We consider a stochastic model, called the replicator coalescent, describing a system of blocks of k different types that undergo pairwise mergers at rates depending on the block types: with rate $C_{ij}\geq 0$ blocks of type i and j merge, resulting in a single block of type i. The replicator coalescent can be seen as a generalisation of Kingman’s coalescent death chain in a multi-type setting, although without an underpinning exchangeable partition structure. The name is derived from a remarkable connection between the instantaneous dynamics of this multi-type coalescent when issued from an arbitrarily large number of blocks, and the so-called replicator equations from evolutionary game theory. By dilating time arbitrarily close to zero, we see that initially, on coming down from infinity, the replicator coalescent behaves like the solution to a certain replicator equation. Thereafter, stochastic effects are felt and the process evolves more in the spirit of a multi-type death chain.

Regression is a fundamental prediction task common in data-centric engineering applications that involves learning mappings between continuous variables. In many engineering applications (e.g., structural health monitoring), feature-label pairs used to learn such mappings are of limited availability, which hinders the effectiveness of traditional supervised machine learning approaches. This paper proposes a methodology for overcoming the issue of data scarcity by combining active learning (AL) for regression with hierarchical Bayesian modeling. AL is an approach for preferentially acquiring feature-label pairs in a resource-efficient manner. In particular, the current work adopts a risk-informed approach that leverages contextual information associated with regression-based engineering decision-making tasks (e.g., inspection and maintenance). Hierarchical Bayesian modeling allow multiple related regression tasks to be learned over a population, capturing local and global effects. The information sharing facilitated by this modeling approach means that information acquired for one engineering system can improve predictive performance across the population. The proposed methodology is demonstrated using an experimental case study. Specifically, multiple regressions are performed over a population of machining tools, where the quantity of interest is the surface roughness of the workpieces. An inspection and maintenance decision process is defined using these regression tasks, which is in turn used to construct the active-learning algorithm. The novel methodology proposed is benchmarked against an uninformed approach to label acquisition and independent modeling of the regression tasks. It is shown that the proposed approach has superior performance in terms of expected cost—maintaining predictive performance while reducing the number of inspections required.

Dientamoeba fragilis (D. fragilis) is an intestinal protozoan parasite with uncertain pathogenic potential. In the United States, data on D. fragilis in the era of molecular detection are limited. The aim of this retrospective chart review was to evaluate the epidemiology and clinical characteristics of D. fragilis cases identified using polymerase chain reaction assays between 2016 and 2024 at our academic medical centre located in Utah. We identified 28 unique cases with varying gastrointestinal symptomatology including diarrhoea, abdominal pain, nausea, vomiting, and bloating. Approximately half (52%) of patients with follow-up data demonstrated improvement in symptoms following initial treatment for D. fragilis. The overall prevalence of D. fragilis was low among those tested (0.6% positivity). Additional research, including case-control studies, is needed to better describe the etiologic role of D. fragilis.

An overview of changes in the classification of personality disorders from ICD-10 to ICD-11 is presented. The new classification incorporates a dimensional approach centred on severity with five domains available to describe personality pathology. The potential clinical utility of the new approach is discussed.

The fossil record is spatiotemporally heterogeneous: taxon occurrence data have patchy spatial distributions, and this patchiness varies through time. Large-scale quantitative paleobiology studies that fail to account for heterogeneous sampling coverage will generate uninformative inferences at best and confidently draw wrong conclusions at worst. Explicitly spatial methods of standardization are necessary for analyses of large-scale fossil datasets, because nonspatial sample standardization, such as diversity rarefaction, is insufficient to reduce the signal of varying spatial coverage through time or between environments and clades. Spatial standardization should control both geographic area and dispersion (spread) of fossil localities. In addition to standardizing the spatial distribution of data, other factors may be standardized, including environmental heterogeneity or the number of publications or field collecting units that report taxon occurrences. Using a case study of published global Paleobiology Database occurrences, we demonstrate strong signals of sampling; without spatial standardization, these sampling signatures could be misattributed to biological processes. We discuss practical issues of implementing spatial standardization via subsampling and present the new R package divvy to improve the accessibility of spatial analysis. The software provides three spatial subsampling approaches, as well as related tools to quantify spatial coverage. After reviewing the theory, practice, and history of equalizing spatial coverage between data comparison groups, we outline priority areas to improve related data collection, analysis, and reporting practices in paleobiology.

Depression is a major public health concern. Depressed individuals have received increasing treatment with antidepressants in Western countries. In this study, we examine the relationship among individual symptoms (sadness, worry and unhappiness), human development factors and antidepressant use in 29 OECD countries. We report that increased antidepressant prescribing is not associated with decreased prevalence of sadness, worry or unhappiness. However, income, education and life expectancy (measured using the Human Development Index) are associated with lower prevalence of all these symptoms. This suggests that increasing spending on depression treatment may not be as effective as general public health interventions at reducing depression in communities.

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.

We present the third data release from the Parkes Pulsar Timing Array (PPTA) project. The release contains observations of 32 pulsars obtained using the 64-m Parkes ‘Murriyang’ radio telescope. The data span is up to 18 yr with a typical cadence of 3 weeks. This data release is formed by combining an updated version of our second data release with $\sim$3 yr of more recent data primarily obtained using an ultra-wide-bandwidth receiver system that operates between 704 and 4032 MHz. We provide calibrated pulse profiles, flux density dynamic spectra, pulse times of arrival, and initial pulsar timing models. We describe methods for processing such wide-bandwidth observations and compare this data release with our previous release.

Perceived relationships between timbres are critical in electroacoustic music. Most studies assume timbres have fixed inter-relationships, but we tested whether distinct tasks change these. Thirty short sounds were used, from five categories: acoustic instruments, impulse responses, convolutions of the preceding, environmental sounds and computer-manipulated instrumental sounds. In Task 1, 46 non-musicians formed a ‘cohesive’ sonic ordering of unlabelled icons (sounds attached). In Task 2, they categorised the icons into four boxes. In Task 3 listeners separately ordered the sounds from each of Task 2’s boxes using the approach of Task 1. Tasks 1 and 2/3 revealed distinct orderings, consistent with conceptual flexibility. To analyse the orderings, we replaced conventional distance by adjacency measures, and described each system as a network (rather than spatial positions), confirming that the two task outcomes were distinct. Network analyses also showed that the two systems were mechanistically distinct and allowed us to predict temporally changing networks, modelling the observed networks as successive perceptions. Further simulated networks generated with the temporal model readily encompassed all possible pairings between the sounds and not just those we observed. The temporal network model thus confirms conceptual flexibility even in untrained listeners, clearly suitable for a composer to use.

The classical Greeks give us a substance ontology grounded in ‘being qua being’ or ‘being per se’ (to on he on) that guarantees a permanent and unchanging subject as the substratum for the human experience. With the combination of eidos and telos as the formal and final cause of independent things such as persons, this ‘substance’ necessarily persists through change. This substratum or essence includes its purpose for being, and is defining of the ‘what-it-means-to-be-a-thing-of-this-kind’ of any particular thing in setting a closed, exclusive boundary and the strict identity necessary for it to be this, and not that.

In the Yijing 易經 or Book of Changes we find a vocabulary that makes explicit cosmological assumptions that are a stark alternative to this substance ontology, and provides the interpretive context for the Confucian canons by locating them within a holistic, organic, and ecological worldview. To provide a meaningful contrast with this fundamental assumption of on or ‘being’ we might borrow the Greek notion of zoe or ‘life’ and create the neologism ‘zoe-tology’ as ‘the art of living’. This cosmology begins from ‘living’ (sheng 生) itself as the motive force behind change, and gives us a world of boundless ‘becomings’: not ‘things’ that are, but ‘events’ that are happening, a contrast between an ontological conception of the human ‘being’ and a process conception of what I will call human ‘becomings’.

The United States Department of Agriculture Agricultural Marketing Service (USDA AMS) began publishing formula base price information in August 2021. Considerable variation in the types of cattle priced via formula has raised questions about the level of base price transparency that can be gleaned from formula base price reports. This study employs 6 years of transactions to estimate hedonic models assessing the capability of existing data to describe variation in formula base prices. Results suggest factors beyond those reported to USDA AMS by packers influence base prices. We offer suggestions for improved data collection to make hedonic modeling of base prices more effective for reporting market information.

There is an urgent need to improve the clinical management of major depressive disorder (MDD), which has become increasingly prevalent over the past two decades. Several gaps and challenges in the awareness, detection, treatment, and monitoring of MDD remain to be addressed. Digital health technologies have demonstrated utility in relation to various health conditions, including MDD. Factors related to the COVID-19 pandemic have accelerated the development of telemedicine, mobile medical apps, and virtual reality apps and have continued to introduce new possibilities across mental health care. Growing access to and acceptance of digital health technologies present opportunities to expand the scope of care and to close gaps in the management of MDD. Digital health technology is rapidly evolving the options for nonclinical support and clinical care for patients with MDD. Iterative efforts to validate and optimize such digital health technologies, including digital therapeutics and digital biomarkers, continue to improve access to and quality of personalized detection, treatment, and monitoring of MDD. The aim of this review is to highlight the existing gaps and challenges in depression management and discuss the current and future landscape of digital health technology as it applies to the challenges faced by patients with MDD and their healthcare providers.