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

Critical assessment of protein intrinsic disorder prediction

Abstract: Intrinsically disordered proteins, defying the traditional protein structure–function paradigm, are a challenge to study experimentally. Because a large part of our knowledge rests on computational predictions, it is crucial that their accuracy is high. The Critical Assessment of protein Intrinsic Disorder prediction (CAID) experiment was established as a community-based blind test to determine the state of the art in prediction of intrinsically disordered regions and the subset of residues involved in binding. A total of 43 methods were evaluated on a dataset of 646 proteins from DisProt. The best methods use deep learning techniques and notably outperform physicochemical methods. The top disorder predictor has Fmax = 0.483 on the full dataset and Fmax = 0.792 following filtering out of bona fide structured regions. Disordered binding regions remain hard to predict, with Fmax = 0.231. Interestingly, computing times among methods can vary by up to four orders of magnitude

SPOT-Disorder2: Improved Protein Intrinsic Disorder Prediction by Ensembled Deep Learning

Intrinsically disordered or unstructured proteins (or regions in proteins) have been found to be important in a wide range of biological functions and implicated in many diseases. Due to the high cost and low efficiency of experimental determination of intrinsic disorder and the exponential increase of unannotated protein sequences, developing complementary computational prediction methods has been an active area of research for several decades. Here, we employed an ensemble of deep Squeeze-and-Excitation residual inception and long short-term memory (LSTM) networks for predicting protein intrinsic disorder with input from evolutionary information and predicted one-dimensional structural properties. The method, called SPOT-Disorder2, offers substantial and consistent improvement not only over our previous technique based on LSTM networks alone, but also over other state-of-the-art techniques in three independent tests with different ratios of disordered to ordered amino acid residues, and for sequences with either rich or limited evolutionary information. More importantly, semi-disordered regions predicted in SPOT-Disorder2 are more accurate in identifying molecular recognition features (MoRFs) than methods directly designed for MoRFs prediction. SPOT-Disorder2 is available as a web server and as a standalone program at https://sparks-lab.org/server/spot-disorder2/

Liquid Metals as Efficient High-Temperature Heat-Transport Fluids

Liquid metals appear to be attractive heat-transport fluids, in particular if looking at their high thermal conductivities and low viscosities. Despite some pioneering technical applications in the past, complex handling, special requirements, safety concerns, and structural degradation of the materials have prevented their widespread application. However, progress in research and development on liquid-metal science and technology has advanced considerably in the last decade, and this has opened the gate to their broader use in the short term. This requires a more differentiated view on liquid metals, particularly on the specific properties of individual fluids within the context of specific applications. By doing so, many commonly mentioned prejudices vanish or are of minor significance. At the Karlsruhe Institute of Technology, a comprehensive research program on liquid-metal technology has been pursued for more than 50years, and some of the advances in different applications will be outlined in this article

Experimental and numerical investigation of liquid-metal free-surface flows in spallation targets

Accelerator Driven Systems (ADS) are extensively investigated for the transmutation of high-level nuclear waste within many worldwide research programs. The first advanced design of an ADS system is currently developed in SCK•CEN, Mol, Belgium: the Multi-purpose hYbrid Research Reactor for High-tech Applications (MYRRHA). Many European research programs support the design of MYRRHA. In the framework of the Euratom project 'Thermal Hydraulics of Innovative nuclear Systems (THINS)' a liquid-metal free-surface experiment is performed at the Karlsruhe Liquid Metal Laboratory (KALLA) of Karlsruhe Institute of Technology (KIT). The experiment investigates a full-scale model of the concentric free-surface spallation target of MYRRHA using Lead Bismuth Eutectic (LBE) as coolant. In parallel, numerical free surface models are developed and tested which are reviewed in the article. A volume-of-fluid method, a moving mesh model, a free surface model combining the Level-Set method with Large-Eddy Simulation model and a smoothed-particle hydrodynamics approach are investigated. Verification of the tested models is based on the experimental results obtained within the THINS project and on previous water experiments performed at the University Catholic de Louvain (UCL) within the Euratom project 'EUROpean Research Programme for the TRANSmutation of High Level Nuclear Waste in Accelerator Driven System (EUROTRANS)'. The design of the target enables a high fluid velocity and a stable surface at the beam entry. The purpose of this paper is to present an overview of both experimental and numerical results obtained for free surface target characterization. Without entering in technical details, the status, the major achievements and lessons for the future with respect to model development are described as well as some applications, which were carried out within the work package 'multi-phase flow' of THINS