2,066 research outputs found
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
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