Showing papers by "Wrocław University of Technology published in 2021"

SARS-CoV-2 M pro inhibitors and activity-based probes for patient-sample imaging

Abstract: In December 2019, the first cases of infection with a novel coronavirus, SARS-CoV-2, were diagnosed. Currently, there is no effective antiviral treatment for COVID-19. To address this emerging problem, we focused on the SARS-CoV-2 main protease that constitutes one of the most attractive antiviral drug targets. We have synthesized a combinatorial library of fluorogenic substrates with glutamine in the P1 position. We used it to determine the substrate preferences of the SARS-CoV and SARS-CoV-2 main proteases. On the basis of these findings, we designed and synthesized a potent SARS-CoV-2 inhibitor (Ac-Abu-DTyr-Leu-Gln-VS, half-maximal effective concentration of 3.7 µM) and two activity-based probes, for one of which we determined the crystal structure of its complex with the SARS-CoV-2 Mpro. We visualized active SARS-CoV-2 Mpro in nasopharyngeal epithelial cells of patients suffering from COVID-19 infection. The results of our work provide a structural framework for the design of inhibitors as antiviral agents and/or diagnostic tests.

Thermoelectric generator (TEG) technologies and applications

Abstract: Nowadays humans are facing difficult issues, such as increasing power costs, environmental pollution and global warming. In order to reduce their consequences, scientists are concentrating on improving power generators focused on energy harvesting. Thermoelectric generators (TEGs) have demonstrated their capacity to transform thermal energy directly into electric power through the Seebeck effect. Due to the unique advantages they present, thermoelectric systems have emerged during the last decade as a promising alternative among other technologies for green power production. In this regard, thermoelectric device output prediction is important both for determining the future use of this new technology and for specifying the key design parameters of thermoelectric generators and systems. Moreover, TEGs are environmentally safe, work quietly as they do not include mechanical mechanisms or rotating elements and can be manufactured on a broad variety of substrates such as silicon, polymers and ceramics. In addition, TEGs are position-independent, have a long working life and are ideal for bulk and compact applications. Furthermore, Thermoelectric generators have been found as a viable solution for direct generation of electricity from waste heat in industrial processes. This paper presents in-depth analysis of TEGs, beginning with a comprehensive overview of their working principles such as the Seebeck effect, the Peltier effect, the Thomson effect and Joule heating with their applications, materials used, Figure of Merit, improvement techniques including different thermoelectric material arrangements and technologies used and substrate types. Moreover, performance simulation examples such as COMSOL Multiphysics and ANSYS-Computational Fluid Dynamics are investigated.

3D printing filament as a second life of waste plastics-a review.

Abstract: In recent times, the issue of plastic recycling has become one of the leading issues of environmental protection and waste management. Polymer materials have been found an application in many areas of daily life and industry. Along with their extended use, the problem of plastic wastes appeared because, after withdrawal from use, they became persistent and noxious wastes. The possibility of reusing polymeric materials gives a possibility of valorization-a second life-and enables effective waste utilization to obtain consumable products. The 3D printing market is a well-growing sector. Printable filaments can be made from a variety of thermoplastic materials, including those from recycling. This paper focuses on a review of the available literature on the production of filaments for 3D printers from recycled polymers as the alternative to present approach of central selective collection of plastics. The possibility of recycling of basic thermoplastic materials and the impact of processing on their physicochemical and mechanical properties were verified (Lanzotti et al. 2019). In addition, commercially available filaments produced from recycled materials and devices which allow self-production of filaments to 3D printing from plastic waste were reviewed.

Review and Comparison of Commonly Used Activation Functions for Deep Neural Networks

Abstract: The primary neural networks’ decision-making units are activation functions. Moreover, they evaluate the output of networks neural node; thus, they are essential for the performance of the whole network. Hence, it is critical to choose the most appropriate activation function in neural networks calculation. Acharya et al. (2018) suggest that numerous recipes have been formulated over the years, though some of them are considered deprecated these days since they are unable to operate properly under some conditions. These functions have a variety of characteristics, which are deemed essential to successfully learning. Their monotonicity, individual derivatives, and finite of their range are some of these characteristics. This research paper will evaluate the commonly used additive functions, such as swish, ReLU, Sigmoid, and so forth. This will be followed by their properties, own cons and pros, and particular formula application recommendations.

SARS-CoV-2 in animals: potential for unknown reservoir hosts and public health implications.

Abstract: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, previously 2019-nCoV) is suspected of having originated in 2019 in China from a coronavirus infected bat of the genus Rhinolophus. Following the initial emergence, possibly facilitated by a mammalian bridge host, SARS-CoV-2 is currently transmitted across the globe via efficient human-to-human transmission. Results obtained from experimental studies indicate that animal species such as cats, ferrets, raccoon dogs, cynomolgus macaques, rhesus macaques, white-tailed deer, rabbits, Egyptian fruit bats, and Syrian hamsters are susceptible to SARS-CoV-2 infection, and that cat-to-cat and ferret-to-ferret transmission can take place via contact and air. However, natural infections of SARS-CoV-2 have been reported only in pet dogs and cats, tigers, lions, snow leopards, pumas, and gorillas at zoos, and farmed mink and ferrets. Even though human-to-animal spillover has been reported at several instances, SARS-CoV-2 transmission from animals-to-humans has only been reported from mink-to-humans in mink farms. Following the rapid transmission of SARS-CoV-2 within the mink population, a new mink-associated SARS-CoV-2 variant emerged that was identified in both humans and mink. The increasing reports of SARS-CoV-2 in carnivores indicate the higher susceptibility of animal species belonging to this order. The sporadic reports of SARS-CoV-2 infection in domestic and wild animal species require further investigation to determine if SARS-CoV-2 or related Betacoronaviruses can get established in kept, feral or wild animal populations, which may eventually act as viral reservoirs. This review analyzes the current evidence of SARS-CoV-2 natural infection in domestic and wild animal species and their possible implications on public health.

Nutritional significance of amino acids, vitamins and minerals as nutraceuticals in poultry production and health – a comprehensive review

Abstract: Nutraceuticals have gained immense importance in poultry science recently considering the nutritional and beneficial health effects of their constituents. Besides providing nutritional requirements to birds, nutraceuticals have beneficial pharmacological effects, for example, they help in establishing normal physiological health status, prevent diseases and thereby improve production performance. Nutraceuticals include amino acids, vitamins, minerals, enzymes, etc. which are important for preventing oxidative stress, regulating the immune response and maintaining normal physiological, biochemical and homeostatic mechanisms. Nutraceuticals help in supplying nutrients in balanced amounts for supporting the optimal growth performance in modern poultry flocks, and as a dietary supplement can reduce the use of antibiotics. The application of antibiotic growth enhancers in poultry leads to the propagation of antibiotic-resistant microbes and drug residues; therefore, they have been restricted in many countries. Thus, there is a demand for natural feed additives that lead to the same growth enhancement without affecting the health. Nutraceuticals substances have an essential role in the development of the animals' normal physiological functions and in protecting them against infectious diseases. In this review, the uses of amino acids, vitamins and minerals as well as their mode of action in growth promotion and elevation of immune system are discussed.

Prediction of Compressive Strength of Fly Ash Based Concrete Using Individual and Ensemble Algorithm.

Abstract: Machine learning techniques are widely used algorithms for predicting the mechanical properties of concrete This study is based on the comparison of algorithms between individuals and ensemble approaches, such as bagging Optimization for bagging is done by making 20 sub-models to depict the accurate one Variables like cement content, fine and coarse aggregate, water, binder-to-water ratio, fly-ash, and superplasticizer are used for modeling Model performance is evaluated by various statistical indicators like mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) Individual algorithms show a moderate bias result However, the ensemble model gives a better result with R2 = 0911 compared to the decision tree (DT) and gene expression programming (GEP) K-fold cross-validation confirms the model’s accuracy and is done by R2, MAE, MSE, and RMSE Statistical checks reveal that the decision tree with ensemble provides 25%, 121%, and 49% enhancement for errors like MAE, MSE, and RMSE between the target and outcome response

Prediction of Chronic Kidney Disease - A Machine Learning Perspective

Abstract: Chronic Kidney Disease is one of the most critical illness nowadays and proper diagnosis is required as soon as possible. Machine learning technique has become reliable for medical treatment. With the help of a machine learning classifier algorithms, the doctor can detect the disease on time. For this perspective, Chronic Kidney Disease prediction has been discussed in this article. Chronic Kidney Disease dataset has been taken from the UCI repository. Seven classifier algorithms have been applied in this research such as artificial neural network, C5.0, Chi-square Automatic interaction detector, logistic regression, linear support vector machine with penalty L1 & with penalty L2 and random tree. The important feature selection technique was also applied to the dataset. For each classifier, the results have been computed based on (i) full features, (ii) correlation-based feature selection, (iii) Wrapper method feature selection, (iv) Least absolute shrinkage and selection operator regression, (v) synthetic minority over-sampling technique with least absolute shrinkage and selection operator regression selected features, (vi) synthetic minority over-sampling technique with full features. From the results, it is marked that LSVM with penalty L2 is giving the highest accuracy of 98.86% in synthetic minority over-sampling technique with full features. Along with accuracy, precision, recall, F-measure, area under the curve and GINI coefficient have been computed and compared results of various algorithms have been shown in the graph. Least absolute shrinkage and selection operator regression selected features with synthetic minority over-sampling technique gave the best after synthetic minority over-sampling technique with full features. In the synthetic minority over-sampling technique with least absolute shrinkage and selection operator selected features, again linear support vector machine gave the highest accuracy of 98.46%. Along with machine learning models one deep neural network has been applied on the same dataset and it has been noted that deep neural network achieved the highest accuracy of 99.6%.

Identification of ebselen and its analogues as potent covalent inhibitors of papain-like protease from SARS-CoV-2.

Abstract: An efficient treatment against a COVID-19 disease, caused by the novel coronavirus SARS-CoV-2 (CoV2), remains a challenge. The papain-like protease (PLpro) from the human coronavirus is a protease that plays a critical role in virus replication. Moreover, CoV2 uses this enzyme to modulate the host's immune system to its own benefit. Therefore, it represents a highly promising target for the development of antiviral drugs. We used Approximate Bayesian Computation tools, molecular modelling and enzyme activity studies to identify highly active inhibitors of the PLpro. We discovered organoselenium compounds, ebselen and its structural analogues, as a novel approach for inhibiting the activity of PLproCoV2. Furthermore, we identified, for the first time, inhibitors of PLproCoV2 showing potency in the nanomolar range. Moreover, we found a difference between PLpro from SARS and CoV2 that can be correlated with the diverse dynamics of their replication, and, putatively to disease progression.

[Methylhydrazinium]2PbBr4, a Ferroelectric Hybrid Organic–Inorganic Perovskite with Multiple Nonlinear Optical Outputs

Abstract: An expansive library of structurally complex two-dimensional (2D) and three-dimensional (3D) lead halide perovskites has emerged over the past decade, finding applications in various aspects of pho

Objective comparison of methods to decode anomalous diffusion.

Abstract: Deviations from Brownian motion leading to anomalous diffusion are found in transport dynamics from quantum physics to life sciences. The characterization of anomalous diffusion from the measurement of an individual trajectory is a challenging task, which traditionally relies on calculating the trajectory mean squared displacement. However, this approach breaks down for cases of practical interest, e.g., short or noisy trajectories, heterogeneous behaviour, or non-ergodic processes. Recently, several new approaches have been proposed, mostly building on the ongoing machine-learning revolution. To perform an objective comparison of methods, we gathered the community and organized an open competition, the Anomalous Diffusion challenge (AnDi). Participating teams applied their algorithms to a commonly-defined dataset including diverse conditions. Although no single method performed best across all scenarios, machine-learning-based approaches achieved superior performance for all tasks. The discussion of the challenge results provides practical advice for users and a benchmark for developers.

A Review on the Adaption of Alginate-Gelatin Hydrogels for 3D Cultures and Bioprinting.

Abstract: Sustaining the vital functions of cells outside the organism requires strictly defined parameters In order to ensure their optimal growth and development, it is necessary to provide a range of nutrients and regulators Hydrogels are excellent materials for 3D in vitro cell cultures Their ability to retain large amounts of liquid, as well as their biocompatibility, soft structures, and mechanical properties similar to these of living tissues, provide appropriate microenvironments that mimic extracellular matrix functions The wide range of natural and synthetic polymeric materials, as well as the simplicity of their physico-chemical modification, allow the mechanical properties to be adjusted for different requirements Sodium alginate-based hydrogel is a frequently used material for cell culture The lack of cell-interactive properties makes this polysaccharide the most often applied in combination with other materials, including gelatin The combination of both materials increases their biological activity and improves their material properties, making this combination a frequently used material in 3D printing technology The use of hydrogels as inks in 3D printing allows the accurate manufacturing of scaffolds with complex shapes and geometries The aim of this paper is to provide an overview of the materials used for 3D cell cultures, which are mainly alginate–gelatin hydrogels, including their properties and potential applications

A look into the future of the COVID-19 pandemic in Europe: an expert consultation.

Abstract: How will the coronavirus disease 2019 (COVID-19) pandemic develop in the coming months and years? Based on an expert survey, we examine key aspects that are likely to influence the COVID-19 pandemic in Europe. The challenges and developments will strongly depend on the progress of national and global vaccination programs, the emergence and spread of variants of concern (VOCs), and public responses to non-pharmaceutical interventions (NPIs). In the short term, many people remain unvaccinated, VOCs continue to emerge and spread, and mobility and population mixing are expected to increase. Therefore, lifting restrictions too much and too early risk another damaging wave. This challenge remains despite the reduced opportunities for transmission given vaccination progress and reduced indoor mixing in summer 2021. In autumn 2021, increased indoor activity might accelerate the spread again, whilst a necessary reintroduction of NPIs might be too slow. The incidence may strongly rise again, possibly filling intensive care units, if vaccination levels are not high enough. A moderate, adaptive level of NPIs will thus remain necessary. These epidemiological aspects combined with economic, social, and health-related consequences provide a more holistic perspective on the future of the COVID-19 pandemic.

Objective comparison of methods to decode anomalous diffusion

Abstract: Deviations from Brownian motion leading to anomalous diffusion are ubiquitously found in transport dynamics, playing a crucial role in phenomena from quantum physics to life sciences. The detection and characterization of anomalous diffusion from the measurement of an individual trajectory are challenging tasks, which traditionally rely on calculating the mean squared displacement of the trajectory. However, this approach breaks down for cases of important practical interest, e.g., short or noisy trajectories, ensembles of heterogeneous trajectories, or non-ergodic processes. Recently, several new approaches have been proposed, mostly building on the ongoing machine-learning revolution. Aiming to perform an objective comparison of methods, we gathered the community and organized an open competition, the Anomalous Diffusion challenge (AnDi). Participating teams independently applied their own algorithms to a commonly-defined dataset including diverse conditions. Although no single method performed best across all scenarios, the results revealed clear differences between the various approaches, providing practical advice for users and a benchmark for developers.

COVID-19 Impact on Operation and Energy Consumption of Heating, Ventilation and Air-Conditioning (HVAC) Systems

Abstract: Heating, ventilation and air-conditioning (HVAC) system is favourable for regulating indoor temperature, relative humidity, airflow pattern and air quality. However, HVAC systems may turn out to be the culprit of microbial contamination in enclosed spaces and deteriorate the environment due to inappropriate design and operation. In the context of COVID-19, significant transformations and new requirements are occurring in HVAC systems. Recently, several updated operational guidelines for HVAC systems have been issued by various institutions to control the airborne transmission and mitigate infection risks in enclosed environments. Challenges and innovations emerge in response to operational variations of HVAC systems. To efficiently prevent the spread of the pandemic and reduce infection risks, it is essential to have an overall understanding of impacts caused by COVID-19 on HVAC systems. Therefore, the objectives of this article are to: (a) provide a comprehensive review of the airborne transmission characteristics of SARS-CoV-2 in enclosed spaces and a theoretical basis for HVAC operation guideline revision; (b) investigate HVAC-related guidelines to clarify the operational variations of HVAC systems during the pandemic; (c) analyse how operational variations of HVAC systems affect energy consumption; and (d) identify the innovations and research trends concerning future HVAC systems. Furthermore, this paper compares the energy consumption of HVAC system operation during the normal times versus pandemic period, based on a case study in China, providing a reference for other countries around the world. Results of this paper offer comprehensive insights into how to keep indoor environments safe while maintaining energy-efficient operation of HVAC systems.

Role of PFKFB3 and PFKFB4 in Cancer: Genetic Basis, Impact on Disease Development/Progression, and Potential as Therapeutic Targets.

Abstract: Glycolysis is a crucial metabolic process in rapidly proliferating cells such as cancer cells. Phosphofructokinase-1 (PFK-1) is a key rate-limiting enzyme of glycolysis. Its efficiency is allosterically regulated by numerous substances occurring in the cytoplasm. However, the most potent regulator of PFK-1 is fructose-2,6-bisphosphate (F-2,6-BP), the level of which is strongly associated with 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase activity (PFK-2/FBPase-2, PFKFB). PFK-2/FBPase-2 is a bifunctional enzyme responsible for F-2,6-BP synthesis and degradation. Four isozymes of PFKFB (PFKFB1, PFKFB2, PFKFB3, and PFKFB4) have been identified. Alterations in the levels of all PFK-2/FBPase-2 isozymes have been reported in different diseases. However, most recent studies have focused on an increased expression of PFKFB3 and PFKFB4 in cancer tissues and their role in carcinogenesis. In this review, we summarize our current knowledge on all PFKFB genes and protein structures, and emphasize important differences between the isoenzymes, which likely affect their kinase/phosphatase activities. The main focus is on the latest reports in this field of cancer research, and in particular the impact of PFKFB3 and PFKFB4 on tumor progression, metastasis, angiogenesis, and autophagy. We also present the most recent achievements in the development of new drugs targeting these isozymes. Finally, we discuss potential combination therapies using PFKFB3 inhibitors, which may represent important future cancer treatment options.

Caution, "normal" BMI: health risks associated with potentially masked individual underweight-EPMA Position Paper 2021.

Abstract: An increasing interest in a healthy lifestyle raises questions about optimal body weight. Evidently, it should be clearly discriminated between the standardised “normal” body weight and individually optimal weight. To this end, the basic principle of personalised medicine “one size does not fit all” has to be applied. Contextually, “normal” but e.g. borderline body mass index might be optimal for one person but apparently suboptimal for another one strongly depending on the individual genetic predisposition, geographic origin, cultural and nutritional habits and relevant lifestyle parameters—all included into comprehensive individual patient profile. Even if only slightly deviant, both overweight and underweight are acknowledged risk factors for a shifted metabolism which, if being not optimised, may strongly contribute to the development and progression of severe pathologies. Development of innovative screening programmes is essential to promote population health by application of health risks assessment, individualised patient profiling and multi-parametric analysis, further used for cost-effective targeted prevention and treatments tailored to the person. The following healthcare areas are considered to be potentially strongly benefiting from the above proposed measures: suboptimal health conditions, sports medicine, stress overload and associated complications, planned pregnancies, periodontal health and dentistry, sleep medicine, eye health and disorders, inflammatory disorders, healing and pain management, metabolic disorders, cardiovascular disease, cancers, psychiatric and neurologic disorders, stroke of known and unknown aetiology, improved individual and population outcomes under pandemic conditions such as COVID-19. In a long-term way, a significantly improved healthcare economy is one of benefits of the proposed paradigm shift from reactive to Predictive, Preventive and Personalised Medicine (PPPM/3PM). A tight collaboration between all stakeholders including scientific community, healthcare givers, patient organisations, policy-makers and educators is essential for the smooth implementation of 3PM concepts in daily practice.

Application of Novel Machine Learning Techniques for Predicting the Surface Chloride Concentration in Concrete Containing Waste Material.

Abstract: Structures located on the coast are subjected to the long-term influence of chloride ions, which cause the corrosion of steel reinforcements in concrete elements. This corrosion severely affects the performance of the elements and may shorten the lifespan of an entire structure. Even though experimental activities in laboratories might be a solution, they may also be problematic due to time and costs. Thus, the application of individual machine learning (ML) techniques has been investigated to predict surface chloride concentrations (Cc) in marine structures. For this purpose, the values of Cc in tidal, splash, and submerged zones were collected from an extensive literature survey and incorporated into the article. Gene expression programming (GEP), the decision tree (DT), and an artificial neural network (ANN) were used to predict the surface chloride concentrations, and the most accurate algorithm was then selected. The GEP model was the most accurate when compared to ANN and DT, which was confirmed by the high accuracy level of the K-fold cross-validation and linear correlation coefficient (R2), mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) parameters. As is shown in the article, the proposed method is an effective and accurate way to predict the surface chloride concentration without the inconveniences of laboratory tests.

Self-esteem and cyberchondria: The mediation effects of health anxiety and obsessive–compulsive symptoms in a community sample

Abstract: Cyberchondria refers to the excessive and repeated searching for medical information on the Internet and may be considered as health-related problematic Internet use. Previous findings indicated that cyberchondria is positively associated with health anxiety and obsessive–compulsive symptoms. Also, research suggests that excessive or problematic Internet use as well as health worries and compulsive behaviors are present among individuals with low self-esteem. This study sought to examine: (1) the association between self-esteem and cyberchondria, and (2) the mediating role of health anxiety and obsessive–compulsive symptoms in the relationship between self-esteem and cyberchondria. Participants (N = 207) from a community sample completed self-report measures assessing global self-esteem, health anxiety, obsessive–compulsive symptoms, and cyberchondria. We found that self-esteem directly predicted cyberchondria and that health anxiety and obsessive–compulsive symptoms parallelly mediated the relationship between self-esteem and cyberchondria. These findings suggest that low self-esteem, health anxiety and obsessive–compulsive symptoms can be considered vulnerability factors for cyberchondria. In addition, the reverse mediation model indicated that cyberchondria potentially predicts self-esteem both directly and through health anxiety and obsessive–compulsive symptoms. The bidirectional relationship among the analyzed variables are discussed in the context of potential psychological predictors and consequences of cyberchondria and possible mechanisms explaining cyberchondria. The current study provides further insight into the conceptualization of cyberchondria and the feasibility of specific treatment directions.

A review of membrane crystallization, forward osmosis and membrane capacitive deionization for liquid mining

Abstract: Socio-economic development and new technological advancements have greatly increased the demand for metals, minerals and nutrients. Thus, substantial interest in developing technologies to recover these commodities from seawater, various brines and wastewater streams (industrial and domestic) has emerged. Less explored and innovative membrane processes including membrane crystallization (MCr), forward osmosis (FO) and membrane capacitive deionization (MCDI) are gaining interest in this regard. The current study provides a critical review of the current trends in applying MCr, FO and MCDI for recovery of metals, minerals and nutrients from various streams. The processes are compared in terms of types of fouling, energy consumption, overall composition of suitable feed solutions, feasible concentration ranges and potential to recover the targeted metal from a multi-component solution. The ultimate objective is to establish future research directions for further improvement of each process and to identify which of the processes is more suitable under a given scenario.

Metal-insulator transition in transition metal dichalcogenide heterobilayer moiré superlattices

Abstract: Moir\'e superlattices formed in two-dimensional semiconductor heterobilayers provide a new realization of Hubbard model physics in which the number of electrons per effective atom can be tuned at will. We report on an exact diagonalization study of the electronic properties of half-filled narrow moir\'e bands in which correlation strengths are varied by changing twist angles or interaction strengths. We construct a phase diagram for the bilayer, identifying where the metal-insulator phase transition occurs, estimating the sizes of the charge gaps in the insulating phase, and commenting on the nature of the transition and the importance of subdominant interaction parameters.