Surface ozone is an air pollutant that contributes to hundreds of thousands of premature deaths annually. Accurate short-term ozone forecasts may allow improved policy actions to reduce the risk to human health. However, forecasting surface ozone is a difficult problem as its concentrations are controlled by a number of physical and chemical processes that act on varying timescales. We implement a state-of-the-art transformer-based model, the temporal fusion transformer, trained on observational data from three European countries. In four-day forecasts of daily maximum 8-hour ozone (DMA8), our novel approach is highly skillful (MAE = 4.9 ppb, coefficient of determination $ {\mathrm{R}}^2=0.81 $) and generalizes well to data from 13 other European countries unseen during training (MAE = 5.0 ppb, $ {\mathrm{R}}^2=0.78 $). The model outperforms other machine learning models on our data (ridge regression, random forests, and long short-term memory networks) and compares favorably to the performance of other published deep learning architectures tested on different data. Furthermore, we illustrate that the model pays attention to physical variables known to control ozone concentrations and that the attention mechanism allows the model to use the most relevant days of past ozone concentrations to make accurate forecasts on test data. The skillful performance of the model, particularly in generalizing to unseen European countries, suggests that machine learning methods may provide a computationally cheap approach for accurate air quality forecasting across Europe.

We present new numerical solutions for nonlinear standing water waves when the effects of both gravity and surface tension are considered. For small values of the surface tension parameter, solutions are shown to exhibit highly oscillatory capillary waves (parasitic ripples), which are both time- and space-periodic, and which lie on the surface of an underlying gravity-driven standing wave. Our numerical scheme combines a time-dependent conformal mapping together with a shooting method, for which the residual is minimised by Newton iteration. Previous numerical investigations typically clustered gridpoints near the wave crest, and thus lacked the fine detail across the domain required to capture this phenomenon of small-scale parasitic ripples. The amplitude of these ripples is shown to be exponentially small in the zero surface tension limit, and their behaviour is linked to (or explains) the generation of an elaborate bifurcation structure.

We extend the target authors' moral disciplining theory (MDT) by discussing signaling, proscriptive and prescriptive morality, and the dynamics by which signaling may operate in tandem with proscriptive and prescriptive forms of moral disciplining. We also suggest that MDT can help explain challenges to economic and social progress by revealing fundamental tensions between puritanical intuitions and liberal ideals.

The Five Domains model is influential in contemporary studies of animal welfare. It was originally presented as a conceptual model to understand the types of impact that procedures may impose on experimental animals. Its application has since broadened to cover a wide range of animal species and forms of animal use. However, it has also increasingly been applied as an animal welfare assessment tool, which is the focus of this paper. Several critical limitations associated with this approach have not been widely acknowledged, including that: (1) it relies upon expert or stakeholder opinion, with little transparency around the selection of these individuals; (2) quantitative scoring is typically attempted despite the absence of clear principles for aggregation of welfare measures and few attempts to account for uncertainty; (3) there have been few efforts to measure the repeatability of findings; and (4) it does not consider indirect and unintentional impacts such as those imposed on non-target animals. These deficiencies lead to concerns surrounding testability, repeatability and the potential for manipulation. We provide suggestions for refinement of how the Five Domains model is applied to partially address these limitations. We argue that the Five Domains model is useful for systematic consideration of all sources of possible welfare compromise and enhancement, but is not, in its current state, fit-for-purpose as an assessment tool. We argue for wider acknowledgment of the operational limits of using the model as an assessment tool, prioritisation of the studies needed for its validation, and encourage improvements to this approach.

Knowledge graphs have become a common approach for knowledge representation. Yet, the application of graph methodology is elusive due to the sheer number and complexity of knowledge sources. In addition, semantic incompatibilities hinder efforts to harmonize and integrate across these diverse sources. As part of The Biomedical Translator Consortium, we have developed a knowledge graph–based question-answering system designed to augment human reasoning and accelerate translational scientific discovery: the Translator system. We have applied the Translator system to answer biomedical questions in the context of a broad array of diseases and syndromes, including Fanconi anemia, primary ciliary dyskinesia, multiple sclerosis, and others. A variety of collaborative approaches have been used to research and develop the Translator system. One recent approach involved the establishment of a monthly “Question-of-the-Month (QotM) Challenge” series. Herein, we describe the structure of the QotM Challenge; the six challenges that have been conducted to date on drug-induced liver injury, cannabidiol toxicity, coronavirus infection, diabetes, psoriatic arthritis, and ATP1A3-related phenotypes; the scientific insights that have been gleaned during the challenges; and the technical issues that were identified over the course of the challenges and that can now be addressed to foster further development of the prototype Translator system. We close with a discussion on Large Language Models such as ChatGPT and highlight differences between those models and the Translator system.

Newcastle disease (ND) is a notifiable disease affecting chickens and other avian species caused by virulent strains of Avian paramyxovirus type 1 (APMV-1). While outbreaks of ND can have devastating consequences, avirulent strains of APMV-1 generally cause subclinical infections or mild disease. However, viruses can cause different levels of disease in different species and virulence can evolve following cross-species transmission events. This report describes the detection of three cases of avirulent APMV-1 infection in Great Britain (GB). Case 1 emerged from the ‘testing to exclude’ scheme in chickens in Shropshire while cases 2 and 3 were made directly from notifiable avian disease investigations in chicken broilers in Herefordshire and on premises in Wiltshire containing ducks and mixed species, respectively). Class II/genotype I.1.1 APMV-1 from case 1 shared 99.94% identity to the Queensland V4 strain of APMV-1. Class II/genotype II APMV-1 was detected from case 2 while the class II/genotype I.2 virus from case 3 aligned closely with strains isolated from Anseriformes. Exclusion of ND through rapid detection of avirulent APMV-1 is important where clinical signs caused by avirulent or virulent APMV-1s could be ambiguous. Understanding the diversity of APMV-1s circulating in GB is critical to understanding disease threat from these adaptable viruses.

The U.S. Department of Agriculture–Agricultural Research Service (USDA-ARS) has been a leader in weed science research covering topics ranging from the development and use of integrated weed management (IWM) tactics to basic mechanistic studies, including biotic resistance of desirable plant communities and herbicide resistance. ARS weed scientists have worked in agricultural and natural ecosystems, including agronomic and horticultural crops, pastures, forests, wild lands, aquatic habitats, wetlands, and riparian areas. Through strong partnerships with academia, state agencies, private industry, and numerous federal programs, ARS weed scientists have made contributions to discoveries in the newest fields of robotics and genetics, as well as the traditional and fundamental subjects of weed–crop competition and physiology and integration of weed control tactics and practices. Weed science at ARS is often overshadowed by other research topics; thus, few are aware of the long history of ARS weed science and its important contributions. This review is the result of a symposium held at the Weed Science Society of America’s 62nd Annual Meeting in 2022 that included 10 separate presentations in a virtual Weed Science Webinar Series. The overarching themes of management tactics (IWM, biological control, and automation), basic mechanisms (competition, invasive plant genetics, and herbicide resistance), and ecosystem impacts (invasive plant spread, climate change, conservation, and restoration) represent core ARS weed science research that is dynamic and efficacious and has been a significant component of the agency’s national and international efforts. This review highlights current studies and future directions that exemplify the science and collaborative relationships both within and outside ARS. Given the constraints of weeds and invasive plants on all aspects of food, feed, and fiber systems, there is an acknowledged need to face new challenges, including agriculture and natural resources sustainability, economic resilience and reliability, and societal health and well-being.

Aerial application of an herbicide mixture of triclopyr, dicamba, picloram, and aminopyralid is used to control dense infestations of exotic conifers, notably lodgepole pine (Pinus contorta Douglas ex Loudon), in New Zealand. The rates of herbicide applied to control these tree weeds has the potential for off-target impacts through persistence in the forest floor, soil, and water. Persistence of three of these herbicides was investigated in cast needles, forest floor (litter, fermented humic layer [LFH]), and soil following their operational aerial application (triclopyr: 18 kg ai ha−1; dicamba: 5 kg ai ha−1; picloram: 2 kg ai ha−1) at three sites across New Zealand (KF, MD, GE) with dense invasions of P. contorta. Water was collected from a local stream at two sites (KF, MD) in the days/months after spraying. Active ingredients detected across all sites in cast needles, LFH, and mineral soil generally reflected their application rates, with total amounts comprising 81% triclopyr, 14% dicamba, and 5% picloram. Most of the active ingredients were detected in the LFH (59%), a heavy lignin-rich layer of dead needles overlaying the soil. All three herbicides persisted in this layer, at all sites, for up to 2 yr (at study termination). Only triclopyr was detected in mineral soil, where it declined to below detection levels (0.2 mg kg−1) within 1 yr. All three herbicides were detected in stream water on the day of spray application at KF, and during a rainfall event 1 mo later. However, amounts did not exceed New Zealand environmental and drinking water standards, an outcome attributed to a 30-m no-spray buffer zone used at this site. At MD, herbicides were detectable in water up to 4 mo after spraying, with amounts exceeding New Zealand drinking water standards on one occasion, 1 mo after spray application. No spray buffer zones were used at the MD site.

One in eight individuals worldwide lives with a mental health disorder. For many European countries, the prevalence is even higher, with one in four people reporting mental health problems [1]. Three-quarters of all mental health disorders develop before age 25, with many presenting initially in undiagnosed forms already in the mid-teens and eventually manifesting as severe disorders and lasting into old age [2]. There is also growing evidence that mental health disorder symptoms cross diagnoses and people frequently have more than one mental health disorder [3].

Clinical trials face many challenges with meeting projected enrollment and retention goals. A study’s recruitment materials and messaging convey necessary key information and therefore serve as a critical first impression with potential participants. Yet study teams often lack the resources and skills needed to develop engaging, culturally tailored, and professional-looking recruitment materials. To address this gap, the Recruitment Innovation Center recently developed a Recruitment & Retention Materials Content and Design Toolkit, which offers research teams guidance, actionable tips, resources, and customizable templates for creating trial-specific study materials. This paper seeks to describe the creation and contents of this new toolkit.

Paleoproterozoic massive Cu-Zn±Pb±Au±Ag sulphide deposits metamorphosed to the middle-upper amphibolite facies in central-south Colorado formed in a volcanic arc setting on the edge of the Yavapai crustal province. Previously published U-Pb ages on spatially related granitoids range from ∼1.9 to ∼1.1 Ga, while Pb isotope studies on galena from massive sulphides suggest mineralization formed at around 1.8–1.7 Ga. Some deposits in the Dawson-Green Mountain trend (DGMT) and the Gunnison belt are composed of Cu-Zn-Au-(Pb-Ag) mineralization that were overprinted by later Au-(Ag-Cu-Bi-Se-Te) mineralization. Sulphide mineralization is spatially related to amphibolite and bimodal, mafic-felsic volcanic rocks (gabbro, amphibolite, rhyolite and dacite) and granitoids, but it occurs mostly in biotite-garnet-quartz±sillimanite±cordierite schists and gneisses, spatially related to nodular sillimanite rocks, and in some locations, exhalative rocks (iron formations, gahnite-rich rocks and quartz-garnetite). The major metallic minerals of the massive sulphides include chalcopyrite, sphalerite, pyrite, pyrrhotite, and magnetite, with minor galena and gahnite. Altered rocks intimately associated with mineralization primarily consist of various amphiboles (gedrite, tremolite and hornblende), gahnite, biotite, garnet, cordierite, carbonate and rare högbomite. The Zn/Cd ratios of sphalerite (44 to 307) in deposits in the DGMT fall within the range of global volcanogenic massive sulphide (VMS) deposits but overlap with sphalerite from sedimentary exhalative (Sedex) deposits. Sulphur isotope values of sulphides (δ34S = −3.3 to +6.5) suggest sulphur was largely derived from magmatic sources, and that variations in isotopic values resulting from thermochemical sulphate reduction are due to small differences in physicochemical conditions. The preferred genetic model is for the deposits to be bimodal-mafic (Gunnison) to mafic-siliciclastic VMS deposits (Cotopaxi, Cinderella-Bon Ton, DGMT).

New technologies and disruptions related to Coronavirus disease-2019 have led to expansion of decentralized approaches to clinical trials. Remote tools and methods hold promise for increasing trial efficiency and reducing burdens and barriers by facilitating participation outside of traditional clinical settings and taking studies directly to participants. The Trial Innovation Network, established in 2016 by the National Center for Advancing Clinical and Translational Science to address critical roadblocks in clinical research and accelerate the translational research process, has consulted on over 400 research study proposals to date. Its recommendations for decentralized approaches have included eConsent, participant-informed study design, remote intervention, study task reminders, social media recruitment, and return of results for participants. Some clinical trial elements have worked well when decentralized, while others, including remote recruitment and patient monitoring, need further refinement and assessment to determine their value. Partially decentralized, or “hybrid” trials, offer a first step to optimizing remote methods. Decentralized processes demonstrate potential to improve urban-rural diversity, but their impact on inclusion of racially and ethnically marginalized populations requires further study. To optimize inclusive participation in decentralized clinical trials, efforts must be made to build trust among marginalized communities, and to ensure access to remote technology.

The Critically Endangered African wild ass Equus africanus is one of the most threatened equids, with fewer than 400 individuals persisting in the Danakil Desert (Eritrea), and fewer than 600 globally. To effectively conserve the species, it is essential to determine the extent of available suitable habitats and understand the environmental factors that most influence its current distribution. During 2016–2019 we observed African wild asses, recorded their locations during both the wet and dry seasons and analysed the bioclimatic data and topography using the maximum entropy species distribution model. Distance from water sources and precipitation of the driest month were the top predictors of suitable habitat for the dry season, whereas seasonal temperature variability and precipitation during the warmest quarter were the top predictors for the wet season. Model performances were high, with area under the curve values of 0.97 and 0.98 for the dry and wet seasons, respectively. In the Danakil Desert of Eritrea, the extent of optimal habitat for African wild asses is estimated to be 130 km2 in the dry season and 739 km2 in the wet season, with a potential range of 11,000 km2 for both seasons. Our model results also indicate that in the dry season 89 km2 of the Messir Plateau is optimal habitat, and the entire plateau area of 124 km2 provides optimal habitat during the wet season. These findings provide wildlife management authorities with substantive information and rationale for the establishment of a protected area on the Messir Plateau for African wild asses in Eritrea.