Graph theoretic methods for the analysis of structural relationships in biological macromolecules

Subgraph isomorphism and maximum common subgraph isomorphism algorithms from graph theory provide an effective and an efficient way of identifying structural relationships between biological macromolecules. They thus provide a natural complement to the pattern matching algorithms that are used in bioinformatics to identify sequence relationships. Examples are provided of the use of graph theory to analyze proteins for which three-dimensional crystallographic or NMR structures are available, focusing on the use of the Bron-Kerbosch clique detection algorithm to identify common folding motifs and of the Ullmann subgraph isomorphism algorithm to identify patterns of amino acid residues. Our methods are also applicable to other types of biological macromolecule, such as carbohydrate and nucleic acid structures

Overview: Computer vision and machine learning for microstructural characterization and analysis

The characterization and analysis of microstructure is the foundation of microstructural science, connecting the materials structure to its composition, process history, and properties. Microstructural quantification traditionally involves a human deciding a priori what to measure and then devising a purpose-built method for doing so. However, recent advances in data science, including computer vision (CV) and machine learning (ML) offer new approaches to extracting information from microstructural images. This overview surveys CV approaches to numerically encode the visual information contained in a microstructural image, which then provides input to supervised or unsupervised ML algorithms that find associations and trends in the high-dimensional image representation. CV/ML systems for microstructural characterization and analysis span the taxonomy of image analysis tasks, including image classification, semantic segmentation, object detection, and instance segmentation. These tools enable new approaches to microstructural analysis, including the development of new, rich visual metrics and the discovery of processing-microstructure-property relationships.Comment: submitted to Materials and Metallurgical Transactions

Promoting Inquiry in Science Classrooms in European Schools : a Handbook for Tutors

In Scotland, PISCES stands for Promoting Inquiry Skills for a Curriculum for Excellence in Science. It is a CPD module for teachers of science, which has been developed in Scotland with the support of the S-TEAM project. However, this Handbook uses our international acronym in which PISCES stands for Promoting Inquiry in Science Classrooms in European Schools . It is, we believe, potentially equally as successful across Europe as it was designed around the idea of empowering teachers to think for themselves how to make their practice more-inquiry based, wherever they are. It is recognised that some school, social, policy and cultural environments may be more supportive of the idea of ‘more inquiry-based practice’ than others. PISCES empowers teachers to make small or large changes to their practice, according to those sorts of contextual factors, their own aims and how they perceive the needs of their pupils. You will note that we have been careful to use the word ‘more’ in ‘more inquiry-based.’ As befits the idea of empowerment to adapt to one’s own context, there is no single model of inquiry being ‘pushed’ here. Indeed, we count it as a measure of success of PISCES that the teachers who have participated did very different things in making their practice more inquiry-based. Strathclyde University is a leading partner in S-TEAM. Members of Strathclyde University, along with the Development Officer for Curriculum for Excellence for East Lothian, successfully developed and delivered a pilot version of PISCES as a module to a group of East Lothian teachers, in 2010/11. The module resulted in successful ‘experiments in practice’ and increased awareness of the benefits of inquiry-based teaching and learning. The same group of teachers have also taken part in a follow-up course (ARIES: Advanced Resources for Inquiry and Evaluation in Science). PISCES is a high quality CPD programme, valued by teachers and supportive of their professional self-development. It can be applied to both primary and secondary teaching, in all science subjects. Pupils benefit from learning experiences, which develop scientific inquiry skills. Feedback from participating teachers has been consistently positive

Recruitment kinetics of DNA repair proteins Mdc1 and Rad52 but not 53BP1 depend on damage complexity.

The recruitment kinetics of double-strand break (DSB) signaling and repair proteins Mdc1, 53BP1 and Rad52 into radiation-induced foci was studied by live-cell fluorescence microscopy after ion microirradiation. To investigate the influence of damage density and complexity on recruitment kinetics, which cannot be done by UV laser irradiation used in former studies, we utilized 43 MeV carbon ions with high linear energy transfer per ion (LET = 370 keV/µm) to create a large fraction of clustered DSBs, thus forming complex DNA damage, and 20 MeV protons with low LET (LET = 2.6 keV/µm) to create mainly isolated DSBs. Kinetics for all three proteins was characterized by a time lag period T(0) after irradiation, during which no foci are formed. Subsequently, the proteins accumulate into foci with characteristic mean recruitment times τ(1). Mdc1 accumulates faster (T(0) = 17 ± 2 s, τ(1) = 98 ± 11 s) than 53BP1 (T(0) = 77 ± 7 s, τ(1) = 310 ± 60 s) after high LET irradiation. However, recruitment of Mdc1 slows down (T(0) = 73 ± 16 s, τ(1) = 1050 ± 270 s) after low LET irradiation. The recruitment kinetics of Rad52 is slower than that of Mdc1, but exhibits the same dependence on LET. In contrast, the mean recruitment time τ(1) of 53BP1 remains almost constant when varying LET. Comparison to literature data on Mdc1 recruitment after UV laser irradiation shows that this rather resembles recruitment after high than low LET ionizing radiation. So this work shows that damage quality has a large influence on repair processes and has to be considered when comparing different studies

Reducing model bias in a deep learning classifier using domain adversarial neural networks in the MINERvA experiment

We present a simulation-based study using deep convolutional neural networks (DCNNs) to identify neutrino interaction vertices in the MINERvA passive targets region, and illustrate the application of domain adversarial neural networks (DANNs) in this context. DANNs are designed to be trained in one domain (simulated data) but tested in a second domain (physics data) and utilize unlabeled data from the second domain so that during training only features which are unable to discriminate between the domains are promoted. MINERvA is a neutrino-nucleus scattering experiment using the NuMI beamline at Fermilab. $A$-dependent cross sections are an important part of the physics program, and these measurements require vertex finding in complicated events. To illustrate the impact of the DANN we used a modified set of simulation in place of physics data during the training of the DANN and then used the label of the modified simulation during the evaluation of the DANN. We find that deep learning based methods offer significant advantages over our prior track-based reconstruction for the task of vertex finding, and that DANNs are able to improve the performance of deep networks by leveraging available unlabeled data and by mitigating network performance degradation rooted in biases in the physics models used for training.Comment: 41 page