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Abstract Extraction of chemical formulas from images was not in the top priority of Computer Vision tasks for a while. The complexity both on the input and prediction sides has made this task challenging for the conventional Artificial Intelligence and Machine Learning problems. A binary input image which might seem trivial for convolutional analysis was not easy to classify, since the provided sample was not representative of the given molecule: to describe the same formula, a variety of graphical representations which do not resemble each other can be used. Considering the variety of molecules, the problem shifted from classification to that of formula generation, which makes Natural Language Processing (NLP) a good candidate for an effective solution. This paper describes the evolution of approaches from rule-based structure analyses to complex statistical models, and compares the efficiency of models and methodologies used in the recent years. Although the latest achievements deliver ideal results on particular datasets, the authors mention possible problems for various scenarios and provide suggestions for further development.

The scope of this article is to present an overview of the Density Functional based Tight Binding (DFTB) method and its applications. The paper introduces the basics of DFTB and its standard formulation up to second order. It also addresses methodological developments such as third order expansion, inclusion of non-covalent interactions, schemes to solve the self-interaction error, implementation of long-range short-range separation, treatment of excited states via the time-dependent DFTB scheme, inclusion of DFTB in hybrid high-level/low level schemes (DFT/DFTB or DFTB/MM), fragment decomposition of large systems, large scale potential energy landscape exploration with molecular dynamics in ground or excited states, non-adiabatic dynamics. A number of applications are reviewed, focusing on -(i)- the variety of systems that have been studied such as small molecules, large molecules and biomolecules, bare or functionalized clusters, supported or embedded systems, and -(ii)- properties and processes, such as vibrational spectroscopy, collisions, fragmentation, thermodynamics or non-adiabatic dynamics. Finally outlines and perspectives are given.

Abstract IgE and not IgG is usually associated with allergy. IgE lodged on mast cells in skin or gut and basophils in the blood allows for the prolonged duration of allergy through the persistent expression of high affinity IgE receptors. However, many allergic reactions are not dependent on IgE and are generated in the absence of allergen specific and even total IgE. Instead, IgG plasma cells are involved in induction of, and for much of the pathogenesis of, allergic diseases. The pattern of IgG producing plasma cells in atopic children and the tendency for direct or further class switching to IgE are the principle factors responsible for long‐lasting sensitization of mast cells in allergic children. Indirect class switching from IgG producing plasma cells has been shown to be the predominant pathway for production of IgE while a Th2 microenvironment, genetic predisposition, and the concentration and nature of allergens together act on IgG plasma cells in the atopic tendency to undergo further immunoglobulin gene recombination. The seminal involvement of IgG in allergy is further indicated by the principal role of IgG4 in the natural resolution of allergy and as the favourable immunological response to immunotherapy. This paper will look at allergy through the role of different antibodies than IgE and give current knowledge of the nature and role of IgG antibodies in the start, maintenance and resolution of allergy.

Augmented Reality (AR) tools are increasingly finding their way into education settings. Although their use is still not widespread in educational contexts, the research literature indicates their potential and effectiveness. However, overall and specifically for the education sector there are still numerous research gaps. This study investigates how the use of head-mounted AR displays such as the Microsoft HoloLens can change learning and what needs to be considered from a didactic perspective. The researched sample consists of 18 student teachers with a nature and technology teaching profile of a German-speaking university of teacher education. The data collection included a written questionnaire, video recordings of a teaching unit with HoloLens examining molecular structures, and one-to-one semi-structured interviews. The results of questionnaires and interviews presented in this paper show that all students were highly motivated to work with this technology in teacher education. The usability of the HoloLens was rated very satisfactory, although many students expressed minor problems. Most students attributed a positive impact on learning to the AR device and stated that the usage of the devices increased their motivation for learning the topic. Overall, the results show that the use of AR in teacher education is considered very valuable and should be increasingly employed in the future.

We discuss a numerical model (macro/micro/nanoscopic) to enable more accurate analysis of electro-hydro-dynamic (EMHD) processes in water at the level of atoms. Dedicated experiments have shown that inserting a relatively homogeneous periodic structure (deionized, degassed, or distilled H2O) in a magnetic field will influence the atomic basis, molecules, and relevant bonds. In this context, the present paper focuses on the designing, analysis, and evaluation of the behavior of an extensive system that represents H2O from the microscopic perspective, and it also outlines the properties and changes of the bonds in the examined water samples. Complementarily, a simple example is used to define the results obtained from analyses of the generated spiral static gradient magnetic and non-stationary gradient electromagnetic fields from the frequency range of f = 1 GHz to 10 GHz.

VAMDC bridged the gap between atomic and molecular (A&M) producers and users through providing an interoperable e-infrastructure connecting A&M databases, as well as tools to extract and manipulate those data. The current paper highlights the usage of the VAMDC Portal, recalls how data citation is implemented within VAMDC and provides insights about usage of VAMDC that will increase the impact factor of A&M producers and will offer a more reliable citation of A&M datasets included in application fields.