bluuebunny/crossref_metadata_2025 huggingface dataset:
Interactions between Humans and Dogs during the COVID-19 Pandemic: Recent Updates and Future Perspectives
Authors: Kamel Mohamed S. , El-Sayed Amr A. , Munds Rachel A. , Verma Mohit S.
Published: February 2023

COVID-19 is one of the deadliest epidemics. This pandemic is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), but the role of dogs in spreading the disease in human society is poorly understood. This review sheds light on the limited susceptibility of dogs to COVID-19 infections which is likely attributed to the relatively low levels of angiotensin-converting enzyme 2 (ACE2) in the respiratory tract and the phylogenetic distance of ACE2 in dogs from the human ACE2 receptor. The low levels of ACE2 affect the binding affinity between spike and ACE2 proteins resulting in it being uncommon for dogs to spread the disease. To demonstrate the role of dogs in spreading COVID-19, we reviewed the epidemiological studies and prevalence of SARS-CoV-2 in dogs. Additionally, we discussed the use of detection dogs as a rapid and reliable method for effectively discriminating between SARS-CoV-2 infected and non-infected individuals using different types of samples (secretions, saliva, and sweat). We considered the available information on COVID-19 in the human–dog interfaces involving the possibility of transmission of COVID-19 to dogs by infected individuals and vice versa, the human–dog behavior changes, and the importance of preventive measures because the risk of transmission by domestic dogs remains a concern.
Natural Medicine a Promising Candidate in Combating Microbial Biofilm
Authors: Shamim Athar , Ali Asgar , Iqbal Zeenat , Mirza Mohd Aamir , Aqil Mohd , Kawish S. M. , Siddiqui Ayesha , Kumar Vijay , Naseef Punnoth Poonkuzhi , Alshadidi Abdulkhaliq Ali F. , Saheer Kuruniyan Mohamed
Published: February 2023

Studies on biofilm-related infections are gaining prominence owing to their involvement in most clinical infections and seriously threatening global public health. A biofilm is a natural form of bacterial growth ubiquitous in ecological niches, considered to be a generic survival mechanism adopted by both pathogenic and non-pathogenic microorganisms and entailing heterogeneous cell development within the matrix. In the ecological niche, quorum sensing is a communication channel that is crucial to developing biofilms. Biofilm formation leads to increased resistance to unfavourable ecological effects, comprising resistance to antibiotics and antimicrobial agents. Biofilms are frequently combated with modern conventional medicines such as antibiotics, but at present, they are considered inadequate for the treatment of multi-drug resistance; therefore, it is vital to discover some new antimicrobial agents that can prevent the production and growth of biofilm, in addition to minimizing the side effects of such therapies. In the search for some alternative and safe therapies, natural plant-derived phytomedicines are gaining popularity among the research community. Phytomedicines are natural agents derived from natural plants. These plant-derived agents may include flavonoids, terpenoids, lectins, alkaloids, polypeptides, polyacetylenes, phenolics, and essential oils. Since they are natural agents, they cause minimal side effects, so could be administered with dose flexibility. It is vital to discover some new antimicrobial agents that can control the production and growth of biofilms. This review summarizes and analyzes the efficacy characteristics and corresponding mechanisms of natural-product-based antibiofilm agents, i.e., phytochemicals, biosurfactants, antimicrobial peptides, and their sources, along with their mechanism, quorum sensing signalling pathways, disrupting extracellular matrix adhesion. The review also provides some other strategies to inhibit biofilm-related illness. The prepared list of newly discovered natural antibiofilm agents could help in devising novel strategies for biofilm-associated infections.
Pulmonary Pharmacokinetic and Pharmacodynamic Evaluation of Ampicillin/Sulbactam Regimens for Pneumonia Caused by Various Bacteria, including Acinetobacter baumannii
Authors: Onita Tetsushu , Ikawa Kazuro , Ishihara Noriyuki , Tamaki Hiroki , Yano Takahisa , Naora Kohji , Morikawa Norifumi
Published: February 2023

This study aimed to assess the dosing regimens of ampicillin/sulbactam for pneumonia based on pulmonary pharmacokinetic (PK)/pharmacodynamic (PD) target attainment. Using the literature data, we developed pulmonary PK models and estimated the probabilities of attaining PK/PD targets in lung tissue. Against bacteria other than A. baumannii (the general treatment), the PK/PD target was set as both 50% time above the minimum inhibitory concentration (T > MIC) for ampicillin and 50% T > 0.5 MIC for sulbactam. For the A. baumannii treatment, the PK/PD target was set as 60% T > MIC for sulbactam. The pulmonary PK/PD breakpoint was defined as the highest minimum inhibitory concentration (MIC) at which the target attainment probability in the lung tissue was ≥90%. The lung tissue/serum area under the drug concentration–time curve from 0 to 3 h (AUC0–3h) ratios for ampicillin and sulbactam were 0.881 and 0.368, respectively. The ampicillin/sulbactam AUC0–3h ratio in the lung tissue was 3.89. For the general treatment, the pulmonary PK/PD breakpoint for ampicillin/sulbactam at 3 g four times daily in typical patients with creatinine clearance (CLcr) of 60 mL/min was 2 μg/mL, which covered the MIC90s (the MICs that inhibited the growth of 90% of the strains) of most gram-positive and gram-negative bacteria. For the A. baumannii treatment, the pulmonary PK/PD breakpoint for ampicillin/sulbactam at 9 g 4-h infusion three times daily (27 g/day) in patients with a CLcr of 60 mL/min was 4 μg/mL, which covered the MIC90 of A. baumannii. A PK/PD evaluation for pneumonia should be performed in the lung tissue (the target site) rather than in the blood because sulbactam concentrations are lower in lung tissue. These findings should facilitate the selection of ampicillin/sulbactam regimens for pneumonia caused by various bacteria, including A. baumannii.
Nanomaterials and Coatings for Managing Antibiotic-Resistant Biofilms
Authors: Ferreres Guillem , Ivanova Kristina , Ivanov Ivan , Tzanov Tzanko
Published: February 2023

Biofilms are a global health concern responsible for 65 to 80% of the total number of acute and persistent nosocomial infections, which lead to prolonged hospitalization and a huge economic burden to the healthcare systems. Biofilms are organized assemblages of surface-bound cells, which are enclosed in a self-produced extracellular polymer matrix (EPM) of polysaccharides, nucleic acids, lipids, and proteins. The EPM holds the pathogens together and provides a functional environment, enabling adhesion to living and non-living surfaces, mechanical stability, next to enhanced tolerance to host immune responses and conventional antibiotics compared to free-floating cells. Furthermore, the close proximity of cells in biofilms facilitates the horizontal transfer of genes, which is responsible for the development of antibiotic resistance. Given the growing number and impact of resistant bacteria, there is an urgent need to design novel strategies in order to outsmart bacterial evolutionary mechanisms. Antibiotic-free approaches that attenuate virulence through interruption of quorum sensing, prevent adhesion via EPM degradation, or kill pathogens by novel mechanisms that are less likely to cause resistance have gained considerable attention in the war against biofilm infections. Thereby, nanoformulation offers significant advantages due to the enhanced antibacterial efficacy and better penetration into the biofilm compared to bulk therapeutics of the same composition. This review highlights the latest developments in the field of nanoformulated quorum-quenching actives, antiadhesives, and bactericides, and their use as colloid suspensions and coatings on medical devices to reduce the incidence of biofilm-related infections.
Virucidal Activity of Lemon Essential Oil against Feline Calicivirus Used as Surrogate for Norovirus
Authors: Pellegrini Francesco , Camero Michele , Catella Cristiana , Fracchiolla Giuseppe , Sblano Sabina , Patruno Giovanni , Trombetta Claudia Maria , Galgano Michela , Pratelli Annamaria , Tempesta Maria , Martella Vito , Lanave Gianvito
Published: February 2023

Norovirus (NoV) is regarded as a common cause of acute gastrointestinal illness worldwide in all age groups, with substantial morbidity across health care and community settings. The lack of in vitro cell culture systems for human NoV has prompted the use of cultivatable caliciviruses (such as feline calicivirus, FCV, or murine NoV) as surrogates for in vitro evaluation of antivirals. Essential oils (EOs) may represent a valid tool to counteract viral infections, particularly as food preservatives. In the present study, the virucidal efficacy of lemon EO (LEO) against FCV was assessed in vitro. The gas chromatography hyphenated with mass spectrometry (GC/MS) technique was used to reveal the chemical composition of LEO. The following small molecules were detected as major components of LEO: limonene (53%), β-pinene (14.5%), γ-terpinene (5.9%), citral (3.8%), α-pinene (2.4%), and β-thujene (1.94%). LEO at 302.0 μg/mL, exceeding the maximum non cytotoxic limit, significantly decreased viral titre of 0.75 log10 TCID50/50 μL after 8 h. Moreover, virucidal activity was tested using LEO at 3020.00 μg/mL, determining a reduction of viral titre as high as 1.25 log10 TCID50/50 μL after 8 h of time contact. These results open up perspectives for the development of alternative prophylaxis approaches for the control of NoV infection.
Cerium-Doped Self-Assembling Nanoparticles as a Novel Anti-Oxidant Delivery System Preserving Mitochondrial Function in Cortical Neurons Exposed to Ischemia-like Conditions
Authors: Nele Valeria , Tedeschi Valentina , Campani Virginia , Ciancio Raffaella , Angelillo Alessia , Graziano Sossio Fabio , De Rosa Giuseppe , Secondo Agnese
Published: February 2023

Neurodegenerative diseases are characterized by mitochondrial dysfunction leading to abnormal levels of reactive oxygen species (ROS), making the use of ROS-scavenging nanomaterials a promising therapeutic approach. Here, we combined the unique ROS-scavenging properties of cerium-based nanomaterials with the lipid self-assembling nanoparticles (SANP) technology. We optimized the preparation of cerium-doped SANP (Ce-SANP) and characterized the formulations in terms of both physiochemical and biological properties. Ce-SANP exhibited good colloidal properties and were able to mimic the activity of two ROS-scavenging enzymes, namely peroxidase and super oxide dismutase. Under ischemia-like conditions, Ce-SANP could rescue neuronal cells from mitochondrial suffering by reducing ROS production and preventing ATP level reduction. Furthermore, Ce-SANP prevented mitochondrial Ca2+ homeostasis dysfunction, partially restoring mitochondrial Ca2+ handling. Taken together, these results highlight the potential of the anti-oxidant Ce-SANP platform technology to manage ROS levels and mitochondrial function for the treatment of neurodegenerative diseases.
The Carotenoid Diatoxanthin Modulates Inflammatory and Angiogenesis Pathways In Vitro in Prostate Cancer Cells
Authors: Sansone Clementina , Pistelli Luigi , Calabrone Luana , Del Mondo Angelo , Fontana Angelo , Festa Marco , Noonan Douglas M. , Albini Adriana , Brunet Christophe
Published: February 2023

Xanthophylls, a group of carotenoids, have attracted attention as human health benefit compounds thanks to their functionality and bioavailability. The great antioxidant and anti-inflammatory abilities of diatoxanthin (Dt), a photoprotective xanthophyll synthetized by diatoms, were recently documented. This study investigates the capacity of Dt to intercept prostate cancer progression in vitro on different human cell lines, exploring its role against cancer proliferation and angiogenesis. Our results highlighted the chemopreventive role of Dt already at low concentration (44.1 pM) and suggest that the Dt-induced cancer cell death occurred through oxidative stress mechanisms. This hypothesis was supported by variations on the expression of key genes and proteins. Oxidative stress cell deaths (e.g., ferroptosis) are recently described types of cell death that are closely related to the pathophysiological processes of many diseases, such as tumors. Nonetheless, the interest of Dt was further strengthened by its ability to inhibit angiogenesis. The results are discussed considering the actual progress and requirements in cancer therapy, notably for prostate cancer.
Dietary Antioxidants and Chronic Diseases
Authors: Zujko Małgorzata Elżbieta , Witkowska Anna Maria
Published: February 2023

Chronic diseases, most notably diabetes, cancer, cardiovascular diseases, neurodegenerative diseases, thyroid diseases, and allergic diseases are major causes of death, disability, and a lower quality of life in various populations [...]
Nondestructive Determination of Leaf Nitrogen Content in Corn by Hyperspectral Imaging Using Spectral and Texture Fusion
Authors: Wu Ling , Gong Yuanjuan , Bai Xiaoping , Wang Wei , Wang Zhuo
Published: February 2023

The nitrogen content is an important indicator affecting corn plants’ growth status. Most of the standard hyperspectral imaging-based techniques for nondestructive detection of crop nitrogen content use a single feature as the input variable of the model, which reduces the generalization ability of the prediction model. To this end, a prediction model for the nitrogen content of corn leaves based on the fusion of image and spectral features is proposed. In this study, corn leaves at the modulation stage were studied, samples with different nitrogen levels were numbered, and their hyperspectral data in the wavelength range of 400~1100 nm were collected. The average spectrum of the models was used as valid spectral information. First-order derivatives, standard normal variables transformation (SNV), Savitzky-Golay (S-G) smoothing, and normalization were selected to preprocess the spectral features. The CARS-SPA algorithm was used to screen sensitive spectral variables. The gray level co-currency matrix (GLCM) was chosen to extract the texture image features of the test samples. Corn leaf spectral and texture image features were fused and modeled as target features. Partial least squares regression (PLSR) and support vector machine regression (SVR) were used to predict corn leaves’ nitrogen content. The results showed that the image and spectral-based fusion models improved the prediction performance to some extent compared to the univariate models. The PLSR model based on feature fusion predicted the best results, in which the RP2 and RMSEP were 0.987 and 0.047. This method provides a reliable theoretical basis and technical support for developing nondestructive and accurate detection of nitrogen content in corn leaves.
Polyphenol Content, Mineral Compounds Composition, Antimicrobial and Antioxidant Activities of Selected Medicinal Herbs from Slovak Republic
Authors: Ivanišová Eva , Horňák Marek , Čech Matej , Harangozo Ľuboš , Kačániová Miroslava , Grygorieva Olga , Kowalczewski Przemysław Łukasz
Published: February 2023

The aim of this study was to determine antioxidant activity (DPPH and phosphomolybdenum method), polyphenols content (total polyphenols, flavonoids, and phenolic acids), mineral compounds composition (Cu, Zn, Mn, Fe, Cr, Ni, Co, Pb and Cd) and antimicrobial activity (with disc diffusion method) of medicinal herbs traditionally used in the Slovak republic. The tested plants belonged to the Primulaceae, Urticaceae, Grossulariaceae, Rosaceae, Lamiaceae, Asteraceae, Equisetaceae, Tropaeolaceae, and Plantaginaceae families. The highest antioxidant activities were found in samples of Rosa canina L. (DPPH—29.43 ± 0.11 mg TE/g; TE—Trolox equivalent) and Fragaria vesca L. (phosphomolybdenum method—679.56 ± 3.06 mg TE/g), both from the Rosaceae family. Total polyphenols (determined using the Folin–Ciocâlteu-reagent) were most abundant in a sample of Fragaria vesca L.—124.51 ± 5.05 mg GAE/g (GAE—gallic acid equivalent), total flavonoids (determined using the aluminum chloride method)—in a sample of Primula veris L.—48.35 ± 3.77 mg QE/g (QE—quercetin equivalent), and total phenolic acids (determined using Arnova reagent)—in a sample of Thymus serpyllum L.—102.31 ± 2.89 mg CAE/g (CAE—caffeic acid equivalent). Regarding mineral compounds composition, samples of Fragaria vesca L. and Thymus serpyllum L. showed the highest levels of iron. In samples of Calendula officinalis L. and Trapaeolum majus L., the highest amounts of zinc were determined, while copper was the most abundant in samples of Urtica dioica L. and Melissa officinalis L. The amounts of heavy metals were within legally acceptable limits. The extract of Equisetum arvense L. showed the strongest inhibitory activity towards Clostridium perfringens CCM 4991 (6 mm), while the one from Mentha piperita L.—towards Candida glabrata CCM 8270 (4.83 mm) and Candida tropicalis CCM 8223 (4.33 mm).
Vibration Assessment of a 12-kW Self-Propelled Riding-Type Automatic Onion Transplanter for Transplanting Performance and Operator Comfort
Authors: Chowdhury Milon , Reza Md Nasim , Ali Mohammod , Kabir Md Shaha Nur , Kiraga Shafik , Lim Seung-Jin , Choi Il-Su , Chung Sun-Ok
Published: February 2023

Vibration assessment of upland crop machinery under development is essential because high vibrational exposures affect machine efficiency, service life of components, degradation of the working environment, and cause health risks to the operator. It is intensively assessed for automobiles as well as large off-road agricultural vehicles (i.e., tractors). However, it is mostly overlooked in the case of the small or medium riding-type upland utility vehicles. Therefore, the vibration exposures of a 12-kilowatt self-propelled riding-type automatic onion transplanter were measured and evaluated to assess the performance of onion transplantation and the operator’s comfort in this study. Different types of driving surfaces, operating statuses (static and driving), and load conditions were considered to analyze the vibration exposure. The precision of transplantations was evaluated while operating the transplanter on the soil surface with different driving speeds and load conditions. Tri-axial accelerometers and a LabVIEW-coded program were used for data acquisition. The vibrational exposures were evaluated based on ISO standards, and power spectral density (PSD) was estimated to assess the major frequencies. According to the statistical analysis, the daily exposure value (A(8)) and the vibration dose value (VDV) varied from 10 to 15 ms−2 and 20 to 31 ms−1.75, respectively, which exceeded the ISO 2631-1 standards (i.e., A(8): 1.15 ms−2 and VDV: 21 ms−1.75). The calculated health risk factor (RA) was moderate. Moreover, a high weighted acceleration (around 8 ms−2) was observed on the seedling conveyor belt, which might result in missing seedlings during transplanting. The vibration exposures of the developed onion transplanter need to be minimized following the ISO standards, and vibration reduction would also improve the market competitiveness.
Bridge Health Monitoring Using Proper Orthogonal Decomposition and Transfer Learning
Authors: Ardani Samira , Eftekhar Azam Saeed , Linzell Daniel G.
Published: February 2023

This study focuses on developing and examining the effectiveness of Transfer Learning (TL) for structural health monitoring (SHM) systems that transfer knowledge about damage states from one structure (i.e., the source domain) to another structure (i.e., the target domain). Transfer Learning (TL) is an efficient method for knowledge transfer and mapping from source to target domains. In addition, Proper Orthogonal Modes (POMs), which help classify behavior and health, provide a promising tool for damage identification in structural systems. Previous investigations show that damage intensity and location are highly correlated with POM variations for structures under unknown loads. To train damage identification algorithms based on POMs and ML, one generally needs to use multiple simulations to generate damage scenarios. The developed process is applied to a simply supported truss span in a multi-span railway bridge. TL is first used to obtain relationships between POMs for two modeled bridges: one being a source model (i.e., labeled) and the other being the target modeled bridge (i.e., unlabeled). This technique is then implemented to develop POMs for a damaged, unknown target using TL that links source and target POMs. It is shown that the trained knowledge from one bridge was effectively generalized to other, somewhat similar, bridges in the population.
Metamodelling of Manufacturing Processes and Automation Workflows towards Designing and Operating Digital Twins
Authors: Stavropoulos Panagiotis , Papacharalampopoulos Alexios , Sabatakakis Kyriakos , Mourtzis Dimitris
Published: February 2023

The automation of workflows for the optimization of manufacturing processes through digital twins seems to be achievable nowadays. The enabling technologies of Industry 4.0 have matured, while the plethora of available sensors and data processing methods can be used to address functionalities related to manufacturing processes, such as process monitoring and control, quality assessment and process modelling. However, technologies succeeding Computer-Integrated Manufacturing and several promising techniques, such as metamodelling languages, have not been exploited enough. To this end, a framework is presented, utilizing an automation workflow knowledge database, a classification of technologies and a metamodelling language. This approach will be highly useful for creating digital twins for both the design and operation of manufacturing processes, while keeping humans in the loop. Two process control paradigms are used to illustrate the applicability of such an approach, under the framework of certifiable human-in-the-loop process optimization.
Selection of a Photovoltaic Panel Cooling Technique Using Multi-Criteria Decision Analysis
Authors: Kaneesamkandi Zakariya , Rehman Ateekh Ur
Published: February 2023

Photovoltaic panel performance in terms of its efficiency and durability is severely affected by operating temperature when the temperature is much higher than the nominal operating cell temperature in hot climates. Different cooling methods have been reported over several decades, but photovoltaic panel manufacturers or users are yet to adopt a popular method of panel cooling. This is the main concern of the present work. Potential cooling solutions differ in terms of their criteria for performance evaluation, which are efficiency enhancement, costs, reliability, environmental aspects and ergonomics. Hence, there is a need to identify the optimum cooling method. Eight different cooling methods were identified, and the analysis was made with the multi-criteria analysis tool on the different possible attributes. Two different climate zones with different weight schemes are considered for the evaluation process, and the best to the worst cooling solutions have been identified. Five different scenarios depending on the importance given to each evaluation criterion are analyzed. The best cooling method to the worst cooling method has been arranged under each scenario. When the efficiency of operation was given maximum weight, aluminum fin cooling proved to be the best panel cooling method. When the emission reduction criterion was given maximum weight, thermosiphon cooling was the best cooling option. A comparison of the results indicates that thermosiphon works out to be the best option. The second-best method was found to be forced convection cooling when equal weights were applied and thermosiphon cooling when a 40% weight on efficiency enhancement criteria was applied, which is a more practical weight distribution. Phase change cooling and forced convection cooling had the poorest performance among the different cooling methods for all the weighing scenarios.
Design of a Low-Energy Earth-Moon Flight Trajectory Using a Planar Auxiliary Problem
Authors: Nikolichev Ilya , Sesyukalov Vladimir
Published: February 2023

The paper presents a sufficiently simple technique for designing a low-energy flight trajectory of a spacecraft (SC) from the Earth to the Moon with insertion into a low circular orbit of the latter. The proposed technique is based on the solution and subsequent analysis of a special model problem, which is a variant of the restricted circular four-body problem (RC4BP) Earth-Sun-SC-Moon; for which it is assumed that the planes of the orbits of all considered bodies coincides. The planar motion of the center of mass of the SC relative to the Earth is considered as perturbed (Sun, Moon). To describe it, equations in osculating elements are used, obtained by using the method of variation of constants based on the analytical solution of the planar circular restricted problem of two bodies (RC2BP)—Earth-SC, for which the rotation of the main axes of the coordinate system (the main plane) is synchronized with the motion of the Sun. The trajectory problem of designing a SC flight from a low circular near-Earth orbit to a low circular selenocentric one (“full” motion model—a restricted four-body problem (R4BP), an ephemeris model) is considered as an optimization one in the impulse formulation. The solution of the main problem is carried out in few (three) stages, on each the appropriate solution of the current variant of the auxiliary problem is determined, which is subsequently used as the basis of the initial approximation to the main one.
Maximization of CO2 Capture Capacity Using Recent RUNge Kutta Optimizer and Fuzzy Model
Authors: Nassef Ahmed M. , Rezk Hegazy , Alahmer Ali , Abdelkareem Mohammad Ali
Published: February 2023

This study aims to identify the optimal operating parameters for the carbon dioxide (CO2) capture process using a combination of artificial intelligence and metaheuristics techniques. The main objective of the study is to maximize CO2 capture capacity. The proposed method integrates fuzzy modeling with the RUNge Kutta optimizer (RUN) to analyze the impact of three operational factors: carbonation temperature, carbonation duration, and H2O-to-CO2 flow rate ratio. These factors are considered to maximize the CO2 capture. A fuzzy model was developed based on the measured data points to simulate the CO2 capture process in terms of the stated parameters. The model was then used to identify the optimal values of carbonation temperature, carbonation duration, and H2O-to-CO2 flow rate ratio using RUN. The results of the proposed method are then compared with an optimized performance using the response surface methodology (RSM) and measured data to demonstrate the superiority of the proposed strategy. The results of the study showed that the suggested technique increased the CO2 capture capacity from 6.39 to 6.99 by 10.08% and 9.39%, respectively, compared to the measured and RSM methods. This implies that the proposed method is an effective approach to maximize the CO2 capture capacity. The results of this study can be used to improve the performance of the CO2 capture process in various industrial applications.
A Study of The Stochastic Burgers’ Equation Using The Dynamical Orthogonal Method
Authors: El-Beltagy Mohamed , Mahdi Ragab , Noor Adeeb
Published: February 2023

In the current work, the stochastic Burgers’ equation is studied using the Dynamically Orthogonal (DO) method. The DO presents a low-dimensional representation for the stochastic fields. Unlike many other methods, it has a time-dependent property on both the spatial basis and stochastic coefficients, with flexible representation especially in the strongly transient and nonstationary problems. We consider a computational study and application of the DO method and compare it with the Polynomial Chaos (PC) method. For comparison, both the stochastic viscous and inviscid Burgers’ equations are considered. A hybrid approach, combining the DO and PC is proposed in case of deterministic initial conditions to overcome the singularities that occur in the DO method. The results are verified with the stochastic collocation method. Overall, we observe that the DO method has a higher rate of convergence as the number of modes increases. The DO method is found to be more efficient than PC for the same level of accuracy, especially for the case of high-dimensional parametric spaces. The inviscid Burgers’ equation is analyzed to study the shock wave formation when using the DO after suitable handling of the convective term. The results show that the sinusoidal wave shape is distorted and sharpened as the time evolves till the shock wave occurs.
The Threat of Adversarial Attack on a COVID-19 CT Image-Based Deep Learning System
Authors: Li Yang , Liu Shaoying
Published: February 2023

The coronavirus disease 2019 (COVID-19) rapidly spread around the world, and resulted in a global pandemic. Applying artificial intelligence to COVID-19 research can produce very exciting results. However, most research has focused on applying AI techniques in the study of COVID-19, but has ignored the security and reliability of AI systems. In this paper, we explore adversarial attacks on a deep learning system based on COVID-19 CT images with the aim of helping to address this problem. Firstly, we built a deep learning system that could identify COVID-19 CT images and non-COVID-19 CT images with an average accuracy of 76.27%. Secondly, we attacked the pretrained model with an adversarial attack algorithm, i.e., FGSM, to cause the COVID-19 deep learning system to misclassify the CT images, and the classification accuracy of non-COVID-19 CT images dropped from 80% to 0%. Finally, in response to this attack, we proposed how a more secure and reliable deep learning model based on COVID-19 medical images could be built. This research is based on a COVID-19 CT image recognition system, which studies the security of a COVID-19 CT image-based deep learning system. We hope to draw more researchers’ attention to the security and reliability of medical deep learning systems.
The Importance of Studying Factors That Affect the In Vitro Evaluation of Semen Quality to Predict Potential Fertility in Males
Authors: Silvestre Miguel Angel , Soler Carles , Mocé Eva , Roldan Eduardo R. S. , Yániz Jesús L.
Published: February 2023

The presence of sub-fertile or infertile males in farms or artificial insemination (AI) centres has a great impact on the reproductive and economic performance of the livestock industry [...]
KinFams: De-Novo Classification of Protein Kinases Using CATH Functional Units
Authors: Adeyelu Tolulope , Bordin Nicola , Waman Vaishali P. , Sadlej Marta , Sillitoe Ian , Moya-Garcia Aurelio A. , Orengo Christine A.
Published: February 2023

Protein kinases are important targets for treating human disorders, and they are the second most targeted families after G-protein coupled receptors. Several resources provide classification of kinases into evolutionary families (based on sequence homology); however, very few systematically classify functional families (FunFams) comprising evolutionary relatives that share similar functional properties. We have developed the FunFam-MARC (Multidomain ARchitecture-based Clustering) protocol, which uses multi-domain architectures of protein kinases and specificity-determining residues for functional family classification. FunFam-MARC predicts 2210 kinase functional families (KinFams), which have increased functional coherence, in terms of EC annotations, compared to the widely used KinBase classification. Our protocol provides a comprehensive classification for kinase sequences from >10,000 organisms. We associate human KinFams with diseases and drugs and identify 28 druggable human KinFams, i.e., enriched in clinically approved drugs. Since relatives in the same druggable KinFam tend to be structurally conserved, including the drug-binding site, these KinFams may be valuable for shortlisting therapeutic targets. Information on the human KinFams and associated 3D structures from AlphaFold2 are provided via our CATH FTP website and Zenodo. This gives the domain structure representative of each KinFam together with information on any drug compounds available. For 32% of the KinFams, we provide information on highly conserved residue sites that may be associated with specificity.
The Extracellular Vesicles Proteome of Endometrial Cells Simulating the Receptive Menstrual Phase Differs from That of Endometrial Cells Simulating the Non-Receptive Menstrual Phase
Authors: Hart Amber Rose , Khan Norhayati Liaqat Ali , Dissanayake Keerthie , Godakumara Kasun , Andronowska Aneta , Eapen Saji , Heath Paul R , Fazeli Alireza
Published: February 2023

Successful embryo implantation into a receptive endometrium requires mutual endometrial-embryo communication. Recently, the function of extracellular vehicles (EVs) in cell-to-cell interaction in embryo-maternal interactions has been investigated. We explored isolated endometrial-derived EVs, using RL95-2 cells as a model of a receptive endometrium, influenced by the menstrual cycle hormones estrogen (E2; proliferative phase), progesterone (P4; secretory phase), and estrogen plus progesterone (E2P4; the receptive phase). EV sized particles were isolated by differential centrifugation and size exclusion chromatography. Nanoparticle tracking analysis was used to examine the different concentrations and sizes of particles and EV proteomic analysis was performed using shotgun label-free mass spectrometry. Our results showed that although endometrial derived EVs were secreted in numbers independent of hormonal stimulation, EV sizes were statistically modified by it. Proteomics analysis showed that hormone treatment changes affect the endometrial EV’s proteome, with proteins enhanced within the EV E2P4 group shown to be involved in different processes, such as embryo implantation, endometrial receptivity, and embryo development, supporting the concept of a communication system between the embryo and the maternal endometrium via EVs.
Early Prediction of Dementia Using Feature Extraction Battery (FEB) and Optimized Support Vector Machine (SVM) for Classification
Authors: Javeed Ashir , Dallora Ana Luiza , Berglund Johan Sanmartin , Idrisoglu Alper , Ali Liaqat , Rauf Hafiz Tayyab , Anderberg Peter
Published: February 2023

Dementia is a cognitive disorder that mainly targets older adults. At present, dementia has no cure or prevention available. Scientists found that dementia symptoms might emerge as early as ten years before the onset of real disease. As a result, machine learning (ML) scientists developed various techniques for the early prediction of dementia using dementia symptoms. However, these methods have fundamental limitations, such as low accuracy and bias in machine learning (ML) models. To resolve the issue of bias in the proposed ML model, we deployed the adaptive synthetic sampling (ADASYN) technique, and to improve accuracy, we have proposed novel feature extraction techniques, namely, feature extraction battery (FEB) and optimized support vector machine (SVM) using radical basis function (rbf) for the classification of the disease. The hyperparameters of SVM are calibrated by employing the grid search approach. It is evident from the experimental results that the newly pr oposed model (FEB-SVM) improves the dementia prediction accuracy of the conventional SVM by 6%. The proposed model (FEB-SVM) obtained 98.28% accuracy on training data and a testing accuracy of 93.92%. Along with accuracy, the proposed model obtained a precision of 91.80%, recall of 86.59, F1-score of 89.12%, and Matthew’s correlation coefficient (MCC) of 0.4987. Moreover, the newly proposed model (FEB-SVM) outperforms the 12 state-of-the-art ML models that the researchers have recently presented for dementia prediction.
Antimicrobial and Antiviral Compounds of Phlomis viscosa Poiret
Authors: Yarmolinsky Ludmila , Nakonechny Faina , Budovsky Arie , Zeigerman Haim , Khalfin Boris , Sharon Eyal , Yarmolinsky Leonid , Ben-Shabat Shimon , Nisnevitch Marina
Published: February 2023

Phlomis viscosa Poiret (an evergreen shrub) represents a valuable source of medicinal compounds. In this study, we discovered compounds with antimicrobial and antiviral properties. The aim of this study was to identify compounds of P. viscosa and estimate the antimicrobial and antiviral activity of its phytochemicals. The volatile compounds were identified using gas chromatography/mass spectrometry (GC/MS) analysis. For the identification of nonvolatile components of the extracts, high-performance liquid chromatography (HPLC), liquid chromatography–electrospray ionization-mass spectrometry (LC-ESI-MS) and matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry (MALDI-TOF-MS) were applied. Quercetin 3-O-rutinoside and hesperidin caused a significant decrease in the bacterial concentration of Agrobacterium tumefaciens, Xylella fastidiosa and Pseudomonas syringae (p < 0.001). The growth of drug-resistant microorganisms (Escherichia coli, Klebsiella pneumoniae, Acinetobacter baumannii, Serratia marcescens and Salmonella enteritidis) was inhibited by quercetin 3-O-rutinoside, quercetin 3-O-arabinoside and hesperidin. In addition, these compounds demonstrated antiquorum-sensing properties. Diosmin, hesperidin and quercetin 3-O-arabinoside significantly inhibited varicella zoster virus (VZV) (p < 0.001). Quercetin 3-O-rutinoside and quercetin 3-O-arabinoside were effective against herpes simplex virus 1 (HSV-1), including mutant strains.
The Interaction of Gut Microbiota and Heart Failure with Preserved Ejection Fraction: From Mechanism to Potential Therapies
Authors: Yu Wei , Jiang Yufeng , Xu Hui , Zhou Yafeng
Published: February 2023

Heart failure with preserved ejection fraction (HFpEF) is a disease for which there is no definite and effective treatment, and the number of patients is more than 50% of heart failure (HF) patients. Gut microbiota (GMB) is a general term for a group of microbiota living in humans’ intestinal tracts, which has been proved to be related to cardiovascular diseases, including HFpEF. In HFpEF patients, the composition of GMB is significantly changed, and there has been a tendency toward dysbacteriosis. Metabolites of GMB, such as trimethylamine N-oxide (TMAO), short-chain fatty acids (SCFAs) and bile acids (BAs) mediate various pathophysiological mechanisms of HFpEF. GMB is a crucial influential factor in inflammation, which is considered to be one of the main causes of HFpEF. The role of GMB in its important comorbidity—metabolic syndrome—also mediates HFpEF. Moreover, HF would aggravate intestinal barrier impairment and microbial translocation, further promoting the disease progression. In view of these mechanisms, drugs targeting GMB may be one of the effective ways to treat HFpEF. This review focuses on the interaction of GMB and HFpEF and analyzes potential therapies.
Lab-on-a-Chip Electrochemical Biosensors for Foodborne Pathogen Detection: A Review of Common Standards and Recent Progress
Authors: Zolti Or , Suganthan Baviththira , Ramasamy Ramaraja P.
Published: February 2023

Foodborne pathogens are an important diagnostic target for the food, beverage, and health care industries due to their prevalence and the adverse effects they can cause to public health, food safety, and the economy. The standards that determine whether a given type of food is fit for consumption are set by governments and must be taken into account when designing a new diagnostic tool such as a biosensor platform. In order to meet these stringent detection limits, cost, and reliability standards, recent research has been focused on developing lab-on-a-chip-based approaches for detection devices that use microfluidic channels and platforms. The microfluidics-based devices are designed, developed, and used in different ways to achieve the established common standards for food pathogen testing that enable high throughput, rapid detection, low sample volume, and minimal pretreatment procedures. Combining microfluidic approaches with electrochemical biosensing could offer affordable, portable, and easy to use devices for food pathogen diagnostics. This review presents an analysis of the established common standards and the recent progress made in electrochemical sensors toward the development of future lab-on-a-chip devices that will aid ‘collection-to-detection’ using a single method and platform.
Effects of Patchwise Sampling Strategy to Three-Dimensional Convolutional Neural Network-Based Alzheimer’s Disease Classification
Authors: Shen Xiaoqi , Lin Lan , Xu Xinze , Wu Shuicai
Published: February 2023

In recent years, the rapid development of artificial intelligence has promoted the widespread application of convolutional neural networks (CNNs) in neuroimaging analysis. Although three-dimensional (3D) CNNs can utilize the spatial information in 3D volumes, there are still some challenges related to high-dimensional features and potential overfitting issues. To overcome these problems, patch-based CNNs have been used, which are beneficial for model generalization. However, it is unclear how the choice of a patchwise sampling strategy affects the performance of the Alzheimer’s Disease (AD) classification. To this end, the present work investigates the impact of a patchwise sampling strategy for 3D CNN based AD classification. A 3D framework cascaded by two-stage subnetworks was used for AD classification. The patch-level subnetworks learned feature representations from local image patches, and the subject-level subnetwork combined discriminative feature representations from all patch-level subnetworks to generate a classification score at the subject level. Experiments were conducted to determine the effect of patch partitioning methods, the effect of patch size, and interactions between patch size and training set size for AD classification. With the same data size and identical network structure, the 3D CNN model trained with 48 × 48 × 48 cubic image patches showed the best performance in AD classification (ACC = 89.6%). The model trained with hippocampus-centered, region of interest (ROI)-based image patches showed suboptimal performance. If the pathological features are concentrated only in some regions affected by the disease, the empirically predefined ROI patches might be the right choice. The better performance of cubic image patches compared with cuboidal image patches is likely related to the pathological distribution of AD. The image patch size and training sample size together have a complex influence on the performance of the classification. The size of the image patches should be determined based on the size of the training sample to compensate for noisy labels and the problem of the curse of dimensionality. The conclusions of the present study can serve as a reference for the researchers who wish to develop a superior 3D patch-based CNN model with an appropriate patch sampling strategy.
Increasing Couriers’ Job Satisfaction through Social-Sustainability Practices: Perceived Fairness and Psychological-Safety Perspectives
Authors: Pang Qiwei , Fang Mingjie , Wang Lu , Mi Kena , Su Miao
Published: February 2023

Due to the spike in online-retail demand during the pandemic, couriers confront increased workload and safety concerns, posing significant social-sustainability challenges for courier companies. This study explores the impact of social-sustainability practices on couriers’ job satisfaction in the context of the COVID-19 pandemic. We designed the research model from the theoretical lens of Maslow’s hierarchy of needs, equity, and psychological-safety theories. We collected the views of 428 couriers from the Chinese market, where there is a developed e-commerce industry. The structural-equation-model analysis results found that social-sustainability practices such as working environment, working conditions, health and safety, education, and training positively affected the job satisfaction of couriers during the pandemic through the mediators (psychological safety and perceived fairness). These findings provide empirical recommendations for improving employees’ job satisfaction in courier companies during COVID-19 and addressing the social-sustainability issues of courier companies.
The Role of CSR Information on Social Media to Promote the Communicative Behavior of Customers: An Emotional Framework Enriching Behavioral Sciences Literature
Authors: Li Zhijuan , Sial Muhammad Safdar , Wu Hualiang , Căpușneanu Sorinel , Barbu Cristian-Marian
Published: February 2023

Studies have shown that an organization’s corporate social responsibility (CSR) activities affect customer behaviors such as loyalty and satisfaction. In spite of this, the role of social media in informing customers about a brand’s CSR activities and in fostering customer advocacy behavior (CADB) has been underexplored. To fill this knowledge gap, this study investigates the relationship between the CSR-related communication of a banking organization and CADB. This study also examines how emotions such as customer–company identification (CCI) and gratitude as a mediator and a moderator. Using a self-administered questionnaire (n = 302), we collected data from banking customers. Hypotheses were evaluated by using structural equation modeling, which revealed that CSR positively predicts CADB, whereas there are mediating and moderating functions of CCI and GA. Theoretically, this study highlights the role of human emotions in behavior formation from the standpoint of social media. Practically, this study provides important insights for the banking sector’s administrators to realize the important role of CSR communication, using different social networking websites, for converting customers into brand advocates.
The Use of Advanced Environmentally Friendly Systems in the Insulation and Reconstruction of Buildings
Authors: Zach Jiří , Novák Vítězslav , Peterková Jitka , Bubeník Jan , Košir Mitja , Božiček David , Krejza Zdeněk
Published: February 2023

This study is devoted to the possibility of using advanced insulation materials, such as Vacuum Insulation Panels (VIP), in the insulation and reconstruction of buildings, in connection with the green elements that are installed on the facade in the case of the use of external thermal insulation composite systems (ETICS). The use of VIP as part of the insulation system will result in a significant reduction in the required thickness of the insulation layer. In turn, the reduced overall thickness of the system will allow for easier direct anchoring of the elements of the green facade through the insulating layer to the base of the structure. The research carried out proves that, by using VIP in the insulation system (with a VIP thickness of 30 mm in combination with 20 mm of extruded polystyrene XPS), the thermal insulation properties can be significantly improved and, thus, the thickness of the insulation system can be reduced to 1/3 of the thickness of conventional insulation (while achieving the same thermal resistance), thereby enabling the anchoring of green elements on the surface of such an insulation system.