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Abstract The primary objective of this study is to understand the regulatory role of epigenetics in thyroid-associated ophthalmopathy (TAO) using multi-omics sequencing data. We utilized tRFs sequencing data, DNA methylation sequencing data, and lncRNA/circRNA/mRNA sequencing data, as well as several RNA methylation target prediction websites, to analyze the regulatory effect of DNA methylation, non-coding RNA, and RNA methylation on TAO-associated genes. Through differential expression analysis, we identified 1019 differentially expressed genes, 985 differentially methylated genes, and 2601 non-coding RNA. Functional analysis showed that differentially expressed genes were mostly associated with the PI3K signaling pathway and the IL17 signaling pathway. Genes regulated by DNA epigenetic regulatory networks were mainly related to the Cytokine-cytokine receptor interaction pathway, whereas genes regulated by RNA epigenetic regulatory networks were primarily related to the T cell receptor signaling pathway. Finally, our integrated regulatory network analysis revealed that epigenetics mainly impacts the occurrence of TAO through its effects on key pathways such as cell killing, cytokine production, and immune response. In summary, this study is the first to reveal a new mechanism underlying the development of TAO and provides new directions for future TAO research.

Abstract The complexity of hydraulic fracture network generation during the fracturing of shale reservoirs is a key indicator of the effectiveness of fracture stimulation. To obtain as large a reservoir stimulation volume as possible, this paper reviews articles on the study of hydraulic fracture propagation mechanism during hydraulic fracturing, analyses the factors affecting hydraulic fracture propagation, and classifies them into two categories: geological factors and engineering factors. In particular, the geological factors affecting hydraulic fracture propagation are classified into five categories: mineral composition of the shale, connections between mineral grains, defects in the shale, geostress, and temperature. Various influencing factors act together, resulting in the hydraulic fracture propagation path is difficult to predict. Therefore, this paper firstly explores the hydraulic fracture propagation pattern under the action of single geological factors and specifies its action mechanism; secondly, it also analyses the hydraulic fracture propagation pattern under the combined action of multiple geological factors and analyses its action mechanism. It is clear that relatively high brittle mineral content and temperature, low stress anisotropy and cementation strength, and a more developed natural fracture network are conducive to the generation of a complex fracture network. By analyzing the influence mechanism of single factors and multiple factors, the influence mechanism of geological factors on hydraulic fracture propagation is identified, guiding the optimal design of hydraulic fracturing.

ObjectiveTo evaluate efficacy and safety of traditional Chinese medicine (TCM) combined with Western medicine in treatment of Sjogren’s syndrome (SS).MethodsCNKI, WanFang, VIP, CBM, Sinomed, PubMed, Embase, and Web of Science were searched to collect randomized controlled trials (RCTs) of TCM combined with conventional western medicine (CWM) in treating SS from the time of their estalishment to May 2023. The researchers independently screened the literature and extracted data for quality evaluation. Analyses were performed using Review Manager (version 5.4) and R-4.3.1.ResultsA total of 66 RCTs were included, with a sample size of 5,052, involving four kinds of TCM (total glucosides of paeony capsules, tripterygium glycosides tablet, Xinfeng capsule and Jinju Qingrun capsule) and three kinds of CWM(hydroxychloroquine sulfate, Iguratimod and glucocorticoid). The network meta-analysis results showed that IGU + HCQ + TGP ranked the highest in reducing ESR and IgG and improving the Schirmer test when the three drugs were combined. When the two drugs are combined, IGU + GC and TGT + TGP are good choices for reducing erythrocyte sedimentation rate (ESR) and Immunoglobulin G (IgG). Although TGP + HCQ vs. HCQ had the most studies, TGP combined with HCQ did not rank high in each outcome indicator. It is recommended to use TGT and XFG in decreasing ESR and IgG for a single drug. JJQR have an advantageous role in relieving xerostomia and dry eyes.ConclusionTCM combined with CWM has a very significant effect on treating SS compared with CWM alone. According to the network meta-analysis, the best intervention measures of different TCMs for different outcome indicators were obtained.Systematic Review Registration[https://www.crd.york.ac.uk/prospero/], identifier [CRD42023451845].

IntroductionRheumatoid arthritis (RA) patients often experience significant hand dysfunction. Yijinjing, a traditional Chinese exercise, has been recognized for its benefits to mind–body health. This study aimed to evaluate the efficacy and safety of Yijinjing in improving hand dysfunction among individuals with RA.MethodsThis research was conducted as a single-center, outcome-blinded, randomized controlled trial. A total of 66 eligible RA participants were randomly assigned in a 1:1 ratio to either the Yijinjing exercise group (YJJG) with sessions conducted three times per week over a 12-week period or the control group (CG), which maintained ordinary activities. Various outcomes were assessed, including the Michigan Hand Outcomes Questionnaire (MHQ), handgrip strength, active range of motion (AROM), RA disease activity, the Health Assessment Questionnaire Disability Index (HAQ-DI), levels of anxiety and depression, and wrist ultrasound, all of which were collected at baseline and the week 12.ResultsAt the end of the 12 weeks, the YJJG demonstrated significant improvements in MHQ scores compared to the CG (p 0.05). Additionally, morning stiffness duration, Disease Activity Score 28-ESR (DAS28-ESR), erythrocyte sedimentation rate (ESR), and C-reactive protein (CRP) levels showed significant improvement in the YJJG (p

The hypoxic microenvironment, characterized by significantly reduced oxygen levels within tissues, has emerged as a critical factor in the pathogenesis and progression of various autoimmune diseases (AIDs). Central to this process is the hypoxia-inducible factor-1 (HIF-1), which orchestrates a wide array of cellular responses under low oxygen conditions. This review delves into the multifaceted roles of the hypoxic microenvironment in modulating immune cell function, particularly highlighting its impact on immune activation, metabolic reprogramming, and angiogenesis. Specific focus is given to the mechanisms by which hypoxia contributes to the development and exacerbation of diseases such as rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), multiple sclerosis (MS), and dermatomyositis (DM). In these conditions, the hypoxic microenvironment not only disrupts immune tolerance but also enhances inflammatory responses and promotes tissue damage. The review also discusses emerging therapeutic strategies aimed at targeting the hypoxic pathways, including the application of HIF-1α inhibitors, mTOR inhibitors, and other modulators of the hypoxic response. By providing a comprehensive overview of the interplay between hypoxia and immune dysfunction in AIDs, this review offers new perspectives on the underlying mechanisms of these diseases and highlights potential avenues for therapeutic intervention.

Epidemiological studies have demonstrated exposure to cadmium ion (Cd2+) is significantly associated with the incidence and aggravation of nonalcoholic fatty liver disease (NAFLD) to non-alcoholic steatohepatitis (NASH). Cd2+ exposure could alter lipid metabolism, and changed lipid metabolites are significantly associated with NASH. Arachidonic acid (ArA) is an omega-6 polyunsaturated fatty acid. Promotion of ArA synthesis and profile changes by Cd2+ exposure potentially to cause NAFLD. ArA metabolism pathway has been identified to enrich in Cd2+ exposure-facilitated NASH. ArA could be generation an impressive metabolic profile through mainly three pathways, including Cyclooxygenases (COX), Lipoxygenases (LOX) and Cytochrome P450 (CYP450) pathway. However, the functions of these metabolites and underlying mechanism in hepatic inflammation are still not clear. In present study, by integrative transcriptomics and metabolomics analysis, we identified that the fatty acid metabolic process and the pro-inflammatory NF-κB signaling pathway were enriched in Cd2+-regulated differentially expressed genes (DEGs) and Cd2+-altered differential metabolites, such as, fatty acid biosynthesis, degradation, and ArA metabolism. The metabolites levels of LOX pathway products 5-HETE and leukotriene C4 (LTC4), and COX catalytic product prostaglandin D2 (PGD2) were significantly elevated in Cd2+ exposed mouse livers. 5-HETE, LTC4, and PGD2 were significantly positive correlated with NF-κB signaling. In addition, the synthase of 20-Hydroxyeicosatetraenoic acid (20-HETE), CYP450 gene 4 family (CYP4A32), was also involved in NF-κB signaling network. Results from both in vitro and in vivo proved that Cd2+ exposure increased ArA metabolite to PGD2 and 20-HETE, and upregulated the mRNA level of their catalytic enzyme PGDS and CYP4A32. Cd2+-induced ArA metabolite to PGD2 and 20-HETE promoted activation of TLR4/IκBα/NF-κB signaling and pro-inflammatory of hepatocytes. Our study explores novel molecular mechanism of Cd2+ exposure-aggravated liver diseases and provides potential novel targets for in hepatic inflammatory treatments and prevention.

IntroductionGenistin, as a kind of natural isoflavone glycoside, has good biological activity, and its weak absorption makes it closely related to intestinal flora. However, the role of the intestinal flora is still unclear and whether the metabolites produced by the intestinal flora are absorbed systemically is also variable.MethodsGenistin was fermented for 24 h based on fecal bacteria fermentation technology. The components were qualitatively and quantitatively analyzed by HPLC and UHPLC-Q-Exactive Orbitrap Mass spectrometry. The composition of intestinal flora in fermentation samples from fecal bacteria was detected by 16S rRNA sequencing. Five representative probiotics were cultured in vitro and fermented with genistin to determine similarities and differences in genistin metabolites by different bacteria at different times. Finally, the absorption results of metabolites by fermentation were verified by a Caco-2 cell monolayer.ResultsThe HPLC results of fecal fermentation showed that genistein levels increased from 0.0139 ± 0.0057 mg/mL to 0.0426 ± 0.0251 mg/mL and two new metabolites were produced. A total of 46 metabolites following fecal fermentation were identified, resulting from various biotransformation reaction products, such as decarbonylation, hydroxylation, and methylation. Simultaneously, the 16S rRNA results showed that the intestinal flora changed significantly before and after fermentation and that the intestinal microorganisms in the control (Con) group and the fermentation (Fer) group showed a significant separation trend. Five genera, Lactobacillus, Bifidobacterium, Parabacteroides, Sutterella, and Dorea, were considered the dominant flora for genistin fermentation. The qualitative results of fermentation of genistin by five probiotics at different times showed that there were significant differences in small molecule metabolites by fermentation of different bacteria. Meanwhile, most metabolites could be identified following fecal bacteria fermentation, which verified the importance of the dominant bacteria in the feces for the biotransformation of components. Finally, the absorption results of the metabolites based on the Caco-2 cell monolayer showed that 14 metabolites could be absorbed into the circulation in vivo through the mesentery.DiscussionThe small molecule metabolites of genistin by fermentation of fecal bacteria can be well absorbed systemically by the body. These studies provide a reference value for explaining the transformation and absorption of flavonoid glycosides in the intestine.

BackgroundCoronary heart disease (CHD) is a leading cause of death worldwide and imposes a significant economic burden. TikTok has risen as a favored platform within the social media sphere for disseminating CHD-related information and stands as a pivotal resource for patients seeking knowledge about CHD. However, the quality of such content on TikTok remains largely unexplored. ObjectiveThis study aims to assess the quality of information conveyed in TikTok CHD-related videos. MethodsA comprehensive cross-sectional study was undertaken on TikTok videos related to CHD. The sources of the videos were identified and analyzed. The comprehensiveness of content was assessed through 6 questions addressing the definition, signs and symptoms, risk factors, evaluation, management, and outcomes. The quality of the videos was assessed using 3 standardized evaluative instruments: DISCERN, the Journal of the American Medical Association (JAMA) benchmarks, and the Global Quality Scale (GQS). Furthermore, correlative analyses between video quality and characteristics of the uploaders and the videos themselves were conducted. ResultsThe search yielded 145 CHD-related videos from TikTok, predominantly uploaded by health professionals (n=128, 88.3%), followed by news agencies (n=6, 4.1%), nonprofit organizations (n=10, 6.9%), and for-profit organizations (n=1, 0.7%). Content comprehensiveness achieved a median score of 3 (IQR 2-4). Median values for the DISCERN, JAMA, and GQS evaluations across all videos stood at 27 (IQR 24-32), 2 (IQR 2-2), and 2 (IQR 2-3), respectively. Videos from health professionals and nonprofit organizations attained significantly superior JAMA scores in comparison to those of news agencies (P

Background: Bilateral first rib fractures are rare. This article presented the diagnosis and treatment of a case of bilateral first rib fractures with multi-organ complications and discussed the injury mechanism. Case presentation: A 15-year-old girl fell off a motorcycle. She complained of right neck root pain and right upper limb weakness. The myodynamia of the right upper limb was grade 0, and the sensation disappeared below the level of the elbow joint. The computed tomography (CT) showed bilateral first rib fractures and transverse process fracture of the 6th cervical vertebra. Chest CT revealed a massive hemothorax in the right thoracic cavity, and head magnetic resonance imaging showed bilateral cerebellar infarction. Cervical computed tomography angiography (CTA) revealed a lumen occlusion at the origin of the right subclavian artery. The patient underwent an emergency thoracoscopy, and a re-examination of chest CT indicated that no obvious pleural effusion was found after the hemothorax was cleared. The patient underwent right subclavian arteriography and interventional endovascular thrombolysis, and the right subclavicular artery was patency postoperative. Bilateral first rib fractures and cerebellar infarction were treated conservatively. The brachial plexus injury did not show any signs of recovery after conservative treatment, and she was recommended to be transferred to a superior hospital for surgical treatment. Conclusions: The injury mechanism of bilateral first rib fractures with multi-organ complications was closely related to the initial factor of the right neck root colliding with a bulge on the ground. We believe that the fractures occur as a result of a combination including a high energy trauma from direct impact and a low-energy mechanism from violent muscle contraction caused by neck hyperextension. This case report was helpful for clinicians to understand bilateral first rib fractures and their complications.

Cadmium (Cd) exposure is considered as non-infectious stressor to human and animal health. Recent studies suggest that the immunotoxicity of low dose Cd is not directly apparent, but disrupts the immune responses when infected with some bacteria or virus. But how Cd alters the adaptive immunity organ and cells remains unclear. In this study, we applied lipopolysaccharide (LPS, infectious stressor) to induced inflammation in spleen tissues and T cells, and investigated the effects after Cd exposure and the underlying mechanism. Cd exposure promoted LPS-induced the expressions of the inflammatory factors, induced abnormal initiation of autophagy, but blocked autophagic flux. The effects Cd exposure under LPS activation were reversed by the autophagy promoter Rapamycin. Under LPS activation conditions, Cd also induced oxidative stress by increasing the levels of reactive oxygen species (ROS) and malondialdehyde (MDA), and reducing total antioxidant capacity (T-AOC) activity. The increased superoxide dismutase (SOD) activity after Cd exposure might be a negative feedback or passive adaptive regulation of oxidative stress. Cd-increased autophagic flux inhibition and TNF-α expression were reversed by ROS scavenger α-tocopherol (TCP). Furthermore, under LPS activation condition, Cd promoted activation of toll-like receptor 4 (TLR4)/IκBα/NFκ-B signaling pathway and increased TLR4 protein stability, which were abolished by the pretreatment of Rapamycin. The present study confirmed that, by increasing ROS-mediated inhibiting autophagic degradation of TLR4, Cd promoted LPS-induced inflammation in spleen T cells. This study identified the mechanism of autophagy in Cd-aggravated immunotoxicity under infectious stress, which could arouse public attention to synergistic toxicity of Cd and bacterial or virus infection.

Ensuring the safety of autonomous vehicles (AVs) in complex and high-risk traffic scenarios remains a critical unresolved challenge. Current AV planning methods exhibit limitations in generating robust driving trajectories that effectively avoid collisions, highlighting the urgent need for improved planning strategies to address these issues. This paper introduces a novel iterative optimization framework that incorporates safety-critical trajectory generation to enhance AV planning. The use of the HighD dataset, which is collected using the wide-area remote sensing capabilities of unmanned aerial vehicles (UAVs), is fundamental to the framework. Remote sensing enables large-scale real-time observation of traffic conditions, providing precise data on vehicle dynamics, road structures, and surrounding environments. To generate safety-critical trajectories, the decoder within the conditional variational auto-encoder (CVAE) is innovatively designed through a data-mechanism integration method, ensuring that these trajectories strictly adhere to vehicle kinematic constraints. Furthermore, two parallel CVAEs (Dual-CVAE) are trained collaboratively by a shared objective function to implicitly model the multi-vehicle interactions. Inspired by the concept of “learning to collide”, adversarial optimization is integrated into the Dual-CVAE (Adv. Dual-CVAE), facilitating efficient generation from normal to safety-critical trajectories. Building upon this, these generated trajectories are then incorporated into an iterative optimization framework, significantly enhancing the AV’s planning ability to avoid collisions. This framework decomposes the optimization process into stages, initially addressing normal trajectories and progressively tackling more safety-critical and collision trajectories. Finally, comparative case studies of enhancing AV planning are conducted and the simulation results demonstrate that the proposed method can efficiently enhance AV planning by generating safety-critical trajectories.

Insufficient studies in characterizing vertical structure of Chlorophyll-a (Chl-a) in the ocean critically limit better understanding about marine ecosystem based on global climate change. In this study, we developed a Gaussian-activation deep neural network (Gaussian-DNN) model to assess vertical Chl-a structure in the upper ocean at high spatial resolution. Our Gaussian-DNN model used the input variables including satellite data of sea surface Chl-a and in-situ vertical physics profiles (temperature and salinity) in the northwestern Pacific Ocean (NWPO). After validation test based on two independent datasets of BGC-Argo and ship measurement, we applied the Gaussian-DNN model to reconstruct temporal evolution of 3-D Chl-a structure in the NWPO. Our modelling results successfully explain over 80% of the Chl-a vertical profiles in the NWPO at a horizontal resolution of 1° × 1° and 1 m vertical resolution within upper 300 meters during 2004 to 2022. Moreover, according to our modelling results, the Subsurface Chlorophyll Maxima (SCMs) and total Chl-a within 0-300 m depths were extracted and presented seasonal variability overlapping longer-time trends of spatial discrepancies all over the NWPO. In addition, our sensitivity testing suggested that sea-water temperatures predominantly control 3-D structures of the Chl-a in the tropical NWPO, while salinity played a key role in the temperate gyre of the NWPO. Here, our development of the Gaussian-DNN model may also be applied to craft long term, 3-D Chl-a products in the global ocean.

BackgroundAt the intersection of neural monitoring and decoding, event-related potential (ERP) based on electroencephalography (EEG) has opened a window into intrinsic brain function. The stability of ERP makes it frequently employed in the field of neuroscience. However, project-specific custom code, tracking of user-defined parameters, and the large diversity of commercial tools have limited clinical application.MethodsWe introduce an open-source, user-friendly, and reproducible MATLAB toolbox named EPAT that includes a variety of algorithms for EEG data preprocessing. It provides EEGLAB-based template pipelines for advanced multi-processing of EEG, magnetoencephalography, and polysomnogram data. Participants evaluated EEGLAB and EPAT across 14 indicators, with satisfaction ratings analyzed using the Wilcoxon signed-rank test or paired t-test based on distribution normality.ResultsEPAT eases EEG signal browsing and preprocessing, EEG power spectrum analysis, independent component analysis, time-frequency analysis, ERP waveform drawing, and topological analysis of scalp voltage. A user-friendly graphical user interface allows clinicians and researchers with no programming background to use EPAT.ConclusionThis article describes the architecture, functionalities, and workflow of the toolbox. The release of EPAT will help advance EEG methodology and its application to clinical translational studies.

Parkinson’s disease (PD), ranking as the second most prevalent neurodegenerative disorder globally, presents a pressing need for innovative animal models to deepen our understanding of its pathophysiology and explore potential therapeutic interventions. The development of such animal models plays a pivotal role in unraveling the complexities of PD and investigating promising treatment avenues. In this study, we employed transcriptome sequencing on BmN cells treated with 1 μg/ml rotenone, aiming to elucidate the underlying toxicological mechanisms. The investigation brought to light a significant reduction in mitochondrial membrane potential induced by rotenone, subsequently triggering mitophagy. Notably, the PTEN induced putative kinase 1 (PINK1)/Parkin pathway emerged as a key player in the cascade leading to rotenone-induced mitophagy. Furthermore, our exploration extended to silkworms exposed to 50 μg/ml rotenone, revealing distinctive motor dysfunction as well as inhibition of Tyrosine hydroxylase (TH) gene expression. These observed effects not only contribute valuable insights into the impact and intricate mechanisms of rotenone exposure on mitophagy but also provide robust scientific evidence supporting the utilization of rotenone in establishing a PD model in the silkworm. This comprehensive investigation not only enriches our understanding of the toxicological pathways triggered by rotenone but also highlights the potential of silkworms as a valuable model organism for PD research.

The Eastern Indian Ocean (EIO) is an ideal region to explore the variability and controlling mechanisms of the seawater carbonate system and their potential influence on global climate change due to the distinctive environmental features, while studies in the EIO is far from sufficient. The spatiotemporal distributions of pH, dissolved inorganic carbon (DIC), alkalinity (Alk), and partial pressure of carbon dioxide (pCO2) were investigated in the EIO during autumn 2020 and spring 2021. The respective quantitative contributions of different controlling processes to DIC were further delineated. Significant seasonal variations were observed in the study area. Overall, the surface pH was lower and DIC, Alk, and pCO2 were higher during spring 2021 than during autumn 2020. The pH generally decreased from east to west during autumn 2020, whereas it decreased from north to south during spring 2021. The low values of DIC and Alk that were detected in the Bay of Bengal in these two seasons were mainly attributed to the influence of river inputs. Coastal upwelling during monsoon periods led to higher pCO2 and DIC values near Sumatra and Sri Lanka during spring 2021. The relationships of carbonate system parameters with different types of nutrients and different sized chlorophyll-a in the two seasons indicated the shifts of nutrients utilized by the phytoplankton, and phytoplankton species dominated the carbonate system variabilities. In vertical profiles, carbonate system parameters showed strong correlations with other physical and biogeochemical parameters, and these correlations were more robust during spring 2021 than during autumn 2020. The average sea–air flux of CO2 was 10.00 mmol m−2 d−1 during autumn 2020 and was 16.00 mmol m−2 d−1 during spring 2021, which revealed that the EIO served as a CO2 source during the study period. In addition, the separation of different controlling processes of DIC indicated stronger mixing processes, less CaCO3 precipitation, more intensive sea–air exchange, and weaker photosynthesis during spring 2021 than during autumn 2020.

Abstract Background Fringe is a glycosyltransferase involved in tumor occurrence and metastasis. However, a comprehensive analysis of the Fringe family members lunatic fringe (LFNG), manic fringe (MFNG), radical fringe (RFNG) in human cancers is lacking. Methods In this study, we performed a pan-cancer analysis of Fringe family members in 33 cancer types with transcriptomic, genomic, methylation data from The Cancer Genome Atlas (TCGA) project. The correlation between Fringe family member expression and patient overall survival, copy number variation, methylation, Gene Ontology enrichment, and tumor-infiltrating lymphocytes (TILs) was investigated by using multiple databases, such as cBioPortal, Human Protein Atlas, GeneCards, STRING, MSigDB, TISIDB, and TIMER2. In vitro experiments and immunohistochemical assays were performed to validate our findings. Results High expression levels of LFNG, MFNG, RFNG were closely associated with poor overall survival in multiple cancers, particularly in pancreatic adenocarcinoma (PAAD), uveal melanoma (UVM), and brain lower-grade glioma (LGG). Copy number variation analysis revealed that diploid and gain mutations of LFNG was significantly increased in PAAD and stomach adenocarcinoma (STAD), and significantly associated with the methylation levels in promoter regions. Significant differential genes between high and low expression groups of these Fringe family members were found to be consistently enriched in immune response and T cell activation pathway, extracellular matrix adhesion pathway, RNA splicing and ion transport pathways. Correlation between the abundance of tumor-infiltrating lymphocytes (TILs) and LFNG, MFNG, and RFNG expression showed that high LFNG expression was associated with lower TIL levels, particularly in PAAD. In vitro experiment by using pancreatic cancer PANC1 cells showed that LFNG overexpression promoted cell proliferation and invasion. Immunohistochemical assay in 90 PAAD patients verified the expression level of LFNG and its relationship with the prognosis. Conclusions Our study provides a relatively comprehensive understanding of the expression, mutation, copy number, promoter methylation level changes along with prognosis values of LFNG, MFNG, and RFNG in different tumors. High LFNG expression may serve as a poor prognosis molecular marker for PAAD.

Compared with traditional genetic markers, genomic approaches have proved valuable to the conservation of endangered species. Paeonia ludlowii having rarely and pure yellow flowers, is one of the world's most famous tree peonies. However, only several wild populations remain in the Yarlung Zangbo Valley (Nyingchi and Shannan regions, Xizang) in China due to increasing anthropogenic impact on the natural habitats. We used genome-wide single nucleotide polymorphisms to elucidate the spatial pattern of genetic variation, population structure and demographic history of P. ludlowii from the fragmented region comprising the entire range of this species, aiming to provide a basis for conserving the genetic resources of this species. Unlike genetic uniformity among populations revealed in previous studies, we found low but varied levels of intra-population genetic diversity, in which lower genetic diversity was detected in the population in Shannan region compared to those in Nyingzhi region. These spatial patterns may be likely associated with different population sizes caused by micro-environment differences in these two regions. Additionally, low genetic differentiation among populations (Fst = 0.0037) were detected at the species level. This line of evidence, combined with the result of significant genetic differentiation between the two closest populations and lack of isolation by distance, suggested that shared ancestry among now remnant populations rather than contemporary genetic connectivity resulted in subtle population structure. Demographic inference suggested that P. ludlowii probably experienced a temporal history of sharp population decline during the period of Last Glacial Maximum, and a subsequent bottleneck event resulting from prehistoric human activities on the Qinghai-Tibet Plateau. All these events, together with current habitat fragment and excavation might contribute to the endangered status of P. ludlowii. Our study improved the genetic characterization of the endangered tree peony (P. ludlowii) in China, and these genetic inferences should be considered when making different in situ and ex situ conservation actions for P. ludlowii in this evolutionary hotspot region.

Visual object tracking, pivotal for applications like earth observation and environmental monitoring, encounters challenges under adverse conditions such as low light and complex backgrounds. Traditional tracking technologies often falter, especially when tracking dynamic objects like aircraft amidst rapid movements and environmental disturbances. This study introduces an innovative adaptive multimodal image object-tracking model that harnesses the capabilities of multispectral image sensors, combining infrared and visible light imagery to significantly enhance tracking accuracy and robustness. By employing the advanced vision transformer architecture and integrating token spatial filtering (TSF) and crossmodal compensation (CMC), our model dynamically adjusts to diverse tracking scenarios. Comprehensive experiments conducted on a private dataset and various public datasets demonstrate the model’s superior performance under extreme conditions, affirming its adaptability to rapid environmental changes and sensor limitations. This research not only advances visual tracking technology but also offers extensive insights into multisource image fusion and adaptive tracking strategies, establishing a robust foundation for future enhancements in sensor-based tracking systems.

Abstract Changes in ocean ventilation have been pivotal in regulating carbon sequestration and release on centennial to millennial timescales. However, paleoceanographic reconstructions documenting changes in deep-ocean ventilation using 14C dating, may bear multidimensional explanations, obfuscating the roles of ocean ventilation played on climate evolution. Here, we show that previously inferred poorly ventilated conditions in the North Atlantic were linked to enhanced pre-aged organic carbon (OC) input during Heinrich Stadial 1 (HS1). The 14C age of sedimentary OC was approximately 13,345 ± 692 years older than the coeval foraminifera in the central North Atlantic during HS1, which is coupled to a ventilation age of 5,169 ± 660 years. Old OC was mainly of terrigenous origin and exported to the North Atlantic by ice-rafting. Remineralization of old terrigenous OC in the ocean may have contributed to, at least in part, the anomalously old ventilation ages reported for the high-latitude North Atlantic during HS1.

Abstract Background Ceratostigma, a genus in the Plumbaginaceae, is an ecologically dominant group of shrubs, subshrub and herb mainly distributed in Qinghai-Tibet Plateau and North China. Ceratostigma has been the focal group in several studies, owing to their importance in economic and ecological value and unique breeding styles. Despite this, the genome information is limited and interspecific relationships within the genus Cerotastigma remains unexplored. Here we sequenced, assembled and characterized the 14 plastomes of five species, and conducted phylogenetic analyses of Cerotastigma using plastomes and nuclear ribosomal DNA (nrDNA) data. Results Fourteen Cerotastigma plastomes possess typical quadripartite structures with lengths from 164,076 to 168,355 bp that consist of a large single copy, a small single copy and a pair of inverted repeats, and contain 127–128 genes, including 82–83 protein coding genes, 37 transfer RNAs and eight ribosomal RNAs. All plastomes are highly conservative and similar in gene order, simple sequence repeats (SSRs), long repeat repeats and codon usage patterns, but some structural variations in the border of single copy and inverted repeats. Mutation hotspots in coding (Pi values > 0.01: matK, ycf3, rps11, rps3, rpl22 and ndhF) and non-coding regions (Pi values > 0.02: trnH-psbA, rps16-trnQ, ndhF-rpl32 and rpl32-trnL) were identified among plastid genomes that could be served as potential molecular markers for species delimitation and genetic variation studies in Cerotastigma. Gene selective pressure analysis showed that most protein-coding genes have been under purifying selection except two genes. Phylogenetic analyses based on whole plastomes and nrDNA strongly support that the five species formed a monophyletic clade. Moreover, interspecific delimitation was well resolved except C. minus, individuals of which clustered into two main clades corresponding to their geographic distributions. The topology inferred from the nrDNA dataset was not congruent with the tree derived from the analyses of the plastid dataset. Conclusion These findings represent the first important step in elucidating plastome evolution in this widespread distribution genus Cerotastigma in the Qinghai-Tibet Plateau. The detailed information could provide a valuable resource for understanding the molecular dynamics and phylogenetic relationship in the family Plumbaginaceae. Lineage genetic divergence within C. minus was perhaps promoted by geographic barriers in the Himalaya and Hengduan Mountains region, but introgression or hybridization could not be completely excluded.

Abstract Airborne mineral dust is sensitive to climatic changes, but its response to orbital forcing is still not fully understood. Here, we present a reconstruction of dust input to the Subarctic Pacific Ocean covering the past 190 kyr. The dust composition record is indicative of source moisture conditions, which were dominated by precessional variations. In contrast, the dust flux record is dominated by obliquity variations and displays an out‐of‐phase relationship with a dust record from the mid‐latitude North Pacific Ocean. Climate model simulations suggest precession likely drove changes in the aridity and extent of dust source regions. Additionally, the obliquity variations in dust flux can be explained by meridional shifts in the North Pacific westerly jet, driven by changes in the meridional atmospheric temperature gradient. Overall, our findings suggest that North Pacific dust input was primarily modulated by orbital‐controlled source aridity and the strength and position of the westerly winds.

With the development of artificial intelligence and breakthroughs in deep learning, large-scale foundation models (FMs), such as generative pre-trained transformer (GPT), Sora, etc., have achieved remarkable results in many fields including natural language processing and computer vision. The application of FMs in autonomous driving holds considerable promise. For example, they can contribute to enhancing scene understanding and reasoning. By pre-training on rich linguistic and visual data, FMs can understand and interpret various elements in a driving scene, and provide cognitive reasoning to give linguistic and action instructions for driving decisions and planning. Furthermore, FMs can augment data based on the understanding of driving scenarios to provide feasible scenes of those rare occurrences in the long tail distribution that are unlikely to be encountered during routine driving and data collection. The enhancement can subsequently lead to improvement in the accuracy and reliability of autonomous driving systems. Another testament to the potential of FMs’ applications lies in world models, exemplified by the DREAMER series, which showcases the ability to comprehend physical laws and dynamics. Learning from massive data under the paradigm of self-supervised learning, world models can generate unseen yet plausible driving environments, facilitating the enhancement in the prediction of road users’ behaviors and the off-line training of driving strategies. In this paper, we synthesize the applications and future trends of FMs in autonomous driving. By utilizing the powerful capabilities of FMs, we strive to tackle the potential issues stemming from the long-tail distribution in autonomous driving, consequently advancing overall safety in this domain.

BackgroundHeart failure (HF) is a complex and life-threatening syndrome associated with significant morbidity and mortality. While TikTok has gained popularity as a social media platform for sharing HF-related information, the quality of such content on TikTok remains unexplored.MethodsA cross-sectional analysis was conducted on TikTok videos related to HF in China. The sources of the videos were identified and analyzed. The content comprehensiveness of the videos was evaluated using six questions that covered definition, signs and symptoms, risk factors, evaluation, management, and outcomes. The reliability and quality of the videos were assessed using three standardized evaluation instruments: DISCERN, JAMA benchmarks, and the Global Quality Scale. Additionally, the correlation between video quality and video characteristics was further investigated.ResultsAmong the video sources, 92.2% were attributed to health professionals, while news agencies and non-profit organizations accounted for 5.7% and 2.1%, respectively. The content comprehensiveness score for the videos was 3.36 (SD 3.56), with news agencies receiving the highest scores of 4.06 (SD 3.31). The median DISCERN, JAMA, and GQS scores for all 141 videos were 26.50 (IQR 25.00–28.750), 2.00 (IQR 2.00–2.00), and 2.00 (IQR 2.00–2.00), respectively. Videos from health professionals had significantly higher JAMA scores compared to those from non-profit organizations (P

Abstract Nanotechnology-enabled sensors or nanosensors are emerging as promising new tools for various in-vivo life science applications such as biosensing, components of delivery systems, and probes for spatial bioimaging. However, as with a wide range of synthetic biomaterials, tissue responses have been observed depending on cell types and various nanocomponent properties. The tissue response is critical for determining the acute and long term health of the organism and the functional lifetime of the material in-vivo. While nanomaterial properties can contribute significantly to the tissue response, it may be possible to circumvent adverse reactions by formulation of the encapsulation vehicle. In this study, five formulations of poly (ethylene glycol) diacrylate (PEGDA) hydrogel-encapsulated fluorescent nanosensors were implanted into SKH-1E mice, and the inflammatory responses were tracked in order to determine the favorable design rules for hydrogel encapsulation and minimization of such responses. Hydrogels with higher crosslinking density were found to allow faster resolution of acute inflammation. Five different immunocompromised mice lines were utilized for comparison across different inflammatory cell populations and responses. Degradation products of the gels were also characterized. Finally, the importance of the tissue response in determining functional lifetime was demonstrated by measuring the time-dependent nanosensor deactivation following implantation into animal models.

A widely utilized tool in reactor analysis is passive tracers that report the residence time distribution, allowing estimation of the conversion and other properties of the system. Recently, advances in microrobotics have introduced powered and functional entities with sizes comparable to some traditional tracers. This has motivated the concept of Smart Tracers that could record the local chemical concentrations, temperature, or other conditions as they progress through reactors. Herein, the design constraints and advantages of Smart Tracers by simulating their operation in a laminar flow reactor model conducting chemical reactions of various orders are analyzed. It is noted that far fewer particles are necessary to completely map even the most complex concentration gradients compared with their conventional counterparts. Design criteria explored herein include sampling frequency, memory storage capacity, and ensemble number necessary to achieve the required accuracy to inform a reactor model. Cases of severe particle diffusion and sensor noise appear to bind the functional upper limit of such probes and require consideration for future design. The results of the study provide a starting framework for applying the new technology of microrobotics to the broad and impactful set of problems classified as chemical reactor analysis.

Abstract With the increase in penetration of wind generation on interconnected power systems, the importance of wind power ramp forecasting has continuously grown. Large power ramps caused by sudden weather changes raise more concerns due to their significant impact on the power system economics and stability. Correct wind power ramp forecasts can help the system operators and utility companies to tradeoff the risks when scheduling wind energy in the electricity market. In this paper, a day‐ahead wind power ramp forecasting algorithm is developed to provide probabilistic ramp forecasts for look‐ahead times up to 48 h using hourly wind speed forecasts from Environment Canada High Resolution Deterministic Prediction System (HRDPS). An image‐based similarity search strategy has been designed to build a direct link between the wind speed forecasts and the wind power ramp prediction, thus reducing the impact of the uncertainty from both the power production forecast model and the ramp identification process on the forecasting accuracy. A performance assessment and validation of the proposed ramp event forecasting method is conducted by using the forecast and operation data from six investigated wind farms across Canada.

Context Ferulic acid (FA) has antioxidative and anti-inflammatory effects, and is a promising drug to treat sepsis.Objective To study the therapeutic effect of FA in sepsis-induced acute lung injury (ALI) and its underlying mechanisms.Materials and methods The caecal ligation and puncture (CLP) manoeuvre was applied to establish a murine model of sepsis-induced ALI, and female BALB/c mice (6 mice per group) were subjected to 100 mg/kg FA or 0.8 mg/kg ferrostatin-1 (Fer-1, ferroptosis inhibitor) treatment to clarify the role of FA in preserving alveolar epithelial barrier function and inhibiting ferroptosis. Lipopolysaccharide (LPS; 500 ng/mL)-induced cell models were prepared and subjected to FA (0.1 μM), sh-Nrf2, and Fe (Fe-citrate, ferroptosis inducer; 5 M) treatment to study the in vitro effect of FA on LPS-induced alveolar epithelial cell injury and the role of the Nrf2/HO-1 pathway.Results We found that FA decreased the lung injury score (48% reduction), lung wet/dry weight ratio (33% reduction), and myeloperoxidase activity (58% reduction) in sepsis-induced ALI. Moreover, FA inhibited ferroptosis of alveolar epithelial cells and improved alveolar epithelial barrier dysfunction. The protective role of FA against alveolar epithelial barrier dysfunction could be reversed by the ferroptosis inducer Fe-citrate, suggesting that FA alleviates alveolar epithelial barrier dysfunction by inhibiting ferroptosis. Mechanistically, we found that FA inhibited ferroptosis of alveolar epithelial cells by activating the Nrf2/HO-1 pathway.Conclusion Collectively, our data highlighted the alleviatory role of ferulic acid in sepsis-induced ALI by activating the Nrf2/HO-1 pathway and inhibiting ferroptosis, offering a new basis for sepsis treatment.

Abstract Background Wickerhamomyces anomalus (W. anomalus) is a kind of non-Saccharomyces yeast that has a variety of unique physiological characteristics and metabolic features and is widely used in many fields, such as food preservation, biomass energy, and aquaculture feed protein production. However, the mechanism of W. anomalus response to ethanol stress is still unclear, which greatly limits its application in the production of ethanol beverages and ethanol fuels. Therefore, we checked the effects of ethanol stress on the morphology, the growth, and differentially expressed genes (DEGs) and metabolites (DEMs) of W. anomalus. Results High concentrations of ethanol (9% ethanol and 12% ethanol) remarkably inhibited the growth of W. anomalus. Energy metabolism, amino acid metabolism, fatty acids metabolism, and nucleic acid metabolism were significantly influenced when exposing to 9% ethanol and 12% ethanolstress, which maybe universal for W. anomalus to response to different concentrations of ethanol stressl Furthermore, extracellular addition of aspartate, glutamate, and arginine significantly abated ethanol damage and improved the survival rate of W. anomalus. Conclusions The results obtained in this study provide insights into the mechanisms involved in W. anomalus response to ethanol stress. Therefore, new strategies can be realized to improve the ethanol tolerance of W. anomalus through metabolic engineering.

The mechanism of the last deglacial global warming is key for future climate. Here, the authors shed light on the pivotal role of the thermal coupling between the western Pacific warm pool and the Southern Ocean.

Based on marine multiproxy records, a new study outlines the role of larger heat discharge of the pan-Arctic Rivers in determining the pronounced sea ice retreat over the East Siberian Arctic Shelf in the mid-Holocene.

Abstract Background Clinical decision of extubation is a challenge in the treatment of patient with invasive mechanical ventilation (IMV), since existing extubation protocols are not capable of precisely predicting extubation failure (EF). This study aims to develop and validate interpretable recurrent neural network (RNN) models for dynamically predicting EF risk. Methods A retrospective cohort study was conducted on IMV patients from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Time series with a 4-h resolution were built for all included patients. Two types of RNN models, the long short-term memory (LSTM) and the gated recurrent unit (GRU), were developed. A stepwise logistic regression model was used to select key features for developing light-version RNN models. The RNN models were compared to other five non-temporal machine learning models. The Shapley additive explanations (SHAP) value was applied to explain the influence of the features on model prediction. Results Of 8,599 included patients, 2,609 had EF (30.3%). The area under receiver operating characteristic curve (AUROC) of LSTM and GRU showed no statistical difference on the test set (0.828 vs. 0.829). The light-version RNN models based on the 26 features selected out of a total of 89 features showed comparable performance as their corresponding full-version models. Among the non-temporal models, only the random forest (RF) (AUROC: 0.820) and the extreme gradient boosting (XGB) model (AUROC: 0.823) were comparable to the RNN models, but their calibration was deviated. Conclusions The RNN models have excellent predictive performance for predicting EF risk and have potential to become real-time assistant decision-making systems for extubation.

IntroductionCurrently, deep-learning-based prediction of Significant Wave Height (SWH) is mostly performed for a single location in the ocean or simply relies on a single factor (SF). Such approaches have the disadvantage of lacking spatial correlations or dynamic complexity, leading to an inevitable growth of the prediction error with time.MethodsHere, attempting a solution, we develop a Multi-Factor (MF) data-driven 2D SWH prediction model for the Bohai, Yellow, and East China Seas (BYECS). Our model is developed based on a multi-channel PredRNN algorithm that is an improved deep-learning calculation of the ConvLSTM.ResultsIn our model, the MF of historical SWH, 10 m surface winds, ocean surface currents, bathymetries, and open boundaries are used to predict 2D SWH in the next 1-72h. Our modeled SWHs show the correlation coefficients as 0.98, 0.90, and 0.87 for the next 6h, 24h, and 72h, respectively.DiscussionAccording to the ablation experiments, winds are the dominant factor in the MF model and the memory-decoupling module is the key improvement of the PredRNN compared to the ConvLSTM. Furthermore, when the historical SWH is excluded from the input, the correlation coefficients remain around 0.95 in the 1-72h prediction due to the elimination of the error accumulation. It was worse than the MF-PredRNN with the historical SWH before 10h but better than it after 10h. Overall, for the prediction of SWH in the BYECS, our MF-PredRNN-based 2D SWH prediction model significantly improves the accuracy and extends the effective prediction time length.

Abstract Nanoparticle corona phase (CP) design offers a unique approach toward molecular recognition (MR) for sensing applications. Single-walled carbon nanotube (SWCNT) CPs can additionally transduce MR through its band-gap photoluminescence (PL). While DNA oligonucleotides have been used as SWCNT CPs, no generalized scheme exists for MR prediction de novo due to their sequence-dependent three-dimensional complexity. This work generated the largest DNA-SWCNT PL response library of 1408 elements and leveraged machine learning (ML) techniques to understand MR and DNA sequence dependence through local (LFs) and high-level features (HLFs). Out-of-sample analysis of our ML model showed significant correlations between model predictions and actual sensor responses for 6 out of 8 experimental conditions. Different HLF combinations were found to be uniquely correlated with different analytes. Furthermore, models utilizing both LFs and HLFs show improvement over that with HLFs alone, demonstrating that DNA-SWCNT CP engineering is more complex than simply specifying molecular properties.

Background Although the machine learning model developed on electronic health records has become a promising method for early predicting hospital mortality, few studies focus on the approaches for handling missing data in electronic health records and evaluate model robustness to data missingness. This study proposes an attention architecture that shows excellent predictive performance and is robust to data missingness. Methods Two public intensive care unit databases were used for model training and external validation, respectively. Three neural networks (masked attention model, attention model with imputation, attention model with missing indicator) based on the attention architecture were developed, using masked attention mechanism, multiple imputation, and missing indicator to handle missing data, respectively. Model interpretability was analyzed by attention allocations. Extreme gradient boosting, logistic regression with multiple imputation and missing indicator (logistic regression with imputation, logistic regression with missing indicator) were used as baseline models. Model discrimination and calibration were evaluated by area under the receiver operating characteristic curve, area under precision-recall curve, and calibration curve. In addition, model robustness to data missingness in both model training and validation was evaluated by three analyses. Results In total, 65,623 and 150,753 intensive care unit stays were respectively included in the training set and the test set, with mortality of 10.1% and 8.5%, and overall missing rate of 10.3% and 19.7%. attention model with missing indicator had the highest area under the receiver operating characteristic curve (0.869; 95% CI: 0.865 to 0.873) in external validation; attention model with imputation had the highest area under precision-recall curve (0.497; 95% CI: 0.480–0.513). Masked attention model and attention model with imputation showed better calibration than other models. The three neural networks showed different patterns of attention allocation. In terms of robustness to data missingness, masked attention model and attention model with missing indicator are more robust to missing data in model training; while attention model with imputation is more robust to missing data in model validation. Conclusions The attention architecture has the potential to become an excellent model architecture for clinical prediction task with data missingness.

Abstract Background Plastid genomes (plastomes) present great potential in resolving multiscale phylogenetic relationship but few studies have focused on the influence of genetic characteristics of plastid genes, such as genetic variation and phylogenetic discordance, in resolving the phylogeny within a lineage. Here we examine plastome characteristics of Cycas L., the most diverse genus among extant cycads, and investigate the deep phylogenetic relationships within Cycas by sampling 47 plastomes representing all major clades from six sections. Results All Cycas plastomes shared consistent gene content and structure with only one gene loss detected in Philippine species C. wadei. Three novel plastome regions (psbA-matK, trnN-ndhF, chlL-trnN) were identified as containing the highest nucleotide variability. Molecular evolutionary analysis showed most of the plastid protein-coding genes have been under purifying selection except ndhB. Phylogenomic analyses that alternatively included concatenated and coalescent methods, both identified four clades but with conflicting topologies at shallow nodes. Specifically, we found three species-rich Cycas sections, namely Stangerioides, Indosinenses and Cycas, were not or only weakly supported as monophyly based on plastomic phylogeny. Tree space analyses based on different tree-inference methods both revealed three gene clusters, of which the cluster with moderate genetic properties showed the best congruence with the favored phylogeny. Conclusions Our exploration in plastomic data for Cycas supports the idea that plastid protein-coding genes may exhibit discordance in phylogenetic signals. The incongruence between molecular phylogeny and morphological classification reported here may largely be attributed to the uniparental attribute of plastid, which cannot offer sufficient information to resolve the phylogeny. Contrasting to a previous consensus that genes with longer sequences and a higher proportion of variances are superior for phylogeny reconstruction, our result implies that the most effective phylogenetic signals could come from loci that own moderate variation, GC content, sequence length, and underwent modest selection.

Accurate perception, especially situational awareness, is central to the evolution of autonomous driving. This necessitates understanding both the traffic conditions and driving intentions of surrounding vehicles. Given the unobservable nature of driving intentions, the hidden Markov model (HMM) has emerged as a popular tool for intention recognition, owing to its ability to relate observable and hidden variables. However, HMM does not account for the inconsistencies present in time series data, which are crucial for intention recognition. Specifically, HMM overlooks the fact that recent observations offer more reliable insights into a vehicle’s driving intention. To address the aforementioned limitations, we introduce a time-sequenced weights hidden Markov model (TSWHMM). This model amplifies the significance of recent observations in recognition by integrating a discount factor during the observation sequence probability computation, making it more aligned with practical requirements. Regarding the model’s input, in addition to easily accessible states of a target vehicle, such as lateral speed and heading angle, we also introduced lane hazard factors that reflect collision risks to capture the traffic environment information surrounding the vehicle. Experiments on the HighD dataset show that TSWHMM achieves recognition accuracies of 94.9% and 93.4% for left and right lane changes, surpassing both HMM and recurrent neural networks (RNN). Moreover, TSWHMM recognizes lane-changing intentions earlier than its counterparts. In tests involving more complex roundabout scenarios, TSWHMM achieves an accuracy of 87.3% and can recognize vehicles’ intentions to exit the roundabout 2.09 s in advance.

As a gymnosperm group, cycads are known for their ancient origin and specialized coralloid root, which can be used as an ideal system to explore the interaction between host and associated microorganisms. Previous studies have revealed that some nitrogen-fixing cyanobacteria contribute greatly to the composition of the endophytic microorganisms in cycad coralloid roots. However, the roles of host and environment in shaping the composition of endophytic bacteria during the recruitment process remain unclear. Here, we determined the diversity, composition, and function prediction of endophytic bacteria from the coralloid roots of a widely cultivated cycad, Cycas revoluta Thunb. Using next-generation sequencing techniques, we comprehensively investigated the diversity and community structure of the bacteria in coralloid roots and bulk soils sampled from 11 sites in China, aiming to explore the variations in core endophytic bacteria and to predict their potential functions. We found a higher microbe diversity in bulk soils than in coralloid roots. Meanwhile, there was no significant difference in the diversity and composition of endophytic bacteria across different localities, and the same result was found after removing cyanobacteria. Desmonostoc was the most dominant in coralloid roots, followed by Nostoc, yet these two cyanobacteria were not shared by all samples. Rhodococcus, Edaphobacter, Niastella, Nordella, SH-PL14, and Virgisporangium were defined as the core microorganisms in coralloid roots. A function prediction analysis revealed that endophytic bacteria majorly participated in the plant uptake of phosphorus and metal ions and in disease resistance. These results indicate that the community composition of the bacteria in coralloid roots is affected by both the host and environment, in which the host is more decisive. Despite the very small proportion of core microbes, their interactions are significant and likely contribute to functions related to host survival. Our study contributes to an understanding of microbial diversity and composition in cycads, and it expands the knowledge on the association between hosts and symbiotic microbes.

High- and low-latitude climatic processes in the North Pacific Ocean are important components of the global climate system. For example, the interplay among North Pacific atmospheric circulation, ocean circulation, and biological productivity affects atmospheric carbon dioxide levels and marine oxygen concentrations. Here we review recent research on the North Pacific paleoclimatic and paleoceanographic evolution during the late Quaternary and its response to external forcings such as orbital insolation, ice-sheet extent, and greenhouse gas concentrations. First, we summarize the principles and application of relative paleointensity as a critical chronological tool in North Pacific paleoclimate research. Second, we illustrate the latest discoveries on the interaction between North Pacific Intermediate Water formation and high-to-low latitude teleconnection processes. Third, recent progress in linking dust fluxes and marine productivity and their global significance for the carbon cycle are presented. Finally, several key scientific problems are highlighted for future research on ocean-atmosphere-climate interactions in the North Pacific, pointing to the importance of combining paleo-records and modeling simulations. Overall, this review also aims to provide a broad insight into possible future changes of ocean-atmosphere circulation in the North Pacific region under a rapidly warming climate.

Objective: Adiponectin is an adipocyte-derived hormone with an important role in glucose metabolism. The present study explored the effect of adiponectin in diverse population groups on pre-diabetes and newly diagnosed diabetes. Methods: A total of 3300 individuals were enrolled and their data were collected in the analyses dataset from December 2018 to October 2019. Cluster analysis was conducted based on age, BMI, waistline, body fat, systolic blood pressure, triglycerides, and glycosylated hemoglobin 1c. Cluster analysis divided the participants into four groups: a young-healthy group, an elderly-hypertension group, a high glucose–lipid group, and an obese group. Odds ratio (OR) and 95% CIs were calculated using multivariate logistic regression analysis. Results: Compared with the first quartile of adiponectin, the risk of pre -diabetes of fourth quartile was decreased 61% (aOR = 0.39, 95% CI (0.20–0.73)) in the young-healthy group; and the risk of diabetes of fourth quartile was decrease d 85% (aOR = 0.15, 95% CI (0.02–0.67)) in the obese group. There were no significan t correlations between the adiponectin level and diabetes/pre-diabetes in the other two groups. Additionally, receiver operating characteristic curve analysis indicated that adiponectin could significantly improve the diagnosis based on models in the young -healthy group (from 0.640 to 0.675) and the obese group (from 0.714 to 0.761). Conclusions: Increased adiponectin levels were associated with decreased risk of pre-diabetes in the young-healthy population, and with a decreased the risk of diabetes in the obese population. An increased adiponectin level is an independent protective factor for pre-diabetes and diabetes in a specific population in south China.

Subsurface temperature and salinity in the Western Pacific Warm Pool were linked to the shallow overturning circulation and varied on orbital timescales over the past 360,000 years in El Niño/Southern Oscillation-like processes, suggest foraminifera proxy records and numerical modelling.

Abstract Hainan Island had experienced several cold–warm and dry–humid fluctuations since the Late Pleistocene period, resulting in separating and connecting from the mainland several times with the cyclic rise and fall of sea level. The fluctuations can change the biota and ecological environment in the island. Cycas taiwaniana Carruthers is endemic to Hainan Island and is classified as endangered by the International Union for Conservation of Nature (IUCN). To comprehensively understand the genetic dynamics of C. taiwaniana, we sampled 12 wild populations in Hainan Island and one cultivated population in Fujian province, and analyzed the genetic diversity, genetic structure, and demographic history based on the molecular data. Results revealed that C. taiwaniana had relatively low genetic diversity and high genetic differentiation. Haplotypes of C. taiwaniana diversified during the Pleistocene based on the chloroplast DNA (cpDNA) and the concatenated nuclear DNA (nDNA) data. Genetic cluster analyses based on the microsatellite (SSR) data showed that the 12 wild populations were separated into three clusters which could be three evolutionary significant units (ESUs), indicating three basic units of protection were identified. Moreover, we also confirmed the cultivated population FJ derived from the DLS1‐GSL clade. Demographic inference from different data was discordant, but overall, it uncovered that C. taiwaniana had experienced population contraction events twice during the Pleistocene and Holocene, and then expanded recently. Our study elucidated the population genetic characteristics of C. taiwaniana, and guided us to develop targeted conservation and management strategies for this endangered species.

Both changing tectonics and climate may shape the phylogeographic patterns of plant species. The dry-hot valleys in southwestern China harbor a high number of endemic plants. In this study, we investigated the evolutionary history and potential distribution of an endemic shrub Himalrandia lichiangensis (Rubiaceae), to evaluate the effects of tectonic and climatic processes on this thermophilic plant species from the dry-hot valleys. By sequencing DNA from four plastid non-coding regions (psbM-trnD, trnD-trnT, atpB-rbcL and accD-psaI) and the CAMX1F-CAMX2R region and ITS for 423 individuals from 23 populations, we investigated the genetic diversity, phylogeographical pattern and population dynamics of H. lichiangensis. We found a high degree of differentiation in H. lichiangensis during the middle Miocene (15-13 Myr), possibly triggered by the rapid tectonic uplift event in this period area. accompanied by frequent orogeneses in this period. This hypothesis is also supported by the association between genetic differentiation and altitudinal gradients among populations. The middle reach of the Jinsha River, which harbors the greatest genetic diversity, is most likely to have been a refugia for H. lichiangensis during Quaternary. We also detected a strong barrier effect between the Nanpan River and Jinsha River, suggesting the river system may play a role in geographical isolation between clades on both sides of the barrier. The Maximum Entropy Model (MaxEnt) results showed that future climate warming will lead to the niche expansion in some areas for H. lichiangensis but will also cause a scattered and fragmented distribution. Given the high among-population differentiation and no recent expansion detected in H. lichiangensis, its current phylogeographical pattern is possibly due to a long-term geographical barrier caused by uplifting mountains since the Miocene, as well as Quaternary climate refugia isolated also by high mountains. This study illustrated tectonic and climatic processes may have a continuous effect on plant phylogeography and offers insights into the origin of biodiversity and endemism in the dry-hot valleys of southwestern China.

QingreHuoxue decoction (QRHX) is a well-known traditional Chinese medicine formula for treating rheumatoid arthritis (RA). It is not known how QRHX is involved in bone metabolism regulation and thereby alleviatesbone injury caused by arthritis. In this study, we used collagen-induced arthritis (CIA) rat model to evaluate the effects of QRHX onarthriticdiarthrodial joints and the underlying molecular mechanisms. Our results showed that QRHX significantly reduced foot joint volume, synovial inflammatory infiltration and the arthritis index (AI) score. Micro-CT imaging showed that toe bone and ankle joint were eroded and destroyed progressively during RA process, andcompensatory hyperplasia appeared in late stage RA. QRHX treatment inhibited foot bone erosion of early and mid-stage RA, and improved bone mineral density and trabecular fine structure in late RA.This studyunlocked the whole process details of ankle and toe bone injuryduring RA, as well as the bone metabolic characteristics under QRHX treatmentin CIArats. QRHX was confirmed to inhibite the expression of RANKL and DKK-1 mRNA and protein, enhance the expression of β-catenin. Meanwhile, QRHX reduced the pro-inflammatorycytokines IL-17, IL-1β and TNF-αand upregulated anti-inflammatory IL-4 in serum, improving local and systemic inflammatory microenvironment. These results demonstrated that QRHX had sustained protective effects on diarthrodial joints/bone during RA by a synergistic regulation mechanism, which reverses the imbalance of bone metabolismthroughinhibiting RANKL related osteoclast pathway and promotingWnt/β-catenin related osteoblast activation pathway.

Endophytes are essential in plant succession and evolution, and essential for stress resistance. Coralloid root is a unique root structure found in cycads that has played a role in resisting adverse environments, yet the core taxa and microbial community of different Cycas species have not been thoroughly investigated. Using amplicon sequencing, we successfully elucidated the microbiomes present in coralloid roots of 10 Cycas species, representing all four sections of Cycas in China. We found that the endophytic bacteria in coralloid roots, i.e., Cyanobacteria, were mainly composed of Desmonostoc_PCC-7422, Nostoc_PCC-73102 and unclassified_f__Nostocaceae. Additionally, the Ascomycota fungi of Exophiala, Paraboeremia, Leptobacillium, Fusarium, Alternaria, and Diaporthe were identified as the core fungi taxa. The Ascomycota fungi of Nectriaceae, Herpotrichiellaceae, Cordycipitaceae, Helotiaceae, Diaporthaceae, Didymellaceae, Clavicipitaceae and Pleosporaceae were identified as the core family taxa in coralloid roots of four sections. High abundance but low diversity of bacterial community was detected in the coralloid roots, but no significant difference among species. The fungal community exhibited much higher complexity compared to bacteria, and diversity was noted among different species or sections. These core taxa, which were a subset of the microbiome that frequently occurred in all, or most, individuals of Cycas species, represent targets for the development of Cycas conservation.

Eight samples of Eupatorium heterophyllum leaves were collected at different locations in Yunnan and Sichuan provinces in China, and their chemical constituents were investigated. Thirteen previously undescribed sesquiterpene lactones—seven germacranolides, three eudesmanolides, two guaianolides, and a 2-norelemanolide—were isolated, and their structures were elucidated based on extensive spectroscopic analyses. The major constituents in the six samples from northwestern Yunnan and Sichuan are hiyodorilactones A and B, whereas that in the two samples from the region near Kunming, Yunnan is eupatoriopicrin. These results and previously reported results suggest the presence of locality-dependent intra-specific diversity in the chemical constituents of E. heterophyllum leaves.

Abstract Background Early prediction of hospital mortality is crucial for ICU patients with sepsis. This study aimed to develop a novel blending machine learning (ML) model for hospital mortality prediction in ICU patients with sepsis. Methods Two ICU databases were employed: eICU Collaborative Research Database (eICU-CRD) and Medical Information Mart for Intensive Care III (MIMIC-III). All adult patients who fulfilled Sepsis-3 criteria were identified. Samples from eICU-CRD constituted training set and samples from MIMIC-III constituted test set. Stepwise logistic regression model was used for predictor selection. Blending ML model which integrated nine sorts of basic ML models was developed for hospital mortality prediction in ICU patients with sepsis. Model performance was evaluated by various measures related to discrimination or calibration. Results Twelve thousand five hundred fifty-eight patients from eICU-CRD were included as the training set, and 12,095 patients from MIMIC-III were included as the test set. Both the training set and the test set showed a hospital mortality of 17.9%. Maximum and minimum lactate, maximum and minimum albumin, minimum PaO2/FiO2 and age were important predictors identified by both random forest and extreme gradient boosting algorithm. Blending ML models based on corresponding set of predictors presented better discrimination than SAPS II (AUROC, 0.806 vs. 0.771; AUPRC 0.515 vs. 0.429) and SOFA (AUROC, 0.742 vs. 0.706; AUPRC 0.428 vs. 0.381) on the test set. In addition, calibration curves showed that blending ML models had better calibration than SAPS II. Conclusions The blending ML model is capable of integrating different sorts of basic ML models efficiently, and outperforms conventional severity scores in predicting hospital mortality among septic patients in ICU.