Search for Multimessenger Sources of Gravitational Waves and High-energy Neutrinos with Advanced LIGO during Its First Observing Run, ANTARES, and IceCube

Astrophysical sources of gravitational waves, such as binary neutron star and black hole mergers or core-collapse supernovae, can drive relativistic outflows, giving rise to non-thermal high-energy emission. High-energy neutrinos are signatures of such outflows. The detection of gravitational waves and high-energy neutrinos from common sources could help establish the connection between the dynamics of the progenitor and the properties of the outflow. We searched for associated emission of gravitational waves and high-energy neutrinos from astrophysical transients with minimal assumptions using data from Advanced LIGO from its first observing run O1, and data from the Antares and IceCube neutrino observatories from the same time period. We focused on candidate events whose astrophysical origins could not be determined from a single messenger. We found no significant coincident candidate, which we used to constrain the rate density of astrophysical sources dependent on their gravitational-wave and neutrino emission processes

Patisiran, an RNAi therapeutic, for hereditary transthyretin amyloidosis

BACKGROUND Patisiran, an investigational RNA interference therapeutic agent, specifically inhibits hepatic synthesis of transthyretin. METHODS In this phase 3 trial, we randomly assigned patients with hereditary transthyretin amyloidosis with polyneuropathy, in a 2:1 ratio, to receive intravenous patisiran (0.3 mg per kilogram of body weight) or placebo once every 3 weeks. The primary end point was the change from baseline in the modified Neuropathy Impairment Score+7 (mNIS+7; range, 0 to 304, with higher scores indicating more impairment) at 18 months. Other assessments included the Norfolk Quality of Life-Diabetic Neuropathy (Norfolk QOL-DN) questionnaire (range, −4 to 136, with higher scores indicating worse quality of life), 10-m walk test (with gait speed measured in meters per second), and modified body-mass index (modified BMI, defined as [weight in kilograms divided by square of height in meters]×albumin level in grams per liter; lower values indicated worse nutritional status). RESULTS A total of 225 patients underwent randomization (148 to the patisiran group and 77 to the placebo group). The mean (±SD) mNIS+7 at baseline was 80.9±41.5 in the patisiran group and 74.6±37.0 in the placebo group; the least-squares mean (±SE) change from baseline was −6.0±1.7 versus 28.0±2.6 (difference, −34.0 points; P<0.001) at 18 months. The mean (±SD) baseline Norfolk QOL-DN score was 59.6±28.2 in the patisiran group and 55.5±24.3 in the placebo group; the least-squares mean (±SE) change from baseline was −6.7±1.8 versus 14.4±2.7 (difference, −21.1 points; P<0.001) at 18 months. Patisiran also showed an effect on gait speed and modified BMI. At 18 months, the least-squares mean change from baseline in gait speed was 0.08±0.02 m per second with patisiran versus −0.24±0.04 m per second with placebo (difference, 0.31 m per second; P<0.001), and the least-squares mean change from baseline in the modified BMI was −3.7±9.6 versus −119.4±14.5 (difference, 115.7; P<0.001). Approximately 20% of the patients who received patisiran and 10% of those who received placebo had mild or moderate infusion-related reactions; the overall incidence and types of adverse events were similar in the two groups. CONCLUSIONS In this trial, patisiran improved multiple clinical manifestations of hereditary transthyretin amyloidosis

Automated Glioblastoma Segmentation Based on a Multiparametric Structured Unsupervised Classification

Automatic brain tumour segmentation has become a key component for the future of brain tumour treatment. Currently, most of brain tumour segmentation approaches arise from the supervised learning standpoint, which requires a labelled training dataset from which to infer the models of the classes. The performance of these models is directly determined by the size and quality of the training corpus, whose retrieval becomes a tedious and time-consuming task. On the other hand, unsupervised approaches avoid these limitations but often do not reach comparable results than the supervised methods. In this sense, we propose an automated unsupervised method for brain tumour segmentation based on anatomical Magnetic Resonance (MR) images. Four unsupervised classification algorithms, grouped by their structured or non-structured condition, were evaluated within our pipeline. Considering the non-structured algorithms, we evaluated K-means, Fuzzy K-means and Gaussian Mixture Model (GMM), whereas as structured classification algorithms we evaluated Gaussian Hidden Markov Random Field (GHMRF). An automated postprocess based on a statistical approach supported by tissue probability maps is proposed to automatically identify the tumour classes after the segmentations. We evaluated our brain tumour segmentation method with the public BRAin Tumor Segmentation (BRATS) 2013 Test and Leaderboard datasets. Our approach based on the GMM model improves the results obtained by most of the supervised methods evaluated with the Leaderboard set and reaches the second position in the ranking. Our variant based on the GHMRF achieves the first position in the Test ranking of the unsupervised approaches and the seventh position in the general Test ranking, which confirms the method as a viable alternative for brain tumour segmentation.EFG was supported by Programa Torres Quevedo, Ministerio de Educacion y Ciencia, co-funded by the European Social Fund (PTQ-1205693). EFG, JMGG, and JVM were supported by Red Tematica de Investigacion Cooperativa en Cancer, (RTICC) 2013-2016 (RD12/0036/0020). JMGG was supported by Project TIN2013-43457-R: Caracterizacion de firmas biologicas de glioblastomas mediante modelos no-supervisados de prediccion estructurada basados en biomarcadores de imagen, co-funded by the Ministerio de Economia y Competitividad of Spain; CON2014001 UPV-IISLaFe: Unsupervised glioblastoma tumor components segmentation based on perfusion multiparametric MRI and spatio/temporal constraints; and CON2014002 UPV-IISLaFe: Empleo de segmentacion no supervisada multiparametrica basada en perfusion RM para la caracterizacion del edema peritumoral de gliomas y metastasis cerebrales unicas, funded by Instituto de Investigacion Sanitaria H. Universitario y Politecnico La Fe. This work was partially supported by the Instituto de Aplicaciones de las Tecnologias de la Informacion y las Comunicaciones Avanzadas (ITACA). Veratech for Health S.L. provided support in the form of salaries for author EF-G, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of this author is articulated in the "author contributions" section. This does not alter the authors' adherence to PLOS ONE policies on sharing data and materials.Juan Albarracín, J.; Fuster García, E.; Manjón Herrera, JV.; Robles Viejo, M.; Aparici, F.; Marti-Bonmati, L.; García Gómez, JM. (2015). Automated Glioblastoma Segmentation Based on a Multiparametric Structured Unsupervised Classification. PLoS ONE. 10(5):1-20. https://doi.org/10.1371/journal.pone.0125143S120105Wen, P. Y., Macdonald, D. R., Reardon, D. A., Cloughesy, T. F., Sorensen, A. G., Galanis, E., … Chang, S. M. (2010). Updated Response Assessment Criteria for High-Grade Gliomas: Response Assessment in Neuro-Oncology Working Group. Journal of Clinical Oncology, 28(11), 1963-1972. doi:10.1200/jco.2009.26.3541Bauer, S., Wiest, R., Nolte, L.-P., & Reyes, M. (2013). A survey of MRI-based medical image analysis for brain tumor studies. Physics in Medicine and Biology, 58(13), R97-R129. doi:10.1088/0031-9155/58/13/r97Dolecek, T. A., Propp, J. M., Stroup, N. E., & Kruchko, C. (2012). CBTRUS Statistical Report: Primary Brain and Central Nervous System Tumors Diagnosed in the United States in 2005-2009. Neuro-Oncology, 14(suppl 5), v1-v49. doi:10.1093/neuonc/nos218Gordillo, N., Montseny, E., & Sobrevilla, P. (2013). State of the art survey on MRI brain tumor segmentation. Magnetic Resonance Imaging, 31(8), 1426-1438. doi:10.1016/j.mri.2013.05.002Verma, R., Zacharaki, E. I., Ou, Y., Cai, H., Chawla, S., Lee, S.-K., … Davatzikos, C. (2008). Multiparametric Tissue Characterization of Brain Neoplasms and Their Recurrence Using Pattern Classification of MR Images. Academic Radiology, 15(8), 966-977. doi:10.1016/j.acra.2008.01.029Jensen, T. R., & Schmainda, K. M. (2009). Computer-aided detection of brain tumor invasion using multiparametric MRI. Journal of Magnetic Resonance Imaging, 30(3), 481-489. doi:10.1002/jmri.21878Breiman, L. (2001). Machine Learning, 45(1), 5-32. doi:10.1023/a:1010933404324Wagstaff KL. Intelligent Clustering with instance-level constraints. PhD Thesis, Cornell University. 2002.Fletcher-Heath, L. M., Hall, L. O., Goldgof, D. B., & Murtagh, F. R. (2001). Automatic segmentation of non-enhancing brain tumors in magnetic resonance images. Artificial Intelligence in Medicine, 21(1-3), 43-63. doi:10.1016/s0933-3657(00)00073-7Nie, J., Xue, Z., Liu, T., Young, G. S., Setayesh, K., Guo, L., & Wong, S. T. C. (2009). Automated brain tumor segmentation using spatial accuracy-weighted hidden Markov Random Field. Computerized Medical Imaging and Graphics, 33(6), 431-441. doi:10.1016/j.compmedimag.2009.04.006Zhu, Y., Young, G. S., Xue, Z., Huang, R. Y., You, H., Setayesh, K., … Wong, S. T. (2012). Semi-Automatic Segmentation Software for Quantitative Clinical Brain Glioblastoma Evaluation. Academic Radiology, 19(8), 977-985. doi:10.1016/j.acra.2012.03.026Zhang, Y., Brady, M., & Smith, S. (2001). Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Transactions on Medical Imaging, 20(1), 45-57. doi:10.1109/42.906424Vijayakumar, C., Damayanti, G., Pant, R., & Sreedhar, C. M. (2007). Segmentation and grading of brain tumors on apparent diffusion coefficient images using self-organizing maps. Computerized Medical Imaging and Graphics, 31(7), 473-484. doi:10.1016/j.compmedimag.2007.04.004Prastawa, M., Bullitt, E., Moon, N., Van Leemput, K., & Gerig, G. (2003). Automatic brain tumor segmentation by subject specific modification of atlas priors1. Academic Radiology, 10(12), 1341-1348. doi:10.1016/s1076-6332(03)00506-3Gudbjartsson, H., & Patz, S. (1995). The rician distribution of noisy mri data. Magnetic Resonance in Medicine, 34(6), 910-914. doi:10.1002/mrm.1910340618Buades, A., Coll, B., & Morel, J. M. (2005). A Review of Image Denoising Algorithms, with a New One. Multiscale Modeling & Simulation, 4(2), 490-530. doi:10.1137/040616024Manjón, J. V., Coupé, P., Martí-Bonmatí, L., Collins, D. L., & Robles, M. (2009). Adaptive non-local means denoising of MR images with spatially varying noise levels. Journal of Magnetic Resonance Imaging, 31(1), 192-203. doi:10.1002/jmri.22003Sled, J. G., Zijdenbos, A. P., & Evans, A. C. (1998). A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Transactions on Medical Imaging, 17(1), 87-97. doi:10.1109/42.668698Tustison, N. J., Avants, B. B., Cook, P. A., Yuanjie Zheng, Egan, A., Yushkevich, P. A., & Gee, J. C. (2010). N4ITK: Improved N3 Bias Correction. IEEE Transactions on Medical Imaging, 29(6), 1310-1320. doi:10.1109/tmi.2010.2046908Manjón, J. V., Coupé, P., Buades, A., Collins, D. L., & Robles, M. (2010). MRI Superresolution Using Self-Similarity and Image Priors. International Journal of Biomedical Imaging, 2010, 1-11. doi:10.1155/2010/425891Rousseau, F. (2010). A non-local approach for image super-resolution using intermodality priors☆. Medical Image Analysis, 14(4), 594-605. doi:10.1016/j.media.2010.04.005Protter, M., Elad, M., Takeda, H., & Milanfar, P. (2009). Generalizing the Nonlocal-Means to Super-Resolution Reconstruction. IEEE Transactions on Image Processing, 18(1), 36-51. doi:10.1109/tip.2008.2008067Manjón, J. V., Coupé, P., Buades, A., Fonov, V., Louis Collins, D., & Robles, M. (2010). Non-local MRI upsampling. Medical Image Analysis, 14(6), 784-792. doi:10.1016/j.media.2010.05.010Kassner, A., & Thornhill, R. E. (2010). Texture Analysis: A Review of Neurologic MR Imaging Applications. American Journal of Neuroradiology, 31(5), 809-816. doi:10.3174/ajnr.a2061Ahmed, S., Iftekharuddin, K. M., & Vossough, A. (2011). Efficacy of Texture, Shape, and Intensity Feature Fusion for Posterior-Fossa Tumor Segmentation in MRI. IEEE Transactions on Information Technology in Biomedicine, 15(2), 206-213. doi:10.1109/titb.2011.2104376Lloyd, S. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129-137. doi:10.1109/tit.1982.1056489Dunn, J. C. (1973). A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters. Journal of Cybernetics, 3(3), 32-57. doi:10.1080/01969727308546046Bezdek, J. C. (1981). Pattern Recognition with Fuzzy Objective Function Algorithms. doi:10.1007/978-1-4757-0450-1Hammersley, JM, Clifford, P. Markov fields on finite graphs and lattices. 1971.Komodakis, N., & Tziritas, G. (2007). Approximate Labeling via Graph Cuts Based on Linear Programming. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(8), 1436-1453. doi:10.1109/tpami.2007.1061Komodakis, N., Tziritas, G., & Paragios, N. (2008). Performance vs computational efficiency for optimizing single and dynamic MRFs: Setting the state of the art with primal-dual strategies. Computer Vision and Image Understanding, 112(1), 14-29. doi:10.1016/j.cviu.2008.06.007Fonov, V., Evans, A. C., Botteron, K., Almli, C. R., McKinstry, R. C., & Collins, D. L. (2011). Unbiased average age-appropriate atlases for pediatric studies. NeuroImage, 54(1), 313-327. doi:10.1016/j.neuroimage.2010.07.033Fonov, V., Evans, A., McKinstry, R., Almli, C., & Collins, D. (2009). Unbiased nonlinear average age-appropriate brain templates from birth to adulthood. NeuroImage, 47, S102. doi:10.1016/s1053-8119(09)70884-5Klein, A., Andersson, J., Ardekani, B. A., Ashburner, J., Avants, B., Chiang, M.-C., … Parsey, R. V. (2009). Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. NeuroImage, 46(3), 786-802. doi:10.1016/j.neuroimage.2008.12.037AVANTS, B., EPSTEIN, C., GROSSMAN, M., & GEE, J. (2008). Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain. Medical Image Analysis, 12(1), 26-41. doi:10.1016/j.media.2007.06.004Saez C, Robles M, Garcia-Gomez JM. Stability metrics for multi-source biomedical data based on simplicial projections from probability distribution distances. Statistical Methods in Medical Research 2014; In press

Search for eccentric black hole coalescences during the third observing run of LIGO and Virgo

Despite the growing number of confident binary black hole coalescences observed through gravitational waves so far, the astrophysical origin of these binaries remains uncertain. Orbital eccentricity is one of the clearest tracers of binary formation channels. Identifying binary eccentricity, however, remains challenging due to the limited availability of gravitational waveforms that include effects of eccentricity. Here, we present observational results for a waveform-independent search sensitive to eccentric black hole coalescences, covering the third observing run (O3) of the LIGO and Virgo detectors. We identified no new high-significance candidates beyond those that were already identified with searches focusing on quasi-circular binaries. We determine the sensitivity of our search to high-mass (total mass M&gt;70 M⊙) binaries covering eccentricities up to 0.3 at 15 Hz orbital frequency, and use this to compare model predictions to search results. Assuming all detections are indeed quasi-circular, for our fiducial population model, we place an upper limit for the merger rate density of high-mass binaries with eccentricities 0&lt;e≤0.3 at 0.33 Gpc−3 yr−1 at 90\% confidence level

Black holes, gravitational waves and fundamental physics: a roadmap

The grand challenges of contemporary fundamental physics—dark matter, dark energy, vacuum energy, inflation and early universe cosmology, singularities and the hierarchy problem—all involve gravity as a key component. And of all gravitational phenomena, black holes stand out in their elegant simplicity, while harbouring some of the most remarkable predictions of General Relativity: event horizons, singularities and ergoregions. The hitherto invisible landscape of the gravitational Universe is being unveiled before our eyes: the historical direct detection of gravitational waves by the LIGO-Virgo collaboration marks the dawn of a new era of scientific exploration. Gravitational-wave astronomy will allow us to test models of black hole formation, growth and evolution, as well as models of gravitational-wave generation and propagation. It will provide evidence for event horizons and ergoregions, test the theory of General Relativity itself, and may reveal the existence of new fundamental fields. The synthesis of these results has the potential to radically reshape our understanding of the cosmos and of the laws of Nature. The purpose of this work is to present a concise, yet comprehensive overview of the state of the art in the relevant fields of research, summarize important open problems, and lay out a roadmap for future progress. This write-up is an initiative taken within the framework of the European Action on 'Black holes, Gravitational waves and Fundamental Physics'

Search for gravitational-lensing signatures in the full third observing run of the LIGO-Virgo network

Gravitational lensing by massive objects along the line of sight to the source causes distortions of gravitational wave-signals; such distortions may reveal information about fundamental physics, cosmology and astrophysics. In this work, we have extended the search for lensing signatures to all binary black hole events from the third observing run of the LIGO--Virgo network. We search for repeated signals from strong lensing by 1) performing targeted searches for subthreshold signals, 2) calculating the degree of overlap amongst the intrinsic parameters and sky location of pairs of signals, 3) comparing the similarities of the spectrograms amongst pairs of signals, and 4) performing dual-signal Bayesian analysis that takes into account selection effects and astrophysical knowledge. We also search for distortions to the gravitational waveform caused by 1) frequency-independent phase shifts in strongly lensed images, and 2) frequency-dependent modulation of the amplitude and phase due to point masses. None of these searches yields significant evidence for lensing. Finally, we use the non-detection of gravitational-wave lensing to constrain the lensing rate based on the latest merger-rate estimates and the fraction of dark matter composed of compact objects

Ultralight vector dark matter search using data from the KAGRA O3GK run

Among the various candidates for dark matter (DM), ultralight vector DM can be probed by laser interferometric gravitational wave detectors through the measurement of oscillating length changes in the arm cavities. In this context, KAGRA has a unique feature due to differing compositions of its mirrors, enhancing the signal of vector DM in the length change in the auxiliary channels. Here we present the result of a search for U(1)B−L gauge boson DM using the KAGRA data from auxiliary length channels during the first joint observation run together with GEO600. By applying our search pipeline, which takes into account the stochastic nature of ultralight DM, upper bounds on the coupling strength between the U(1)B−L gauge boson and ordinary matter are obtained for a range of DM masses. While our constraints are less stringent than those derived from previous experiments, this study demonstrates the applicability of our method to the lower-mass vector DM search, which is made difficult in this measurement by the short observation time compared to the auto-correlation time scale of DM