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Despite considerable work in recent years, pinpointing the time when angiosperms originated has been challenging. However, the rapid development of molecular clock methodology has provided new tools to resolve this conundrum. In particular, the fossilized birth-death model establishes a rich interplay between molecules and stratigraphy by incorporating fossils explicitly into dating analyses. In this study, we apply Bayesian node dating and the skyline fossilized birth-death model, which differ in how the calibration is applied, to estimate the crown age of angiosperms. Node dating analyses with different calibration strategies show that the posterior distribution is strongly constrained by the effective prior at the node of crown angiosperms, dominated by the maximum age constraint. Using the skyline fossilized birth-death model, we reveal that assigning different priors for origin time resulted in similar crown ages for angiosperms. Moreover, the oldest fossils play a significant role in time estimates, and the dating results are robust to sampling assumptions of extant taxa. Our dating analyses indicate a largely Triassic crown age (255–202 Ma) for angiosperms, the period when mammals, dinosaurs, and squamate reptiles first appeared, and highlight the potential of morphological data to redefine the timeline of angiosperms. 

ABSTRACT High-fidelity three-dimensional (3D) models of skeletal specimens underpin many ecological and evolutionary analyses. Here we present a fully open pipeline inside the 3D Slicer platform that couples automatic image masking by the Segment Anything Model (SAM) with surface reconstruction by the OpenDroneMap (NodeODM) engine, all wrapped in a user-friendly extension. To test accuracy, we photographed 14 mountain-beaver skulls, reconstructed 3D models with the new pipeline and with our previous workflow and compared each model to its micro-CT reference using mean surface distance, root mean square error (RMSE), Hausdorff, and Chamfer metrics. Our improved pipeline that integrates masking to the model reconstructed lowered mean distance and RMSE by 10–15% across specimens and reduced visible artefacts around thin elements such as zygomatic arches; Hausdorff distance changed little, indicating that gains were global rather than confined to outliers. Our new extension provides a convenient workflow that integrates masking, scaling, and reconstruction under one interface. 

Abstract MotivationAs the SARS-CoV-2 virus rapidly evolves, predicting the trajectory of viral mutations has become a critical yet complex task. A deep understanding of future mutation patterns, in particular the mutations that will prevail in the near future, is vital in steering diagnostics, therapeutics, and vaccine strategies for disease control. ResultsIn this study, we developed a model to forecast future SARS-CoV-2 mutation surges in real-time, using historical mutation frequency data from the USA. We transformed the temporal prediction problem into a supervised learning framework using a sliding window approach. This involved breaking the time series of mutation frequencies into very short segments. Considering the time-dependent nature of the data, we focused on modeling the first-order derivative of the mutation frequency. We predicted the final derivative in each segment based on the preceding derivatives, employing various machine learning methods, including random forest, XGBoost, support vector machine, and neural network models. Empowered by the novel transformation strategy and the high capacity of machine learning models, we observed low prediction error that is confined within 0.1% and 1% when making predictions of mutation rates for the future 30 and 80 days, respectively. In addition, the method also led to a notable increase in prediction accuracy compared to traditional time-series models, as evidenced by much lower MAE (Mean Absolute Error) and MSE (Mean Squared Error) for predictions made within different time horizons. To further assess the method’s effectiveness and robustness in predicting mutation patterns for unforeseen mutations, we first designed a synthetic case where we categorized all mutations into three major patterns. The model demonstrated its robustness by accurately predicting unseen mutation patterns when training on data from two pattern categories while testing on the third pattern category, showcasing its potential in forecasting a variety of mutation trajectories. We then applied our method to prediction for a recent time frame between 1 January 2025 and 10 June 2025, for both the USA and UK, where the model training was conducted using frequency sequence data collected between 12 December 2019 and 26 January 2023 in the USA. The model demonstrated superior performance for both datasets. Availability and implementationTo enhance accessibility and utility, we built our methodology into a GitHub package (https://github.com/ZhouXY199502/SWD). Our method has the potential applicability to study other infectious diseases or forecasting tasks, thus extending its relevance beyond the current COVID pandemic. 

Microscale heating platforms capable of generating localized temperature rises can find applications in wide‐ranging areas including nanomaterials synthesis and microscale thermometry. Here, commercially available optical calibration samples called Ronchi rulings, which consist of an array of chrome lines on a float glass substrate, are demonstrated to serve as reconfigurable Joule heaters. Electrical connections are formed by wire bonding onto the chrome to Joule heat individual lines and monitor their temperature rises using electrical resistance thermometry. Tests across multiple heater lines demonstrate a negative temperature coefficient of resistance with an average value of −6.93 × 10⁻⁴ ± 8.18 × 10⁻⁵ K⁻¹. Under Joule heating, temperature rises exceeding 100 K are measured. To characterize the temperature gradient across the chrome line and glass, a noncontact optical thermometry technique based on the temperature‐dependent luminescence of upconverting nanoparticles (UCNPs) is used, producing temperature measurements that match finite element simulations. A 1:1 area ratio between the chrome lines and glass offers a high probability of finding UCNPs across both materials. The temperature rise on chrome determined from luminescence thermometry, electrical resistance thermometry, and simulations are also consistent. Furthermore, over 50% of the peak temperature rise is maintained along the neighboring glass region. 

Understanding howAI recommendationswork can help the younger generation become more informed and critical consumers of the vast amount of information they encounter daily. However, young learners with limited math and computing knowledge often find AI concepts too abstract. To address this, we developed Briteller, a light-based recommendation system that makes learning tangible. By exploring and manipulating light beams, Briteller enables children to understand an AI recommender system’s core algorithmic building block, the dot product, through hands-on interactions. Initial evaluations with ten middle school students demonstrated the effectiveness of this approach, using embodied metaphors, such as "merging light" to represent addition. To overcome the limitations of the physical optical setup, we further explored how AR could embody multiplication, expand data vectors with more attributes, and enhance contextual understanding. Our findings provide valuable insights for designing embodied and tangible learning experiences that make AI concepts more accessible to young learners.