In this paper we evaluate the three main methods for correcting the susceptibility-induced artefact in diffusion-weighted magnetic-resonance (DW-MR) data, and assess how correction is affected by the ...susceptibility field's interaction with motion. The susceptibility artefact adversely impacts analysis performed on the data and is typically corrected in post-processing. Correction strategies involve either registration to a structural image, the application of an acquired field-map or the use of additional images acquired with different phase-encoding. Unfortunately, the choice of which method to use is made difficult by the absence of any systematic comparisons of them. In this work we quantitatively evaluate these methods, by extending and employing a recently proposed framework that allows for the simulation of realistic DW-MR datasets with artefacts. Our analysis separately evaluates the ability for methods to correct for geometric distortions and to recover lost information in regions of signal compression. In terms of geometric distortions, we find that registration-based methods offer the poorest correction. Field-mapping techniques are better, but are influenced by noise and partial volume effects, whilst multiple phase-encode methods performed best. We use our simulations to validate a popular surrogate metric of correction quality, the comparison of corrected data acquired with AP and LR phase-encoding, and apply this surrogate to real datasets. Furthermore, we demonstrate that failing to account for the interaction of the susceptibility field with head movement leads to increased errors when analysing DW-MR data. None of the commonly used post-processing methods account for this interaction, and we suggest this may be a valuable area for future methods development.
In this paper we demonstrate a simulation framework that enables the direct and quantitative comparison of post-processing methods for diffusion weighted magnetic resonance (DW-MR) images. DW-MR ...datasets are employed in a range of techniques that enable estimates of local microstructure and global connectivity in the brain. These techniques require full alignment of images across the dataset, but this is rarely the case. Artefacts such as eddy-current (EC) distortion and motion lead to misalignment between images, which compromise the quality of the microstructural measures obtained from them. Numerous methods and software packages exist to correct these artefacts, some of which have become de-facto standards, but none have been subject to rigorous validation. In the literature, improved alignment is assessed using either qualitative visual measures or quantitative surrogate metrics. Here we introduce a simulation framework that allows for the direct, quantitative assessment of techniques, enabling objective comparisons of existing and future methods. DW-MR datasets are generated using a process that is based on the physics of MRI acquisition, which allows for the salient features of the images and their artefacts to be reproduced. We apply this framework in three ways. Firstly we assess the most commonly used method for artefact correction, FSL's eddy_correct, and compare it to a recently proposed alternative, eddy. We demonstrate quantitatively that using eddy_correct leads to significant errors in the corrected data, whilst eddy is able to provide much improved correction. Secondly we investigate the datasets required to achieve good correction with eddy, by looking at the minimum number of directions required and comparing the recommended full-sphere acquisitions to equivalent half-sphere protocols. Finally, we investigate the impact of correction quality by examining the fits from microstructure models to real and simulated data.
•We propose a framework for assessing artefact correction techniques in diffusion MRI.•The proposed framework simulates realistic diffusion MRI data, with artefacts.•We apply the framework to assess two widely-used FSL tools: eddy_correct and eddy.•Eddy outperforms eddy_correct in correcting motion and eddy-current distortion.•We show that poor correction adversely impacts the estimation of microstructure.
The Pfizer-BioNTech (BNT162b2) and the Oxford-AstraZeneca (ChAdOx1 nCoV-19) COVID-19 vaccines have shown excellent safety and efficacy in phase 3 trials. We aimed to investigate the safety and ...effectiveness of these vaccines in a UK community setting.
In this prospective observational study, we examined the proportion and probability of self-reported systemic and local side-effects within 8 days of vaccination in individuals using the COVID Symptom Study app who received one or two doses of the BNT162b2 vaccine or one dose of the ChAdOx1 nCoV-19 vaccine. We also compared infection rates in a subset of vaccinated individuals subsequently tested for SARS-CoV-2 with PCR or lateral flow tests with infection rates in unvaccinated controls. All analyses were adjusted by age (≤55 years vs >55 years), sex, health-care worker status (binary variable), obesity (BMI <30 kg/m2vs ≥30 kg/m2), and comorbidities (binary variable, with or without comorbidities).
Between Dec 8, and March 10, 2021, 627 383 individuals reported being vaccinated with 655 590 doses: 282 103 received one dose of BNT162b2, of whom 28 207 received a second dose, and 345 280 received one dose of ChAdOx1 nCoV-19. Systemic side-effects were reported by 13·5% (38 155 of 282 103) of individuals after the first dose of BNT162b2, by 22·0% (6216 of 28 207) after the second dose of BNT162b2, and by 33·7% (116 473 of 345 280) after the first dose of ChAdOx1 nCoV-19. Local side-effects were reported by 71·9% (150 023 of 208 767) of individuals after the first dose of BNT162b2, by 68·5% (9025 of 13 179) after the second dose of BNT162b2, and by 58·7% (104 282 of 177 655) after the first dose of ChAdOx1 nCoV-19. Systemic side-effects were more common (1·6 times after the first dose of ChAdOx1 nCoV-19 and 2·9 times after the first dose of BNT162b2) among individuals with previous SARS-CoV-2 infection than among those without known past infection. Local effects were similarly higher in individuals previously infected than in those without known past infection (1·4 times after the first dose of ChAdOx1 nCoV-19 and 1·2 times after the first dose of BNT162b2). 3106 of 103 622 vaccinated individuals and 50 340 of 464 356 unvaccinated controls tested positive for SARS-CoV-2 infection. Significant reductions in infection risk were seen starting at 12 days after the first dose, reaching 60% (95% CI 49–68) for ChAdOx1 nCoV-19 and 69% (66–72) for BNT162b2 at 21–44 days and 72% (63–79) for BNT162b2 after 45–59 days.
Systemic and local side-effects after BNT162b2 and ChAdOx1 nCoV-19 vaccination occur at frequencies lower than reported in phase 3 trials. Both vaccines decrease the risk of SARS-CoV-2 infection after 12 days.
ZOE Global, National Institute for Health Research, Chronic Disease Research Foundation, National Institutes of Health, UK Medical Research Council, Wellcome Trust, UK Research and Innovation, American Gastroenterological Association.
Diphenylborinic acid serves as a cocatalyst for site- and stereoselective C–H alkylation reactions of carbohydrates under photoredox conditions using quinuclidine as the hydrogen atom transfer ...mediator. Products arising from selective abstraction of the equatorial hydrogens of cis-1,2-diol moieties, followed by C–C bond formation with net retention of configuration, are obtained. Computational modeling supports a mechanism involving formation of a tetracoordinate borinic ester, which accelerates hydrogen atom transfer with the quinuclidine-derived radical cation through polarity-matching and/or ion-pairing effects.
Perfluoroaryl-substituted tellurophenes act as anion receptors through noncovalent chalcogen bonding interactions. Linking two tellurophenes through an ethynylene group results in a significant level ...of chelate cooperativity, thus demonstrating that chalcogen bonding can be used to achieve multidentate anion recognition.
Interfaces impede heat flow in micro/nanostructured systems. Conventional theories for interfacial thermal transport were derived based on bulk phonon properties of the materials making up the ...interface without explicitly considering the atomistic interfacial details, which are found critical to correctly describing thermal boundary conductance. Recent theoretical studies predicted the existence of localized phonon modes at the interface which can play an important role in understanding interfacial thermal transport. However, experimental validation is still lacking. Through a combination of Raman spectroscopy and high-energy-resolution electron energy-loss spectroscopy in a scanning transmission electron microscope, we report the experimental observation of localized interfacial phonon modes at ~12 THz at a high-quality epitaxial Si-Ge interface. These modes are further confirmed using molecular dynamics simulations with a high-fidelity neural network interatomic potential, which also yield thermal boundary conductance agreeing well with that measured in time-domain thermoreflectance experiments. Simulations find that the interfacial phonon modes have an obvious contribution to the total thermal boundary conductance. Our findings significantly contribute to the understanding of interfacial thermal transport physics and have impact on engineering thermal boundary conductance at interfaces in applications such as electronics thermal management and thermoelectric energy conversion.
Despite its great potential in studying brain anatomy and structure, diffusion magnetic resonance imaging (dMRI) is marred by artefacts more than any other commonly used MRI technique. In this paper ...we present a non-parametric framework for detecting and correcting dMRI outliers (signal loss) caused by subject motion.
Signal loss (dropout) affecting a whole slice, or a large connected region of a slice, is frequently observed in diffusion weighted images, leading to a set of unusable measurements. This is caused by bulk (subject or physiological) motion during the diffusion encoding part of the imaging sequence. We suggest a method to detect slices affected by signal loss and replace them by a non-parametric prediction, in order to minimise their impact on subsequent analysis. The outlier detection and replacement, as well as correction of other dMRI distortions (susceptibility-induced distortions, eddy currents (EC) and subject motion) are performed within a single framework, allowing the use of an integrated approach for distortion correction. Highly realistic simulations have been used to evaluate the method with respect to its ability to detect outliers (types 1 and 2 errors), the impact of outliers on retrospective correction of movement and distortion and the impact on estimation of commonly used diffusion tensor metrics, such as fractional anisotropy (FA) and mean diffusivity (MD). Data from a large imaging project studying older adults (the Whitehall Imaging sub-study) was used to demonstrate the utility of the method when applied to datasets with severe subject movement.
The results indicate high sensitivity and specificity for detecting outliers and that their deleterious effects on FA and MD can be almost completely corrected.
•We present a framework for correction of distortions, subject movement and signal dropout in diffusion weighted images.•It has been validated on realistic simulated data.•One can reliably correct for signal dropout as long as the affected slices constitute no more than 10% of the total.
Quality control (QC) is a fundamental component of any study. Diffusion MRI has unique challenges that make manual QC particularly difficult, including a greater number of artefacts than other MR ...modalities and a greater volume of data. The gold standard is manual inspection of the data, but this process is time-consuming and subjective. Recently supervised learning approaches based on convolutional neural networks have been shown to be competitive with manual inspection. A drawback of these approaches is they still require a manually labelled dataset for training, which is itself time-consuming to produce and still introduces an element of subjectivity. In this work we demonstrate the need for manual labelling can be greatly reduced by training on simulated data, and using a small amount of labelled data for a final calibration step. We demonstrate its potential for the detection of severe movement artefacts, and compare performance to a classifier trained on manually-labelled real data.
•We demonstrate a classifier for the quality control of DW-MRI data.•The classifier is trained to spot movement-corrupted volumes.•It greatly reduces the need for labelled training data by making use of simulation.•The classifier performance is similar to a classifier trained entirely on real data.
Site-selective functionalization of hydroxy groups in sugar derivatives is a major challenge in carbohydrate synthesis. Methods for achieving this goal will provide efficient access to new ...sugar-derived chemical building blocks and will facilitate the preparation or late-stage modification of complex oligosaccharides for applications in glycobiology research and drug discovery. Here, we describe site-selective, copper-promoted couplings of boronic acids with carbohydrate derivatives. These reactions generate sugar-derived aryl ethers, a structural class that is challenging to generate by other means and has not previously been accessed in a site-selective fashion. Experimental evidence and computational modeling suggest that the formation of a sugar-derived boronic ester intermediate is crucial to the selectivity of these processes, accelerating the arylation of an adjacent hydroxy group. The results demonstrate how the interactions of sugars with boron compounds can be combined with transition metal catalysis to achieve new chemical reactivity.
Most motion correction methods work by aligning a set of volumes together, or to a volume that represents a reference location. These are based on an implicit assumption that the subject remains ...motionless during the several seconds it takes to acquire all slices in a volume, and that any movement occurs in the brief moment between acquiring the last slice of one volume and the first slice of the next. This is clearly an approximation that can be more or less good depending on how long it takes to acquire one volume and in how rapidly the subject moves. In this paper we present a method that increases the temporal resolution of the motion correction by modelling movement as a piecewise continous function over time. This intra-volume movement correction is implemented within a previously presented framework that simultaneously estimates distortions, movement and movement-induced signal dropout. We validate the method on highly realistic simulated data containing all of these effects. It is demonstrated that we can estimate the true movement with high accuracy, and that scalar parameters derived from the data, such as fractional anisotropy, are estimated with greater fidelity when data has been corrected for intra-volume movement. Importantly, we also show that the difference in fidelity between data affected by different amounts of movement is much reduced when taking intra-volume movement into account. Additional validation was performed on data from a healthy volunteer scanned when lying still and when performing deliberate movements. We show an increased correspondence between the “still” and the “movement” data when the latter is corrected for intra-volume movement. Finally we demonstrate a big reduction in the telltale signs of intra-volume movement in data acquired on elderly subjects.
•We introduce a method to correct for intra-volume movement into an existing framework for movement and distortion correction.•It has been validated on realistic simulated data and on experimental data with deliberate movement.•The results indicate that one can reliably reverse the adverse effects of intra-volume movement.
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