Reconstructing Galaxy Spectral Energy Distributions from Broadband Photometry

We present a novel approach to photometric redshifts, one that merges the advantages of both the template fitting and empirical fitting algorithms, without any of their disadvantages. This technique derives a set of templates, describing the spectral energy distributions of galaxies, from a catalog with both multicolor photometry and spectroscopic redshifts. The algorithm is essentially using the shapes of the templates as the fitting parameters. From simulated multicolor data we show that for a small training set of galaxies we can reconstruct robustly the underlying spectral energy distributions even in the presence of substantial errors in the photometric observations. We apply these techniques to the multicolor and spectroscopic observations of the Hubble Deep Field building a set of template spectra that reproduced the observed galaxy colors to better than 10%. Finally we demonstrate that these improved spectral energy distributions lead to a photometric-redshift relation for the Hubble Deep Field that is more accurate than standard template-based approaches.Comment: 23 pages, 8 figures, LaTeX AASTeX, accepted for publication in A

A neural network algorithm for cloud fraction estimation using NASA-Aura OMI VIS radiance measurements

The discrimination of cloudy from cloud-free pixels is required in almost any estimate of a parameter retrieved from satellite data in the ultraviolet (UV), visible (VIS) or infrared (IR) parts of the electromagnetic spectrum. In this paper we report on the development of a neural network (NN) algorithm to estimate cloud fractions using radiances measured at the top of the atmosphere with the NASA-Aura Ozone Monitoring Instrument (OMI). We present and discuss the results obtained from the application of two different types of neural networks, i.e., extreme learning machine (ELM) and back propagation (BP). The NNs were trained with an OMI data sets existing of six orbits, tested with three other orbits and validated with another two orbits. The results were evaluated by comparison with cloud fractions available from the MODerate Resolution Imaging Spectrometer (MODIS) flying on Aqua in the same constellation as OMI, i.e., with minimal time difference between the OMI and MODIS observations. The results from the ELM and BP NNs are compared. They both deliver cloud fraction estimates in a fast and automated way, and they both performs generally well in the validation. However, over highly reflective surfaces, such as desert, or in the presence of dust layers in the atmosphere, the cloud fractions are not well predicted by the neural network. Over ocean the two NNs work equally well, but over land ELM performs better

Medico-legal autopsy in postoperative hemodynamic collapse following coronary artery bypass surgery

Sudden unexpected postoperative hemodynamic collapse with a high mortality develops in 1–3% of patients undergoing coronary artery bypass surgery (CABG). The contribution of surgical graft complications to this serious condition is poorly known and their demonstration at autopsy is a challenging task. Isolated CABG was performed in 8,807 patients during 1988–1999. Of the patients, 76 (0.9%) developed sudden postoperative hemodynamic collapse resulting in subsequent emergency reopening of the median sternotomy and open cardiac massage. Further emergency reoperation could be performed in 62 (82%) whereas 14 patients died prior to reoperation and a further 21 did not survive the reoperation or died a few days later. All 35 (46%) patients who did not survive were subjected to medico-legal autopsy combined with postmortem cast angiography. By combining clinical data with autopsy and angiography data, various types of graft complications were observed in 27 (36%, 1.3 per patient) of the 76 patients with hemodynamic collapse. There were no significant differences in the frequency (33 vs. 40%) or number of complicated grafts per patient (1.2 vs. 1.4) between those who survived reoperation and who did not. Autopsy detected 25 major and minor findings not diagnosed clinically. Postmortem cast angiography visualized 2 graft twists not possible to detect by autopsy dissection only. Surgical graft complications were the most frequent single cause for sudden postoperative hemodynamic collapse in CABG patients leading to a fatal outcome in almost half of the cases. Postmortem angiography improved the accuracy of autopsy diagnostics of graft complications

Estimating bias in causes of death ascertainment in the Finnish Randomized Study of Screening for Prostate Cancer

Background: Precise cause of death (CoD) ascertainment is crucial in any cancer screening trial to avoid bias from misclassification due to excessive recording of diagnosed cancer as a CoD in death certificates instead of non-cancer disease that actually caused death. We estimated whether there was bias in CoD determination between screening (SA) and control arms (CA) in a population-based prostate cancer (PCa) screening trial. Methods: Our trial is the largest component of the European Randomized Study of Screening for Prostate Cancer with more than 80,000 men. Randomly selected deaths in men with PCa (N = 442/2568 cases, 17.2%) were reviewed by an independent CoD committee. Median follow-up was 16.8 years in both arms. Results: Overdiagnosis of PCa was present in the SA as the risk ratio for PCa incidence was 1.19 (95% confidence interval (CI) 1.14-1.24). The hazard ratio (HR) for PCa mortality was 0.94 (95% CI 0.82-1.08) in favor of the SA. Agreement with official CoD registry was 94.6% (k = 0.88) in the SA and 95.4% (k = 0.91) in the CA. Altogether 14 PCa deaths were estimated as false-positive in both arms and exclusion of these resulted in HR 0.92 (95% CI 0.80-1.06). Conclusions: A small differential misclassification bias in ascertainment of CoD was present, most likely due to attribution bias (overdiagnosis in the SA). Maximum precision in CoD ascertainment can only be achieved with independent review of all deaths in the diseased population. However, this is cumbersome and expensive and may provide little benefit compared to random sampling. (C) 2016 Elsevier Ltd. All rights reserved.Peer reviewe

Entropy Encoding, Hilbert Space and Karhunen-Loeve Transforms

By introducing Hilbert space and operators, we show how probabilities, approximations and entropy encoding from signal and image processing allow precise formulas and quantitative estimates. Our main results yield orthogonal bases which optimize distinct measures of data encoding.Comment: 25 pages, 1 figur

Increased tooth brushing frequency is associated with reduced gingival pocket bacterial diversity in patients with intracranial aneurysms

Objectives The objective of this study was to investigate the association of tooth brushing frequency and bacterial communities of gingival crevicular fluid in patients subjected to preoperative dental examination prior to operative treatment for unruptured intracranial aneurysms. Methods Gingival crevicular fluid samples were taken from their deepest gingival pocket from a series of hospitalized neurosurgical patients undergoing preoperative dental screening (n = 60). The patients were asked whether they brushed their teeth two times a day, once a day, or less than every day. Total bacterial DNA was isolated and the V3–V4 region of the 16S rRNA gene was amplificated. Sequencing was performed with Illumina’s 16S metagenomic sequencing library preparation protocol and data were analyzed with QIIME (1.9.1) and R statistical software (3.3.2). Results Bacterial diversity (Chao1 index) in the crevicular fluid reduced along with reported tooth brushing frequency (p = 0.0002; R2 = 34%; p (adjusted with age and sex) = 0.09; R2 = 11%) showing that patients who reported brushing their teeth twice a day had the lowest bacterial diversity. According to the differential abundant analysis between the tooth brushing groups, tooth brushing associated with two phyla of fusobacteria [p = 0.0001; p = 0.0007], and one bacteroidetes (p = 0.004) by reducing their amounts. Conclusions Tooth brushing may reduce the gingival bacterial diversity and the abundance of periodontal bacteria maintaining oral health and preventing periodontitis, and thus it is highly recommended for neurosurgical patients

Data Mining and Machine Learning in Astronomy

We review the current state of data mining and machine learning in astronomy. 'Data Mining' can have a somewhat mixed connotation from the point of view of a researcher in this field. If used correctly, it can be a powerful approach, holding the potential to fully exploit the exponentially increasing amount of available data, promising great scientific advance. However, if misused, it can be little more than the black-box application of complex computing algorithms that may give little physical insight, and provide questionable results. Here, we give an overview of the entire data mining process, from data collection through to the interpretation of results. We cover common machine learning algorithms, such as artificial neural networks and support vector machines, applications from a broad range of astronomy, emphasizing those where data mining techniques directly resulted in improved science, and important current and future directions, including probability density functions, parallel algorithms, petascale computing, and the time domain. We conclude that, so long as one carefully selects an appropriate algorithm, and is guided by the astronomical problem at hand, data mining can be very much the powerful tool, and not the questionable black box.Comment: Published in IJMPD. 61 pages, uses ws-ijmpd.cls. Several extra figures, some minor additions to the tex