Catalytic stereoselective [2,3]-rearrangement reactions

The authors thank the Royal Society for a University Research Fellowship (A.D.S.), the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-20013) ERC Grant Agreement No. 279850 (J.E.T., T.H.W., K.K.), and the European Union (Marie Curie ITN ‘SuBiCat’ PITN-GA-2013-607044) (S.S.M.S.) for financial support.[2,3]-Sigmatropic rearrangement processes of allylic ylides or their equivalents can be applied to a variety of different substrates and generate products of wide interest and applicability to organic synthesis. This review describes the development and applications of stereoselective [2,3]-rearrangement reactions in which a sub-stoichiometric amount of a catalyst is used in either the formation of the reactive intermediate or the [2,3]-rearrangement step itself.PostprintPeer reviewe

LegoNet: Alternating Model Blocks for Medical Image Segmentation

Since the emergence of convolutional neural networks (CNNs), and later vision transformers (ViTs), the common paradigm for model development has always been using a set of identical block types with varying parameters/hyper-parameters. To leverage the benefits of different architectural designs (e.g. CNNs and ViTs), we propose to alternate structurally different types of blocks to generate a new architecture, mimicking how Lego blocks can be assembled together. Using two CNN-based and one SwinViT-based blocks, we investigate three variations to the so-called LegoNet that applies the new concept of block alternation for the segmentation task in medical imaging. We also study a new clinical problem which has not been investigated before, namely the right internal mammary artery (RIMA) and perivascular space segmentation from computed tomography angiography (CTA) which has demonstrated a prognostic value to major cardiovascular outcomes. We compare the model performance against popular CNN and ViT architectures using two large datasets (e.g. achieving 0.749 dice similarity coefficient (DSC) on the larger dataset). We evaluate the performance of the model on three external testing cohorts as well, where an expert clinician made corrections to the model segmented results (DSC>0.90 for the three cohorts). To assess our proposed model for suitability in clinical use, we perform intra- and inter-observer variability analysis. Finally, we investigate a joint self-supervised learning approach to assess its impact on model performance. The code and the pretrained model weights will be available upon acceptance.Comment: 12 pages, 5 figures, 4 table

Cool Subdwarf Investigations (CSI) I: New Thoughts for the Spectral Types of K and M Subdwarfs

Using new spectra of 88 K and M-type subdwarfs, we consider novel methods for assigning their spectral types and take steps toward developing a comprehensive spectral sequence for subdwarf types K3.0 to M6.0. The types are assigned based on the overall morphology of spectra covering 6000\AA to 9000\AA. The types and sequence presented link the spectral types of cool subdwarfs to their main sequence counterparts, with emphasis on the relatively opacity-free region from 8200--9000\AA. When available, supporting abundance, kinematic, and trigonometric parallax information is used to provide more complete portraits of the observed subdwarfs. We find that the CaHn (n $=$ 1--3) and TiO5 indices often used for subdwarf spectral typing are affected in complicated ways by combinations of subdwarfs' temperatures, metallicities, and gravities, and we use model grids to evaluate the trends in all three parameters. Because of the complex interplay of these three characteristics, it is not possible to identify a star as an ``extreme'' subdwarf simply based on very low metallicity, and we suggest that the modifiers ``extreme'' or ``ultra'' only outline locations on spectroscopic indices plots, and should not be used to imply low or very low metallicity stars. In addition, we propose that ``VI'' be used to identify a star as a subdwarf, rather than the confusing ``sd'' prefix, which is also used for hot O and B subdwarfs that are unrelated to the cool subdwarfs discussed in this paper.Comment: 84 pages, 35 figures, accepted to A

Anatomy of viscera of giant panda

23 p. : ill. ; 24 cm.Includes bibliographical references (p. 23)

Deweyan tools for inquiry and the epistemological context of critical pedagogy

This article develops the notion of resistance as articulated in the literature of critical pedagogy as being both culturally sponsored and cognitively manifested. To do so, the authors draw upon John Dewey\u27s conception of tools for inquiry. Dewey provides a way to conceptualize student resistance not as a form of willful disputation, but instead as a function of socialization into cultural models of thought that actively truncate inquiry. In other words, resistance can be construed as the cognitive and emotive dimensions of the ongoing failure of institutions to provide ideas that help individuals both recognize social problems and imagine possible solutions. Focusing on Dewey\u27s epistemological framework, specifically tools for inquiry, provides a way to grasp this problem. It also affords some innovative solutions; for instance, it helps conceive of possible links between the regular curriculum and the study of specific social justice issues, a relationship that is often under-examined. The aims of critical pedagogy depend upon students developing dexterity with the conceptual tools they use to make meaning of the evidence they confront; these are background skills that the regular curriculum can be made to serve even outside social justice-focused curricula. Furthermore, the article concludes that because such inquiry involves the exploration and potential revision of students\u27 world-ordering beliefs, developing flexibility in how one thinks may be better achieved within academic subjects and topics that are not so intimately connected to students\u27 current social lives, especially where students may be directly implicated

Deep-Learning for Epicardial Adipose Tissue Assessment with Computed Tomography: Implications for Cardiovascular Risk Prediction

Background: Epicardial adipose tissue (EAT) volume is a marker of visceral obesity that can be measured in coronary computed tomography angiograms (CCTA). The clinical value of integrating this measurement in routine CCTA interpretation has not been documented./ Objectives: This study sought to develop a deep-learning network for automated quantification of EAT volume from CCTA, test it in patients who are technically challenging, and validate its prognostic value in routine clinical care./ Methods: The deep-learning network was trained and validated to autosegment EAT volume in 3,720 CCTA scans from the ORFAN (Oxford Risk Factors and Noninvasive Imaging Study) cohort. The model was tested in patients with challenging anatomy and scan artifacts and applied to a longitudinal cohort of 253 patients post-cardiac surgery and 1,558 patients from the SCOT-HEART (Scottish Computed Tomography of the Heart) Trial, to investigate its prognostic value./ Results: External validation of the deep-learning network yielded a concordance correlation coefficient of 0.970 for machine vs human. EAT volume was associated with coronary artery disease (odds ratio [OR] per SD increase in EAT volume: 1.13 [95% CI: 1.04-1.30]; P = 0.01), and atrial fibrillation (OR: 1.25 [95% CI:1.08-1.40]; P = 0.03), after correction for risk factors (including body mass index). EAT volume predicted all-cause mortality (HR per SD: 1.28 [95% CI: 1.10-1.37]; P = 0.02), myocardial infarction (HR: 1.26 [95% CI:1.09-1.38]; P = 0.001), and stroke (HR: 1.20 [95% CI: 1.09-1.38]; P = 0.02) independently of risk factors in SCOT-HEART (5-year follow-up). It also predicted in-hospital (HR: 2.67 [95% CI: 1.26-3.73]; P ≤ 0.01) and long-term post–cardiac surgery atrial fibrillation (7-year follow-up; HR: 2.14 [95% CI: 1.19-2.97]; P ≤ 0.01). Conclusions: Automated assessment of EAT volume is possible in CCTA, including in patients who are technically challenging; it forms a powerful marker of metabolically unhealthy visceral obesity, which could be used for cardiovascular risk stratification

Crystal structure of rhodopsin bound to arrestin by femtosecond X-ray laser.

G-protein-coupled receptors (GPCRs) signal primarily through G proteins or arrestins. Arrestin binding to GPCRs blocks G protein interaction and redirects signalling to numerous G-protein-independent pathways. Here we report the crystal structure of a constitutively active form of human rhodopsin bound to a pre-activated form of the mouse visual arrestin, determined by serial femtosecond X-ray laser crystallography. Together with extensive biochemical and mutagenesis data, the structure reveals an overall architecture of the rhodopsin-arrestin assembly in which rhodopsin uses distinct structural elements, including transmembrane helix 7 and helix 8, to recruit arrestin. Correspondingly, arrestin adopts the pre-activated conformation, with a ∼20° rotation between the amino and carboxy domains, which opens up a cleft in arrestin to accommodate a short helix formed by the second intracellular loop of rhodopsin. This structure provides a basis for understanding GPCR-mediated arrestin-biased signalling and demonstrates the power of X-ray lasers for advancing the frontiers of structural biology

Hippocampal subfields at ultra high field MRI: An overview of segmentation and measurement methods

The hippocampus is one of the most interesting and studied brain regions because of its involvement in memory functions and its vulnerability in pathological conditions, such as neurodegenerative processes. In the recent years, the increasing availability of Magnetic Resonance Imaging (MRI) scanners that operate at ultra-high field (UHF), that is, with static magnetic field strength ≥7T, has opened new research perspectives. Compared to conventional high-field scanners, these systems can provide new contrasts, increased signal-to-noise ratio and higher spatial resolution, thus they may improve the visualization of very small structures of the brain, such as the hippocampal subfields. Studying the morphometry of the hippocampus is crucial in neuroimaging research because changes in volume and thickness of hippocampal subregions may be relevant in the early assessment of pathological cognitive decline and Alzheimer's Disease (AD). The present review provides an overview of the manual, semi-automated and fully automated methods that allow the assessment of hippocampal subfield morphometry at UHF MRI, focusing on the different hippocampal segmentation produced. © 2017 Wiley Periodicals, Inc