UC Riverside

Reassortment between different influenza strains occurs when they co-infect the same host cell. The emergence of a reassortant virus depends on both its intrinsic fitness and extrinsic factors, including preexisting humoral immunity. The generation of pandemic strains, such as H2N2 and H3N2, and zoonotic influenza A viruses, such as H5N6, H5N8, and H7N9, in birds is suggested to be the result of extensive selection by preexisting antibodies. To further explore the role of humoral immunity in reassortment, we generated two divergent fluorescent protein-expressing viruses and used strain-specific and cross-reactive monoclonal antibodies (mAbs) to assess the impact of cross-immunity on reassortment. Our results indicate that all mAbs altered the genotypic diversity and significantly reduced the release of progeny virions in co-infected cells both in vitro and in vivo. Moreover, antibody transfer studies in mice revealed protection from challenge with divergent pathogenicity profiles. Notably, selection driven by a strain-specific mAb depended on its neutralizing specificity, whereas the selection driven by broadly reactive mAbs was independent of neutralization specificity. Our findings demonstrate that preexisting neutralizing antibodies shape reassortment and that strain-specific neutralizing antibodies promote antigenic shift during co-infection, which is not the case for broadly cross-reactive antibodies that recognize influenza viruses from different subtypes.

A zoonotic Babesia species previously referred to as Babesia sp. MO1 is formally described and named here as Babesia hegotelforum sp. nov. This taxon is distinct from Babesia divergens based on genome-wide sequence divergence, phylogenetic placement, host associations, and clinical presentation. The parasite infects erythrocytes of humans, and eastern cottontail rabbits (Sylvilagus floridanus), and is transmitted by Ixodes dentatus. The holotype consists of a Giemsa-stained thin blood smear and cryopreserved infected erythrocytes from the cloned isolate BML-Bh-B12 at ≤10 passages in continuous in vitro culture. Paratype material includes five additional clones (BML-Bh-H1, BML-Bh-F12, BML-Bh-H6, BML-Bh-A3, and BML-Bh-F1) derived from BEI Resources strain NR-50441, along with the original mixed isolate NR-50441. This species description meets the requirements of the International Code of Zoological Nomenclature and establishes Babesia hegotelforum sp. nov. as a distinct species of clinical and epidemiological significance in North America.

Endovascular thrombectomy (EVT) has transformed the treatment of acute ischemic stroke (AIS). However, a substantial proportion of AIS patients experience poor outcomes despite successful recanalization, often due to severe neurological deterioration or life-threatening complications. Early identification of these high-risk patients remains a major unmet need. In this study, we developed and validated machine-learning (ML) models that integrate automated quantitative brain arterial morphology and collateral grading with demographic, clinical, laboratory, and imaging variables to predict major post-EVT complications and early neurological outcomes. Using a prospectively collected database of 727 AIS patients that underwent EVT, we developed ML models to incorporate patient-specific vascular morphometry with conventional clinical, laboratory, and imaging data to predict emergence of early neurological deterioration (END), symptomatic intracranial hemorrhage (sICH), malignant brain edema (MBE) requiring surgical decompression, and neurogenic respiratory failure and dysphagia requiring tracheostomy/gastrostomy (TC/PEG). Our analysis of morphological features, including increased tortuosity and reduced vessel diameter, showed strong associations with complications. Morphology-informed (MI) models consistently outperformed baseline-clinical (BC) models for patients with END (AUROC 0.81 for MI model vs. 0.73 for BC), sICH (AUROC 0.68 MI vs. 0.56 BC model), MBE (AUROC 0.67 MI model vs. 0.56 BC), or patients who underwent TC/PEG (AUROC 0.66MI vs. 0.58 BC model). Statistical testing confirmed significant AUROC improvements for END, sICH and mRS (p < 0.05), Finally, patient-specific calibrated probability profiles enabled individualized, multidimensional risk stratification, revealing distinct complication-specific risk patterns across patients. These findings demonstrate that cerebrovascular structure—an often overlooked yet physiologically fundamental determinant of ischemic injury and reperfusion dynamics—provides significant predictive information that is not captured by standard clinical or visual imaging assessments. Automated vascular segmentation and collateral grading techniques enable rapid and objective integration of cerebrovascular metrics into prognostic models, offering a scalable tool for precision risk stratification, supporting earlier intervention, targeted monitoring, and improved post-EVT management.

Abstract: We investigate interseismic locking on the Kamchatka megathrust, source of the 2025 M w 8.8 earthquake, using a data‐driven probabilistic framework. Horizontal GNSS velocities constrain a boundary element model on a triangulated megathrust geometry, while an algorithmic sampling strategy estimates the locking probability for fault elements without prescribing asperity locations a priori. The resulting probability distribution reveals a locked region whose cumulative slip deficit since the previous megathrust earthquake (M w 9.0, 1952) yields a moment deficit comparable to the 2025 release. Hierarchical clustering of the locking models identifies smaller asperities that coincide with the 2025 rupture initiation zone. Slip in the 1952 and 2025 events shows strong spatial correspondence with high locking probabilities, indicating that persistent asperities dominate moment release. Lower shallow slip in 2025 contrasts with the greater near‐trench slip inferred for the 1952 tsunami, highlighting variable shallow slip as a major source of uncertainty for tsunami hazard estimation.

PURPOSE: Conventional arterial spin labeling (ASL) reconstruction suffers from sign-flipping errors and/or noise bias under strong background suppression (BS), limiting SNR optimization. We propose reference-superimposed reconstruction (RS-recon) to enable ideal BS and robust signal recovery. METHODS: RS-recon superimposes high-SNR M0 k-space data onto ASL k-space before reconstruction, with subsequent reference subtraction. Four volunteers underwent 3T scanning with pulsed ASL, pseudo-continuous ASL, and dual-module velocity-selective ASL across varying BS levels. RS-recon integrated reconstruction pipelines were compared to standard magnitude (Mag), complex subtraction (ComplexSub), and complex (Complex) reconstruction pipelines. The performance was evaluated by metrics including ASL signal, temporal SNR (tSNR), artifacts, misalignment robustness, and generalized auto-calibrating partially parallel acquisitions (GRAPPA) compatibility. RESULTS: RS-recon eliminated subtraction artifacts due to signal sign-flipping, recovered negative ASL signals, and enabled ideal BS for maximizing SNR. It significantly improved ASL signal and tSNR in gray/white matter across all labeling methods and different BS conditions. RS-recon corrected phase errors under low-SNR conditions, preserved signal signs, maintained Gaussian noise distribution, remained robust against misalignments between the reference and the ASL images, and integrated well with GRAPPA. Mag + RS performance matched complex-based (ComplexSub and Complex) RS pipelines and is recommended for its simplicity, compatibility, and robustness overall. CONCLUSION: RS-recon offers a simple, robust strategy for ASL image reconstruction, resolving subtraction errors even with magnitude output and enabling optimal background suppression. It enhances signal fidelity, SNR, and phase accuracy while integrating seamlessly with existing reconstruction pipelines and parallel imaging.

Dissolved organic matter (DOM) is a central component of Earth’s carbon cycle and one of the planet’s most chemically diverse pools, yet the molecular structures of its constituents remain largely unresolved. This limitation has hindered our ability to link DOM composition to microbial processes and ecosystem function. Here we present ENVnet, a global molecular repository built from tandem mass spectrometry data collected across 13 terrestrial and aquatic environment types, including 419 newly generated samples that expand publicly available DOM metabolomics data and cover previously underrepresented environments. By computationally deconvolving chimeric mass spectra, a longstanding challenge in environmental metabolomics, we recover high-quality fragmentation data for >22,000 distinct molecular features (defined by a specific precursor mass and fragmentation pattern). Using ENVnet, we uncover conserved and environment-specific molecular patterns in DOM composition and underlying biogeochemical processes. We also use molecular features encoded in ENVnet to train predictive models of DOM persistence, allowing molecular-level assessment of microbial turnover in independent systems.

Abstract: We present an analysis of XMM-Newton and Chandra observations of 85 nearby main-sequence FGK stars with age estimates ranging from 0.2 to 12 Gyr. We measure quiescent 0.3–10 keV luminosities, variability metrics, and multitemperature thermal plasma spectral parameters. Quiescent spectra are typically described by three characteristic plasma components ( kT  ≈ 0.1, 0.4, 0.8 keV); the fraction of flux from T ≥ 7 MK rises with X-ray surface flux, reaching ∼50% for F X  ≳ 10 6 erg cm −2 s −1 . We derive relations between emission measure-weighted coronal temperature and both L X and F X , enabling temperature-informed count rate conversions for faint sources. We quantify how bandpass conversions (ROSAT 0.1–2.4 keV versus XMM-Newton 0.3–10 keV) depend on temperature and show that inferred ROSAT-band L X broadly follows the canonical t −1.5 decay, while the harder band exhibits increased scatter at >4 Gyr. Several stars show excess activity suggestive of age errors, inclination effects, or unresolved companions. Some of these “outlier” stars are potential direct imaging targets for the Habitable Worlds Observatory, and detailed characterization of these stars is needed to inform their likely influence on the atmospheric evolution of orbiting planets.

Rhodium supported on titania (Rh/TiO 2 ) is an active catalyst for the reverse water gas shift reaction, yet the nature of the active sites for this reaction and others remain under debate due to the dynamic nature of the Rh coordination. Rhodium supported on titania (Rh/TiO 2 ) is an active catalyst for the reverse water gas shift reaction, yet the nature of the active sites for this reaction and others remain under debate. Single atom Rh sites have frequently been proposed as key sites, making it essential to monitor size changes of Rh species under in situ conditions to establish the structure–function relationship. However, surface-sensitive in situ measurements of nanosized particles remain experimentally challenging and have focused on metal oxide single crystal model systems. Here, we apply ambient pressure X-ray photoelectron spectroscopy (APXPS) to Rh/TiO 2 powdered catalysts under oxidizing and reducing environments. We find size-dependent binding energy shifts in both oxidized and reduced Rh species. By deconvoluting this size effect from oxidation state core level shifts, APXPS can provide qualitative evidence of Rh cluster size changes. Potential electronic effects responsible for these shifts are explored with density functional theory calculations. Ex situ transmission electron microscopy gives insight into particle size while Raman spectroscopy identifies Rh oxide phases. Correlative X-ray absorption spectroscopy and pair distribution function (PDF) measurements confirm the structural changes in the APXPS results. These findings offer direct insight into the dynamic behavior of Rh catalyst sintering and fragmentation based on core-level shifts.