Depression, a leading cause of global disability, arises from a multifaceted combination of genetic and environmental components. This study explores the relationship between major depressive disorder (MDD) polygenic scores (PGS), characteristics and symptoms of depression, and community-shared socioeconomic factors derived from postal code data in a cohort of 12,646 individuals from the Australian Genetics of Depression Study (AGDS). Our findings reveal that people living in areas with relatively higher socioeconomic advantages and education/occupation scores are more likely to report experiencing fewer depressive symptoms during their worst depressive period, as well as fewer number of lifetime episodes. Additionally, participants who reported depression onset later in life tend to currently reside in wealthier areas. Interestingly, no significant interaction between genetic and socioeconomic factors was observed, suggesting their independent contribution to depression outcomes. This research underscores the importance of integrating socioeconomic factors into psychiatric evaluation and care, and points to the critical role of public policy in addressing mental health disparities driven by socioeconomic factors. Future research should aim to further elucidate the causal relationships within these associations and explore the potential for integrated genetic and socioeconomic approaches in mental health interventions.

The stars of the Milky Way carry the chemical history of our Galaxy in their atmospheres as they journey through its vast expanse. Like barcodes, we can extract the chemical fingerprints of stars from high-resolution spectroscopy. The fourth data release (DR4) of the Galactic Archaeology with HERMES (GALAH) Survey, based on a decade of observations, provides the chemical abundances of up to 32 elements for 917 588 stars that also have exquisite astrometric data from the Gaia satellite. For the first time, these elements include life-essential nitrogen to complement carbon, and oxygen as well as more measurements of rare-earth elements critical to modern-life electronics, offering unparalleled insights into the chemical composition of the Milky Way. For this release, we use neural networks to simultaneously fit stellar parameters and abundances across the whole wavelength range, leveraging synthetic grids computed with Spectroscopy Made Easy. These grids account for atomic line formation in non-local thermodynamic equilibrium for 14 elements. In a two-iteration process, we first fit stellar labels to all 1 085 520 spectra, then co-add repeated observations and refine these labels using astrometric data from Gaia and 2MASS photometry, improving the accuracy and precision of stellar parameters and abundances. Our validation thoroughly assesses the reliability of spectroscopic measurements and highlights key caveats. GALAH DR4 represents yet another milestone in Galactic archaeology, combining detailed chemical compositions from multiple nucleosynthetic channels with kinematic information and age estimates. The resulting dataset, covering nearly a million stars, opens new avenues for understanding not only the chemical and dynamical history of the Milky Way but also the broader questions of the origin of elements and the evolution of planets, stars, and galaxies.

The Lyman alpha (Ly$\alpha$) forest in the spectra of $z\gt5$ quasars provides a powerful probe of the late stages of the epoch of reionisation (EoR). With the recent advent of exquisite datasets such as XQR-30, many models have struggled to reproduce the observed large-scale fluctuations in the Ly$\alpha$ opacity. Here we introduce a Bayesian analysis framework that forward-models large-scale lightcones of intergalactic medium (IGM) properties and accounts for unresolved sub-structure in the Ly$\alpha$ opacity by calibrating to higher-resolution hydrodynamic simulations. Our models directly connect physically intuitive galaxy properties with the corresponding IGM evolution, without having to tune ‘effective’ parameters or calibrate out the mean transmission. The forest data, in combination with UV luminosity functions and the CMB optical depth, are able to constrain global IGM properties at percent level precision in our fiducial model. Unlike many other works, we recover the forest observations without invoking a rapid drop in the ionising emissivity from $z\sim7$ to 5.5, which we attribute to our sub-grid model for recombinations. In this fiducial model, reionisation ends at $z=5.44\pm0.02$ and the EoR mid-point is at $z=7.7\pm0.1$. The ionising escape fraction increases towards faint galaxies, showing a mild redshift evolution at fixed UV magnitude, $M_\textrm{UV}$. Half of the ionising photons are provided by galaxies fainter than $M_\textrm{UV} \sim -12$, well below direct detection limits of optical/NIR instruments including $\textit{ JWST}$. We also show results from an alternative galaxy model that does not allow for a redshift evolution in the ionising escape fraction. Despite being decisively disfavoured by the Bayesian evidence, the posterior of this model is in qualitative agreement with that from our fiducial model. We caution, however, that our conclusions regarding the early stages of the EoR and which sources reionised the Universe are more model-dependent.

In many areas of The Gambia, West Africa, population crowding in a degraded environment has forced close interactions of diurnal primate species with humans. We assessed intestinal parasitic infection prevalence and diversity in 4 diurnal non-human primate (NHP) species, Chlorocebus sabaeus, Erythrocebus patas, Papio papio and Piliocolobus badius across 13 sampling sites. The effect of human activity, determined by the human activity index, and NHP group size on parasite richness was assessed using a generalized linear mixed model (GLMM). The most common protozoa identified were Entamoeba coli (30%) and Iodamoeba buetschlii (25%). The most common helminths were Strongyloides fuelleborni (11%), Oesophagostomum spp. (9%) and Trichuris trichiura (9%). Two of six (6%) Cyclospora spp. infections detected sequenced as Cyclospora cercopitheci (both in C. sabaeus). The more arboreal P. badius trended towards a lower prevalence of intestinal parasites, although this was not statistically significant (χ2 P = 0.105). Human activity or group size did not have any significant effect on parasite richness for P. badius (P = 0.161 and P = 0.603) or P. papio (P = 0.817 and P = 0.607, respectively). There were insufficient observations to fit a GLMM to E. patas or C. sabaeus. Our reports present the richness and diversity of intestinal parasites in 4 diurnal NHPs in The Gambia, West Africa. Despite desertification and habitat loss, our results indicate that the prevalence and diversity of intestinal parasites in Gambian NHPs are seemingly unaffected by human activity. Further investigation with a larger dataset is required to better elucidate these findings.

Research is only beginning to shape our understanding of eating disorders as metabolic-psychiatric illnesses. How eating disorders (EDs) are classified is essential to future research for understanding the etiology of these severe illnesses and both developing and tailoring effective treatments. The gold standard for classification for research and diagnostic purposes has primarily been and continues to be the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). With the reconceptualization of EDs comes new challenges of considering how EDs are classified to reflect clinical reality, prognosis and lived experience. In this article, we explore the DSM-5 method of categorical classification and how it may not accurately represent the fluidity in which EDs present themselves. We discuss alternative methods of conceptualizing EDs, and their relevance and implications for genetic research.

Foliar-applied postemergence applications of glufosinate are often applied to glufosinate-resistant crops to provide nonselective weed control without significant crop injury. Rainfall, air temperature, solar radiation, and relative humidity near the time of application have been reported to affect glufosinate efficacy. However, previous research may have not captured the full range of weather variability to which glufosinate may be exposed before or following application. Additionally, climate models suggest more extreme weather will become the norm, further expanding the weather range to which glufosinate can be exposed. The objective of this research was to quantify the probability of successful weed control (efficacy ≥85%) with glufosinate applied to some key weed species across a broad range of weather conditions. A database of >10,000 North American herbicide evaluation trials was used in this study. The database was filtered to include treatments with a single postemergence application of glufosinate applied to waterhemp [Amaranthus tuberculatus (Moq.) Sauer], morningglory species (Ipomoea spp.), and/or giant foxtail (Setaria faberi Herrm.) <15 cm in height. These species were chosen because they are well represented in the database and listed as common and troublesome weed species in both corn (Zea mays L.) and soybean [Glycine max (L.) Merr.] (Van Wychen 2020, 2022). Individual random forest models were created. Low rainfall (≤20 mm) over the 5 d before glufosinate application was detrimental to the probability of successful control of A. tuberculatus and S. faberi. Lower relative humidity (≤70%) and solar radiation (≤23 MJ m−1 d−1) on the day of application reduced the probability of successful weed control in most cases. Additionally, the probability of successful control decreased for all species when average air temperature over the first 5 d after application was ≤25 C. As climate continues to change and become more variable, the risk of unacceptable control of several common species with glufosinate is likely to increase.

Recent advancements in Earth system science have been marked by the exponential increase in the availability of diverse, multivariate datasets characterised by moderate to high spatio-temporal resolutions. Earth System Data Cubes (ESDCs) have emerged as one suitable solution for transforming this flood of data into a simple yet robust data structure. ESDCs achieve this by organising data into an analysis-ready format aligned with a spatio-temporal grid, facilitating user-friendly analysis and diminishing the need for extensive technical data processing knowledge. Despite these significant benefits, the completion of the entire ESDC life cycle remains a challenging task. Obstacles are not only of a technical nature but also relate to domain-specific problems in Earth system research. There exist barriers to realising the full potential of data collections in light of novel cloud-based technologies, particularly in curating data tailored for specific application domains. These include transforming data to conform to a spatio-temporal grid with minimum distortions and managing complexities such as spatio-temporal autocorrelation issues. Addressing these challenges is pivotal for the effective application of Artificial Intelligence (AI) approaches. Furthermore, adhering to open science principles for data dissemination, reproducibility, visualisation, and reuse is crucial for fostering sustainable research. Overcoming these challenges offers a substantial opportunity to advance data-driven Earth system research, unlocking the full potential of an integrated, multidimensional view of Earth system processes. This is particularly true when such research is coupled with innovative research paradigms and technological progress.

Despite societal perceptions of older adults as vulnerable, literature on resilience suggests that exposure to adversity and resources gained with life experience contribute to adaptation. One way to explore the nature of resilience is to document assets supporting adaptation. Interviews were conducted with older adults living in Canada at two time points during the COVID-19 pandemic, September 2020–May 2021 (T1) and January–August 2022 (T2). Reflexive thematic analysis was completed to report on what older adults identified as assets and how they understood the value of those assets for resilience. Participants indicated that the potential value of their contributions went largely untapped at the level of the community but supported individual and household adaptation. In line with calls for an all-of-society approach to reduce disaster risk and support resilience, creating a culture of inclusivity that recognizes the potential contributions of older adults should be paired with opportunities for action.

Foliar-applied postemergence herbicides are a critical component of corn (Zea mays L.) and soybean [Glycine max (L.) Merr.] weed management programs in North America. Rainfall and air temperature around the time of application may affect the efficacy of herbicides applied postemergence in corn or soybean production fields. However, previous research utilized a limited number of site-years and may not capture the range of rainfall and air temperatures that these herbicides are exposed to throughout North America. The objective of this research was to model the probability of achieving successful weed control (≥85%) with commonly applied postemergence herbicides across a broad range of environments. A large database of more than 10,000 individual herbicide evaluation field trials conducted throughout North America was used in this study. The database was filtered to include only trials with a single postemergence application of fomesafen, glyphosate, mesotrione, or fomesafen + glyphosate. Waterhemp [Amaranthus tuberculatus (Moq.) Sauer], morningglory species (Ipomoea spp.), and giant foxtail (Setaria faberi Herrm.) were the weeds of focus. Separate random forest models were created for each weed species by herbicide combination. The probability of successful weed control deteriorated when the average air temperature within the first 10 d after application was <19 or >25 C for most of the herbicide by weed species models. Additionally, drier conditions before postemergence herbicide application reduced the probability of successful control for several of the herbicide by weed species models. As air temperatures increase and rainfall becomes more variable, weed control with many of the commonly used postemergence herbicides is likely to become less reliable.

Ice rises hold valuable records revealing the ice dynamics and climatic history of Antarctic coastal areas from the Last Glacial Maximum to today. This history is often reconstructed from isochrone radar stratigraphy and simulations focusing on Raymond arch evolution beneath the divides. However, this relies on complex ice-flow models where many parameters are unconstrained by observations. Our study explores quad-polarimetric, phase-coherent radar data to enhance understanding near ice divides and domes, using Hammarryggen Ice Rise (HIR) as a case study. Analysing a 5 km profile intersecting the dome, we derive vertical strain rates and ice-fabric properties. These align with ice core data near the summit, increasing confidence in tracing signatures from the dome to the flanks. The Raymond effect is evident, correlating with surface strain rates and radar stratigraphy. Stability is inferred over millennia for the saddle connecting HIR to the mainland, but dome ice-fabric appears relatively young compared to 2D model predictions. In a broader context, quad-polarimetric measurements provide valuable insights into ice-flow models, particularly for anisotropic rheology. Including quad-polarimetric data advances our ability to reconstruct past ice flow dynamics and climatic history in ice rises.