University of California System

The deep oceans are environments of complex carbon dynamics that have the potential to significantly impact the global carbon cycle. However, the role of hadal zones, particularly hadal trenches (water depth > 6 km), in the oceanic dissolved organic carbon (DOC) cycle is not thoroughly investigated. Here we report distinct DOC signatures in the Japan Trench bottom water. We find that up to 34% ± 7% of the DOC in the trench bottom is removed during the northeastward transport of dissolved carbon along the trench axis. This DOC removal increases the overall DOC recalcitrance of the deep Pacific DOC pool, and is potentially enhanced by the earthquake-triggered physical and biogeochemical processes in the hadal trenches. Radiocarbon analysis on representative oceanic transects further reveals that the Pacific deep-water DOC undergoes distinct removal compared to those in the Atlantic and Indian Oceans along the thermohaline transport. Our findings highlight hadal trenches as previously unrecognized DOC sinks in the deep ocean system, with varying dynamics that warrant further investigation.

GENCODE produces comprehensive reference gene annotation for human and mouse. Entering its twentieth year, the project remains highly active as new technologies and methodologies allow us to catalog the genome at ever-increasing granularity. In particular, long-read transcriptome sequencing enables us to identify large numbers of missing transcripts and to substantially improve existing models, and our long non-coding RNA catalogs have undergone a dramatic expansion and reconfiguration as a result. Meanwhile, we are incorporating data from state-of-the-art proteomics and Ribo-seq experiments to fine-tune our annotation of translated sequences, while further insights into function can be gained from multi-genome alignments that grow richer as more species’ genomes are sequenced. Such methodologies are combined into a fully integrated annotation workflow. However, the increasing complexity of our resources can present usability challenges, and we are resolving these with the creation of filtered genesets such as MANE Select and GENCODE Primary. The next challenge is to propagate annotations throughout multiple human and mouse genomes, as we enter the pangenome era. Our resources are freely available at our web portal www.gencodegenes.org, and via the Ensembl and UCSC genome browsers.

The permeability ($\text{K}$) of tight carbonate rocks is important to maximize the efficiency of hydrocarbon production and overall reservoir management. While such property is crucial for engineering design, conducting experimental tests to determine $\text{K}$ can be both time-consuming and expensive. As such, reliable and high-fidelity models derived with soft computing techniques become useful for estimating $\text{K}$. Using a data set containing samples from 130 data points published in the literature, this work developed a sensitivity-driven Evolutionary Polynomial Regression (EPR) model to predict $\text{K}$. The model computes the permeability, log10$\text{K}$ (mD), as a function of three explanatory variables: porosity, $\phi$ (−), formation factor, $\text{F}$ (−), and the characteristic pore throat diameter, $\text{dPT}$ (m). One unique feature of our approach is that it considers the physical meaning of the variables during the construction of the model. Verification of the methodology was carried out using split-sampling cross-validation. The developed model showed attributes such as parsimony (lower number of parameters and input variables), good predictive capability (accurate tracking observed log10$\text{K}$), generalization ability (preserving physical meaning), and robustness (consistent performance under cross-validation). Sensitivity analysis revealed that the model can adequately simulate the increase in $\text{K}$ with increasing $\phi$ and $\text{dPT}$, as well as its capacity to capture the non-linear relationship between log10$\text{K}$ and $\text{F}$. Comparison of simulated $\text{K}$-values with results of models published in the literature, further validated the ability of our optimum EPR model structure. The proposed model shows potential as a promising method to estimate the permeability of tight carbonate rocks.

Atmospheric rivers (ARs) are filamentary structures within the atmosphere that account for a substantial portion of poleward moisture transport and play an important role in Earth's hydroclimate. However, there is no one quantitative definition for what constitutes an atmospheric river, leading to uncertainty in quantifying how these systems respond to global change. This study seeks to better understand how different AR detection tools (ARDTs) respond to changes in climate states utilizing single‐forcing climate model experiments under the aegis of the Atmospheric River Tracking Method Intercomparison Project (ARTMIP). We compare a simulation with an early Holocene orbital configuration and another with CO2 levels of the Last Glacial Maximum to a preindustrial control simulation to test how the ARDTs respond to changes in seasonality and mean climate state, respectively. We find good agreement among the algorithms in the AR response to the changing orbital configuration, with a poleward shift in AR frequency that tracks seasonal poleward shifts in atmospheric water vapor and zonal winds. In the low CO2 simulation, the algorithms generally agree on the sign of AR changes, but there is substantial spread in their magnitude, indicating that mean‐state changes lead to larger uncertainty. This disagreement likely arises primarily from differences between algorithms in their thresholds for water vapor and its transport used for identifying ARs. These findings warrant caution in ARDT selection for paleoclimate and climate change studies in which there is a change to the mean climate state, as ARDT selection contributes substantial uncertainty in such cases.

A shift in depth range enables marine organisms to adapt to marine heatwaves (MHWs). Subsurface MHWs could limit this pathway, yet their response to climate warming remains unclear. Here, using an eddy-resolving Earth system model forced under a high emission scenario, we project a robust global increase in subsurface MHWs driven by rising subsurface mean temperatures and enhanced temperature variability. Historically, maximum MHW intensity occurs around 100 m depth, which shifts to the faster-warming surface under greenhouse warming. However, removing the long-term warming trend yields an increase in subsurface MHW intensity and annual days greater than that at the surface, especially in large marine ecosystem regions, primarily due to increased variability. Additionally, days of the surface and subsurface concurrent event increase ten times more than those of individual events. Our study highlights a heightened threat to marine organisms under global warming, as the increased subsurface heatwaves reduce their refuge options.

Meal sorting in mosquitoes is a phenomenon whereby ingested blood and sugar meals are directed to different destinations in the alimentary canal. We undertake a systematic analysis and show that entry of blood in the midgut is influenced by blood components, temperature, and feeding mode, while sugar solutions are directed to the crop in a dose-dependent manner. Sweet and nutritive sugars, like sucrose and maltose, enter the crop more efficiently compared to non-sweet or non-nutritive sugars. Additionally, the robustness of meal sorting declines with mosquito age and is compromised in mutants of candidate thermoreceptors. Proper blood meal sorting is crucial for optimal egg production, as disruption of this process by adding sucrose results in reduced fecundity. Furthermore, certain amino acids essential for vitellogenesis are preferentially directed to the midgut. Our findings provide new insights into the meal sorting mechanism, with implications for mosquito reproduction and vectorial capacity.

Knowledge about seafloor depth, or bathymetry, is crucial for various marine activities, including scientific research, offshore industry, safety of navigation, and ocean exploration. Mapping the central Arctic Ocean is challenging due to the presence of perennial sea ice, which limits data collection to icebreakers, submarines, and drifting ice stations. The International Bathymetric Chart of the Arctic Ocean (IBCAO) was initiated in 1997 with the goal of updating the Arctic Ocean bathymetric portrayal. The project team has since released four versions, each improving resolution and accuracy. Here, we present IBCAO Version 5.0, which offers a resolution four times as high as Version 4.0, with 100 × 100 m grid cells compared to 200 × 200 m. Over 25% of the Arctic Ocean is now mapped with individual depth soundings, based on a criterion that considers water depth. Version 5.0 also represents significant advancements in data compilation and computing techniques. Despite these improvements, challenges such as sea-ice cover and political dynamics still hinder comprehensive mapping.

Climate change is projected to cause extensive plant range shifts, and, in many cases such shifts already are underway. Most long‐term studies of range shifts measure emergent changes in species distributions but not the underlying demographic patterns that shape them. To better understand species' elevational range shifts and their underlying demographic processes, we use the powerful approach of rephotography, comparing historical (1978–1982) and modern (2015–2016) photographs taken along a 1000‐m elevational gradient in the Colorado Desert of Southern California. This approach allowed us to track demographic outcomes for 4263 individual plants of 11 long‐lived, perennial species over the past ~36 years. All species showed an upward shift in mean elevation (average = 45 m), consistent with observed increasing temperature and severe drought in the region. We found that varying demographic processes underlaid these elevational shifts, with some species showing higher recruitment and some showing higher survival with increasing elevation. Species with faster life‐history rates (higher background recruitment and mortality rates) underwent larger elevational shifts. Our findings emphasize the importance of demography and life history in shaping range shift responses and future community composition, as well as the sensitivity of desert systems to climate change despite the typical “slow motion” population dynamics of perennial desert plants.

Though advancements have been made in the pharmacologic treatment of myasthenia gravis (MG), surgical resection is not only an option as a last line of defense for those patients who do not respond to medical therapy but also remains vital for those with thymic epithelial tumors (TET). While prior studies have shown the potential superiority of minimally invasive approaches via robotic- and video-assisted thoracoscopic surgery (RATS/VATS) for thymectomy compared to open surgery, in the setting of malignancies, this outcome delineation is controversial. As RATS/VATS may be associated with less post-operative complications in the treatment of TET, some surgeons argue that the open approach is necessary for complete resection (R0 resection) and to prevent potential seeding of the malignancy. In this review article, we will compare the efficacy and implications of the different surgical approaches and techniques themselves in performing a thymectomy for autoimmune and oncologic pathologies.

Plain Language Summary Atmospheric rivers (ARs) are large storm systems originating in tropical and mid‐latitude regions capable of depositing large amounts of precipitation in high latitude regions. Using river ice breakup data throughout Interior Alaska (AK) we set out to explore the relationship between ARs and annual river ice breakup timing from 1980 to 2023. We found that daily air temperature increases can last up to 13 days after an AR event. ARs account for 40% of total precipitation, explain 47% of the variability of precipitation, and make up 59% of extreme precipitation events, on average annually. Using the mass and temperature of precipitation accumulated on the river ice surface, we approximated thermal energy exchange between precipitation and the river ice surface. The magnitude of energy exchange was then correlated to river ice breakup timing. We found that greater amounts of precipitation from both AR and non‐AR induced precipitation, occurring relatively close to river ice breakup dates have little correlation to the breakup date. However, increased precipitation during the coldest period of the year (typically late December to early February) is strongly inversely correlated with river ice breakup timing and seems to delay the breakup date.

Cotton leafroll dwarf virus (CLRDV), a threat to the cotton industry, was first reported in the United States (US) as an emergent pathogen in 2017. Phylogenetic analysis supports the hypothesis that US CLRDV strains are genetically distinct from strains in South America and elsewhere, which is not consistent with the hypothesis that the virus is newly introduced into the country. Using database mining, we evaluated the timeline and geographic distribution of CLRDV in the country. We uncovered evidence that shows CLRDV had been in the US for over a decade before its official first report. CLRDV sequences were detected in datasets derived from Mississippi in 2006, Louisiana in 2015, and California in 2018. Additionally, through field surveys of upland cotton in 2023, we confirmed that CLRDV is present in California, which had no prior reports of the virus. Viral sequences from these old and new datasets exhibited high nucleotide identities (>98%) with recently characterized US isolates, and phylogenetic analyses with their homologs placed these sequences within a US-specific clade, further supporting the earlier presence of CLRDV in the country. Moreover, potential new hosts, including another fiber crop, flax, were determined through data mining. Retrospective analysis suggests CLRDV has been present in the US since at least 2006 (Mississippi). Our findings challenge the current understanding of the arrival and spread of CLRDV in the US, highlight the power of data mining for virus discovery, and underscore the need for further investigation into CLRDV's impact on US cotton.

This paper presents a sparsified Fourier neural operator for coupled time-dependent partial differential equations (ST-FNO) as an efficient machine learning surrogate for fluid and particle-based fusion codes such as NIMROD (Non-Ideal Magnetohydrodynamics with Rotation - Open Discussion) and GTC (Gyrokinetic Toroidal Code). ST-FNO leverages the structures in the governing equations and utilizes neural operators to represent Green's function-like numerical operators in the corresponding numerical solvers. Once trained, ST-FNO can rapidly and accurately predict dynamics in fusion devices compared with first-principle numerical algorithms. In general, ST-FNO represents an efficient and accurate machine learning surrogate for numerical simulators for multi-variable nonlinear time-dependent partial differential equations, with the proposed architectures and loss functions. The efficacy of ST-FNO has been demonstrated using quiescent H-mode simulation data from NIMROD and kink-mode simulation data from GTC. The ST-FNO H-mode results show orders of magnitude reduction in memory and central processing unit usage in comparison with the numerical solvers in NIMROD when computing fields over a selected poloidal plane. The ST-FNO kink-mode results achieve a factor of 2 reduction in the number of parameters compared to baseline FNO models without accuracy loss.

Lower crustal xenoliths from the Missouri Breaks diatremes and Bearpaw Mountains volcanic field in Montana record a multi-billion-year geologic history lasting from the Neoarchean to the Cenozoic. Unusual kyanite-scapolite-bearing mafic granulites equilibrated at approximately 1.8 GPa and 890 °C and 2.3 GPa and 1000 °C (67 and 85 km depth) and have compositions pointing to their origin as arc cumulates, while metapelitic granulites record peak conditions of 1.3 GPa and 775 °C (48 km depth). Rutile from both mafic granulites and metapelites have U-Pb dates that document the eruption of the host rocks at ca. 46 Ma (Big Slide in the Missouri Breaks) and ca. 51 Ma (Robinson Ranch in the Bearpaw Mountains). Detrital igneous zircon in metapelites date back to the Archean, and metamorphic zircon and monazite record a major event beginning at 1800 Ma. Both zircon and monazite from a metapelite from Robinson Ranch also document an earlier metamorphic event at 2200–2000 Ma, likely related to burial/metamorphism in a rift setting. Metapelites from Big Slide show a clear transition from detrital igneous zircon accumulation to metamorphic zircon and monazite growth around 1800 Ma, recording arc magmatism and subsequent continent-continent collision during the Great Falls orogeny, supporting suggestions that the Great Falls tectonic zone is a suture between the Wyoming craton and Medicine Hat block. U-Th-Pb and trace-element depth profiles of zircon and monazite record metasomatism of the lower crust during the Laramide orogeny at ~60 Ma, bolstering recent research pointing to Farallon slab fluid infiltration during the orogeny.

In the field of brain tumor segmentation, models based on CNNs and transformer have received a lot of attention. However, CNNs have limitations in long-range modeling, and although transformers can model at long distances, they have quadratic computational complexity. Recently, state space models (SSM), exemplified by the Mamba model, can achieve linear computational complexity and are adept at long-distance interactions. In this paper, we propose pocket convolution Mamba (P-BTS), which utilizes the PocketNet paradigm, SSM, and patch contrastive learning to achieve an efficient segmentation model. Specifically, the encoder follows the PocketNet paradigm, the SSM is used at the highest level of the model encoder to capture rich semantic information, and patch contrastive learning is achieved through the results of dual-stream data. Meanwhile, we designed a spatial channel attention (SCA) module to enhance control over spatial channels, and a feature complement module (FCM) to facilitate the interaction between low-level features and high-level semantic information. We conducted comprehensive experiments on the BraTS2018 and BraTS2019 datasets, and the results show that P-BTS has excellent segmentation performance. Our code has been released at https://github.com/zzpr/P-BTS.

Background Multiplexed Assays of Variant Effects (MAVEs) can test all possible single variants in a gene of interest. The resulting saturation-style functional data may help resolve variant classification disparities between populations, especially for Variants of Uncertain Significance (VUS). Methods We analyzed clinical significance classifications in 213,663 individuals of European-like genetic ancestry versus 206,975 individuals of non-European-like genetic ancestry from All of Us and the Genome Aggregation Database. Then, we incorporated clinically calibrated MAVE data into the Clinical Genome Resource’s Variant Curation Expert Panel rules to automate VUS reclassification for BRCA1, TP53, and PTEN. Results Using two orthogonal statistical approaches, we show a higher prevalence (p ≤ 5.95e − 06) of VUS in individuals of non-European-like genetic ancestry across all medical specialties assessed in all three databases. Further, in the non-European-like genetic ancestry group, higher rates of Benign or Likely Benign and variants with no clinical designation (p ≤ 2.5e − 05) were found across many medical specialties, whereas Pathogenic or Likely Pathogenic assignments were increased in individuals of European-like genetic ancestry (p ≤ 2.5e − 05). Using MAVE data, we reclassified VUS in individuals of non-European-like genetic ancestry at a significantly higher rate in comparison to reclassified VUS from European-like genetic ancestry (p = 9.1e − 03) effectively compensating for the VUS disparity. Further, essential code analysis showed equitable impact of MAVE evidence codes but inequitable impact of allele frequency (p = 7.47e − 06) and computational predictor (p = 6.92e − 05) evidence codes for individuals of non-European-like genetic ancestry. Conclusions Generation of saturation-style MAVE data should be a priority to reduce VUS disparities and produce equitable training data for future computational predictors.

Ferns belong to species-rich group of land plants, encompassing more than 11,000 extant species, and are crucial for reflecting terrestrial ecosystem changes. However, our understanding of their biodiversity hotspots, particularly in Southeast Asia, remains limited due to scarce genetic data. Despite harboring around one-third of the world’s fern species, less than 6% of Southeast Asian ferns have been DNA-sequenced. In this study, we addressed this gap by sequencing 1,496 voucher-referenced and expert-identified fern samples from (sub)tropical Asia, spanning Malaysia, the Philippines, Taiwan, and Vietnam, to retrieve their rbcL and trnL-F sequences. This DNA barcode collection of Asian ferns encompasses 956 species across 152 genera and 34 families, filling major gaps in fern biodiversity understanding and advancing research in systematics, phylogenetics, ecology and conservation. This dataset significantly expands the Fern Tree of Life to over 6,000 species, serving as a pivotal and global reference for worldwide barcoding identification of ferns.

Adverse Childhood Experiences (ACEs) are very common and presently implicated in 9 out of 10 leading causes of death in the United States. Despite this fact, our mechanistic understanding of how ACEs impact health is limited. Moreover, interventions for reducing stress presently use a one-size-fits-all approach that involves no treatment tailoring or precision. To address these issues, we developed a combined cross-sectional study and randomized controlled trial, called the California Stress, Trauma, and Resilience Study (CalSTARS), to (a) characterize how ACEs influence multisystem biological functioning in adults with all levels of ACE burden and current perceived stress, using multiomics and other complementary approaches, and (b) test the efficacy of our new California Precision Intervention for Stress and Resilience (PRECISE) in adults with elevated perceived stress levels who have experienced the full range of ACEs. The primary trial outcome is perceived stress, and the secondary outcomes span a variety of psychological, emotional, biological, and behavioral variables, as assessed using self-report measures, wearable technologies, and extensive biospecimens (i.e. DNA, saliva, blood, urine, & stool) that will be subjected to genomic, transcriptomic, proteomic, metabolomic, lipidomic, immunomic, and metagenomic/microbiome analysis. In this protocol paper, we describe the scientific gaps motivating this study as well as the sample, study design, procedures, measures, and planned analyses. Ultimately, our goal is to leverage the power of cutting-edge tools from psychology, multiomics, precision medicine, and translational bioinformatics to identify social, molecular, and immunological processes that can be targeted to reduce stress-related disease risk and enhance biopsychosocial resilience in individuals and communities worldwide.