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The Indian Ocean Dipole (IOD) mode exerts distinct impacts on the climate in China and can further affect tropospheric ozone. Using long‐term GEOS‐Chem simulations, we found distinct changes in planetary boundary layer ozone throughout China during positive and negative phases of IOD. In summer, ozone shows synchronized increases except in southern China during positive IOD; the ozone increases are dominated by chemical production and transport in northern and western China, respectively. The increased precursor from biogenic emissions contributed to ozone chemical formation in the northern region, and the increased precipitation and decreased solar radiation hindered ozone production in southern China. Ozone changes show good symmetry over most regions during negative IOD. In autumn, the ozone reduction in southern China shares the same reason as summer, while the chemical increase over northern China is affected more by changes in solar radiation and relative humidity than in the precursor emission. Plain Language Summary Indian Ocean Dipole (IOD) is an important climate variability in the Indian Ocean; here, we found that the variation of IOD from its neutral state to positive or negative phases can strongly modulate ozone within planetary boundary layer (PBL) in China. In summer, positive IOD generally increases ozone over the entire China; northern and western China experience ozone increases mainly due to chemical reactions and regional transport, while southern China sees hindered ozone production from factors like increased rainfall. During autumn for positive events, the ozone reduction in southern China shares the same reason as summer, while northern China is affected more by local weather conditions such as sunlight and humidity rather than increases in reactants during summer. Ozone exhibits opposite changes in negative IOD events due to reverse effects. This research indicates that the IOD's phases influence PBL ozone in China, with varying effects depending on the regions and seasons. Key Points In summer, positive Indian Ocean Dipole (IOD) induces a synchronous increase in planetary boundary layer (PBL) ozone up to 5% throughout China, and vice versa for negative IOD PBL ozone increases over northern and western China in summer for positive IOD is dominated by precursor increase and regional transport PBL ozone increases in autumn are primarily driven by heightened chemical production influenced by meteorological conditions

Adolescents with idiopathic scoliosis (AIS) often exhibit a slender body shape and reduced bone mass, even in the absence of evident vertebral deformities. Although prior studies have implicated microRNAs (miRNAs) in the development and progression of AIS, the precise mechanisms remain poorly understood. Therefore, primary osteoblasts and plasma samples from AIS patients and controls were isolated and associated mechanism was investigated in this study. We observed impaired osteogenic capacity of AIS-osteoblasts, and further identified a significant elevation of miRNA-130b-3p in AIS patients compared to controls through RNA sequencing of plasma samples. The expression levels of miR-130b-3p were validated in an independent cohort of 40 individuals using qPCR. Dual-energy X-ray absorptiometry showed reduced bone mineral density (BMD) in AIS patients. And the correlation analysis revealed a significant negative relationship between miR-130b-3p levels and BMD. Additionally, transcriptomic analysis and dual-luciferase assays confirmed that overexpression of miR-130b-3p in primary osteoblasts inhibited the activation of the ERK1/2 signaling pathway by targeting IGF1, thereby disrupting bone metabolism. Meanwhile, knockdown of miR-130b-3p in AIS-derived osteoblasts improved osteogenic function. In zebrafish, miR-130b-3p overexpression delayed vertebral development and induced spinal deformities. In summary, this study identifies a significant increase of miR-130b-3p in AIS patients and demonstrates its role in impairing osteogenic function through suppression of the IGF1/ERK signaling pathway.

Titanium alloys represent the prevailing material employed in orthopedic implants, which are present in millions of patients worldwide. The prolonged presence of these implants in the human body has raised concerns about possible health effects. This study presents a comprehensive analysis of titanium implants and surrounding tissue samples obtained from patients who underwent revision surgery for therapeutic reasons. The surface of the implants exhibited nano-scale corrosion defects, and nanoparticles were deposited in adjacent samples. In addition, muscle in close proximity to the implant showed clear evidence of fibrotic proliferation, with titanium content in the muscle tissue increasing the closer it was to the implant. Transcriptomics analysis revealed SNAI2 upregulation and activation of PI3K/AKT signaling. In vivo rodent and zebrafish models validated that titanium implant or nanoparticles exposure provoked collagen deposition and disorganized muscle structure. Snai2 knockdown significantly reduced implant-associated fibrosis in both rodent and zebrafish models. Cellular experiments demonstrated that titanium dioxide nanoparticles (TiO 2 NPs) induced fibrotic gene expression at sub-cytotoxic doses, whereas Snai2 knockdown significantly reduced TiO 2 NPs-induced fibrotic gene expression. The in vivo and in vitro experiments collectively demonstrated that Snai2 plays a pivotal role in mediating titanium-induced fibrosis. Overall, these findings indicate a significant release of titanium nanoparticles from the implants into the surrounding tissues, resulting in muscular fibrosis, partially through Snai2-dependent signaling. Graphical Abstract

Iron overload-associated osteoporosis presents a significant challenge to bone health. This study examines the effects of arecoline (ACL), an alkaloid found in areca nut, on bone metabolism under iron overload conditions induced by ferric ammonium citrate (FAC) treatment. The results indicate that ACL mitigates the FAC-induced inhibition of osteogenesis in zebrafish larvae, as demonstrated by increased skeletal mineralization and upregulation of osteogenic genes. ACL attenuates FAC-mediated suppression of osteoblast differentiation and mineralization in MC3T3-E1 cells. RNA sequencing analysis suggests that the protective effects of ACL are related to the regulation of ferroptosis. We demonstrate that ACL inhibits ferroptosis, including oxidative stress, lipid peroxidation, mitochondrial damage, and cell death under FAC exposure. In this study, we have identified heme oxygenase-1 (HO-1) as a critical mediator of ACL inhibiting ferroptosis and promoting osteogenesis, which was validated by HO-1 knockdown and knockout experiments. The study links ACL to HO-1 activation and ferroptosis regulation in the context of bone metabolism. These findings provide new insights into the mechanisms underlying the modulation of osteogenesis by ACL. Targeting the HO-1/ferroptosis axis is a promising therapeutic approach for treating iron overload-induced bone diseases.

Parameter estimation, which represents a classical inverse problem, is often ill-posed as different parameter combinations can yield identical outputs. This non-uniqueness presents a critical barrier to accurate and unique identification. Here we introduce a parameter estimation framework to address such limits: the Joint Conditional Diffusion Model-based Inverse Problem Solver. By leveraging the stochasticity of diffusion models, it produces candidate solutions that capture underlying parameter distributions conditioned on the observations. Joint conditioning on multiple observations further narrows the posterior distributions of non-identifiable parameters. For composite load model parameterization, a challenging task in dynamic power systems, the proposed method achieves a 58.6% reduction in parameter estimation error compared to the single-condition model. It also accurately replicates system’s dynamic responses under various electrical faults with root mean square errors below 4 × 10 −3 , exhibiting comprehensive advantages in calibration and efficiency over existing methods. Given its data-driven nature, it provides a general framework for parameter estimation while effectively mitigating the non-uniqueness problem across scientific domains. Feiqin Zhu, Dmitrii Torbunov and colleagues propose a probabilistic parameter estimation framework to address limits of parameter non-uniqueness in dynamical systems. Study on a composite load model in dynamic power systems demonstrates its effectiveness in reducing parameter estimation uncertainties and improving cross-event generalization.

Quantifying parametric uncertainty using observations from individual sites provides a critical foundation for Earth system modeling, serving as a necessary first step before scaling up to regional or global applications. This study introduces a novel computational framework designed to enhance model predictability by reducing parametric uncertainty and assessing site and observable generalizability using various observational constraints. The framework integrates five components: Model Simulation, Statistical Emulation, Global Sensitivity Analysis (GSA), Model Calibration, and Model Prediction. Using the E3SM land model, we simulated site‐level land‐atmosphere carbon and energy fluxes from 2003 to 2007 across five evergreen needleleaf FLUXNET sites, perturbing 26 vegetation‐related model parameters. Gaussian process emulators were employed to expedite GSA and model calibration. Four critical parameters that strongly influence selected land‐atmosphere fluxes were identified by GSA. Bayesian approaches were used to infer parameter probability distributions leveraging synthetic data and FLUXNET observations. The results reveal that posterior parameter distributions vary significantly across different sites and observables within the same plant functional type. Probabilistic predictions indicate that parameters calibrated at one site can enhance predictive accuracy at other sites, although site heterogeneity may sometimes outweigh parametric uncertainty. Additionally, the probabilistic predictions demonstrate that calibration for one variable can also improve predictability for other variables, thereby maximizing predictive capabilities with limited observations. This framework provides a powerful approach for reducing parametric uncertainty in Earth system models and deepening our understanding of carbon dynamics and energy cycles. Its adaptability makes it a valuable tool for broader applications in Earth system modeling. Plain Language Summary Understanding and predicting how carbon and energy move between the land and atmosphere is a key goal in Earth system science. But making accurate predictions can be difficult because many model parameters, key values used in the model's math equations, are uncertain. In this study, we developed a framework to improve predictions by better estimating these parameters at specific sites. We used the E3SM land model (ELM) to simulate carbon and energy fluxes at five evergreen forest sites, and applied a machine learning tool to build a surrogate of ELM to speed up the analysis. This surrogate helped us identify the most important parameters and adjust them using real‐world data from tower‐based flux measurements. We then tested how well parameter values calibrated at one site could be applied to other sites, and whether calibrating the model with one type of observation (like carbon fluxes) could also improve predictions of other variables (like energy fluxes). Our results show that both the location of observations and the type of variable used for calibration affect how well the model performs. This framework offers a practical way to improve land model predictions, especially when observations are limited. Key Points A computational framework for uncertainty quantification is developed for Earth system model (ELM), with applicability to other ELMs Site‐level uncertainty quantification improves model predictability and guides transferable parameterization across sites and observables Synthetic data are used for parameter estimation to disentangle model error and parametric uncertainty

Cobalt alloys have numerous applications, especially as critical components in orthopedic biomedical implants. However, recent investigations have revealed potential hazards associated with the release of nanoparticles from cobalt-based implants during implantation. This can lead to their accumulation and migration within the body, resulting in adverse reactions such as organ toxicity. Despite being a primary interface for cobalt nanoparticle (CoNP) exposure, skeletal muscle lacks comprehensive long-term impact studies. This study evaluated whether selenium nanoparticles (SeNPs) could mitigate CoNP toxicity in muscle cells and zebrafish models. CoNPs dose-dependently reduced C2C12 viability while elevating reactive oxygen species (ROS) and apoptosis. However, low-dose SeNPs attenuated these adverse effects. CoNPs downregulated myogenic genes and α-smooth muscle actin (α-SMA) expression in C2C12 cells; this effect was attenuated by SeNP cotreatment. Zebrafish studies confirmed CoNP toxicity, as it decreased locomotor performance while inducing muscle injury, ROS generation, malformations, and mortality. However, SeNPs alleviated these detrimental effects. Overall, SeNPs mitigated CoNP-mediated cytotoxicity in muscle cells and tissue through antioxidative and antiapoptotic mechanisms. This suggests that SeNP-coated implants could be developed to eliminate cobalt nanoparticle toxicity and enhance the safety of metallic implants.

Introduction: The escalating global surge in Rifampicin-resistant strains poses a formidable challenge to the worldwide campaign against tuberculosis (TB), particularly in developing countries. The frequent reports of suboptimal treatment outcomes, complications, and the absence of definitive treatment guidelines for Rifampicin-resistant spinal TB (DSTB) contribute significantly to the obstacles in its effective management. Consequently, there is an urgent need for innovative and efficacious drugs to address Rifampicin-resistant spinal tuberculosis, minimizing the duration of therapy sessions. This study aims to investigate potential targets for DSTB through comprehensive proteomic and pharmaco-transcriptomic analyses. Methods: Mass spectrometry-based proteomics analysis was employed to validate potential DSTB-related targets. PPI analysis confirmed by Immunohistochemistry (IHC) and Western blot analysis. Results: The proteomics analysis revealed 373 differentially expressed proteins (DEPs), with 137 upregulated and 236 downregulated proteins. Subsequent Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses delved into the DSTB-related pathways associated with these DEPs. In the context of network pharmacology analysis, five key targets—human leukocyte antigen A chain (HLAA), human leukocyte antigen C chain (HLA-C), HLA Class II Histocompatibility Antigen, DRB1 Beta Chain (HLA-DRB1), metalloproteinase 9 (MMP9), and Phospholipase C-like 1 (PLCL1)—were identified as pivotal players in pathways such as “Antigen processing and presentation” and “Phagosome,” which are crucially enriched in DSTB. Moreover, pharmaco-transcriptomic analysis can confirm that 58 drug compounds can regulate the expression of the key targets. Discussion: This research confirms the presence of protein alterations during the Rifampicin-resistant process in DSTB patients, offering novel insights into the molecular mechanisms underpinning DSTB. The findings suggest a promising avenue for the development of targeted drugs to enhance the management of Rifampicin-resistant spinal tuberculosis.

Tropospheric ozone, as a critical atmospheric component, plays an important role in influencing radiation equilibrium and ecological health. It is affected not only by anthropogenic activities but also by natural climate variabilities. Here we examine the tropospheric ozone changes in China associated with the eastern Pacific (EP) and central Pacific (CP) El Niño using satellite observations from 2007 to 2017 and GEOS-Chem simulations from 1980 to 2017. GEOS-Chem reasonably reproduced the satellite-retrieved lower tropospheric ozone (LTO) changes despite a slight underestimation. In general, both types of El Niño exert negative impacts on LTO concentration in China, except for southeastern China during the pre-CP El Niño autumn and post-EP El Niño summer. Ozone budget analysis further reveals that for both events, LTO changes are dominated by the transport processes controlled by circulation patterns and the chemical processes influenced by local meteorological anomalies associated with El Niño, especially the changes in solar radiation and relative humidity. The differences between EP- and CP-induced LTO changes mostly lie in southern China. The different strengths, positions, and duration of the western North Pacific anomalous anticyclone induced by tropical warming are likely responsible for the different EP and CP LTO changes. During the post-EP El Niño summer, the Indian Ocean capacitor effect also plays an important role in mediating LTO changes over southern China.