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

Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering

This publication is the Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering from July 6-8, 2022. The EG-ICE International Workshop on Intelligent Computing in Engineering brings together international experts working on the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolution of challenges such as supporting multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways. &nbsp

Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering

This publication is the Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering from July 6-8, 2022. The EG-ICE International Workshop on Intelligent Computing in Engineering brings together international experts working on the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolution of challenges such as supporting multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways. &nbsp

Intrinsic hybrid composites for lightweight structures - new process chain approaches

This publication describes new process chain approaches for the manufacturing of intrinsic hybrid composites for lightweight structures. The introduced process chains show a variety of different part and sample types, like insert technology for fastening of hollow hybrid shafts and profiles. Another field of research are hybrid laminates with different layers of carbon fiber reinforced plastics stacked with aluminum or steel sheets. The derived process chains base on automated fiber placement, resin transfer molding, deep drawing, rotational molding and integral tube blow molding

It keeps on turning: Effects of prolonged long-term left ventricular assist device support as a bridge to heart transplantation

Objectives: Increasing incidence of end-stage heart failure has moved the therapy with left ventricular assist devices to the forefront of surgical treatment. Moreover, continuous sophistication in this technology has resulted in increasing proportion of patients on prolonged support. Early and late complications after left ventricular assist device as a bridge to transplantation and present factors associated with long-term support and long-term outcomes of patients supported for at least 1 year were compared. Methods: A total of 163 consecutive patients who underwent left ventricular assist device implantation as bridge to transplantation were included. A total of 79 patients were supported for at least 1 year (long-term support), whereas 84 patients were supported for less than 1 year (short-term group). Results: Factors associated with a successful long-term support were male gender (p < 0.001), cessation of smoking at least 6 months prior to surgery (p = 0.045), previous implantation of implantable cardioverter defibrillator (p = 0.001) and rapid postoperative extubation (p = 0.018). Regarding echocardiographic parameters, higher left ventricular mass (p = 0.013) and larger left ventricular-end systolic (p = 0.008) and diastolic (p = 0.005) diameters prior to left ventricular assist device implantation were associated with long-term support. Short-term group showed higher mortality and higher proportion of patients who underwent device exchange due to device failure, and left ventricular assist device explantation for myocardial recovery was less frequent in the long-term support (p < 0.001). In addition, patients from the long-term support had significantly higher incidence of higher-grade aortic regurgitation (p = 0.005). Conclusion: Prolonged left ventricular assist device support as bridge to transplantation is associated with lower mortality and lower incidence of device failure requiring device exchange. However, long-term support reduces the chance of device explantation for myocardial recovery and increases the incidence of higher-grade aortic regurgitation in the follow-up