Storage and redistribution of anthropogenic CO2 in the western North Pacific: The role of subtropical mode water transportation.

Oceanic uptake and storage of anthropogenic CO2 (CANT) are regulated by ocean circulation and ventilation. To decipher the storage and redistribution of CANT in the western North Pacific, where a major CANT sink develops, we investigated the water column carbonate system, dissolved inorganic radiocarbon and ancillary parameters in May and August 2018, spanning the Kuroshio Extension (KE, 35-39 °N), Kuroshio Recirculation (KR, 27-35 °N) and subtropical (21-27 °N) zones. Water column CANT inventories were estimated to be 40.5 ± 1.1 mol m-2 in the KR zone and 37.2 ± 0.9 mol m-2 in the subtropical zone. In comparison with historical data obtained in 2005, relatively high rates of increase of the CANT inventory of 1.05 ± 0.20 and 1.03 ± 0.12 mol m-2 yr-1 in the recent decade were obtained in the KR and subtropical zones, respectively. Our water-mass-based analyses suggest that formation and transport of subtropical mode water dominate the deep penetration, storage, and redistribution of CANT in those two regions. In the KE zone, however, both the water column CANT inventory and the decadal CANT accumulation rate were small and uncertain owing to the dynamic hydrology, where the naturally uplifting isopycnal surfaces make CANT penetration relatively shallow. The findings of this study improve the understanding of the spatiotemporal variations of CANT distribution, storage, and transport in the western North Pacific.

Unambiguous Models and Machine Learning Strategies for Anomalous Extreme Events in Turbulent Dynamical System.

Data-driven modeling methods are studied for turbulent dynamical systems with extreme events under an unambiguous model framework. New neural network architectures are proposed to effectively learn the key dynamical mechanisms including the multiscale coupling and strong instability, and gain robust skill for long-time prediction resistive to the accumulated model errors from the data-driven approximation. The machine learning model overcomes the inherent limitations in traditional long short-time memory networks by exploiting a conditional Gaussian structure informed of the essential physical dynamics. The model performance is demonstrated under a prototype model from idealized geophysical flow and passive tracers, which exhibits analytical solutions with representative statistical features. Many attractive properties are found in the trained model in recovering the hidden dynamics using a limited dataset and sparse observation time, showing uniformly high skill with persistent numerical stability in predicting both the trajectory and statistical solutions among different statistical regimes away from the training regime. The model framework is promising to be applied to a wider class of turbulent systems with complex structures.

Analysis of the heterogeneity and complexity of murine extraorbital lacrimal gland via single-cell RNA sequencing.

PURPOSE: The lacrimal gland is essential for maintaining ocular surface health and avoiding external damage by secreting an aqueous layer of the tear film. However, a healthy lacrimal gland's inventory of cell types and heterogeneity remains understudied. METHODS: Here, 10X Genome-based single-cell RNA sequencing was used to generate an unbiased classification of cellular diversity in the extraorbital lacrimal gland (ELG) of C57BL/6J mice. From 43,850 high-quality cells, we produced an atlas of cell heterogeneity and defined cell types using classic marker genes. The possible functions of these cells were analyzed through bioinformatics analysis. Additionally, the CellChat was employed for a preliminary analysis of the cell-cell communication network in the ELG. RESULTS: Over 37 subclasses of cells were identified, including seven types of glandular epithelial cells, three types of fibroblasts, ten types of myeloid-derived immune cells, at least eleven types of lymphoid-derived immune cells, and five types of vascular-associated cell subsets. The cell-cell communication network analysis revealed that fibroblasts and immune cells play a pivotal role in the dense intercellular communication network within the mouse ELG. CONCLUSIONS: This study provides a comprehensive transcriptome atlas and related database of the mouse ELG.

S1PR3 inhibition protects against LPS-induced ARDS by inhibiting NF-κB and improving mitochondrial oxidative phosphorylation.

BACKGROUND: Inflammation and endothelial barrier dysfunction are the major pathophysiological changes in acute respiratory distress syndrome (ARDS). Sphingosine-1-phosphate receptor 3 (S1PR3), a G protein-coupled receptor, has been found to mediate inflammation and endothelial cell (EC) integrity. However, the function of S1PR3 in ARDS has not been fully elucidated. METHODS: We used a murine lipopolysaccharide (LPS)-induced ARDS model and an LPS- stimulated ECs model to investigate the role of S1PR3 in anti-inflammatory effects and endothelial barrier protection during ARDS. RESULTS: We found that S1PR3 expression was increased in the lung tissues of mice with LPS-induced ARDS. TY-52156, a selective S1PR3 inhibitor, effectively attenuated LPS-induced inflammation by suppressing the expression of proinflammatory cytokines and restored the endothelial barrier by repairing adherens junctions and reducing vascular leakage. S1PR3 inhibition was achieved by an adeno-associated virus in vivo and a small interfering RNA in vitro. Both the in vivo and in vitro studies demonstrated that pharmacological or genetic inhibition of S1PR3 protected against ARDS by inhibiting the NF-κB pathway and improving mitochondrial oxidative phosphorylation. CONCLUSIONS: S1PR3 inhibition protects against LPS-induced ARDS via suppression of pulmonary inflammation and promotion of the endothelial barrier by inhibiting NF-κB and improving mitochondrial oxidative phosphorylation, indicating that S1PR3 is a potential therapeutic target for ARDS.

Activated Clec4nhi Neutrophils Aggravate Lung Injury in an Endothelial IGFBP7-Dependent Manner.

The role of endothelial cells in acute lung injury (ALI) has been widely elaborated, but little is known about the role of different subtypes of endothelial cells in ALI. ALI models were established by lipopolysaccharide. Single-cell RNA sequencing was used to identify differential molecules in endothelial subtypes and the heterogeneity of lung immune cells. Specific antibodies were used to block insulin-like growth factor binding protein 7 (IGFBP7), and AAVshIGP7 was used to specifically knock down IGFBP7. Here, we found that IGFBP7 was the most differentially expressed molecule in diverse subsets of endothelial cells and that IGFBP7 was strongly associated with inflammatory responses. Elevated IGFBP7 significantly exacerbated barrier dysfunction in ALI, whereas blockade of IGFBP7 partially reversed barrier damage. General capillary cells are the primary source of elevated serum IGFBP7 after ALI. Using single-cell RNA sequencing, we identified significantly increased Clec4nhi neutrophils in mice with ALI, whereas IGFBP7 knockdown significantly reduced infiltration of Clec4nhi cells and mitigated barrier dysfunction in ALI. In addition, we found that IGFBP7 activated the NF-κB signaling axis by promoting phosphorylation and ubiquitination degradation of F-box/WD repeat-containing protein 2 (FBXW2), thereby exacerbating barrier dysfunction in ALI. Taken together, our data indicate that blockade of serum IGFBP7 or IGFBP7 depletion in general capillary cells reversed barrier damage in ALI. Therefore, targeting IGFBP7 depletion could be a novel strategy for treating ALI.

Chronic sleep deprivation impairs retinal circadian transcriptome and visual function.

Sleep loss is common in modern society and is increasingly associated with eye diseases. However, the precise effects of sleep loss on retinal structure and function, particularly on the retinal circadian system, remain largely unexplored. This study investigates these effects using a chronic sleep deprivation (CSD) model in mice. Our investigation reveals that CSD significantly alters the retinal circadian transcriptome, leading to remarkable changes in the temporal patterns of enriched pathways. This perturbation extends to metabolic and immune-related transcriptomes, coupled with an accumulation of reactive oxygen species in the retina. Notably, CSD rhythmically affects the thickness of the ganglion cell complex, along with diurnal shifts in microglial migration and morphology within the retina. Most critically, we observe a marked decrease in both scotopic and photopic retinal function under CSD conditions. These findings underscore the broad impact of sleep deprivation on retinal health, highlighting its role in altering circadian gene expression, metabolism, immune response, and structural integrity. Our study provides new insights into the broader impact of sleep loss on retinal health.

Dynamics of the surface carbonate system in oil fields with a high concentration of wells on the northwestern South China Sea shelf.

Oil exploitation may pose adverse effects on marine ecosystems, but its impacts on surface carbonate dynamics remain unknown. In a carbonate system with low air-sea ∆pCO2, such as the South China Sea (SCS), human activities may affect the pCO2 distribution patterns and potentially alter CO2 sink or source at the surface. This study investigates the surface carbonate system in two oil fields, namely the Wenchang Oil Feld and Enping Oil Feld, located on the northwestern SCS (NWSCS) shelf. In Enping Oil Field, although there is a slight increase in surface pCO2 due to probable total alkalinity (TA) consumption from CaCO3 precipitation, strong biological production makes the plume water a strong CO2 sink. Similarly, the biological processes dominated the pCO2 variability in Wenchang Oil Feld, exhibiting high values in its central area. In NWSCS, the influence of shelf water was observed during both cruises. And the pCO2 drawdown caused by the decreased sea surface temperature (SST) and CO2 outgassing outweighed their increases via enhanced vertical mixing, leading to a pCO2 drawdown from September to October within this water mass. More importantly, there were no significant disparities observed in carbonate parameters at stations along transects with and without wells, and the observed parameter values in this study fell within the range reported previously on the nSCS shelf with similar controlling processes. Thus the impact of oil exploitation on carbonate dynamics is negligible, and the characteristics of the carbonate system in oil field are primarily governed by natural processes such as the mixing of plume water and basin water, CaCO3 precipitation and the changes in SST. The provided data establish a crucial baseline for detecting future alterations in carbonate chemistry within oil fields, and the rapid fluctuations in sea surface pCO2 highlight the need for higher spatiotemporal resolution observation.

Few-Shot Object Detection With Self-Supervising and Cooperative Classifier.

Few-shot object detection (FSOD), which detects novel objects with only a few training instances, has recently attracted more attention. Previous works focus on making the most use of label information of objects. Still, they fail to consider the structural and semantic information of the image itself and solve the misclassification between data-abundant base classes and data-scarce novel classes efficiently. In this article, we propose FSOD with Self-Supervising and Cooperative Classifier ( [Formula: see text]) approach to deal with those concerns. Specifically, we analyze the underlying performance degradation of novel classes in FSOD and discover that false-positive samples are the main reason. By looking into these false-positive samples, we further notice that misclassifying novel classes as base classes are the main cause. Thus, we introduce double RoI heads into the existing Fast-RCNN to learn more specific features for novel classes. We also consider using self-supervised learning (SSL) to learn more structural and semantic information. Finally, we propose a cooperative classifier (CC) with the base-novel regularization to maximize the interclass variance between base and novel classes. In the experiment, [Formula: see text] outperforms all the latest baselines in most cases on PASCAL VOC and COCO.

Advances in relationship between pyroptosis and pulmonary arterial hypertension and therapeutic drugs / 中国药理学通报

Pyroptosis is the programmed death of cells accompanied by an inflammatory response and is widely involved in the development of a variety of diseases, such as infectious diseases, cardiovascular diseases, and neurodegeneration. It has been shown that cellular scorching is involved in the pathogenesis of pulmonary arterial hypertension ( PAH) in cardiovascular diseases. Patients with PAH have perivascular inflammatory infiltrates in lungs, pulmonary vasculopathy exists in an extremely inflam-matory microenvironment, and pro-inflammatory factors in cellular scorching drive pulmonary vascular remodelling in PAH patients. This article reviews the role of cellular scorch in the pathogenesis of PAH and the related research on drugs for the treatment of PAH, with the aim of providing new ideas for clinical treatment of PAH.

Single-session intervention on growth mindset on negative emotions for university student mental health (U-SIGMA): a protocol of two-armed randomized controlled trial.

BACKGROUND: The university years are a developmentally crucial phase and a peak period for the onset of mental disorders. The beliefs about the changeability of negative emotion may play an important role in help-seeking. The brief digital growth mindset intervention is potentially scalable and acceptable to enhance adaptive coping and help-seeking for mental health needs in university students. We adapted the Single-session Intervention on Growth Mindset for adolescents (SIGMA) to be applied in university students (U-SIGMA). This protocol introduces a two-armed waitlist randomized controlled trial study to examine the effectiveness and acceptability of U-SIGMA in promoting help-seeking among university students in the Greater Bay Area. METHODS: University students (N = 250, ages 18-25) from universities in the Greater Bay Area will be randomized to either the brief digital growth mindset intervention group or the waitlist control group. Participants will report on the mindsets of negative emotions, perceived control over anxiety, attitude toward help-seeking, physical activity, hopelessness, psychological well-being, depression, anxiety, and perceived stress at baseline and the 2-week and 8-week follow-ups through web-based surveys. A 30-min digital intervention will be implemented in the intervention group, with a pre- and post-intervention survey collecting intervention feedback, while the control group will receive the link for intervention after 8 weeks. DISCUSSION: This protocol introduces the implementation plan of U-SIMGA in multi-cities of the Greater Bay Area. The findings are expected to help provide pioneer evidence for the effectiveness and acceptability of the brief digital intervention for university students in the Chinese context and beyond and contribute to the development of accessible and effective prevention and early intervention for university students' mental health. TRIAL REGISTRATION: HKU Clinical Trials Registry: HKUCTR-3012; Registered 14 April 2023.  http://www.hkuctr.com/Study/Show/7a3ffbc0e03f4d1eac0525450fc5187e .

Peak-tracking BOTDA with dynamic ternary search.

We propose a peak-tracking BOTDA (PT-BOTDA) equipped with an efficient dynamic Brillouin frequency shift (BFS) searching scheme based on ternary search. The proposed scheme establishes a feedback loop between the selected frequency and the corresponding Brillouin gain to reduce the required number of scanning frequencies in one measurement. We also demonstrate the performance evaluation of the proposed scheme under scenarios with different searching granularities and dynamic sensing ranges. Experimental results indicate that in all situations, the proposed PT-BOTDA can achieve at least 85% and 97% reduction in the number of scanning frequencies for 1-MHz and 0.1-MHz frequency steps, respectively, with a 3-meter spatial resolution, while maintaining a convincing BFS searching accuracy under sufficient SNR condition using a smaller searching interval.

High-order moment closure models with random batch method for efficient computation of multiscale turbulent systems.

We propose a high-order stochastic-statistical moment closure model for efficient ensemble prediction of leading-order statistical moments and probability density functions in multiscale complex turbulent systems. The statistical moment equations are closed by a precise calibration of the high-order feedbacks using ensemble solutions of the consistent stochastic equations, suitable for modeling complex phenomena including non-Gaussian statistics and extreme events. To address challenges associated with closely coupled spatiotemporal scales in turbulent states and expensive large ensemble simulation for high-dimensional systems, we introduce efficient computational strategies using the random batch method (RBM). This approach significantly reduces the required ensemble size while accurately capturing essential high-order structures. Only a small batch of small-scale fluctuation modes is used for each time update of the samples, and exact convergence to the full model statistics is ensured through frequent resampling of the batches during time evolution. Furthermore, we develop a reduced-order model to handle systems with really high dimensions by linking the large number of small-scale fluctuation modes to ensemble samples of dominant leading modes. The effectiveness of the proposed models is validated by numerical experiments on the one-layer and two-layer Lorenz '96 systems, which exhibit representative chaotic features and various statistical regimes. The full and reduced-order RBM models demonstrate uniformly high skill in capturing the time evolution of crucial leading-order statistics, non-Gaussian probability distributions, while achieving significantly lower computational cost compared to direct Monte-Carlo approaches. The models provide effective tools for a wide range of real-world applications in prediction, uncertainty quantification, and data assimilation.

Mechanisms of Extraorbital Lacrimal Gland Aging in Mice: An Integrative Analysis of the Temporal Transcriptome.

Purpose: This study used high-throughput RNA sequencing (RNA-Seq) and bioinformatics analysis to investigate the altered transcriptome profile of aging lacrimal glands in mice that occurs over the course of a 24-hour cycle. Methods: Male C57BL/6J mice aged 12 weeks (young) and 20 months (aging) were housed in a pathogen-free setting with a 12-hour light/12-hour dark cycle. Throughout a 24-hour cycle, mouse extraorbital lacrimal glands (ELGs) were collected at eight time points at three-hour intervals. To prepare for the high-throughput RNA-Seq, whole mRNA was extracted. Differentially expressed genes (DEGs) in the young and aging groups were subjected to bioinformatic analysis based on diurnal patterns. Furthermore, the cell populations in which significant DEGs express and signaling pathways occur were validated at the single-cell RNA sequencing (scRNA-seq) level. Results: The total transcriptome composition was significantly altered in aging ELGs compared with that in young mouse ELGs at eight time points during the 24-hour cycle, with 864 upregulated and 228 downregulated DEGs, which were primarily enriched in inflammatory pathways. Further comparative analysis of the point-to-point transcriptome revealed that aging ELGs underwent alterations in the temporal transcriptome profile in several pathways, including the inflammation-related, metabolism-related, mitochondrial bioenergetic function-associated, synaptome neural activity-associated, cell processes-associated, DNA processing-associated and fibrosis-associated pathways. Most of these pathways occurred separately in distinct cell populations. Conclusions: Transcriptome profiles of aging lacrimal glands undergo considerable diurnal time-dependent changes; this finding offers a comprehensive source of information to better understand the pathophysiology of lacrimal gland aging and its underlying mechanisms.

Phylogeography and Genetic Structure of the Bush Cricket Decma fissa (Orthoptera, Tettigoniidae) in Southern China.

Decma fissa is the most widely distributed species of the genus Decma occuring in southern China. This study presents the first phylogeographic work of D. fissa based on COI, Cytb and ITS sequence. We examined genetic diversity with ITS and mitochondrial sequence respectively, and phylogenetic work was based on the mitochondrial data. A high-level genetic diversity was revealed based on mitochondrial data but a low-level diversity was shown with ITS sequence. For the mitochondrial data, divergence time analysis displayed five lineages. Based on the Mantel test, geographic and genetic distances among D. fissa populations revealed a significant positive correlation. Bayesian skyline plot (BSP) analyses implied that none of three major lineages of D. fissa was seemingly affected by the last glacial maximum (LGM, 0.015-0.025 Mya). Ecological niche modeling was used to predict the distribution of D. fissa in four periods (LGM, Mid-Holocene, current and 2070) in China. Analysis of the ancestral area reconstruction indicated that D. fissa occurred in the South China area.

Biogeochemical and physical controls on ammonium accumulation on the Chukchi shelf, western Arctic Ocean.

Spatial variability of ammonium concentrations along repeat transects were examined on the Chukchi shelf during 2012-2018. Two distinct near-bottom high ammonium pools (>1 µmol/kg) near 67.5°N and 72.5°N of the transects were identified in all years. The accumulation of ammonium in the regions is driven primarily by a combination of biogeochemical processes (e.g., dynamic bacterial remineralization of organic matter) and physical controls (e.g., strong density-contrast barrier limits upward mixing of ammonium). The ammonium pool on the shelf may became larger in the expectation of the stronger bacterial remineralization following elevate primary production, and may have potential impact on the structure and productivity of ecosystem on the Chukchi shelf.

Concentrations and controls of dissolved inorganic carbon in Arctic summer sea ice and adjacent surface seawaters.

The carbonate chemistry of sea ice plays a critical role in global ocean carbon cycles, particularly in polar regions which are subject to significant climate change-induced sea ice variation. However, less is known about the interaction of carbonate system between sea ice and its adjacent seawaters due to sparse sampling and disparities in reported results. Here we provide an insight into this issue by collecting and measuring dissolved inorganic carbon (DIC) and associated environmental parameters in Arctic sea ice during a cruise in the summer of 2014. Our observations show that DIC in Arctic summer sea ice has a mean concentration of 463.3 ± 213.0 µmol/kg and appears to be controlled mainly by the fraction of brine water in the ice. The low Chl a and nutrients content in sea ice indicate minor contribution of biological uptake to sea-ice DIC in the western Arctic Ocean. The DIC concentration in surface water (<100 m depth) decreased from a mean of 2108.3 ± 45.4 µmol/kg in 1994 to a mean of 2052.4 ± 98.6 µmol/kg in 2014, due to the enhanced sea ice melting that dilutes the DIC concentrations of surrounding seawaters.