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Resolving how Earth surface redox conditions evolved through the Proterozoic Eon is fundamental to understanding how biogeochemical cycles have changed through time. The redox sensitivity of cerium relative to other rare earth elements and its uptake in carbonate minerals make the Ce anomaly (Ce/Ce*) a particularly useful proxy for capturing redox conditions in the local marine environment. Here, we report Ce/Ce* data in marine carbonate rocks through 3.5 billion years of Earth’s history, focusing in particular on the mid-Proterozoic Eon (i.e., 1.8 – 0.8 Ga). To better understand the role of atmospheric oxygenation, we use Ce/ Ce* data to estimate the partial pressure of atmospheric oxygen (pO2) through this time. Our thermodynamics-based modeling supports a major rise in atmospheric oxygen level in the aftermath of the Great Oxidation Event (~ 2.4 Ga), followed by invariant pO2 of about 1% of present atmospheric level through most of the Proterozoic Eon (2.4 to 0.65 Ga).

Strontium isotope ratios (87Sr/86Sr) are gaining considerable interest as a geolocation tool and are now widely applied in archaeology, ecology, and forensic research. However, their application for provenance requires the development of baseline models predicting surficial 87Sr/86Sr variations (\"isoscapes\"). A variety of empirically-based and process-based models have been proposed to build terrestrial 87Sr/86Sr isoscapes but, in their current forms, those models are not mature enough to be integrated with continuous-probability surface models used in geographic assignment. In this study, we aim to overcome those limitations and to predict 87Sr/86Sr variations across Western Europe by combining process-based models and a series of remote-sensing geospatial products into a regression framework. We find that random forest regression significantly outperforms other commonly used regression and interpolation methods, and efficiently predicts the multi-scale patterning of 87Sr/86Sr variations by accounting for geological, geomorphological and atmospheric controls. Random forest regression also provides an easily interpretable and flexible framework to integrate different types of environmental auxiliary variables required to model the multi-scale patterning of 87Sr/86Sr variability. The method is transferable to different scales and resolutions and can be applied to the large collection of geospatial data available at local and global levels. The isoscape generated in this study provides the most accurate 87Sr/86Sr predictions in bioavailable strontium for Western Europe (R2 = 0.58 and RMSE = 0.0023) to date, as well as a conservative estimate of spatial uncertainty by applying quantile regression forest. We anticipate that the method presented in this study combined with the growing numbers of bioavailable 87Sr/86Sr data and satellite geospatial products will extend the applicability of the 87Sr/86Sr geo-profiling tool in provenance applications.

The subaerial exposure of the modern continental crust through time remains intensely debated, with estimates of the first exposure ranging from the late Archean to the Neoproterozoic. To constrain when and how much of the continental crust was exposed subaerially during Earth's history, we trained a supervised machine learning model on the compositions of modern subaerial and submerged basalts. Then, we applied this well‐trained model to a refined worldwide data set of basaltic compositions and calculated the mean proportions of basalts erupted subaerially since 3.8 billion years ago (Ga). Our results suggest that ∼20% of the basalts were exposed subaerially in the early Archean, which may have driven the synthesis of biopolymers crucial to the origins and evolution of life. The proportion of subaerial basalts increased markedly during two stages between the late Archean and the Paleoproterozoic before reaching the present‐day level no earlier than ∼1.8 Ga. Plain Language Summary Although land only covers about 30% of Earth's surface, it plays a crucial role in our planet's ecosystem by interacting with the ocean and atmosphere. Scientists are still debating when and how much land was above sea level throughout history. In our study, we used a machine learning technique to analyze the chemical compositions of basalts and classified rocks that formed on land and under water. We then used this program to study rocks from different times in Earth's history and estimate how much land was above the oceans over time. We found that about 15%–30% of the continents were above the oceans about 3.8 to 2.8 billion years ago (Ga). This may have helped life evolve by creating molecules essential for living things. The amount of land above water increased significantly during two periods between 2.8 and 1.8 Ga and reached today's level no earlier than 1.8 Ga. Key Points Improve the geochemical data quality of global basaltic rocks Machine learning can effectively classify subaerially and subaqueously erupted basalts The subaerial proportion of basalts may reach present‐day level at about 1.8 billion years ago to a maximum

Background Celastrol has been proven effective in anti-inflammatory but was limited in the clinic due to the poor solubility and side effects induced by low bioavailability. Osteoarthritis has acidic and inflammatory environment. Our aim was to load celastrol into HMSNs and capped with chitosan to construct a pH-responsive nanoparticle medicine (CSL@HMSNs-Cs), which is of high solubility for osteoarthritis intra-articular injection treatment. Methods The CSL@HMSNs-Cs were assembled and the characteristics were measured. The CSL@HMSNs-Cs was applied in vitro in the chondrocytes collected from rats cartilage tissue and in vivo in the MIA induced knee osteoarthritis rats via intra-articular injection. Cytotoxicity assay, pH-responsive release, pain behavior, MRI, safranin o fast green staining, ELISA and western blot analysis were applied to evaluate the bioavailability and therapeutic effect of CSL@HMSNs-Cs. Results CSL@HMSNs-Cs was stable due to the protection of the chitosan layers in alkaline environment (pH = 7.7) but revealed good solubility and therapeutic effect in acidic environment (pH = 6.0). The cytotoxicity assay showed no cytotoxicity at relatively low concentration (200 μg/mL) and the cell viability of chondrocytes stimulated by IL-1β was increased in CSL@HMSNs-Cs group. Paw withdrawal threshold in CSL@HMSNs-Cs group is increased, and MRI and Safranin O Fast Green staining showed improvements in articular surface erosion and joint effusion. The upregulated expression levels of IL-1β, TNF-α, IL-6, MMP-3 and MMP-13 and NF-κB signaling pathway of chondrocytes were inhibited in CSL@HMSNs-Cs group. Conclusion Hollow mesoporous silica nanoparticles were an ideal carrier for natural drugs with poor solubility and were of high biocompatibility for intra-articular injection. These intra-articular injectable CSL@HMSNs-Cs with improved solubility, present a pH-responsive therapeutic strategy against osteoarthritis.

Background Intestinal ischemia/reperfusion (I/R) injury commonly occurs during perioperative periods, resulting in high morbidity and mortality on a global scale. Dexmedetomidine (Dex) is a selective α2-agonist that is frequently applied during perioperative periods for its analgesia effect; however, its ability to provide protection against intestinal I/R injury and underlying molecular mechanisms remain unclear. Methods To fill this gap, the protection of Dex against I/R injury was examined in a rat model of intestinal I/R injury and in an inflammation cell model, which was induced by tumor necrosis factor-alpha (TNF-α) plus interferon-gamma (IFN-γ) stimulation. Results Our data demonstrated that Dex had protective effects against intestinal I/R injury in rats. Dex was also found to promote mitophagy and inhibit apoptosis of enteric glial cells (EGCs) in the inflammation cell model. PINK1 downregulated p53 expression by promoting the phosphorylation of HDAC3. Further studies revealed that Dex provided protection against experimentally induced intestinal I/R injury in rats, while enhancing mitophagy, and suppressing apoptosis of EGCs through SIRT3-mediated PINK1/HDAC3/p53 pathway in the inflammation cell model. Conclusion Hence, these findings provide evidence supporting the protective effect of Dex against intestinal I/R injury and its underlying mechanism involving the SIRT3/PINK1/HDAC3/p53 axis.

Metabolic reprogramming is one of the hallmarks of cancer. Nrf2 pathway is one of the critical signaling cascades involved in cell defense and survival against oxidative stress. The significance of Nrf2 in cancer metabolism begins to be recognized. In this minireview, we focus on the Nrf2-mediated cancer metabolic reprogramming and intend to highlight the role of Nrf2 in the regulation of malignant transformation, cancer proliferation, and the development of treatment resistance via metabolic adaptations. We hope for the development of noninvasive biomarkers and novel therapeutic approaches for cancer based on Nrf2-directed cancer metabolic reprogramming in the near future.

Foundering of mafic lower continental crust into underlying convecting mantle has been proposed as one means to explain the unusually evolved chemical composition of Earth's continental crust 1 , 2 , yet direct evidence of this process has been scarce. Here we report that Late Jurassic high-magnesium andesites, dacites and adakites (siliceous lavas with high strontium and low heavy-rare-earth element and yttrium contents) from the North China craton have chemical and petrographic features consistent with their origin as partial melts of eclogite that subsequently interacted with mantle peridotite. Similar features observed in adakites and some Archaean sodium-rich granitoids of the tonalite-trondhjemite-granodiorite series have been interpreted to result from interaction of slab melts with the mantle wedge. Unlike their arc-related counterparts, however, the Chinese magmas carry inherited Archaean zircons and have neodymium and strontium isotopic compositions overlapping those of eclogite xenoliths derived from the lower crust of the North China craton. Such features cannot be produced by crustal assimilation of slab melts, given the high Mg#, nickel and chromium contents of the lavas. We infer that the Chinese lavas derive from ancient mafic lower crust that foundered into the convecting mantle and subsequently melted and interacted with peridotite. We suggest that lower crustal foundering occurred within the North China craton during the Late Jurassic, and thus provides constraints on the timing of lithosphere removal beneath the North China craton.

As one of the basic supporting technologies of 5G system, wireless sensor networks technology is facing a new challenge to improve its transmission energy efficiency. This paper considers combining simultaneous wireless information and power transfer (SWIPT) technique and routing technique, and applying them to multi-hop clustered wireless sensor networks (MCWSN), where each node can decode information and harvest energy from a received radio-frequency signal. And the relay nodes in MCWSN can utilize the harvest energy to forward data to their next hop nodes according to the routing scheme. First, we formulate an energy-efficient routing problem of MCWSN with SWIPT. Then, a heuristic energy efficient cooperative SWIPT routing algorithm (EECSR) is presented to find a transmission path with the maximum energy efficiency. Specifically, in EECSR, the resource allocation problem in each hop of the path is transformed to some equivalent convex optimization problems, which are resolved via dual decomposition. Moreover, a distributed routing protocol based on EECSR is proposed. As far as we know, this is the first solution that considers energy efficiency optimization based on routing and SWIPT in MCWSN. Simulation results show that our EECSR algorithm has high energy efficiency and good robustness. And our distributed routing protocol has better real-time performance than traditional protocols.

Glutamine is a known regulator of vascular smooth muscle cell (VSMC) function, but the molecular pathways underlying this response remain incompletely understood. This study investigated how glutamine metabolism influences VSMC behavior and identified the responsible enzymes and metabolites. Glutamine deprivation markedly reduced VSMC proliferation, migration, and collagen synthesis, while modestly decreasing viability. Pharmacological inhibition of glutaminase-1 (GLS1) or aminotransferases (AT) similarly suppressed these cellular functions, whereas inhibiting glutamate dehydrogenase 1 (GLUD1) had no effect. Metabolite analysis revealed that glutamine deprivation or AT inhibition, but not GLUD1 inhibition, reduced intracellular α-ketoglutarate (αKG) concentrations, establishing AT as the primary enzyme converting glutamine-derived glutamate to αKG. To identify which metabolite drives VSMC responses, glutamine-starved cells were supplemented with various glutamine-derived molecules. The cell-permeable αKG analog dimethyl-αKG significantly restored VSMC proliferation, migration, collagen synthesis, and survival, while ammonia only enhanced viability, demonstrating αKG’s primary role in mediating glutamine-dependent functions. These findings establish that glutamine metabolism via the GLS1-AT-αKG pathway is a critical driver of VSMC activation and survival. Targeting this glutamine-αKG metabolic axis through GLS1 inhibition, AT blockade, or downstream αKG disruption offers a compelling therapeutic strategy for ameliorating fibroproliferative vascular diseases, including atherosclerosis, post-angioplasty restenosis, and pulmonary hypertension.

Background The relationship between surgical sperm retrieval of different etiologies and clinical pregnancy is unclear. We aimed to develop a robust and interpretable machine learning (ML) model for predicting clinical pregnancy using the SHapley Additive exPlanation (SHAP) association of surgical sperm retrieval from testes of different etiologies. Methods A total of 345 infertile couples who underwent intracytoplasmic sperm injection (ICSI) treatment with surgical sperm retrieval due to different etiologies from February 2020 to March 2023 at the reproductive center were retrospectively analyzed. The six machine learning (ML) models were used to predict the clinical pregnancy of ICSI. After evaluating the performance characteristics of the six ML models, the Extreme Gradient Boosting model (XGBoost) was selected as the best model, and SHAP was utilized to interpret the XGBoost model for predicting clinical pregnancies and to reveal the decision-making process of the model. Results Combining the area under the receiver operating characteristic curve (AUROC), accuracy, precision, recall, F1 score, brier score, and the area under the precision-recall (P-R) curve (AP), the XGBoost model has the best performance (AUROC: 0.858, 95% confidence interval (CI): 0.778–0.936, accuracy: 79.71%, brier score: 0.151). The global summary plot of SHAP values shows that the female age is the most important feature influencing the model output. The SHAP plot showed that younger age in females, bigger testicular volume (TV), non-tobacco use, higher anti-müllerian hormone (AMH), lower follicle-stimulating hormone (FSH) in females, lower FSH in males, the temporary ejaculatory disorders (TED) group, and not the non-obstructive azoospermia (NOA) group all resulted in an increased probability of clinical pregnancy. Conclusions The XGBoost model predicts clinical pregnancies associated with testicular sperm retrieval of different etiologies with high accuracy, reliability, and robustness. It can provide clinical counseling decisions for patients with surgical sperm retrieval of various etiologies.