The reactivity of transverse waves in detonations of methane, oxygen and nitrogen are experimentally assessed using MHz rate schlieren and chemiluminescence imaging. In these highly unstable mixtures, the mode of wave propagation is more complex than what is described by the cellular instability model that is conventionally used for weakly unstable mixtures. Behind the low-speed leading shock in unstable waves, the processed gas remains essentially unreacted until transverse waves reach this region. In highly unstable waves, the transverse waves have a range of reactivity, that is rates of reaction in the flow immediately behind the wave. In this study, we present examples of transverse waves for near-limit detonations and analyse four cases in detail. In some cases, these waves appear to be essentially non-reactive or cause very slow reaction. In other cases, the transverse waves can be highly reactive. In the most extreme example, the transverse wave is propagating at the Chapman–Jouguet speed with a small reaction zone, i.e. a transverse detonation. A reactive oblique shock model is used to approximate the triple-point configuration of this case as a double-Mach reflection, which shows good agreement with the images. The reaction evolution along path lines is analysed using detailed reaction mechanisms and considerations about flow-field unsteadiness. Length scales of the energy release and expansion processes within the reaction zone region are used to explain the observed modes of wave propagation and interaction.

This study examined whether supplementation with collagen peptides (CP) affects appetite and post-exercise energy intake in healthy active females.

In this randomised, double-blind crossover study, 15 healthy females (23 ± 3 y) consumed 15 g/day of CP or a taste matched non-energy control (CON) for 7 days. On day 7, participants cycled for 45 min at ∼55% Wmax, before consuming the final supplement. Sixty min post supplementation an ad libitum meal was provided, and energy intake recorded. Subjective appetite sensations were measured daily for 6 days (pre- and 30 min post-supplement), and pre (0 min) to 280 min post-exercise on day 7. Blood glucose and hormone concentrations (total ghrelin, glucagon-like peptide-1 (GLP-1), and peptide YY (PYY), cholecystokinin (CCK), dipeptidyl peptidase-4 (sDPP4), leptin, and insulin, were measured fasted at baseline (day 0), then pre-breakfast (0 min), post-exercise (100 min), post-supplement (115, 130, 145, 160 min) and post-meal (220, 280 min) on day 7.

Ad-libitum energy intake was ∼10% (∼41kcal) lower in the CP trial (P=0.037). There was no difference in gastrointestinal symptoms or subjective appetite sensations throughout the trial (P≥0.412). Total plasma GLP-1 (area under the curve, CON: 6369±2330; CP: 9064±3021 pmol/L; P<0.001) and insulin (+80% at peak) were higher after CP (P<0.001). Plasma ghrelin and leptin were lower in CP (condition effect; P≤0.032). PYY, CCK, sDPP4 and glucose were not different between CP and placebo (P≥0.100).

CP supplementation following exercise increased GLP-1 and insulin concentrations and reduced ad libitum energy intake at a subsequent meal in physically active females.

This study explored whether lifestyle therapy that promoted adherence to a Mediterranean-style diet as a treatment for depression led to environmental co-benefits. Participants (n 75 complete case) were Australian adults in the Curbing Anxiety and Depression using Lifestyle Medicine non-inferiority, randomised controlled trial, which showed that lifestyle therapy was non-inferior to psychotherapy in reducing depressive symptoms, when delivered in group format via video conferencing over an 8-week treatment period. In this secondary analysis, we hypothesised that the lifestyle arm would be superior to the psychotherapy arm in reducing the environmental impact of self-reported diet over time. Dietary intake derived from FFQ at baseline and 8 weeks was transformed into environmental impact scores by calculating global warming potential (GWP)*. GWP* was calculated for total dietary intake and distinct food groups (Australian Dietary Guidelines and NOVA classifications). Within-arm changes in GWP* over time were calculated using the median difference. Neither arm showed significant changes. Between-arm differences in percentage change in GWP* scores over time were analysed using generalised estimating equations models. No between-arm difference for total GWP* score was found (β = 11·06 (–7·04, 29·15)). When examining distinct food groups, results were mixed. These novel findings contribute to the sparse evidence base that has measured the environmental impact of diets in a clinical trial context. Whilst lifestyle therapy that reduced depressive symptoms did not have clear environmental benefits relative to psychotherapy, nutritional counselling that focuses on the environmental impact of food choices may drive more pronounced planetary co-benefits.

Heath forests, or known locally as kerangas, in Indonesia and Malaysia form a distinct and understudied ecoregion. We document the distribution and ecological significance of the largest extent of kerangas in Kalimantan, Indonesian Borneo. We mapped 16,586 km2 of kerangas to the nearest one square kilometre across Kalimantan, showing a significant reduction from previous estimates. About 19% of this area exists as a poorly documented mosaic landscape in Central Kalimantan’s Rungan-Kahayan region. Here, peat-based forests transition to heath and dipterocarp forests, making it difficult to reliably classify these forests for conservation planning. Using remote sensing and tree plot data, we identified three forest types—kerangas, low pole, and mixed swamp. Vegetation structure is influenced by soil, topography, and hydrology, while peat depth and elevation affect species diversity. Our findings indicate that these forests are dynamic ecosystems with diverse vegetation communities adapted to peat as well as sandy soils. Lowland heath forests in Rungan-Kahayan exhibits higher tree densities compared to other Bornean heath forests, reflecting unique ecological adaptations to challenging environments. Despite covering just 3% of Kalimantan’s forest area, these ecosystems remain largely unprotected, facing threats from land conversion and fire. Our study highlights the ecological complexity of kerangas and underscores the urgent need for targeted conservation and further research on these forests.

Plants in the genera Astragalus and Oxytropis, collectively referred to as “locoweeds,” contain swainsonine, a toxic alkaloid synthesized by their fungal endophyte Alternaria sect. Undifilum. The ecological role of this endophyte across the mutualism–commensalism–parasitism continuum is unknown. We examined the fitness traits of Astragalus and Oxytropis species growing with and without the endophyte, in a 9-yr, common-garden experiment. Silky crazyweed (Oxytropis sericea Nutt.) and woolly loco (Astragalus mollissimus Torr.) plants germinated from seeds that naturally host the endophyte (E+) and with it mechanically removed (E−) were established in a common garden in southwest Montana. We measured mortality, gas exchange, flower and seed production, seed germination, and final biomass. Astragalus mollissimus plants grew as annuals under common-garden conditions regardless of endophyte status. Oxytropis sericea plants grew as perennials with survival unaffected by endophyte; however, E+ O. sericea plants produced slightly more reproductive stems, flowers per stem, and crown and stem biomass. Maternal effects detected in the parental generation disappeared in subsequent generations. Gas exchange, fecundity, and seed germination were unaffected by endophyte. Contrary to our initial hypothesis of mutualism, the endophyte did not improve host survival or fecundity, nor did we detect transgenerational effects. However, the endophyte did slightly increase the number of reproductive stems and flowers per stem and crown and stem mass in O. sericea, suggesting endophytic effects on carbohydrate biochemistry and pollination parameters should be examined. Lack of selection for or against endophyte-containing plants allows both nontoxic and toxic swainsonine-producing plants to persist in Astragalus and Oxytropis populations, posing a continued threat to grazing livestock.

Threat sensitivity, an individual difference construct reflecting variation in responsiveness to threats of various types, predicts physiological reactivity to aversive stimuli and shares heritable variance with anxiety disorders in adults. However, no research has been conducted yet with youth to examine the heritability of threat sensitivity or evaluate the role of genetic versus environmental influences in its relations with mental health problems. The current study addressed this gap by evaluating the psychometric properties of a measure of this construct, the 20-item Trait Fear scale (TF-20), and examining its phenotypic and genotypic correlations with different forms of psychopathology in a sample of 346 twin pairs (121 monozygotic), aged 9–14 years. Analyses revealed high internal consistency and test-retest reliability for the TF-20. Evidence was also found for its convergent and discriminant validity in terms of phenotypic and genotypic correlations with measures of fear-related psychopathology. By contrast, the TF-20’s associations with depressive conditions were largely attributable to environmental influences. Extending prior work with adults, current study findings provide support for threat sensitivity as a genetically-influenced liability for phobic fear disorders in youth.

Stochastic actor-oriented models (SAOMs) were designed in the social network setting to capture network dynamics representing a variety of influences on network change. The standard framework assumes the observed networks are free of false positive and false negative edges, which may be an unrealistic assumption. We propose a hidden Markov model (HMM) extension to these models, consisting of two components: 1) a latent model, which assumes that the unobserved, true networks evolve according to a Markov process as they do in the SAOM framework; and 2) a measurement model, which describes the conditional distribution of the observed networks given the true networks. An expectation-maximization algorithm is developed for parameter estimation. We address the computational challenge posed by a massive discrete state space, of a size exponentially increasing in the number of vertices, through the use of the missing information principle and particle filtering. We present results from a simulation study, demonstrating our approach offers improvement in accuracy of estimation, in contrast to the standard SAOM, when the underlying networks are observed with noise. We apply our method to functional brain networks inferred from electroencephalogram data, revealing larger effect sizes when compared to the naive approach of fitting the standard SAOM.

We report the lattice parameters and cell volume for cristobalite powder added at 35 wt% to Ba-Al-Silicate glass (CGI930) as reflowed bulk glass bars where the embedded cristobalite phase is constrained within the glass matrix. Analysis confirms that the room temperature lattice parameters and cell volume obtained for the bulk glass–ceramic are larger compared with single-phase cristobalite powders. The increased volume of the cristobalite phase in a glass matrix is driven by tensile stresses developed at the interface between the cristobalite and matrix glass phase, and this stress impacts the phase transition temperature and thermal hysteresis of the cristobalite phase. In situ high-temperature measurements confirm that the tetragonal to cubic α–β phase transformation of the cristobalite phase within the glass matrix is ~195 °C with complete suppression of hysteresis behavior. In contrast, bulk glass–ceramic material ground to a powder form displays the expected thermal hysteresis behavior and more comparable phase transition temperatures of 245 °C on heating and 220 °C on cooling. Isothermal holds at varying temperatures above or near the α–β phase transition suggest that the cristobalite phase does not undergo significant relaxation within the matrix phase to reduce accumulated stress imposed by the constraining matrix glassy phase.

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.

Incorporating paleontological data into phylogenetic inference can greatly enrich our understanding of evolutionary relationships by providing insights into the diversity and morphological evolution of a clade over geological timescales. Phylogenetic analysis of fossil data has been significantly aided by the introduction of the fossilized birth–death (FBD) process, a model that accounts for fossil sampling through time. A decade on from the first implementation of the FBD model, we explore its use in more than 170 empirical studies, summarizing insights gained through its application. We identify a number of challenges in applying the model in practice: it requires a working knowledge of paleontological data and their complex properties, Bayesian phylogenetics, and the mechanics of evolutionary models. To address some of these difficulties, we provide an introduction to the Bayesian phylogenetic framework, discuss important aspects of paleontological data, and finally describe the assumptions of the models used in paleobiology. We also present a number of exemplar empirical studies that have used the FBD model in different ways. Through this review, we aim to provide clarity on how paleontological data can best be used in phylogenetic inference. We hope to encourage communication between model developers and empirical researchers, with the ultimate goal of developing models that better reflect the data we have and the processes that generated them.

Objectives/Goals: Our study’s objective is to evaluate RadOnc-GPT, a GPT-4o powered LLM, in generating responses to in-basket messages related to prostate cancer treatment in the Radiation Oncology department. By integrating it with electronic health record (EHR) systems, the goal is to assess its impact on clinician workload, response quality, and efficiency in healthcare communication. Methods/Study Population: RadOnc-GPT was integrated with patient EHRs from both hospital-wide and radiation-oncology-specific databases. The study examined 158 pre-recorded in-basket message interactions from 90 non-metastatic prostate cancer patients. Quantitative natural language processing analysis and two randomized single-blinded grading studies, involving four clinicians and four nurses, were conducted to evaluate RadOnc-GPT’s response quality in completeness, correctness, clarity, empathy, and estimated editing time. Response times were measured to estimate the time saved for clinicians and nurses. The study population included patient messages across all phases of care (pre-, during, and post-treatment) for those undergoing radiotherapy. Results/Anticipated Results: In the single-blinded grader study, clinician graders evaluated 316 responses (158 from human care teams and 158 from RadOnc-GPT). Results showed RadOnc-GPT outperformed human responses in empathy and clarity, while humans excelled in completeness and correctness. Sentiment analyses using TextBlob and VADER revealed RadOnc-GPT responses had a positive mean score of 0.25, whereas human responses clustered around neutral. VADER analysis indicated a high median score for RadOnc-GPT, nearing 1.0, reflecting predominantly positive sentiment, while human responses displayed a broader sentiment range, indicating sensitivity to context. Clinicians averaged 3.60 minutes (SD 1.44) to respond, compared to 6.39 minutes (SD 4.05) for nurses, highlighting RadOnc-GPT’s efficiency in generating timely responses. Discussion/Significance of Impact: RadOnc-GPT effectively generated responses to individualized patient in-basket messages, comparable to those from radiation oncologists and nurses. While human oversight is still necessary to avoid errors, RadOnc-GPT can speed up response times and reduce pressure on care teams, shifting their role from drafting to reviewing responses.

Experiments are reported that add to the growing literature on the voluntary provision of public goods. Information conditions are manipulated to address whether early findings of above-equilibrium contributions to a public good are a result of complete information regarding the symmetry of the game. No significant information effect was found. Further, by examining designs with an interior Nash equilibrium, this research suggests that the nonzero contributions observed in the previous dominant strategy environments, where the prediction was a zero level of provision of the public good, were not simply transitional errors as the system converged to a boundary equilibrium.

Interseeding alfalfa (Medicago sativa L.) into corn (Zea mays L.) is a novel approach that increases the production of high-quality forage and reduces the risk of nutrient and soil loss from cropland. Annual grass weeds like yellow foxtail [Setaria pumila (Poir.) Roem. & Schult.] can reduce the success of alfalfa establishment and are difficult to manage in the interseeding system. This study evaluated ground cover, fall biomass, and fall plant density of interseeded alfalfa in response to varying populations of S. pumila. Our goal was to identify a threshold for initiating control of annual grasses to ensure good establishment of alfalfa in this intercropping system. Ground cover of interseeded alfalfa growing under corn declined as S. pumila density increased from 0 to 125 plants m−2 in July, August, and October with the sharpest decline in August (up to a 70% reduction in alfalfa cover). This reduction in ground cover was associated with a decline in postestablishment shoot and root mass and a reduction in alfalfa plant density from 246 to 146 plants m−2 in October. Results suggest that June S. pumila populations should be kept to less than 50 plants m−2 to obtain recommended fall alfalfa densities of 200 plants m−2 that are needed to maximize alfalfa yield the following year. This research provides crucial information to practitioners on when annual grass management is needed to ensure successful alfalfa establishment in this interseeded system.

For near-future missions planed for Mars Sample Return (MSR), an international working group organized by the Committee on Space Research (COSPAR) developed the sample safety assessment framework (SSAF). For the SSAF, analytical instruments were selected by taking the practical limitations of hosting them within a facility with the highest level of biosafety precautions (biosafety level 4) and the precious nature of returned samples into account. To prepare for MSR, analytical instruments of high sensitivity need to be tested on effective Mars analogue materials. As an analogue material, we selected a rock core of basalt, a prominent rock type on the Martian surface. Two basalt samples with aqueous alteration cached in Jezero crater by the Perseverance rover are planned to be returned to Earth. Our previously published analytical procedures using destructive but spatially sensitive instruments such as nanoscale secondary ion mass spectrometry (NanoSIMS) and transmission electron microscopy coupled to energy-dispersive spectroscopy revealed microbial colonization at clay-filled fractures. With an aim to test the capability of an analytical instrument listed in SSAF, we now extend that work to conventional Fourier transform infrared (FT-IR) microscopy with a spatial resolution of 10 μm. Although Fe-rich smectite called nontronite was identified after crushing some portion of the rock core sample into powder, the application of conventional FT-IR microscopy is limited to a sample thickness of <30 μm. In order to obtain IR-based spectra without destructive preparation, a new technique called optical-photothermal infrared (O-PTIR) spectroscopy with a spatial resolution of 0.5 μm was applied to a 100 μm thick section of the rock core. By O-PTIR spectroscopic analysis of the clay-filled fracture, we obtained in-situ spectra diagnostic to microbial cells, consistent with our previously published data obtained by NanoSIMS. In addition, nontronite identification was also possible by O-PTIR spectroscopic analysis. From these results, O-PTIR spectroscopy is suggested be superior to deep ultraviolet fluorescence microscopy/μ-Raman spectroscopy, particularly for smectite identification. A simultaneous acquisition of the spatial distribution of structural motifs associated with biomolecules and smectites is critical for distinguishing biological material in samples as well as characterizing an abiotic background.