingirika Eric Zadok , El Hosseiny Ahmed , Bakheit Sheri Magdy Saleeb , Arafeh Rami , Amleh Asma
Published: July 2020

Medicinal plants are potential sources for a wide range of complex compounds with probable anticancer activity. Ephedra foeminea Forssk. (E. foeminea), a medicinal plant found in the Eastern Mediterranean, has recently been gaining popularity as a cancer remedy; there is, however, a paucity of empirical evidence supporting this claim. In this study, the effect of E. foeminea ethyl acetate, ethanol, and water crude extracts on viability, migratory ability, and the steady‐state mRNA levels of genes involved in these processes was, respectively, examined using MTT assay, wound healing assay, and reverse transcriptase PCR (RT‐PCR). The study concludes that all extracts significantly reduce human osteosarcoma U2OS percentage viability in a dose‐ and time‐dependent manner, with varying potencies. The least half‐maximal inhibitory concentration (IC50) was observed in the water extract after 48 h incubation (30.761 ± 1.4 μg/mL) followed by the ethyl acetate extract after 72 h incubation (80.35 ± 1.233 μg/mL) and finally the ethanol extract after 48 h incubation (97.499 ± 1.188 μg/mL). Ethanol extract significantly reduced U2OS percentage wound closure. On the other hand, both ethanol and water extracts considerably reduced the steady‐state mRNA expression of beta-catenin, promoting both cell proliferation and migration in osteosarcoma by regulating target genes. Additionally, E. foeminea showed no hemolytic activity. These effects suggest that E. foeminea decreases U2OS cell viability and migratory ability by modulating the expression of critical genes involved in regulating these processes and is likely cytocompatible with human erythrocytes.
Identification and Pathogenicity of Fungal Pathogens Associated with Stem End Rots of Avocado Fruits in Kenya
Authors: Wanjiku E. K. , Waceke J. W. , Wanjala B. W. , Mbaka J. N.
Published: July 2020

Losses associated with stem end rot (SER) of avocado fruits have been reported in all avocado growing regions of the world. In Kenya, mature avocado fruits present SER symptoms during storage and marketing, but the disease causal agent(s) has not been established. This study aimed to identify the fungal pathogen(s) associated with avocado SER in Kenya and evaluate its pathogenicity. Fungal isolates were collected from symptomatic avocado fruits from randomly selected orchards and major markets within Murang'a County, a major avocado growing region in Kenya, between September 2017 and March 2018. A total of 207 and 125 fungal isolates, recovered from orchards and major markets, respectively, were identified morphologically and further confirmed by molecular techniques. The identified isolates were Lasiodiplodia theobromae (39.8%), Neofusicoccum parvum (24.4%), Nectria pseudotrichia (18.4%), Fusarium solani (7.2%), F. oxysporum (5.1%), F. equiseti (3.9%), and Geotricum candidum (1.2%). Geotricum candidum was exclusively recovered from fruits from the market. In the pathogenicity test, L. theobromae, N. parvum, and N. pseudotrichia caused the most severe SER symptoms. Consequently, they were considered to be the major pathogens of SER of avocado fruits in Kenya. To our knowledge, this is the first report of SER pathogen of avocado fruits in Kenya. Given the significant contribution of avocado fruits to household income and foreign exchange in Kenya, this information is significant to further develop management strategies of postharvest loss of avocado fruits in Kenya.
Fatigue Driving Prediction on Commercial Dangerous Goods Truck Using Location Data: The Relationship between Fatigue Driving and Driving Environment
Authors: Niu Shifeng , Li Guiqiang
Published: July 2020

The approaches monitoring fatigue driving are studied because of the fact that traffic accidents caused by fatigue driving often have fatal consequences. This paper proposes a new approach to predict driving fatigue using location data of commercial dangerous goods truck (CDT) and driver’s yawn data. The proposed location data are from an existing dataset of a transportation company that was collected from 166 vehicles and drivers in an actual driving environment. Six different categories of the predictor set are considered as fatigue-related indexes including travel time, day of week, road type, continuous driving time, average velocity, and overall mileage. The driver’s yawn data are used as a proxy for ground truth for the classification algorithm. From the six different categories of the predictor set, we obtain a set of 17 predictor variables to train logistic regression, neural network, and random forest classifiers. Then, we evaluate the predictive performance of the classifiers based on three indexes: accuracy, F1-measure, and area under the ROC curve (AUROC). The results show that the random forest is more suitable for predicting fatigue driving using location data according to its best accuracy (74.18%), F1-measure (62.02%), and AUROC (0.8059). Finally, we analyze the relationship between fatigue driving and driving environment according to variable importance described by random forest. In summary, our results obviously exhibit the potential of location data for reducing the accident rate caused by fatigue driving in practice.
Nonperturbative and Thermal Dynamics of Confined Fields in Dual QCD
Authors: Chandola H. C. , Rawat Deependra Singh , Yadav Dinesh , Pandey H. C. , Dehnen H.
Published: July 2020

In order to study the detailed dynamics and associated nonperturbative features of QCD, a dual version of the color gauge theory based on a topologically viable homogeneous fibre bundle approach has been analysed taking into account its magnetic symmetry structure. In the dynamically broken phase of magnetic symmetry, the associated flux tube structure on a S2 sphere in the magnetically condensed state of the dual QCD vacuum has been analyzed for the profiles of the color electric field using flux quantization and stability conditions. The color electric field has its intimate association with the vector mode of the magnetically condensed QCD vacuum, and such field configurations have been analyzed to show that the color electric flux gets localized towards the poles for a large sphere case while it gets uniformly distributed for the small sphere case in the infrared sector of QCD. The critical flux tube densities have been computed for various couplings and are shown to be in agreement with that for lead-ion central collisions in the near infrared sector of QCD. The possible annihilation/unification of flux tubes under some typical flux tube density and temperature conditions in the magnetic symmetry broken phase of QCD has also been analyzed and shown to play an important role in the process of QGP formation. The thermal variation of the profiles of the color electic field is further investigated which indicates the survival of flux tubes even in the thermal domain that leads the possibility of the formation of some exotic states like QGP in the intermediate regime during the quark-hadron phase transition.
Time Function Model of Surface Subsidence Based on Inversion Analysis in Deep Soil Strata
Authors: Han Jihuan , Hu Chenchen , Zou Jiuqun
Published: July 2020

As a common geological disaster, surface subsidence caused by mining underground resources has always been a hot and difficult topic in the civil engineering field. Aimed at the shortcomings of existing time function models in predicting mining subsidence in deep soil strata, a more accurate and reasonable time function model, called the composite function model, was established based on an inverted analysis of measured data. The results showed that the composite function model could describe the whole subsidence process of a deep soil surface and agreed well with the measured data. The model parameters were calculated by specific formulas, which improved the reliability of the subsidence prediction results under different mining conditions. The new model provided important guiding significance for preventing subsidence geological disasters and determining the coal mining time under the buildings, the railways, and the water bodies in deep soil strata.
Evaluation of IL-29 in Euthyroid Patients with Graves’ Orbitopathy: A Preliminary Study
Authors: Falkowski Bogusz , Szczepanek-Parulska Ewelina , Sawicka-Gutaj Nadia , Krygier Aleksandra , Ruchala Marek
Published: July 2020

Background. The most frequent cause of hyperthyroidism is Graves’ disease (GD). Orbitopathy is the most prevalent and recognizable extrathyroidal manifestation of Graves’ disease with unrevealed pathogenesis. Interleukin 29 (IL-29) is a relatively newly discovered inflammatory cytokine. Thus, the aim of this study was to evaluate the relationship between IL-29 and Graves’ orbitopathy (GO) in euthyroid patients. Methods. Thirty-one euthyroid patients with Graves’ disease and with active GO [clinical activity score CAS≥3/7], seventeen euthyroid patients with GD but without GO, and seventy-two healthy control subjects (CS) matched for age and gender were enrolled in the study. The following parameters were evaluated in every participant: thyroid-related hormones and autoantibodies and inflammatory markers (white blood cells, hsCRP). ELISA assay was applied to measure the concentration of IL-29. Results. We found higher level of IL-29 in GO group in comparison with CS [165 (133-747) vs. 62 (62-217) pg/mL, p<0.001]. Furthermore, participants in the subgroup with GD with GO as compared with GD without GO had higher concentration of IL-29 [165 (133-747) vs. 62 (62-558) pg/mL, p=0.031]. The ROC analysis for IL-29 revealed IL-29 cut-off of 105 pg/mL (sensitivity 1.000 and specificity 0.597) as the best value significantly indicating the presence of GO in GD [area under the ROC curve (AUC): 0.739, 95% confidence interval (CI): 0.646-0.833, p<0.001]. Conclusions. The present study revealed for the first time an elevated level of IL-29 in the serum of patients with GD and GO that might suggest its involvement in the pathogenesis of GD ocular complications.
Improved Classification of White Blood Cells with the Generative Adversarial Network and Deep Convolutional Neural Network
Authors: Almezhghwi Khaled , Serte Sertan
Published: July 2020

White blood cells (leukocytes) are a very important component of the blood that forms the immune system, which is responsible for fighting foreign elements. The five types of white blood cells include neutrophils, eosinophils, lymphocytes, monocytes, and basophils, where each type constitutes a different proportion and performs specific functions. Being able to classify and, therefore, count these different constituents is critical for assessing the health of patients and infection risks. Generally, laboratory experiments are used for determining the type of a white blood cell. The staining process and manual evaluation of acquired images under the microscope are tedious and subject to human errors. Moreover, a major challenge is the unavailability of training data that cover the morphological variations of white blood cells so that trained classifiers can generalize well. As such, this paper investigates image transformation operations and generative adversarial networks (GAN) for data augmentation and state-of-the-art deep neural networks (i.e., VGG-16, ResNet, and DenseNet) for the classification of white blood cells into the five types. Furthermore, we explore initializing the DNNs’ weights randomly or using weights pretrained on the CIFAR-100 dataset. In contrast to other works that require advanced image preprocessing and manual feature extraction before classification, our method works directly with the acquired images. The results of extensive experiments show that the proposed method can successfully classify white blood cells. The best DNN model, DenseNet-169, yields a validation accuracy of 98.8%. Particularly, we find that the proposed approach outperforms other methods that rely on sophisticated image processing and manual feature engineering.
Vehicle Logo Recognition with Small Sample Problem in Complex Scene Based on Data Augmentation
Authors: Ke Xiao , Du Pengqiang
Published: July 2020

Automatic identification for vehicles is an important topic in the field of Intelligent Transportation Systems (ITS), and the vehicle logo is one of the most important characteristics of a vehicle. Therefore, vehicle logo detection and recognition are important research topics. Because of the problems that the area of a vehicle logo is too small to be detected and the dataset is too small to train for complex scenes, considering the speed of recognition and the robustness to complex scenes, we use deep learning methods which are based on data optimization for vehicle logo in complex scenes. We propose three augmentation strategies for vehicle logo data: cross-sliding segmentation method, small frame method, and Gaussian Distribution Segmentation method. For the problem of small sample size, we use cross-sliding segmentation method, which can effectively increase the amount of data without changing the aspect ratio of the original vehicle logo image. To expand the area of the logos in the images, we develop the small frame method which improves the detection results of the small area vehicle logos. In order to enrich the position diversity of vehicle logo in the image, we propose Gaussian Distribution Segmentation method, and the result shows that this method is very effective. The F1 value of our method in the YOLO framework is 0.7765, and the precision is greatly improved to 0.9295. In the Faster R-CNN framework, the F1 value of our method is 0.7799, which is also better than before. The results of experiments show that the above optimization methods can better represent the features of the vehicle logos than the traditional method, and the experimental results have been improved.
Gnathic Bones and Hyperparathyroidism: A Review on the Metabolic Bony Changes Affecting the Mandible and Maxilla in case of Hyperparathyroidism
Authors: Ibrahem Hazim Mahmoud
Published: July 2020

Parathyroid glands secrete the parathyroid hormone that plays an essential role in bone remodeling. Excessive production of parathyroid hormone causes a common metabolic bone disorder known as hyperparathyroidism that is classified into primary, secondary, or tertiary. In hyperparathyroidism, the late bony complication is manifested as a giant cell osteolytic lesion called “brown tumor.” Primary hyperparathyroidism is usually a sporadic disorder, but in minority of cases it occurs in inherited forms, and one of these forms is the hyperparathyroidism-jaw tumor syndrome, which is characterized by primary hyperparathyroidism and ossifying fibroma in the mandible and/or maxilla.
Adaptive Output Feedback Control for the Trajectory Tracking of High-Speed Trains with Disturbance Uncertainties on the Basis of Neural Network Observers
Authors: Liu Yang , Li Weidong
Published: July 2020

The dynamic model of high-speed trains (HSTs) is nonlinear and uncertain; hence, with the decrease in the running interval of HSTs, an accurate and safe train operation control algorithm is required. In this study, an adaptive output feedback trajectory tracking control method for HSTs is proposed on the basis of neural network observers. The proposed method aims to solve problems, such as the immeasurable speed, model parameter disturbance, and unknown external disturbance of HSTs. In this method, a neural network adaptive observer is designed to estimate the velocity of an HST. Another neural network model is used to approximate the model uncertainties. Moreover, a robust controller is constructed by considering the train position and velocity tracking errors. In the proposed observer/controller, the bound function of estimator errors is introduced to increase the accuracy and safety of the tracking system. Furthermore, the adaptive update value of the neural networks, output weights, and bound function are performed online. All adaptive algorithms and the observer/controller are synthesized in nonlinear control systems. The error signals of the closed-loop trajectory tracking system are uniformly and eventually bounded through a formal proof on the basis of the Lyapunov methods. Simulation examples illustrate that the proposed controller is robust and has excellent tracking accuracy for system model parameter and external disturbance.
Code Design for Moving Target-Detecting Radar in Nonhomogeneous Signal-Dependent Clutter
Authors: Zhang Jindong , Du Yin , Yan He
Published: July 2020

In this paper, we investigate the code design problem of improving the detection performance of a moving target in the presence of nonhomogeneous signal-dependent clutter for moving target-detecting (MTD) radar systems. The optimization metric is constructed based on the signal to clutter and noise ratio (SCNR) of interpulse matched filtering. Under the frameworks of cyclic and majorization-minimization algorithms, we propose a novel algorithm, named CMMCODE, to tackle the code design optimization problem in the case of unknown precise target Doppler information and nonhomogeneous clutter. In the white-noise case, the simplified algorithm is also given based on CMMCODE algorithm. The presented algorithm is computationally efficient and convergent. Numerical examples show the effectiveness of the proposed algorithms.
A SAR Image Target Recognition Approach via Novel SSF-Net Models
Authors: Wang Wei , Zhang Chengwen , Tian Jinge , Ou Jianping , Li Ji
Published: July 2020

With the wide application of high-resolution radar, the application of Radar Automatic Target Recognition (RATR) is increasingly focused on how to quickly and accurately distinguish high-resolution radar targets. Therefore, Synthetic Aperture Radar (SAR) image recognition technology has become one of the research hotspots in this field. Based on the characteristics of SAR images, a Sparse Data Feature Extraction module (SDFE) has been designed, and a new convolutional neural network SSF-Net has been further proposed based on the SDFE module. Meanwhile, in order to improve processing efficiency, the network adopts three methods to classify targets: three Fully Connected (FC) layers, one Fully Connected (FC) layer, and Global Average Pooling (GAP). Among them, the latter two methods have less parameters and computational cost, and they have better real-time performance. The methods were tested on public datasets SAR-SOC and SAR-EOC-1. The experimental results show that the SSF-Net has relatively better robustness and achieves the highest recognition accuracy of 99.55% and 99.50% on SAR-SOC and SAR-EOC-1, respectively, which is 1% higher than the comparison methods on SAR-EOC-1.
Experimental Study on Fatigue Performance of Negative Bending Moment of Steel‐Concrete Continuous Composite Box Girder
Authors: Gao Pu , Li Kuan , Zheng Yuanxun
Published: July 2020

The experimental work presents results on the fatigue performance of composite beams in the negative moment region and the changes of stiffness and deformation of composite beams under repeated loads; fatigue tests were carried out on two double‐layer composite beams. The fatigue performance of composite beams with different reinforcement ratios under complete shear connection and the variation of deflection, strain of the reinforcement, strain of steel beam, and crack growth under fatigue load were obtained. The results showed that the fatigue resistance performance of concrete slab with low reinforcement ratio was much lower than that of concrete slab with high reinforcement ratio whereas, under the fatigue load, the stress of the welding nail in the negative moment region was small and the slip was almost negligible. The degradation of stiffness and the development of cracks were mainly due to the degradation of bond‐slip between the concrete and reinforcement. The fatigue failure mode was the fracture of the upper reinforcement in negative moment region. The results obtained in this study are helpful in the design of composite beam.
Association of Lifestyle and Body Composition on Risk Factors of Cardiometabolic Diseases and Biomarkers in Female Adolescents
Authors: Miranda Valter Paulo Neves , dos Santos Amorim Paulo Roberto , Bastos Ronaldo Rocha , Canabrava Karina Lúcia Ribeiro , Júnior Márcio Vidigal Miranda , Faria Fernanda Rocha , do Carmo Castro Franceschini Sylvia , do Carmo Gouveia Peluzio Maria , Priore Silvia Eloiza
Published: July 2020

Background. Female adolescents are considered a risk group for cardiometabolic disease due to their lifestyle (LS). Objective. To evaluate the association between LS classes and body composition groups with cardiometabolic disease risk factors and pro- and anti-inflammatory biomarkers in female adolescents. Methods. This cross-sectional study was carried out with female adolescents aged 14 to 19 years, from Viçosa-MG, Brazil. Latent class analysis assessed LS classes. Kinanthropometric measurements were taken together with the body fat percentage (BF%), being analyzed by the Dual Energy X-ray Absorptiometry (DEXA) equipment. Blood pressure and biochemical parameters were analyzed in the Health Division of the Federal University of Viçosa. The pro- and anti-inflammatory biomarkers were analyzed using Luminex technology. Associations with biomarkers were estimated by multiple linear regression. Results. 405 female adolescents were evaluated. The majority, 82.57%, 72.90%, and 65.31%, were classified as inactive by the number of steps, with high screen and cell phone time, respectively. Furthermore, 41.55% did meet the minimum of 60 minutes of moderate-to-vigorous physical activity (MVPA) and 54.69% had high values of BF% (DEXA). The “Sedentary & Inactive LS” class together with the high levels of weight and BF% were associated with increased levels of blood pressure, lipid profile, and uric acid. It was also found that “Inactive & Sedentary LS”, high BF%, insulin resistance, and ultra-sensitive C-reactive protein were associated with the concentrations of proinflammatory biomarkers of tumor necrosis factor-α, interleukin-6, and leptin. Conclusion. We concluded that female adolescents with overweight/obese and high BF% presented higher values of anthropometric indicators, levels of blood pressure, concentration of uric acid and hs-CRP, and lower concentration of HDL. Inactive and Sedentary lifestyle of these girls, along with excess body fat, insulin resistance, and higher concentrations of hs-CRP were associated with the higher concentration proinflammatory markers.
Neoepitopes as Difference Makers for General Cancer Vaccines?
Authors: Filderman Jessica N. , Storkus Walter J.
Published: July 2020

The cancer mutanome has been associated with disease prognosis as well as response to interventional immunotherapy and provides a substrate for development of personalized vaccines targeting tumor neoepitopes. Recent findings suggest that neoantigen-based vaccines may represent general interventional approaches for patients with solid cancers, regardless of their inherent mutational burden.

See related article by Fang et al., p. 4511
The ERG-Regulated LINC00920 Promotes Prostate Cancer Cell Survival via the 14-3-3ϵ–FOXO Pathway
Authors: Angeles Arlou Kristina , Heckmann Doreen , Flosdorf Niclas , Duensing Stefan , Sültmann Holger
Published: July 2020

Numerous noncoding transcripts have been reported to correlate with cancer development and progression. Nevertheless, there remains a paucity of long noncoding RNAs (lncRNA) with well-elucidated functional roles. Here, we leverage the International Cancer Genome Consortium-Early Onset Prostate Cancer transcriptome and identify the previously uncharacterized lncRNA LINC00920 to be upregulated in prostate tumors. Phenotypic characterization of LINC00920 revealed its positive impact on cellular proliferation, colony formation, and migration. We demonstrate that LINC00920 transcription is directly activated by ERG, an oncogenic transcription factor overexpressed in 50% of prostate cancers. Chromatin isolation by RNA purification-mass spectrometry revealed the interaction of LINC00920 with the 14-3-3ϵ protein, leading to enhanced sequestration of tumor suppressive FOXO1. Altogether, our results provide a rationale on how ERG overexpression, partly by driving LINC00920 transcription, could confer survival advantage to prostate cancer cells and potentially prime PTEN-intact prostate cells for cellular transformation through FOXO inactivation.

Implications:

The study describes a novel lncRNA-mediated mechanism of regulating the FOXO signaling pathway and provides additional insight into the role of ERG in prostate cancer cells.
The Hoarding Phenomenon in Schizophrenia Spectrum Disorders
Authors: Schou Marie Lundholm , Urfer-Parnas Annick , Parnas Josef
Published: July 2020

The presence of hoarding behavior among patients with schizophrenia has been known for more than a century. Nevertheless, only a few studies have examined the hoarding phenomenon among patients with schizophrenia, and no studies have examined the potential motivation. Hoarding disorder became a separate diagnosis in DSM-5. Studies about hoarding disorder use primarily quantitative approaches (e.g., scales, self-administered questionnaires, and structured interviews) when assessing the patients. The main objectives of this study were to examine the meaning of hoarding for patients with schizophrenia spectrum disorders and to explore associated psychopathology which may motivate the hoarding. We have examined 13 patients diagnosed with schizophrenia spectrum disorders showing hoarding behavior. The patients underwent semistructured, narrative interviews about the following domains: the time of onset of hoarding, the meaning of hoarding and associated psychopathology, the presence of insight into hoarding, and problematic issues about discarding. The study shows that hoarding begins very early in life for these patients and can be motivated by various psychopathologies. Diminished sense of basic self and transitivistic experiences were mentioned by several patients as motivations for collecting objects. Furthermore, the difficulty with discarding reflected a huge ambivalence and ruminations. Our findings suggest that thorough and comprehensive assessments of people with hoarding symptoms are necessary to assure the correct diagnosis and to understand the patient. An increased knowledge of the patients’ motivations for hoarding behavior may also be useful for the treating personnel and may prevent social adversities. We think it is questionable that in the DSM-5 hoarding is not mentioned in the description of schizophrenia but has been elevated to a separate disorder.
Two Amino Acids Proximate to the Renin Cleavage Site of Human Angiotensinogen Do Not Affect Blood Pressure and Atherosclerosis in Mice—Brief Report
Authors: Wu Chia-Hua , Wu Congqing , Howatt Deborah A. , Moorleghen Jessica J. , Cassis Lisa A. , Daugherty Alan , Lu Hong S.
Published: July 2020

Objective:

Renin cleavage of angiotensinogen has species specificity. As the residues at positions 11 and 12 are different between human angiotensinogen and mouse angiotensinogen, we determined whether these 2 residues in angiotensinogen affect renin cleavage and angiotensin II-mediated blood pressure regulation and atherosclerosis using an adenoassociated viral approach for manipulating angiotensinogen in vivo.

Approach and Results:

Hepatocyte-specific angiotensinogen deficient (hepAGT
−/−
) mice in an LDL receptor–deficient background were infected with adenoassociated virals containing a null insert, human angiotensinogen, or mouse angiotensinogen expressing the same residues of the human protein at positions 11 and 12 (mouse angiotensinogen [L11V;Y12I]). Expression of human angiotensinogen in hepAGT
−/−
mice led to high plasma human angiotensinogen concentrations without changes in plasma endogenous mouse angiotensinogen, plasma renin concentrations, blood pressure, or atherosclerosis. This is consistent with human angiotensinogen not being cleaved by mouse renin. To determine whether the residues at positions 11 and 12 in human angiotensinogen lead to the inability of mouse renin to cleave human angiotensinogen, hepAGT
−/−
mice were injected with adenoassociated viral vector encoding mouse angiotensinogen (L11V;Y12I). Expression of mouse angiotensinogen (L11V;Y12I) in hepAGT
−/−
mice resulted in increased plasma mouse angiotensinogen concentrations, reduced renin concentrations, and increased renal AngII concentrations that were comparable to their concentrations in hepAGT
+/+
mice. This mouse angiotensinogen variant increased blood pressure and atherosclerosis in hepAGT
−/−
mice to the magnitude of hepAGT
+/+
mice.

Conclusions:

Replacement of L11 and Y12 to V11 and I12, respectively, in mouse angiotensinogen does not affect renin cleavage, blood pressure, and atherosclerosis in LDL receptor–deficient mice.
Nicotine Modulates CTSS (Cathepsin S) Synthesis and Secretion Through Regulating the Autophagy-Lysosomal Machinery in Atherosclerosis
Authors: Ni Huaner , Xu Shuang , Chen Hangwei , Dai Qiuyan
Published: July 2020

Objective:

Increased CTSS (cathepsin S) has been reported to play a critical role in atherosclerosis progression. Both CTSS synthesis and secretion are essential for exerting its functions. However, the underlying mechanisms contributing to CTSS synthesis and secretion in atherosclerosis remain unclear.

Approach and Results:

In this study, we showed that nicotine activated autophagy and upregulated CTSS expression in vascular smooth muscle cells and in atherosclerotic plaques. Western blotting and immunofluorescent staining showed that nicotine inhibited the mTORC1 (mammalian target of rapamycin complex 1) activity, promoted the nuclear translocation of TFEB (transcription factor EB), and upregulated the expression of CTSS. Chromatin immunoprecipitation-qualificative polymerase chain reaction, electrophoretic mobility shift assay, and luciferase reporter assay further demonstrated that TFEB directly bound to the
CTSS
promoter. mTORC1 inhibition by nicotine or rapamycin promoted lysosomal exocytosis and CTSS secretion. Live cell assays and IP-MS (immunoprecipitation-mass spectrometry) identified that the interactions involving Rab10 (Rab10, member RAS oncogene family) and mTORC1 control CTSS secretion. Nicotine promoted vascular smooth muscle cell migration by upregulating CTSS, and CTSS inhibition suppressed nicotine-induced atherosclerosis in vivo.

Conclusions:

We concluded that nicotine mediates CTSS synthesis and secretion through regulating the autophagy-lysosomal machinery, which offers a potential therapeutic target for atherosclerosis treatment.
Acquired von Willebrand Syndrome Hiding Inherited von Willebrand Disease Can Explain Severe Bleeding in Patients With Aortic Stenosis
Authors: Casonato Alessandra , Galletta Eva , Cella Giuseppe , Barbon Giovanni , Daidone Viviana
Published: July 2020

Objective:

Aortic stenosis may be complicated by an acquired von Willebrand syndrome that rarely causes significant bleeding, raising the question of why it does so in a few cases. To seek an explanation, we studied 5 severe bleeder aortic stenosis patients in a cohort of 49 patients, using the flowchart for inherited von Willebrand disease.

Approach and Results:

All 5 patients were lacking in large and intermediate VWF (von Willebrand factor) multimers, 3 had reduced plasma and platelet VWF levels, and none showed PFA100 closure. Two patients (those with most multimers missing) also had a short VWF half-life. Genetic analyses on the 3 patients with reduced platelet VWF levels revealed that one carried both the c.1164C>G and the c.7880G>A mutations, and another carried the c.3390C>T mutation, while the third had one of the 2 VWF alleles relatively less expressed than the other (25% versus 75%). No genetic alterations emerged in the other 2 patients. Successful replacement of the stenotic aortic valve, performed in the 2 patients with VWF mutations, did not correct their abnormal VWF multimer picture—unlike what happened in the aortic stenosis patients without bleeding symptoms.

Conclusions:

Our findings suggest that acquired von Willebrand syndrome can develop in patients with hitherto-undiagnosed inherited von Willebrand disease. Since von Willebrand disease is the most common bleeding disorder, this possibility should be considered in aortic stenosis patients—especially those with a more severe bleeding history and more disrupted VWF laboratory patterns—because they risk hemorrhage during aortic valve replacement.