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ERG preserves endothelial identity to limit atherosclerosis
Identifying multiple drivers of stratified deformation in soft deltaic deposits using integrated geosensing
The impact of metastability on the high-pressure behavior of cerium
Abstract The structures adopted by solids under pressure are often assumed to reflect thermodynamic equilibrium, yet in many materials phase selection is strongly influenced by kinetic pathways and microstructural inheritance. Elemental cerium (Ce) exemplifies this challenge, with decades of conflicting reports describing different high-pressure crystal structures emerging under nominally identical conditions. Here we use neutron diffraction from large ( ~ 60 mm 3 ) sample volumes to follow the structural evolution in ultra-high-purity Ce during controlled pressure-temperature cycling between 85 and 295 K and up to 8 GPa. We find that the crystal structure formed at high pressure depends on the compression pathway: slow compression ( ~ 0.25 GPa hr −1 ) at room temperature favors an orthorhombic phase ( $${\alpha }^{{\prime} }$$ α ′ ), whereas slow ( ~ 0.25 GPa hr −1 ) and also moderately faster ( ~ 0.5 GPa hr −1 ) compression at low temperature stabilizes a pure monoclinic phase ( α ″ ). The low-temperature phase persists metastably over a wide temperature range but transforms irreversibly upon heating above ~ 280 K, or modest pressure cycling. Remarkably, the lower-pressure γ phase remains trapped far beyond the expected stability field, persisting to the highest pressures studied. These observations show that phase selection in Ce is governed by kinetics and microstructural memory rather than equilibrium thermodynamics or sample morphology alone, establishing path dependence as a defining feature of its high-pressure behavior.
An artificial neural network with analytical self-attenuation correction for rapid efficiency calibration of HPGe detectors
Abstract Efficiency calibration of HPGe detectors for environmental gamma-ray spectrometry requires knowing the full energy peak efficiency (FEPE) for each combination of photon energy, sample geometry and material composition. We present a hybrid model, combining an artificial neural network with a fixed analytical correction layer, that decouples this problem into a multilayer perceptron—trained on a single reference material to learn FEPE as a function of energy and sample height—and the analytical layer, which corrects for self-attenuation using XCOM mass attenuation coefficients. Two levels of geometric correction are evaluated: the classical parallel beam (PB) formula and a geometric ray-tracing (RT) approach with solid-angle weighting. The methodology is validated on two HPGe detectors of fundamentally different geometry—a well-type and a planar extended-range (XtRa) detector—using characterised PENELOPE Monte Carlo models as the data source. For each detector, the model is trained with IAEA-RGU-1 and tested against eight materials spanning densities from $${1.0}\,\text {g cm}^{-3}$$ (water) to $${2.7}\,\text {g cm}^{-3}$$ (ilmenite), at unseen energies and unseen sample heights. The solid-angle weighted ray-tracing correction is universally superior to the parallel beam formula for both detector geometries, achieving a mean pipeline MAPE across all eight test materials of 2.7% ( $$R^2 = 0.994$$ ) for the well-type detector and 1.8% ( $$R^2 = 0.999$$ ) for the XtRa, compared to 4.0% and 2.4% with the parallel beam formula. With the ray-tracing correction, the pipeline MAPE remains below 5% for all materials on both detectors, including ilmenite ( $$\rho = {2.7}\,\text {g cm}^{-3}$$ ). Once trained, the model yields FEPE predictions in under one second for any sample height and material, compared to tens to hundreds of seconds per Monte Carlo simulation, depending on sample size and the statistical precision required.
Cerebellar neural populations orchestrate dopamine reward signaling with single-trial precision
Design optimization and electromagnetic performance investigation of a magnetically integrated transformer-type controllable reactor for reactive power compensation
Biodiversity buffers forest ecosystems from compound climate extremes
Evaluating the tourism ecological security in rural regions using the DPSIR framework
Synchrotron cryo-soft X-ray microscopy reveals bacterial interactions with nanostructured calcium phosphate substrates
Abstract Nanostructured synthetic bone grafts offer a promising strategy to prevent bacterial colonisation while supporting bone regeneration. Here, calcium-deficient hydroxyapatite (CDHA) nanopillars synthesized from α-tricalcium phosphate exhibit contact-killing activity against Gram-positive Bacillus subtilis . Unlike inert bactericidal surfaces, CDHA exchanges ions with the surrounding environment, introducing chemical interactions alongside mechanical damage. Using synchrotron cryo-soft X-ray tomography and spectromicroscopy, we visualise bacterial ultrastructure and intracellular ionic changes in fully hydrated cells at subcellular resolution. Some bacteria exposed to the nanotopographies display membrane rupture, cytosolic leakage, and multivesicular body formation, indicating severe stress responses. XANES cryo-spectromicroscopy and linear absorption coefficient analysis identify a bacterial subpopulation with increased intracellular calcium, correlating with calcium release from the substrate and confirmed by confocal microscopy. However, this calcium accumulation does not affect viability, consistent with the halotolerant nature of B. subtilis . These findings provide insights into the combined effects of nanotopography and surface chemistry on bacterial responses.
From public weather narratives to solar-market risk decisions using constrained language-model features
Electric current as a stabilizing thermodynamic field in metallic crystals
MGCP-FER: Meta Control Genetic Programming (MCGP-FER) based feature extraction and fusion for facial expression recognition
Reconfigurable single-shot spectroscopy reaching 86 femtometers resolution
Impact of adding silver-doped carbon nanotube fillers to heat-cured acrylic denture base on flexural strength, contact angle and surface roughness
Abstract Acrylic resin, also known as poly (methyl methacrylate) (PMMA), is still the preferred material to fabricate denture bases. Studies were conducted to enhance its mechanical and physical properties. The current in vitro study investigated the effect of adding 0.05 wt% Ag-doped carbon nanotube (CNT) fillers to a heat-cured PMMA-based denture base material on its flexural strength, contact angle, and surface roughness. A total of 60 heat-cured acrylic specimens were prepared. The specimens were divided into two groups ( n = 30/group), according to the additive used: (a) control group, using heat-cured PMMA; (b) treated group, using an additive powder prepared by mixing 0.05 wt-% Ag-doped CNT nanoparticles with heat-cured PMMA. The flexural strength (three-point bending test), contact angle (sessile drop method), and surface roughness (Microscope Image Analysis Software) for each group were evaluated ( n = 10/test for each subgroup). Data were analyzed using the independent sample t-test ( p ≤ 0.05). The flexural strength of the treated group with Ag-doped CNT (107.9 MPa) was significantly higher than that of the control heat-cured PMMA (70.8 MPa). Similarly, the contact angle of the treated group (101.9°) was significantly higher than that of the control group (80.8°). Regarding the average surface roughness (R a ), no significant difference was observed between the treated group (11.99 R a ) and the control group (12.42 R a ). Reinforcing heat-cured acrylic denture base with 0.05 wt-% Ag-doped CNTs enhances flexural strength and hydrophobicity without affecting surface roughness, making it a promising alternative to conventional denture base materials.
Transcriptomic analysis of tissue-resident memory T cells of the fallopian tube reveals a precursor immune surveillance network for ovarian cancer prevention
Abstract The fallopian tube (FT) is increasingly recognized as the origin of high-grade serous ovarian cancer (HGSOC), yet its immune landscape remains poorly understood. Here, we employ single-cell RNA sequencing and paired T-cell receptor sequencing to profile tissue-resident memory-like T cells (TRML) from patient-derived matched non-cancerous FT, metastatic tumors, and peripheral blood. We identify substantial clonal and functional overlap (18.4%) between FT-derived and tumor-infiltrating TRMLs, exceeding that of circulating T cells. Shared clonotypes are preferentially enriched in exhausted CD8+ subsets, including a previously uncharacterized SIK3-high subset linked to epigenetic plasticity and metabolic adaptation. Functionally, FT-derived TRMLs recognize autologous tumor antigens, showing strong interferon-γ (IFN-γ) responses and CD137 upregulation in organoid coculture assays, supporting a role in early immune surveillance. Notably, FT TRMLs exhibit lower exhaustion than tumor counterparts, suggesting therapeutic potential. These findings reveal a precursor immune surveillance network in the FT and support leveraging FT-resident T cells for cancer immunotherapy and prevention.
Energy-based evaluation of soil-structure interaction in 3D arch dams using free-vibration decay
Large-scale data-driven inverse EM design based on metacircuit-embedded surface
Abstract General-purpose electromagnetic model is central to intelligent electromagnetic applications. However, its development is limited by severe scarcity of training data, arising from the high computational cost of full-wave simulations and reliance on specialized topologies that yield single EM function. To overcome this challenge, we introduce a metacircuit-embedded surface (MCES) framework, consisting of a fixed structural topology and flexible metacircuits that can be realized through lumped components. By shifting design focus from full-wave simulation to circuit-level modeling, over six million samples can be easily generated with laptop computation resources. Subsequently, an MCES-based forward and inverse AI design method was developed, providing rich design capability in frequency, amplitude, phase and polarization domains. Finally, three kinds of classical metasurfaces were designed and fabricated, showing superior performance compared to reported advanced designs. Overall, the proposed MCES-based framework greatly raises the design efficiency and training scale, paving the way for future general-purpose electromagnetic model.
Association between multi-intensity olfactory test performance and the Montreal Cognitive Assessment score after adjustment for gray matter volume
Abstract Previous studies, including ours, have reported that olfactory test scores (OTSs) are associated with gray matter volumes (GMVs) in brain regions including the hippocampus and amygdala, even after adjustment for the Japanese version of the Montreal Cognitive Assessment (MoCA-J) score. However, it has not been comprehensively investigated whether decreases in GMV fully explain the association between olfactory and cognitive function test scores. We analyzed the association between OTSs obtained using multiple odor intensities and the MoCA-J score in 1,444 adults (36.1% male) aged 31–91 years using multivariable regression models adjusted separately for (1) whole-brain GMV, (2) total GMV across olfactory limbic (memory-related) regions, and (3) total GMV across brain regions significantly associated with the MoCA-J score. Among participants aged ≥ 65 years, OTSs showed a positive association with the MoCA-J score across all three GMV-adjusted models (standardized β = 0.318, p = 0.001; β = 0.302, p = 0.002; and β = 0.308, p = 0.001, respectively), although effect sizes were small. These findings support the possibility that decreases in GMV may not fully explain the relationship between olfactory and cognitive function in older adults. Longitudinal studies are needed to clarify the causal relationship between olfactory function and cognitive decline.
Integrative structural interactomics reveals protein organization and structure in a giant virus
Abstract Giant viruses are large DNA viruses that infect unicellular and multicellular eukaryotes and form exceptionally large extracellular particles. (Meta)genomics and (meta)transcriptomics have provided insight into their diverse coding repertoire, but many of the proteins remain to be characterized as they lack homology with known proteins. Here, we integrate cross-linking mass spectrometry, quantitative proteomics, computational tools and cryo-EM data to characterize the protein architecture of intact melbournevirus particles. Based on this, we allocate 88 viral proteins to different virion sub-compartments and propose topologies of 25 inner membrane proteins. We assign eight components of the capsid in cryo-EM data, including proteins that tether the capsid shell to the membrane, reflecting key points in virion maturation. The data provide a valuable resource and demonstrate the power of an integrative approach to gain system-level structural insights into a poorly characterized biological system.