Coasting in live-bearing fish: The drag penalty of being pregnant

Swimming performance of pregnant live-bearing fish is presumably constrained by the additional drag associated with the reproductive burden. Yet, it is still unclear how and to what extent the reproductive investment affects body drag of the females. We examined the effect of different levels of reproductive investment on body drag. The biggest measured increase in body volume due to pregnancy was about 43%, linked to a wetted area increase of about 16% and 69% for the frontal area. We printed three-dimensional models of live-bearing fish in a straight body posture representing different reproductive allocation (RA) levels. We measured the drag and visualized the flow around these models in a flow tunnel at different speeds. Drag grew in a power fashion with speed and exponentially with the increase of RA, thus drag penalty for becoming thicker was relatively low for low speeds compared to high ones. We show that the drag increase with increasing RA was most probably due to bigger regions of flow separation behind the enlarged belly. We suggest that the rising drag penalty with an increasing RA, possibly together with pregnancy-related negative effects on muscle- and abdominal bending performance, will reduce the maximum swimming speed

Successful implementation of new technologies in nursing care: a questionnaire survey of nurse-users

<p>Abstract</p> <p>Background</p> <p>A growing number of new technologies are becoming available within nursing care that can improve the quality of care, reduce costs, or enhance working conditions. However, such effects can only be achieved if technologies are used as intended. The aim of this study is to gain a better understanding of determinants influencing the success of the introduction of new technologies as perceived by nursing staff.</p> <p>Methods</p> <p>The study population is a nationally representative research sample of nursing staff (further referred to as the Nursing Staff Panel), of whom 685 (67%) completed a survey questionnaire about their experiences with recently introduced technologies. Participants were working in Dutch hospitals, psychiatric organizations, care organizations for mentally disabled people, home care organizations, nursing homes or homes for the elderly.</p> <p>Results</p> <p>Half of the respondents were confronted with the introduction of a new technology in the last three years. Only half of these rated the introduction of the technology as positive.</p> <p>The factors most frequently mentioned as impeding actual use were related to the (kind of) technology itself, such as malfunctioning, ease of use, relevance for patients, and risks to patients. Furthermore nursing staff stress the importance of an adequate innovation strategy.</p> <p>Conclusions</p> <p>A prerequisite for the successful introduction of new technologies is to analyse determinants that may impede or enhance the introduction among potential users. For technological innovations special attention has to be paid to the (perceived) characteristics of the technology itself.</p

Mining the Herschel-astrophysical terahertz large area survey : Submillimetre-selected blazars in equatorial fields

The Herschel-Astrophysical Terahertz Large Area Survey (H-ATLAS) provides an unprecedented opportunity to search for blazars at sub-mm wavelengths. We cross-matched the Faint Images of the Radio Sky at Twenty-cm (FIRST) radio source catalogue with the 11 655 sources brighter than 35 mJy at 500 μm in the ∼135 deg2 of the sky covered by the H-ATLAS equatorial fields at 9h and 15h, plus half of the field at 12h. We found that 379 of the H-ATLAS sources have a FIRST counterpart within 10 arcsec, including eight catalogued blazars (plus one known blazar that was found at the edge of one of the H-ATLAS maps). To search for additional blazar candidates we have devised new diagnostic diagrams and found that known blazars occupy a region of the log(S500μm/S350μm) versus log(S500μm/S1.4 GHz) plane separated from that of sub-mm sources with radio emission powered by star formation, but shared with radio galaxies and steep-spectrum radio quasars. Using this diagnostic we have selected 12 further possible candidates that turn out to be scattered in the (r-z) versus (u-r) plane or in the Wide-Field Infrared Survey Explorer colour-colour diagram, where known blazars are concentrated in well defined strips. This suggests that the majority of them are not blazars. Based on an inspection of all the available photometric data, including unpublished VISTA Kilo-degree Infrared Galaxy survey photometry and new radio observations, we found that the spectral energy distributions (SEDs) of only one out of the 12 newly selected sources are compatible with being synchrotron dominated at least up to 500 μm, i.e. with being a blazar. Another object may consist of a faint blazar nucleus inside a bright star-forming galaxy. The possibility that some blazar hosts are endowed with active star formation is supported by our analysis of the SEDs of Planck Early Release Compact Source Catalogue blazars detected at both 545 and 857 GHz. The estimated rest-frame synchrotron peak frequencies of H-ATLAS blazars are in the range 11.5 ≤ log (νpeak, Hz) ≤ 13.7, implying that these objects are low synchrotron peak. Six of them also show evidence of an ultraviolet excess that can be attributed to emission from the accretion disc. Allowing for the possibility of misidentifications and of contamination of the 500 μm flux density by the dusty torus or by the host galaxy, we estimate that there are seven or eight pure synchrotron sources brighter than S500μm = 35 mJy over the studied area, a result that sets important constraints on blazar evolutionary models.Peer reviewe

Using Neural Networks for Relation Extraction from Biomedical Literature

Using different sources of information to support automated extracting of relations between biomedical concepts contributes to the development of our understanding of biological systems. The primary comprehensive source of these relations is biomedical literature. Several relation extraction approaches have been proposed to identify relations between concepts in biomedical literature, namely, using neural networks algorithms. The use of multichannel architectures composed of multiple data representations, as in deep neural networks, is leading to state-of-the-art results. The right combination of data representations can eventually lead us to even higher evaluation scores in relation extraction tasks. Thus, biomedical ontologies play a fundamental role by providing semantic and ancestry information about an entity. The incorporation of biomedical ontologies has already been proved to enhance previous state-of-the-art results.Comment: Artificial Neural Networks book (Springer) - Chapter 1

Herschel-ATLAS: VISTA VIKING near-IR counterparts in the Phase 1 GAMA 9h data

We identify near-infrared Ks band counterparts to Herschel-ATLAS sub-mm sources, using a preliminary object catalogue from the VISTA VIKING survey. The sub-mm sources are selected from the H-ATLAS Phase 1 catalogue of the GAMA 9h field, which includes all objects detected at 250, 350 or 500 um with the SPIRE instrument. We apply and discuss a likelihood ratio (LR) method for VIKING candidates within a search radius of 10" of the 22,000 SPIRE sources with a 5 sigma detection at 250 um. We find that 11,294(51%) of the SPIRE sources have a best VIKING counterpart with a reliability $R\ge 0.8$, and the false identification rate of these is estimated to be 4.2%. We expect to miss ~5% of true VIKING counterparts. There is evidence from Z-J and J-Ks colours that the reliable counterparts to SPIRE galaxies are marginally redder than the field population. We obtain photometric redshifts for ~68% of all (non-stellar) VIKING candidates with a median redshift of 0.405. Comparing to the results of the optical identifications supplied with the Phase I catalogue, we find that the use of medium-deep near-infrared data improves the identification rate of reliable counterparts from 36% to 51%.Comment: 20 pages, 20 figures, 3 tables, accepted by MNRA

Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy

Abstract: Purpose: Early clinical recognition of sepsis can be challenging. With the advancement of machine learning, promising real-time models to predict sepsis have emerged. We assessed their performance by carrying out a systematic review and meta-analysis. Methods: A systematic search was performed in PubMed, Embase.com and Scopus. Studies targeting sepsis, severe sepsis or septic shock in any hospital setting were eligible for inclusion. The index test was any supervised machine learning model for real-time prediction of these conditions. Quality of evidence was assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology, with a tailored Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) checklist to evaluate risk of bias. Models with a reported area under the curve of the receiver operating characteristic (AUROC) metric were meta-analyzed to identify strongest contributors to model performance. Results: After screening, a total of 28 papers were eligible for synthesis, from which 130 models were extracted. The majority of papers were developed in the intensive care unit (ICU, n = 15; 54%), followed by hospital wards (n = 7; 25%), the emergency department (ED, n = 4; 14%) and all of these settings (n = 2; 7%). For the prediction of sepsis, diagnostic test accuracy assessed by the AUROC ranged from 0.68–0.99 in the ICU, to 0.96–0.98 in-hospital and 0.87 to 0.97 in the ED. Varying sepsis definitions limit pooling of the performance across studies. Only three papers clinically implemented models with mixed results. In the multivariate analysis, temperature, lab values, and model type contributed most to model performance. Conclusion: This systematic review and meta-analysis show that on retrospective data, individual machine learning models can accurately predict sepsis onset ahead of time. Although they present alternatives to traditional scoring systems, between-study heterogeneity limits the assessment of pooled results. Systematic reporting and clinical implementation studies are needed to bridge the gap between bytes and bedside