The Murchison Widefield Array (MWA) has observed the entire southern sky (Declination, $\delta< 30^{\circ}$ ) at low radio frequencies, over the range 72–231MHz. These observations constitute the GaLactic and Extragalactic All-sky MWA (GLEAM) Survey, and we use the extragalactic catalogue (EGC) (Galactic latitude, $|b| >10^{\circ}$ ) to define the GLEAM 4-Jy (G4Jy) Sample. This is a complete sample of the ‘brightest’ radio sources ( $S_{\textrm{151\,MHz}}>4\,\text{Jy}$ ), the majority of which are active galactic nuclei with powerful radio jets. Crucially, low-frequency observations allow the selection of such sources in an orientation-independent way (i.e. minimising the bias caused by Doppler boosting, inherent in high-frequency surveys). We then use higher-resolution radio images, and information at other wavelengths, to morphologically classify the brightest components in GLEAM. We also conduct cross-checks against the literature and perform internal matching, in order to improve sample completeness (which is estimated to be $>95.5$ %). This results in a catalogue of 1863 sources, making the G4Jy Sample over 10 times larger than that of the revised Third Cambridge Catalogue of Radio Sources (3CRR; $S_{\textrm{178\,MHz}}>10.9\,\text{Jy}$ ). Of these G4Jy sources, 78 are resolved by the MWA (Phase-I) synthesised beam ( $\sim2$ arcmin at 200MHz), and we label 67% of the sample as ‘single’, 26% as ‘double’, 4% as ‘triple’, and 3% as having ‘complex’ morphology at $\sim1\,\text{GHz}$ (45 arcsec resolution). We characterise the spectral behaviour of these objects in the radio and find that the median spectral index is $\alpha=-0.740 \pm 0.012$ between 151 and 843MHz, and $\alpha=-0.786 \pm 0.006$ between 151MHz and 1400MHz (assuming a power-law description, $S_{\nu} \propto \nu^{\alpha}$ ), compared to $\alpha=-0.829 \pm 0.006$ within the GLEAM band. Alongside this, our value-added catalogue provides mid-infrared source associations (subject to 6” resolution at 3.4 $\mu$ m) for the radio emission, as identified through visual inspection and thorough checks against the literature. As such, the G4Jy Sample can be used as a reliable training set for cross-identification via machine-learning algorithms. We also estimate the angular size of the sources, based on their associated components at $\sim1\,\text{GHz}$ , and perform a flux density comparison for 67 G4Jy sources that overlap with 3CRR. Analysis of multi-wavelength data, and spectral curvature between 72MHz and 20GHz, will be presented in subsequent papers, and details for accessing all G4Jy overlays are provided at https://github.com/svw26/G4Jy.

The entire southern sky (Declination, $\delta< 30^{\circ}$ ) has been observed using the Murchison Widefield Array (MWA), which provides radio imaging of $\sim$ 2 arcmin resolution at low frequencies (72–231 MHz). This is the GaLactic and Extragalactic All-sky MWA (GLEAM) Survey, and we have previously used a combination of visual inspection, cross-checks against the literature, and internal matching to identify the ‘brightest’ radio-sources ( $S_{\mathrm{151\,MHz}}>4$ Jy) in the extragalactic catalogue (Galactic latitude, $|b| >10^{\circ}$ ). We refer to these 1 863 sources as the GLEAM 4-Jy (G4Jy) Sample, and use radio images (of ${\leq}45$ arcsec resolution), and multi-wavelength information, to assess their morphology and identify the galaxy that is hosting the radio emission (where appropriate). Details of how to access all of the overlays used for this work are available at https://github.com/svw26/G4Jy. Alongside this we conduct further checks against the literature, which we document here for individual sources. Whilst the vast majority of the G4Jy Sample are active galactic nuclei with powerful radio-jets, we highlight that it also contains a nebula, two nearby, star-forming galaxies, a cluster relic, and a cluster halo. There are also three extended sources for which we are unable to infer the mechanism that gives rise to the low-frequency emission. In the G4Jy catalogue we provide mid-infrared identifications for 86% of the sources, and flag the remainder as: having an uncertain identification (129 sources), having a faint/uncharacterised mid-infrared host (126 sources), or it being inappropriate to specify a host (2 sources). For the subset of 129 sources, there is ambiguity concerning candidate host-galaxies, and this includes four sources (B0424–728, B0703–451, 3C 198, and 3C 403.1) where we question the existing identification.

Development of immunocontraceptives for wild rabbit populations requires selection of both effective antigens and effective delivery systems. Recombinant rabbit zona pellucida glycoprotein B (ZPB) produced in eukaryotic cells in vitro was an effective antigen and induced sustained infertility in 70% of female rabbits. This required two boosts and serum antibody titers of 12 800 or greater. Antibody titers in females were low after the initial immunization, as might be expected with a self-antigen; however, male rabbits had a strong antibody response, indicating that the protein was immunologically foreign. To develop a delivery system, ZPB was delivered by infection with a recombinant myxoma virus. In contrast to the results with ZPB protein, infection of rabbits induced a similar serum antibody response to ZPB in both sexes. This indicated that presentation of ZPB in the context of a virus infection was able to overcome tolerance in females. However, the antibody titers were lower than 12 800, and only 25% of female rabbits were infertile. This antibody response was boosted by injections of recombinant ZPB protein, after which 80% of female rabbits were infertile. Infertility was associated with antibody binding to zonae and varying degrees of ovarian pathology characterized by follicular degeneration and substantial depletion of primordial follicles. Oocyte and follicular degeneration appeared to be the principal mechanism of infertility and may be primarily induced by antibodies to ZPB.

We present a detailed analysis of the chemistry and kinematics of red giants in the dwarf irregular galaxy NGC 6822. Spectroscopy at « 8500Å was acquired for 72 red giant stars across two fields using FORS2 at the VLT. Line of sight extinction was individually estimated for each target star to accommodate the variable reddening across NGC 6822. The mean radial velocity was found to be xv rad y "´52.8˘2.2 km s´1 with dispersion σ v " 24.1 km s´1, in agreement with other studies. Ca ii triplet equivalent widths were converted into [Fe/H] metallicities using a V magnitude proxy for surface gravity. The average metallicity was x[Fe/H]y "´0.84˘0.04 with dispersion σ " 0.31 dex and interquartile range 0.48. Our assignment of individual reddening values makes our analysis more sensitive to spatial variations in metallicity than previous studies. We divide our sample into metal rich and metal poor stars; the former were found to cluster towards small radii with the metal poor stars more evenly distributed across the galaxy. The velocity dispersion of the metal poor stars was found to be higher than that of the metal rich stars (σ vMP " 27.4 km s´1; σ vMR " 21.1 km s´1); combined with the age-metallicity relation this indicates that the older populations have either been dynamically heated during their lifetimes or were born in a less disklike distribution than the younger stars.. The low ratio v rot {σ v suggests that within the inner 10 1 , NGC 6822's stars are dynamically decoupled from the H I gas, and possibly distributed in a thick disc or spheroid structure.

The volume of data that will be produced by the next generation of astrophysical instruments represents a significant opportunity for making unplanned and unexpected discoveries. Conversely, finding unexpected objects or phenomena within such large volumes of data presents a challenge that may best be solved using computational and statistical approaches. We present the application of a coarse-grained complexity measure for identifying interesting observations in large astronomical datasets. This measure, which has been termed apparent complexity, has been shown to model human intuition and perceptions of complexity. Apparent complexity is computationally efficient to derive and can be used to segment and identify interesting observations in very large datasets based on their morphological complexity. We show using data from the Australia Telescope Large Area Survey (ATLAS) that the apparent complexity can be combined with clustering methods to provide an automated process for distinguishing between images of galaxies which have been classified as having simple and complex morphologies. The approach generalises well when applied to new data after being calibrated on a smaller dataset, where it performs better than tested classification methods using pixel data. This generalisability positions apparent complexity as a suitable machine learning feature for identifying complex observations with unanticipated features.