Translational research needs to show value through impact on measures that matter to the public, including health and societal benefits. To this end, the Translational Science Benefits Model (TSBM) identified four categories of impact: Clinical, Community, Economic, and Policy. However, TSBM offers limited guidance on how these areas of impact relate to equity. Central to the structure of our Center for American Indian and Alaska Native Diabetes Translation Research are seven regional, independent Satellite Centers dedicated to community-engaged research. Drawing on our collective experience, we provide empirical evidence about how TSBM applies to equity-focused research that centers community partnerships and recognizes Indigenous knowledge. For this special issue – “Advancing Understanding and Use of Impact Measures in Implementation Science” – our objective is to describe and critically evaluate gaps in the fit of TSBM as an evaluation approach with sensitivity to health equity issues. Accordingly, we suggest refinements to the original TSBM Logic model to add: 1) community representation as an indicator of providing community partners “a seat at the table” across the research life cycle to generate solutions (innovations) that influence equity and to prioritize what to evaluate, and 2) assessments of the representativeness of the measured outcomes and benefits.

Behavioural treatments are recommended first-line for insomnia, but long-term benzodiazepine receptor agonist (BZRA) use remains common and engaging patients in a deprescribing consultation is challenging. Few deprescribing interventions directly target patients. Prescribers’ support of patient-targeted interventions may facilitate their uptake. Recently assessed in the Your Answers When Needing Sleep in New Brunswick (YAWNS NB) study, Sleepwell (mysleepwell.ca) was developed as a direct-to-patient behaviour change intervention promoting BZRA deprescribing and non-pharmacological insomnia management. BZRA prescribers of YAWNS NB participants were invited to complete an online survey assessing the acceptability of Sleepwell as a direct-to-patient intervention. The survey was developed using the seven construct components of the theoretical framework of acceptability (TFA) framework. Respondents (40/250, 17.2%) indicated high acceptability, with positive responses per TFA construct averaging 32.3/40 (80.7%). Perceived as an ethical, credible, and useful tool, Sleepwell also promoted prescriber–patient BZRA deprescribing engagements (11/19, 58%). Prescribers were accepting of Sleepwell and supported its application as a direct-to-patient intervention.

Odd Radio Circles (ORCs) are a class of low surface brightness, circular objects approximately one arcminute in diameter. ORCs were recently discovered in the Australian Square Kilometre Array Pathfinder (ASKAP) data and subsequently confirmed with follow-up observations on other instruments, yet their origins remain uncertain. In this paper, we suggest that ORCs could be remnant lobes of powerful radio galaxies, re-energised by the passage of a shock. Using relativistic hydrodynamic simulations with synchrotron emission calculated in post-processing, we show that buoyant evolution of remnant radio lobes is alone too slow to produce the observed ORC morphology. However, the passage of a shock can produce both filled and edge-brightnened ORC-like morphologies for a wide variety of shock and observing orientations. Circular ORCs are predicted to have host galaxies near the geometric centre of the radio emission, consistent with observations of these objects. Significantly offset hosts are possible for elliptical ORCs, potentially causing challenges for accurate host galaxy identification. Observed ORC number counts are broadly consistent with a paradigm in which moderately powerful radio galaxies are their progenitors.

Adsorption of uranyl to SWy-1 montmorillonite was evaluated experimentally and results were modeled to identify likely surface complexation reactions responsible for removal of uranyl from solution. Uranyl was contacted with SWy-1 montmorillonite in a NaCIO4 electrolyte solution at three ionic strengths (I = 0.001, 0.01, 0.1), at pH 4 to 8.5, in a N2(g) atmosphere. At low ionic strength, adsorption decreased from 95% at pH 4 to 75% at pH 6.8. At higher ionic strength, adsorption increased with pH from initial values less than 75%; adsorption edges for all ionic strengths coalesced above a pH of 7. A site-binding model was applied that treated SWy-1 as an aggregate of fixed-charge sites and edge sites analogous to gibbsite and silica. The concentration of fixed-charge sites was estimated as the cation exchange capacity, and non-preference exchange was assumed in calculating the contribution of fixed-charge sites to total uranyl adsorption. The concentration of edge sites was estimated by image analysis of transmission electron photomicrographs. Adsorption constants for uranyl binding to gibbsite and silica were determined by fitting to experimental data, and these adsorption constants were then used to simulate SWy-1 adsorption results. The best simulations were obtained with an ionization model in which AlOH2+ was the dominant aluminol surface species throughout the experimental range in pH. The pH-dependent aqueous speciation of uranyl was an important factor determining the magnitude of uranyl adsorption. At low ionic strength and low pH, adsorption by fixed-charge sites was predominant. The decrease in adsorption with increasing pH was caused by the formation of monovalent aqueous uranyl species, which were weakly bound to fixed-charge sites. At higher ionic strengths, competition with Na+ decreased the adsorption of UO22+ to fixed-charge sites. At higher pH, the most significant adsorption reactions were the binding of UO22+ to AlOH and of (UO2)3(OH)5+ to SiOH edge sites. Near-saturation of AlOH sites by UO22+ allowed significant contributions of SiOH sites to uranyl adsorption.

To investigate the symptoms of SARS-CoV-2 infection, their dynamics and their discriminatory power for the disease using longitudinally, prospectively collected information reported at the time of their occurrence. We have analysed data from a large phase 3 clinical UK COVID-19 vaccine trial. The alpha variant was the predominant strain. Participants were assessed for SARS-CoV-2 infection via nasal/throat PCR at recruitment, vaccination appointments, and when symptomatic. Statistical techniques were implemented to infer estimates representative of the UK population, accounting for multiple symptomatic episodes associated with one individual. An optimal diagnostic model for SARS-CoV-2 infection was derived. The 4-month prevalence of SARS-CoV-2 was 2.1%; increasing to 19.4% (16.0%–22.7%) in participants reporting loss of appetite and 31.9% (27.1%–36.8%) in those with anosmia/ageusia. The model identified anosmia and/or ageusia, fever, congestion, and cough to be significantly associated with SARS-CoV-2 infection. Symptoms’ dynamics were vastly different in the two groups; after a slow start peaking later and lasting longer in PCR+ participants, whilst exhibiting a consistent decline in PCR- participants, with, on average, fewer than 3 days of symptoms reported. Anosmia/ageusia peaked late in confirmed SARS-CoV-2 infection (day 12), indicating a low discrimination power for early disease diagnosis.

Environmental sensors are crucial for monitoring weather conditions and the impacts of climate change. However, it is challenging to place sensors in a way that maximises the informativeness of their measurements, particularly in remote regions like Antarctica. Probabilistic machine learning models can suggest informative sensor placements by finding sites that maximally reduce prediction uncertainty. Gaussian process (GP) models are widely used for this purpose, but they struggle with capturing complex non-stationary behaviour and scaling to large datasets. This paper proposes using a convolutional Gaussian neural process (ConvGNP) to address these issues. A ConvGNP uses neural networks to parameterise a joint Gaussian distribution at arbitrary target locations, enabling flexibility and scalability. Using simulated surface air temperature anomaly over Antarctica as training data, the ConvGNP learns spatial and seasonal non-stationarities, outperforming a non-stationary GP baseline. In a simulated sensor placement experiment, the ConvGNP better predicts the performance boost obtained from new observations than GP baselines, leading to more informative sensor placements. We contrast our approach with physics-based sensor placement methods and propose future steps towards an operational sensor placement recommendation system. Our work could help to realise environmental digital twins that actively direct measurement sampling to improve the digital representation of reality.

Far from being a monolithic approach to psychotherapy, cognitive behavioural therapy (CBT) is in fact an umbrella term to describe a family of psychological therapies that share many common features but also have nuanced differences. Of the CBTs, two are often conflated under the ‘CBT’ moniker, namely cognitive therapy (CT) and rational emotive behaviour therapy (REBT). In this article, we explore some of the key differences and similarities between CT and REBT, touching on philosophy, practical implementation, and literature. We provide a brief hypothetical case study to demonstrate the different ways a therapist using CT and REBT might tackle the same client problem. We do not declare either approach superior, but suggest each might have their advantages in certain contexts and acknowledge that skilful practitioners could, and often do, integrate both approaches. As CBT continues to evolve and move into new areas, it is important that psychology practitioners and researchers are clear about which specific approach to CBT they are delivering, measuring and/or reporting on.

Oil palm is one of Southeast Asia’s most common crops, and its expansion has caused substantial modification of natural habitats and put increasing pressure on biodiversity. Rising global demand for vegetable oil, coupled with oil palm’s high yield per unit area and the versatility of the palm oil product, has driven the expansion of oil palm agriculture in the region. Therefore, it is critical to identify management practices that can support biodiversity in plantations without exacerbating negative impacts on the environment. This study focuses on day-flying Lepidoptera (butterflies and moths), which contribute to the ecosystem functioning as pollinators, prey, and herbivore species. We assessed whether density and behaviours of day-flying Lepidoptera varied between different habitats within oil palm plantations and across seasons. We surveyed the density and behaviours of Lepidoptera communities in mature industrial oil palm plantations within the Biodiversity and Ecosystem Function in Tropical Agriculture (BEFTA) Programme sites, in Riau, Indonesia. We surveyed two distinct habitats within the plantations in March and September 2013: Edge habitats, which were bordered by plantation roads on one side, and Core habitats in the centre of oil palm planting blocks. We conducted analyses on the effect of habitat type and season on both the overall density and behaviour of Lepidoptera communities and, independently, on the most common species. In our surveys, we observed 1464 individuals across 41 species, with a significantly higher density in Edge than in Core habitats. While there was no significant difference between overall density in March and September surveys, there was an interaction between season and habitat, with density increasing more markedly in Edge than Core areas in September. There was also a significant effect of habitat and season on behavioural time budget for the community as a whole, with more active behaviours, such as foraging and mating, being recorded more frequently in Edge than Core habitats, and more commonly in September than March. The effect of habitat type, season, and their interaction differed between the six most common species. Our findings indicate that Lepidoptera abundance is affected by habitat characteristics in a plantation and can therefore be influenced by plantation management practices. In particular, our study highlights the value of road edges and paths in plantations for day-flying Lepidoptera. We suggest that increased non-crop vegetation in these areas, achieved through reduced clearing practices or planting of flowering plants, could foster abundant and active butterfly communities in plantations. These practices could form part of sustainability management recommendations for oil palm, such as those of the Roundtable on Sustainable Palm Oil.

Increasing litter size has long been a goal of pig breeders and producers, and may have implications for pig (Sus scrofa domesticus) welfare. This paper reviews the scientific evidence on biological factors affecting sow and piglet welfare in relation to large litter size. It is concluded that, in a number of ways, large litter size is a risk factor for decreased animal welfare in pig production. Increased litter size is associated with increased piglet mortality, which is likely to be associated with significant negative animal welfare impacts. In surviving piglets, many of the causes of mortality can also occur in non-lethal forms that cause suffering. Intense teat competition may increase the likelihood that some piglets do not gain adequate access to milk, causing starvation in the short term and possibly long-term detriments to health. Also, increased litter size leads to more piglets with low birth weight which is associated with a variety of negative long-term effects. Finally, increased production pressure placed on sows bearing large litters may produce health and welfare concerns for the sow. However, possible biological approaches to mitigating health and welfare issues associated with large litters are being implemented. An important mitigation strategy is genetic selection encompassing traits that promote piglet survival, vitality and growth. Sow nutrition and the minimisation of stress during gestation could also contribute to improving outcomes in terms of piglet welfare. Awareness of the possible negative welfare consequences of large litter size in pigs should lead to further active measures being taken to mitigate the mentioned effects.

Increasing litter size has long been a goal of pig (Sus scrofa domesticus) breeders and producers in many countries. Whilst this has economic and environmental benefits for the pig industry, there are also implications for pig welfare. Certain management interventions are used when litter size routinely exceeds the ability of individual sows to successfully rear all the piglets (ie viable piglets outnumber functional teats). Such interventions include: tooth reduction; split suckling; cross-fostering; use of nurse sow systems and early weaning, including split weaning; and use of artificial rearing systems. These practices raise welfare questions for both the piglets and sow and are described and discussed in this review. In addition, possible management approaches which might mitigate health and welfare issues associated with large litters are identified. These include early intervention to provide increased care for vulnerable neonates and improvements to farrowing accommodation to mitigate negative effects, particularly for nurse sows. An important concept is that management at all stages of the reproductive cycle, not simply in the farrowing accommodation, can impact on piglet outcomes. For example, poor stockhandling at earlier stages of the reproductive cycle can create fearful animals with increased likelihood of showing poor maternal behaviour. Benefits of good sow and litter management, including positive human-animal relationships, are discussed. Such practices apply to all production situations, not just those involving large litters. However, given that interventions for large litters involve increased handling of piglets and increased interaction with sows, there are likely to be even greater benefits for management of hyper-prolific herds.

Surgical castration is a painful procedure that is routinely performed without pain relief on commercial pig (Sus scrofa domesticus) farms. Previous research has focused on quantifying piglet pain response through behaviours. However, to date, behavioural sampling methodologies used to quantify pain associated with castration have not been validated. Therefore, the objective of this study was to validate scan sampling methodologies (2-min, 3-min, 5-min, 10-min and 15-min intervals) to quantify piglet pain responses expressed by castrated piglets’ behaviour. A total of 39 Yorkshire-Landrace × Duroc male piglets (five days of age) were surgically castrated using a scalpel blade. Behaviour frequency and duration (scratching, spasms, stiffness, tail wagging and trembling) of each piglet were continuously collected for the first 15 min of the following hours relative to castration (-24, 1-8 and 24). To determine if the sampling interval accurately reflected true duration and frequency for each behaviour, as determined by continuous observation, criteria previously utilised from other behavioural validation studies were used: coefficient of determination above 0.9, slope not statistically different from one and intercept not statistically different from zero. No scan sampling interval provided accurate estimates for any behavioural indicators of pain. The results of this study suggest that continuous sampling is the most appropriate methodology to fully capture behaviour specific to pain associated with castration. Using validated behavioural methodologies in future research can assist in the development of objective, science-based protocols for managing pig pain.

While unobscured and radio-quiet active galactic nuclei are regularly being found at redshifts $z > 6$ , their obscured and radio-loud counterparts remain elusive. We build upon our successful pilot study, presenting a new sample of low-frequency-selected candidate high-redshift radio galaxies (HzRGs) over a sky area 20 times larger. We have refined our selection technique, in which we select sources with curved radio spectra between 72–231 MHz from the GaLactic and Extragalactic All-sky Murchison Widefield Array (GLEAM) survey. In combination with the requirements that our GLEAM-selected HzRG candidates have compact radio morphologies and be undetected in near-infrared $K_{\rm s}$ -band imaging from the Visible and Infrared Survey Telescope for Astronomy Kilo-degree Infrared Galaxy (VIKING) survey, we find 51 new candidate HzRGs over a sky area of approximately $1200\ \mathrm{deg}^2$ . Our sample also includes two sources from the pilot study: the second-most distant radio galaxy currently known, at $z=5.55$ , with another source potentially at $z \sim 8$ . We present our refined selection technique and analyse the properties of the sample. We model the broadband radio spectra between 74 MHz and 9 GHz by supplementing the GLEAM data with both publicly available data and new observations from the Australia Telescope Compact Array at 5.5 and 9 GHz. In addition, deep $K_{\rm s}$ -band imaging from the High-Acuity Widefield K-band Imager (HAWK-I) on the Very Large Telescope and from the Southern Herschel Astrophysical Terahertz Large Area Survey Regions $K_{\rm s}$ -band Survey (SHARKS) is presented for five sources. We discuss the prospects of finding very distant radio galaxies in our sample, potentially within the epoch of reionisation at $z \gtrsim 6.5$ .