Actuaries must model mortality to understand, manage and price risk. Continuous-time methods offer considerable practical benefits to actuaries analysing portfolio mortality experience. This paper discusses six categories of advantage: (i) reflecting the reality of data produced by everyday business practices, (ii) modelling rapid changes in risk, (iii) modelling time- and duration-varying risk, (iv) competing risks, (v) data-quality checking and (vi) management information. Specific examples are given where continuous-time models are more useful in practice than discrete-time models.

We reprise some common statistical models for actuarial mortality analysis using grouped counts. We then discuss the benefits of building mortality models from the most elementary items. This has two facets. First, models are better based on the mortality of individuals, rather than groups. Second, models are better defined in continuous time, rather than over fixed intervals like a year. We show how Poisson-like likelihoods at the “macro” level are built up by product integration of sequences of infinitesimal Bernoulli trials at the “micro” level. Observed data is represented through a stochastic mortality hazard rate, and counting processes provide the natural notation for left-truncated and right-censored actuarial data, individual or age-grouped. Together these explain the “pseudo-Poisson” behaviour of survival model likelihoods.

Recent changes to US research funding are having far-reaching consequences that imperil the integrity of science and the provision of care to vulnerable populations. Resisting these changes, the BJPsych Portfolio reaffirms its commitment to publishing mental science and advancing psychiatric knowledge that improves the mental health of one and all.

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.

Aerosol-cloud interactions contribute significant uncertainty to modern climate model predictions. Analysis of complex observed aerosol-cloud parameter relationships is a crucial piece of reducing this uncertainty. Here, we apply two machine learning methods to explore variability in in-situ observations from the NASA ACTIVATE mission. These observations consist of flights over the Western North Atlantic Ocean, providing a large repository of data including aerosol, meteorological, and microphysical conditions in and out of clouds. We investigate this dataset using principal component analysis (PCA), a linear dimensionality reduction technique, and an autoencoder, a deep learning non-linear dimensionality reduction technique. We find that we can reduce the dimensionality of the parameter space by more than a factor of 2 and verify that the deep learning method outperforms a PCA baseline by two orders of magnitude. Analysis in the low dimensional space of both these techniques reveals two consistent physically interpretable regimes—a low pollution regime and an in-cloud regime. Through this work, we show that unsupervised machine learning techniques can learn useful information from in-situ atmospheric observations and provide interpretable results of low-dimensional variability.

Measurement error undermines the accuracy of dietary intake data. The 24-h dietary recall (24HR) is the standard data collection method in nutrition surveillance. Several neurocognitive processes underpin the act of recall, and individuals differ in their performance of these processes. This study aimed to investigate whether variation in neurocognitive processes, measured using four cognitive tasks, was associated with variation in measurement error of 24HR. Participants (n 139) completed the Trail Making Test, the Wisconsin Card Sorting Test, the Visual Digit Span and the Vividness of Visual Imagery questionnaire. During a controlled feeding study, participants completed three technology-assisted 24HR: the Automated Self-Administered Dietary Assessment Tool, Intake24 and an Interviewer-Administered Image-Assisted 24HR (IA-24HR) 1 week apart. The percentage error between reported and true energy intakes was calculated. Using linear regression, the association between cognitive task scores and absolute percentage error in estimated energy intake was assessed. Longer time spent completing the Trail Making Test, an indicator of visual attention and executive functioning, was associated with greater error in energy intake estimation using ASA24 (B 0·13, 95 % CI 0·04, 0·21) and Intake24 (B 0·10, 95 % CI 0·02, 0·19). Regression models explained 13·6 % (ASA24) and 15·8 % (Intake24) of the variance in energy estimation error. No cognitive task scores were associated with error using IA-24HR. This study demonstrates that variation between individuals in neurocognitive processes explains some of the variation in 24HR error. Further investigation into the role of neurocognitive processes in 24HR and their role in the reliability of dietary intake data is warranted.

There is a growing awareness that diversity, health equity, and inclusion play a significant role in improving patient outcomes and advancing knowledge. The Pediatric Heart Network launched an initiative to incorporate diversity, health equity, and inclusion into its 2021 Scholar Award Funding Opportunity Announcement. This manuscript describes the process of incorporating diversity, health equity, and inclusion into the Pediatric Heart Network Scholar Award and the lessons learned. Recommendations for future Pediatric Heart Network grant application cycles are made which could be replicated by other funding agencies.

This research paper describes a test of association of sire with susceptibility to mastitis, using a custom-bred population of dairy cattle. We hypothesised that sire daughters ranked as more resistant to intramammary infections in their first two lactations would be more resistant to an intramammary challenge in their third lactation. Mastitis phenotypes were generated for a Holstein-Friesian × Jersey crossbred research herd of 864 cows, bred from six defined sires and managed as two cohorts in a seasonal calving system in New Zealand. Naturally occurring new intramammary infections (IMI) and clinical mastitis (CM) were monitored in their first two lactations from herd records, milking staff observations and bacteriology of quarter milk samples collected at four time-points during each lactation. The animals retained to their third lactation were then exposed to a single intramammary challenge with Streptococcus uberis. We used a relative risk (RR) analysis to rank performance of sire daughters for pathogen-specific phenotypes for new IMI and CM, and somatic cell count (SCC) traits, and their clinical outcomes to the challenge. Generally, daughters of sire B had the highest RR for new IMI or CM by a major pathogen, whereas daughters of sires A and C had a consistently lower risk. The RR for sires E, D and F were intermediate and inconsistent across major pathogens. Daughters of sire B ranked highest for all CM cases and SCC traits whereas sires A and C ranked lowest. Following intramammary challenge, daughters of sires A and C were more likely to develop CM, whereas daughters of sire B and F were less likely to develop CM. Thus, the hypothesis was rejected. The results revealed strong associations between sire and pathogen-specific mastitis phenotypes, and validated use of SCC and CM traits in sire selection and breeding programmes to improve mastitis resistance.

Previous studies by Electron Spin Resonance (ESR) have established the substitution of Fe3+ and Mg2+ in the kaolinite structure. It is shown that Fe2+ can substitute in kaolinite and stabilize defects which are detectable by ESR in a manner identical to Mg2+. The development of methods of preparing a synthetic kaolinite doped with Fe2+ is described in detail. It is shown that the main ESR signals, which occur at g = 2.0 in natural kaolinites and which previously have been interpreted in terms of iron and magnesium, can be attributed to iron alone.

Few studies have examined the genetic population structure of vector-borne microparasites in wildlife, making it unclear how much these systems can reveal about the movement of their associated hosts. This study examined the complex host–vector–microbe interactions in a system of bats, wingless ectoparasitic bat flies (Nycteribiidae), vector-borne microparasitic bacteria (Bartonella) and bacterial endosymbionts of flies (Enterobacterales) across an island chain in the Gulf of Guinea, West Africa. Limited population structure was found in bat flies and Enterobacterales symbionts compared to that of their hosts. Significant isolation by distance was observed in the dissimilarity of Bartonella communities detected in flies from sampled populations of Eidolon helvum bats. These patterns indicate that, while genetic dispersal of bats between islands is limited, some non-reproductive movements may lead to the dispersal of ectoparasites and associated microbes. This study deepens our knowledge of the phylogeography of African fruit bats, their ectoparasites and associated bacteria. The results presented could inform models of pathogen transmission in these bat populations and increase our theoretical understanding of community ecology in host–microbe systems.

NASA’s all-sky survey mission, the Transiting Exoplanet Survey Satellite (TESS), is specifically engineered to detect exoplanets that transit bright stars. Thus far, TESS has successfully identified approximately 400 transiting exoplanets, in addition to roughly 6 000 candidate exoplanets pending confirmation. In this study, we present the results of our ongoing project, the Validation of Transiting Exoplanets using Statistical Tools (VaTEST). Our dedicated effort is focused on the confirmation and characterisation of new exoplanets through the application of statistical validation tools. Through a combination of ground-based telescope data, high-resolution imaging, and the utilisation of the statistical validation tool known as TRICERATOPS, we have successfully discovered eight potential super-Earths. These planets bear the designations: TOI-238b (1.61$^{+0.09} _{-0.10}$ R$_\oplus$), TOI-771b (1.42$^{+0.11} _{-0.09}$ R$_\oplus$), TOI-871b (1.66$^{+0.11} _{-0.11}$ R$_\oplus$), TOI-1467b (1.83$^{+0.16} _{-0.15}$ R$_\oplus$), TOI-1739b (1.69$^{+0.10} _{-0.08}$ R$_\oplus$), TOI-2068b (1.82$^{+0.16} _{-0.15}$ R$_\oplus$), TOI-4559b (1.42$^{+0.13} _{-0.11}$ R$_\oplus$), and TOI-5799b (1.62$^{+0.19} _{-0.13}$ R$_\oplus$). Among all these planets, six of them fall within the region known as ‘keystone planets’, which makes them particularly interesting for study. Based on the location of TOI-771b and TOI-4559b below the radius valley we characterised them as likely super-Earths, though radial velocity mass measurements for these planets will provide more details about their characterisation. It is noteworthy that planets within the size range investigated herein are absent from our own solar system, making their study crucial for gaining insights into the evolutionary stages between Earth and Neptune.

Scanning, transmission, and analytical electron microscopy studies of shales from the Salton Sea geothermal field revealed that phyllosilicates progress through zones of illite-muscovite (115°−220°C), chlorite (220°−310°C), and biotite (310°C). These phyllosilicates occur principally as discrete, euhedral to subhedral crystals which partly fill pore space. The structural and chemical heterogeneity, which is typical of phyllosilicates in shales subject to diagenesis, is generally absent. Textures and microstructures indicate that the mineral progression involves dissolution of detrital phases, mass transport through interconnecting pore space, and direct crystallization of phyllosilicates from solution.

Phyllosilicate stability relations indicate that either increase in temperature or changing ion concentrations in solutions with depth are capable of explaining the observed mineral zoning. Textural and compositional data suggest that the observed mineral assemblages and the interstitial fluids approach equilibrium relative to the original detrital suites. The alteration process may have occurred in a single, short-lived, episodic hydrothermal event in which the original detrital phases (smectite, etc.) reacted directly to precipitate illite, chlorite, or biotite at different temperatures (depths) without producing intermediate phases.