We report on a search for gravitational waves from coalescing compact binaries, of total mass between 2 and $35M_{\odot }$, using LIGO observations between November 14, 2006 and May 18, 2007. No gravitational-wave signals were detected. We report upper limits on the rate of compact binary coalescence as a function of total mass. The LIGO cumulative 90%-confidence rate upper limits of the binary coalescence of neutron stars, black holes and black hole-neutron star systems are $1.4\times {10}^{-2}$, $7.3\times {10}^{-4}$ and $3.6\times {10}^{-3}{\mathrm{yr}}^{-1}{\mathrm{L}}_{10}^{-1}$, respectively, where ${\mathrm{L}}_{10}$ is ${10}^{10}$ times the blue solar luminosity.

According to general relativity a perturbed black hole will settle to a stationary configuration by the emission of gravitational radiation. Such a perturbation will occur, for example, in the coalescence of a black hole binary, following their inspiral and subsequent merger. At late times the waveform is a superposition of quasinormal modes, which we refer to as the ringdown. The dominant mode is expected to be the fundamental mode, $l=m=2$. Since this is a well-known waveform, matched filtering can be implemented to search for this signal using LIGO data. We present a search for gravitational waves from black hole ringdowns in the fourth LIGO science run S4, during which LIGO was sensitive to the dominant mode of perturbed black holes with masses in the range of $10M_{\odot }$ to $500M_{\odot }$, the regime of intermediate-mass black holes, to distances up to 300 Mpc. We present a search for gravitational waves from black hole ringdowns using data from S4. No gravitational wave candidates were found; we place a 90%-confidence upper limit on the rate of ringdowns from black holes with mass between $85M_{\odot }$ and $390M_{\odot }$ in the local universe, assuming a uniform distribution of sources, of $3.2\times {10}^{-5}{\mathrm{yr}}^{-1}{\mathrm{Mpc}}^{-3}=1.6\times {10}^{-3}{\mathrm{yr}}^{-1}{L}_{10}^{-1},$where $L_{10}$ is ${10}^{10}$ times the solar blue-light luminosity.

Data from the LIGO Livingston interferometer and the ALLEGRO resonant-bar detector, taken during LIGO’s fourth science run, were examined for cross correlations indicative of a stochastic gravitational-wave background in the frequency range 850–950 Hz, with most of the sensitivity arising between 905 and 925 Hz. ALLEGRO was operated in three different orientations during the experiment to modulate the relative sign of gravitational-wave and environmental correlations. No statistically significant correlations were seen in any of the orientations, and the results were used to set a Bayesian 90% confidence level upper limit of ${\Omega }_{\mathrm{gw}}(f)\le 1.02$, which corresponds to a gravitational-wave strain at 915 Hz of $1.5\times {10}^{-23}{\mathrm{Hz}}^{-1/2}$. In the traditional units of $h_{100}^2{\Omega }_{\mathrm{gw}}(f)$, this is a limit of 0.53, 2 orders of magnitude better than the previous direct limit at these frequencies. The method was also validated with successful extraction of simulated signals injected in hardware and software.

Medical imaging is essential nowadays throughout medical education, research, and care. Accordingly, international efforts have been made to set large-scale image repositories for these purposes. Yet, to date, browsing of large-scale medical image repositories has been troublesome, time-consuming, and generally limited by text search engines. A paradigm shift, by means of a query-by-example search engine, would alleviate these constraints and beneficially impact several practical demands throughout the medical field. The current project aims to address this gap in medical imaging consumption by developing a content-based image retrieval (CBIR) system, which combines two image processing architectures based on deep learning. Furthermore, a first-of-its-kind intelligent visual browser was designed that interactively displays a set of imaging examinations with similar visual content on a similarity map, making it possible to search for and efficiently navigate through a large-scale medical imaging repository, even if it has been set with incomplete and curated metadata. Users may, likewise, provide text keywords, in which case the system performs a content- and metadata-based search. The system was fashioned with an anonymizer service and designed to be fully interoperable according to international standards, to stimulate its integration within electronic healthcare systems and its adoption for medical education, research and care. Professionals of the healthcare sector, by means of a self-administered questionnaire, underscored that this CBIR system and intelligent interactive visual browser would be highly useful for these purposes. Further studies are warranted to complete a comprehensive assessment of the performance of the system through case description and protocolized evaluations by medical imaging specialists.

Promoter identification is a fundamental step in understanding bacterial gene regulation mechanisms. However, accurate and fast classification of bacterial promoters continues to be challenging. New methods based on deep convolutional networks have been applied to identify and classify bacterial promoters recognized by sigma ($\sigma$) factors and RNA polymerase subunits which increase affinity to specific DNA sequences to modulate transcription and respond to nutritional or environmental changes. This work presents a new multiclass promoter prediction model by using convolutional neural networks (CNNs), denoted as PromoterLCNN, which classifies Escherichia coli promoters into subclasses ${\sigma }^{70}$, ${\sigma }^{24}$, ${\sigma }^{32}$, ${\sigma }^{38}$, ${\sigma }^{28}$, and ${\sigma }^{54}$. We present a light, fast, and simple two-stage multiclass CNN architecture for promoter identification and classification. Training and testing were performed on a benchmark dataset, part of RegulonDB. Comparative performance of PromoterLCNN against other CNN-based classifiers using four parameters (Acc, Sn, Sp, MCC) resulted in similar or better performance than those that commonly use cascade architecture, reducing time by approximately 30–90% for training, prediction, and hyperparameter optimization without compromising classification quality.