Showing papers by "Technical University of Ostrava published in 2020"

Tissue clearing and its applications in neuroscience

Abstract: State-of-the-art tissue-clearing methods provide subcellular-level optical access to intact tissues from individual organs and even to some entire mammals. When combined with light-sheet microscopy and automated approaches to image analysis, existing tissue-clearing methods can speed up and may reduce the cost of conventional histology by several orders of magnitude. In addition, tissue-clearing chemistry allows whole-organ antibody labelling, which can be applied even to thick human tissues. By combining the most powerful labelling, clearing, imaging and data-analysis tools, scientists are extracting structural and functional cellular and subcellular information on complex mammalian bodies and large human specimens at an accelerated pace. The rapid generation of terabyte-scale imaging data furthermore creates a high demand for efficient computational approaches that tackle challenges in large-scale data analysis and management. In this Review, we discuss how tissue-clearing methods could provide an unbiased, system-level view of mammalian bodies and human specimens and discuss future opportunities for the use of these methods in human neuroscience.

Chatbots for learning: A review of educational chatbots for the Facebook Messenger

Abstract: With the exponential growth in the mobile device market over the last decade, chatbots are becoming an increasingly popular option to interact with users, and their popularity and adoption are rapidly spreading. These mobile devices change the way we communicate and allow ever-present learning in various environments. This study examined educational chatbots for Facebook Messenger to support learning. The independent web directory was screened to assess chatbots for this study resulting in the identification of 89 unique chatbots. Each chatbot was classified by language, subject matter and developer's platform. Finally, we evaluated 47 educational chatbots using the Facebook Messenger platform based on the analytic hierarchy process against the quality attributes of teaching, humanity, affect, and accessibility. We found that educational chatbots on the Facebook Messenger platform vary from the basic level of sending personalized messages to recommending learning content. Results show that chatbots which are part of the instant messaging application are still in its early stages to become artificial intelligence teaching assistants. The findings provide tips for teachers to integrate chatbots into classroom practice and advice what types of chatbots they can try out.

Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers-A study to show how popularity is affecting accuracy in social media.

Abstract: COVID-19 originally known as Corona VIrus Disease of 2019, has been declared as a pandemic by World Health Organization (WHO) on 11th March 2020. Unprecedented pressures have mounted on each country to make compelling requisites for controlling the population by assessing the cases and properly utilizing available resources. The rapid number of exponential cases globally has become the apprehension of panic, fear and anxiety among people. The mental and physical health of the global population is found to be directly proportional to this pandemic disease. The current situation has reported more than twenty four million people being tested positive worldwide as of 27th August, 2020. Therefore, it is the need of the hour to implement different measures to safeguard the countries by demystifying the pertinent facts and information. This paper aims to bring out the fact that tweets containing all handles related to COVID-19 and WHO have been unsuccessful in guiding people around this pandemic outbreak appositely. This study analyzes two types of tweets gathered during the pandemic times. In one case, around twenty three thousand most re-tweeted tweets within the time span from 1st Jan 2019 to 23rd March 2020 have been analyzed and observation says that the maximum number of the tweets portrays neutral or negative sentiments. On the other hand, a dataset containing 226,668 tweets collected within the time span between December 2019 and May 2020 have been analyzed which contrastingly show that there were a maximum number of positive and neutral tweets tweeted by netizens. The research demonstrates that though people have tweeted mostly positive regarding COVID-19, yet netizens were busy engrossed in re-tweeting the negative tweets and that no useful words could be found in WordCloud or computations using word frequency in tweets. The claims have been validated through a proposed model using deep learning classifiers with admissible accuracy up to 81%. Apart from these the authors have proposed the implementation of a Gaussian membership function based fuzzy rule base to correctly identify sentiments from tweets. The accuracy for the said model yields up to a permissible rate of 79%.

Building Fast Diffusion Channel by Constructing Metal Sulfide/Metal Selenide Heterostructures for High-Performance Sodium Ion Batteries Anode

Abstract: Heterostructure engineering is one of the most promising modification strategies toward improving sluggish kinetics for the anode of sodium ion batteries (SIBs). Herein, we report a systemic invest...

CLIJ: GPU-accelerated image processing for everyone.

Abstract: Graphics processing units (GPU) allow image processing at unprecedented speed. We present CLIJ, a Fiji plugin enabling end-users with entry level experience in programming to benefit from GPU-accelerated image processing. Freely programmable workflows can speed up image processing in Fiji by factor 10 and more using high-end GPU hardware and on affordable mobile computers with built-in GPUs.

Quantum Key Distribution: A Networking Perspective

Abstract: The convergence of quantum cryptography with applications used in everyday life is a topic drawing attention from the industrial and academic worlds. The development of quantum electronics has led to the practical achievement of quantum devices that are already available on the market and waiting for their first application on a broader scale. A major aspect of quantum cryptography is the methodology of Quantum Key Distribution (QKD), which is used to generate and distribute symmetric cryptographic keys between two geographically separate users using the principles of quantum physics. In previous years, several successful QKD networks have been created to test the implementation and interoperability of different practical solutions. This article surveys previously applied methods, showing techniques for deploying QKD networks and current challenges of QKD networking. Unlike studies focusing on optical channels and optical equipment, this survey focuses on the network aspect by considering network organization, routing and signaling protocols, simulation techniques, and a software-defined QKD networking approach.

A Review of Signal Processing Techniques for Non-Invasive Fetal Electrocardiography

Abstract: Fetal electrocardiography (fECG) is a promising alternative to cardiotocography continuous fetal monitoring. Robust extraction of the fetal signal from the abdominal mixture of maternal and fetal electrocardiograms presents the greatest challenge to effective fECG monitoring. This is mainly due to the low amplitude of the fetal versus maternal electrocardiogram and to the non-stationarity of the recorded signals. In this review, we highlight key developments in advanced signal processing algorithms for non-invasive fECG extraction and the available open access resources (databases and source code). In particular, we highlight the advantages and limitations of these algorithms as well as key parameters that must be set to ensure their optimal performance. Improving or combining the current or developing new advanced signal processing methods may enable morphological analysis of the fetal electrocardiogram, which today is only possible using the invasive scalp electrocardiography method.

Foliar Application of Low Concentrations of Titanium Dioxide and Zinc Oxide Nanoparticles to the Common Sunflower under Field Conditions.

Abstract: Nano-fertilisers have only recently been introduced to intensify plant production, and there still remains inadequate scientific knowledge on their plant-related effects This paper therefore compares the effects of two nano-fertilisers on common sunflower production under field conditions The benefits arising from the foliar application of micronutrient-based zinc oxide fertiliser were compared with those from the titanium dioxide plant-growth enhancer Both the zinc oxide (ZnO) and titanium dioxide (TiO2) were delivered by foliar application in nano-size at a concentration of 26 mg·L-1 The foliar-applied nanoparticles (NPs) had good crystallinity and a mean size distribution under 30 nm There were significant differences between these two experimental treatments in the leaf surfaces' trichomes diversity, ratio, width, and length at the flower-bud development stage Somewhat surprisingly, our results established that the ZnO-NPs treatment induced generally better sunflower physiological responses, while the TiO2-NPs primarily affected quantitative and nutritional parameters such as oil content and changed sunflower physiology to early maturation There were no differences detected in titanium or zinc translocation or accumulation in the fully ripe sunflower seeds compared to the experimental controls, and our positive results therefore encourage further nano-fertiliser research

Agrowaste derived biochars impregnated with ZnO for removal of arsenic and lead in water

Abstract: Using residual biomass for biochar production to be applied for water treatment is a cost effective and environmental-friendly alternative to activated carbon. However, biochars are materials with low textural properties (total specific area and total pore volume) and hence lower adsorption capacity compared to activated carbon. In that sense, this study aimed to impregnate ZnO on biochar derived from agricultural residual biomass to improve its As(V) and Pb(II) adsorption capacity. Biochars derived from corn cob and coffee husk were prepared by carbonization in mild conditions and then impregnated with ZnO using precipitation method. The resulting materials were comprehensively characterised describing their textural, chemical, surface, morphological and structural properties. Adsorption capacity of the produced materials was tested with As(V) and Pb(II) in kinetic and equilibrium experiments. The ZnO impregnation of the biochars derived from both precursors improves their adsorption capacities and, in most cases, accelerates the rate of adsorption of both pollutants. The best results were obtained by corncob derived ZnO impregnated biochar (CC-ZnO) reaching a maximum equilibrium adsorption capacity of 25.9 mg of As(V)/g and at least 25.8 mg of Pb(II)/g. The corncob derived ZnO impregnated biochar is a suitable adsorbent candidate for the use in the removal of As and Pb from polluted water.

Automated Detection of Acute Lymphoblastic Leukemia From Microscopic Images Based on Human Visual Perception.

Abstract: Microscopic image analysis plays a significant role in initial leukemia screening and its efficient diagnostics. Since the present conventional methodologies partly rely on manual examination, which is time consuming and depends greatly on the experience of domain experts, automated leukemia detection opens up new possibilities to minimize human intervention and provide more accurate clinical information. This paper proposes a novel approach based on conventional digital image processing techniques and machine learning algorithms to automatically identify acute lymphoblastic leukemia from peripheral blood smear images. To overcome the greatest challenges in the segmentation phase, we implemented extensive pre-processing and introduced a three-phase filtration algorithm to achieve the best segmentation results. Moreover, sixteen robust features were extracted from the images in the way that hematological experts do, which significantly increased the capability of the classifiers to recognize leukemic cells in microscopic images. To perform the classification, we applied two traditional machine learning classifiers, the artificial neural network and the support vector machine. Both methods reached a specificity of 95.31%, and the sensitivity of the support vector machine and artificial neural network reached 98.25 and 100%, respectively.

Border Collie Optimization

Abstract: In recent times, several metaheuristic algorithms have been proposed for solving real world optimization problems. In this paper, a new metaheuristic algorithm, called the Border Collie Optimization is introduced. The algorithm is developed by mimicking the sheep herding styles of Border Collie dogs. The Border Collie's unique herding style from the front as well as from the sides is adopted successfully in this paper. In this algorithm, the entire population is divided into two parts viz., dogs and sheep. This is done to equally focus on both exploration and exploitation of the search space. The Border Collie utilizes a predatory move called eyeing. This technique of the dogs is utilized to prevent the algorithm from getting stuck into local optima. A sensitivity analysis of the proposed algorithm has been carried out using the Sobol's sensitivity indices with the Sobol g-function for tuning of parameters. The proposed algorithm is applied on thirty-five benchmark functions. The proposed algorithm provides very competitive results, when compared with seven state-of-the-art algorithms like Ant Colony optimization, Differential algorithm, Genetic algorithm, Grey-wolf optimizer, Harris Hawk optimization, Particle Swarm optimization and Whale optimization algorithm. The performance of the proposed algorithm is analytically and visually tested by different methods to judge its supremacy. Finally, the statistical significance of the proposed algorithm is established by comparing it with other algorithms by employing Kruskal-Wallis test and Friedman test.

Comparison of Smoothing Filters in Analysis of EEG Data for the Medical Diagnostics Purposes.

Abstract: This paper covers a brief review of both the advantages and disadvantages of the implementation of various smoothing filters in the analysis of electroencephalography (EEG) data for the purpose of potential medical diagnostics. The EEG data are very prone to the occurrence of various internal and external artifacts and signal distortions. In this paper, three types of smoothing filters were compared: smooth filter, median filter and Savitzky–Golay filter. The authors of this paper compared those filters and proved their usefulness, as they made the analyzed data more legible for diagnostic purposes. The obtained results were promising, however, the studies on finding perfect filtering methods are still in progress.

Defect‐Enhanced CO2 Reduction Catalytic Performance in O‐Terminated MXenes

Abstract: Electrochemical carbon dioxide reduction reaction (CO2 RR) represents a promising way to generate fuels and chemical feedstock sustainably. Recently, studies have shown that two-dimensional metal carbides and nitrides (MXenes) can be promising CO2 RR electrocatalysts due to the alternating -C and -H coordination with intermediates that decouples scaling relations seen on transition metal catalysts. However, further by tuning the electronic and surface structure of MXenes it should still be possible to reach higher turnover number and selectivities. To this end, defect engineering of MXenes for electrochemical CO2 RR has not been investigated to date. In this work, first-principles modelling simulations are employed to systematically investigate CO2 RR on M2 XO2 -type MXenes with transition metal and carbon/nitrogen vacancies. We found that the -C-coordinated intermediates take the form of fragments (e. g., *COOH, *CHO) whereas the -H-coordinated intermediates form a complete molecule (e. g., *HCOOH, *H2 CO). Interestingly, the fragment-type intermediates become more strongly bound when transition-metal vacancies are present on most MXenes, while the molecule-type intermediates are largely unaffected, allowing the CO2 RR overpotential to be tuned. The most promising defective MXene is Hf2 NO2 containing Hf vacancies, with a low overpotential of 0.45 V. More importantly, through electronic structure analysis it could be observed that the Fermi level of the MXene changes significantly in the presence of vacancies, indicating that the Fermi level shift can be used as an ideal descriptor to rapidly predict the catalytic performance of defective MXenes. Such an evaluation strategy is applicable to other catalysts beyond MXenes, which could enhance high throughput screening efforts for accelerated catalyst discovery.

Regionalized tissue fluidization is required for epithelial gap closure during insect gastrulation.

Abstract: Many animal embryos pull and close an epithelial sheet around the ellipsoidal egg surface during a gastrulation process known as epiboly. The ovoidal geometry dictates that the epithelial sheet first expands and subsequently compacts. Moreover, the spreading epithelium is mechanically stressed and this stress needs to be released. Here we show that during extraembryonic tissue (serosa) epiboly in the insect Tribolium castaneum, the non-proliferative serosa becomes regionalized into a solid-like dorsal region with larger non-rearranging cells, and a more fluid-like ventral region surrounding the leading edge with smaller cells undergoing intercalations. Our results suggest that a heterogeneous actomyosin cable contributes to the fluidization of the leading edge by driving sequential eviction and intercalation of individual cells away from the serosa margin. Since this developmental solution utilized during epiboly resembles the mechanism of wound healing, we propose actomyosin cable-driven local tissue fluidization as a conserved morphogenetic module for closure of epithelial gaps.

A self-adaptive spherical search algorithm for real-world constrained optimization problems

Abstract: Determination of the global optimum of complex non-convex optimization problems of the real-world applications has remained a challenging task. Many researchers have been developing various types of effective direct search-based methods to tackle these problems. In this paper, we introduce a new variant of the recently developed Spherical Search (SS) algorithm, which contains a powerful and effective self-adaptation structure to enhance the performance. To analyze the performance, proposed algorithm is tested on the 57 test problems collected from different real-world applications. The obtained results statistically confirm the efficacy and efficiency of the proposed algorithm.

Influence of Scanning Strategy Parameters on Residual Stress in the SLM Process According to the Bridge Curvature Method for AISI 316L Stainless Steel

Abstract: The present paper deals with the investigation and comparison of the influence of scanning strategy on residual stress in the selective laser melting (SLM) process. For the purpose of the experiment, bridge geometry samples were printed by a 3D metal printer, which exhibited tension after cutting from the substrate, slightly bending the samples toward the laser melting direction. Samples were produced with the variation of process parameters and with a change in scanning strategy which plays a major role in stress generation. It was evaluated using the Bridge Curvature Method (BCM) and optical microscopy. At the end, a recommendation was made.

Effect of thermomechanical processing via rotary swaging on properties and residual stress within tungsten heavy alloy

Abstract: The effects of cold and warm rotary swaging and subsequent post-process annealing on mechanical properties, residual stress, and structure development within WNiCo powder-based pseudo-alloy were predicted numerically and investigated experimentally. The swaging temperature of 900 °C imparted increase in the Young’s and shear moduli; the post-process annealing at 900 °C also imparted decrease in the residual stress values, primarily due to structure recovery introduced within the matrix. Cold rotary swaging at 20 °C imparted ultimate tensile strength of almost 1 900 MPa, while warm rotary swaging at 900 °C introduced increased plasticity (almost 24 % after a single swaging pass). Post-process heat treatment promoted diffusion of W to the Ni/Co matrix, which increased strength, but remarkably decreased elongation to failure and residual stress. Numerically predicted results of mechanical behaviour corresponded to the experimental results and confirmed the favourable effects of the selected thermomechanical treatments on WNiCo performance.

Imaging plant germline differentiation within Arabidopsis flowers by light sheet microscopy.

Abstract: In higher plants, germline differentiation occurs during a relatively short period within developing flowers. Understanding of the mechanisms that govern germline differentiation lags behind other plant developmental processes. This is largely because the germline is restricted to relatively few cells buried deep within floral tissues, which makes them difficult to study. To overcome this limitation, we have developed a methodology for live imaging of the germ cell lineage within floral organs of Arabidopsis using light sheet fluorescence microscopy. We have established reporter lines, cultivation conditions, and imaging protocols for high-resolution microscopy of developing flowers continuously for up to several days. We used multiview imagining to reconstruct a three-dimensional model of a flower at subcellular resolution. We demonstrate the power of this approach by capturing male and female meiosis, asymmetric pollen division, movement of meiotic chromosomes, and unusual restitution mitosis in tapetum cells. This method will enable new avenues of research into plant sexual reproduction.

Influence of high temperature synthesis on the structure of graphitic carbon nitride and its hydrogen generation ability

Abstract: Graphitic carbon nitride (g-C3N4) was obtained by thermal polymerization of dicyandiamide, thiourea or melamine at high temperatures (550 and 600 °C), using different heating rates (2 or 10 °C min-1) and synthesis times (0 or 4 h) The effects of the synthesis conditions and type of the precursor on the efficiency of g-C3N4 were studied The most efficient was the synthesis from dicyandiamide, 53%, while the efficiency in the process of synthesis from melamine and thiourea were much smaller, 26% and 11%, respectively On the basis of the results provided by X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), infrared spectroscopy (FTIR), ultraviolet-visible spectroscopy (UV-vis), thermogravimetric analysis (TGA), elemental analysis (EA), the best precursor and the optimum conditions of synthesis of g-C3N4 were identified to get the product of the most stable structure, the highest degree of ordering and condensation of structure and finally the highest photocatalytic activity It was found that as the proton concentration decreased and the degree of condensation increased, the hydrogen yields during the photocatalytic decomposition of water-methanol solution were significantly enhanced The generation of hydrogen was 1200 µmol g-1 and the selectivity towards hydrogen of more than 98%

Detection of Atrial Fibrillation Episodes in Long-Term Heart Rhythm Signals Using a Support Vector Machine.

Abstract: Atrial fibrillation (AF) is a serious heart arrhythmia leading to a significant increase of the risk for occurrence of ischemic stroke. Clinically, the AF episode is recognized in an electrocardiogram. However, detection of asymptomatic AF, which requires a long-term monitoring, is more efficient when based on irregularity of beat-to-beat intervals estimated by the heart rate (HR) features. Automated classification of heartbeats into AF and non-AF by means of the Lagrangian Support Vector Machine has been proposed. The classifier input vector consisted of sixteen features, including four coefficients very sensitive to beat-to-beat heart changes, taken from the fetal heart rate analysis in perinatal medicine. Effectiveness of the proposed classifier has been verified on the MIT-BIH Atrial Fibrillation Database. Designing of the LSVM classifier using very large number of feature vectors requires extreme computational efforts. Therefore, an original approach has been proposed to determine a training set of the smallest possible size that still would guarantee a high quality of AF detection. It enables to obtain satisfactory results using only 1.39% of all heartbeats as the training data. Post-processing stage based on aggregation of classified heartbeats into AF episodes has been applied to provide more reliable information on patient risk. Results obtained during the testing phase showed the sensitivity of 98.94%, positive predictive value of 98.39%, and classification accuracy of 98.86%.

3D Hierarchical, Nanostructured Chitosan/PLA/HA Scaffolds Doped with TiO2/Au/Pt NPs with Tunable Properties for Guided Bone Tissue Engineering

Abstract: Bone tissue is the second tissue to be replaced. Annually, over four million surgical treatments are performed. Tissue engineering constitutes an alternative to autologous grafts. Its application requires three-dimensional scaffolds, which mimic human body environment. Bone tissue has a highly organized structure and contains mostly inorganic components. The scaffolds of the latest generation should not only be biocompatible but also promote osteoconduction. Poly (lactic acid) nanofibers are commonly used for this purpose; however, they lack bioactivity and do not provide good cell adhesion. Chitosan is a commonly used biopolymer which positively affects osteoblasts' behavior. The aim of this article was to prepare novel hybrid 3D scaffolds containing nanohydroxyapatite capable of cell-response stimulation. The matrixes were successfully obtained by PLA electrospinning and microwave-assisted chitosan crosslinking, followed by doping with three types of metallic nanoparticles (Au, Pt, and TiO2). The products and semi-components were characterized over their physicochemical properties, such as chemical structure, crystallinity, and swelling degree. Nanoparticles' and ready biomaterials' morphologies were investigated by SEM and TEM methods. Finally, the scaffolds were studied over bioactivity on MG-63 and effect on current-stimulated biomineralization. Obtained results confirmed preparation of tunable biomimicking matrixes which may be used as a promising tool for bone-tissue engineering.

Industry-scale application and evaluation of deep learning for drug target prediction

Abstract: Artificial intelligence (AI) is undergoing a revolution thanks to the breakthroughs of machine learning algorithms in computer vision, speech recognition, natural language processing and generative modelling. Recent works on publicly available pharmaceutical data showed that AI methods are highly promising for Drug Target prediction. However, the quality of public data might be different than that of industry data due to different labs reporting measurements, different measurement techniques, fewer samples and less diverse and specialized assays. As part of a European funded project (ExCAPE), that brought together expertise from pharmaceutical industry, machine learning, and high-performance computing, we investigated how well machine learning models obtained from public data can be transferred to internal pharmaceutical industry data. Our results show that machine learning models trained on public data can indeed maintain their predictive power to a large degree when applied to industry data. Moreover, we observed that deep learning derived machine learning models outperformed comparable models, which were trained by other machine learning algorithms, when applied to internal pharmaceutical company datasets. To our knowledge, this is the first large-scale study evaluating the potential of machine learning and especially deep learning directly at the level of industry-scale settings and moreover investigating the transferability of publicly learned target prediction models towards industrial bioactivity prediction pipelines.

Development of fresh and fully recrystallized microstructures through friction stir processing of a rare earth bearing magnesium alloy

Abstract: In the present study the friction stir processing (FSP), as an effective thermomechanical processing routine, has been conducted under the different rotational speeds on a solution treated rare earth bearing magnesium alloy. The various processing conditions result in a wide range of Zener-Holloman parameters to elaborate the desired microstructure in respect of the grain size. The obtained results indicate that applying the process on the primary solution treated material leads to the development of fully recrystallized microstructures, where the capability of refinement is directly affected through controlling the strain rate and temperature. In this regard, the resulting microstructures have been classified into “fresh and recrystallized” and “recrystallized and deformed” regions. The grain orientation speared (GOS) maps have also been employed to separate the recrystallized grains and deformed ones. Interestingly, the “recrystallized and deformed” microstructure holds a specified recrystallization texture, and exhibits unexpected hardenability during subsequent room temperature deformation. This is supported by Schmid factor analysis and fractography examinations revealing activation of basal slip and twinning systems in “recrystallized and deformed” microstructure.

Quantitative Morphology of Cerebral Thrombi Related to Intravital Contraction and Clinical Features of Ischemic Stroke.

Abstract: Background and Purpose: The purpose was to assess quantitatively and qualitatively the composition and structure of cerebral thrombi and correlate them with the signs of intravital clot contraction...