Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Representing and operating with model differences

Models and metamodels play a cornerstone role in Model-Driven Software Development (MDSD). Models conform to metamodels, which usually specify domain-specific languages that allow to represent the various facets of a system in terms of models. This paper discusses the problem of calculating differences between models conforming to arbitrary metamodels, something essential in any MDSD environment for dealing with the management of changes and evolution of software models. We present a metamodel for representing the differences as models, too, following the MDSD “everything is a model” principle. The Difference Metamodel, together with the difference and other related operations (do, undo and composition) presented here have been specified in Maude and integrated in an Eclipse-developed environment

Revealing hidden physical nonclassicality with nonnegative polynomials

Understanding quantum phenomena which go beyond classical concepts is a focus of modern quantum physics. Here, we show how the theory of nonnegative polynomials emerging around Hilbert's 17th problem, can be used to optimally exploit data capturing the nonclassical nature of light. Specifically, we show that nonnegative polynomials can reveal nonclassicality in data even when it is hidden from standard detection methods up to now. Moreover, the abstract language of nonnegative polynomials also leads to a unified mathematical approach to nonclassicality for light and spin systems, allowing us to map methods for one to the other. Conversely, the physical problems arising also inspire several mathematical insights into characterisation of nonnegative polynomials.Comment: 16 pages, 5 figures, comments are welcome

A manifesto for semantic model differencing

Abstract. Models are heavily used in software engineering and together with their systems they evolve over time. Thus, managing their changes is an important challenge for system maintainability. Existing approaches to model differencing concentrate on heuristics matching between model syntactic level. While showing some success, these approaches are inherently limited to comparing syntactic structures. This paper is a manifesto for research on semantic model differencing. We present our vision to develop semantic diff operators for model comparisons: operators whose input consists of two models and whose output is a set of diff witnesses, instances of one model that are not instances of the other. In particular, if the models are syntactically different but there are no diff witnesses, the models are semantically equivalent. We demonstrate our vision using two concrete diff operators, for class diagrams and for activity diagrams. We motivate the use of semantic diff operators, briefly discuss the algorithms to compute them, list related challenges, and show their application and potential use as new fundamental building blocks for change management in model-driven engineering.

Tissue-Engineered Small Diameter Arterial Vascular Grafts from Cell-Free Nanofiber PCL/Chitosan Scaffolds in a Sheep Model.

Tissue engineered vascular grafts (TEVGs) have the potential to overcome the issues faced by existing small diameter prosthetic grafts by providing a biodegradable scaffold where the patient's own cells can engraft and form functional neotissue. However, applying classical approaches to create arterial TEVGs using slow degrading materials with supraphysiological mechanical properties, typically results in limited host cell infiltration, poor remodeling, stenosis, and calcification. The purpose of this study is to evaluate the feasibility of novel small diameter arterial TEVGs created using fast degrading material. A 1.0mm and 5.0mm diameter TEVGs were fabricated with electrospun polycaprolactone (PCL) and chitosan (CS) blend nanofibers. The 1.0mm TEVGs were implanted in mice (n = 3) as an unseeded infrarenal abdominal aorta interposition conduit., The 5.0mm TEVGs were implanted in sheep (n = 6) as an unseeded carotid artery (CA) interposition conduit. Mice were followed with ultrasound and sacrificed at 6 months. All 1.0mm TEVGs remained patent without evidence of thrombosis or aneurysm formation. Based on small animal outcomes, sheep were followed with ultrasound and sacrificed at 6 months for histological and mechanical analysis. There was no aneurysm formation or calcification in the TEVGs. 4 out of 6 grafts (67%) were patent. After 6 months in vivo, 9.1 ± 5.4% remained of the original scaffold. Histological analysis of patent grafts demonstrated deposition of extracellular matrix constituents including elastin and collagen production, as well as endothelialization and organized contractile smooth muscle cells, similar to that of native CA. The mechanical properties of TEVGs were comparable to native CA. There was a significant positive correlation between TEVG wall thickness and CD68+ macrophage infiltration into the scaffold (R2 = 0.95, p = 0.001). The fast degradation of CS in our novel TEVG promoted excellent cellular infiltration and neotissue formation without calcification or aneurysm. Modulating host macrophage infiltration into the scaffold is a key to reducing excessive neotissue formation and stenosis