bluuebunny/crossref_metadata_2025 huggingface dataset:
The Effect of Weather in Soccer Results: An Approach Using Machine Learning Techniques
Authors: Iskandaryan Ditsuhi , Ramos Francisco , Palinggi Denny Asarias , Trilles Sergio
Published: September 2020

The growing popularity of soccer has led to the prediction of match results becoming of interest to the research community. The aim of this research is to detect the effects of weather on the result of matches by implementing Random Forest, Support Vector Machine, K-Nearest Neighbors Algorithm, and Extremely Randomized Trees Classifier. The analysis was executed using the Spanish La Liga and Segunda division from the seasons 2013–2014 to 2017–2018 in combination with weather data. Two tasks were proposed as part of this study: the first was to find out whether the game will end in a draw, a win by the hosts or a victory by the guests, and the second was to determine whether the match will end in a draw or if one of the teams will win. The results show that, for the first task, Extremely Randomized Trees Classifier is a better method, with an accuracy of 65.9%, and, for the second task, Support Vector Machine yielded better results with an accuracy of 79.3%. Moreover, it is possible to predict whether the game will end in a draw or not with 0.85 AUC-ROC. Additionally, for comparative purposes, the analysis was also performed without weather data.
Measurement and Analysis of Vibration Levels for Truck Transport Environment in Korea
Authors: Park Jongmin , Choi Sangil , Jung Hyun Mo
Published: September 2020

The first step in the appropriate packaging design of food, agricultural and industrial products is to conduct an accurate simulation of the vehicle transport environment, in which a power spectral density (PSD) profile is applied. Although several researchers have mentioned the limitations of PSD-based simulation, it is still widely used because accelerated test conditions can be easily generated from the PSD acquired from a particular transport section. In this study, three representative trucks and transport test routes of domestic freight transport were selected to develop a simulation protocol for a truck transport environment in Korea. These studies are needed to compare domestic transport vibration levels with those presented by the International Standards (ASTM: West Conshohocken, USA, ISTA: Chicago, USA) and to simulate damage to packaged products by domestic transport environments. The composite PSD profile for the truck transport environment was established by dividing it into high-and low-level composite PSD profiles representing the top 30% and lower 70%, respectively, of the measured vibration events based on the root-mean-square acceleration (rms G) of the measured vibration events. Also, the effects of these variables on the truck vibration level were analyzed by extracting data corresponding to the truck’s pre-planned travel speed and road conditions in the vibration records measured on the test route. Moreover, kurtosis, skewness, and normal quantile-quantity (Q-Q) analyses were conducted to understand the statistical characteristics of Korea’s truck transport environment. Statistical analysis showed that the measured vibration events had a heavy-tailed distribution and skewed to the right, causing dissymmetry. The overall rms G of the developed high-level and low-level composite PSD profiles in the range from 1 to 250 Hz were 0.47 and 0.32 for leaf-spring trucks and 0.30 and 0.14 for air-ride trucks, respectively.
Air-Coupled, Contact, and Immersion Ultrasonic Non-Destructive Testing: Comparison for Bonding Quality Evaluation
Authors: Yilmaz Bengisu , Asokkumar Aadhik , Jasiūnienė Elena , Kažys Rymantas Jonas
Published: September 2020

The objective of this study is to compare the performance of different ultrasonic non-destructive testing (NDT) techniques for bonding quality evaluation. Aluminium-epoxy-aluminium single lap joints containing debonding in the form of release film inclusions have been investigated using three types of ultrasonic NDT methods: contact testing, immersion testing, and air-coupled testing. Apart from the traditional bulk wave ultrasound, guided wave testing was also performed using air coupled and contact transducers for the excitation of guided waves. Guided wave propagation within adhesive bond was numerically simulated. A wide range of inspection frequencies causing different ultrasonic wavelengths has been investigated. Average errors in defect sizing per ultrasonic wavelength have been used as a feature to determine the performance of each ultrasonic NDT technique. The best performance is observed with bulk wave investigations. Particularly, the higher frequencies (10–50 MHz) in the immersion testing performed significantly better than air-coupled testing (300 kHz); however, air coupled investigations have other advantages as contactless inspection. Whereas guided wave inspections show relatively lower accuracy in defect sizing, they are good enough to detect the presence of the debonding and enable to inspect long range. Even though each technique has its advantages and limitations, guided wave techniques can be practical for the preliminary in-situ inspection of adhesively bonded specimens.
Reconstruction of Late Pleistocene-Holocene Deformation through Massive Data Collection at Krafla Rift (NE Iceland) Owing to Drone-Based Structure-from-Motion Photogrammetry
Authors: Bonali Fabio Luca , Tibaldi Alessandro , Corti Noemi , Fallati Luca , Russo Elena
Published: September 2020

In the present work, we demonstrate how drone surveys coupled with structure-from-motion (SfM) photogrammetry can help to collect huge amounts of very detailed data even in rough terrains where logistics can affect classical field surveys. The area of study is located in the NW part of the Krafla Fissure Swarm (NE Iceland), a volcanotectonic rift composed of eruptive centres, extension fractures, and normal faults. The surveyed sector is characterized by the presence of a hyaloclastite ridge composed of deposits dated, on a stratigraphic basis, to the Weichselian High Glacial (29.1–12.1 ka BP), and a series of lava flows mostly dating back to 11–12 ka BP. The integration of remotely sensed surveys and field inspections enabled us to recognize that this segment of the Krafla rift is made of grabens arranged en-échelon with a left-stepping geometry. A major graben increases in width in correspondence of the hyaloclastite cone; we interpret this geometry as resulting from the mechanical contrast between the stiffer lava succession and the softer hyaloclastites, which favours the development of concentric faults. We also measured a total extension of 16.6 m and 11.2 m along the fractures affecting the lava units, and a total extension in the hyaloclastites of 29.3 m. This produces an extension rate of 1.4 mm/yr in the Holocene lavas and 1.7 ± 0.7 mm/yr in the Weichselian hyaloclastite deposits. The spreading direction we obtained for this area is N97.7° E, resulting from the av. of 568 opening direction values.
A Novel Approach to Shadow Boundary Detection Based on an Adaptive Direction-Tracking Filter for Brain-Machine Interface Applications
Authors: Ju Ziyi , Gun Li , Hussain Amir , Mahmud Mufti , Ieracitano Cosimo
Published: September 2020

In this paper, a Brain-Machine Interface (BMI) system is proposed to automatically control the navigation of wheelchairs by detecting the shadows on their route. In this context, a new algorithm to detect shadows in a single image is proposed. Specifically, a novel adaptive direction tracking filter (ADT) is developed to extract feature information along the direction of shadow boundaries. The proposed algorithm avoids extraction of features around all directions of pixels, which significantly improves the efficiency and accuracy of shadow features extraction. Higher-order statistics (HOS) features such as skewness and kurtosis in addition to other optical features are used as input to different Machine Learning (ML) based classifiers, specifically, a Multilayer Perceptron (MLP), Autoencoder (AE), 1D-Convolutional Neural Network (1D-CNN) and Support Vector Machine (SVM), to perform the shadow boundaries detection task. Comparative results demonstrate that the proposed MLP-based system outperforms all the other state-of-the-art approaches, reporting accuracy rates up to 84.63%.
A Discriminative Multi-Output Gaussian Processes Scheme for Brain Electrical Activity Analysis
Authors: Torres-Valencia Cristian , Orozco Álvaro , Cárdenas-Peña David , Álvarez-Meza Andrés , Álvarez Mauricio
Published: September 2020

The study of brain electrical activity (BEA) from different cognitive conditions has attracted a lot of interest in the last decade due to the high number of possible applications that could be generated from it. In this work, a discriminative framework for BEA via electroencephalography (EEG) is proposed based on multi-output Gaussian Processes (MOGPs) with a specialized spectral kernel. First, a signal segmentation stage is executed, and the channels from the EEG are used as the model outputs. Then, a novel covariance function within the MOGP known as the multispectral mixture kernel (MOSM) allows us to find and quantify the relationships between different channels. Several MOGPs are trained from different conditions grouped in bi-class problems, and the discrimination is performed based on the likelihood score of the test signals against all the models. Finally, the mean likelihood is computed to predict the correspondence of new inputs with each class’s existing models. Results show that this framework allows us to model the EEG signals adequately using generative models and allows analyzing the relationships between channels of the EEG for a particular condition. At the same time, the set of trained MOGPs is well suited to discriminate new input data.
Review on Short-circuit Current Analysis and Suppression Techniques for MMC-HVDC Transmission Systems
Authors: Qin Boyu , Liu Wansong , Zhang Ruowei , Liu Jialing , Li Hengyi
Published: September 2020

The modular multilevel converter (MMC) has been widely adopted in high voltage direct current (HVDC) transmission systems due to its significant advantages. MMC-HVDC is developing towards multi-terminal direct current (MTDC) power grid for reliability enhancement. However, there exist a huge amplitude and a steep rise in fault current due to the low impedances of DC lines and MMCs, which threaten the security and reliability of the DC power grids. It is necessary to restrain the DC short circuit current in order to ensure the safe and stable operation of DC power grids. This paper gives a comprehensive review and evaluation of the proposed DC short-circuit current analysis and suppression techniques used in MMC-based MTDC power girds, in terms of MMC modeling, short circuit calculation, and suppression method. In addition, future trends of countermeasures to short circuit current in MMC-based MTDC power grids are also discussed.
A Three-Dimensional and Bi-objective Topological Optimization Approach Based on Piezoelectric Energy Harvester
Authors: Liu Yizhi , Huang Ziyu , Gao Yufei
Published: September 2020

Topological optimization can realize the optimization of the mass distribution in the whole objective domain. Compared with morphology and size optimization, it has a higher degree of freedom. In this work, the three-dimensional topological optimization based on piezoelectric materials was discussed. Using the Optimality Criteria, topology optimization was applied to the cantilever piezoelectric transducer. The structure optimization was realized with the voltage and stiffness as the multi-objective function. The corresponding codes are given to show the process of optimization. With 70% of the origin volume, the bi-objective optimization increases the global stiffness by 50.9% and the voltage by 30%. As the iteration process shows, the results of bi-objective optimization prove the value of additive mass at the bottom of the cantilever. This lays the foundation for future piezoelectric transducer structural optimization. Using only stiffness as the objective, the final objective increases inconspicuously. Bi-objective optimization shows its superiority. There are quite a few papers that research the combination of stiffness and voltage, and research which studies three-dimensionality is a point of innovation. Furthermore, this is also the first time a piezoelectric topology code has been shared.
Influence of Biomass Absorptivity on the Process of Sinter Charge Pelletisation
Authors: Džupková Martina , Fröhlichová Mária , Legemza Jaroslav , Findorák Róbert , Hudák Jozef
Published: September 2020

Capillary water absorption of materials is a very important factor in the process of pre-treatment of fine-grained materials. Materials that are in a moisturized state capable of forming a firm, compact pellet are, thanks to this particular physical property, suitable for utilisation in sinter charge preparation within the process of sintering iron-ore raw materials. The pelletising ability of coke dust is generally known and coke dust exhibits good pelletisability. From the ecological point of view, an alternative to coke dust is currently biomass, which has a great potential for industrial applications, including use in the agglomeration process. Understanding of how biomass behaves during pre-pelletisation is very important and for the sintering process, it is essential. The purpose of pre-pelletisation of the sinter charge is to achieve its optimal permeability in the sintering process. The experiment described in the article was carried out using wood biomass—oak and pine sawdust, as well as plant biomass—Miscanthus sinensis and Lavandula angustifolia. The evaluation was carried out by applying the capillary water absorption test and the free-fall drop test. As different types of biomass have different chemical compositions, heating capacities, grain morphologies, and chemical and physical properties, the testing was carried out with several types of biomass. The capillary water absorption was examined in terms of different granulometries, and the impact of the type of liquid medium was analysed. It was observed that different types of biomass differ in their ability to absorb liquids. Another finding was that the type of a liquid medium had a significant effect on the pelletising ability of biomass, which was determined by the surface tension and the ability to form liquid bridges between the grains. Research results indicate an excellent pelletising ability of the Miscanthus sinensis grass. The wettability of oak and pine sawdust determines its application in the pelletising process. It may be concluded, based on the research, that Lavandula angustifolia is not a suitable alternative to coke dust due to its low ability to form pellets.
Sustainable Competitive Advantage Driven by Big Data Analytics and Innovation
Authors: Ramadan Muawia , Shuqqo Hana , Qtaishat Layalee , Asmar Hebaa , Salah Bashir
Published: September 2020

Big data analytics (BDA) is one of the main pillars of Industry 4.0. It has become a promising tool for supporting the competitive advantages of firms by enhancing data-driven performance. Meanwhile, the scarcity of resources on a worldwide level has forced firms to consider sustainable-based performance as a critical issue. Additionally, the literature confirms that BDA and innovation can enhance firms’ performance, leading to competitive advantage. However, there is a lack of studies that examine whether or not BDA and innovation alone can sustain a firm’s competitive advantage. Drawing on previous studies and dynamic capability theory, this study proposes that big data analytics capabilities (BDAC), supported by a high level of data availability (DA), can improve innovation capabilities (IC) and, hence, lead to the development of a sustainable competitive advantage (SCA). This study examines the proposed hypotheses by surveying 117 manufacturing firms and analyzing responses via partial least squares–structural equation modeling (PLS-SEM) statistical software. Findings reveal that BDAC relies significantly on the degree of DA and has a significant role in increasing IC. Furthermore, the analysis confirms that IC has a significant and direct effect on a firm’s SCA, while BDAC has no direct effect on SCA. This study provides valuable insights for manufacturing firms to improve their sustainable business performance and theoretical and practical insights into BDA implementation issues in attaining sustainability in processes.
Total and Sustainable Valorisation of Olive Pomace Using a Fractionation Approach
Authors: Ribeiro Tânia B. , Oliveira Ana L. , Costa Cristina , Nunes João , Vicente António A. , Pintado Manuela
Published: September 2020

Olive pomace management represents a great concern to the olive oil industry. This work focused on the development of a “zero waste” strategy for olive pomace based on a fractionation approach resulting in the obtention of different value-added fractions. The physicochemical composition of edible fractions obtained (liquid and pulp) was analysed. The potential use as a solid biofuel of the non-edible fraction (stones) was evaluated. High amounts of hydroxytyrosol (513.61–625.76 mg/100 g dry weight) were present in the liquid fraction. Pulp fraction was demonstrated to be a good source of fibre (53–59% dry weight) with considerable antioxidant activity both from free and bound phenolics. The stones fraction exhibited substantial high heating values (18.65–18.94 megajoule (MJ/kg). All these results support the added value of the olive pomace fractions combining the biofuel potential from the stones fraction and the functional food ingredients’ potential both from liquid and pulp fractions. The present methodology seems to be a feasible whole valorisation approach to achieve the circularity in the olive oil sector, prioritising obtaining high over low added-value products.
A Sparse Autoencoder-Based Unsupervised Scheme for Pump Fault Detection and Isolation
Authors: Liang Xiaoxia , Duan Fang , Bennett Ian , Mba David
Published: September 2020

Pumps are one of the most critical machines in the petrochemical process. Condition monitoring of such parts and detecting faults at an early stage are crucial for reducing downtime in the production line and improving plant safety, efficiency and reliability. This paper develops a fault detection and isolation scheme based on an unsupervised machine learning method, sparse autoencoder (SAE), and evaluates the model on industrial multivariate data. The Mahalanobis distance (MD) is employed to calculate the statistical difference of the residual outputs between monitoring and normal states and is used as a system-wide health indicator. Furthermore, fault isolation is achieved by a reconstruction-based two-dimensional contribution map, in which the variables with larger contributions are responsible for the detected fault. To demonstrate the effectiveness of the proposed scheme, two case studies are carried out based on a multivariate data set from a pump system in an oil and petrochemical factory. The classical principal component analysis (PCA) method is compared with the proposed method and results show that SAE performs better in terms of fault detection than PCA, and can effectively isolate the abnormal variables, which can hence help effectively trace the root cause of the detected fault.
T-Spline Surface Toolpath Generation Using Watershed-Based Feature Recognition
Authors: Liu Yazui , Zhao Gang , Han Pengfei
Published: September 2020

The freeform surface is treated as a single machining region for most traditional toolpath generation algorithms. However, due to the complexity of a freeform surface, it is impossible to produce a high-quality surface using one unique machining process. Hence, region-based methods are widely investigated for freeform surface machining to achieve an optimized toolpath. The Non-Uniform Rational B-spline Surface (NURBS) represented freeform surface is not suitable for region-based toolpath generation because of the surface gaps caused by NURBS trimming and merging operations. To solve the limitation of the NURBS, T-spline is proposed with the advantages of being gap-free, having less control points, and local refinement, which is an ideal tool for region-based toolpath generation. Thus, T-spline is introduced to represent a freeform surface for its toolpath generation in the paper. A region-based toolpath generation method for the T-spline surface is proposed based on watershed technology. Firstly, watershed-based feature recognition is presented to divide the T-spline surface into a set of sub-regions. Secondly, the concept of a PolyBoundingBox that consists of a set of minimum bounding boxes is proposed to describe the sub-regions, and Manufacturing-Suitable Regions are constructed with the help of T-spline local refinement and the PolyBoundingBox. In the end, an optimized multi-rectangles toolpath generation algorithm is applied for sub-regions. The proposed method is tested using three synthetic T-spline surfaces, and the comparison results show the advantage in toolpath length and toolpath reversing number.
Performance of Hybrid Filter in a Microgrid Integrated Power System Network Using Wavelet Techniques
Authors: Das Soumya Ranjan , Ray Prakash K. , Sahoo Arun Kumar , Ramasubbareddy Somula , Babu Thanikanti Sudhakar , Kumar Nallapaneni Manoj , Haes Alhelou Hassan , Siano Pierluigi
Published: September 2020

Nowadays, the application of distributed energy sources (DES) has been extensively employed to serve the power system by supplying the power into the grid and improving the power quality (PQ). Therefore, DES is one solution that can efficiently overcome the energy crisis and climate change problems. The DES, such as solar photovoltaic (PV), wind turbine (WT), and battery energy storage systems (BESS), are incorporated to form the microgrid (MG), which are interfaced with the power system. However, interfacing MG to the power system is undoubtedly a big challenge. Therefore, more focus is required on the control strategy to control the MG with the power system. To address the PQ problems, a controlled MG integrated with a hybrid shunt active power filter (HSAPF) is provided in this work. For controlling the MG integrated HSAPF, different control strategies are applied. In this work, a learning-based incremental conductance (LINC) technique is used as a maximum power point tracking (MPPT) for tracking the maximum power in PV and WT. The voltage source inverter (VSI) of HSAPF is controlled using a wavelet-based technique with a synchronous reference frame (SRF). The main focus is to improve the PQ by compensating the harmonics and regulating the reactive power in both grid-interactive and islanded condition and also supply continuous and adequate power to the non-linear load. The power system model has been developed with MATLAB/Simulink tool, which shows the efficiency of the proposed method. The results obtained have been satisfactorily under various operating conditions and can be validated further using the real-time dSPACE.
Identification of Risk Factors and Machine Learning-Based Prediction Models for Knee Osteoarthritis Patients
Authors: Kokkotis Christos , Moustakidis Serafeim , Giakas Giannis , Tsaopoulos Dimitrios
Published: September 2020

Knee Osteoarthritis (KOA) is a multifactorial disease that causes low quality of life, poor psychology and resignation from life. Furthermore, KOA is a big data problem in terms of data complexity, heterogeneity and size as it has been commonly considered in the literature with most of the reported studies being limited in the amount of information they can adequately process. The aim of this paper is: (i) To provide a robust feature selection (FS) approach that could identify important risk factors which contribute to the prediction of KOA and (ii) to develop machine learning (ML) prediction models for KOA. The current study considers multidisciplinary data from the osteoarthritis initiative (OAI) database, the available features of which come from heterogeneous sources such as questionnaire data, physical activity indexes, self-reported data about joint symptoms, disability and function as well as general health and physical exams’ data. The novelty of the proposed FS methodology lies on the combination of different well-known approaches including filter, wrapper and embedded techniques, whereas feature ranking is decided on the basis of a majority vote scheme to avoid bias. The validation of the selected factors was performed in data subgroups employing seven well-known classifiers in five different approaches. A 74.07% classification accuracy was achieved by SVM on the group of the first fifty-five selected risk factors. The effectiveness of the proposed approach was evaluated in a comparative analysis with respect to classification errors and confusion matrices to confirm its clinical relevance. The results are the basis for the development of reliable tools for the prediction of KOA progression.
Arching Detection Method of Slab Track in High-Speed Railway Based on Track Geometry Data
Authors: Ma Zhuoran , Gao Liang , Zhong Yanglong , Ma Shuai , An Bolun
Published: September 2020

During the long-term service of slab track, various external factors (such as complicated temperature) can result in a series of slab damages. Among them, slab arching changes the structural mechanical properties, deteriorates the track geometry conditions, and even threatens the operation of trains. Therefore, it is necessary to detect slab arching accurately to achieve effective maintenance. However, the current damage detection methods cannot satisfy high accuracy and low cost simultaneously, making it difficult to achieve large-scale and efficient arching detection. To this end, this paper proposed a vision-based arching detection method using track geometry data. The main works include: (1) data nonlinear deviation correction and arching characteristics analysis; (2) data conversion and augmentation; (3) design and experiments of convolutional neural network- based detection model. The results show that the proposed method can detect arching damages effectively, and the F1-score reaches 98.4%. By balancing the sample size of each pattern, the performance can be further improved. Moreover, the method outperforms the plain deep learning network. In practice, the proposed method can be employed to detect slab arching and help to make maintenance plans. The method can also be applied to the data-based detection of other structural damages and has broad prospects.
Monitoring Thermal and Non-Thermal Treatments during Processing of Muscle Foods: A Comprehensive Review of Recent Technological Advances
Authors: Hassoun Abdo , Ojha Shikha , Tiwari Brijesh , Rustad Turid , Nilsen Heidi , Heia Karsten , Cozzolino Daniel , Bekhit Alaa El-Din , Biancolillo Alessandra , Wold Jens Petter
Published: September 2020

Muscle food products play a vital role in human nutrition due to their sensory quality and high nutritional value. One well-known challenge of such products is the high perishability and limited shelf life unless suitable preservation or processing techniques are applied. Thermal processing is one of the well-established treatments that has been most commonly used in order to prepare food and ensure its safety. However, the application of inappropriate or severe thermal treatments may lead to undesirable changes in the sensory and nutritional quality of heat-processed products, and especially so for foods that are sensitive to thermal treatments, such as fish and meat and their products. In recent years, novel thermal treatments (e.g., ohmic heating, microwave) and non-thermal processing (e.g., high pressure, cold plasma) have emerged and proved to cause less damage to the quality of treated products than do conventional techniques. Several traditional assessment approaches have been extensively applied in order to evaluate and monitor changes in quality resulting from the use of thermal and non-thermal processing methods. Recent advances, nonetheless, have shown tremendous potential of various emerging analytical methods. Among these, spectroscopic techniques have received considerable attention due to many favorable features compared to conventional analysis methods. This review paper will provide an updated overview of both processing (thermal and non-thermal) and analytical techniques (traditional methods and spectroscopic ones). The opportunities and limitations will be discussed and possible directions for future research studies and applications will be suggested.
Effective RNA Regulation by Combination of Multiple Programmable RNA-Binding Proteins
Authors: Sugimoto Misaki , Suda Akiyo , Futaki Shiroh , Imanishi Miki
Published: September 2020

RNAs play important roles in gene expression through translation and RNA splicing. Regulation of specific RNAs is useful to understand and manipulate specific transcripts. Pumilio and fem-3 mRNA-binding factor (PUF) proteins, programmable RNA-binding proteins, are promising tools for regulating specific RNAs by fusing them with various functional domains. The key question is: How can PUF-based molecular tools efficiently regulate RNA functions? Here, we show that the combination of multiple PUF proteins, compared to using a single PUF protein, targeting independent RNA sequences at the 3′ untranslated region (UTR) of a target transcript caused cooperative effects to regulate the function of the target RNA by luciferase reporter assays. It is worth noting that a higher efficacy was achieved with smaller amounts of each PUF expression vector introduced into the cells compared to using a single PUF protein. This strategy not only efficiently regulates target RNA functions but would also be effective in reducing off-target effects due to the low doses of each expression vector.
Analysis of Random Dynamics of Cell Segmented by a Modified Active Contour Method
Authors: Hyun Ji Yeon , Ha Seungeon , Baek Jongmin , Han Junghun , An Honggi , Woo Sung-Hun , Kim Yoon Suk , Lee Sang Woo , Yang Sejung , Lee Sei Young
Published: September 2020

To understand the dynamics of a living system, the analysis of particular and/or cellular dynamics has been performed based on shape-based center point detection. After collecting sequential time-lapse images of cellular dynamics, the trajectory of a moving object is determined from the set of center points of the cell analyzed from each image. The accuracy of trajectory is significant in understanding the stochastic nature of the dynamics of biological objects. In this study, to localize a cellular object in time-lapse images, three different localization methods, namely radial symmetry, circular Hough transform, and modified active contour, were considered. To analyze the accuracy of cellular dynamics, several statistical parameters such as mean square displacement and velocity autocorrelation function were employed, and localization error derived from these was reported for each localization method. In particular, through denoising using a Poisson noise filter, improved localization characteristics could be achieved. The modified active contour with denoising reduced localization error significantly, and thus allowed for accurate estimation of the statistical parameters of cellular dynamics.
(+)-(E)-Chrysanthenyl Acetate: A Molecule with Interesting Biological Properties Contained in the Anthemis secundiramea (Asteraceae) Flowers
Authors: Di Napoli Michela , Maresca Viviana , Varcamonti Mario , Bruno Maurizio , Badalamenti Natale , Basile Adriana , Zanfardino Anna
Published: September 2020

Anthemis secundiramea is a perennial herb native widespread throughout the Mediterranean basin. The oil obtained from the flowers of this plant has antimicrobial properties against gram-positive and -negative bacteria, and inhibits the biofilm formation. The extract of A. secundiramea also has antioxidant activity—increasing the activity of different enzymes (SOD, CAT, and GPx). Surprisingly, in the oil extracted from the flowers, there is a single molecule, called (+)-(E)-chrysanthenyl acetate: This makes the A. secundiramea flowers extract extremely interesting for future topical, cosmetic, and nutraceutical applications.
Nanostructured Biosilica of Diatoms: From Water World to Biomedical Applications
Authors: Tramontano Chiara , Chianese Giovanna , Terracciano Monica , de Stefano Luca , Rea Ilaria
Published: September 2020

Diatoms—unicellular photosynthetic algae—are promising natural sources of nanostructured silica. These microorganisms produce in their membrane approximately a highly ordered porous cell wall called a frustule as protection from environmental stress. Diatom frustules consist of hydrated silica that show peculiar properties including biocompatibility, tailorable surface chemistry, chemical inertness, and thermal stability. Frustules harvested from aquatic ecosystems or diatomaceous fossil sediments represent an excellent cost-effective source of biosilica for a broad range of biomedical applications. The porous ultrastructure of the frustules displays a large surface area available for coating with various biomolecules through different functionalization methods. In this review article, we highlight the main features of diatom biosilica and present some of the most advantageous properties that support the employment of frustules in the field of drug delivery, biosensing, and regenerative medicine. In particular, it is offered an insight into the most common functionalization strategies through which diatom physicochemical properties can be modified and tailored according to the described field of application.
Comparing Simulations of Umbrella-Cloud Growth and Ash Transport with Observations from Pinatubo, Kelud, and Calbuco Volcanoes
Authors: Mastin Larry G. , Van Eaton Alexa R.
Published: September 2020

The largest explosive volcanic eruptions produce umbrella clouds that drive ash radially outward, enlarging the area that impacts aviation and ground-based communities. Models must consider the effects of umbrella spreading when forecasting hazards from these eruptions. In this paper we test a version of the advection–dispersion model Ash3d that considers umbrella spreading by comparing its simulations with observations from three well-documented umbrella-forming eruptions: (1) the 15 June 1991 eruption of Pinatubo (Philippines); (2) the 13 February 2014 eruption of Kelud (Indonesia); and (3) phase 2 of the 22–23 April 2015 eruption of Calbuco (Chile). In volume, these eruptions ranged from several cubic kilometers dense-rock equivalent (DRE) for Pinatubo to about one tenth for Calbuco. In mass eruption rate (MER), they ranged from 108–109 kg s−1 at Pinatubo to 9–16 × 106 kg s−1 at Calbuco. For each case we ran simulations that considered umbrella growth and ones that did not. All umbrella-cloud simulations produced a cloud whose area was within ~25% of the observed cloud by the end of the eruption. By the eruption end, the simulated areas of the Pinatubo, Kelud, and Calbuco clouds were 851, 53.2, and 100 × 103 km2 respectively. These areas were 2.2, 2.2, and 1.5 times the areas calculated in simulations that ignored umbrella growth. For Pinatubo and Kelud, the umbrella simulations provided better agreement with the observed cloud area than the non-umbrella simulations. Each of these simulations extended 24 h from the eruption start. After the eruption ended, the difference in cloud area (umbrella minus non-umbrella) at Pinatubo persisted for many hours; at Kelud it diminished and became negative after 14 h and at Calbuco it became negative after ~23 h. The negative differences were inferred to result from the fact that non-umbrella simulations distributed ash over a wider vertical extent in the plume, and that wind shear spread the cloud out in multiple directions. Thus, for some smaller eruptions, wind shear can produce a larger cloud than might be produced by umbrella spreading alone.
Reducing SO2 Doses in Red Wines by Using Grape Stem Extracts as Antioxidants
Authors: Esparza Irene , Martínez-Inda Blanca , Cimminelli María José , Jimeno-Mendoza Maria Carmen , Moler José Antonio , Jiménez-Moreno Nerea , Ancín-Azpilicueta Carmen
Published: September 2020

SO2 is a very important wine preservative. However, there are several drawbacks associated with the use of SO2 in wine. The aim of this work is to evaluate the effect of the partial substitution of SO2 in the Tempranillo wine by a Mazuelo grape stem extract and by a commercial vine wood extract (Vinetan®). The results were compared with a control sample (with no addition of any extract). After 12 months of storage in a bottle, total anthocyanin content, together with total polyphenol and flavonoid content were slightly higher for control wines than for those treated with extracts. These differences were of little relevance, as no differences in antioxidant activity were found between any of the wines at the end of the study. The sensory analysis revealed that the use of both extracts as partial substitutes of SO2 could lead to wines with good organoleptic properties, similar or even better to the control ones.
Take Advantage of Glutamine Anaplerosis, the Kernel of the Metabolic Rewiring in Malignant Gliomas
Authors: Martins Filipa , Gonçalves Luís G. , Pojo Marta , Serpa Jacinta
Published: September 2020

Glutamine is a non-essential amino acid that plays a key role in the metabolism of proliferating cells including neoplastic cells. In the central nervous system (CNS), glutamine metabolism is particularly relevant, because the glutamine-glutamate cycle is a way of controlling the production of glutamate-derived neurotransmitters by tightly regulating the bioavailability of the amino acids in a neuron-astrocyte metabolic symbiosis-dependent manner. Glutamine-related metabolic adjustments have been reported in several CNS malignancies including malignant gliomas that are considered ‘glutamine addicted’. In these tumors, glutamine becomes an essential amino acid preferentially used in energy and biomass production including glutathione (GSH) generation, which is crucial in oxidative stress control. Therefore, in this review, we will highlight the metabolic remodeling that gliomas undergo, focusing on glutamine metabolism. We will address some therapeutic regimens including novel research attempts to target glutamine metabolism and a brief update of diagnosis strategies that take advantage of this altered profile. A better understanding of malignant glioma cell metabolism will help in the identification of new molecular targets and the design of new therapies.
Omics-Based Platforms: Current Status and Potential Use for Cholangiocarcinoma
Authors: Chang Yu-Chan , Chen Ming-Huang , Yeh Chun-Nan , Hsiao Michael
Published: September 2020

Cholangiocarcinoma (CCA) has been identified as a highly malignant cancer that can be transformed from epithelial cells of the bile duct, including intrahepatic, perihilar and extrahepatic. High-resolution imaging tools (abdominal ultrasound, computed tomography and percutaneous transhepatic cholangial drainage) are recruited for diagnosis. However, the lack of early diagnostic biomarkers and treatment evaluation can lead to serious outcomes and poor prognosis (i.e., CA19-9, MUC5AC). In recent years, scientists have established a large number of omics profiles to reveal underlying mechanisms and networks (i.e., IL-6/STAT3, NOTCH). With these results, we achieved several genomic alteration events (i.e., TP53mut, KRASmut) and epigenetic modifications (i.e., DNA methylation, histone modification) in CCA cells and clinical patients. Moreover, we reviewed candidate gene (such as NF-kB, YAP1) that drive gene transcription factors and canonical pathways through transcriptomics profiles (including microarrays and next-generation sequencing). In addition, the proteomics database also indicates which molecules and their directly binding status could trigger dysfunction signatures in tumorigenesis (carbohydrate antigen 19-9, mucins). Most importantly, we collected metabolomics datasets and pivotal metabolites. These results reflect the pharmacotherapeutic options and evaluate pharmacokinetic/pharmacodynamics in vitro and in vivo. We reversed the panels and selected many potentially small compounds from the connectivity map and L1000CDS2 system. In this paper, we summarize the prognostic value of each candidate gene and correlate this information with clinical events in CCA. This review can serve as a reference for further research to clearly investigate the complex characteristics of CCA, which may lead to better prognosis, drug repurposing and treatment strategies.
Lyme Disease Biosensors: A Potential Solution to a Diagnostic Dilemma
Authors: Flynn Connor , Ignaszak Anna
Published: September 2020

Over the past four decades, Lyme disease has remained a virulent and pervasive illness, persisting throughout North America and many other regions of the world. Recent increases in illness in many countries has sparked a renewed interest in improved Lyme diagnostics. While current standards of diagnosis are acceptable for the late stages of the disease, it remains difficult to accurately diagnose early forms of the illness. In addition, current diagnostic methods tend to be relatively expensive and require a large degree of laboratory-based analysis. Biosensors represent the fusion of biological materials with chemical techniques to provide simple, inexpensive alternatives to traditional diagnostic methods. Lyme disease biosensors have the potential to better diagnose early stages of the illness and provide possible patients with an inexpensive, commercially available test. This review examines the current state of Lyme disease biosensing, with a focus on previous biosensor development and essential future considerations.
Emotional Dysregulation Mechanisms in Psychosomatic Chronic Diseases Revealed by the Instability Coefficient
Authors: Ciuluvica (Neagu) Cristina , Amerio Paolo , Grossu Ioan Valeriu
Published: September 2020

In the present work, we analyzed some emotional mechanisms (emotion dysregulation—ED, negative affect—NA, and emotional vulnerability) involved in chronic diseases by means of an interdisciplinary approach. We started from the conceptualization of emotions as a complex dynamic system that can be investigated and understood within a framework inspired by Chaos Theory. An “instability coefficient” Δ was computed to analyze ED mechanisms, NA, and emotional vulnerability in different disease groups (blood cancer, breast cancer, hypertension) as well as in healthy persons. This coefficient, recently defined by our group, computes the Euclidian distance between the pairs of vectors whose components are similar or reverted items of a test measuring ED. The emotional and somatic systems were considered as two complex dynamical systems in interaction. Due to this interaction, and as a result of the laws of complexity, a small perturbation in an inner state of the emotional system could generate an important reaction in the somatic system in time. The emotional vulnerability reflected by high values of Δ was associated with the chronic disease condition. The differences between illness groups and healthy persons, as well as between the three disease groups in Δ values, were analyzed. The results showed that there were significant differences between the chronic disease groups in Δ values. The most highly significant differences in Δ values were reported between the breast cancer group and the healthy group on one hand and between the breast cancer group and the blood cancer group on the other hand. The less significant differences in Δ values were noticed between the hypertension group and the control group. Δ was significant in predicting ED and NA. Compared to the classical approaches, the original contribution of our research is that these results encourage us to propose this interdisciplinary method of assessment as a challenging, valid tool of investigation and understanding of complex phenomena that occur in the emotional and somatic system.
Brain Response Induced with Paired Associative Stimulation Is Related to Repetition Suppression of Motor Evoked Potential
Authors: Kariminezhad Shohreh , Karhu Jari , Säisänen Laura , Reijonen Jusa , Könönen Mervi , Julkunen Petro
Published: September 2020

Repetition suppression (RS), i.e., the reduction of neuronal activity upon repetition of an external stimulus, can be demonstrated in the motor system using transcranial magnetic stimulation (TMS). We evaluated the RS in relation to the neuroplastic changes induced by paired associative stimulation (PAS). An RS paradigm, consisting of 20 trains of four identical suprathreshold TMS pulses 1 s apart, was assessed for motor-evoked potentials (MEPs) in 16 healthy subjects, before and following (at 0, 10, and 20 min) a common PAS protocol. For analysis, we divided RS into two components: (1) the ratio of the second MEP amplitude to the first one in RS trains, i.e., the “dynamic” component, and (2) the mean of the second to fourth MEP amplitudes, i.e., the “stable” component. Following PAS, five subjects showed change in the dynamic RS component. However, nearly all the individuals (n = 14) exhibited change in the stable component (p < 0.05). The stable component was similar between subjects showing increased MEPs and those showing decreased MEPs at this level (p = 0.254). The results suggest the tendency of the brain towards a stable state, probably free from the ongoing dynamics, following PAS.
Statistical Permutation Test Reveals Progressive and Region-Specific Iron Accumulation in the Thalami of Children with Aspartylglucosaminuria
Authors: Sairanen Viljami , Tokola Anna , Tikkanen Ritva , Laine Minna , Autti Taina
Published: September 2020

Aspartylglucosaminuria (AGU) is a rare lysosomal storage disorder causing developmental delay, intellectual disability, and eventual death. A distinct feature in AGU is iron accumulation within the thalamus. Our aim is to demonstrate that susceptibility-weighted images (SWI) could be used as an MRI biomarker to evaluate the response within the AGU population to newly evolving treatments. SWI from 16 patients with AGU and 16 age-matched controls were used in the analysis. Thalamic volume with an iron accumulation was identified using a permutation test. Group differences were investigated for both the complete thalamus and the iron accumulation regions. Group-wise age correlation within these volumes were assessed with analysis of variance and multivariate regression. We found a statistically significant and large difference (p-value = 0.01, Cohen’s D = 0.97) for the whole thalamus comparison and an even greater difference in the iron accumulation regions (p-value < 0.01, Cohen’s D = 3.52). Furthermore, we found strong evidence for iron accumulation as a linear function of age with R2 = 0.65 only for AGU. The statistical analysis of SWI provides tools for assessing the degree of iron accumulation. This method could be used to study the response to treatments, in that a successful treatment would be expected to result in a decline in iron accumulation.