CHIANTI - an atomic database for emission lines. VII. New Data for X-rays and other improvements

The CHIANTI atomic database contains atomic energy levels, wavelengths, radiative transition probabilities, and collisional excitation data for a large number of ions of astrophysical interest. CHIANTI also includes a suite of IDL routines to calculate synthetic spectra and carry out plasma diagnostics. Version 5 has been released, which includes several new features, as well as new data for many ions. The new features in CHIANTI are as follows: the inclusion of ionization and recombination rates to individual excited levels as a means to populate atomic levels; data for Kα and Kβ emission from Fe ii to Fe xxiv; new data for high-energy configurations in Fe xvii to Fe xxiii; and a complete reassessment of level energies and line identifications in the X-ray range, multitemperature particle distributions, and photoexcitation from any user-defined radiation field. New data for ions already in the database, as well as data for ions not present in earlier versions of the database, are also included. Version 5 of CHIANTI represents a major improvement in the calculation of line emissivities and synthetic spectra in the X-ray range and expands and improves theoretical spectra calculations in all other wavelength ranges

An automatic deep learning approach for coronary artery calcium segmentation

Coronary artery calcium (CAC) is a significant marker of atherosclerosis and cardiovascular events. In this work we present a system for the automatic quantification of calcium score in ECG-triggered non-contrast enhanced cardiac computed tomography (CT) images. The proposed system uses a supervised deep learning algorithm, i.e. convolutional neural network (CNN) for the segmentation and classification of candidate lesions as coronary or not, previously extracted in the region of the heart using a cardiac atlas. We trained our network with 45 CT volumes; 18 volumes were used to validate the model and 56 to test it. Individual lesions were detected with a sensitivity of 91.24%, a specificity of 95.37% and a positive predicted value (PPV) of 90.5%; comparing calcium score obtained by the system and calcium score manually evaluated by an expert operator, a Pearson coefficient of 0.983 was obtained. A high agreement (Cohen's k = 0.879) between manual and automatic risk prediction was also observed. These results demonstrated that convolutional neural networks can be effectively applied for the automatic segmentation and classification of coronary calcifications

Sub-Doppler laser cooling of potassium atoms

We investigate sub-Doppler laser cooling of bosonic potassium isotopes, whose small hyperfine splitting has so far prevented cooling below the Doppler temperature. We find instead that the combination of a dark optical molasses scheme that naturally arises in this kind of systems and an adiabatic ramping of the laser parameters allows to reach sub-Doppler temperatures for small laser detunings. We demonstrate temperatures as low as 25(3)microK and 47(5)microK in high-density samples of the two isotopes 39K and 41K, respectively. Our findings will find application to other atomic systems.Comment: 7 pages, 9 figure

Continuing data analysis of the AS/E grazing incidence X-ray telescope experiment on the OSO-4 satellite

The work to correct and extend the calculation of the theoretical solar X-ray spectrum produced during earlier OSO-4 data analysis is reported along with the work to formulate models of active regions, and compare these models with the experimental values. An atlas of solar X-ray photographs is included, and solar X-ray observations are correlated with the solar wind

Ontological Levels in Histological Imaging

Paper presented at the 9th edition of the Formal Ontology in Information Systems conference, FOIS 2016, July 6–9, 2016, Annecy, FranceThis is the author accepted manuscript. The final version is available from IOS Press via the DOI in this record.In this paper we present an ontological perspective on ongoing work in histological and histopathological imaging involving the quantitative and algorithmic analysis of digitised images of cells and tissues. We present the derivation of consistent histological models from initially captured images of prepared tissue samples as a progression through a number of ontological levels, each populated by its distinctive classes of entities related in systematic ways to entities at other levels. We see this work as contributing to ongoing efforts to provide a consistent and widely accepted suite of ontological resources such as those currently constituting the OBO Foundry, and where possible we draw links between our work and existing ontologies within that suite.This research is supported by EPSRC through funding under grant EP/M023869/1 “Novel context-based segmentation algorithms for intelligent microscopy”

Reconnection in a slow Coronal Mass Ejection

This paper aims at studying reconnection occurring in the aftermath of the 28 May 2004, CME, first imaged by the LASCO (Large Angle and Spectrometric Coronagraph) C2 at 11:06 UT. The CME was observed in White Light and UV radiation: images acquired by the LASCO C2 and C3 coronagraphs and spectra acquired by UVCS (Ultraviolet Coronagraph Spectrometer) allowed us to identify the level at which field lines, stretched outwards by the CME ejection, reconnect below the CME bubble. As the CME propagates outwards, reconnection occurs at increasingly higher levels. The process goes on at a low pace for several hours: here we give the profile of the reconnection rate vs. heliocentric distance over a time interval of &asymp;14 h after the CME onset, extending estimates of the reconnection rate to larger distances than previously inferred by other authors. The reconnection rate appears to decrease with time/altitude. We also calculate upper and lower limits to the density in the diffusion region between 4 and 7 <I>R</I>_&#x2299; and conclude by comparing estimates of the classical and anomalous resistivity in the diffusion region with the value inferred from the data. The latter turns out to be &ge;5 order of magnitudes larger than predicted by classical or anomalous theories, pointing to the need of identifying the process responsible for the observed value

Automatic thresholding from the gradients of region boundaries

We present an approach for automatic threshold segmentation of greyscale images. The procedure is inspired by a reinterpretation of the strategy observed in human operators when adjusting thresholds manually and interactively by means of ‘slider’ controls. The approach translates into two methods. The first one is suitable for single or multiple global thresholds to be applied globally to images and consists of searching for a threshold value that generates a phase whose boundary coincides with the largest gradients in the original image. The second method is a variation, implemented to operate on the discrete connected components of the thresholded phase (i.e. the binary regions) independently. Consequently, this becomes an adaptive local threshold procedure, which operates relative to regions, rather than to local image subsets as is the case in most local thresholding methods previously published. Adding constraints for specifying certain classes of expected objects in the images can improve the output of the method over the traditional ‘segmenting first, then classify’ approach.The research reported in this paper was supported by the Engineering and Physical Sciences Research Council (EPSRC), UK through funding under grant EP/M023869/1 ‘Novel contextbased segmentation algorithms for intelligent microscopy’

Quantitative analysis of the epithelial lining architecture in radicular cysts and odontogenic keratocysts

BACKGROUND: This paper describes a quantitative analysis of the cyst lining architecture in radicular cysts (of inflammatory aetiology) and odontogenic keratocysts (thought to be developmental or neoplastic) including its 2 counterparts: solitary and associated with the Basal Cell Naevus Syndrome (BCNS). METHODS: Epithelial linings from 150 images (from 9 radicular cysts, 13 solitary keratocysts and 8 BCNS keratocysts) were segmented into theoretical cells using a semi-automated partition based on the intensity of the haematoxylin stain which defined exclusive areas relative to each detected nucleus. Various morphometrical parameters were extracted from these "cells" and epithelial layer membership was computed using a systematic clustering routine. RESULTS: Statistically significant differences were observed across the 3 cyst types both at the morphological and architectural levels of the lining. Case-wise discrimination between radicular cysts and keratocyst was highly accurate (with an error of just 3.3%). However, the odontogenic keratocyst subtypes could not be reliably separated into the original classes, achieving discrimination rates slightly above random allocations (60%). CONCLUSION: The methodology presented is able to provide new measures of epithelial architecture and may help to characterise and compare tissue spatial organisation as well as provide useful procedures for automating certain aspects of histopathological diagnosis