Direct prediction of phonon density of states with Euclidean neural networks

Machine learning has demonstrated great power in materials design, discovery, and property prediction. However, despite the success of machine learning in predicting discrete properties, challenges remain for continuous property prediction. The challenge is aggravated in crystalline solids due to crystallographic symmetry considerations and data scarcity. Here we demonstrate the direct prediction of phonon density of states using only atomic species and positions as input. We apply Euclidean neural networks, which by construction are equivariant to 3D rotations, translations, and inversion and thereby capture full crystal symmetry, and achieve high-quality prediction using a small training set of $\sim 10^{3}$ examples with over 64 atom types. Our predictive model reproduces key features of experimental data and even generalizes to materials with unseen elements,and is naturally suited to efficiently predict alloy systems without additional computational cost. We demonstrate the potential of our network by predicting a broad number of high phononic specific heat capacity materials. Our work indicates an efficient approach to explore materials' phonon structure, and can further enable rapid screening for high-performance thermal storage materials and phonon-mediated superconductors.Comment: 21 pages total, 5 main figures + 16 supplementary figures. To appear in Advanced Science (2021

Machine Learning on Neutron and X-Ray Scattering

Neutron and X-ray scattering represent two state-of-the-art materials characterization techniques that measure materials' structural and dynamical properties with high precision. These techniques play critical roles in understanding a wide variety of materials systems, from catalysis to polymers, nanomaterials to macromolecules, and energy materials to quantum materials. In recent years, neutron and X-ray scattering have received a significant boost due to the development and increased application of machine learning to materials problems. This article reviews the recent progress in applying machine learning techniques to augment various neutron and X-ray scattering techniques. We highlight the integration of machine learning methods into the typical workflow of scattering experiments. We focus on scattering problems that faced challenge with traditional methods but addressable using machine learning, such as leveraging the knowledge of simple materials to model more complicated systems, learning with limited data or incomplete labels, identifying meaningful spectra and materials' representations for learning tasks, mitigating spectral noise, and many others. We present an outlook on a few emerging roles machine learning may play in broad types of scattering and spectroscopic problems in the foreseeable future.Comment: 56 pages, 12 figures. Feedback most welcom

G_s protein peptidomimetics as allosteric modulators of the β₂-adrenergic receptor

A series of G(s) protein peptidomimetics were designed and synthesised based on the published X-ray crystal structure of the active state β(2)-adrenergic receptor (β(2)AR) in complex with the G(s) protein (PDB 3SN6). We hypothesised that such peptidomimetics may function as allosteric modulators that target the intracellular G(s) protein binding site of the β(2)AR. Peptidomimetics were designed to mimic the 15 residue C-terminal α-helix of the G(s) protein and were pre-organised in a helical conformation by (i, i + 4)-stapling using copper catalysed azide alkyne cycloaddition. Linear and stapled peptidomimetics were analysed by circular dichroism (CD) and characterised in a membrane-based cAMP accumulation assay and in a bimane fluorescence assay on purified β(2)AR. Several peptidomimetics inhibited agonist isoproterenol (ISO) induced cAMP formation by lowering the ISO maximal efficacy up to 61%. Moreover, some peptidomimetics were found to significantly decrease the potency of ISO up to 39-fold. In the bimane fluorescence assay none of the tested peptidomimetics could stabilise an active-like conformation of β(2)AR. Overall, the obtained pharmacological data suggest that some of the peptidomimetics may be able to compete with the native G(s) protein for the intracellular binding site to block ISO-induced cAMP formation, but are unable to stabilise an active-like receptor conformation

Rethinking Service Systems and Public Policy: A Transformative Refugee Service Experience Framework

The global refugee crisis is a complex humanitarian problem. Service researchers can assist in solving this crisis because refugees are immersed in complex human service systems. Drawing on marketing, sociology, transformative service, and consumer research literature, this study develops a Transformative Refugee Service Experience Framework to enable researchers, service actors, and public policy makers to navigate the challenges faced throughout a refugee’s service journey. The primary dimensions of this framework encompass the spectrum from hostile to hospitable refugee service systems and the resulting suffering or well-being in refugees’ experiences. The authors conceptualize this at three refugee service journey phases (entry, transition, and exit) and at three refugee service system levels (macro, meso, and micro) of analysis. The framework is supported by brief examples from a range of service-related refugee contexts as well as a Web Appendix with additional cases. Moreover, the authors derive a comprehensive research agenda from the framework, with detailed research questions for public policy and (service) marketing researchers. Managerial directions are provided to increase awareness of refugee service problems; stimulate productive interactions; and improve collaboration among public and nonprofit organizations, private service providers, and refugees. Finally, this work provides a vision for creating hospitable refugee service systems

Localization Techniques for Non-Palpable Breast Lesions : Current Status, Knowledge Gaps, and Rationale for the MELODY Study (EUBREAST-4/iBRA-NET, NCT 05559411)

Funding: The MELODY study will be financially supported by: Hologic, Merit Medical, Endomag and Sirius Medical.Peer reviewedPublisher PD

Consensus on exercise reporting template (Cert): Modified delphi study

© 2016 American Physical Therapy Association. Background. Exercise interventions are often incompletely described in reports of clinical trials, hampering evaluation of results and replication and implementation into practice. Objective. The aim of this study was to develop a standardized method for reporting exercise programs in clinical trials: the Consensus on Exercise Reporting Template (CERT). Design and Methods. Using the EQUATOR Network’s methodological framework, 137 exercise experts were invited to participate in a Delphi consensus study. A list of 41 items was identified from a meta-epidemiologic study of 73 systematic reviews of exercise. For each item, participants indicated agreement on an 11-point rating scale. Consensus for item inclusion was defined a priori as greater than 70% agreement of respondents rating an item 7 or above. Three sequential rounds of anonymous online questionnaires and a Delphi workshop were used. Results. There were 57 (response rate=42%), 54 (response rate=95%), and 49 (response rate=91%) respondents to rounds 1 through 3, respectively, from 11 countries and a range of disciplines. In round 1, 2 items were excluded; 24 items reached consensus for inclusion (8 items accepted in original format), and 16 items were revised in response to participant suggestions. Of 14 items in round 2, 3 were excluded, 11 reached consensus for inclusion (4 items accepted in original format), and 7 were reworded. Sixteen items were included in round 3, and all items reached greater than 70% consensus for inclusion. Limitations. The views of included Delphi panelists may differ from those of experts who declined participation and may not fully represent the views of all exercise experts. Conclusions. The CERT, a 16-item checklist developed by an international panel of exercise experts, is designed to improve the reporting of exercise programs in all evaluative study designs and contains 7 categories: materials, provider, delivery, location, dosage, tailoring, and compliance. The CERT will encourage transparency, improve trial interpretation and replication, and facilitate implementation of effective exercise interventions into practice

Differently Pre-treated Alfalfa Silages Affect the in vitro Ruminal Microbiota Composition

Alfalfa (Medicago sativa L.) silage (AS) is an important feedstuff in ruminant nutrition. However, its high non-protein nitrogen content often leads to poor ruminal nitrogen retention. Various pre-ensiling treatments differing with respect to dry matter concentrations, wilting intensities and sucrose addition have been previously shown to improve the quality and true protein preservation of AS, and have substantial effects on in vitro ruminal fermentation of the resulting silages. However, it is unknown how these pre-ensiling treatments affect the ruminal microbiota composition, and whether alterations in the microbiota explain previously observed differences in ruminal fermentation. Therefore, during AS incubation in a rumen simulation system, liquid and solid phases were sampled 2 and 7 days after first incubating AS, representing an early (ET) and late (LT) time point, respectively. Subsequently, DNA was extracted and qPCR (bacteria, archaea, and anaerobic fungi) and prokaryotic 16S rRNA gene amplicon sequence analyses were performed. At the ET, high dry matter concentration and sucrose addition increased concentrations of archaea in the liquid phase (P = 0.001) and anaerobic fungi in the solid phase (P < 0.001). At the LT, only sucrose addition increased archaeal concentration in the liquid phase (P = 0.014) and anaerobic fungal concentration in the solid phase (P < 0.001). Bacterial concentrations were not affected by pre-ensiling treatments. The prokaryotic phylogenetic diversity index decreased in the liquid phase from ET to LT (P = 0.034), whereas the solid phase was not affected (P = 0.060). This is suggestive of a general adaption of the microbiota to the soluble metabolites released from the incubated AS, particularly regarding the sucrose-treated AS. Redundancy analysis of the sequence data at the genus level indicated that sucrose addition (P = 0.001), time point (P = 0.001), and their interaction (P = 0.001) affected microbial community composition in both phases. In summary, of the pre-ensiling treatments tested sucrose addition had the largest effect on the microbiota, and together with sampling time point affected microbiota composition in both phases of the rumen simulation system. Thus, microbiota composition analysis helped to understand the ruminal fermentation patterns, but could not fully explain them

Direct Prediction of Phonon Density of States With Euclidean Neural Networks

© 2021 The Authors. Advanced Science published by Wiley-VCH GmbH Machine learning has demonstrated great power in materials design, discovery, and property prediction. However, despite the success of machine learning in predicting discrete properties, challenges remain for continuous property prediction. The challenge is aggravated in crystalline solids due to crystallographic symmetry considerations and data scarcity. Here, the direct prediction of phonon density-of-states (DOS) is demonstrated using only atomic species and positions as input. Euclidean neural networks are applied, which by construction are equivariant to 3D rotations, translations, and inversion and thereby capture full crystal symmetry, and achieve high-quality prediction using a small training set of (Formula presented.) examples with over 64 atom types. The predictive model reproduces key features of experimental data and even generalizes to materials with unseen elements, and is naturally suited to efficiently predict alloy systems without additional computational cost. The potential of the network is demonstrated by predicting a broad number of high phononic specific heat capacity materials. The work indicates an efficient approach to explore materials' phonon structure, and can further enable rapid screening for high-performance thermal storage materials and phonon-mediated superconductors