Applicability domains of neural networks for toxicity prediction

In this paper, the term "applicability domain" refers to the range of chemical compounds for which the statistical quantitative structure-activity relationship (QSAR) model can accurately predict their toxicity. This is a crucial concept in the development and practical use of these models. First, a multidisciplinary review is provided regarding the theory and practice of applicability domains in the context of toxicity problems using the classical QSAR model. Then, the advantages and improved performance of neural networks (NNs), which are the most promising machine learning algorithms, are reviewed. Within the domain of medicinal chemistry, nine different methods using NNs for toxicity prediction were compared utilizing 29 alternative artificial intelligence (AI) techniques. Similarly, seven NN-based toxicity prediction methodologies were compared to six other AI techniques within the realm of food safety, 11 NN-based methodologies were compared to 16 different AI approaches in the environmental sciences category and four specific NN-based toxicity prediction methodologies were compared to nine alternative AI techniques in the field of industrial hygiene. Within the reviewed approaches, given known toxic compound descriptors and behaviors, we observed a difficulty in being able to extrapolate and predict the effects with untested chemical compounds. Different methods can be used for unsupervised clustering, such as distance-based approaches and consensus-based decision methods. Additionally, the importance of model validation has been highlighted within a regulatory context according to the Organization for Economic Co-operation and Development (OECD) principles, to predict the toxicity of potential new drugs in medicinal chemistry, to determine the limits of detection for harmful substances in food to predict the toxicity limits of chemicals in the environment, and to predict the exposure limits to harmful substances in the workplace. Despite its importance, a thorough application of toxicity models is still restricted in the field of medicinal chemistry and is virtually overlooked in other scientific domains. Consequently, only a small proportion of the toxicity studies conducted in medicinal chemistry consider the applicability domain in their mathematical models, thereby limiting their predictive power to untested drugs. Conversely, the applicability of these models is crucial; however, this has not been sufficiently assessed in toxicity prediction or in other related areas such as food science, environmental science, and industrial hygiene. Thus, this review sheds light on the prevalent use of Neural Networks in toxicity prediction, thereby serving as a valuable resource for researchers and practitioners across these multifaceted domains that could be extended to other fields in future research

Experimental and Theoretical Analysis of Pressure Coupled Infusion Gyration for Fibre Production

In this work, we uncover the science of the combined application of external pressure, controlled infusion of polymer solution and gyration in the field of nanofiber preparation. This novel application takes gyration-based method into another new arena through enabling the mass production of exceedingly fine (few nanometres upwards) nanofibres in a single step. Polyethylene oxide (PEO) was used as a model polymer in the experimental study, which shows the use of this novel method to fabricate polymeric nanofibres and nanofibrous mats under different combinations of operating parameters, including working pressure, rotational speed, infusion rate and collection distance. The morphologies of the nanofibres were characterised using scanning electron microscopy, and the anisotropy of alignment of fibre was studied using two dimensional fast Fourier transform analysis. A correlation between the product morphology and the processing parameters is established. The response surface models of the experimental process were developed using the least squares fitting. A systematic description of the PCIG spinning was developed to help us obtain a clear understanding of the fibre formation process of this novel application. The input data we used are the conventional mean of fibre diameter measurements obtained from our experimental works. In this part, both linear and nonlinear fitting formats were applied, and the successes of the fitted models were mainly evaluated using Adjusted R2 and Akaike Information Criterion (AIC). The correlations and effects of individual parameters and their interactions were explicitly studied. The modelling results indicated the polymer concentration has the most significant impact on fibre diameters. A self-defined objective function was studied with the best-fitted model to optimise the experimental process for achieving the desired nanofibre diameters and narrow standard deviations. The experimental parameters were optimised by several algorithms, and the most favoured sets of parameters recommended by the non-linear interior point methods were further validated through a set of additional experiments. The results of validation indicated that pressure coupled infusion gyration offers a facile way for forming nanofibres and nanofibre assemblies, and the developed model has a good prediction power of experimental parameters that are possible to be useful for achieving the desirable PEO nanofibres

Друга міжнародна конференція зі сталого майбутнього: екологічні, технологічні, соціальні та економічні питання (ICSF 2021). Кривий Ріг, Україна, 19-21 травня 2021 року

Second International Conference on Sustainable Futures: Environmental, Technological, Social and Economic Matters (ICSF 2021). Kryvyi Rih, Ukraine, May 19-21, 2021.Друга міжнародна конференція зі сталого майбутнього: екологічні, технологічні, соціальні та економічні питання (ICSF 2021). Кривий Ріг, Україна, 19-21 травня 2021 року

OCM 2015 - 2nd International Conference on Optical Characterization of Materials: March 18th - 19th, 2015, Karlsruhe, Germany

Each material has its own specific spectral signature independent if it is food, plastics, or minerals. During the conference we will discuss new trends and developments in material characterization. You also will be informed about latest highlights to identify spectral footprints and their realizations in industry

Beating the system : accelerating commercialization of new materials

Thesis (Ph. D.)--Massachusetts Institute of Technology, Engineering Systems Division, Technology, Management, and Policy Program, February 2005.Includes bibliographical references (p. 233-249).Over the past century, materials have faced notoriously long delays between invention and commercialization. These delays make private investment very difficult, and can prevent good materials from reaching markets. A systematic exploration of the commercial histories of major commodity thermoplastics was performed, which showed that these delays were attributable to technical deficiencies in materials and obstacles in the application value chains. Contrary to popular wisdom, material costs, competitive materials, and serendipity were much smaller factors in commercialization delay. The factors that led to insertion of plastics into applications were different from the factors that led to post-insertion growth. The major plastics showed a characteristic pattern of commercialization. First, they entered simple, small applications in which they solved new problems. They then progressed to achieve insertion in a single major application, which they continue to dominate today. Having established themselves with this application, they found insertion in a wide range of large applications. The commercialization pattern can be explained in large part by the concept of switching costs. As knowledge of a material increases, switching costs are reduced; as value chain complexity increases, switching costs increase. The earliest applications required little understanding of plastics and had simple value chains, so switching costs were low, corresponding to fast commercialization. Later applications had more complex value chains and required much more detailed understanding of the failure modes and processing parameters of the material, corresponding to high switching costs and slow commercialization. Materials can be deployed into(cont.) many markets. By strategically selecting application markets, materials producers can significantly improve the probability that new materials will be adopted and can shorten the period of commercialization. Early markets should be selected based on the ability of the material to solve unique problems and the simplicity of the application value chain. When market selection is not an option, materials producers can integrate forward in the value chain to shorten commercialization times, but capital requirements are very high. Once integrated into an application, the safest competitive position for materials is to be the lowest cost option that meets the exact needs of the application.by Christopher Scott Musso.Ph.D

Shortest Route at Dynamic Location with Node Combination-Dijkstra Algorithm

Abstract— Online transportation has become a basic requirement of the general public in support of all activities to go to work, school or vacation to the sights. Public transportation services compete to provide the best service so that consumers feel comfortable using the services offered, so that all activities are noticed, one of them is the search for the shortest route in picking the buyer or delivering to the destination. Node Combination method can minimize memory usage and this methode is more optimal when compared to A* and Ant Colony in the shortest route search like Dijkstra algorithm, but can’t store the history node that has been passed. Therefore, using node combination algorithm is very good in searching the shortest distance is not the shortest route. This paper is structured to modify the node combination algorithm to solve the problem of finding the shortest route at the dynamic location obtained from the transport fleet by displaying the nodes that have the shortest distance and will be implemented in the geographic information system in the form of map to facilitate the use of the system. Keywords— Shortest Path, Algorithm Dijkstra, Node Combination, Dynamic Location (key words

PhD students´day FMST 2023

The authors gave oral presentations of their work online as part of a Doctoral Students’ Day held on 15 June 2023, and they reflect the challenging work done by the students and their supervisors in the fields of metallurgy, materials engineering and management. There are 82 contributions in total, covering a range of areas – metallurgical technology, thermal engineering and fuels in industry, chemical metallurgy, nanotechnology, materials science and engineering, and industrial systems management. This represents a cross-section of the diverse topics investigated by doctoral students at the faculty, and it will provide a guide for Master’s graduates in these or similar disciplines who are interested in pursuing their scientific careers further, whether they are from the faculty here in Ostrava or engineering faculties elsewhere in the Czech Republic. The quality of the contributions varies: some are of average quality, but many reach a standard comparable with research articles published in established journals focusing on disciplines of materials technology. The diversity of topics, and in some cases the excellence of the contributions, with logical structure and clearly formulated conclusions, reflect the high standard of the doctoral programme at the faculty.Ostrav

Forecasting nanoparticle toxicity using nonlinear predictive regressor learning systems

Nanoparticle (NP) toxicity is determined by a vast number of topological, sterical, physico-chemical as well as biological properties, rendering a priori evaluation of the effect of NP on biological tissue as arduous as it is necessary and urgent. We aimed at mining the HORIZON 2020 MODENA COST NP cytotoxicity database through nonlinear predictive regressor learning systems in order to assess the power of available NP descriptors and assay characteristics in predicting NP toxicity. Specifically, we assessed the results of cytotoxicity assays performed on 57 NP and trained two different nonlinear regressors (Support Vector Regressors [SVR] with polynomical kernels and Radial Basis Function [RBF] regressors) within a nested-cross validation scheme for parameter optimization to predict toxicity as quantified by EC25, EC50 and slope while using the regressional ReliefF algorithm (RReliefF) for feature selection. Available NP attributes were material, coating, cell type, dispersion protocol, shape, 1st and 2nd dimension, aspect ratio, surface area, zeta potential and size in situ. In most regressor learning systems, after feature selection with the RReliefF algorithm, the correlation between real and estimated toxicity endpoint values increased monotonically with the number of included features, reaching values above 0.90. The best performance was obtained with RBF regressors, and the most informative features in predicting toxicity endpoints were related to nanoparticle structure. These trends did not change significantly between toxicity endpoints. In conclusion, EC25, EC50 and slope can be predicted with high correlation using purely data-driven, machine learning methods in Adenosine triphosphate (ATP)-based NP cytotoxicity assays