Despite cold-water coral (CWC) reefs being considered biodiversity hotspots, very little is known about the main processes driving their morphological development. Indeed, there is a considerable knowledge gap in quantitative experimental studies that help understand the interaction between reef morphology, near-bed hydrodynamics, coral growth, and (food) particle transport processes. In the present study, we performed a 2-month long flume experiment in which living coral nubbins were placed on a reef patch to determine the effect of a unidirectional flow on the growth and physiological condition of Lophelia pertusa. Measurements revealed how the presence of coral framework increased current speed and turbulence above the frontal part of the reef patch, while conditions immediately behind it were characterised by an almost stagnant flow and reduced turbulence. Owing to the higher current speeds that likely promoted a higher food encounter rate and intake of ions involved in the calcification process, the coral nubbins located on the upstream part of the reef presented a significantly enhanced average growth and a lower expression of stress-related enzymes than the downstream ones. Yet, further experiments would be needed to fully quantify how the variations in water hydrodynamics modify particle encounter and ion intake rates by coral nubbins located in different parts of a reef, and how such discrepancies may ultimately affect coral growth. Nonetheless, the results acquired here denote that a reef influenced by a unidirectional water flow would grow into the current: a pattern of reef development that coincides with that of actual coral reefs located in similar water flow settings. Ultimately, the results of this study suggest that at the local scale coral reef morphology has a direct effect on coral growth thus, indicating that the spatial patterns of living CWC colonies in reef patches are the result of spatial self-organisation.
Integrative toxicological approaches are crucial to understand the “whole picture” regarding the environmental hazardous potential of the solvents to license.
Face recognition has become one of the most important modalities of biometrics in recent years. It widely utilises deep learning computer vision tools and adopts large collections of unconstrained face images of celebrities for training. Such choice of the data is related to its public availability when existing document compliant face image collections are hardly accessible due to security and privacy issues. Such inconsistency between the training data and deploy scenario may lead to a leak in performance in biometric systems, which are developed specifically for dealing with ID document compliant images. To mitigate this problem, we propose to regularise the training of the deep face recognition network with a specific sample mining strategy, which penalises the samples by their estimated quality. In addition to several considered quality metrics in recent work, we also expand our deep learning strategy to other sophisticated quality estimation methods and perform experiments to better understand the nature of quality sampling. Namely, we seek for the penalising manner (sampling character) that better satisfies the purpose of adapting deep learning face recognition for images of ID and travel documents. Extensive experiments demonstrate the efficiency of the approach for ID document compliant face images.
Heterogeneity within and between data sets is one of the primary impediments to sound and efficient data analysis. This heterogeneity can arise from many sources: data collection practices can change over time, even within the same study; rigorous standards for data encoding may be missing, leading to inter-individual heterogeneity in data encoding; or the data may originally have been collected for a different purpose (e.g. EHR data). Additionally, narrative text, such as medical statements, are inherently unstructured and require curation before analysis. No matter which source(s) heterogeneity derives from, curation of the data is necessary for efficient, accurate and reproducible data analysis. This not only includes correcting errors and standardizing encoding, but also extends to enriching the data through mapping it to relevant medical ontologies, such as SNOMED CT, or other standardized terminologies, such as Research Resource Identifiers. To solve the most common issues in clinical and phenotypic data curation, we have developed the AccurateTM data curation and ontology mapping solution. It combines an intuitive web-based user interface for data cleaning with efficient solutions for semi-automated ontology mapping of both structured data and narrative text. For structured data, tokenized and stemmed data items are mapped against ontologies indexed in Elasticsearch. Term names, their synonyms and the local ontology structure are then used to query the target ontology, with a list of best matches returned along with a quality score for the mapping. Ontology tagging of narrative text is based on a sentence-based deep learning approach, analyzing sentences to classify and ontology map identified text units. In practice, combining a bidirectional long short term memory network with a conditional random field model into a named entity recognition system (bio-NER). Preliminary benchmarking of the bio-NER system on the MIMIC III data set suggests good specificity and sensitivity for identification of biomedically relevant concepts. In summary, we here present an intuitive and highly efficient solution for curating clinical and phenotypic data, as well as enriching it using ontology mapping of both structured and narrative data. Citation Format: Henrik Edgren, Beatriz Mano, Maria Laaksonen. Efficient curation and ontology mapping of clinical and phenotypic data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2276.
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