Deep Learning using K-space Based Data Augmentation for Automated Cardiac MR Motion Artefact Detection

Quality assessment of medical images is essential for complete automation of image processing pipelines. For large population studies such as the UK Biobank, artefacts such as those caused by heart motion are problematic and manual identification is tedious and time-consuming. Therefore, there is an urgent need for automatic image quality assessment techniques. In this paper, we propose a method to automatically detect the presence of motion-related artefacts in cardiac magnetic resonance (CMR) images. As this is a highly imbalanced classification problem (due to the high number of good quality images compared to the low number of images with motion artefacts), we propose a novel k-space based training data augmentation approach in order to address this problem. Our method is based on 3D spatio-temporal Convolutional Neural Networks, and is able to detect 2D+time short axis images with motion artefacts in less than 1ms. We test our algorithm on a subset of the UK Biobank dataset consisting of 3465 CMR images and achieve not only high accuracy in detection of motion artefacts, but also high precision and recall. We compare our approach to a range of state-of-the-art quality assessment methods.Comment: Accepted for MICCAI2018 Conferenc

Anti-cancer and potential chemopreventive actions of ginseng by activating Nrf2 (NFE2L2) anti-oxidative stress/anti-inflammatory pathways

This article reviews recent basic and clinical studies of ginseng, particularly the anti-cancer effects and the potential chemopreventive actions by activating the transcriptional factor, nuclear factor (erythroid-derived 2)-like 2 (Nrf2 or NFE2L2)-mediated anti-oxidative stress or anti-inflammatory pathways. Nrf2 is a novel target for cancer prevention as it regulates the antioxidant responsive element (ARE), a critical regulatory element in the promoter region of genes encoding cellular phase II detoxifying and anti-oxidative stress enzymes. The studies on the chemopreventive effects of ginseng or its components/products showed that Nrf2 could also be a target for ginseng's actions. A number of papers also demonstrated the anti-inflammatory effects of ginseng. Targeting Nrf2 pathway is a novel approach to the investigation of ginseng's cancer chemopreventive actions, including some oxidative stress and inflammatory conditions responsible for the initiation, promotion and progression of carcinogenesis

Free Ferrous Ions Sustain Activity of Mammalian Stearoyl-CoA Desaturase-1

Mammalian stearoyl-CoA desaturase-1 (SCD1) introduces a double-bond to a saturated long-chain fatty acid in a reaction catalyzed by a diiron center. The diiron center is well-coordinated by conserved histidine residues and is thought to remain with the enzyme. However, we find here that SCD1 progressively loses its activity during catalysis and becomes fully inactive after about nine turnovers. Further studies show that the inactivation of SCD1 is due to the loss of an iron (Fe) ion in the diiron center and that the addition of free ferrous ions (F

Sub-wavelength terahertz beam profiling of a THz source via an all-optical knife-edge technique

Terahertz technologies recently emerged as outstanding candidates for a variety of applications in such sectors as security, biomedical, pharmaceutical, aero spatial, etc. Imaging the terahertz field, however, still remains a challenge, particularly when sub-wavelength resolutions are involved. Here we demonstrate an all-optical technique for the terahertz near-field imaging directly at the source plane. A thin layer (<100 nm-thickness) of photo carriers is induced on the surface of the terahertz generation crystal, which acts as an all-optical, virtual blade for terahertz near-field imaging via a knife-edge technique. Remarkably, and in spite of the fact that the proposed approach does not require any mechanical probe, such as tips or apertures, we are able to demonstrate the imaging of a terahertz source with deeply sub-wavelength features (<30 μm) directly in its emission plane

Influence of oxygen pressure and aging on LaAlO3 films grown by pulsed laser deposition on SrTiO3 substrates

The crystal structures of LaAlO3 films grown by pulsed laser deposition on SrTiO3 substrates at oxygen pressure of 10-3 mbar or 10-5 mbar, where kinetics of ablated species hardly depend on oxygen background pressure, are compared. Our results show that the interface between LaAlO3 and SrTiO3 is sharper when the oxygen pressure is lower. Over time, the formation of various crystalline phases is observed while the crystalline thickness of the LaAlO3 layer remains unchanged. X-ray scattering as well as atomic force microscopy measurements indicate three-dimensional growth of such phases, which appear to be fed from an amorphous capping layer present in as-grown samples

Language Models as Knowledge Bases?

Recent progress in pretraining language models on large textual corpora led to a surge of improvements for downstream NLP tasks. Whilst learning linguistic knowledge, these models may also be storing relational knowledge present in the training data, and may be able to answer queries structured as "fill-in-the-blank" cloze statements. Language models have many advantages over structured knowledge bases: they require no schema engineering, allow practitioners to query about an open class of relations, are easy to extend to more data, and require no human supervision to train. We present an in-depth analysis of the relational knowledge already present (without fine-tuning) in a wide range of state-of-the-art pretrained language models. We find that (i) without fine-tuning, BERT contains relational knowledge competitive with traditional NLP methods that have some access to oracle knowledge, (ii) BERT also does remarkably well on open-domain question answering against a supervised baseline, and (iii) certain types of factual knowledge are learned much more readily than others by standard language model pretraining approaches. The surprisingly strong ability of these models to recall factual knowledge without any fine-tuning demonstrates their potential as unsupervised open-domain QA systems. The code to reproduce our analysis is available at https://github.com/facebookresearch/LAMA