Experimental study of spectral and spatial distribution of solar X-rays

The study of the physical conditions within the solar corona and the development of instrumentation and technical expertise necessary for advanced studies of solar X-ray emission are reported. Details are given on the Aerobee-borne-X-ray spectrometer/monochromator and also on the observing program. Preliminary discussions of some results are presented and include studies of helium-like line emission, mapping O(VII) and Ne(IX) lines, survey of O(VII) and Ne(IX) lines, study of plage regions and small flares, and analysis of line emission from individual active regions. It is concluded that the use of large-area collimated Bragg spectrometers to scan narrow wavelength intervals and the capability of the SPARCS pointing control to execute a complex observing program are established

Non-thermal recombination - a neglected source of flare hard X-rays and fast electron diagnostic

Context. Flare Hard X-Rays (HXRs) from non-thermal electrons are commonly treated as solely bremsstrahlung (f-f), recombination (f-b) being neglected. This assumption is shown to be substantially in error, especially in hot sources, mainly due to recombination onto Fe ions. Aims. We analyse the effects on HXR spectra and electron diagnostics by including non-thermal recombination onto heavy elements in our model. Methods. Using Kramers hydrogenic cross sections with effective Z, we calculate f-f and f-b spectra for power-law electron spectra, in both thin and thick target limits, and for Maxwellians, with summation over all important ions. Results. We find that non-thermal electron recombination, especially onto Fe, must, in general, be included together with f-f, for reliable spectral interpretation, when the HXR source is hot. f-b contribution is greatest when the electron spectral index is large, and any low energy cut-off small. f-b spectra recombination edges mean a cut-off in F(E) appears as a HXR feature at Photon energy = Ec + Vz, offering an Ec diagnostic. Including f-b lowers, greatly in some cases, the F(E) needed for prescribed HXR fluxes and, even when small, seriously distorts F(E) as inferred by inversion or forward fitting based on f-f alone. Conclusions. f-b recombination from non-thermal electrons can be an important contributor to HXR spectra and should be included in spectral analyses, especially for hot sources. Accurate results will require use of better cross sections than ours and consideration of source ionisation structure.Comment: 13 pages, 2 tables, 9 figures, Accepted for publication in A&

Cross-platform comparison and visualisation of gene expression data using co-inertia analysis

BACKGROUND: Rapid development of DNA microarray technology has resulted in different laboratories adopting numerous different protocols and technological platforms, which has severely impacted on the comparability of array data. Current cross-platform comparison of microarray gene expression data are usually based on cross-referencing the annotation of each gene transcript represented on the arrays, extracting a list of genes common to all arrays and comparing expression data of this gene subset. Unfortunately, filtering of genes to a subset represented across all arrays often excludes many thousands of genes, because different subsets of genes from the genome are represented on different arrays. We wish to describe the application of a powerful yet simple method for cross-platform comparison of gene expression data. Co-inertia analysis (CIA) is a multivariate method that identifies trends or co-relationships in multiple datasets which contain the same samples. CIA simultaneously finds ordinations (dimension reduction diagrams) from the datasets that are most similar. It does this by finding successive axes from the two datasets with maximum covariance. CIA can be applied to datasets where the number of variables (genes) far exceeds the number of samples (arrays) such is the case with microarray analyses. RESULTS: We illustrate the power of CIA for cross-platform analysis of gene expression data by using it to identify the main common relationships in expression profiles on a panel of 60 tumour cell lines from the National Cancer Institute (NCI) which have been subjected to microarray studies using both Affymetrix and spotted cDNA array technology. The co-ordinates of the CIA projections of the cell lines from each dataset are graphed in a bi-plot and are connected by a line, the length of which indicates the divergence between the two datasets. Thus, CIA provides graphical representation of consensus and divergence between the gene expression profiles from different microarray platforms. Secondly, the genes that define the main trends in the analysis can be easily identified. CONCLUSIONS: CIA is a robust, efficient approach to coupling of gene expression datasets. CIA provides simple graphical representations of the results making it a particularly attractive method for the identification of relationships between large datasets

Flows in the solar atmosphere due to the eruptions on the 15th July, 2002

<p>Which kind of flows are present during flares? Are they compatible with the present understanding of energy release and which model best describes the observations? We analyze successive flare events in order to answer these questions. The flares were observed in the magnetically complex NOAA active region (AR) 10030 on 15 July 2002. One of them is of GOES X-class. The description of these flares and how they relate to the break-out model is presented in Gary & Moore (2004). The Coronal Diagnostic Spectrometer on board SOHO observed this active region for around 14 h. The observed emission lines provided data from the transition region to the corona with a field of view covering more than half of the active region. In this paper we analyse the spatially resolved flows seen in the atmosphere from the preflare to the flare stages. We find evidence for evaporation occurring before the impulsive phase. During the main phase, the ongoing magnetic reconnection is demonstrated by upflows located at the edges of the flare loops (while downflows are found in the flare loops themselves). We also report the impact of a filament eruption on the atmosphere, with flows up to 300 km s<sup>-1</sup> observed at transition-region temperatures in regions well away from the location of the pre-eruptive filament. Our results are consistent with the predictions of the break out model before the impulsive phase of the flare; while, as the flare progresses, the directions of the flows are consistent with flare models invoking evaporation followed by cooling and downward plasma motions in the flare loops.</p&gt

Relating near-Earth observations of an interplanetary coronal mass ejection to the conditions at its site of origin in the solar corona

A halo coronal mass ejection (CME) was detected on January 20, 2004. We use solar remote sensing data (SOHO, Culgoora) and near-Earth in situ data (Cluster) to identify the CME source event and show that it was a long duration flare in which a magnetic flux rope was ejected, carrying overlying coronal arcade material along with it. We demonstrate that signatures of both the arcade material and the flux rope material are clearly identifiable in the Cluster and ACE data, indicating that the magnetic field orientations changed little as the material traveled to the Earth, and that the methods we used to infer coronal magnetic field configurations are effective

A multivariate approach to the integration of multi-omics datasets

Background: To leverage the potential of multi-omics studies, exploratory data analysis methods that provide systematic integration and comparison of multiple layers of omics information are required. We describe multiple co-inertia analysis (MCIA), an exploratory data analysis method that identifies co-relationships between multiple high dimensional datasets. Based on a covariance optimization criterion, MCIA simultaneously projects several datasets into the same dimensional space, transforming diverse sets of features onto the same scale, to extract the most variant from each dataset and facilitate biological interpretation and pathway analysis. Results: We demonstrate integration of multiple layers of information using MCIA, applied to two typical “omics” research scenarios. The integration of transcriptome and proteome profiles of cells in the NCI-60 cancer cell line panel revealed distinct, complementary features, which together increased the coverage and power of pathway analysis. Our analysis highlighted the importance of the leukemia extravasation signaling pathway in leukemia that was not highly ranked in the analysis of any individual dataset. Secondly, we compared transcriptome profiles of high grade serous ovarian tumors that were obtained, on two different microarray platforms and next generation RNA-sequencing, to identify the most informative platform and extract robust biomarkers of molecular subtypes. We discovered that the variance of RNA-sequencing data processed using RPKM had greater variance than that with MapSplice and RSEM. We provided novel markers highly associated to tumor molecular subtype combined from four data platforms. MCIA is implemented and available in the R/Bioconductor “omicade4” package. Conclusion: We believe MCIA is an attractive method for data integration and visualization of several datasets of multi-omics features observed on the same set of individuals. The method is not dependent on feature annotation, and thus it can extract important features even when there are not present across all datasets. MCIA provides simple graphical representations for the identification of relationships between large datasets

Flows and Non-thermal Velocities in Solar Active Regions Observed with the Extreme-ultraviolet Imaging Spectrometer on Hinode: A Tracer of Active Region Sources of Heliospheric Magnetic Fields?

From Doppler velocity maps of active regions constructed from spectra obtained by the Extreme-ultraviolet Imaging Spectrometer (EIS) on the Hinode spacecraft we observe large areas of outflow (20-50 km/s) that can persist for at least a day. These outflows occur in areas of active regions that are faint in coronal spectral lines formed at typical quiet Sun and active region temperatures. The outflows are positively correlated with non-thermal velocities in coronal plasmas. The bulk mass motions and non-thermal velocities are derived from spectral line centroids and line widths, mostly from a strong line of Fe XII at 195.12 Angstroms. The electron temperature of the outflow regions estimated from an Fe XIII to Fe XII line intensity ratio is about 1.2-1.4 MK. The electron density of the outflow regions derived from a density sensitive intensity ratio of Fe XII lines is rather low for an active region. Most regions average around 7E10+8 cm(-3), but there are variations on pixel spatial scales of about a factor of 4. We discuss results in detail for two active regions observed by EIS. Images of active regions in line intensity, line width, and line centroid are obtained by rastering the regions. We also discuss data from the active regions obtained from other orbiting spacecraft that support the conclusions obtained from analysis of the EIS spectra. The locations of the flows in the active regions with respect to the longitudinal photospheric magnetic fields suggest that these regions might be tracers of long loops and/or open magnetic fields that extend into the heliosphere, and thus the flows could possibly contribute significantly to the solar wind.Comment: one tex file, 11 postscript figure file

iBBiG: iterative binary bi-clustering of gene sets

Motivation: Meta-analysis of genomics data seeks to identify genes associated with a biological phenotype across multiple datasets; however, merging data from different platforms by their features (genes) is challenging. Meta-analysis using functionally or biologically characterized gene sets simplifies data integration is biologically intuitive and is seen as having great potential, but is an emerging field with few established statistical methods. Results: We transform gene expression profiles into binary gene set profiles by discretizing results of gene set enrichment analyses and apply a new iterative bi-clustering algorithm (iBBiG) to identify groups of gene sets that are coordinately associated with groups of phenotypes across multiple studies. iBBiG is optimized for meta-analysis of large numbers of diverse genomics data that may have unmatched samples. It does not require prior knowledge of the number or size of clusters. When applied to simulated data, it outperforms commonly used clustering methods, discovers overlapping clusters of diverse sizes and is robust in the presence of noise. We apply it to meta-analysis of breast cancer studies, where iBBiG extracted novel gene set—phenotype association that predicted tumor metastases within tumor subtypes

How Can Active Region Plasma Escape into the Solar Wind from below a Closed Helmet Streamer?

Recent studies show that active-region (AR) upflowing plasma, observed by the EUV-Imaging Spectrometer (EIS), onboard Hinode, can gain access to open field-lines and be released into the solar wind (SW) via magnetic-interchange reconnection at magnetic null-points in pseudo-streamer configurations. When only one bipolar AR is present on the Sun and it is fully covered by the separatrix of a streamer, such as AR 10978 in December 2007, it seems unlikely that the upflowing AR plasma can find its way into the slow SW. However, signatures of plasma with AR composition have been found at 1 AU by Culhane et al. (2014) apparently originating from the West of AR 10978. We present a detailed topology analysis of AR 10978 and the surrounding large-scale corona based on a potential-field source-surface (PFSS) model. Our study shows that it is possible for the AR plasma to get around the streamer separatrix and be released into the SW via magnetic reconnection, occurring in at least two main steps. We analyse data from the Nan\c{c}ay Radioheliograph (NRH) searching for evidence of the chain of magnetic reconnections proposed. We find a noise storm above the AR and several varying sources at 150.9 MHz. Their locations suggest that they could be associated with particles accelerated during the first-step reconnection process and at a null point well outside of the AR. However, we find no evidence of the second-step reconnection in the radio data. Our results demonstrate that even when it appears highly improbable for the AR plasma to reach the SW, indirect channels involving a sequence of reconnections can make it possible.Comment: 26 pages, 10 figures. appears in Solar Physics, 201

Predicting Housing Abandonment with the Philadelphia Neighborhood Information System

Several large US cities, including Chicago, Los Angeles, New York, and Philadelphia, have developed information systems to distribute property-level housing data to community organizations and municipal agencies. These early warning systems are also intended to predict which properties are at greatest risk of abandonment, but they have rarely used statistical modeling to support such forecasts. This study used logistic regression to analyze data from the Philadelphia Neighborhood Information System in order to determine which properties were most likely to become imminently dangerous. Several different characteristics of the property, including whether it was vacant, had outstanding housing code violations, and tax arrearages as well as characteristics of nearby properties were identified as significant predictors. Challenges common to the development of early warning systems - including integrating administrative data, defining abandonment, and modeling temporal and spatial data - are discussed along with policy implications for cities like Philadelphia that have thousands of vacant and abandoned properties