Morphologically-Identified Merging Galaxies in the SWIRE Fields

We investigate the evolutional and environmental effects on star formation efficiency for more than 400 merging galaxies. The ~400 merging systems, with photometric redshifts smaller than 0.7, are obtained from a catalog of ~15000 morphologically identified merging galaxies derived from observations of the Canada-France-Hawaii Telescope. We also obtained the IR data of the merging galaxies from the Spitzer Wide-area InfraRed Extragalactic Survey (SWIRE). The redshift differences \Delta z between the member galaxies of these merging pairs show a large distribution with 0 < \Delta z < 0.4. We divide our merging pairs into two sub-samples with \Delta z 0.05 for further analyses. We find a statistically significant anti-correlation between the specific star formation rate (SSFR) and the separation of the merging galaxies for both sub-samples. Our analyses also show that although most of the merging systems do have enhanced star formation activity, only very rare ones display extremely high SFRs. Additionally, the SSFR of the merging galaxies also decreases when the magnitude difference between two member galaxies becomes large. However, we find that for the merging pairs with large luminosity contrast, the fainter components show higher SSFR than the brighter ones. Finally, there is a higher fraction of gas-poor mergers in galaxy clusters, and the SSFR of gas-rich mergers is reduced in cluster environments.Comment: 32 pages, 12 figures and 7 tables; accepted for publication in Ap

Central Engine Memory of Gamma-Ray Bursts and Soft Gamma-Ray Repeaters

Gamma-ray Bursts (GRBs) are bursts of $\gamma$-rays generated from relativistic jets launched from catastrophic events such as massive star core collapse or binary compact star coalescence. Previous studies suggested that GRB emission is erratic, with no noticeable memory in the central engine. Here we report a discovery that similar light curve patterns exist within individual bursts for at least some GRBs. Applying the Dynamic Time Warping (DTW) method, we show that similarity of light curve patterns between pulses of a single burst or between the light curves of a GRB and its X-ray flare can be identified. This suggests that the central engine of at least some GRBs carries "memory" of its activities. We also show that the same technique can identify memory-like emission episodes in the flaring emission in Soft Gamma-Ray Repeaters (SGRs), which are believed to be Galactic, highly magnetized neutron stars named magnetars. Such a phenomenon challenges the standard black hole central engine models for GRBs, and suggest a common physical mechanism behind GRBs and SGRs, which points towards a magnetar central engine of GRBs.Comment: 7 pages, 4 figures, ApJ Letters in pres

Integrating hot and cool intelligences: Thinking Broadly about Broad Abilities

Although results from factor-analytic studies of the broad, second-stratum abilities of human intelligence have been fairly consistent for decades, the list of broad abilities is far from complete, much less understood. We propose criteria by which the list of broad abilities could be amended and envision alternatives for how our understanding of the hot intelligences (abilities involving emotionally-salient information) and cool intelligences (abilities involving perceptual processing and logical reasoning) might be integrated into a coherent theoretical framework

Preliminary prediction of individual response to electroconvulsive therapy using whole-brain functional magnetic resonance imaging data.

Electroconvulsive therapy (ECT) works rapidly and has been widely used to treat depressive disorders (DEP). However, identifying biomarkers predictive of response to ECT remains a priority to individually tailor treatment and understand treatment mechanisms. This study used a connectome-based predictive modeling (CPM) approach in 122 patients with DEP to determine if pre-ECT whole-brain functional connectivity (FC) predicts depressive rating changes and remission status after ECT (47 of 122 total subjects or 38.5% of sample), and whether pre-ECT and longitudinal changes (pre/post-ECT) in regional brain network biomarkers are associated with treatment-related changes in depression ratings. Results show the networks with the best predictive performance of ECT response were negative (anti-correlated) FC networks, which predict the post-ECT depression severity (continuous measure) with a 76.23% accuracy for remission prediction. FC networks with the greatest predictive power were concentrated in the prefrontal and temporal cortices and subcortical nuclei, and include the inferior frontal (IFG), superior frontal (SFG), superior temporal (STG), inferior temporal gyri (ITG), basal ganglia (BG), and thalamus (Tha). Several of these brain regions were also identified as nodes in the FC networks that show significant change pre-/post-ECT, but these networks were not related to treatment response. This study design has limitations regarding the longitudinal design and the absence of a control group that limit the causal inference regarding mechanism of post-treatment status. Though predictive biomarkers remained below the threshold of those recommended for potential translation, the analysis methods and results demonstrate the promise and generalizability of biomarkers for advancing personalized treatment strategies

Three-dimensional multifractal analysis of trabecular bone under clinical computed tomography

Purpose: An adequate understanding of bone structural properties is critical for predicting fragility conditions caused by diseases such as osteoporosis, and in gauging the success of fracture prevention treatments. In this work we aim to develop multiresolution image analysis techniques to extrapolate high-resolution images predictive power to images taken in clinical conditions. Methods: We performed multifractal analysis (MFA) on a set of 17 ex vivo human vertebrae clinical CT scans. The vertebræ failure loads (FFailure) were experimentally measured. We combined bone mineral density (BMD) with different multifractal dimensions, and BMD with multiresolution statistics (e.g., skewness, kurtosis) of MFA curves, to obtain linear models to predict FFailure. Furthermore we obtained short- and long-term precisions from simulated in vivo scans, using a clinical CT scanner. Ground-truth data - high-resolution images - were obtained with a High-Resolution Peripheral Quantitative Computed Tomography (HRpQCT) scanner. Results: At the same level of detail, BMD combined with traditional multifractal descriptors (Lipschitz-Hölder exponents), and BMD with monofractal features showed similar prediction powers in predicting FFailure (87%, adj. R2). However, at different levels of details, the prediction power of BMD with multifractal features raises to 92% (adj. R2) of FFailure. Our main finding is that a simpler but slightly less accurate model, combining BMD and the skewness of the resulting multifractal curves, predicts 90% (adj. R2) of FFailure. Conclusions: Compared to monofractal and standard bone measures, multifractal analysis captured key insights in the conditions leading to FFailure. Instead of raw multifractal descriptors, the statistics of multifractal curves can be used in several other contexts, facilitating further research.Fil: Baravalle, Rodrigo Guillermo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; ArgentinaFil: Thomsen, Felix Sebastian Leo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Sur; ArgentinaFil: Delrieux, Claudio Augusto. Universidad Nacional del Sur; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Lu, Yongtao. Dalian University of Technology; ChinaFil: Gómez, Juan Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; ArgentinaFil: Stošić, Borko. Universidade Federal Rural Pernambuco; BrasilFil: Stošić, Tatijana. Universidade Federal Rural Pernambuco; Brasi

Gendered behavior as a disadvantage in open source software development

Women are severely marginalized in software development, especially in open source. In this article we argue that disadvantage is more due to gendered behavior than to categorical discrimination: women are at a disadvantage because of what they do, rather than because of who they are. Using data on entire careers of users from GitHub.com, we develop a measure to capture the gendered pattern of behavior: We use a random forest prediction of being female (as opposed to being male) by behavioral choices in the level of activity, specialization in programming languages, and choice of partners. We test differences in success and survival along both categorical gender and the gendered pattern of behavior. We find that 84.5% of women's disadvantage (compared to men) in success and 34.8% of their disadvantage in survival are due to the female pattern of their behavior. Men are also disadvantaged along their interquartile range of the female pattern of their behavior, and users who don't reveal their gender suffer an even more drastic disadvantage in survival probability. Moreover, we do not see evidence for any reduction of these inequalities in time. Our findings are robust to noise in gender recognition, and to taking into account particular programming languages, or decision tree classes of gendered behavior. Our results suggest that fighting categorical gender discrimination will have a limited impact on gender inequalities in open source software development, and that gender hiding is not a viable strategy for women

Predicting spectral features in galaxy spectra from broad-band photometry

We explore the prospects of predicting emission line features present in galaxy spectra given broad-band photometry alone. There is a general consent that colours, and spectral features, most notably the 4000 A break, can predict many properties of galaxies, including star formation rates and hence they could infer some of the line properties. We argue that these techniques have great prospects in helping us understand line emission in extragalactic objects and might speed up future galaxy redshift surveys if they are to target emission line objects only. We use two independent methods, Artifical Neural Neworks (based on the ANNz code) and Locally Weighted Regression (LWR), to retrieve correlations present in the colour N-dimensional space and to predict the equivalent widths present in the corresponding spectra. We also investigate how well it is possible to separate galaxies with and without lines from broad band photometry only. We find, unsurprisingly, that recombination lines can be well predicted by galaxy colours. However, among collisional lines some can and some cannot be predicted well from galaxy colours alone, without any further redshift information. We also use our techniques to estimate how much information contained in spectral diagnostic diagrams can be recovered from broad-band photometry alone. We find that it is possible to classify AGN and star formation objects relatively well using colours only. We suggest that this technique could be used to considerably improve redshift surveys such as the upcoming FMOS survey and the planned WFMOS survey.Comment: 10 pages 7 figures summitted to MNRA

An Investigation of the Large-scale Variability of the Apparently Single Wolf-Rayet Star WR 1

In recent years, much studies have focused on determining the origin of the large-scale line-profile and/or photometric patterns of variability displayed by some apparently single Wolf-Rayet stars, with the existence of an unseen (collapsed?) companion or of spatially extended wind structures as potential candidates. We present observations of WR 1 which highlight the unusual character of the variations in this object. Our narrowband photometric observations reveal a gradual increase of the stellar continuum flux amounting to Delta v = 0.09 mag followed by a decline on about the same timescale (3-4 days). Only marginal evidence for variability is found during the 11 following nights. Strong, daily line-profile variations are also observed but they cannot be easily linked to the photometric variations. Similarly to the continuum flux variations, coherent time-dependent changes are observed in 1996 in the centroid, equivalent width, and skewness of He II 4686. Despite the generally coherent nature of the variations, we do not find evidence in our data for the periods claimed in previous studies. While the issue of a cyclical pattern of variability in WR 1 is still controversial, it is clear that this object might constitute in the future a cornerstone for our understanding of the mechanisms leading to the formation of largely anisotropic outflows in Wolf-Rayet stars.Comment: 11 pages, 9 figures, accepted for publication in Astronomy & Astrophysic

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin