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.

While cancer remains the leading cause of occupational death among firefighters, cancer mitigation efforts are not always successful. This study examines how health practices become durable behavioral changes within the professional field of California firefighters. Drawing on 48 in-depth interviews, we examine how health uptake has been successful in a field where resistance would be expected based on the entrenched tradition and masculine culture. Integrating Bourdieu's framework with Fundamental Cause Theory, we show that cultural, symbolic, and social capital must be reconfigured concurrently to transform the entrenched habitus and enhance cancer mitigation. Specifically, acquiring new knowledge on cancer risks, reclassifying clean gear as a new symbol of professionalism, and solidifying the new knowledge and symbol through cohesive social networks can facilitate habitus transformation and produce sustained behavioral changes in cancer mitigation. Our research enriches both theoretical perspectives and extends medical sociology's understanding of how to overcome sociocultural barriers to pro-health actions.

Infants learn through everyday interactions with the physical and social environment. For infants with cerebral palsy (CP), motor impairments may disrupt everyday learning opportunities. How can we measure real-world motor behavior and learning opportunities in infants with CP? Machine learning models have been developed to quantify body position throughout a day in infants with typical development (TD) using wearable sensor data. However, these models have not been validated in infants with motor impairments. This study assessed the validity of body position classification using machine learning and sensors in infants with CP. Ten infants with CP (7-18 months; one session each) and 19 infants with TD (4-12 months; 45 sessions) wore four inertial sensors on their legs throughout a day. Ninety minutes were video recorded and manually scored for body position in five categories: supine, prone, sitting, standing, and held. Random forest classifiers were trained to predict body position from sensor-derived motion features. Models trained on datasets that varied in size (9, 45, 54 sessions) and group composition (CP, TD, CP and TD) were compared to determine the most accurate model for infants with CP. Larger training sets and training sets that included data from infants with CP were the most accurate; the final models achieved similar performance in CP and TD (86% and 89% accuracy), captured meaningful individual differences (ICCs = 0.682-0.999), and generated predictions that were correlated with motor skill assessments. Findings demonstrate that wearable sensors and machine learning can accurately classify real-world body position in infants with CP.

Abstract: Characterization of exoplanet atmospheres requires a close interplay between observations, modelling, and experimental data. The accuracy of input data used in atmospheric models is essential because it impacts our interpretation of planetary spectra with retrieval codes. Molecular absorption cross sections in the UV–visible range are fundamental input parameters, determining chemical kinetics, particularly on temperate terrestrial planets. However, several atmospheric species remain poorly, or even entirely, uncharacterized. This is the case for HSO, a radical with unconstrained photolysis cross sections, often approximated by hydroperoxyl (HO 2 ). Sulphur chemistry can strongly influence the composition of rocky exoplanets, particularly in anoxic environments where volcanic SO 2 —the main source of HSO—is more efficiently photolyzed, and sulphur aerosols such as S 8 can form. HSO photolysis contributes to the formation of such sulphur chains, which have been proposed as indirect signatures of volcanic outgassing. Assessing the sensitivity of photochemical models to different UV–visible cross section prescriptions for HSO is therefore important for guiding its prioritization among poorly characterized atmospheric species. Here, we derive an updated HSO cross-section prescription from simulated HSO 2 data, providing a more reliable representation of HSO photolysis than HO 2 . We compare results from these new cross sections to the default prescription for temperate terrestrial planets with Archean-like atmospheres. We find that our updated HSO cross-section prescription enhances aerosol scattering and absorption signatures in transmission, emission, and reflection spectra for planets orbiting G- and K-type stars.

Abstract: Biomass burning (BB) is a major organic aerosol source during urban winter pollution events. BB particles are rich in brown carbon (BrC) and absorb light to produce photooxidants such as oxidizing triplet excited states of organic chromophores (3C*). In midlatitudes, semivolatile phenols (ArOH) from BB are oxidized to form phenolic secondary organic aerosol (SOA) under moderate temperatures and sunlight. Here, we explore whether phenolic SOA is also formed in a cold, relatively dark, high-latitude city during winter. We use measurements from the 2022 ALPACA field campaign in Fairbanks, Alaska, incorporated into the PACT-1D model with added emissions, partitioning, and chemistry for 34 semivolatile BB phenols. The model includes phenol oxidation in the gas phase and in two populations of particles─aqueous and organic. Despite short pollutant residence times within the polluted surface layer, simulated phenolic SOA formation is fast, generating up to 4.5 μg m–3, with 99% produced by 3C* in organic particles. The modeled phenolic SOA explains up to 34% of measured daytime oxidized organic aerosol (OOA), indicating it is a significant portion of the poorly understood OOA in Fairbanks. Our results reveal that BrC photochemistry can be an important source of atmospheric oxidizing capacity even during high-latitude winters.

Uncertainty in long-term climate outcomes arises not only from physical processes but also from societal responses to climate variability and change. Here we embed a range of temperature anomalies into an empirically-informed, coupled climate–social model to investigate how natural temperature variability, through its influence on societal decision-making, shapes global emissions trajectories. Using Monte Carlo ensembles spanning social, political, and technological parameters, we find that anomalous mid-century warmth strengthens public support for mitigation and accelerates the timing of net-zero emissions by several years, whereas anomalous cooling delays net-zero achievement. The response scales with the magnitude and persistence of variability with stronger and longer-lasting temperature anomalies producing larger shifts in mitigation timing. Across ensembles, high-variability climates are associated with higher emissions pathways and a greater likelihood of failing to reach net zero by 2100. Externally forced cooling events, such as volcanic eruptions, produce sharp impacts, delaying net zero by nearly a decade through temporary declines in public support for climate mitigation. These findings reveal that climate variability can cascade through perception, politics, and technology adoption to alter the pace of global mitigation, highlighting the sensitivity of emissions pathways to short-term climate signals and the role of societal interpretation of those signals.

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.

Predicting which non-native plant species will become established and where is critical for conserving and managing biodiversity. Theory suggests that the mycorrhizal strategy of non-native plants may predict their establishment success. Here we combine a global dataset of 440,788 vegetation plots with data on plant native status and mycorrhizal type to assess mycorrhizal strategy of non-native plants. The mycorrhizal strategy of non-native plants varies strongly across biomes. Across grassland and desert biomes, non-native species are more frequently non-mycorrhizal than native species, whereas in other biomes non-native species are more likely to be mycorrhizal, most commonly arbuscular-mycorrhizal. Disturbance type and intensity are key predictors of mycorrhizal strategy of non-native species, as mycorrhizal species are favoured by landscape modification and non-mycorrhizal species by natural and human-caused disturbance events. Facultatively mycorrhizal species are consistently under-represented among non-native plants compared with natives, suggesting that symbiotic flexibility does not confer an advantage for non-natives as previously expected. Our study shows that non-native mycorrhizal strategy varies across biogeographical contexts and disturbance, highlighting the need for region-specific prevention and management approaches to plant species introductions.