Tautomerism in large databases

We have used the Chemical Structure DataBase (CSDB) of the NCI CADD Group, an aggregated collection of over 150 small-molecule databases totaling 103.5 million structure records, to conduct tautomerism analyses on one of the largest currently existing sets of real (i.e. not computer-generated) compounds. This analysis was carried out using calculable chemical structure identifiers developed by the NCI CADD Group, based on hash codes available in the chemoinformatics toolkit CACTVS and a newly developed scoring scheme to define a canonical tautomer for any encountered structure. CACTVS’s tautomerism definition, a set of 21 transform rules expressed in SMIRKS line notation, was used, which takes a comprehensive stance as to the possible types of tautomeric interconversion included. Tautomerism was found to be possible for more than 2/3 of the unique structures in the CSDB. A total of 680 million tautomers were calculated from, and including, the original structure records. Tautomerism overlap within the same individual database (i.e. at least one other entry was present that was really only a different tautomeric representation of the same compound) was found at an average rate of 0.3% of the original structure records, with values as high as nearly 2% for some of the databases in CSDB. Projected onto the set of unique structures (by FICuS identifier), this still occurred in about 1.5% of the cases. Tautomeric overlap across all constituent databases in CSDB was found for nearly 10% of the records in the collection

State of the Art on Neural Rendering

Efficient rendering of photo-realistic virtual worlds is a long standing effort of computer graphics. Modern graphics techniques have succeeded in synthesizing photo-realistic images from hand-crafted scene representations. However, the automatic generation of shape, materials, lighting, and other aspects of scenes remains a challenging problem that, if solved, would make photo-realistic computer graphics more widely accessible. Concurrently, progress in computer vision and machine learning have given rise to a new approach to image synthesis and editing, namely deep generative models. Neural rendering is a new and rapidly emerging field that combines generative machine learning techniques with physical knowledge from computer graphics, e.g., by the integration of differentiable rendering into network training. With a plethora of applications in computer graphics and vision, neural rendering is poised to become a new area in the graphics community, yet no survey of this emerging field exists. This state-of-the-art report summarizes the recent trends and applications of neural rendering. We focus on approaches that combine classic computer graphics techniques with deep generative models to obtain controllable and photo-realistic outputs. Starting with an overview of the underlying computer graphics and machine learning concepts, we discuss critical aspects of neural rendering approaches. This state-of-the-art report is focused on the many important use cases for the described algorithms such as novel view synthesis, semantic photo manipulation, facial and body reenactment, relighting, free-viewpoint video, and the creation of photo-realistic avatars for virtual and augmented reality telepresence. Finally, we conclude with a discussion of the social implications of such technology and investigate open research problems

Challenges in QCD matter physics - The Compressed Baryonic Matter experiment at FAIR

Substantial experimental and theoretical efforts worldwide are devoted to explore the phase diagram of strongly interacting matter. At LHC and top RHIC energies, QCD matter is studied at very high temperatures and nearly vanishing net-baryon densities. There is evidence that a Quark-Gluon-Plasma (QGP) was created at experiments at RHIC and LHC. The transition from the QGP back to the hadron gas is found to be a smooth cross over. For larger net-baryon densities and lower temperatures, it is expected that the QCD phase diagram exhibits a rich structure, such as a first-order phase transition between hadronic and partonic matter which terminates in a critical point, or exotic phases like quarkyonic matter. The discovery of these landmarks would be a breakthrough in our understanding of the strong interaction and is therefore in the focus of various high-energy heavy-ion research programs. The Compressed Baryonic Matter (CBM) experiment at FAIR will play a unique role in the exploration of the QCD phase diagram in the region of high net-baryon densities, because it is designed to run at unprecedented interaction rates. High-rate operation is the key prerequisite for high-precision measurements of multi-differential observables and of rare diagnostic probes which are sensitive to the dense phase of the nuclear fireball. The goal of the CBM experiment at SIS100 (sqrt(s_NN) = 2.7 - 4.9 GeV) is to discover fundamental properties of QCD matter: the phase structure at large baryon-chemical potentials (mu_B > 500 MeV), effects of chiral symmetry, and the equation-of-state at high density as it is expected to occur in the core of neutron stars. In this article, we review the motivation for and the physics programme of CBM, including activities before the start of data taking in 2022, in the context of the worldwide efforts to explore high-density QCD matter.Comment: 15 pages, 11 figures. Published in European Physical Journal

Gauging the Effectiveness of Educational Technology Integration in Education: What the Best-Quality Meta-Analyses Tell Us

This chapter examines quantitative research in the literature of technology integration in education from the perspective of the meta-analyses of primary studies conducted from 1982 to 2015. The intent is to identify and review the best of these meta-analyses. Fifty-two meta-analyses were originally identified and evaluated for methodological quality using the Meta-Analysis Methodological Quality Review Guide (MMQRG), and the best 20 were selected and are included for review here. Some describe the effects of technology integration within specific content areas and some are more general. Technology integration in education is one of the most fluid areas of research, reflecting the incredible pace of the evolution of computer-based tools and applications. Just navigating through the vast primary empirical literature presents a real challenge to those interested in evaluating the educational effectiveness of technology. Systematic reviews in the field are numerous and quite diverse in their methodological quality, introducing potential bias in the interpretation of findings (Bernard RM, Borokhovski E, Schmid RF, Tamim RM. J Comput High Educ 26(3):183–209, 2014), thus bringing into question their applied value. This chapter identifies and reviews the best of these meta-analyses. In addition to overall statistical analyses of this collection, the findings of six of the most recent and best meta-analyses (after 2010) are summarized in more detail. The discussion focuses on the interpretation of the current findings, considers future alternatives to primary research in this area, and examines how meta-analysts might address them

Professional Learning Through Everyday Work: How Finance Professionals Self-Regulate Their Learning

Professional learning is a critical component of ongoing improvement and innovation and the adoption of new practices in the workplace. Professional learning is often achieved through learning embedded in everyday work tasks. However, little is known about how professionals self-regulate their learning through regular work activities. This paper explores how professionals in the finance sector (n-30) self-regulate their learning through day-to-day work. Analysis focuses on three sub-processes of self-regulated learning that have been identified as significant predictors of good self-regulated learning at work. A key characteristic of good self-regulation is viewing learning as a form of long-term, personalised self-improvement. This study provides a foundation for future policy and planning in organisations aiming to encourage self-regulated learning

The learning styles neuromyth:when the same term means different things to different teachers

Alexia Barrable - ORCID: 0000-0002-5352-8330 https://orcid.org/0000-0002-5352-8330Although learning styles (LS) have been recognised as a neuromyth, they remain a virtual truism within education. A point of concern is that the term LS has been used within theories that describe them using completely different notions and categorisations. This is the first empirical study to investigate education professionals’ conceptualisation, as well as means of identifying and implementing LS in their classroom. A sample of 123 education professionals were administered a questionnaire consisting both closed- and open-ended questions. Responses were analysed using thematic analysis. LS were found to be mainly conceptualised within the Visual-Auditory-(Reading)-Kinaesthetic (VAK/VARK) framework, as well as Gardner’s multiple intelligences. Moreover, a lot of education professionals confused theories of learning (e.g., behavioural or cognitive theories) with LS. In terms of identifying LS, educators reported using a variety of methods, spanning from observation and everyday contact to the use of tests. The ways LS were implemented in the classroom were numerous, comprising various teaching aids, participatory techniques and motor activities. Overall, we argue that the extended use of the term LS gives the illusion of a consensus amongst educators, when a closer examination reveals that the term LS is conceptualised, identified and implemented idiosyncratically by different individuals. This study aims to be of use to pre-service and in-service teacher educators in their effort to debunk the neuromyth of LS and replace it with evidence-based practices.https://doi.org/10.1007/s10212-020-00485-236pubpub

The serious games ecosystem: Interdisciplinary and intercontextual praxis

This chapter will situate academia in relation to serious games commercial production and contextual adoption, and vice-versa. As a researcher it is critical to recognize that academic research of serious games does not occur in a vaccum. Direct partnerships between universities and commercial organizations are increasingly common, as well as between research institutes and the contexts that their serious games are deployed in. Commercial production of serious games and their increased adoption in non-commercial contexts will influence academic research through emerging impact pathways and funding opportunities. Adding further complexity is the emergence of commercial organizations that undertake their own research, and research institutes that have inhouse commercial arms. To conclude, we explore how these issues affect the individual researcher, and offer considerations for future academic and industry serious games projects

Identification of regulatory variants associated with genetic susceptibility to meningococcal disease

Non-coding genetic variants play an important role in driving susceptibility to complex diseases but their characterization remains challenging. Here, we employed a novel approach to interrogate the genetic risk of such polymorphisms in a more systematic way by targeting specific regulatory regions relevant for the phenotype studied. We applied this method to meningococcal disease susceptibility, using the DNA binding pattern of RELA - a NF-kB subunit, master regulator of the response to infection - under bacterial stimuli in nasopharyngeal epithelial cells. We designed a custom panel to cover these RELA binding sites and used it for targeted sequencing in cases and controls. Variant calling and association analysis were performed followed by validation of candidate polymorphisms by genotyping in three independent cohorts. We identified two new polymorphisms, rs4823231 and rs11913168, showing signs of association with meningococcal disease susceptibility. In addition, using our genomic data as well as publicly available resources, we found evidences for these SNPs to have potential regulatory effects on ATXN10 and LIF genes respectively. The variants and related candidate genes are relevant for infectious diseases and may have important contribution for meningococcal disease pathology. Finally, we described a novel genetic association approach that could be applied to other phenotypes

Identification of regulatory variants associated with genetic susceptibility to meningococcal disease.

Non-coding genetic variants play an important role in driving susceptibility to complex diseases but their characterization remains challenging. Here, we employed a novel approach to interrogate the genetic risk of such polymorphisms in a more systematic way by targeting specific regulatory regions relevant for the phenotype studied. We applied this method to meningococcal disease susceptibility, using the DNA binding pattern of RELA - a NF-kB subunit, master regulator of the response to infection - under bacterial stimuli in nasopharyngeal epithelial cells. We designed a custom panel to cover these RELA binding sites and used it for targeted sequencing in cases and controls. Variant calling and association analysis were performed followed by validation of candidate polymorphisms by genotyping in three independent cohorts. We identified two new polymorphisms, rs4823231 and rs11913168, showing signs of association with meningococcal disease susceptibility. In addition, using our genomic data as well as publicly available resources, we found evidences for these SNPs to have potential regulatory effects on ATXN10 and LIF genes respectively. The variants and related candidate genes are relevant for infectious diseases and may have important contribution for meningococcal disease pathology. Finally, we described a novel genetic association approach that could be applied to other phenotypes