Perfusion measured by laser speckle contrast imaging as a predictor for expansion of psoriasis lesions

BACKGROUND: Skin microvasculature changes are crucial in psoriasis development and correlate with perfusion. The noninvasive Handheld Perfusion Imager (HAPI) examines microvascular skin perfusion in large body areas using laser speckle contrast imaging (LSCI). OBJECTIVES: To (i) assess whether increased perilesional perfusion and perfusion inhomogeneity are predictors for expansion of psoriasis lesions and (ii) assess feasibility of the HAPI system in a mounted modality. METHODS: In this interventional pilot study in adults with unstable plaque psoriasis, HAPI measurements and color photographs were performed for lesions present on one body region at week 0, 2, 4, 6 and 8. The presence of increased perilesional perfusion and perfusion inhomogeneity was determined. Clinical outcome was categorized as increased, stable or decreased lesion surface between visits. Patient feedback was collected on a 10‐point scale. RESULTS: In total, 110 lesions with a median follow‐up of 6 (IQR 6.0) weeks were assessed in 6 patients with unstable plaque psoriasis. Perfusion data was matched to 281 clinical outcomes after two weeks. A mixed multinomial logistic regression model revealed a predictive value of perilesional increased perfusion (OR 9.90; p < 0.001) and perfusion inhomogeneity (OR 2.39; p = 0.027) on lesion expansion after two weeks compared to lesion stability. HAPI measurements were considered fast, patient‐friendly and important by patients. CONCLUSION: Visualization of increased perilesional perfusion and perfusion inhomogeneity by noninvasive whole field LSCI holds potential for prediction of psoriatic lesion expansion. Furthermore, the HAPI is a feasible and patient‐friendly tool

Multilingual Fine-Grained Entity Typing

Many entity recognition approaches classify recognised entities into a limited set of coarse-grained entity types. However, for deeper natural language analysis and end-user tasks, fine-grained entity types are more useful. For example, while standard named entity recognition may determine that an entity is a person knowing whether that entity is a politician or an actor is important for determining whether, in a subsequent relation extraction task, a relation should be acts or governs. Currently, fine-grained entity typing has only been investigated for English. In this paper, we present a fine-grained entity typing system for Dutch and Spanish using training data extracted from Wikipedia and DBpedia. Our system achieves comparable performance to English with an F1 measure of .90 on over 40 types for both Dutch and Spanish

Linked Open Piracy: A story about e-Science, Linked Data, and statistics

There is an abundance of semi-structured reports on events being written and made available on the World Wide Web on a daily basis. These reports are primarily meant for human use. A recent movement is the addition of RDF metadata to make automatic processing by computers easier. A fine example of this movement is the open government data initiative which, by representing data from spreadsheets and textual reports in RDF, strives to speed up the creation of geographical mashups and visual analytic applications. In this paper, we present a newly linked dataset and the method we used to automatically translate semi-structured reports on the Web to an RDF event model. We demonstrate how the semantic representation layer makes it possible to easily analyze and visualize the aggregated reports to answer domain questions through a SPARQL client for the R statistical programming language. We showcase our method on piracy attack reports issued by the International Chamber of Commerce (ICC-CCS). Our pipeline includes conversion of the reports to RDF, linking their parts to external resources from the linked open data cloud and exposing them to the Web