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

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

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

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