Terrace grading of SiGe for high-quality virtual substrates

Silicon germanium (SiGe) virtual substrates of final germanium composition x = 0.50 have been fabricated using solid-source molecular beam epitaxy with a thickness of 2 µm. A layer structure that helps limit the size of dislocation pileups associated with the modified Frank–Read dislocation multiplication mechanism has been studied. It is shown that this structure can produce lower threading dislocation densities than conventional linearly graded virtual substrates. Cross-sectional transmission electron microscopy shows the superior quality of the dislocation network in the graded regions with a lower rms roughness shown by atomic force microscopy. X-ray diffractometry shows these layers to be highly relaxed. This method of Ge grading suggests that high-quality virtual substrates can be grown considerably thinner than with conventional grading methods

Adaptive division of growth and development between hosts in helminths with two‐host life cycles

Parasitic worms (helminths) with complex life cycles divide growth and development between successive hosts. Using data from 597 species of acanthocephalans, cestodes, and nematodes with two‐host life cycles, we found that helminths with larger intermediate hosts were more likely to infect larger, endothermic definitive hosts, although some evolutionary shifts in definitive host mass occurred without changes in intermediate host mass. Life‐history theory predicts parasites to shift growth to hosts in which they can grow rapidly and/or safely. Accordingly, helminth species grew relatively less as larvae and more as adults if they infected smaller intermediate hosts and/or larger, endothermic definitive hosts. Growing larger than expected in one host, relative to host mass/endothermy, was not associated with growing less in the other host, implying a lack of cross‐host trade‐offs. Rather, some helminth orders had both large larvae and large adults. Within these taxa, however, size at maturity in the definitive host was unaffected by changes to larval growth, as predicted by optimality models. Parasite life‐history strategies were mostly (though not entirely) consistent with theoretical expectations, suggesting that helminths adaptively divide growth and development between the multiple hosts in their complex life cycles.Peer Reviewe

Gravity-induced vacuum dominance

It has been widely believed that, except in very extreme situations, the influence of gravity on quantum fields should amount to just small, sub-dominant contributions. This view seemed to be endorsed by the seminal results obtained over the last decades in the context of renormalization of quantum fields in curved spacetimes. Here, however, we argue that this belief is false by showing that there exist well-behaved spacetime evolutions where the vacuum energy density of free quantum fields is forced, by the very same background spacetime, to become dominant over any classical energy-density component. This semiclassical gravity effect finds its roots in the infrared behavior of fields on curved spacetimes. By estimating the time scale for the vacuum energy density to become dominant, and therefore for backreaction on the background spacetime to become important, we argue that this vacuum dominance may bear unexpected astrophysical and cosmological implications.Comment: To appear in Phys. Rev. Lett

Acceleration of the universe, vacuum metamorphosis, and the large-time asymptotic form of the heat kernel

We investigate the possibility that the late acceleration observed in the rate of expansion of the universe is due to vacuum quantum effects arising in curved spacetime. The theoretical basis of the vacuum cold dark matter (VCDM), or vacuum metamorphosis, cosmological model of Parker and Raval is revisited and improved. We show, by means of a manifestly nonperturbative approach, how the infrared behavior of the propagator (related to the large-time asymptotic form of the heat kernel) of a free scalar field in curved spacetime causes the vacuum expectation value of its energy-momentum tensor to exhibit a resonance effect when the scalar curvature R of the spacetime reaches a particular value related to the mass of the field. we show that the back reaction caused by this resonance drives the universe through a transition to an accelerating expansion phase, very much in the same way as originally proposed by Parker and Raval. Our analysis includes higher derivatives that were neglected in the earlier analysis, and takes into account the possible runaway solutions that can follow from these higher-derivative terms. We find that the runaway solutions do not occur if the universe was described by the usual classical FRW solution prior to the growth of vacuum energy-density and negative pressure (i.e., vacuum metamorphosis) that causes the transition to an accelerating expansion of the universe in this theory.Comment: 33 pages, 3 figures. Submitted to Physical Review D15 (Dec 23, 2003). v2: 1 reference added. No other change

Awaking the vacuum in relativistic stars

Void of any inherent structure in classical physics, the vacuum has revealed to be incredibly crowded with all sorts of processes in relativistic quantum physics. Yet, its direct effects are usually so subtle that its structure remains almost as evasive as in classical physics. Here, in contrast, we report on the discovery of a novel effect according to which the vacuum is compelled to play an unexpected central role in an astrophysical context. We show that the formation of relativistic stars may lead the vacuum energy density of a quantum field to an exponential growth. The vacuum-driven evolution which would then follow may lead to unexpected implications for astrophysics, while the observation of stable neutron-star configurations may teach us much on the field content of our Universe.Comment: To appear in Phys. Rev. Let

Primary Carcinoid Tumor of the Ileal Efferent Limb of an Ileovesicostomy: A Case Report

We report on the evaluation and management of a 47-year-old white male found to have primary carcinoid tumor of the ileal segment of his diverting ileovesicostomy thirty-five months after initial creation. Subsequent to presentation with intermittent gross hematuria, CT urogram highlights an 8 mm enhancing lesion near the enterovesical junction of urinary diversion. Office cystoscopy confirms presence of a lesion that was later endoscopically resected and found to be a well-differentiated carcinoid tumor. Evaluation with serum markers, direct visualization utilizing endoscopy, and imaging was without finding of alternate primary or metastatic lesions. The patient ultimately had the proximal ileal portion of his ileovesicostomy excised and the distal portion converted into an ileal conduit. After briefly discussing the carcinoid tumor and the carcinoid syndrome it may cause, we review the literature on the incidence of carcinoid tumors in a population requiring the use of intestine in the urinary tract

DeepBrainPrint: A Novel Contrastive Framework for Brain MRI Re-Identification

Recent advances in MRI have led to the creation of large datasets. With the increase in data volume, it has become difficult to locate previous scans of the same patient within these datasets (a process known as re-identification). To address this issue, we propose an AI-powered medical imaging retrieval framework called DeepBrainPrint, which is designed to retrieve brain MRI scans of the same patient. Our framework is a semi-self-supervised contrastive deep learning approach with three main innovations. First, we use a combination of self-supervised and supervised paradigms to create an effective brain fingerprint from MRI scans that can be used for real-time image retrieval. Second, we use a special weighting function to guide the training and improve model convergence. Third, we introduce new imaging transformations to improve retrieval robustness in the presence of intensity variations (i.e. different scan contrasts), and to account for age and disease progression in patients. We tested DeepBrainPrint on a large dataset of T1-weighted brain MRIs from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and on a synthetic dataset designed to evaluate retrieval performance with different image modalities. Our results show that DeepBrainPrint outperforms previous methods, including simple similarity metrics and more advanced contrastive deep learning frameworks

Use of Rho kinase Inhibitors in Ophthalmology: A Review of the Literature

The use of Rho Kinase (ROCK) inhibitors as therapeutic agents in ophthalmology has been a topic of discussion for several years, particularly in the realm of glaucoma, Fuchs’ endothelial dystrophy, and diabetic retinopathy. In this review, the authors provide a detailed and comprehensive overview of the published literature on the use of Rho kinase inhibitors for the aforementioned purposes. A thorough search of several databases was conducted to find sufficient literature on ROCK inhibitors. This research found strong evidence demonstrating that inhibition of Rho kinase significantly decreases IOP, increases healing of the corneal endothelium, and decreases progression of diabetic retinopathy. The main side effect of ROCK inhibitors is conjunctival hyperemia that is often present in more than half of the patients in certain formulations. Additional clinical trials investigating the reviewed treatment options of Rho kinase inhibitors are necessary to further validate previous findings on the topic. Nonetheless, it is clear that Rho kinase inhibitors have the potential to be another potent therapeutic option for several chronic diseases in ophthalmology

An efficient semi-supervised quality control system trained using physics-based MRI-artefact generators and adversarial training

© 2023 The Author(s). Published by Elsevier B.V. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/Large medical imaging data sets are becoming increasingly available. A common challenge in these data sets is to ensure that each sample meets minimum quality requirements devoid of significant artefacts. Despite a wide range of existing automatic methods having been developed to identify imperfections and artefacts in medical imaging, they mostly rely on data-hungry methods. In particular, the scarcity of artefact-containing scans available for training has been a major obstacle in the development and implementation of machine learning in clinical research. To tackle this problem, we propose a novel framework having four main components: (1) a set of artefact generators inspired by magnetic resonance physics to corrupt brain MRI scans and augment a training dataset, (2) a set of abstract and engineered features to represent images compactly, (3) a feature selection process that depends on the class of artefact to improve classification performance, and (4) a set of Support Vector Machine (SVM) classifiers trained to identify artefacts. Our novel contributions are threefold: first, we use the novel physics-based artefact generators to generate synthetic brain MRI scans with controlled artefacts as a data augmentation technique. This will avoid the labour-intensive collection and labelling process of scans with rare artefacts. Second, we propose a large pool of abstract and engineered image features developed to identify 9 different artefacts for structural MRI. Finally, we use an artefact-based feature selection block that, for each class of artefacts, finds the set of features that provide the best classification performance. We performed validation experiments on a large data set of scans with artificially-generated artefacts, and in a multiple sclerosis clinical trial where real artefacts were identified by experts, showing that the proposed pipeline outperforms traditional methods. In particular, our data augmentation increases performance by up to 12.5 percentage points on the accuracy, F1, F2, precision and recall. At the same time, the computation cost of our pipeline remains low – less than a second to process a single scan – with the potential for real-time deployment. Our artefact simulators obtained using adversarial learning enable the training of a quality control system for brain MRI that otherwise would have required a much larger number of scans in both supervised and unsupervised settings. We believe that systems for quality control will enable a wide range of high-throughput clinical applications based on the use of automatic image-processing pipelines.Peer reviewe