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Artesunate was reported to have inhibition effect on tumors via amplified oxidative stress while the lack of intratumoral ferrous ions supply greatly hinders its efficacy. Herein, the AS/GOD@HAZnO NPs we proposed could be efficiently taken in by the affinity between hyaluronic acid and the CD44 receptors. DLS and TEM results manifested the nano-size (~ 160 nm) and circular shape of AS/GOD@HAZnO NPs. Due to the acid-responsive degradation, AS/GOD@HAZnO NPs realized responsive release (up to 80%) in acid environment while only 20% was released in neutral medium. The cellular and in vivo experiment showed that co-delivery of AS and GOD via HAZnO NPs could effectively induce the overproduction of ROS and cut the glucose supply of tumor cells, and thus result in efficient cell apoptosis and tumor inhibition.

Learning outcomes can be predicted with machine learning algorithms that assess students’ online behavior data. However, there have been few generalized predictive models for a large number of blended courses in different disciplines and in different cohorts. In this study, we examined learning outcomes in terms of learning data in all of the blended courses offered at a Chinese university and proposed a new classification method of blended courses, in which students were primarily clustered on the basis of their online learning behaviors in blended courses using the expectation–maximization algorithm. Then, the blended courses were classified on the basis of the cluster of students who were present in the course and had the highest proportion. The advantage of this method is that the criteria used for classification of the blended courses are clearly defined on the basis of students' online behavior data, so it can easily be used by machine learning systems to algorithmically classify blended courses based on log data collected from a learning management system. Drawing on the classification of the blended courses, we also proposed and validated a general model using the random forest algorithm to predict learning outcomes based on students’ online behaviors in blended courses with different disciplines and different cohorts. The findings of this study indicated that after blended courses were classified on the basis of students’ online behavior, prediction accuracy in each category increased. The overall accuracies for Course I (380 courses out of 661 after screening), L (14 courses out of 661 after screening), A (237 courses out of 661 after screening), V (8 courses out of 661 after screening), and H (22 courses out of 661 after screening) were 38.2%, 48.4%, 42.3%, 42.4%, and 74.7%, respectively. According to these results, it was found that a prerequisite for the accurate prediction of students' learning outcomes in a blended course was that most students should be highly engaged in a variety of online learning activities rather than being focused on only one type of activity, such as only watching online videos or submitting online assignments. The prediction model achieved accuracies of 80.6%, 85.3%, 63%, 54.8%, and 14.3% for grades A, B, C, D, and F in Course H, respectively. The results demonstrated the potential of the proposed model for accurately predicting learning outcomes in blended courses. Finally, we found that there was no single online learning behavior that had a dominant effect on the prediction of students' final grades.

This research has two aims: (1) to bridge a gap in blended learning research - the lack of a systems approach to the understanding of blended learning research and practice, and (2) to promote a more comprehensive understanding of what has been achieved and what needs to be achieved in blended learning research and practice. To achieve these aims, we first assess the strengths and limitations in existing models of blended learning, then propose a framework for blended learning that is grounded in the complex adaptive systems theory. The proposed framework sees blended learning as a system consisting of six essential subsystems, and all the subsystems relate to and interact upon one another. The proposed framework is then applied to the review of 87 empirical studies from the current blended learning literature. The review identifies several gaps in current blended learning research and practice, and advances our understanding of some untapped potential of this new system of learning. We hope that this research will shed light on critical issues in understanding blended learning and in scaling up its implementation in tertiary education.

REY‐rich mud, consisting of deep‐sea sediments with high concentrations of rare‐earth elements and yttrium (REY), holds significant economic potential. Many studies have been conducted on biogenic apatite, ferromanganese micronodule, and phillipsite within these deposits to ascertain the REY enrichment mechanisms. However, the knowledge of clay minerals in REY‐rich mud, which is the predominant component of pelagic sediments, is still limited. In this study, two adjacent gravity cores (core GC02: REY‐rich mud; core GC03: typical sediments of equatorial Pacific) were collected from the Clarion‐Clipperton Fracture Zone (CCFZ) of the Eastern Pacific to study the role of different clay minerals in REY enrichment. The clay minerals in core GC03 and core GC02 are primarily illite (averaging 60%) and smectite (averaging 63%), respectively, and the smectite in core GC02 was mainly Fe‐rich, which was probably formed via the reaction between opal and FeOOH. Moreover, multiple studies have reported similar smectite enrichment in REY‐rich mud, suggesting that it is a common characteristic. The presumed hydrothermal or volcanic origination of smectite in REY‐rich layers of core GC02 indicates the essential role of hydrothermal and volcanic activities in REY‐rich mud formation during the Oligocene in the western CCFZ. Key Points Smectite is likely to be commonly enriched in rare‐earth elements and yttrium (REY)‐rich mud of the Pacific Authigenic smectite probably originated from hydrothermal or volcanic activity and concentrated moderate amounts of REY The REY‐rich mud of the western Clarion‐Clipperton Fracture Zone presumably formed through hydrothermal or volcanic activities during the Oligocene

IntroductionThe Yangtze River Basin (YRB) is a region of immense economic and ecological significance in China, whose complex topography and climatic variability render it particularly susceptible to landslide disasters.MethodsIn this study, landslide spatial density (LSD) is adopted as a quantitative indicator and multiple linear regression analysis alongside the geographic detector method are employed to evaluate the influence of natural and anthropogenic factors on LSD. A Composite Human Activity Intensity Index (CHAII) is developed from nighttime light intensity, population density, and distances to impermeable surfaces and cultivated land. Factors analyzed include CHAII, slope, topographic ruggedness, precipitation, and distances to river and fault lines. ResultsResults reveal that precipitation and distance to fault are the most significant drivers of LSD across the YRB, with precipitation exhibiting the highest explanatory power. CHAII, precipitation, and topographic ruggedness show strong positive correlations with LSD, whereas slope, distance to river, and distance to fault are negatively correlated. Notably, slopes of 20°–30° correspond to reduced LSD, suggesting a localized mitigating effect. Regionally, intense precipitation in the upper YRB substantially amplifies landslide risk even under low levels of human activity, whereas in the middle YRB natural and anthropogenic factors jointly influence LSD, reflecting a transitional zone. In the lower YRB, interactions between human activity and natural factors become more pronounced, increasing spatial heterogeneity of LSD.DiscussionThe findings provide important scientific insights for landslide risk management and contribute to the sustainable development of the YRB.

With the rapid development and widespread application of multibeam echo-sounding systems, large-scale and high-resolution seafloor topography can be efficiently acquired, enabling precise mapping of seabed terrain. However, due to complex oceanographic conditions, instrumental noise, and acoustic interferences, the acquired multibeam data often contain outliers that deviate from the true seafloor surface. These outliers can distort the representation of seafloor topography, adversely affecting subsequent geological analysis and engineering applications. To address this issue, a hybrid outlier detection method combining CUBE filtering with the Isolation Forest (IForest) algorithm, termed CUBE-IForest, is proposed. The method first employs CUBE filtering to remove gross outliers based on local uncertainty estimation, followed by the application of IForest to identify subtle anomalies in the refined data, achieving hierarchical detection of outliers. Experimental results based on in situ multibeam bathymetric data from the northeastern Pacific demonstrate that compared with traditional filtering methods the CUBE-IForest approach significantly improves detection accuracy and reduces both false positive and false negative rates by approximately 30%, confirming its efficiency and reliability in seafloor mapping and analysis.

The biological macromolecule Nocardia rubra cell-wall skeleton (Nr-CWS) has well-established immune-stimulating and anti-tumor activities. However, the role of Nr-CWS on natural killer (NK) cells remains unclear. Here, we explore the function and related mechanisms of Nr-CWS on NK cells. Using a tumor-bearing model, we show that Nr-CWS has slightly effect on solid tumor. In addition, using a tumor metastasis model, we show that Nr-CWS suppresses the lung metastasis induced by B16F10 melanoma cells in mice, which indicates that Nr-CWS may up-regulate the function of NK cells. Further investigation demonstrated that Nr-CWS can increase the expression of TRAIL and FasL on spleen NK cells from Nr-CWS treated B16F10 tumor metastasis mice. The spleen index and serum levels of TNF-α, IFN-γ, and IL-2 in B16F10 tumor metastasis mice treated with Nr-CWS were significantly increased. In vitro , the studies using purified or sorted NK cells revealed that Nr-CWS increases the expression of CD69, TRAIL, and FasL, decreases the expression of CD27, and enhances NK cell cytotoxicity. The intracellular expression of IFN-γ, TNF-α, perforin (prf), granzyme-B (GrzB), and secreted TNF-α, IFN-γ, IL-6 of the cultured NK cells were significantly increased after treatment with Nr-CWS. Overall, the findings indicate that Nr-CWS could suppress the lung metastasis induced by B16F10 melanoma cells, which may be exerted through its effect on NK cells by promoting NK cell terminal differentiation (CD27 low CD11b high ), and up-regulating the production of cytokines and cytotoxic molecules.

This research explored the origin and paleoenvironmental significance of sediments from the Cosmonaut Sea, Antarctica, focusing on the period since the Last Glacial Maximum (LGM, 26,000 cal a BP). Sediment samples from core ANT37-C5/6-07 were subjected to AMS 14 C dating, clay-mineral assemblage analysis, grain size evaluation, and geochemical testing. Results indicated illite as the dominant clay mineral (average 46%), followed by kaolinite (22%) and smectite (21%), with chlorite (11%) being the least abundant. Comparison with previous studies suggested that these sediments are largely derived from weathered material from Prydz Bay and Enderby Land coastal regions. The study of mineral ratios, geochemical elements, and sediment grain size, alongside δ 18 O values from the East Antarctica EDML ice core, revealed that the ice sheet in the study area retreated around 18600 cal a BP, melted more markedly during 16800-15000 cal a BP, tended to expand during 14800-13500 cal a BP, and then the ice sheet remained in a state of retreat until it expanded again around 5000 cal a BP. It is largely synchronous with the phased changes in the Antarctic climate since the LGM (26ka) of the Cosmonaut Sea. Notably, the sediment record aligns with major paleoclimatic events, including Heinrich Stadial 1 and the Younger Dryas in the northern hemisphere and the Antarctic cold reversal, reflecting a climatic ‘seesaw’ effect. These findings suggest that the sedimentary record in the Cosmonaut Sea is a sensitive indicator of climatic conditions, highlighting a history of glacial movements and revealing East Antarctica’s climatic fluctuations. Additionally, the research indicates that the regional ice sheet is more sensitive to climatic changes than previously believed, underscoring its instability.

Research on changes in the redox conditions of bottom waters is essential for understanding deep water circulation, global ocean currents, climate change, and ecosystem health. Through sedimentary geological methods, a deeper understanding of the complex relationships between various environmental changes can be achieved, providing detailed evidence and theoretical support for global climate change research. The Ross Sea in Antarctica plays a key role in the formation of Antarctic bottom water (AABW), and the complex climate changes since the last glacial maximum (LGM) make it particularly significant for study. This research analyzes core ANT32-RB16C from the Ross Sea using geochemical proxies such as major and trace elements, grain size, and redox-sensitive indicators like Mn/Ti, Co/Ti, Mo/Ti, Cd/Ti, U/Th, and Ni/Co molar concentration ratios. Combining this data with a previously established chronological framework, the study explores the evolution of redox conditions in the Ross Sea’s deep waters since the LGM. The results show that the deep waters have remained oxygen-rich since the LGM, with significant changes in four stages. Stage 1 (24.7–15.7 cal ka BP): Strong oxidizing conditions, likely due to enhanced formation of Ross Sea bottom water (RSBW), increasing oxygen levels. Stage 2 (15.7–4.5 cal ka BP): Weakened oxidizing conditions as temperatures rose and ice shelves retreated, increasing primary productivity and depleting oxygen. Stage 3 (4.5–1.5 cal ka BP): Continued decline in oxidizing conditions, possibly linked to high primary productivity and oxygen consumption. Stage 4 (1.5 cal ka BP to present): A rapid recovery of oxidizing conditions, likely driven by temperature drops, increased RSBW formation, and decreased productivity.

This research delves into the interaction between carbon isotopes, ice-rafted debris (IRD), and Circumpolar Deep Water (CDW) in the Amundsen Sea, West Antarctic. Utilizing sediment core ANT36-A11-04, we traced the source of the organic matter though an analysis of the total organic carbon (TOC), stable carbon isotopes (δ 13 C org ), and nitrogen content. We identified six environmental events in this region since the Mid-Holocene, which were discerned through a comparative analysis of the δ 13 C org , TOC, and IRD content. These events were closely linked to variations in the intensity of the CDW. Notably, the synchronous occurrence of a negative shift in the δ 13 C org value and increases in TOC and IRD highlight the significant impact of CDW intrusion, underlining the pivotal role of the CDW in the regional environmental evolution. Specifically, intensified upwelling of the CDW was correlated with increased heat and nutrients, enhanced glacier melting, phytoplankton blooms, higher TOC content, augmented deposition of IRD, and finally resulted in a negative shift in the δ 13 C org value. We present a comprehensive picture of the local environmental evolution in the Amundsen Sea, characterized as a marine-glacial-biological coupling model, thereby contributing to a broader understanding of Antarctic environmental dynamics.