Spectral Decomposition of Broad-Line AGNs and Host Galaxies

Using an eigenspectrum decomposition technique, we separate the host galaxy from the broad line active galactic nucleus (AGN) in a set of 4666 spectra from the Sloan Digital Sky Survey (SDSS), from redshifts near zero up to about 0.75. The decomposition technique uses separate sets of galaxy and quasar eigenspectra to efficiently and reliably separate the AGN and host spectroscopic components. The technique accurately reproduces the host galaxy spectrum, its contributing fraction, and its classification. We show how the accuracy of the decomposition depends upon S/N, host galaxy fraction, and the galaxy class. Based on the eigencoefficients, the sample of SDSS broad-line AGN host galaxies spans a wide range of spectral types, but the distribution differs significantly from inactive galaxies. In particular, post-starburst activity appears to be much more common among AGN host galaxies. The luminosities of the hosts are much higher than expected for normal early-type galaxies, and their colors become increasingly bluer than early-type galaxies with increasing host luminosity. Most of the AGNs with detected hosts are emitting at between 1% and 10% of their estimated Eddington luminosities, but the sensitivity of the technique usually does not extend to the Eddington limit. There are mild correlations among the AGN and host galaxy eigencoefficients, possibly indicating a link between recent star formation and the onset of AGN activity. The catalog of spectral reconstruction parameters is available as an electronic table.Comment: 18 pages; accepted for publication in A

Using artificial intelligence to reduce diagnostic workload without compromising detection of urinary tract infections

Background A substantial proportion of microbiological screening in diagnostic laboratories is due to suspected urinary tract infections (UTIs), yet approximately two thirds of urine samples typically yield negative culture results. By reducing the number of query samples to be cultured and enabling diagnostic services to concentrate on those in which there are true microbial infections, a significant improvement in efficiency of the service is possible. Methodology Screening process for urine samples prior to culture was modelled in a single clinical microbiology laboratory covering three hospitals and community services across Bristol and Bath, UK. Retrospective analysis of all urine microscopy, culture, and sensitivity reports over one year was used to compare two methods of classification: a heuristic model using a combination of white blood cell count and bacterial count, and a machine learning approach testing three algorithms (Random Forest, Neural Network, Extreme Gradient Boosting) whilst factoring in independent variables including demographics, historical urine culture results, and clinical details provided with the specimen. Results A total of 212,554 urine reports were analysed. Initial findings demonstrated the potential for using machine learning algorithms, which outperformed the heuristic model in terms of relative workload reduction achieved at a classification sensitivity > 95%. Upon further analysis of classification sensitivity of subpopulations, we concluded that samples from pregnant patients and children (age 11 or younger) require independent evaluation. First the removal of pregnant patients and children from the classification process was investigated but this diminished the workload reduction achieved. The optimal solution was found to be three Extreme Gradient Boosting algorithms, trained independently for the classification of pregnant patients, children, and then all other patients. When combined, this system granted a relative workload reduction of 41% and a sensitivity of 95% for each of the stratified patient groups. Conclusion Based on the considerable time and cost savings achieved, without compromising the diagnostic performance, the heuristic model was successfully implemented in routine clinical practice in the diagnostic laboratory at Severn Pathology, Bristol. Our work shows the potential application of supervised machine learning models in improving service efficiency at a time when demand often surpasses resources of public healthcare providers

Zoledronate rescues immunosuppressed monocytes in sepsis patients

Severe sepsis is often accompanied by a transient immune paralysis, which is associated with enhanced susceptibility to secondary infections and poor clinical outcomes. The functional impairment of antigen‐presenting cells is considered to be a major hallmark of this septic immunosuppression, with reduced HLA‐DR expression on circulating monocytes serving as predictor of mortality. Unconventional lymphocytes like γδ T cells have the potential to restore immune defects in a variety of pathologies including cancer but their use to rescue sepsis‐induced immunosuppression has not been investigated. Our own previous work showed that Vγ9/Vδ2+ γδ T cells are potent activators of monocytes from healthy volunteers in vitro, and in individuals with osteoporosis after first‐time administration of the anti‐bone resorption drug zoledronate in vivo. We show here that zoledronate readily induces upregulation of HLA‐DR, CD40 and CD64 on monocytes from both healthy controls and sepsis patients, which could be abrogated by neutralising the pro‐inflammatory cytokines IFN‐γ and TNF‐α in the cultures. In healthy controls, the upregulation of HLA‐DR on monocytes was proportional to the baseline percentage of Vγ9/Vδ2 T cells in the PBMC population. Of note, a proportion of sepsis patients studied here did not show a demonstrable response to zoledronate, predominantly patients with microbiologically confirmed bloodstream infections, compared to sepsis patients with more localised infections marked by negative blood cultures. Taken together, our results suggest that zoledronate can, at least in some individuals, rescue immunosuppressed monocytes during acute sepsis and thus may help improve clinical outcomes during severe infection

GeoWaVe: Geometric median clustering with weighted voting for ensemble clustering of cytometry data

Motivation Clustering is an unsupervised method for identifying structure in unlabelled data. In the context of cytometry, it is typically used to categorise cells into subpopulations of similar phenotypes. However, clustering is greatly dependent on hyperparameters and the data to which it is applied as each algorithm makes different assumptions and generates a different ‘view’ of the dataset. As such, the choice of clustering algorithm can significantly influence results, and there is often not one preferred method but different insights to be obtained from different methods. To overcome these limitations, consensus approaches are needed that directly address the effect of competing algorithms. To the best of our knowledge, consensus clustering algorithms designed specifically for the analysis of cytometry data are lacking. Results We present a novel ensemble clustering methodology based on geometric median clustering with weighted voting (GeoWaVe). Compared to graph ensemble clustering methods that have gained popularity in scRNA-seq analysis, GeoWaVe performed favourably on different sets of high-dimensional mass and flow cytometry data. Our findings provide proof of concept for the power of consensus methods to make the analysis, visualisation and interpretation of cytometry data more robust and reproducible. The wide availability of ensemble clustering methods is likely to have a profound impact on our understanding of cellular responses, clinical conditions, and therapeutic and diagnostic options

CytoPy: An autonomous cytometry analysis framework

Cytometry analysis has seen a considerable expansion in recent years in the maximum number of parameters that can be acquired in a single experiment. In response to this technological advance there has been an increased effort to develop new computational methodologies for handling high-dimensional single cell data acquired by flow or mass cytometry. Despite the success of numerous algorithms and published packages to replicate and outperform traditional manual analysis, widespread adoption of these techniques has yet to be realised in the field of immunology. Here we present CytoPy, a Python framework for automated analysis of cytometry data that integrates a document-based database for a data-centric and iterative analytical environment. In addition, our algorithm agnostic design provides a platform for open-source cytometry bioinformatics in the Python ecosystem. We demonstrate the ability of CytoPy to phenotype T cell subsets in whole blood samples even in the presence of significant batch effects due to technical and user variation. The complete analytical pipeline was then used to immunophenotype the local inflammatory infiltrate in individuals with and without acute bacterial infection. CytoPy is open-source and licensed under the MIT license. CytoPy is open source and available at https://github.com/burtonrj/CytoPy, with notebooks accompanying this manuscript (https://github.com/burtonrj/CytoPyManuscript) and software documentation at https://cytopy.readthedocs.io/

Level Set Approach to Reversible Epitaxial Growth

We generalize the level set approach to model epitaxial growth to include thermal detachment of atoms from island edges. This means that islands do not always grow and island dissociation can occur. We make no assumptions about a critical nucleus. Excellent quantitative agreement is obtained with kinetic Monte Carlo simulations for island densities and island size distributions in the submonolayer regime.Comment: 7 pages, 9 figure

Pre-cooling for endurance exercise performance in the heat: a systematic review.

PMCID: PMC3568721The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1741-7015/10/166. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Endurance exercise capacity diminishes under hot environmental conditions. Time to exhaustion can be increased by lowering body temperature prior to exercise (pre-cooling). This systematic literature review synthesizes the current findings of the effects of pre-cooling on endurance exercise performance, providing guidance for clinical practice and further research

Gender and Acute Myocardial Infarction: Is There a Different Response to Thrombolysis?

AbstractObjectives. This study sought to 1) determine the effect of gender on early and late infarct-related artery patency and reocclusion after thrombolytic therapy for acute myocardial infarction; 2) examine the effect of gender on left ventricular function in response to injury/reperfusion; and 3) assess the independent contribution of gender to early (30-day) mortality after acute myocardial infarction.Background. Women have a higher mortality rate than men after myocardial infarction. However, the effect of gender on infarct-related coronary artery patency and left ventricular response to injury/reperfusion have not been fully defined in the thrombolytic era.Methods. Patency rates and global and regional left ventricular function were determined in patients at 90 min and 5 to 7 days after thrombolytic therapy for acute myocardial infarction. The effect of gender on infarct-related artery patency and left ventricular function was determined. Thirty-day mortality differences between women and men were compared.Results. Women were significantly older and had more hypertension, diabetes, hypercholesterolemia, heart failure and shock. They were less likely to have had a previous myocardial infarction, history of smoking or previous bypass surgery. Ninety-minute patency rates (Thrombolysis in Myocardial Infarction [TIMI] flow grade 3) in women and men were 39% and 38%, respectively (p = 0.5). Reocclusion rates were 8.7% in women versus 5.1% in men (p = 0.14). Women had more recurrent ischemia than men (21.4% vs. 17.0%, respectively, p = 0.01). Ninety-minute ejection fraction and regional ventricular function were clinically similar in women and men with TIMI 2 or 3 flow (ejection fraction [mean ± SD]: 63.4 ± 6% vs. 59.4 ± 0.7%, p = 0.02; number of chords: 21.4 ± 0.9 vs. 21.0 ± 1.9, p = 0.7; SD/chord: −2.4 ± 08 vs. −2.4 ± 0.2, p = 0.9, respectively). No clinically significant differences in left ventricular function were noted at 5- to 7-day follow-up. Women had a greater hyperkinetic response than men in the noninfarct zone (SD/chord: 2.4 ± 0.2 vs. 1.7 ± 0.1, p = 0.005). The 30-day mortality rate was 13.1% in women versus 4.8% in men (p ≤ 0.0001). After adjustment for other clinical and angiographic variables, gender remained an independent determinant of 30-day mortality.Conclusions. Women do not differ significantly from men with regard to either early infarct-related artery patency rates or reocclusion after thrombolytic therapy or ventricular functional response to injury/reperfusion. Gender was an independent determinant of 30-day mortality after acute myocardial infarction.(J Am Coll Cardiol 1997;29:35–42)

Non-invasive or minimally invasive autopsy compared to conventional autopsy of suspected natural deaths in adults: a systematic review

Objectives: Autopsies are used for healthcare quality control and improving medical knowledge. Because autopsy rates are declining worldwide, various non-invasive or minimally invasive autopsy methods are now being developed. To investigate whether these might replace the invasive autopsies conventionally performed in naturally deceased adults, we systematically reviewed original prospective validation studies. Materials and methods: We searched six databases. Two reviewers independently selected articles and extracted data. Methods and patient groups were too heterogeneous for meaningful meta-analysis of outcomes. Results: Sixteen of 1538 articles met our inclusion criteria. Eight studies used a blinded comparison; ten included less than 30 appropriate cases. Thirteen studies used radiological imaging (seven dealt solely with non-invasive procedures), two thoracoscopy and laparoscopy, and one sampling without imaging. Combining CT and MR was the best non-invasive method (agreement for cause of death: 70 %, 95%CI: 62.6; 76.4), but minimally invasive methods surpassed non-invasive methods. The highest sensitivity for cause of death (90.9 %, 95%CI: 74.5; 97.6, suspected duplicates excluded) was achieved in recent studies combining CT, CT-angiography and biopsies. Conclusion: Minimally invasive autopsies including biopsies performed best. To establish a feasible alternative to conventional autopsy and to increase consent to post-mortem investigations, further research in larger study groups is needed. Key points: • Health care quality control benefits from clinical feedback provided by (alternative) autopsies. • So far, sixteen studies investigated alternative autopsy methods for naturally deceased adults. • Thirteen studies used radiological imaging modalities, eight tissue biopsies, and three CT-angiography. • Combined CT, CT-angiography and biopsies were most sensitive diagnosing cause of death