A Mean Field Model for the Quadrupolar Phases of UPd3_3

UPd$_3$ is known to exhibit four antiferroquadrupolar ordered phases at low temperatures. We report measurements of the magnetisation and magnetostriction of single crystal UPd$_3$, along the principal symmetry directions, in fields up to 33 T. These results have been combined with recent inelastic neutron and x-ray resonant scattering measurements to construct a mean field model of UPd$_3$ including up to fourth nearest neighbour interactions. In particular we find that anisotropic quadrupolar interactions must be included in order to explain the low temperature structures derived from the scattering data.Comment: 9 pages, 6 figures, 3 table

Uncertainty-Aware Annotation Protocol to Evaluate Deformable Registration Algorithms

Landmark correspondences are a widely used type of gold standard in image registration. However, the manual placement of corresponding points is subject to high inter-user variability in the chosen annotated locations and in the interpretation of visual ambiguities. In this paper, we introduce a principled strategy for the construction of a gold standard in deformable registration. Our framework: (i) iteratively suggests the most informative location to annotate next, taking into account its redundancy with previous annotations; (ii) extends traditional pointwise annotations by accounting for the spatial uncertainty of each annotation, which can either be directly specified by the user, or aggregated from pointwise annotations from multiple experts; and (iii) naturally provides a new strategy for the evaluation of deformable registration algorithms. Our approach is validated on four different registration tasks. The experimental results show the efficacy of suggesting annotations according to their informativeness, and an improved capacity to assess the quality of the outputs of registration algorithms. In addition, our approach yields, from sparse annotations only, a dense visualization of the errors made by a registration method. The source code of our approach supporting both 2D and 3D data is publicly available at https://github.com/LoicPeter/evaluation-deformable-registration

Deep active learning for suggestive segmentation of biomedical image stacks via optimisation of Dice scores and traced boundary length

Manual segmentation of stacks of 2D biomedical images (e.g., histology) is a time-consuming task which can be sped up with semi-automated techniques. In this article, we present a suggestive deep active learning framework that seeks to minimise the annotation effort required to achieve a certain level of accuracy when labelling such a stack. The framework suggests, at every iteration, a specific region of interest (ROI) in one of the images for manual delineation. Using a deep segmentation neural network and a mixed cross-entropy loss function, we propose a principled strategy to estimate class probabilities for the whole stack, conditioned on heterogeneous partial segmentations of the 2D images, as well as on weak supervision in the form of image indices that bound each ROI. Using the estimated probabilities, we propose a novel active learning criterion based on predictions for the estimated segmentation performance and delineation effort, measured with average Dice scores and total delineated boundary length, respectively, rather than common surrogates such as entropy. The query strategy suggests the ROI that is expected to maximise the ratio between performance and effort, while considering the adjacency of structures that may have already been labelled – which decrease the length of the boundary to trace. We provide quantitative results on synthetically deformed MRI scans and real histological data, showing that our framework can reduce labelling effort by up to 60–70% without compromising accuracy

Silicon-Based Solid-State Batteries: Electrochemistry and Mechanics to Guide Design and Operation

Solid-state batteries (SSBs) are promising alternatives to the incumbent lithium-ion technology; however, they face a unique set of challenges that must be overcome to enable their widespread adoption. These challenges include solid-solid interfaces that are highly resistive, with slow kinetics, and a tendency to form interfacial voids causing diminished cycle life due to fracture and delamination. This modeling study probes the evolution of stresses at the solid electrolyte (SE) solid-solid interfaces, by linking the chemical and mechanical material properties to their electrochemical response, which can be used as a guide to optimize the design and manufacture of silicon (Si) based SSBs. A thin-film solid-state battery consisting of an amorphous Si negative electrode (NE) is studied, which exerts compressive stress on the SE, caused by the lithiation-induced expansion of the Si. By using a 2D chemo-mechanical model, continuum scale simulations are used to probe the effect of applied pressure and C-rate on the stress-strain response of the cell and their impacts on the overall cell capacity. A complex concentration gradient is generated within the Si electrode due to slow diffusion of Li through Si, which leads to localized strains. To reduce the interfacial stress and strain at 100% SOC, operation at moderate C-rates with low applied pressure is desirable. Alternatively, the mechanical properties of the SE could be tailored to optimize cell performance. To reduce Si stress, a SE with a moderate Young's modulus similar to that of lithium phosphorous oxynitride (∼77 GPa) with a low yield strength comparable to sulfides (∼0.67 GPa) should be selected. However, if the reduction in SE stress is of greater concern, then a compliant Young's modulus (∼29 GPa) with a moderate yield strength (1-3 GPa) should be targeted. This study emphasizes the need for SE material selection and the consideration of other cell components in order to optimize the performance of thin film solid-state batteries

Lithium-sulfur battery diagnostics through distribution of relaxation times analysis

Electrochemical impedance spectroscopy (EIS) is widely used in battery analysis as it is simple to implement and non-destructive. However, the data provided is a global representation of all electrochemical processes within the cell and much useful information is ambiguous or inaccessible when using traditional analysis techniques. This is a major challenge when EIS is used to analyse systems with complex cell chemistries, like lithium-sulfur (Li-S), one of the strongest candidates to supersede conventional Li-ion batteries. Here we demonstrate the application of distribution of relaxation times (DRT) analysis for quantitative deconvolution of EIS spectra from Li-S batteries, revealing the contributions of (eight) distinct electrode processes to the total cell polarisation. The DRT profile is shown to be strongly dependent on cell state-of-charge, offering a route to automated and on-board analysis of Li-S cells

Does treating obesity stabilize chronic kidney disease?

BACKGROUND: Obesity is a growing health issue in the Western world. Obesity, as part of the metabolic syndrome adds to the morbidity and mortality. The incidence of diabetes and hypertension, two primary etiological factors for chronic renal failure, is significantly higher with obesity. We report a case with morbid obesity whose renal function was stabilized with aggressive management of his obesity. CASE REPORT: A 43-year old morbidly obese Caucasian male was referred for evaluation of his chronic renal failure. He had been hypertensive with well controlled blood pressure with a body mass index of 46 and a baseline serum creatinine of 4.3 mg/dl (estimated glomerular filtration rate of 16 ml/min). He had failed all conservative attempts at weight reduction and hence was referred for a gastric by-pass surgery. Following the bariatric surgery he had approximately 90 lbs. weight loss over 8-months and his serum creatinine stabilized to 4.0 mg/dl. CONCLUSION: Obesity appears to be an independent risk factor for renal failure. Targeting obesity is beneficial not only for better control of hypertension and diabetes, but also possibly helps stabilization of chronic kidney failure

SynthSR: A public AI tool to turn heterogeneous clinical brain scans into high-resolution T1-weighted images for 3D morphometry

Every year, millions of brain magnetic resonance imaging (MRI) scans are acquired in hospitals across the world. These have the potential to revolutionize our understanding of many neurological diseases, but their morphometric analysis has not yet been possible due to their anisotropic resolution. We present an artificial intelligence technique, "SynthSR," that takes clinical brain MRI scans with any MR contrast (T1, T2, etc.), orientation (axial/coronal/sagittal), and resolution and turns them into high-resolution T1 scans that are usable by virtually all existing human neuroimaging tools. We present results on segmentation, registration, and atlasing of >10,000 scans of controls and patients with brain tumors, strokes, and Alzheimer's disease. SynthSR yields morphometric results that are very highly correlated with what one would have obtained with high-resolution T1 scans. SynthSR allows sample sizes that have the potential to overcome the power limitations of prospective research studies and shed new light on the healthy and diseased human brain