Zooniverse

Abstract:

The MeerKAT International GigaHertz Tiered Extragalactic Exploration (MIGHTEE) is one of the MeerKAT large survey projects, designed to pathfind SKA key science. MIGHTEE is undertaking deep radio imaging of four well-observed fields (COSMOS, XMM-LSS, ELAIS S1, and CDFS) totaling 20 square degrees to μJy sensitivities. Broad-band imaging observations between 880 and1690 MHz yield total intensity continuum, spectro-polarimetry, and atomic hydrogen spectral imaging. Early science data from MIGHTEE are being released from initial observations of COSMOS and XMM–LSS. This paper describes the spectro-polarimetric observations, the polarization data processing of the MIGHTEE early science fields, and presents polarization data images and catalogues. The catalogues include radio spectral index, redshift information, and Faraday rotation measure synthesis results for 13 267 total intensity radio sources down to a polarized intensity detection limit of ∼20 μJy bm−1. Polarized signals were detected from 324 sources. For the polarized detections, we include a catalogue of Faraday Depth from both Faraday Synthesis and Q, U fitting, as well as total intensity and polarization spectral indices. The distribution of redshift of the total radio sources and detected polarized sources are the same, with median redshifts of 0.86 and 0.82, respectively. Depolarization of the emission at longer-wavelengths is seen to increase with decreasing total-intensity spectral index, implying that depolarization is intrinsic to the radio sources. No evidence is seen for a redshift dependence of the variance of Faraday depth.

Abstract:

Respiratory failure, malnutrition, aspiration pneumonia and dehydration contribute to mortality in ALS / MND, and loss of verbal communication impacts quality of life. There are few interventions that help with the management of these symptoms alongside pharmacological ones. Neurologic music therapy protocols, which are biomedical interventions, have been demonstrated to be effective for the treatment of human neurodegenerative disorders, but less so with ALS.
Two case studies from a larger, published ALS study were selected for this new report to provide an insight into the practical aspects of music therapy treatment. The home-based protocol was designed to sustain bulbar and respiratory functions of persons with early and mid-stage onset. It was delivered to all participants twice-weekly for six weeks as a part of a 16-week ABA mixed methods study. Feasibility data (recruitment, retention, adherence, tolerability, self-motivation, personal impressions) and 34 biomedical outcome parameters for bulbar and respiratory changes were collected. The two studies highlight the differences in therapy process between participants – one with a spinal onset, slow progression ALS and another with a bulbar onset, rapid progression.
In both cases, music therapy was tolerated well and perceived as pleasant, although moderately challenging. For both participants, developing the sense of agency played an essential role in the therapy process. Minor treatment protocol modifications were needed. Positive changes in the objective measures of respiration, cough, speech and swallowing were observed.
Suggested individual adaptations of the experimental music therapy protocol included modifications of sitting posture, breathing technique, consonant changes in singing exercises, additional pauses and stretching, and changes to preferred song therapeutic performance. A pilot study utilising the modified protocol is called for, followed by an RCT to assess the clinical effectiveness of the innovative MT treatment.

Abstract:

Modern radio astronomy instruments generate vast amounts of data, and the increasingly challenging radio frequency interference (RFI) environment necessitates ever-more sophisticated RFI rejection algorithms. The ‘needle in a haystack’ nature of searches for transients and technosignatures requires us to develop methods that can determine whether a signal of interest has unique properties, or is a part of some larger set of pernicious RFI. In the past, this vetting has required onerous manual inspection of very large numbers of signals. In this paper, we present a fast and modular deep learning algorithm to search for lookalike signals of interest in radio spectrogram data. First, we trained a β-variational autoencoder on signals returned by an energy detection algorithm. We then adapted a positional embedding layer from classical transformer architecture to a embed additional metadata, which we demonstrate using a frequency-based embedding. Next we used the encoder component of the β-variational autoencoder to extract features from small (∼715 Hz, with a resolution of 2.79 Hz per frequency bin) windows in the radio spectrogram. We used our algorithm to conduct a search for a given query (encoded signal of interest) on a set of signals (encoded features of searched items) to produce the top candidates with similar features. We successfully demonstrate that the algorithm retrieves signals with similar appearance, given only the original radio spectrogram data. This algorithm can be used to improve the efficiency of vetting signals of interest in technosignature searches, but could also be applied to a wider variety of searches for ‘lookalike’ signals in large astronomical data sets.