bluuebunny/crossref_metadata_2025 huggingface dataset:
17β-Estradiol Delivered in Eye Drops: Evidence of Impact on Protein Networks and Associated Biological Processes in the Rat Retina through Quantitative Proteomics
Authors: Prokai Laszlo , Zaman Khadiza , Nguyen Vien , Prokai-Tatrai Katalin
Published: January 2020

To facilitate the development of broad-spectrum retina neuroprotectants that can be delivered through topical dosage forms, this proteomics study focused on analyzing target engagements through the identification of functional protein networks impacted after delivery of 17β-estradiol in eye drops. Specifically, the retinae of ovariectomized Brown Norway rats treated with daily eye drops of 17β-estradiol for three weeks were compared to those of vehicle-treated ovariectomized control animals. We searched the acquired raw data against a composite protein sequence database by using Mascot, as well as employed label-free quantification to detect changes in protein abundances. Our investigation using rigorous validation criteria revealed 331 estrogen-regulated proteins in the rat retina (158 were up-regulated, while 173 were down-regulated by 17β-estradiol delivered in eye drops). Comprehensive pathway analyses indicate that these proteins are relevant overall to nervous system development and function, tissue development, organ development, as well as visual system development and function. We also present 18 protein networks with associated canonical pathways showing the effects of treatments for the detailed analyses of target engagements regarding potential application of estrogens as topically delivered broad-spectrum retina neuroprotectants. Profound impact on crystallins is discussed as one of the plausible neuroprotective mechanisms.
Polyelectrolyte Polysaccharide–Gelatin Complexes: Rheology and Structure
Authors: Derkach Svetlana R. , Kuchina Yuliya A. , Kolotova Daria S. , Voron’ko Nikolay G.
Published: January 2020

General features of rheological properties and structural peculiarities of polyelectrolyte polysaccharide–gelatin complexes were discussed in this paper. Experimental results were obtained for typical complexes, such as κ-carrageenan–gelatin, chitosan–gelatin and sodium alginate–gelatin complexes. A rheological method allows us to examine the physical state of a complex in aqueous phase and the kinetics of the sol–gel transition and temperature dependences of properties as a result of structural changes. The storage modulus below the gelation temperature is constant, which is a reflection of the solid-like state of a material. The gels of these complexes are usually viscoplastic media. The quantitative values of the rheological parameters depend on the ratio of the components in the complexes. The formation of the structure as a result of strong interactions of the components in the complexes was confirmed by UV and FTIR data and SEM analysis. Interaction with polysaccharides causes a change in the secondary structure of gelatin, i.e., the content of triple helices in an α-chain increases. The joint analysis of the structural and rheological characteristics suggests that the formation of additional junctions in the complex gel network results in increases in elasticity and hardening compared with those of the native gelatin.
Zwitterion Co-Polymer PEI-SBMA Nanofiltration Membrane Modified by Fast Second Interfacial Polymerization
Authors: Chiao Yu-Hsuan , Patra Tanmoy , Belle Marie Yap Ang Micah , Chen Shu-Ting , Almodovar Jorge , Qian Xianghong , Wickramasinghe S. Ranil , Hung Wei-Song , Huang Shu-Hsien , Chang Yung , Lai Juin-Yih
Published: January 2020

Nanofiltration membranes have evolved as a promising solution to tackle the clean water scarcity and wastewater treatment processes with their low energy requirement and environment friendly operating conditions. Thin film composite nanofiltration membranes with high permeability, and excellent antifouling and antibacterial properties are important component for wastewater treatment and clean drinking water production units. In the scope of this study, thin film composite nanofiltration membranes were fabricated using polyacrylonitrile (PAN) support and fast second interfacial polymerization modification methods by grafting polyethylene amine and zwitterionic sulfobutane methacrylate moieties. Chemical and physical alteration in structure of the membranes were characterized using methods like ATR-FTIR spectroscopy, XPS analysis, FESEM and AFM imaging. The effects of second interfacial polymerization to incorporate polyamide layer and ‘ion pair’ characteristics, in terms of water contact angle and surface charge analysis was investigated in correlation with nanofiltration performance. Furthermore, the membrane characteristics in terms of antifouling properties were evaluated using model protein foulants like bovine serum albumin and lysozyme. Antibacterial properties of the modified membranes were investigated using E. coli as model biofoulant. Overall, the effect of second interfacial polymerization without affecting the selectivity layer of nanofiltration membrane for their potential large-scale application was investigated in detail.
RADet: Refine Feature Pyramid Network and Multi-Layer Attention Network for Arbitrary-Oriented Object Detection of Remote Sensing Images
Authors: Li Yangyang , Huang Qin , Pei Xuan , Jiao Licheng , Shang Ronghua
Published: January 2020

Object detection has made significant progress in many real-world scenes. Despite this remarkable progress, the common use case of detection in remote sensing images remains challenging even for leading object detectors, due to the complex background, objects with arbitrary orientation, and large difference in scale of objects. In this paper, we propose a novel rotation detector for remote sensing images, mainly inspired by Mask R-CNN, namely RADet. RADet can obtain the rotation bounding box of objects with shape mask predicted by the mask branch, which is a novel, simple and effective way to get the rotation bounding box of objects. Specifically, a refine feature pyramid network is devised with an improved building block constructing top-down feature maps, to solve the problem of large difference in scales. Meanwhile, the position attention network and the channel attention network are jointly explored by modeling the spatial position dependence between global pixels and highlighting the object feature, for detecting small object surrounded by complex background. Extensive experiments on two remote sensing public datasets, DOTA and NWPUVHR -10, show our method to outperform existing leading object detectors in remote sensing field.
Detection and Characterization of Active Slope Deformations with Sentinel-1 InSAR Analyses in the Southwest Area of Shanxi, China
Authors: Shi Xuguo , Zhang Li , Zhong Yulong , Zhang Lu , Liao Mingsheng
Published: January 2020

A catastrophic landslide happened on 15 March 2019 in Xiangning County of Shanxi Province, causing 20 fatalities. Such an event makes us realize the significance of loess slope instability detection. Therefore, it is essential to identify the potential active landslides, monitor their displacements, and sort out dominant controlling factors. Synthetic Aperture Radar (SAR) Interferometry (InSAR) has been recognized as an effective tool for geological hazard mapping with wide coverage and high precision. In this study, the time series InSAR analysis method was applied to map the unstable areas in Xiangning County, as well as surrounding areas from C-band Sentinel-1 datasets acquired from March 2017 to 2019. A total number of 597 unstable sites covering 41.7 km2 were identified, among which approximately 70% are located in the mountainous areas which are prone to landslides. In particular, the freezing and thawing cycles might be the primary triggering factor for the failure of the Xiangning landslide. Furthermore, the nonlinear displacements of the active loess slopes within this region were found to be correlated significantly with precipitation. Therefore, a climate-driven displacement model was employed to explore the quantitative relationship between rainfall and nonlinear displacements.
Hand Gesture Recognition Using Compact CNN via Surface Electromyography Signals
Authors: Chen Lin , Fu Jianting , Wu Yuheng , Li Haochen , Zheng Bin
Published: January 2020

By training the deep neural network model, the hidden features in Surface Electromyography(sEMG) signals can be extracted. The motion intention of the human can be predicted by analysis of sEMG. However, the models recently proposed by researchers often have a large number of parameters. Therefore, we designed a compact Convolution Neural Network (CNN) model, which not only improves the classification accuracy but also reduces the number of parameters in the model. Our proposed model was validated on the Ninapro DB5 Dataset and the Myo Dataset. The classification accuracy of gesture recognition achieved good results.
The Influence of Surface Topography on the Weak Ground Shaking in Kathmandu Valley during the 2015 Gorkha Earthquake, Nepal
Authors: van der Meijde Mark , Ashrafuzzaman Md , Kerle Norman , Khan Saad , van der Werff Harald
Published: January 2020

It remains elusive why there was only weak and limited ground shaking in Kathmandu valley during the 25 April 2015 Mw 7.8 Gorkha, Nepal, earthquake. Our spectral element numerical simulations show that, during this earthquake, surface topography restricted the propagation of seismic energy into the valley. The mountains diverted the incoming seismic wave mostly to the eastern and western margins of the valley. As a result, we find de-amplification of peak ground displacement in most of the valley interior. Modeling of alternative earthquake scenarios of the same magnitude occurring at different locations shows that these will affect the Kathmandu valley much more strongly, up to 2–3 times more, than the 2015 Gorkha earthquake did. This indicates that surface topography contributed to the reduced seismic shaking for this specific earthquake and lessened the earthquake impact within the valley.
Analysis of Vibration and Acoustic Signals for Noncontact Measurement of Engine Rotation Speed
Authors: Shan Xuansheng , Tang Lu , Wen He , Martinek Radek , Smulko Janusz
Published: January 2020

The non-contact measurement of engine speed can be realized by analyzing engine vibration frequency. However, the vibration signal is distorted by harmonics and noise in the measurement. This paper presents a novel method for the measurement of engine rotation speed by using the cross-correlation of vibration and acoustic signals. This method can enhance the same frequency components in engine vibration and acoustic signal. After cross-correlation processing, the energy centrobaric correction method is applied to estimate the accurate frequency of the engine’s vibration. This method can be implemented with a low-cost embedded system estimating the cross-correlation. Test results showed that this method outperformed the traditional vibration-based measurement method.
Short Term Traffic State Prediction via Hyperparameter Optimization Based Classifiers
Authors: Zahid Muhammad , Chen Yangzhou , Jamal Arshad , Memon Muhammad Qasim
Published: January 2020

Short-term traffic state prediction has become an integral component of an advanced traveler information system (ATIS) in intelligent transportation systems (ITS). Accurate modeling and short-term traffic prediction are quite challenging due to its intricate characteristics, stochastic, and dynamic traffic processes. Existing works in this area follow different modeling approaches that are focused to fit speed, density, or the volume data. However, the accuracy of such modeling approaches has been frequently questioned, thereby traffic state prediction over the short-term from such methods inflicts an overfitting issue. We address this issue to accurately model short-term future traffic state prediction using state-of-the-art models via hyperparameter optimization. To do so, we focused on different machine learning classifiers such as local deep support vector machine (LD-SVM), decision jungles, multi-layers perceptron (MLP), and CN2 rule induction. Moreover, traffic states are evaluated using traffic attributes such as level of service (LOS) horizons and simple if–then rules at different time intervals. Our findings show that hyperparameter optimization via random sweep yielded superior results. The overall prediction performances obtained an average improvement by over 95%, such that the decision jungle and LD-SVM achieved an accuracy of 0.982 and 0.975, respectively. The experimental results show the robustness and superior performances of decision jungles (DJ) over other methods.
In Situ Wireless Channel Visualization Using Augmented Reality and Ray Tracing
Authors: Koutitas George , Kumar Siddaraju Varun , Metsis Vangelis
Published: January 2020

This article presents a novel methodology for predicting wireless signal propagation using ray-tracing algorithms, and visualizing signal variations in situ by leveraging Augmented Reality (AR) tools. The proposed system performs a special type of spatial mapping, capable of converting a scanned indoor environment to a vector facet model. A ray-tracing algorithm uses the facet model for wireless signal predictions. Finally, an AR application overlays the signal strength predictions on the physical space in the form of holograms. Although some indoor reconstruction models have already been developed, this paper proposes an image to a facet algorithm for indoor reconstruction and compares its performance with existing AR algorithms, such as spatial understanding that are modified to create the required facet models. In addition, the paper orchestrates AR and ray-tracing techniques to provide an in situ network visualization interface. It is shown that the accuracy of the derived facet models is acceptable, and the overall signal predictions are not significantly affected by any potential inaccuracies of the indoor reconstruction. With the expected increase of densely deployed indoor 5G networks, it is believed that these types of AR applications for network visualization will play a key role in the successful planning of 5G networks.
Novel Giant Magnetoimpedance Magnetic Field Sensor
Authors: Gazda Piotr , Szewczyk Roman
Published: January 2020

The idea, design, and tests of the novel GMI sensor are presented, based on the compensation measurement principle, where the local ‘zero-field’ minimum of the double-peak characteristic was utilized as a sensitive null detector. The compensation field was applied in real-time with the help of microprocessor-based, two-step, quasi-Newtonian optimization. The process of material parameters optimization through Joule-annealing of chosen amorphous alloys is described. The presented results of the prototype test unit show linear output characteristic, low measurement uncertainty, and resistance against time and temperature drift.
Use of PVDF Wire Sensors for Leakage Localization in a Fluid-Filled Pipe
Authors: Sun Pingling , Gao Yan , Jin Boao , Brennan Michael J.
Published: January 2020

The detection and location of pipeline leakage can be deduced from the time difference between the arrival leak signals measured by sensors placed at the pipe access points on either side of a suspected leak. Progress has been made in this area to offer a potential improvement over the conventional cross-correlation method for time delay estimation. This paper is concerned with identifying suitable sensors that can be easily deployed to monitor the pipe vibration due to the propagation of leak noise along the pipeline. In response to this, based on the low-frequency propagation characteristics of leak noise in our previous study, polyvinylidene fluoride (PVDF) wire sensors are proposed as a potential solution to detect the pipeline leak signals. Experimental investigations were carried out at a leak detection pipe rig built in the Chinese Academy of Sciences. Their performances for leak detection were shown in comparison with hydrophones. It is suggested that with special considerations given to aspects pertaining to non-intrusive deployment and low cost, the PVDF wire sensors are of particular interest and may lead to a promising replacement for commercial leak noise transducers.
Neuro-Fuzzy Dynamic Position Prediction for Autonomous Work-Class ROV Docking
Authors: Trslić Petar , Omerdic Edin , Dooly Gerard , Toal Daniel
Published: January 2020

This paper presents a docking station heave motion prediction method for dynamic remotely operated vehicle (ROV) docking, based on the Adaptive Neuro-Fuzzy Inference System (ANFIS). Due to the limited power onboard the subsea vehicle, high hydrodynamic drag forces, and inertia, work-class ROVs are often unable to match the heave motion of a docking station suspended from a surface vessel. Therefore, the docking relies entirely on the experience of the ROV pilot to estimate heave motion, and on human-in-the-loop ROV control. However, such an approach is not available for autonomous docking. To address this problem, an ANFIS-based method for prediction of a docking station heave motion is proposed and presented. The performance of the network was evaluated on real-world reference trajectories recorded during offshore trials in the North Atlantic Ocean during January 2019. The hardware used during the trials included a work-class ROV with a cage type TMS, deployed using an A-frame launch and recovery system.
Robust Soft Sensor with Deep Kernel Learning for Quality Prediction in Rubber Mixing Processes
Authors: Zheng Shuihua , Liu Kaixin , Xu Yili , Chen Hao , Zhang Xuelei , Liu Yi
Published: January 2020

Although several data-driven soft sensors are available, online reliable prediction of the Mooney viscosity in industrial rubber mixing processes is still a challenging task. A robust semi-supervised soft sensor, called ensemble deep correntropy kernel regression (EDCKR), is proposed. It integrates the ensemble strategy, deep brief network (DBN), and correntropy kernel regression (CKR) into a unified soft sensing framework. The multilevel DBN-based unsupervised learning stage extracts useful information from all secondary variables. Sequentially, a supervised CKR model is built to explore the relationship between the extracted features and the Mooney viscosity values. Without cumbersome preprocessing steps, the negative effects of outliers are reduced using the CKR-based robust nonlinear estimator. With the help of ensemble strategy, more reliable prediction results are further obtained. An industrial case validates the practicality and reliability of EDCKR.
Mechanical Properties of Optical Fiber Strain Sensing Cables under γ-Ray Irradiation and Large Strain Influence
Authors: Piccolo Arianna , Delepine-Lesoille Sylvie , Friedrich Etienne , Aziri Shasime , Lecieux Yann , Leduc Dominique
Published: January 2020

Optical fiber strain sensing cables are widely used in structural health monitoring; however, the impact of a harsh environment on them is not assessed despite the huge importance of the stable performances of the monitoring systems. This paper analyzes (i) the impact of the different constituent layers on the behavior of a strain sensing cable whose constitutive materials are metal and polyamide, (ii) the radiation influence on the optical fiber strain sensing cable response (500 kGy of γ -rays), and (iii) the behavior of the cable under high axial strain (up to 1%, 10,000 μ ε ). Radiation impact on strain sensitivity is negligible for practical application, i.e., the coefficient changes by 4% at the max. The influence of the composition of the cable is also assessed: the sensitivity differences remain under 15%, a standard variation range when different cable compositions and structures are considered. The elasto-plastic behavior is at the end evaluated, highlighting the residual strain (about 1600 μ ε after imposing 10,000 μ ε ) of the cable (especially for metallic parts).
Demand Responsive Service-based Optimization on Flexible Routes and Departure Time of Community Shuttles
Authors: Xiong Jie , Chen Biao , Li Xiangnan , He Zhengbing , Chen Yanyan
Published: January 2020

This paper investigates the optimal routing design problem of a community shuttle system feeding to metro stations based on demand-responsive service. The solution aims to jointly optimize a set of customized routes and the departure time of each route to provide a flexible shuttle service. Considering a set of on-demand trip requests between bus stops and metro stations, a mixed-integer optimization model is formulated to minimize the total system cost, including the operation cost and passenger’s in-vehicle cost, subject to the constraints on the route length, time window, detours, and vehicle capacity. To solve the problem, two metaheuristic algorithms, i.e. a tabu search (TS) and a variable neighborhood search (VNS), with different internal operators are specifically designed. A case study based on a realistic network is conducted to test the model and the solution, and comparisons of the performance of different algorithms are investigated.
Food Waste Reduction: A Test of Three Consumer Awareness Interventions
Authors: Soma Tammara , Li Belinda , Maclaren Virginia
Published: January 2020

Halving food waste by 2050 as per the Sustainable Development Goal 12.3 is key to securing a food system that is sustainable. One approach to reducing household food waste is through education campaigns. We recruited 501 households divided into three types of intervention groups and compared with a control group to better understand the efficacy of diverse education campaign approaches. Food waste interventions included a passive approach (handouts), a community engagement approach, and a gamification approach. We conducted waste audits, household surveys (pre- and post-intervention), and a focus group at the end of the campaign. The passive and gamification groups had similarly high levels of participation, while participation in the community group was very low. The passive group and the gamification group had higher self-reported awareness of food wasting after the campaign and lower food wastage than the control group. Waste audits found marginally significant differences between the game group and the control (p = 0.07) and no difference between the other campaign groups and the control group in edible food wasted. Frequent gamers were found to generate less edible food waste than infrequent gamers. We conclude that the evidence about the potential for gamification as an effective education change tool is promising and we recommend further study.
Exploring University Performance through Multiple Factor Analysis: A Case Study
Authors: Visbal-Cadavid Delimiro , Martínez-Gómez Mónica , Escorcia-Caballero Rolando
Published: January 2020

Higher Education in Colombia is experiencing a period of shortfalls in funding which has obliged Universities to seek resources from non-governmental sources, in addition to this, the Ministry of National Education (MEN) has implemented a scheme where funds are assigned year on year but this is not considered part of the budgetary base. This study seeks to create a characterisation of the model and proposes the creation of a synthetic performance index based on Multiple Factorial Analysis (MFA). This index enabled us to rank the Higher Education Institutions (HEI) according to their performance and analyse the trajectory of Colombian Universities at a given moment, which meant their evolution over time could be reviewed, moreover within the complexity of the model of indices of results in Colombian higher education, the majority of HEI have a similar, yet, to some degree, distinct structure and display scores which show that within a certain degree of homogeneity, each institution has its strengths in some of the variables considered and its weaknesses in others.
Assessment of Soil and Water Conservation Practices in the Loess Hilly Region Using a Coupled Rainfall-Runoff-Erosion Model
Authors: Cai Mengfan , An Chunjiang , Guy Christophe , Lu Chen
Published: January 2020

Soil and water conservation practices (SWCPs) are widely used to control soil and water loss. Quantifying the effect of SWCPs and climate change on soil and water erosion is important for regional environmental management. In this study, the Soil Conservation Service Curve Number (SCS-CN) and the Modified Universal Soil Loss Equation (MUSLE) were employed to investigate the patterns of surface runoff and soil erosion with different SWCPs in the hilly region on the Loess Plateau of China. The impact of climate change under RCP4.5 and RCP8.5 emission scenarios was considered from 2020 to 2050. Surface runoff grew with the increased rainfall and rainfall erosivity, while soil erosion presented large variations between years due to uneven distribution of rainfall and rainfall erosivity under two scenarios. Different SWCPs significantly reduced surface soil and water loss. Compared with bare slopes, the reduction rates were 15–40% for surface runoff and 35–67% for soil erosion under RCP4.5 and RCP8.5 emission scenarios, respectively. The combination of shrub and horizontal terracing was recommended due to its low water cost for sediment control among seven SWCPs.
Sustainable Business Model Based on Digital Twin Platform Network: The Inspiration from Haier’s Case Study in China
Authors: Li Xiao , Cao Jiarou , Liu Zhenggang , Luo Xinggang
Published: January 2020

Sustainability and digitalization have become the main direction of transformation of enterprises. Building a digital twin platform network can provide enterprises with a comprehensive view of products, manufacture, supply chain, customer experience, and profitability, which is conducive to the construction of a sustainable business model. The purpose of this paper is to study how enterprises use digital twin platform networks to generate economic, social and environmental benefits in various dimensions and their coupling relationships. Based on the literature review, this paper constructs a five-dimensional framework of a sustainable business model, and analyses the coupling relationship between dimensions. Using Haier as a way to verify the five-dimensional framework, it explores the dynamic mechanism of the Haier digital twin platform network, constructs an integrated framework based on coupling perspective and compares it with other two home appliance enterprises. The study shows that, through the digital twin platform network, enterprises can remove the disadvantage of focusing on a single product life cycle, and form a comprehensive network, so as to promote overall sustainable upgrades. This paper draws generic strategies and digital transformation suggestions for enterprises to innovate the sustainable business model. The conclusion enriches the research on sustainable business models both theoretically and practically and provides a feasible reference for the transformation of enterprises in digital economy environment.
Train-Induced Building Vibration and Radiated Noise by Considering Soil Properties
Authors: Zou Chao , Wang Yimin , Tao Ziyu
Published: January 2020

Constructing buildings above subway tracks exploits urban-area space intensively by adopting the three-dimensional overlapping development mode, which is one of the important measures for solving the contradictions among urban population increase, land resource shortage, and environmental protection. However, the vibration generated by the frequent train operations is transmitted to the upper buildings through the track structure and ground soil, which can cause structural vibrations and radiated noise and bring physical and mental side effects to occupants within the buildings. Subway projects are often located in geologically sensitive areas, while the influences of the encountered geological problems on the generation and propagation of structural vibration and structure-radiated noise within the buildings are not yet clear. Hence, this paper presents a method of studying the train-induced vibration transmission from the ground up into the buildings and the structure-radiated noise within the building. The method consists of a train-track model, track-soil-building model, and structure-radiated noise simulation. The impact of soil properties on the building vibration and structure-radiated noise is analyzed and ground-improvement measures are proposed in order to mitigate vibration and structure-radiated noise within buildings. The results show that the interaction between soil and structure has a great impact on vibration transmission from the ground into the building. Good foundations reduce vibration transmission from ground soil up into the building and lead to a lower level of structure-radiated noise. Ground improvements increase the impedance of ground soil, thereby weakening the vibration transmission and lowering the structure-radiated noise.
The Association of Typhoon Intensity Increase with Translation Speed Increase in the South China Sea
Authors: Chang Ya-Ting , Lin I-I , Huang Hsiao-Ching , Liao Yi-Chun , Lien Chun-Chi
Published: January 2020

Tropical cyclone (TC) translation speed is an important parameter. In the context of TC–ocean interaction, faster translation speed can contribute to less TC-induced ocean cooling and thus enables more air–sea enthalpy flux supply to favor TC intensification. In 2018, Kossin published an interesting paper in Nature, reporting a global slow-down of TC translation speed since the 1950s. However, upon close inspection, in the last two decades, TC translation speed actually increased over the western North Pacific (WNP) and neighboring seas. Thus, we are interested to see which sub-region in the WNP and neighboring seas had the largest increase during the last two decades, and whether such increases contribute to TC intensification. Our results found statistically significant translation speed increases (~0.8 ms−1 per decade) over the South China Sea. Ruling out other possible factors that may influence TC intensity (i.e., changes in atmospheric vertical wind shear, pre-TC sea surface temperature or subsurface thermal condition), we suggest, in this research, the possible contribution of TC translation speed increases to the observed TC intensity increases over the South China Sea in the last two decades (1998–2017).
Correction: OpinionML—Opinion Markup Language for Sentiment Representation. Symmetry 2019, 11, 545
Authors: Attik Mohammed , Saad Missen Malik Muhammad , Coustaty Mickaël , Choi Gyu Sang , Alotaibi Fahd Saleh , Akhtar Nadeem , Jhandir Muhammad Zeeshan , Prasath V. B. Surya , Salamat Nadeem , Husnain Mujtaba
Published: January 2020

The authors wish to make the following corrections to their paper [...]
Acknowledgement to Reviewers of Symmetry in 2019
Authors: Symmetry Editorial Office
Published: January 2020

The editorial team greatly appreciates the reviewers who have dedicated their considerable time and expertise to the journal’s rigorous editorial process over the past 12 months, regardless of whether the papers are finally published or not. [...]
FPGA Implementation and Design of a Hybrid Chaos-AES Color Image Encryption Algorithm
Authors: Yang Cheng-Hsiung , Chien Yu-Sheng
Published: January 2020

In this paper, we propose an image encryption algorithm based on four-dimensional chaotic system to generate key and improve advanced encryption standard. The encryption algorithm is optimized by using the pipeline and parallel computing features of Field Programmable Gate Array (FPGA). First, the chaotic system is used as a key generator for the encryption algorithm. Next, in the improved advanced encryption standard, ShiftRows and SubByres are modified with Spin-Sort and Cubic S-Box, and the round of encryption is reduced. We implement the encryption algorithm and the wired image transmission system to the ARM-based SoC-FPGA. The HPS software runs on Linux and is used to control the FPGA encryption algorithm and image transmission. Finally, the results from the encryption security analysis show that the proposed image encryption algorithm is safe and effective.
An Adaptive Face Image Inpainting Algorithm Based on Feature Symmetry
Authors: Niu Zuodong , Li Handong , Li Yao , Mei Yingjie , Yang Jing
Published: January 2020

Face image inpainting technology is an important research direction in image restoration. When the current image restoration methods repair the damaged areas of face images with weak texture, there are problems such as low accuracy of face image decomposition, unreasonable restoration structure, and degradation of image quality after inpainting. Therefore, this paper proposes an adaptive face image inpainting algorithm based on feature symmetry. Firstly, we locate the feature points of the face, and segment the face into four feature parts based on the feature point distribution to define the feature search range. Then, we construct a new mathematical model, introduce feature symmetry to improve priority calculation, and increase the reliability of priority calculation. After that, in the process of searching for matching blocks, we accurately locate similar feature blocks according to the relative position and symmetry criteria of the target block and various feature parts of the face. Finally, we introduced the HSV (Hue, Saturation, Value) color space to determine the best matching block according to the chroma and brightness of the sample, reduce the repair error, and complete the face image inpainting. During the experiment, we firstly performed visual evaluation and texture analysis on the inpainting face image, and the results show that the face image inpainting by our algorithm maintained the consistency of the face structure, and the visual observation was closer to the real face features. Then, we used the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) as objective evaluation indicators; among the five sample face images inpainting results given in this paper, our method was better than the reference methods, and the average PSNR value improved from 2.881–5.776 dB using our method when inpainting 100 face images. Additionally, we used the time required for inpainting the unit pixel to evaluate the inpainting efficiency, and it was improved by 12%–49% with our method when inpainting 100 face images. Finally, by comparing the face image inpainting experiments with the generative adversary network (GAN) algorithm, we discuss some of the problems with the method in this paper based on graphics in repairing face images with large areas of missing features.
New Distance Measures for Dual Hesitant Fuzzy Sets and Their Application to Multiple Attribute Decision Making
Authors: Wang Rugen , Li Weimin , Zhang Tao , Han Qi
Published: January 2020

Multiple attribute decision making (MADM) is full of uncertainty and vagueness due to intrinsic complexity, limited experience and individual cognition. Representative decision theories include fuzzy set (FS), intuitionistic fuzzy set (IFS), hesitant fuzzy set (HFS), dual hesitant fuzzy set (DHFS) and so on. Compared with IFS and HFS, DHFS has more advantages in dealing with uncertainties in real MADM problems and possesses good symmetry. The membership degrees and non-membership degrees in DHFS are simultaneously permitted to represent decision makers’ preferences by a given set having diverse possibilities. In this paper, new distance measures for dual hesitant fuzzy sets (DHFSs) are developed in terms of the mean, variance and number of elements in the dual hesitant fuzzy elements (DHFEs), which overcomes some deficiencies of the existing distance measures for DHFSs. The proposed distance measures are effectively applicable to solve MADM problems where the attribute weights are completely unknown. With the help of the new distance measures, the attribute weights are objectively determined, and the closeness coefficients of each alternative can be objectively obtained to generate optimal solution. Finally, an evaluation problem of airline service quality is conducted by using the distance-based MADM method to demonstrate its validity and applicability.
How the Neurotoxin β-N-Methylamino-l-Alanine Accumulates in Bivalves: Distribution of the Different Accumulation Fractions among Organs
Authors: Lepoutre Alexandra , Faassen Elisabeth J. , Zweers A. J. , Lürling Miquel , Geffard Alain , Lance Emilie
Published: January 2020

The environmental neurotoxin β-methylamino-l-alanine (BMAA) may represent a risk for human health. BMAA accumulates in freshwater and marine organisms consumed by humans. However, few data are available about the kinetics of BMAA accumulation and detoxification in exposed organisms, as well as the organ distribution and the fractions in which BMAA is present in tissues (free, soluble bound or precipitated bound cellular fractions). Here, we exposed the bivalve mussel Dreissena polymorpha to 7.5 µg of dissolved BMAA/mussel/3 days for 21 days, followed by 21 days of depuration in clear water. At 1, 3, 8, 14 and 21 days of exposure and depuration, the hemolymph and organs (digestive gland, the gills, the mantle, the gonad and muscles/foot) were sampled. Total BMAA as well as free BMAA, soluble bound and precipitated bound BMAA were quantified by tandem mass spectrometry. Free and soluble bound BMAA spread throughout all tissues from the first day of exposure to the last day of depuration, without a specific target organ. However, precipitated bound BMAA was detected only in muscles and foot from the last day of exposure to day 8 of depuration, at a lower concentration compared to free and soluble bound BMAA. In soft tissues (digestive gland, gonad, gills, mantle and muscles/foot), BMAA mostly accumulated as a free molecule and in the soluble bound fraction, with variations occurring between the two fractions among tissues and over time. The results suggest that the assessment of bivalve contamination by BMAA may require the quantification of total BMAA in whole individuals when possible.
P-Cresylsulfate, the Protein-Bound Uremic Toxin, Increased Endothelial Permeability Partly Mediated by Src-Induced Phosphorylation of VE-Cadherin
Authors: Chen Shih-Chieh , Huang Shin-Yin , Wu Chia-Chun , Hsu Chiung-Fang
Published: January 2020

The goal of our study was to investigate the impact of p-cresylsulfate (PCS) on the barrier integrity in human umbilical vein endothelial cell (HUVEC) monolayers and the renal artery of chronic kidney disease (CKD) patients. We measured changes in the transendothelial electrical resistance (TEER) of HUVEC monolayers treated with PCS (0.1–0.2 mM) similar to serum levels of CKD patients. A PCS dose (0.2 mM) significantly decreased TEER over a 48-h period. Both PCS doses (0.1 and 0.2 mM) significantly decreased TEER over a 72-h period. Inter-endothelial gaps were observed in HUVECs following 48 h of PCS treatment by immunofluorescence microscopy. We also determined whether PCS induced the phosphorylation of VE-cadherin at tyrosine 658 (Y658) mediated by the phosphorylation of Src. Phosphorylated VE-cadherin (Y658) and phosphorylated Src levels were significantly higher when the cells were treated with 0.1 and 0.2 mM PCS, respectively, compared to the controls. The endothelial barrier dysfunction in the arterial intima in CKD patients was evaluated by endothelial leakage of immunoglobulin G (IgG). Increased endothelial leakage of IgG was related to the declining kidney function in CKD patients. Increased endothelial permeability induced by uremic toxins, including PCS, suggests that uremic toxins induce endothelial barrier dysfunction in CKD patients and Src-mediated phosphorylation of VE-cadherin is involved in increased endothelial permeability induced by PCS exposure.
Bacterial Genotoxin-Induced DNA Damage and Modulation of the Host Immune Microenvironment
Authors: Martin Océane C.B. , Frisan Teresa
Published: January 2020

Bacterial genotoxins (BTGX) induce DNA damage, which results in senescence or apoptosis of the target cells if not properly repaired. Three BTGXs have been identified: the cytolethal distending toxin (CDT) family produced by several Gram-negative bacteria, the typhoid toxin produced by several Salmonella enterica serovars, and colibactin, a peptide-polyketide, produced mainly by the phylogenetic group B2 Escherichia coli. The cellular responses induced by BTGXs resemble those of well-characterized carcinogenic agents, and several lines of evidence indicate that bacteria carrying genotoxin genes can contribute to tumor development under specific circumstances. Given their unusual mode of action, it is still enigmatic why these effectors have been acquired by microbes and what is their role in the context of the biology of the producing bacterium, since it is unlikely that their primary purpose is to induce/promote cancer in the mammalian host. In this review, we will discuss the possibility that the DNA damage induced by BTGX modulates the host immune response, acting as immunomodulator, leading to the establishment of a suitable niche for the producing bacterium. We will further highlight open questions that remain to be solved regarding the biology of this unusual family of bacterial toxins.
TRI Genotyping and Chemotyping: A Balance of Power
Authors: Villafana Ria T. , Ramdass Amanda C. , Rampersad Sephra N.
Published: January 2020

Fusarium is among the top 10 most economically important plant pathogens in the world. Trichothecenes are the principal mycotoxins produced as secondary metabolites by select species of Fusarium and cause acute and chronic toxicity in animals and humans upon exposure either through consumption and/or contact. There are over 100 trichothecene metabolites and they can occur in a wide range of commodities that form food and feed products. This review discusses strategies to mitigate the risk of mycotoxin production and exposure by examining the Fusarium-trichothecene model. Fundamental to mitigation of risk is knowing the identity of the pathogen. As such, a comparison of current, recommended molecular approaches for sequence-based identification of Fusaria is presented, followed by an analysis of the rationale and methods of trichothecene (TRI) genotyping and chemotyping. This type of information confirms the source and nature of risk. While both are powerful tools for informing regulatory decisions, an assessment of the causes of incongruence between TRI genotyping and chemotyping data must be made. Reconciliation of this discordance will map the way forward in terms of optimization of molecular approaches, which includes data validation and sharing in the form of accessible repositories of genomic data and browsers for querying such data.
Amplification of Snake Venom Toxicity by Endogenous Signaling Pathways
Authors: Bickler Philip E.
Published: January 2020

The active components of snake venoms encompass a complex and variable mixture of proteins that produce a diverse, but largely stereotypical, range of pharmacologic effects and toxicities. Venom protein diversity and host susceptibilities determine the relative contributions of five main pathologies: neuromuscular dysfunction, inflammation, coagulopathy, cell/organ injury, and disruption of homeostatic mechanisms of normal physiology. In this review, we describe how snakebite is not only a condition mediated directly by venom, but by the amplification of signals dysregulating inflammation, coagulation, neurotransmission, and cell survival. Although venom proteins are diverse, the majority of important pathologic events following envenoming follow from a small group of enzyme-like activities and the actions of small toxic peptides. This review focuses on two of the most important enzymatic activities: snake venom phospholipases (svPLA2) and snake venom metalloproteases (svMP). These two enzyme classes are adept at enabling venom to recruit homologous endogenous signaling systems with sufficient magnitude and duration to produce and amplify cell injury beyond what would be expected from the direct impact of a whole venom dose. This magnification produces many of the most acutely important consequences of envenoming as well as chronic sequelae. Snake venom PLA2s and MPs enzymes recruit prey analogs of similar activity. The transduction mechanisms that recruit endogenous responses include arachidonic acid, intracellular calcium, cytokines, bioactive peptides, and possibly dimerization of venom and prey protein homologs. Despite years of investigation, the precise mechanism of svPLA2-induced neuromuscular paralysis remains incomplete. Based on recent studies, paralysis results from a self-amplifying cycle of endogenous PLA2 activation, arachidonic acid, increases in intracellular Ca2+ and nicotinic receptor deactivation. When prolonged, synaptic suppression supports the degeneration of the synapse. Interaction between endothelium-damaging MPs, sPLA2s and hyaluronidases enhance venom spread, accentuating venom-induced neurotoxicity, inflammation, coagulopathy and tissue injury. Improving snakebite treatment requires new tools to understand direct and indirect effects of envenoming. Homologous PLA2 and MP activities in both venoms and prey/snakebite victim provide molecular targets for non-antibody, small molecule agents for dissecting mechanisms of venom toxicity. Importantly, these tools enable the separation of venom-specific and prey-specific pathological responses to venom.
Comments on Immunogenicity Associated with Botulinum Toxin Treatment. Toxins 2019, 11, 491
Authors: Foster Keith , Beard Matthew
Published: January 2020

In contrast to the prevailing arguments presented in the current review, the incidence of neutralising antibody (NAb) formation is not a significant issue for any of the present type A therapeutic botulinum neurotoxin (BoNT) products. Furthermore, clinical non-responsiveness is poorly correlated with the presence of NAbs. The overriding evidence supports the view that the rate of NAb formation is low, does not differ significantly between the different type A BoNT products and that it is not the major factor in clinical response. BoNT products are highly effective and important therapies for the treatment of a variety of neurological and non-neurological conditions.
Reply to Comment on Re-Visiting Immunogenicity Associated with Botulinum Toxin Treatment. Toxins 2019, 11, 491
Authors: Bellows Steven , Jankovic Joseph
Published: January 2020

We appreciate the commentary on our article by Foster and Beard, both employees of Ipsen [...]
Diet Breadth Mediates the Prey Specificity of Venom Potency in Snakes
Authors: Lyons Keith , Dugon Michel M. , Healy Kevin
Published: January 2020

Venoms are best known for their ability to incapacitate prey. In predatory groups, venom potency is predicted to reflect ecological and evolutionary drivers relating to diet. While venoms have been found to have prey-specific potencies, the role of diet breadth on venom potencies has yet to be tested at large macroecological scales. Here, using a comparative analysis of 100 snake species, we show that the evolution of prey-specific venom potencies is contingent on the breadth of a species’ diet. We find that while snake venom is more potent when tested on species closely related to natural prey items, we only find this prey-specific pattern in species with taxonomically narrow diets. While we find that the taxonomic diversity of a snakes’ diet mediates the prey specificity of its venom, the species richness of its diet was not found to affect these prey-specific potency patterns. This indicates that the physiological diversity of a species’ diet is an important driver of the evolution of generalist venom potencies. These findings suggest that the venoms of species with taxonomically diverse diets may be better suited to incapacitating novel prey species and hence play an important role for species within changing environments.