Ground state energies of quantum dots in high magnetic fields: A new approach

We present a new method for calculating ground state properties of quantum dots in high magnetic fields. It takes into account the equilibrium positions of electrons in a Wigner cluster to minimize the interaction energy in the high field limit. Assuming perfect spin alignment the many-body trial function is a single Slater determinant of overlapping oscillator functions from the lowest Landau level centered at and near the classical equilibrium positions. We obtain an analytic expression for the ground state energy and present numerical results for up to N=40.Comment: 4 pages, including 2 figures, contribution to the Proceedings of EP2DS-14, submitted to Physica

Age-related increase of kynurenic acid in human cerebrospinal fluid-IgG and beta(2)-microglobulin changes

Kynurenic acid (KYNA) is an endogenous metabolite in the kynurenine pathway of tryptophan degradation and is an antagonist at the glycine site of the N-methyl-D-aspartate as well as at the alpha 7 nicotinic cholinergic receptors. In the brain tissue KYNA is synthesised from L-kynurenine by kynurenine aminotransferases (KAT) I and II. A host of immune mediators influence tryptophan degradation. In the present study, the levels of KYNA in cerebrospinal fluid (CSF) and serum in a group of human subjects aged between 25 and 74 years were determined by using a high performance liquid chromatography method. In CSF and serum KAT I and II activities were investigated by radioenzymatic assay, and the levels of β2-microglobulin, a marker for cellular immune activation, were determined by ELISA. The correlations between neurochemical and biological parameters were evaluated. Two subject groups with significantly different ages, i.e. 50 years, p < 0.001, showed statistically significantly different CSF KYNA levels, i.e. 2.84 ± 0.16 fmol/μl vs. 4.09 ± 0.14 fmol/μl, p < 0.001, respectively; but this difference was not seen in serum samples. Interestingly, KYNA is synthesised in CSF principally by KAT I and not KAT II, however no relationship was found between enzyme activity and ageing. A positive relationship between CSF KYNA levels and age of subjects indicates a 95% probability of elevated CSF KYNA with ageing (R = 0.6639, p = 0.0001). KYNA levels significantly correlated with IgG and β2-microglobulin levels (R = 0.5244, p = 0.0049; R = 0.4253, p = 0.043, respectively). No correlation was found between other biological parameters in CSF or serum. In summary, a positive relationship between the CSF KYNA level and ageing was found, and the data would suggest age-dependent increase of kynurenine metabolism in the CNS. An enhancement of CSF IgG and β2-microglobulin levels would suggest an activation of the immune system during ageing. Increased KYNA metabolism may be involved in the hypofunction of the glutamatergic and/or nicotinic cholinergic neurotransmission in the ageing CNS

Learning under Distributed Weak Supervision

The availability of training data for supervision is a frequently encountered bottleneck of medical image analysis methods. While typically established by a clinical expert rater, the increase in acquired imaging data renders traditional pixel-wise segmentations less feasible. In this paper, we examine the use of a crowdsourcing platform for the distribution of super-pixel weak annotation tasks and collect such annotations from a crowd of non-expert raters. The crowd annotations are subsequently used for training a fully convolutional neural network to address the problem of fetal brain segmentation in T2-weighted MR images. Using this approach we report encouraging results compared to highly targeted, fully supervised methods and potentially address a frequent problem impeding image analysis research

Contrastive learning for view classification of echocardiograms

Analysis of cardiac ultrasound images is commonly performed in routine clinical practice for quantification of cardiac function. Its increasing automation frequently employs deep learning networks that are trained to predict disease or detect image features. However, such models are extremely data-hungry and training requires labelling of many thousands of images by experienced clinicians. Here we propose the use of contrastive learning to mitigate the labelling bottleneck. We train view classification models for imbalanced cardiac ultrasound datasets and show improved performance for views/classes for which minimal labelled data is available. Compared to a naïve baseline model, we achieve an improvement in F1 score of up to 26% in those views while maintaining state-of-the-art performance for the views with sufficiently many labelled training observations

Ground state energies of quantum dots in high magnetic fields: A new approach

We present a new method for calculating ground state properties of quantum dots in high magnetic fields. It takes into account the equilibrium positions of electrons in a Wigner cluster to minimize the interaction energy in the high field limit. Assuming perfect spin alignment the many-body trial function is a single Slater determinant of overlapping oscillator functions from the lowest Landau level centered at and near the classical equilibrium positions. We obtain an analytic expression for the ground state energy and present numerical results for up to N=40.Comment: 4 pages, including 2 figures, contribution to the Proceedings of EP2DS-14, submitted to Physica

Polyunsaturated fatty acids in fishes increase with total lipids irrespective of feeding sources and trophic position

Trophic transfer and retention of dietary compounds are vital for somatic development, reproduction, and survival of aquatic consumers. In this field study, stable carbon and nitrogen isotopes, and fatty acids (FA) contents in invertebrates and fishes of pre-alpine Lake Lunz, Austria, were used to (1) identify the resource use and trophic level of Arctic charr (Salvelinus alpinus), pike (Esox lucius), perch (Perca fluviatilis), brown trout (Salmo trutta), roach (Rutilus rutilus), and minnow (Phoxinus phoxinus) and (2) examine how polyunsaturated fatty acids (PUFA; i.e., omega-3 and -6 PUFA) are related to total lipid status, littoral-pelagic reliance, and trophic position. Stable isotope data suggest that pike, perch, and minnow derived most of their energy from littoral resources, but minnows differed from pike and perch in their trophic position and PUFA composition. The co-occurrence of cyprinids, percids, and pike segregated these fishes into more lipid-rich (roach, minnow) and lipid-poor (pike, percids) species. Although the relatively lipid-poor pike and percids occupied a higher trophic position than cyprinids, there was a concurrent, total lipid-dependent decline in omega-3 and -6 PUFA in these predatory fishes. Results of this lake food-web study demonstrated that total lipids in fish community, littoral-pelagic reliance, and trophic position explained omega-3 and -6 PUFA in dorsal muscle tissues. Omega-3 and -6 PUFA in these fishes decreased with increasing trophic position, demonstrating that these essential FAs did not biomagnify with increasing trophic level. Finally, this lake food-web study provides evidence of fish community-level relationship between total lipid status and PUFA or stable isotope ratios, whereas the strength of such relationships was less strong at the species level.Peer reviewe

Synaptic Cleft Segmentation in Non-Isotropic Volume Electron Microscopy of the Complete Drosophila Brain

Neural circuit reconstruction at single synapse resolution is increasingly recognized as crucially important to decipher the function of biological nervous systems. Volume electron microscopy in serial transmission or scanning mode has been demonstrated to provide the necessary resolution to segment or trace all neurites and to annotate all synaptic connections. Automatic annotation of synaptic connections has been done successfully in near isotropic electron microscopy of vertebrate model organisms. Results on non-isotropic data in insect models, however, are not yet on par with human annotation. We designed a new 3D-U-Net architecture to optimally represent isotropic fields of view in non-isotropic data. We used regression on a signed distance transform of manually annotated synaptic clefts of the CREMI challenge dataset to train this model and observed significant improvement over the state of the art. We developed open source software for optimized parallel prediction on very large volumetric datasets and applied our model to predict synaptic clefts in a 50 tera-voxels dataset of the complete Drosophila brain. Our model generalizes well to areas far away from where training data was available

Fast multiple landmark localisation using a patch-based iterative network

We propose a new Patch-based Iterative Network (PIN) for fast and accurate landmark localisation in 3D medical volumes. PIN utilises a Convolutional Neural Network (CNN) to learn the spatial relationship between an image patch and anatomical landmark positions. During inference, patches are repeatedly passed to the CNN until the estimated landmark position converges to the true landmark location. PIN is computationally efficient since the inference stage only selectively samples a small number of patches in an iterative fashion rather than a dense sampling at every location in the volume. Our approach adopts a multi-task learning framework that combines regression and classification to improve localisation accuracy. We extend PIN to localise multiple landmarks by using principal component analysis, which models the global anatomical relationships between landmarks. We have evaluated PIN using 72 3D ultrasound images from fetal screening examinations. PIN achieves quantitatively an average landmark localisation error of 5.59mm and a runtime of 0.44s to predict 10 landmarks per volume. Qualitatively, anatomical 2D standard scan planes derived from the predicted landmark locations are visually similar to the clinical ground truth

Confident head circumference measurement from ultrasound with real-time feedback for sonographers

Manual estimation of fetal Head Circumference (HC) from Ultrasound (US) is a key biometric for monitoring the healthy development of fetuses. Unfortunately, such measurements are subject to large inter-observer variability, resulting in low early-detection rates of fetal abnormalities. To address this issue, we propose a novel probabilistic Deep Learning approach for real-time automated estimation of fetal HC. This system feeds back statistics on measurement robustness to inform users how confident a deep neural network is in evaluating suitable views acquired during free-hand ultrasound examination. In real-time scenarios, this approach may be exploited to guide operators to scan planes that are as close as possible to the underlying distribution of training images, for the purpose of improving inter-operator consistency. We train on freehand ultrasound data from over 2000 subjects (2848 training/540 test) and show that our method is able to predict HC measurements within 1.81±1.65 mm deviation from the ground truth, with 50% of the test images fully contained within the predicted confidence margins, and an average of 1.82±1.78 mm deviation from the margin for the remaining cases that are not fully contained

Inelastic collapse of a randomly forced particle

We consider a randomly forced particle moving in a finite region, which rebounds inelastically with coefficient of restitution r on collision with the boundaries. We show that there is a transition at a critical value of r, r_c\equiv e^{-\pi/\sqrt{3}}, above which the dynamics is ergodic but beneath which the particle undergoes inelastic collapse, coming to rest after an infinite number of collisions in a finite time. The value of r_c is argued to be independent of the size of the region or the presence of a viscous damping term in the equation of motion.Comment: 4 pages, REVTEX, 2 EPS figures, uses multicol.sty and epsf.st