Bourns College of Engineering

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.

We build upon Haueis and Colaço's account in two ways. First, we argue that the backgrounding of metabolism was a pragmatic response to a data granularity problem faced by traditional cognitive modelers. Then, we suggest that equivalence demonstrations offer a promising pathway for incrementally introducing metabolic considerations into extant modeling practice.

BACKGROUND AND PURPOSE: Collateral circulation is a key determinant of treatment response and outcomes in acute ischemic stroke (AIS), yet its assessment in clinical practice remains limited and subjective. While CTP offers insight into tissue viability, its restricted availability and susceptibility to artifacts reduce its practical utility, particularly in smaller centers. As an accessible alternative, we developed and validated an automated quantitative collateral index (qCI) derived from CTA using a deep learning U-Net segmentation framework and evaluated the ability of CTA-based features to predict poststroke recovery and functional outcomes. MATERIALS AND METHODS: We retrospectively analyzed prospectively collected data from 230 patients with AIS who underwent endovascular thrombectomy (EVT) between 2019 and 2023. CTA scans were segmented using a validated neural network-based vascular extraction model to generate 3D vessel networks and compute morphology metrics (vessel length, branching, fractal dimension, tortuosity). A fully automated qCI was derived through hemispheric comparison of vascular features following spatial registration. Agreement of qCI with clinician grading was quantified. Gradient boosted decision tree models were trained to predict early neurologic deterioration, early neurologic improvement, and 90-day mRS using the following: 1) CTP-only (core, penumbra, mismatch), 2) CTA-only (qCI + morphology), and 3) combined CTA + CTP features. RESULTS: Automated qCI (grades 0-3) showed strong concordance with expert scoring (accuracy 0.863; Pearson R = 0.880; Cohen κ = 0.786). Dichotomized collateral status achieved 0.938 accuracy (area under the receiver operating curve [AUROC] = 0.945). For 90-day mRS prediction, the CTA-only model outperformed the CTP-only model (AUROC, 0.730 versus 0.645) with better calibration (Brier score 0.178 versus 0.295). The combined CTA + CTP model performed best overall (AUROC 0.781), with similar improvements observed for early neurologic deterioration. CTA-derived features led to significant reclassification gains when added to perfusion-based models. CONCLUSIONS: Automated CTA-derived qCI and cerebrovascular morphology provide rapid, objective, and reproducible collateral assessment with agreement with expert grading. These features outperform perfusion metrics in several predictive tasks and further enhance prognostic accuracy when combined with CTP. Because CTA is widely available, qCI offers a scalable, clinically practical tool for improving prediction of stroke outcome, particularly in settings where CTP is unavailable.

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.

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.

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.

Rapid and sensitive detection of plant pathogens, such as the Avocado Sunblotch Viroid (ASBVd), is essential for early disease management and agricultural biosecurity. Yet, most current diagnostic methods not only require relatively large sample inputs but also often lack the ultrasensitivity required for reliable detection with scarce or minimally collected plant material. Here, we report a novel low-input but ultrasensitive diagnostic platform that integrates isothermal recombinase polymerase amplification (RPA), CRISPR-Cas12a detection, and a solid-state nanopore array for the detection of ASBVd. The system leverages CRISPR-Cas12a collateral cleavage activity to generate single-bead fluorescent signals, which are captured by a nanopore array through pressure-driven blockage. Our platform achieves a detection limit down to 1.68 copies/μL while using only 40 nL of bead-fluorophore mixture per readout, which is over 100-fold less than conventional assays based on fluorescent readout using an imaging reader, enabling detection from minimal avocado sample collection. We demonstrate robust binary classification of ASBVd-positive and -negative samples from multiple avocado tissue types and orchards in California. The assay requires just 60 min and operates entirely under isothermal conditions, avoiding the need for bulky PCR instruments and supporting on-site deployment with minimal equipment. This method provides a promising platform for field-deployable, ultrasensitive, and low-input diagnostics of viroids and other low-titer pathogens in plant or clinical settings.

Computational tools for studying the structure of protein complexes are essential for providing mechanistic insights into protein-protein interactions and therapeutic drug design. Here, we present SPICE (Structural Protein Interaction Complex Evaluator), a web-based platform that allows structural biologists to perform rapid, modular analyses of protein complexes directly from Protein Data Bank (PDB) structures. SPICE allows users to define and execute analysis workflows via an intuitive web interface, reducing analysis times from minutes to seconds. The platform offers a broad range of analytical capabilities, including (i) detection of hydrogen bonds, salt bridges, and disulfide bonds; (ii) protein-protein interface mapping; and (iii) computation of solvent accessibility, van der Waals energetics, and other key geometric descriptors. SPICE further provides interactive 3D visualization and supports comparative analyses across multiple complexes, enabling the study of mutational effects and binding variants. The tool is freely available at https://spice.cs.ucr.edu (no registration required).

OBJECTIVE: Elevated kurtosis values have been observed in subcortical grey matter structures of patients with neurodegenerative diseases. Here, we examined relationships between iron measures and kurtosis in iron-rich subcortical grey matter structures.Please check and confirm the affiliation 4 for the author "Xiaoping P. Hu".Affiliation 4 for Xiaoping P. Hu was incorrect since he is not associated with that department. We have removed this affiliation. Thanks!  MATERIALS AND METHODS: Multi-shell diffusion and multi-echo gradient echo acquisitions were used to derive mean kurtosis and iron measures (R₂* and magnetic susceptibility), respectively, in subcortical grey matter nuclei and white matter tracts in a discovery cohort (110 healthy older and 63 younger adults) and replication cohort (72 healthy older adults).Please confirm if the author names are presented accurately and in the correct sequence (Ilana J. Bennett and Xiaoping P. Hu). Also, kindly confirm the details in the metadata are correct.Thanks for asking. We've checked and the names are presented accurately and in the correct sequence. We have corrected some details in the metadata - adding an affiliation to Murphy Shao (Department of Physics) and removing the Materials Science and Engineering affiliation for Xiaoping P. Hu. Is it possible to add an orcid id for Vala Masjedizadeh? His Orcid ID is  0009-0009-3692-2553 Everything else looks okay RESULTS: Iron-rich grey matter regions exhibited higher mean kurtosis, R₂*, and magnetic susceptibility and white matter regions had lower mean kurtosis in the older adult group from the discovery cohort. In both cohorts, mean kurtosis was significantly correlated with R₂* and magnetic susceptibility in iron-rich grey matter nuclei. No association was seen between signal-to-noise ratio and mean kurtosis in any grey matter region, indicating that the increase in mean kurtosis was not due to reduced signal-to-noise. As keywords are mandatory for this journal, please provide 3-6 keywords.I'm not sure where to put the keywords so I'll reply to the query with the keywords. Our keywords are: kurtosis, iron, grey matter, aging CONCLUSION: Our findings indicate that kurtosis is associated with iron-sensitive metrics in iron-rich grey matter structures, suggesting that iron deposits may be contributing to kurtosis.