Dipolar tidal effects in gravitational waves from scalarized black hole binary inspirals in quadratic gravity

Gravitational waves (GWs) from merging binary black holes (BHs) enable unprecedented tests of gravitational theories beyond Einstein's General Relativity (GR) in highly nonlinear, dynamical regimes. Such GW measurements require an accurate description of GW signatures that may arise in alternative gravitational models. In this work, we focus on a class of higher-curvature extensions of GR, the scalar-Gauss-Bonnet theories, where BHs can develop scalar hair. In an inspiraling binary system, this leads to scalar-induced tidal effects in the dynamics and radiation. We calculate the dominant adiabatic dipolar tidal effects via an approximation scheme based on expansions in post-Newtonian, higher-curvature, and tidal corrections. The tidal effects depend on a characteristic scalar Love number, which we compute using BH perturbation theory, and have the same scaling with GW frequency as the higher-curvature corrections. We perform case studies to characterize the net size and parameter dependencies of these effects, showing that at low frequencies, tidal effects dominate over the higher-curvature contributions for small couplings within current bounds, regardless of the total BH mass, while at high frequencies they are subdominant. We further consider prospects observing both of these regimes, which would be interesting for breaking parameter degeneracies, with multiband detections of LISA and ground-based detectors or the Einstein Telescope alone. We also assess the frequency range of the transition between these regimes by numerically solving the energy balance law. Our results highlight the importance of the dipolar scalar tidal effects for BHs with scalar hair, which arise in several beyond-GR paradigms, and provide ready-to-use inputs for improved GW constraints on Gauss-Bonnet theories.Comment: 21 pages, 8 figures, appendice

Imaging agents for mutlimodal in vivo immune cell tracking

The aim of this work was to develop tri-modal cell labels for tracking immune cells $in$ $vivo$, particularly for longitudinal studies of immune cell therapies. Localisation and quantification of the cells is invaluable in determining the effectiveness of these treatments, so imaging agents for fluorescence, $^1$H MRI and $^19$F MRI were combined. Four combinations of agents in a robust scaffold were investigated: luminescent dyes and a $^1$H MRI contrast agent were trapped electrostatically in a silica matrix; luminescent dyes and proton MRI agents were bound to silica and gold nanoparticle surfaces; fluorinated fluorescent dyes were investigated briefly; and all three modalities’ agents were combined by filling and functionalising a silica shell. All of these combinations were characterised and imaged using a clinical 3T MRI scanner. Lead compounds from each section were incubated with white blood cells, examining uptake in monocytes and lymphocytes, with preliminary observations of viability. Detection of labelled cells by their luminescence and MRI contrast enhancement was possible in some cases, though $^19$F MRI still poses some challenges. Detection limits by $^19$FMRI were determined for a range of fluorinated compounds, elucidating the possibilities of translating $^19$F MRI into the clinic with current technology

Cutinase: a new tool for biomodification of synthetic fibers

This paper describes two methods to monitor esterase hydrolysis at the surface of polyester fibres (PETPolyethylene terephthalate). The hydroxyl groups were determined on the fibre surface by alkaline reaction with a reactive dye (CI Reactive Black 5) and colour intensity was determined using a reflectance spectrophotometer. The terephthalic acid solution formed was also quantified after reaction with peroxide by fluorimetric determination of the resulting hydroxyterephthalic acid. Detailed descriptions of those methods are given in this paper

Prospects for detection and application of the alignment of galaxies with the large-scale velocity field

Studies of intrinsic alignment effects mostly focus on the correlations between the shapes of galaxies with each other or with the underlying density field of the large scale structure of the Universe. Lately, the correlation between the shapes of galaxies and the large-scale velocity field has been proposed as an additional probe of the large scale structure. We use a Fisher forecast to make a prediction for the detectability of this velocity-shape correlation with a combination of redshifts and shapes from the 4 MOST +LSST surveys, and radial velocity reconstruction from the Simons Observatory. The signal-to-noise ratio for the velocity-shape (dipole) correlation is 23, relative to 44 for the galaxy density-shape (monopole) correlation and for a maximum wave number of 0.2 Mpc-1 . Increasing the signal-to-noise ratio for higher values of the maximum wave numbers (respectively, 56 and 69, for a maximum wave number of 1 Mpc-1 ) indicate potential gains in the nonlinear regime. Encouraged by these predictions, we discuss two possible applications for the velocity-shape correlation. Measuring the velocity-shape correlation could improve the mitigation of selection effects induced by intrinsic alignments on galaxy clustering. We also find that velocity-shape measurements could potentially aid in determining the scale dependence of intrinsic alignments when multiple shape measurements of the same galaxies are provided

Locating Human Interactions With Discriminatively Trained Deformable Pose+Motion Parts

We model dyadic (two-person) interactions by discriminatively training a spatio-temporal deformable part model of fine-grained human interactions. All interactions involve at most two persons. Our models are capable of localizing human interactions in unsegmented videos, marking the interactions of interest in space and time. Our contributions are as follows: First, we create a model that localizes human interactions in space and time. Second, our models use multiple pose and motion features per part. Third, we experiment with different ways of training our models discriminatively. When testing on the target class our models achieve a mean average precision score of 0.86. Cross dataset tests show that our models generalize well to different environments

Clinical radiography education across Europe

Purpose: To establish a picture of clinical education models within radiography programmes across Europe by surveying higher education institutions registered as affiliate members of the European Federation of Radiography Societies (EFRS). Method: An online survey was developed to ascertain data on: practical training, supervisory arrangements, placement logistics, quality assurance processes, and the assessment of clinical competencies. Responses were identifiable in terms of educational institution and country. All educational institutions who were affiliate members at the time of the study were invited to participate (n=46). Descriptive and thematic analyses are reported. Results: A response rate of 82.6% (n=38) was achieved from educational institutions representing 21 countries. Over half of responding institutions (n=21) allocated in excess of 60 European Credit Transfer and Accumulation System (ECTS) credits to practical training. In nearly three-quarters of clinical placements there was a dedicated clinical practice supervisor in place; two-thirds of these were employed directly by the hospital. Clinical practice supervisors were typically state registered radiographers, who had a number of years of clinical experience and had received specific training for the role. Typical responsibilities included monitoring student progress, providing feedback and completing paperwork, this did however vary between respondents. In almost all institutions there were support systems in place for clinical placement supervisors within their roles. Conclusions: Similarities exist in the provision of clinical radiography education across Europe. Clinical placements are a core component of radiography education and are supported by experienced clinical practice supervisors. Mechanisms are in place for the selection, training and support of clinical practice supervisors. Professional societies should work collaboratively to establish guidelines for effective clinical placements

Lend Me a Hand: Auxiliary Image Data Helps Interaction Detection

In social settings, people interact in close proximity. When analyzing such encounters from video, we are typically interested in distinguishing between a large number of different interactions. Here, we address training deformable part models (DPMs) for the detection of such interactions from video, in both space and time. When we consider a large number of interaction classes, we face two challenges. First, we need to distinguish between interactions that are visually more similar. Second, it becomes more difficult to obtain sufficient specific training examples for each interaction class. In this paper, we address both challenges and focus on the latter. Specifically, we introduce a method to train body part detectors from nonspecific images with pose information. Such resources are widely available. We introduce a training scheme and an adapted DPM formulation to allow for the inclusion of this auxiliary data. We perform cross-dataset experiments to evaluate the generalization performance of our method. We demonstrate that our method can still achieve decent performance, from as few as five training examples

Analyzing time attributes in temporal event sequences

Event data is present in a variety of domains such as electronic health records, daily living activities and web clickstream records. Current visualization methods to explore event data focus on discovering sequential patterns but present limitations when studying time attributes in event sequences. Time attributes are especially important when studying waiting times or lengths of visit in patient flow analysis. We propose a visual analytics methodology that allows the identification of trends and outliers in respect of duration and time of occurrence in event sequences. The proposed method presents event data using a single Sequential and Time Patterns overview. User-driven alignment by multiple events, sorting by sequence similarity and a novel visual encoding of events allows the comparison of time trends across and within sequences. The proposed visualization allows the derivation of findings that otherwise could not be obtained using traditional visualizations. The proposed methodology has been applied to a real-world dataset provided by Sheffield Teaching Hospitals NHS Foundation Trust, for which four classes of conclusions were derived