Simplified, rapid, and inexpensive estimation of water primary productivity based on chlorophyll fluorescence parameter

Primary productivity in water environment relies on the photosynthetic production of microalgae. Chlorophyll fluorescence is widely used to detect the growth status and photosynthetic efficiency of microalgae. In this study, a method was established to determine the Chl a content, cell density of microalgae, and water primary productivity by measuring chlorophyll fluorescence parameter Fo. A significant linear relationship between chlorophyll fluorescence parameter Fo and Chl a content of microalgae, as well as between Fo and cell density, was observed under pure-culture conditions. Furthermore, water samples collected from natural aquaculture ponds were used to validate the correlation between Fo and water primary productivity, which is closely related to Chl a content in water. Thus, for a given pure culture of microalgae or phytoplankton (mainly microalgae) in aquaculture ponds or other natural ponds for which the relationship between the Fo value and Chl a content or cell density could be established, Chl a content or cell density could be determined by measuring the Fo value, thereby making it possible to calculate the water primary productivity. It is believed that this method can provide a convenient way of efficiently estimating the primary productivity in natural aquaculture ponds and bringing economic value in limnetic ecology assessment, as well as in algal bloom monitoring. (C) 2017 Elsevier GmbH. All rights reserved.</p

Anatomical entity recognition with a hierarchical framework augmented by external resource

References to anatomical entities in medical records consist not only of explicit references to anatomical locations, but also other diverse types of expressions, such as specific diseases, clinical tests, clinical treatments, which constitute implicit references to anatomical entities. In order to identify these implicit anatomical entities, we propose a hierarchical framework, in which two layers of named entity recognizers (NERs) work in a cooperative manner. Each of the NERs is implemented using the Conditional Random Fields (CRF) model, which use a range of external resources to generate features. We constructed a dictionary of anatomical entity expressions by exploiting four existing resources, i.e., UMLS, MeSH, RadLex and BodyPart3D, and supplemented information from two external knowledge bases, i.e., Wikipedia and WordNet, to improve inference of anatomical entities from implicit expressions. Experiments conducted on 300 discharge summaries showed a micro-averaged performance of 0.8509 Precision, 0.7796 Recall and 0.8137 F1 for explicit anatomical entity recognition, and 0.8695 Precision, 0.6893 Recall and 0.7690 F1 for implicit anatomical entity recognition. The use of the hierarchical framework, which combines the recognition of named entities of various types (diseases, clinical tests, treatments) with information embedded in external knowledge bases, resulted in a 5.08% increment in F1. The resources constructed for this research will be made publicly available

Annotation samples.

<p>Annotation samples.</p

Numbers of entities in dictionaries.

<p>Numbers of entities in dictionaries.</p

Inter-annotator agreement between A1 and A2.

<p>Inter-annotator agreement between A1 and A2.</p

Performance with training datasets of various sizes using automatically predicted medical concepts.

<p>Performance with training datasets of various sizes using automatically predicted medical concepts.</p

List of features in this task.

<p>List of features in this task.</p

Performance of different feature combinations using automatically predicted medical concepts.

<p>Performance of different feature combinations using automatically predicted medical concepts.</p

Performance of individual feature added to baseline using gold standard medical concepts.

<p>Performance of individual feature added to baseline using gold standard medical concepts.</p

Building the hierarchical feature for implicit anatomical recognizer.

<p>Building the hierarchical feature for implicit anatomical recognizer.</p