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The longevity of cratons usually implies that the entire cratonic lithosphere remained unchanged over billions of years, which is traditionally attributed to their intrinsically buoyant and strong lithospheric roots. By reviewing relevant studies and recent observational constraints, we show that the present cratonic roots are notably denser than the ambient mantle, with the compositional buoyancy offsetting only one-fifth of the negative thermal buoyancy. In addition, the presence of a weak mid-lithospheric discontinuity could decouple the upper and lower lithosphere upon perturbation, allowing delamination of the lower portion, while most of the delaminated lithosphere would eventually relaminate to the base of the lithosphere after sufficient warming inside the convective mantle. This process generates enduring (>100 Myr) and prominent (>1 km) surface uplifts within continents, a mechanism more compatible with data, especially those reflecting lithospheric deformation, than the model of all continents climbing up a steady region of dynamic uplift. Subsequent lithospheric cooling gradually draws the surface down to below sea level, where the lithospheric mantle density reaches a maximum upon formation of the next supercontinent. We argue that such cratonic deformation has happened repeatedly over supercontinent cycles since the Neoproterozoic and has largely shaped the properties of the present cratonic lithosphere. A few new research directions are also suggested. 

Class-incremental learning (CIL) aims to continually learn a sequence of tasks, with each task consisting of a set of unique classes. Graph CIL (GCIL) follows the same setting but needs to deal with graph tasks (e.g., node classification in a graph). The key characteristic of CIL lies in the absence of task identifiers (IDs) during inference, which causes a significant challenge in separating classes from different tasks (i.e., inter-task class separation). Being able to accurately predict the task IDs can help address this issue, but it is a challenging problem. In this paper, we show theoretically that accurate task ID prediction on graph data can be achieved by a Laplacian smoothing-based graph task profiling approach, in which each graph task is modeled by a task prototype based on Laplacian smoothing over the graph. It guarantees that the task prototypes of the same graph task are nearly the same with a large smoothing step, while those of different tasks are distinct due to differences in graph structure and node attributes. Further, to avoid the catastrophic forgetting of the knowledge learned in previous graph tasks, we propose a novel graph prompting approach for GCIL which learns a small discriminative graph prompt for each task, essentially resulting in a separate classification model for each task. The prompt learning requires the training of a single graph neural network (GNN) only once on the first task, and no data replay is required thereafter, thereby obtaining a GCIL model being both replay-free and forget-free. Extensive experiments on four GCIL benchmarks show that i) our task prototype-based method can achieve 100% task ID prediction accuracy on all four datasets, ii) our GCIL model significantly outperforms state-of-the-art competing methods by at least 18% in average CIL accuracy, and iii) our model is fully free of forgetting on the four datasets. Code is available at https://github.com/mala-lab/TPP. 

Abstract Megathrusts at convergent plate boundaries generate the largest and some of the most hazardous earthquakes on Earth. However, their physical properties, including those influencing fault slip accumulation and release and earthquake‐related surface displacements, are still poorly constrained at critical depths. Here, we combine seismic imaging and geodetic modeling to investigate the structure and mechanical behavior of the Main Himalayan Thrust fault (MHT) in the center of the 2015 Mw 7.8 Gorkha rupture in Nepal. Our results from two independent observations consistently suggest the presence of a channel associated with the MHT with high compliance (shear modulus as low as ∼4 GPa) and strain anisotropy (stiffer in the vertical orientation than in the horizontal), likely arising from a weak subducting layer with north‐dipping foliation. Such mechanical heterogeneity significantly influences the quantification of short‐term fault kinematics and associated earthquake potential, with implications on across‐scale dynamics of plate boundaries in Himalaya and elsewhere. 

Elementary science education, particularly in the 4th and 5th grades, is essential for setting the foundation for lifelong science learning, fostering critical thinking, and preparing students for success in science, technology, engineering, and mathematics (STEM) fields. This stage is especially critical for students with disabilities, as achievement gaps between them and their peers emerge during elementary school. Despite this importance, little is known about how science is taught in elementary classrooms during these critical years, particularly for students with disabilities. To address this gap, we surveyed teachers from a nationally representative sample of U.S. schools to examine elementary science education, including instructional practices, allocation of time, and the inclusion and support of students with disabilities. Our findings reveal that limited instructional time is allocated to science, with significant variability across classrooms. The amount of time dedicated to science instruction was significantly influenced by external factors, such as whether science was a tested subject. Students with disabilities often face additional barriers, including being pulled out of science instruction for special education services, resulting in missed opportunities to engage in science. These findings highlight the need to address opportunity gaps in science instruction to ensure all students have meaningful access to quality science education. 

Abstract IntroductionAbnormal angiogenesis is central to vascular disease and cancer, and noninvasive biomarkers of vascular origin are needed to evaluate patients and therapies. Vascular endothelial growth factor receptors (VEGFRs) are often dysregulated in these diseases, making them promising biomarkers, but the need for an invasive biopsy has limited biomarker research on VEGFRs. Here, we pioneer a blood biopsy approach to quantify VEGFR plasma membrane localization on two circulating vascular proxies: circulating endothelial cells (cECs) and circulating progenitor cells (cPCs). MethodsUsing quantitative flow cytometry, we examined VEGFR expression on cECs and cPCs in four age-sex groups: peri/premenopausal females (aged < 50 years), menopausal/postmenopausal females (≥ 50 years), and younger and older males with the same age cut-off (50 years). ResultscECs in peri/premenopausal females consisted of two VEGFR populations: VEGFR-low (~ 55% of population: population medians ~ 3000 VEGFR1 and 3000 VEGFR2/cell) and VEGFR-high (~ 45%: 138,000 VEGFR1 and 39,000–236,000 VEGFR2/cell), while the menopausal/postmenopausal group only possessed the VEGFR-low cEC population; and 27% of cECs in males exhibited high plasma membrane VEGFR expression (206,000 VEGFR1 and 155,000 VEGFR2/cell). The absence of VEGFR-high cEC subpopulations in menopausal/postmenopausal females suggests that their high-VEGFR cECs are associated with menstruation and could be noninvasive proxies for studying the intersection of age-sex in angiogenesis. VEGFR1 plasma membrane localization in cPCs was detected only in menopausal/postmenopausal females, suggesting a menopause-specific regenerative mechanism. ConclusionsOverall, our quantitative, noninvasive approach targeting cECs and cPCs has provided the first insights into how sex and age influence VEGFR plasma membrane localization in vascular cells.