278 research outputs found
funcX: A Federated Function Serving Fabric for Science
Exploding data volumes and velocities, new computational methods and
platforms, and ubiquitous connectivity demand new approaches to computation in
the sciences. These new approaches must enable computation to be mobile, so
that, for example, it can occur near data, be triggered by events (e.g.,
arrival of new data), be offloaded to specialized accelerators, or run remotely
where resources are available. They also require new design approaches in which
monolithic applications can be decomposed into smaller components, that may in
turn be executed separately and on the most suitable resources. To address
these needs we present funcX---a distributed function as a service (FaaS)
platform that enables flexible, scalable, and high performance remote function
execution. funcX's endpoint software can transform existing clouds, clusters,
and supercomputers into function serving systems, while funcX's cloud-hosted
service provides transparent, secure, and reliable function execution across a
federated ecosystem of endpoints. We motivate the need for funcX with several
scientific case studies, present our prototype design and implementation, show
optimizations that deliver throughput in excess of 1 million functions per
second, and demonstrate, via experiments on two supercomputers, that funcX can
scale to more than more than 130000 concurrent workers.Comment: Accepted to ACM Symposium on High-Performance Parallel and
Distributed Computing (HPDC 2020). arXiv admin note: substantial text overlap
with arXiv:1908.0490
Accidental swallowing of partial denture: a case report
We describe a 42-year-old age woman who accidentally swallowed her lower denture, which was composed of eleven teeth. The daily descent of the denture was followed by plain abdominal radiography and physical examination. The image was localized at the left upper quadrant on admission day, but it stopped on its way at the right lower quadrant on day two and three. Since the patient's complaints increased we planned surgical removal of the denture. In this report, we had discussed the diagnosis, follow up and treatment options of swallowed partial denture with current literature review
Total sulfane sulfur bioavailability reflects ethnic and gender disparities in cardiovascular disease
Hydrogen sulfide (H2S) has emerged as an important physiological and pathophysiological signaling molecule in the cardiovascular system influencing vascular tone, cytoprotective responses, redox reactions, vascular adap- tation, and mitochondrial respiration. However, bioavailable levels of H2S in its various biochemical metabolite forms during clinical cardiovascular disease remain poorly understood. We performed a case-controlled study to quantify and compare the bioavailability of various biochemical forms of H2S in patients with and without cardiovascular disease (CVD). In our study, we used the reverse-phase high performance liquid chromatography monobromobimane assay to analytically measure bioavailable pools of H2S. Single nucleotide polymorphisms (SNPs) were also identified using DNA Pyrosequencing. We found that plasma acid labile sulfide levels were significantly reduced in Caucasian females with CVD compared with those without the disease. Conversely, plasma bound sulfane sulfur levels were significantly reduced in Caucasian males with CVD compared with those without the disease. Surprisingly, gender differences of H2S bioavailability were not observed in African Americans, although H2S bioavailability was significantly lower overall in this ethnic group compared to Caucasians. We also performed SNP analysis of H2S synthesizing enzymes and found a significant increase in cystathionine gamma-lyase (CTH) 1364 G-T allele frequency in patients with CVD compared to controls. Lastly, plasma H2S bioavailability was found to be predictive for cardiovascular disease in Caucasian subjects as de- termined by receiver operator characteristic analysis. These findings reveal that plasma H2S bioavailability could be considered a biomarker for CVD in an ethnic and gender manner. Cystathionine gamma-lyase 1346 G-T SNP might also contribute to the risk of cardiovascular disease development
Penis deformity after intra-urethral liquid paraffin administration in a young male: a case report
This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens
Quantification of bound microbubbles in ultrasound molecular imaging
Molecular markers associated with diseases can be visualized and quantified noninvasively with targeted ultrasound contrast agent (t-UCA) consisting of microbubbles (MBs) that can bind to specific molecular targets. Techniques used for quantifying t-UCA assume that all unbound MBs are taken out of the blood pool few minutes after injection and only MBs bound to the molecular markers remain. However, differences in physiology, diseases, and experimental conditions can increase the longevity of unbound MBs. In such conditions, unbound MBs will falsely be quantified as bound MBs. We have developed a novel technique to distinguish and classify bound from unbound MBs. In the post-processing steps, first, tissue motion was compensated using block-matching (BM) techniques. To preserve only stationary contrast signals, a minimum intensity projection (MinIP) or 20th-percentile intensity projection (PerIP) was applied. The after-flash MinIP or PerIP was subtracted from the before-flash MinIP or PerIP. In this way, tissue artifacts in contrast images were suppressed. In the next step, bound MB candidates were detected. Finally, detected objects were tracked to classify the candidates as unbound or bound MBs based on their displacement. This technique was validated in vitro, followed by two in vivo experiments in mice. Tumors (n = 2) and salivary glands of hypercholesterolemic mice (n = 8) were imaged using a commercially available scanner. Boluses of 100 μL of a commercially available t-UCA targeted to angiogenesis markers and untargeted control UCA were injected separately. Our results show considerable reduction in misclassification of unbound MBs as bound ones. Using our method, the ratio of bound MBs in salivary gland for images with targeted UCA versus control UCA was improved by up to two times compared with unprocessed images
Nonlinear Markov Random Fields Learned via Backpropagation
Although convolutional neural networks (CNNs) currently dominate competitions
on image segmentation, for neuroimaging analysis tasks, more classical
generative approaches based on mixture models are still used in practice to
parcellate brains. To bridge the gap between the two, in this paper we propose
a marriage between a probabilistic generative model, which has been shown to be
robust to variability among magnetic resonance (MR) images acquired via
different imaging protocols, and a CNN. The link is in the prior distribution
over the unknown tissue classes, which are classically modelled using a Markov
random field. In this work we model the interactions among neighbouring pixels
by a type of recurrent CNN, which can encode more complex spatial interactions.
We validate our proposed model on publicly available MR data, from different
centres, and show that it generalises across imaging protocols. This result
demonstrates a successful and principled inclusion of a CNN in a generative
model, which in turn could be adapted by any probabilistic generative approach
for image segmentation.Comment: Accepted for the international conference on Information Processing
in Medical Imaging (IPMI) 2019, camera ready versio
Partial Volume Segmentation of Brain MRI Scans of any Resolution and Contrast
Partial voluming (PV) is arguably the last crucial unsolved problem in
Bayesian segmentation of brain MRI with probabilistic atlases. PV occurs when
voxels contain multiple tissue classes, giving rise to image intensities that
may not be representative of any one of the underlying classes. PV is
particularly problematic for segmentation when there is a large resolution gap
between the atlas and the test scan, e.g., when segmenting clinical scans with
thick slices, or when using a high-resolution atlas. In this work, we present
PV-SynthSeg, a convolutional neural network (CNN) that tackles this problem by
directly learning a mapping between (possibly multi-modal) low resolution (LR)
scans and underlying high resolution (HR) segmentations. PV-SynthSeg simulates
LR images from HR label maps with a generative model of PV, and can be trained
to segment scans of any desired target contrast and resolution, even for
previously unseen modalities where neither images nor segmentations are
available at training. PV-SynthSeg does not require any preprocessing, and runs
in seconds. We demonstrate the accuracy and flexibility of the method with
extensive experiments on three datasets and 2,680 scans. The code is available
at https://github.com/BBillot/SynthSeg.Comment: accepted for MICCAI 202
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