Percutaneous management of a saphenous vein graft perforation using a covered stent and final coil embolization technique

Coronary artery perforation as a result of percutaneous coronary intervention is a rare complication which may result in cardiac tamponade, myocardial infarction and death. Perforation of a saphenous vein graft is unusual and generally requires surgical intervention. We describe a novel percutaneous approach that facilitated the successful management of a potentially catastrophic saphenous vein graft (SVG) perforation

Few Shot Learning in Histopathological Images:Reducing the Need of Labeled Data on Biological Datasets

Although deep learning pathology diagnostic algorithms are proving comparable results with human experts in a wide variety of tasks, they still require a huge amount of well annotated data for training. Generating such extensive and well labelled datasets is time consuming and is not feasible for certain tasks and so, most of the medical datasets available are scarce in images and therefore, not enough for training. In this work we validate that the use of few shot learning techniques can transfer knowledge from a well defined source domain from Colon tissue into a more generic domain composed by Colon, Lung and Breast tissue by using very few training images. Our results show that our few-shot approach is able to obtain a balanced accuracy (BAC) of 90% with just 60 training images, even for the Lung and Breast tissues that were not present on the training set. This outperforms the finetune transfer learning approach that obtains 73% BAC with 60 images and requires 600 images to get up to 81% BAC.This study has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 732111 (PICCOLO project)

Research 4.0 : Research in the age of automation

Executive Summary There is a growing consensus that we are at the start of a fourth industrial revolution, driven by developments in Artificial Intelligence, machine learning, robotics, the Internet of Things, 3-D printing, nanotechnology, biotechnology, 5G, new forms of energy storage and quantum computing. This wave of technical innovations is already having a significant impact on how research is conducted, with dramatic change across research methods in recent years within some disciplines, as this project’s interim report set out. Whilst there are a wide range of technologies associated with the fourth industrial revolution, this report primarily seeks to understand what impact Artificial Intelligence (AI) is having on the UK’s research sector and what implications it has for its future, with a particular focus on academic research. Following Hall and Pesenti in their recent government review of the UK’s AI industry, we adopt the following definition: “[AI is] an umbrella term to cover a set of complementary techniques that have developed from statistics, computer science and cognitive psychology. While recognising distinctions between specific technologies and terms (e.g., artificial intelligence vs. machine learning, machine learning vs. deep learning), it is useful to see these technologies as a group, when considering how to support development and use of them.” Hence, we will use ‘AI’ as an umbrella term throughout the report to cover a range of different technologies (e.g., machine learning, data visualisation, robotics). Building on our interim report, we find that AI is increasingly deployed in academic research in the UK in a broad range of disciplines. The combination of an explosion of new digital data sources with powerful new analytical tools represents a ‘double dividend’ for researchers. This is allowing researchers to investigate questions that would have been unanswerable just a decade ago. Whilst there has been considerable take-up of AI in academic research, steps could be taken to ensure even wider adoption of these new techniques and technologies, including wider training in the necessary skills for effective utilisation of AI, faster routes to culture change and greater multidisciplinary collaboration. We also envisage a range of possible scenarios for the future of UK academic research as a result of widespread use of AI. Steps should be taken to steer us towards desirable futures. The research sector is not set in stone; it can and must be shaped by wider society for the good of all. We consider how to achieve this in our recommendations below. We recognise that the Covid-19 pandemic means universities are currently facing significant pressures, with considerable demands on their resources whilst simultaneously facing threats to income. As a result, we acknowledge that most in the sector will be focused on fighting this immediate threat instead of thinking about the long-term future of research. But as we emerge from the current crisis, we urge policy makers and universities to consider our recommendations and take steps to fortify the UK’s position as a place of world-leading research. Indeed, the current crisis has only reminded us of the critical importance of a highly functioning and flourishing research sector

Finding Higgs bosons heavier than 2 m_W in dileptonic W-boson decays

We reconsider observables for discovering a heavy Higgs boson (with m_h > 2m_W) via its di-leptonic decays h -> WW -> l nu l nu. We show that observables generalizing the transverse mass that take into account the fact that both of the intermediate W bosons are likely to be on-shell give a significant improvement over the variables used in existing searches. We also comment on the application of these observables to other decays which proceed via narrow-width intermediates.Comment: v1:4 pages, 1 figure; v2: 6 pages, 2 figures, substantially revise

Hamilton decompositions of regular expanders: applications

In a recent paper, we showed that every sufficiently large regular digraph G on n vertices whose degree is linear in n and which is a robust outexpander has a decomposition into edge-disjoint Hamilton cycles. The main consequence of this theorem is that every regular tournament on n vertices can be decomposed into (n-1)/2 edge-disjoint Hamilton cycles, whenever n is sufficiently large. This verified a conjecture of Kelly from 1968. In this paper, we derive a number of further consequences of our result on robust outexpanders, the main ones are the following: (i) an undirected analogue of our result on robust outexpanders; (ii) best possible bounds on the size of an optimal packing of edge-disjoint Hamilton cycles in a graph of minimum degree d for a large range of values for d. (iii) a similar result for digraphs of given minimum semidegree; (iv) an approximate version of a conjecture of Nash-Williams on Hamilton decompositions of dense regular graphs; (v) the observation that dense quasi-random graphs are robust outexpanders; (vi) a verification of the `very dense' case of a conjecture of Frieze and Krivelevich on packing edge-disjoint Hamilton cycles in random graphs; (vii) a proof of a conjecture of Erdos on the size of an optimal packing of edge-disjoint Hamilton cycles in a random tournament.Comment: final version, to appear in J. Combinatorial Theory

Microbial communities associated with the parasitic copepod Lepeophtheirus salmonis.

Lepeophtheirus salmonis is a naturally occurring marine parasite of salmonid fishes in the Northern hemisphere, and a major problem in salmonid aquaculture. In addition to the direct effects on host fish, L. salmonis may act as a vector for diseases. Here, the microbial community of L. salmonis recovered from whole genome shotgun sequencing was compared between lice sampled from both the Atlantic and the Pacific, laboratory-reared and wild lice, in addition to lice displaying resistance towards chemical treatments. The analysis shows clear differences in the metagenomic composition between the Atlantic and the Pacific Ocean, whereas the resistance status of the L. salmonis or the cultivation did not have significant impact.submittedVersio

Autofluorescence image reconstruction and virtual staining for in-vivo optical biopsying

Modern photonic technologies are emerging, allowing the acquisition of in-vivo endoscopic tissue imaging at a microscopic scale, with characteristics comparable to traditional histological slides, and with a label-free modality. This raises the possibility of an ‘optical biopsy’ to aid clinical decision making. This approach faces barriers for being incorporated into clinical practice, including the lack of existing images for training, unfamiliarity of clinicians with the novel image domains and the uncertainty of trusting ‘black-box’ machine learned image analysis, where the decision making remains inscrutable. In this paper, we propose a new method to transform images from novel photonics techniques (e.g. autofluorescence microscopy) into already established domains such as Hematoxilyn-Eosin (H-E) microscopy through virtual reconstruction and staining. We introduce three main innovations: 1) we propose a transformation method based on a Siamese structure that simultaneously learns the direct and inverse transformation ensuring domain back-transformation quality of the transformed data. 2) We also introduced an embedding loss term that ensures similarity not only at pixel level, but also at the image embedding description level. This drastically reduces the perception distortion trade-off problem existing in common domain transfer based on generative adversarial networks. These virtually stained images can serve as reference standard images for comparison with the already known H-E images. 3) We also incorporate an uncertainty margin concept that allows the network to measure its own confidence, and demonstrate that these reconstructed and virtually stained images can be used on previously-studied classification models of H-E images that have been computationally degraded and de-stained. The three proposed methods can be seamlessly incorporated on any existing architectures. We obtained balanced accuracies of 0.95 and negative predictive values of 1.00 over the reconstructed and virtually stained image-set on the detection of color-rectal tumoral tissue. This is of great importance as we reduce the need for extensive labeled datasets for training, which are normally not available on the early studies of a new imaging technology.The authors would like to thank all pathologists that generated the BIOPOOL dataset (FP7-ICT-296162) that has been used for this work and specially to M. Saiz, A. Gaafar, S. Fernandez, A. Saiz, E. de Miguel, B. Catón, J. J. Aguirre, R. Ruiz, Ma A. Viguri, and R. Rezola