Browse Articles

Slow slip modulates low-frequency seismicity on the Parkfield segment of the San Andreas Fault

Abstract Understanding how slow slip events (SSEs) influence fault behavior is essential for characterizing the fault slip spectrum and its role in earthquake generation. Here, we show that deep learning applied to strainmeter data can detect short-duration SSEs on the San Andreas Fault near Parkfield, enabling an SSE catalog. SSEs are coherently observed across instruments, with evidence from nearby creepmeters. Location analysis indicates shallow depths and slip consistent with right-lateral motion. They follow a cubic moment–duration scaling law, similar to earthquakes and consistent with both subduction zone observations, and linear scaling as an upper bound. Low-frequency earthquakes increase following SSEs, suggesting that slow aseismic slip modulates seismicity. Detecting these SSEs fills an observational gap in slow earthquake studies and highlights their broader relevance. These findings support a continuum between aseismic and seismic slip, where transient deformation in creeping segments perturbs stress in adjacent locked areas, potentially promoting seismic activity.

Semaglutide attenuates neuroinflammation in male mice

Intermetallic charge redistribution restructures the oxygen-bound intermediate network for efficient ethylene electrosynthesis

Merging bioinspired incubation with supramolecular photocatalysis for Michaelis CO2 reduction beyond enzymes

Elevated threat status of large-fruited plants is associated with the extinction of large frugivores in the Caribbean islands

Anthropogenic activities severely impair biodiversity and ecosystem functionality. A prime example is the loss of frugivores and their corresponding seed dispersal services due to defaunation (the decline of animal populations), ultimately affecting the continued persistence of plant species that lack their ecological partners. Loss of frugivores and their biotic interactions can be particularly severe on islands due to their isolation and relatively small area, which is often associated with low functional redundancy. From field observations on the Caribbean Virgin Islands, we found that only 1% of documented seed dispersal events occurred in large-fruited plants, with dispersal probability closely tied to overlap between fruit width and gape size of the largest nonthreatened frugivore. Using fruit–frugivore trait matching to extrapolate this pattern to the rest of the Caribbean, we found that large-fruited plant species are effectively “orphaned” due to the absence of animal dispersers, resulting in elevated rates of endangerment across islands. Threat status of plant species was better explained by trait matching than other pressures such as human use, suggesting that extinction or reduced abundance of large-bodied frugivores is compromising their plant mutualists’ ability to persist. These results argue strongly for the need to restore populations of large-bodied frugivores across the Caribbean, either through population enhancement of currently endangered species or rewilding with close relatives of extinct species.

A data-light composite climate resilience index reveals superior drought resilience of millets in rainfed agroecosystems

“Effects of robot-assisted gait training combined with traditional treatment in hereditary spastic paraplegia: An observational pilot cohort study”

Structural basis for inhibition of TcrXyn10A xylanase by nonspecific xylose binding at subsite − 2

Performance enhancement of THz antenna with dielectric resonator and prediction bandwidth using machine learning approach for 6G applications

Evaluation of a human 3D multi-spheroid model derived from SH-SY5Y cells for cytotoxicity testing

Numerical and transcritical bifurcation analysis of Chikungunya dynamics within a fractional-fractal framework

Deep-learning based image reconstruction enables reduced dose CT pulmonary angiography with non-inferior image quality

Abstract To investigate whether a CT pulmonary angiography (CTPA) protocol with reduced radiation dose and deep-learning based image reconstruction (DLIR) is non-inferior in image quality to standard-dose CTPA using iterative reconstruction. A phantom study was conducted to estimate the additional radiation dose reduction enabled by high-strength deep learning-based image reconstruction (DLIR-H) compared to adaptive statistical iterative reconstruction (ASiR-V 90%). Medium and large phantoms were used to simulate different body sizes. Subsequently, we reduced radiation dose of our clinical CTPA protocol and transitioned to DLIR for image reconstruction. We retrospectively analyzed 307 consecutive patients who were examined before (n = 152) and after (n = 155) this clinically driven change in the CTPA protocol. Objective image quality was quantified and subjective image quality was rated by two radiologists. The non-inferiority margin was pre-specified as a < 5% difference in image quality parameters. In the phantom, DLIR-H allowed radiation dose to be reduced by up to 71% with equivalent or higher signal-to-noise-ratio (SNR) compared to standard-dose examinations reconstructed with ASiR-V 90%. In the patient cohort, radiation dose was reduced by 41% (median DLP 116 vs. 68 mGy*cm; effective dose 1.69 vs. 0.99 mSv, p < 0.001). In the modified protocol, median SNR was superior for the central pulmonary artery (13.6 vs. 22.3) and non-inferior for the segmental pulmonary arteries (16.4 vs. 16.8). Subjective image quality averaged over both readers was superior with the modified protocol. Compared to state-of-the-art iterative reconstruction, DLIR allows radiation dose for CTPA to be reduced by an additional 41% with non-inferior image quality.

Transportation 4.0 planning in emergency medical services considering real-time ambulance two-phase assignment-routing and mission change

Complete chloroplast genomes of four Triumfetta species obtained from herbarium specimens: comparative analysis and identification of a synapomorphic inversion

Probabilistic deep learning framework for dynamic carbon emission accounting of electric buses under grid uncertainty

Hybrid deep ensemble architecture for robust diabetic retinopathy classification: leveraging transfer learning and CNN-transformer synergy

Eco-friendly fabrication of ZnO–Co₃O₄/C S-scheme photocatalyst from bimetallic ZIF for sunlight-powered H₂ generation and CO₂-to-fuel conversion

RefactoCNN-system: an optimized deep learning framework for predicting software refactoring opportunities using CNN-based code analysis

Unveiling the novel role of PGAM5 in rewiring metabolism through PI3K/AKT/mTOR signaling in acute myelogenous leukemia

Identification of targetable epitope surfaces from the high resolution structure of the superantigen Staphylococcal enterotoxin L

Abstract Emetic exotoxins secreted by Staphylococcus species ( Staphylococcal enterotoxins, SEs) are a major cause of food poisoning cases worldwide and many are additionally classified as superantigens—able to potently activate T cells in an antigen independent manner. Fewer than half of the gene products of the known SE genes have been extensively characterized. The gene for Staphylococcal enterotoxin L (SEL) occurs in both foodborne and clinical isolates but no detailed structural characterization has yet been available. We report here the crystal structure of SEL and confirm its function as a superantigen via direct T cell activation assays. By comparison of the SEL sequence with that of its four closest homologues (SEI, SEK, SEM and SEQ), we have identified binding epitopes unique for SEL and mapped these regions onto the structure. These data provide the first high resolution view of SEL and the basis for the development of diagnostic procedures for its specific detection.