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We exploit the Twitter platform to create a dataset of news articles derived from tweets concerning COVID-19, and use the associated tweets to define a number of popularity measures. The focus on (potentially) biomedical news articles allows the quantity of biomedically valid information (as extracted by biomedical relation extraction) to be included in the list of explored features. Aside from forming part of a systematic correlation exploration, the features – ranging from the semantic relations through readability measures to the article’s digital content – are used within a number of machine learning classifier and regression algorithms. Unsurprisingly, the results support that for more complex articles (as determined by a readability measure) more sophisticated syntactic structure may be expected. A weak correlation is found with information within an article suggesting that other factors, such as numbers of videos, have a notable impact on the popularity of a news article. The best popularity prediction performance is obtained using a random forest machine learning algorithm, and the feature describing the quantity of biomedical information is in the top 3 most important features in almost a third of the experiments performed. Additionally, this feature is found to be more valuable than the widely used named entity recognition.

Many automatic semantic relation extraction\ud tools extract subject-predicate-object triples\ud from unstructured text. However, a large quantity of these triples merely represent background knowledge. We explore using full texts\ud of biomedical publications to create a training\ud corpus of informative and important semantic\ud triples based on the notion that the main contributions of an article are summarized in its\ud abstract. This corpus is used to train a deep\ud learning classifier to identify important triples,\ud and we suggest that an importance ranking for\ud semantic triples could also be generated.

Background: The quantity of documents being published requires researchers to\ud specialize to a narrower field, meaning that inferable connections between\ud publications (particularly from different domains) can be missed. This has given\ud rise to automatic literature based discovery (LBD). However, unless heavily\ud filtered, LBD generates more potential new knowledge than can be manually\ud verified and another form of selection is required before the results can be passed\ud onto a user. Since a large proportion of the automatically generated hidden\ud knowledge is valid but generally known, we investigate the hypothesis that non\ud trivial, interesting, hidden knowledge can be treated as an anomaly and identified\ud using anomaly detection approaches.\ud \ud Results: Two experiments are conducted: (1) to avoid errors arising from\ud incorrect extraction of relations, the hypothesis is validated using manually\ud annotated relations appearing in a thesaurus, and (2) automatically extracted\ud relations are used to investigate the hypothesis on publication abstracts. These\ud allow an investigation of a potential upper bound and the detection of limitations\ud yielded by automatic relation extraction.\ud \ud Conclusion: We apply one-class SVM and isolation forest anomaly detection\ud algorithms to a set of hidden connections to rank connections by identifying\ud outlying (interesting) ones and show that the approach increases the F1 measure\ud by a factor of 10 while greatly reducing the quantity of hidden knowledge to\ud manually verify. We also demonstrate the statistical significance of this result.\ud \ud Keywords: literature based discovery; anomaly detection; unified medical\ud language system

We discuss a few selected hypotheses on how the visual system judges differences of color images. We then derive five image difference features from these hypotheses and address their relation to the visual processing. Three models are proposed to combine these features for the prediction of perceived image differences. The parameters of the image difference features are optimized on human image difference assessments. For each model we investigate the impact of individual features on the overall prediction performance. If chromatic features are combined with lightness based features the prediction accuracy on a test dataset is significantly higher than that of the SSIM index which only operates on the achromatic component.

Image similarity describes the quality of an image relative to a reference. Image difference measures are calculated in order to quantify the perceived difference. The concept is also applicable to model where two colour images differ. Existing measures for image similarity focus on high spatial frequency distortions best visible in the luminance channel. Lower frequency distortions resulting from gamut mapping tone mapping or illuminance change are only incorporated as far as they influence luminance. As luminance reduction and chroma reduction are often correlated in real applications we studied image similarity on artificial transformations thus allowing us to reduce luminance or chrominance independently of each other.

We present a corpus of experimental data from psychometric studies on gamut mapping and demonstrate its use to develop image similarity measures. We investigate whether similarity measures based on luminance (SSIM) can be improved when features based on chroma and hue are added. Image similarity measures can be applied to automatically select a good image from a sample of transformed images.

A retrospective study was carried out involving 39 patients with malignant tumours of the head and neck in order to differentiate by CT changes due to local tumour or lymph node recurrences and various post-operative or post-irradiation changes. In these patients, two or more CT examinations of the neck were available, derived from a fourth generation scanner. The suspected lesions were classified according to their type and the appearances correlated with the treatment and clinical findings. There was frequent overlap between the appearances of benign and malignant changes, so that in individual cases a definite decision often proved impossible.

Barley (Hordeum vulgare, cv. Bomi) leaf ADP-glucose pyrophosphorylase (AGP) was purified to near-homogeneity, using ammonium sulfate fractionation and heat treatment as well as ion exchange, hydrophobic, and dye-ligand chromatography. The enzyme was found to be composed of two subunit types of 51 and 54 kDa, which were recognized by the respective rabbit antibodies against the small and/or large subunit of spinach leaf AGP. Substrate kinetics and product inhibition studies, carried out in the direction of ADP-glucose synthesis, suggested a sequential Iso Ordered Bi Bi kinetic mechanism for the enzyme. In addition, inhibition patterns with CrATP, a dead-end inhibitor of ATP-utilizing enzymes, were consistent with ATP (synthesis reaction) and ADP-glucose (pyrophosphorolysis reaction) binding first to different free forms of the enzyme. The AGP was potently activated by 3-phosphoglycerate (PGA) (K alpha of 5 microM) and inhibited by orthophosphate (P(i)), with the ratio of the two effectors playing a major role in modulating AGP activity. At low [PGA]/[P(i)] ratios, P(i) could reverse the activating effect of PGA. However, at the [PGA]/[Pi] ratios of 1.4, or higher, some activating effect of P(i) in the presence of PGA was observed. PGA decreased the Km values for all substrates of AGP (in both the synthesis and pyrophosphorolysis directions). Properties of the barley leaf AGP are compared to those of AGP from barley seed endosperm, which is insensitive to PGA/P(i) regulation.

Background & Aims Most patients with Crohn's disease (CD) eventually require an intestinal resection. However, CD frequently recurs after resection. We performed a randomized trial to compare the ability of infliximab vs placebo to prevent CD recurrence. Methods We evaluated the efficacy of infliximab in preventing postoperative recurrence of CD in 297 patients at 104 sites worldwide from November 2010 through May 2012. All study patients had undergone ileocolonic resection within 45 days before randomization. Patients were randomly assigned (1:1) to groups given infliximab (5 mg/kg) or placebo every 8 weeks for 200 weeks. The primary end point was clinical recurrence, defined as a composite outcome consisting of a CD Activity Index score >200 and a ≥70-point increase from baseline, and endoscopic recurrence (Rutgeerts score ≥i2, determined by a central reader) or development of a new or re-draining fistula or abscess, before or at week 76. Endoscopic recurrence was a major secondary end point. Results A smaller proportion of patients in the infliximab group had a clinical recurrence before or at week 76 compared with the placebo group, but this difference was not statistically significant (12.9% vs 20.0%; absolute risk reduction [ARR] with infliximab, 7.1%; 95% confidence interval: -1.3% to 15.5%; P =.097). A significantly smaller proportion of patients in the infliximab group had endoscopic recurrence compared with the placebo group (30.6% vs 60.0%; ARR with infliximab, 29.4%; 95% confidence interval: 18.6% to 40.2%; P