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Abstract Plants are affected by many environmental factors during their various stages of growth, among which salt stress is a key factor. WRKY transcription factors play important roles in the response to stress in plants. In this study, SmWRKY40 from eggplant (Solanum melongena L.) was found to belong to the subfamily of WRKY transcription factor group II, closely related to the evolution of wild tomato ScWRKY40 (Solanum chilense). The expression of SmWRKY40 could be induced by several abiotic stresses (drought, salt, and high temperature) and ABA to different degrees, with salt stress being the most significant. In Arabidopsis thaliana, the seed germination rate of SmWRKY40 overexpression seedlings was significantly higher than those of the wild type under high concentrations of NaCl and ABA, and root elongation of overexpression lines was also longer than wild type under NaCl treatments. SmWRKY40 overexpression lines were found to enhance Arabidopsis tolerance to salt with lower ROS, MDA, higher soluble protein, proline accumulation, and more active antioxidant enzymes. The expression level of genes related to stress and ABA signaling displayed significant differences in SmWRKY40 overexpression line than that of WT. These results indicate that SmWRKY40 regulates ABA and salt stress responses in Arabidopsis.

Abstract Fiddler crabs, as coastal ecosystem engineers, play a crucial role in enhancing biodiversity and accelerating the flow of material and energy. Here we show how widespread crab burrows modify the carbon sequestration capacity of different habitats across a large climatic gradient. The process of crab burrowing results in the reallocation of sediment organic carbon and humus. Crab burrows can increase more greenhouse gases emissions compared to the sediment matrix (CO2: by 17–30%; CH4: by 49–141%). Straightforward calculations indicate that these increased emissions could offset 35–134% of sediment carbon burial in these two ecosystems. This research highlights the complex interactions between crab burrows, habitat type, and climate which reveal a potential lower carbon sink function of blue carbon ecosystems than previously expected without considering crab burrows.

Abstract Small-cell lung cancer (SCLC) is the most aggressive and lethal type of lung cancer, characterized by limited treatment options, early and frequent metastasis. However, the determinants of metastasis in SCLC are poorly defined. Here, we show that estrogen-related receptor gamma (ERRγ) is overexpressed in metastatic SCLC tumors, and is positively associated with SCLC progression. ERRγ functions as an essential activator of extracellular matrix (ECM) remodeling and cell adhesion, two critical steps in metastasis, by directly regulating the expression of major genes involved in these processes. Genetic and pharmacological inhibition of ERRγ markedly reduces collagen production, cell-matrix adhesion, microfilament production, and eventually blocks SCLC cell invasion and tumor metastasis. Notably, ERRγ antagonists significantly suppressed tumor growth and metastasis and restored SCLC vulnerability to chemotherapy in multiple cell-derived and patient-derived xenograft models. Taken together, these findings establish ERRγ as an attractive target for metastatic SCLC and provide a potential pharmacological strategy for treating this lethal disease.

Abstract Background Drought stress limits significantly the crop productivity. However, plants have evolved various strategies to cope with the drought conditions by adopting complex molecular, biochemical, and physiological mechanisms. Members of the nuclear factor Y (NF-Y) transcription factor (TF) family constitute one of the largest TF classes and are involved in plant responses to abiotic stresses. Results TaNF-YB2, a NY-YB subfamily gene in T. aestivum, was characterized in this study focusing on its role in mediating plant adaptation to drought stress. Yeast two-hybrid (Y-2 H), biomolecular fluoresence complementation (BiFC), and Co-immunoprecipitation (Co-IP) assays indicated that TaNF-YB2 interacts with the NF-YA member TaNF-YA7 and NF-YC family member TaNF-YC7, which constitutes a heterotrimer TaNF-YB2/TaNF-YA7/TaNF-YC7. The TaNF-YB2 transcripts are induced in roots and aerial tissues upon drought signaling; GUS histochemical staining analysis demonstrated the roles of cis-regulatory elements ABRE and MYB situated in TaNF-YB2 promoter to contribute to target gene response to drought. Transgene analysis on TaNF-YB2 confirmed its functions in regulating drought adaptation via modulating stomata movement, osmolyte biosynthesis, and reactive oxygen species (ROS) homeostasis. TaNF-YB2 possessed the abilities in transcriptionally activating TaP5CS2, the P5CS family gene involving proline biosynthesis and TaSOD1, TaCAT5, and TaPOD5, the genes encoding antioxidant enzymes. Positive correlations were found between yield and the TaNF-YB2 transcripts in a core panel constituting 45 wheat cultivars under drought condition, in which two types of major haplotypes including TaNF-YB2-Hap1 and -Hap2 were included, with the former conferring more TaNF-YB2 transcripts and stronger plant drought tolerance. Conclusions TaNF-YB2 is transcriptional response to drought stress. It is an essential regulator in mediating plant drought adaptation by modulating the physiological processes associated with stomatal movement, osmolyte biosynthesis, and reactive oxygen species (ROS) homeostasis, depending on its role in transcriptionally regulating stress response genes. Our research deepens the understanding of plant drought stress underlying NF-Y TF family and provides gene resource in efforts for molecular breeding the drought-tolerant cultivars in T. aestivum.

Abstract The enantioselective synthesis of S-stereogenic sulfinamides has garnered considerable attention due to their structural and physicochemical properties. However, catalytic asymmetric synthesis of sulfinamides still remains daunting challenges, impeding their broad application in drug discovery and development. Here, we present an approach for the synthesis of S-stereogenic sulfinamides through peptide-mimic phosphonium salt-catalyzed asymmetric skeletal reorganization of simple prochiral and/or racemic sulfoximines. This methodology allows for the facile access to a diverse array of substituted sulfinamides with excellent enantioselectivities, accommodating various substituent patterns through desymmetrization or parallel kinetic resolution process. Mechanistic experiments, coupled with density functional theory calculations, clarify a stepwise pathway involving ring-opening and ring-closing processes, with the ring-opening step identified as crucial for achieving stereoselective control. Given the prevalence of S-stereogenic centers in pharmaceuticals, we anticipate that this protocol will enhance the efficient and precise synthesis of relevant chiral molecules and their analogs, thereby contributing to advancements in drug discovery.

Abstract Materials synthesis platforms that are designed for autonomous experimentation are capable of collecting multimodal diagnostic data that can be utilized for feedback to optimize material properties. Pulsed laser deposition (PLD) is emerging as a viable autonomous synthesis tool, and so the need arises to develop machine learning (ML) techniques that are capable of extracting information from in situ diagnostics. Here, we demonstrate that intensified-CCD image sequences of the plasma plume generated during PLD can be used for anomaly detection and the prediction of thin film growth kinetics. We develop multi-output (2 + 1)D convolutional neural network regression models that extract deep features from plume dynamics that not only correlate with the measured chamber pressure and incident laser energy, but more importantly, predict parameters of an auto-catalytic film growth model derived from in situ laser reflectivity experiments. Our results demonstrate how ML with in situ plume diagnostics data in PLD can be utilized to maintain deposition conditions in an optimal regime. Further, the predictive capabilities of plume dynamics on the kinetics of film growth or other film properties prior to deposition provides a means for rapid pre-screening of growth conditions for the non-expert, which promises to accelerate materials optimization with PLD.

SNF1-related protein kinase 2 (SnRK2) family members are essential components of the plant abscisic acid (ABA) signaling pathway initiated by osmotic stress and triggering a drought stress response. This study characterized the molecular properties of TaSnRK2.4 and its function in mediating adaptation to drought in Triticum aestivum. Transcripts of TaSnRK2.4 were upregulated upon drought and ABA signaling and associated with drought- and ABA-responsive cis-elements ABRE and DRE, and MYB and MYC binding sites in the promoter as indicated by reporter GUS protein staining and activity driven by truncations of the promoter. Yeast two-hybrid, BiFC, and Co-IP assays indicated that TaSnRK2.4 protein interacts with TaPP2C01 and an ABF transcription factor (TF) TaABF2. The results suggested that TaSnRK2.4 forms a functional TaPP2C01-TaSnRK2.4-TaABF2 module with its upstream and downstream partners. Transgene analysis revealed that TaSnRK2.4 and TaABF2 positively regulate drought tolerance whereas TaPP2C01 acts negatively by modulating stomatal movement, osmotic adjustment, reactive oxygen species (ROS) homeostasis, and root morphology. Expression analysis, yeast one-hybrid, and transcriptional activation assays indicated that several osmotic stress-responsive genes, including TaSLAC1-4, TaP5CS3, TaSOD5, TaCAT1, and TaPIN4, are regulated by TaABF2. Transgene analysis verified their functions in positively regulating stomatal movement (TaSLAC1-4), proline accumulation (TaP5CS3), SOD activity (TaSOD5), CAT activity (TaCAT1), and root morphology (TaPIN4). There were high correlations between plant biomass and yield with module transcripts in a wheat variety panel cultivated under drought conditions in the field. Our findings provide insights into understanding plant drought response underlying the SnRK2 signaling pathway in common wheat.

[Introduction] Lithium-ion battery has become the most widely used new energy storage technology. However, it is found that the practical service conditions have great impacts on the actual service behaviors of lithium-ion battery energy storage. This paper aims to study the influence of service conditions on the electrochemical performance of lithium-ion battery, so as to provide references for the construction of future lithium-ion battery energy storage projects. [Method] The effects of operating charge interval, discharge rate and operating temperature on the actual performance of Li-ion battery were tested and analyzed. [Result] The working SOC range, charge/discharge rage and operating temperature all have great influence on the actual performance of lithium-ion battery. On the one hand, proper adjustment of charge and discharge range can significantly improve the service life. On the other hand, the current commonly used 2 C configuration mode may significantly reduce the service life, while reducing the rete configuration to 1 C is expected to achieve lower levelized cost of energy although it will increase the initial investment. In addition, temperature control is extremely important to the usage of lithium-ion batteries, even single-digit temperature differences can cause significant battery inconformity over long cycle life. [Conclusion] Lithium-ion battery energy storage with advantages of fast response speed, high adjustment accuracy and flexible configuration will further play an important role in the work of "carbon peak and neutrality" and the gradual construction of the power market. Paying attention to the influence of practical conditions on the performance of lithium-ion battery will further improve the use efficiency of lithium-ion battery energy storage. In summary, the design of the battery working condition according to the demand of the practical application can provide lithium-ion battery energy storage system more excellent performance, and play an important role to realize the "carbon peak and neutrality" goal of our country.

Abstract Background Osteoarthritis (OA) is a common degenerative joint condition marked by inflammation and cartilage breakdown. Currently, there is a dearth of treatment medications that can clearly slow the course of OA. Glaucocalyxin A (GLA) is a diterpene chemical identified and extracted from Rabdosia japonica with antithrombotic, anticoagulant, anti-tumor, anti-inflammatory, anti-oxidant, and other pharmacological properties. Previous research has linked inflammation to abnormalities in the homeostasis of the extracellular matrix (ECM). Although GLA has been shown to have anti-inflammatory qualities, its effects on the progression of OA are unknown. As a result, the goal of this study was to see if GLA could slow the course of OA. Methods ATDC5 cells were stimulated by IL-1β to create an inflammatory chondrocyte damage model. Quantitative polymerase chain reaction, Western Blot, high-density culture, and immunofluorescence were used to detect the expression levels of associated gene phenotypes. We also created a mouse model of OA induced by destabilization of the medial meniscus (DMM) instability, and GLA was administered intraperitoneally once every two days for eight weeks. Mice knee specimens were stained with hematoxylin–eosin, Safranin O/fast green, and immunohistochemical, and the Osteoarthritis Research Society International grade system and Mankin’s score were used to assess the protective effect of GLA on cartilage. Results In vitro and in vivo, we explored the effects and molecular processes of GLA as a therapy for OA. The findings demonstrated that GLA might reduce the expression of associated inflammatory mediators and protect the ECM by inhibiting the NF-κB and MAPK signaling pathways. Animal research revealed that GLA could protect against the DMM-induced OA model mice by stabilizing ECM. Conclusion Taken together, our findings show that GLA has a protective impact on cartilage throughout OA progression, implying that GLA could be employed as a possible therapeutic agent for OA, thus giving a new therapeutic method for the treatment of OA.

The power Internet of Things (IoT) is a significant trend in technology and a requirement for national strategic development. With the deepening digital transformation of the power grid, China’s power system has initially built a power IoT architecture comprising a perception, network, and platform application layer. However, owing to the structural complexity of the power system, the construction of the power IoT continues to face problems such as complex access management of massive heterogeneous equipment, diverse IoT protocol access methods, high concurrency of network communications, and weak data security protection. To address these issues, this study optimizes the existing architecture of the power IoT and designs an integrated management framework for the access of multi-source heterogeneous data in the power IoT, comprising cloud, pipe, edge, and terminal parts. It further reviews and analyzes the key technologies involved in the power IoT, such as the unified management of the physical model, high concurrent access, multi-protocol access, multi-source heterogeneous data storage management, and data security control, to provide a more flexible, efficient, secure, and easy-to-use solution for multi-source heterogeneous data access in the power IoT.

This paper realizes the full-domain collaborative deployment of multiple interference sources of the global satellite navigation system (GNSS) and evaluates the deployment effect to enhance the ability to disturb the attacker and the capability to defend the GNSS during navigation countermeasures. Key evaluation indicators for the jamming effect of GNSS suppressive and deceptive jamming sources are first created, their evaluation models are built, and their detection procedures are sorted out, as the basis for determining the deployment principles. The principles for collaboratively deploying multi-jamming sources are developed to obtain the deployment structures (including the required number, structures in demand, and corresponding positions) of three single interference sources required by collaboratively deploying. Accordingly, simulation and hardware-in-loop testing results are presented to determine a rational configuration of the collaborative deployment of multi-jamming sources in the set situation and further realize the full-domain deployment of an interference network from ground, air to space. Varied evaluation indices for the deployment effect are finally developed to evaluate the deployment effect of the proposed configuration and further verify its reliability and rationality.

This study aims to quantitatively assess the impact of the COVID-19 pandemic on societal electricity consumption behavior, particularly considering the interference of special events. Currently, there is insufficient quantitative analysis of the extent to which the COVID-19 pandemic affects electricity consumption behavior. Therefore, we endeavor to introduce new theories and methods to comprehensively understand the potential impact of the pandemic on electricity usage in this field. Our proposed machine learning methods demonstrate significant results and advantages in two key aspects. Firstly, we innovatively introduce an abnormality detection algorithm based on the lunar calendar, thereby establishing a detection system capable of accurately identifying abnormal fluctuations in electricity consumption, to precisely determine the onset of the pandemic. Secondly, we design an evaluation system that integrates CNN-LSTM predictive models and controlled variable strategies, enabling us to reconstruct electricity usage patterns less affected by the pandemic. By comparing actual electricity consumption with reconstructed patterns, we deeply evaluate the impact of the pandemic on different regions and industries and introduce the pandemic impact percentage as a quantification method to precisely assess the extent of the impact. The findings indicate a significant impact of the COVID-19 pandemic on commercial and industrial electricity consumption. Furthermore, our methods are not limited to pandemic research but can also be applied to investigate the effects of other events such as holidays, typhoons, and special production schedules on electricity consumption. In summary, by delving into the complex associations underlying electricity consumption, this study provides a solid theoretical and methodological foundation for future similar research and offers crucial decision support for the electricity industry and emergency management.

Emerging environmental persistent free radicals (EPFRs), can generate reactive oxygen species (ROS), posing potential exposure risks to human health. Incomplete coal combustion is a major source of EPFRs. Organic carbonaceous fractions are essential and important players in the formation of EPFRs during coal combustion. However, relationship between individual organic carbonaceous and non-carbon fractions with EPFRs in such emissions are not well known. This paper investigated the characteristics of EPFRs discharged from simulated coal combustion. Our results showed that the concentration of EPFRs was major concentrated on PM1.1 (51.66–81.85 %), and more easily oxidized by oxygen resulting in producing more oxygen-centered radicals (semiquinone-type) in PM1.1. The mean of line width (ΔHp-p) was 5.87 ± 0.41G higher than that of biomass combustion, indicating more free radical species were emitted from coal combustion. Humic-like substances-carbon (HULIS-C) was the major contributor of the formation of EPFRs and facilitate the generated of EPFRs. Secondary processes have also contributed to the formation of EPFRs during the coal combustion. Our result also noted that there was no relationship between transition metals and EPFRs, may be due to the variability and complexity of the chemical properties and composition of PM. This is critical for the prediction of geochemical behavior and risk assessment of EPFRs, which can provide basic data to support policy development to address rural air pollutant emissions.

Abstract Mangroves and saltmarshes are biogeochemical hotspots storing carbon in sediments and in the ocean following lateral carbon export (outwelling). Coastal seawater pH is modified by both uptake of anthropogenic carbon dioxide and natural biogeochemical processes, e.g., wetland inputs. Here, we investigate how mangroves and saltmarshes influence coastal carbonate chemistry and quantify the contribution of alkalinity and dissolved inorganic carbon (DIC) outwelling to blue carbon budgets. Observations from 45 mangroves and 16 saltmarshes worldwide revealed that >70% of intertidal wetlands export more DIC than alkalinity, potentially decreasing the pH of coastal waters. Porewater-derived DIC outwelling (81 ± 47 mmol m−2 d−1 in mangroves and 57 ± 104 mmol m−2 d−1 in saltmarshes) was the major term in blue carbon budgets. However, substantial amounts of fixed carbon remain unaccounted for. Concurrently, alkalinity outwelling was similar or higher than sediment carbon burial and is therefore a significant but often overlooked carbon sequestration mechanism.

Abstract Unraveling local dynamic charge processes is vital for progress in diverse fields, from microelectronics to energy storage. This relies on the ability to map charge carrier motion across multiple length- and timescales and understanding how these processes interact with the inherent material heterogeneities. Towards addressing this challenge, we introduce high-speed sparse scanning Kelvin probe force microscopy, which combines sparse scanning and image reconstruction. This approach is shown to enable sub-second imaging (>3 frames per second) of nanoscale charge dynamics, representing several orders of magnitude improvement over traditional Kelvin probe force microscopy imaging rates. Bridging this improved spatiotemporal resolution with macroscale device measurements, we successfully visualize electrochemically mediated diffusion of mobile surface ions on a LaAlO3/SrTiO3 planar device. Such processes are known to impact band-alignment and charge-transfer dynamics at these heterointerfaces. Furthermore, we monitor the diffusion of oxygen vacancies at the single grain level in polycrystalline TiO2. Through temperature-dependent measurements, we identify a charge diffusion activation energy of 0.18 eV, in good agreement with previously reported values and confirmed by DFT calculations. Together, these findings highlight the effectiveness and versatility of our method in understanding ionic charge carrier motion in microelectronics or nanoscale material systems.

Abstract Poly (ADP-ribose) polymerase inhibitors (PARPi) are selectively active in ovarian cancer (OC) with homologous recombination (HR) deficiency (HRD) caused by mutations in BRCA1/2 and other DNA repair pathway members. We sought molecular targeted therapy that induce HRD in HR-proficient cells to induce synthetic lethality with PARPi and extend the utility of PARPi. Here, we demonstrate that lysine-specific demethylase 1 (LSD1) is an important regulator for OC. Importantly, genetic depletion or pharmacological inhibition of LSD1 induces HRD and sensitizes HR-proficient OC cells to PARPi in vitro and in multiple in vivo models. Mechanistically, LSD1 inhibition directly impairs transcription of BRCA1/2 and RAD51, three genes essential for HR, dependently of its canonical demethylase function. Collectively, our work indicates combination with LSD1 inhibitor could greatly expand the utility of PARPi to patients with HR-proficient tumor, warranting assessment in human clinical trials.

Abstract Backgrounds The novel concept of microwave dynamic therapy (MDT) solves the problem of incomplete tumor eradication caused by non-selective heating and uneven temperature distribution of microwave thermal therapy (MWTT) in clinic, but the poor delivery of microwave sensitizer and the obstacle of tumor hypoxic microenvironment limit the effectiveness of MDT. Results Herein, we engineer a liquid metal-based nanozyme LM@ZIF@HA (LZH) with eutectic Gallium Indium (EGaIn) as the core, which is coated with CoNi-bimetallic zeolite imidazole framework (ZIF) and hyaluronic acid (HA). The flexibility of the liquid metal and the targeting of HA enable the nanozyme to be effectively endocytosed by tumor cells, solving the problem of poor delivery of microwave sensitizers. Due to the catalase-like activity, the nanozyme catalyze excess H2O2 in the tumor microenvironment to generate O2, alleviating the restriction of the tumor hypoxic microenvironment and promoting the production of ROS under microwave irradiation. In vitro cell experiments, the nanozyme has remarkable targeting effect, oxygen production capacity, and microwave dynamic effect, which effectively solves the defects of MDT. In the constructed patient-derived xenograft (PDX) model, the nanozyme achieves excellent MDT effect, despite the heterogeneity and complexity of the tumor model that is similar to the histological and pathological features of the patient. The tumor volume in the LZH + MW group is only about 1/20 of that in the control group, and the tumor inhibition rate is as high as 95%. Conclusion The synthesized nanozyme effectively solves the defects of MDT, improves the targeted delivery of microwave sensitizers while regulating the hypoxic microenvironment of tumors, and achieves excellent MDT effect in the constructed PDX model, providing a new strategy for clinical cancer treatment.

In this paper, a continuous-time insider trading model is investigated in which an insider is risk-seeking and market makers may receive partial information on the value of a risky asset. With the help of filtering theory and dynamic programming principle, the uniqueness and existence of linear equilibrium is established. It shows that (ⅰ) as time goes by, the residual information decreases, but both the trading intensity and the market liquidity increases, and (ⅱ) with the partial observation accuracy decreasing, both the market liquidity and the residual information will increase while the trading intensity decreases. On the whole, the risk-seeking insider is eager to trade all the trading period, and for market development, it is necessary to increase the insider's risk-preference behavior appropriately.

Accurately predicting industrial electricity consumption is of paramount importance for optimizing energy management and operational efficiency. Traditional forecasting approaches face significant challenges in capturing the complex factors influencing industrial electricity consumption, often due to the inadequate representation of correlations, thus limiting their predictive capabilities. To overcome these limitations, we propose a novel graph-based forecasting model termed Industry–Geography Time Series Forecasting Model (IG-TFM). Our approach leverages historical electricity consumption data and geographical information relevant to similar industries to construct an industry–geography relationship graph. This graph serves as the foundation of a comprehensive network that encompasses all industries of interest, allowing us to identify sectors closely associated with the target industry. The structured graph data are then processed within a graph convolutional neural network framework, which effectively captures the impact of geographical location, industry similarities, and inter-industry relationships on electricity consumption patterns. Utilizing this enriched representation, we develop our IG-TFM for accurate time series forecasting of industrial electricity consumption. Experiments conducted on real-world data, including 31 industries across 9 cities in a southern province of China, demonstrate the significant advantages of our proposed method across key performance indicators such as the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). These findings underscore the importance and efficacy of employing complex networks to encode sequence-related information, thereby substantially improving prediction accuracy in industrial electricity consumption forecasting.

Abstract Exploration of medicines for efficient and safe management of metabolic‐associated fatty liver disease (MAFLD) remains a challenge. Obeticholic acid (OCA), a selective farnesoid X receptor agonist, has been reported to ameliorate injury and inflammation in various liver diseases. However, its clinical application is mainly limited by poor solubility, low bioavailability, and potential side effects. Herein a hepatic‐targeted nanodrugs composed of OCA and cholesterol‐lowering atorvastatin (AHT) with an ideal active pharmaceutical ingredient (API) content for orally combined treatment of MAFLD is created. Such carrier‐free nanocrystals (OCAHTs) are self‐assembled, not only improving the stability in gastroenteric environments but also achieving hepatic accumulation through the bile acid transporter‐mediated enterohepatic recycling process. Orally administrated OCAHT outperforms the simple combination of OCA and AHT in ameliorating of liver damage and inflammation in both acetaminophen‐challenged mice and high‐fat diet‐induced MAFLD mice with less systematic toxicity. Importantly, OCAHT exerts profoundly reverse effects on MAFLD‐associated molecular pathways, including impairing lipid metabolism, reducing inflammation, and enhancing the antioxidation response. This work not only provides a facile bile acid transporter‐based strategy for hepatic‐targeting drug delivery but also presents an efficient and safe full‐API nanocrystal with which to facilitate the practical translation of nanomedicines against MAFLD.

Abstract Acute lung injury (ALI) is a severe respiratory disease with a high mortality rate. The integrity of the pulmonary endothelial barrier influences the development and prognosis of ALI. Therefore, it has become an important target for ALI treatment. Extracellular vesicles (EVs) are promising nanotherapeutic agents against ALI. Herein, endothelium‐derived engineered extracellular vesicles (eEVs) that deliver microRNA‐125b‐5p (miRNA‐125b) to lung tissues exerting a protective effect on endothelial barrier integrity are reported. eEVs that are modified with lung microvascular endothelial cell‐targeting peptides (LET) exhibit a prolonged retention time in lung tissues and targeted lung microvascular endothelial cells in vivo and in vitro. To improve the efficacy of the EVs, miRNA‐125b is loaded into EVs. Finally, LET‐EVs‐miRNA‐125b is constructed. The results show that compared to the EVs, miRNA‐125b, and EVs‐miRNA‐125b, LET‐EVs‐miRNA‐125b exhibit the most significant treatment efficacy in ALI. Moreover, LET‐EVs‐miRNA‐125b is found to have an important protective effect on endothelial barrier integrity by inhibiting cell apoptosis, promoting angiogenesis, and protecting intercellular junctions. Sequencing analysis reveals that LET‐EVs‐miRNA‐125b downregulates early growth response‐1 (EGR1) levels, which may be a potential mechanism of action. Taken together, these findings suggest that LET‐EVs‐miRNA‐125b can treat ALI by protecting the endothelial barrier integrity.

We search for an optimal filter design for the estimation of stellar metallicity, based on synthetic photometry from Gaia XP spectra convolved with a series of filter-transmission curves defined by different central wavelengths and bandwidths. Unlike previous designs based solely on maximizing metallicity sensitivity, we find that the optimal solution provides a balance between the sensitivity and uncertainty of the spectra. With this optimal filter design, the best precision of metallicity estimates for relatively bright ( G ∼ 11.5) stars is excellent, σ _[Fe/H] = 0.034 dex for FGK dwarf stars, superior to that obtained utilizing custom sensitivity-optimized filters (e.g., SkyMapper v ). By selecting hundreds of high-probability member stars of the open cluster M67, our analysis reveals that the intrinsic photometric-metallicity scatter of these cluster members is only 0.036 dex, consistent with this level of precision. Our results clearly demonstrate that the internal precision of photometric-metallicity estimates can be extremely high, even providing the opportunity to perform chemical tagging for very large numbers of field stars in the Milky Way. This experiment shows that it is crucial to take into account uncertainty alongside the sensitivity when designing filters for measuring the stellar metallicity and other parameters.

Abstract While hexagonal boron nitride (hBN) has been widely used as a buffer or encapsulation layer for emerging electronic devices, interest in utilizing it for large‐area chemical barrier coating has somewhat faded. A chemical vapor deposition process is reported here for the conformal growth of hBN on large surfaces of various alloys and steels, regardless of their complex shapes. In contrast to the previously reported very limited protection by hBN against corrosion and oxidation, protection of steels against 10% HCl and oxidation resistance at 850 °C in air is demonstrated. Furthermore, an order of magnitude reduction in the friction coefficient of the hBN coated steels is shown. The growth mechanism is revealed in experiments on thin metal films, where the tunable growth of single‐crystal hBN with a selected number of layers is demonstrated. The key distinction of the process is the use of N2 gas, which gets activated exclusively on the catalyst's surface and eliminates adverse gas‐phase reactions. This rate‐limiting step allowed independent control of activated nitrogen along with boron coming from a solid source (like elemental boron). Using abundant and benign precursors, this approach can be readily adopted for large‐scale hBN synthesis in applications where cost, production volume, and process safety are essential.

Abstract Nanotechnology offers the possibility of revolutionizing cancer theranostics in the new era of precision oncology. Extracellular vesicles (EVs)-like biomimetic nanoparticles (EBPs) have recently emerged as a promising platform for targeted cancer drug delivery. Compared with conventional synthetic vehicles, EBPs have several advantages, such as lower immunogenicity, longer circulation time, and better targeting capability. Studies on EBPs as cancer therapeutics are rapidly progressing from in vitro experiments to in vivo animal models and early-stage clinical trials. Here, we describe engineering strategies to further improve EBPs as effective anticancer drug carriers, including genetic manipulation of original cells, fusion with synthetic nanomaterials, and direct modification of EVs. These engineering approaches can improve the anticancer performance of EBPs, especially in terms of tumor targeting effectiveness, stealth property, drug loading capacity, and integration with other therapeutic modalities. Finally, the current obstacles and future perspectives of engineered EBPs as the next-generation delivery platform for anticancer drugs are discussed.

The solid-fermented process conditions of producing feruloylated oligosaccharides (FOs) from corn bran were optimized in this study. The fungus Neurospora sitophila (generally recognised as safe, GRAS) was used as fermenting species, and FOs content was considered as the indicator, the single-factor and response surface experiments were performed to optimize the culture medium and culture conditions, including inoculation amount, fermentation time, fermentation temperature, and added amount of water. An FOs yield of 1.52 μmol/g was achieved under the following conditions: 5 g of corn bran and 2% of corn bran weight (w/w) of soybean meal in a 250 mL conical flask, an inoculation size of 1.17×105 spores/flask, a fermentation time of 75 h, a fermentation temperature of 28 ℃, and added water mount of 15.2 mL. The observed yield value was close to the theoretical value predicted by the model (1.56 μmol/g), and increased by 60.83% compared with that of the pre-optimized process. Therefore, the FOs content of the product significantly increased by the optimization of the fermentation process, which offered a novel thought for enhancing the added value and expanding the routes for preparing maize bran-derived FOs.

Abstract Systemic administration of platinum-based drugs has obvious limitations in the treatment of advanced bladder cancer (BC) owing to lower tumor accumulation and uncontrolled release of chemotherapeutics. There is an urgent need for advanced strategies to overcome the current limitations of platinum-based chemotherapy, to achieve maximal therapeutic outcomes with reduced side effects. In this study, self-polymerized platinum (II)-polydopamine nanocomplexes (PtPDs) were tailored for efficient chemo-photoimmunotherapy of BC. PtPDs with high Pt loading content (11.3%) were degradable under the combination of a reductive tumor microenvironment and near-infrared (NIR) light irradiation, thus controlling the release of Pt ions to achieve efficient chemotherapy. In addition, polydopamine promoted stronger photothermal effects to supplement platinum-based chemotherapy. Consequently, PtPDs provided effective chemo-photothermal therapy of MB49 BC in vitro and in vivo, strengthening the immunogenic cell death (ICD) effect and robust anti-tumoral immunity response. When combined with a PD-1 checkpoint blockade, PtPD-based photochemotherapy evoked systemic immune responses that completely suppressed primary and distant tumor growth without inducing systemic toxicities. Our work provides a highly versatile approach through metal-dopamine self-polymerization for the precise delivery of metal-based chemotherapeutic drugs, and may serve as a promising nanomedicine for efficient and safe platinum-based chemotherapy for BC.

[Introduction] In order to guarantee urban power supply and offshore wind power utilization without building new power lines, the paper aims to establish a electricity transmission strategy based on wind-coal-battery- hydrogen -CCUS multi energy coupling and bundling system, which is analyzed theoretically and verified by simulation.[Method] To verify the effectiveness of the strategy, four experimental schemes (separate transmission of wind and coal power, bundled transmission of wind and coal power without operation coupling, bundled transmission of wind and coal power with operation coupling, and wind-coal-battery-hydrogen-CCUS coupled transmission) were set up, and based on the HOMER software environment and particle swarm optimization algorithm, the system operation simulation and index calculation of the test scheme are carried out.[Result] According to the simulation results, compared with normal separate transmission strategy, power transmission line capacity of system applying wind-coal bundled transmission strategy can be decreased by 2.2 GW, with an average utilization rate of 59%, i.e. 21%~23% higher. In the case that the wind power and coal power share the same transmission line without coordinated operation, the wind power curtailment is 164 GWh in total. System applying wind-coal-battery–hydrogen-CCUS bundled transmission strategy could avoid wind power curtailment and reduce carbon emission from unit power supply by about 30~33g/MWh. [Conclusion] In conclusion, the electricity transmission strategy established by this paper is supposed to be feasible and effective and contributing to planning and construction of offshore wind power project.

Cyclic heat treatment is an effective approach for enhancing the mechanical properties of 18Ni(C250) maraging steel, and the selection of cyclic heat treatment temperature is a key factor. In this study, a cyclic heat treatment process with a two-step solution treatment is employed to investigate the influence of cyclic heat treatment temperature, specifically the first solution treatment temperature (920 °C, 950 °C, and 980 °C), on the microstructure and mechanical properties of 18Ni(C250) maraging steel. The results indicate that with an increase in the cyclic heat treatment temperature, the average grain size of the 18Ni(C250) maraging steel decreases initially and then increases. When the cyclic heat treatment temperature reaches 950 °C, the grain size is at its minimum, exhibiting optimal grain uniformity. Additionally, the increase in cyclic heat treatment temperature results in a reduction in the size of martensitic lath with the same orientation inside the grains, along with an increase in the relative quantity of low-angle grain boundaries. Furthermore, the volume fraction and size of retained austenite show a monotonous increase with the rise in the temperature of the cyclic heat treatment, and the rate of increase becomes notably larger when the temperature is raised from 950 °C to 980 °C. Based on the observed microstructural changes, the variation in the mechanical properties of the 18Ni(C250) maraging steel was analyzed. Specifically, as the cyclic heat treatment temperature increases, the tensile strength of the 18Ni(C250) maraging steel initially increases and then stabilizes, while the elongation and fracture toughness exhibit a monotonic increase.

Fruit color is an intuitive quality of horticultural crops that can be used as an evaluation criterion for fruit ripening and is an important factor affecting consumers’ purchase choices. In this study, a genetic population from the cross of green peel ‘Qidong’ and purple peel ‘8 guo’ revealed that the purple to green color of eggplant peel is dominant and controlled by a pair of alleles. Bulked segregant analysis (BSA), SNP haplotyping, and fine genetic mapping delimited candidate genes to a 350 kb region of eggplant chromosome 10 flanked by markers KA2381 and CA8828. One ANS gene (EGP22363) was predicted to be a candidate gene based on gene annotation and sequence alignment of the 350-kb region. Sequence analysis revealed that a single base mutation of ‘T’ to ‘C’ on the exon green peel, which caused hydrophobicity to become hydrophilic serine, led to a change in the three-level spatial structure. Additionally, EGP22363 was more highly expressed in purple peels than in green peels. Collectively, EGP22363 is a strong candidate gene for anthocyanin biosynthesis in purple eggplant peels. These results provide important information for molecular marker-assisted selection in eggplants, and a basis for analyzing the regulatory pathways responsible for anthocyanin biosynthesis in eggplants.

In recent years, the rapid development of exoplanet research has provided us with an opportunity to better understand planetary systems in the universe and to search for signs of life. In order to further investigate the prevalence of habitable exoplanets and to validate planetary formation theories, as well as to comprehend planetary evolution, we have utilized confirmed exoplanet data obtained from the NASA Exoplanet Archive database, including data released by telescopes such as Kepler and TESS. By analyzing these data, we have selected a sample of planets around F, G, K, and M-type stars within a radius range of 1 to 20 R⊕ and with orbital periods ranging from 0.4 days to 400 days. Using the IDEM method based on these data, we calculated the overall formation rate, which is estimated to be 2.02%. Then, we use these data to analyze the relationship among planet formation rates, stellar metallicity, and stellar gravitational acceleration (logg). We firstly find that the formation rate of giant planets is higher around metal-rich stellars, but it inhibits the formation of gas giants when logg > 4.5, yet the stellar metallicity seems to have no effect on the formation rate of smaller planets. Secondly, the host stellar gravitational acceleration affects the relationship between planet formation rate and orbital period. Thirdly, there is a robust power-law relationship between the orbital period of smaller planets and their formation rate. Finally, we find that, for a given orbital period, there is a positive correlation between the planet formation rate and the logg.

Abstract Programmed death-ligand 1 (PD-L1) ensures that tumor cells escape T-cell-mediated tumor immune surveillance. However, gliomas are characteristic of the low immune response and high-resistance therapy, it is necessary to understand molecular regulatory mechanisms in glioblastoma, especially the limited regulation of PD-L1 expression. Herein, we show that low expression of AP-2α is correlated with high expression of PD-L1 in high-grade glioma tissues. AP-2α binds directly to the promoter of the CD274 gene, not only inhibits the transcriptional activity of PD-L1 but enhances endocytosis and degradation of PD-L1 proteins. Overexpression of AP-2α in gliomas enhances CD8+ T cell-mediated proliferation, effector cytokine secretion, and cytotoxicity in vitro. Tfap2a could increase the cytotoxic effect of Cd8+ T cells in CT26, B16F10, and GL261 tumor-immune models, improve anti-tumor immunity, and promote the efficacy of anti-PD-1 therapy. Finally, the EZH2/H3K27Me3/DNMT1 complex mediates the methylation modification of AP-2α gene and maintains low expression of AP-2α in gliomas. 5-Aza-dC (Decitabine) treatment combines with anti-PD-1 immunotherapy to efficiently suppress the progression of GL261 gliomas. Overall, these data support a mechanism of epigenetic modification of AP-2α that contributes to tumor immune evasion, and reactivation of AP-2α synergizes with anti-PD-1 antibodies to increase antitumor efficacy, which may be a broadly applicable strategy in solid tumors.

Abstract Background Sulfur mustard (SM) is a highly toxic chemical warfare agent that has caused numerous casualties during wars and conflicts in the past century. Specific antidotes or therapeutic strategies are rare due to the complicated mechanism of toxicity, which still awaits elucidation. Clinical data show that acute lung injury (ALI) is responsible for most mortality and morbidity after SM exposure. Extracellular vesicles are natural materials that participate in intercellular communication by delivering various substances and can be modified. In this study, we aim to show that extracellular vesicles derived from human umbilical cord mesenchymal stromal cells (hucMSC-EVs) could exert therapeutic effects on SM-induced ALI, and to explain the underlying mechanism of effects. Methods MiR-146a-5p contained in hucMSC-EVs may be involved in the process of hucMSC-EVs modulating the inflammatory response to SM-induced ALI. We utilized miR-146a-5p delivered by extracellular vesicles and further modified hucMSCs with a miR-146a-5p mimic or inhibitor to collect miR-146a-5p-overexpressing extracellular vesicles (miR-146a-5p+-EVs) or miR-146a-5p-underexpressing extracellular vesicles (miR-146a-5p−-EVs), respectively. Through in vivo and in vitro experiments, we investigated the mechanism. Results The effect of miR-146a-5p+-EVs on improving the inflammatory reaction tied to SM injury was better than that of hucMSC-EVs. We demonstrated that miR-146a-5p delivered by hucMSC-EVs targeted TRAF6 to negatively regulate inflammation in SM-induced ALI models in vitro and in vivo. Conclusion In summary, miR-146a-5p delivered by hucMSC-EVs targeted TRAF6, causing hucMSC-EVs to exert anti-inflammatory effects in SM-induced ALI; thus, hucMSC-EVs treatment may be a promising clinical therapeutic after SM exposure. Graphical Abstract

Different concentrations (0–0.2 wt.%) of multiwall carbon nanotubes (MWCNT) was used to improve the mechanical properties of cementitious composites. The first part of the work showed that adding 0.05 wt.% of MWCNT produced a maximum reinforcement in terms of flexural and compressive strength. The second, the role of MWCNT on mechanical strength improvement was investigated. MWCNTs can absorb water to weaken the cement hydration at the beginning, and then, release water to promote the hydration of cement. The third part focused on using a home-made four-point bending testing device to study the flexural creep behavior under high stress levels (70, 80 and 90% of the flexural strength) and the results are discussed in terms of creep strain and Poisson ratio difference. The data obtained were also compared with the empirical creep models and the creep coefficients obtained were agreement with that from the AASHTO model. Finally, a regression analysis was used to fit the creep compliance of MWCNTs reinforced cementitious composites for the range of conditions studied. The results showed that the creep compliance can be fitted by a power-law for a stress level below 0.85, while a linear function was obtained above.

Operation control of power systems has become challenging with an increase in the scale and complexity of power distribution systems and extensive access to renewable energy. Therefore, improvement of the ability of data-driven operation management, intelligent analysis, and mining is urgently required. To investigate and explore similar regularities of the historical operating section of the power distribution system and assist the power grid in obtaining high-value historical operation, maintenance experience, and knowledge by rule and line, a neural information retrieval model with an attention mechanism is proposed based on graph data computing technology. Based on the processing flow of the operating data of the power distribution system, a technical framework of neural information retrieval is established. Combined with the natural graph characteristics of the power distribution system, a unified graph data structure and a data fusion method of data access, data complement, and multi-source data are constructed. Further, a graph node feature-embedding representation learning algorithm and a neural information retrieval algorithm model are constructed. The neural information retrieval algorithm model is trained and tested using the generated graph node feature representation vector set. The model is verified on the operating section of the power distribution system of a provincial grid area. The results show that the proposed method demonstrates high accuracy in the similarity matching of historical operation characteristics and effectively supports intelligent fault diagnosis and elimination in power distribution systems.

The construction of new power systems presents higher requirements for the Power Internet of Things (PIoT) technology. The “source-grid-load-storage” architecture of a new power system requires PIoT to have a stronger multi- source heterogeneous data fusion ability. Native graph databases have great advantages in dealing with multi-source heterogeneous data, which make them suitable for an increasing number of analytical computing tasks. However, only few existing graph database products have native support for matrix operation-related interfaces or functions, resulting in low efficiency when handling matrix calculations that are commonly encountered in power grids. In this paper, the matrix computation process is expressed by a strategy called graph description, which relies on the natural connection between the matrix and structure of the graph. Based on that, we implement matrix operations on graph database, including matrix multiplication, matrix decomposition, etc. Specifically, only the nodes relevant to the computation and their neighbors are concerned in the process, which prunes the influence of zero elements in the matrix and avoids useless iterations compared to the conventional matrix computation. Based on the graph description, a series of power grid computations can be implemented on graph database, which reduces redundant data import and export operations while leveraging the parallel computing capability of graph database. It promotes the efficiency of PIoT when handling multi-source heterogeneous data. An comprehensive experimental study over two different scale power system datasets compares the proposed method with Python and MATLAB baselines. The results reveal the superior performance of our proposed method in both power flow and N-1 contingency computations.

Abstract Background Recent studies have shown that immunotherapies, including peptide vaccines, remain promising strategies for patients with lower grade glioma (LGG); however new biomarkers need to be developed to identify patients who may benefit from therapy. We aimed to investigate the feasibility of liquid biopsy-based gene signatures in predicting the prognosis of LGG patients, as well as the benefits of immunotherapy. Methods We evaluated the association between circulating immune cells and treatment response by analyzing peripheral blood mononuclear cell (PBMC) samples from LGG patients receiving peptide vaccine immunotherapy, identified response-related genes (RRGs), and constructed RRG-related Response Score. In addition, RRG-related RiskScore was constructed in LGG tumor samples based on RRGs; association analysis for RiskScore and characteristics of TME as well as patient prognosis were performed in two LGG tumor datasets. The predictive power of RiskScore for immunotherapy benefits was analyzed in an anti-PD-1 treatment cohort. Results This study demonstrated the importance of circulating immune cells, including monocytes, in the immunotherapeutic response and prognosis of patients with LGG. Overall, 43 significant RRGs were identified, and three clusters with different characteristics were identified in PBMC samples based on RRGs. The constructed RRG-related Response Score could identify patients who produced a complete response to peptide vaccine immunotherapy and could predict prognosis. Additionally, three subtypes were identified in LGG tumors based on RRGs, with subtype 2 being an immune “hot” phenotype suitable for immune checkpoint therapy. The constructed RRG-related RiskScore was significantly positively correlated with the level of tumor immune cell infiltration. Patients with high RiskScore had a worse prognosis and were more likely to respond to immune checkpoint therapy. The therapeutic advantage and clinical benefits of patients with a high RiskScore were confirmed in an anti-PD-1 treatment cohort. Conclusion This study confirmed the potential of liquid biopsy for individualized treatment selection in LGG patients and determined the feasibility of circulating immune cells as biomarkers for LGG. Scoring systems based on RRGs can predict the benefits of immunotherapy and prognosis in patients with LGG. This work would help to increase our understanding of the clinical significance of liquid biopsy and more effectively guide individualized immunotherapy strategies.

Abstract Background Exosomes are nanosized membranous vesicles secreted by various types of cells, which facilitate intercellular communication by transporting bioactive compounds. Exosomes are abundant in biological fluids including semen, and their protein composition and the potential of seminal plasma exosomes (SPEs) as fertility biomarkers were elucidated in humans, however, little information is available regarding buffalo (Bubalus bubalis). Here, we examined protein correlation between spermatozoa, seminal plasma (SP), and SPEs, and we compared and analyzed protein differences between high-motility (H-motility) and low-motility (L-motility) SPEs in buffalo. Results SPEs were concentrated and purified by ultracentrifugation combined with sucrose density gradient centrifugation, followed by verification using western blotting, nanoparticle tracking analysis, and transmission electron microscopy. Protein composition in spermatozoa, SP and SPEs, and protein difference in H- and L-motility SPEs were identified by LC-MS/MS proteomic analysis and were functionally analyzed through comprehensive bioinformatics. Many SPEs proteins originated from spermatozoa and SP, and nearly one third were also present in spermatozoa and SP. A series of proteins associated with reproductive processes including sperm capacitation, spermatid differentiation, fertilization, sperm-egg recognition, membrane fusion, and acrosome reaction were integrated in a functional network. Comparative proteomic analyses showed 119 down-regulated and 41 up-regulated proteins in L-motility SPEs, compared with H-motility SPEs. Gene Ontology (GO) enrichment of differentially expressed proteins (DEPs) showed that most differential proteins were located in sperm and vesicles, with activities of hydrolase and metalloproteinase, and were involved in sperm-egg recognition, fertilization, single fertilization, and sperm-zona pellucida binding processes, etc. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that differential proteins were mainly involved in the PPRP signaling pathway, calcium signaling pathway, and cAMP signaling pathway, among others. Furthermore, 6 proteins associated with reproduction were validated by parallel reaction monitoring analysis. Conclusion This study provides a comprehensive description of the seminal plasma exosome proteome and may be of use for further screening of biomarkers associated with male infertility.

Coke production is an important source of environmental polycyclic aromatic compounds (PACs), including parent polycyclic aromatic hydrocarbons (PAHs) and their derivatives. The focus near coking plants has primarily been on parent-PAH contamination, with less attention given to highly toxic derivatives. In this study, soil samples were collected from both within and outside of a coking plant. The concentrations of parent-PAHs and their derivatives, including methylated-PAHs, oxygenated-PAHs, and nitrated-PAHs, were examined. Spatial interpolation was employed to determine their spatial distribution patterns. Methods for identifying potential sources and conducting incremental lifetime cancer risk analysis were used. This could achieve a comprehensive understanding of the status of PAC pollution and the associated health risks caused by coke production. The concentrations of total PACs inside the plant ranged from 7.4 to 115.8 mg/kg, higher than those outside (in the range of 0.2 to 65.7 mg/kg). The spatial distribution of parent-PAH concentration and their derivatives consistently decreased with increasing distance from the plant. A significant positive correlation (p < 0.05) among parent-PAHs and their derivatives was observed, indicating relatively consistent sources. Based on diagnostic ratios, the potential emission sources of soil PACs could be attributed to coal combustion and vehicle emissions, while principal component analysis–multiple linear regression further indicated that primary emissions and secondary formation jointly influenced the PAC content, accounting for 60.4% and 39.6%, respectively. The exposure risk of soil PACs was dominated by 16 priority control PAHs; the non-priority PAHs’ contribution to the exposure risk was only 6.4%.

Gaia DR3 photometry and Blue Photometer (BP) and Red Photometer (RP) spectra have been widely used as reference in photometric calibrations. In this work, we check the spatial uniformity of Gaia DR3 photometry and BP/RP spectra by comparing the BP, RP, and G -band photometry with the synthetic ones from the BP/RP spectra. The discrepancies have a small dispersion of 1.07, 0.55, and 1.02 mmag for the BP, RP, and G bands, respectively. However, the discrepancies exhibit obvious spatial patterns, which are clearly associated with Gaia’s scanning law. The patterns observed in the BP and G bands are similar, with discrepancies between photometry and spectra being more pronounced in these bands compared to the RP band. A further independent test with the Dark Energy Survey DR2 photometry reveals that the spatial patterns are more likely attributed to the Gaia DR3 BP/RP spectra, particularly in the BP band. On one hand, our results confirm the high spatial uniformity of Gaia data at the mmag level. On the other hand, our results suggest that the spatial uniformity of Gaia DR3 BP/RP spectra is not as good as that of Gaia DR3 photometry, and could be further improved in the future.

Using the homogeneous X-ray catalog from ROSAT observations, we conducted a comprehensive investigation into stellar X-ray activity–rotation relations for both single and binary stars. Generally, the relation for single stars consists of two distinct regions: a weak decay region, indicating a continued dependence of the magnetic dynamo on stellar rotation rather than a saturation regime with constant activity, and a rapid decay region, where X-ray activity is strongly correlated with the Rossby number. Detailed analysis reveals more fine structures within the relation: in the extremely fast-rotating regime, a decrease in X-ray activity was observed with increasing rotation rate, referred to as supersaturation, while in the extremely slow-rotating region, the relation flattens, mainly due to the scattering of F stars. This scattering may result from intrinsic variability in stellar activities over one stellar cycle or the presence of different dynamo mechanisms. Binaries exhibit a similar relation to that of single stars while the limited sample size prevented the identification of fine structures in the relation for binaries. We calculated the mass-loss rates of planetary atmospheres triggered by X-ray emissions from host stars. Our findings indicate that for an Earthlike planet within the stellar habitable zone, it would easily lose its entire primordial H/He envelope (equating to about 1% of the planetary mass).

We present a comprehensive recalibration of narrowband/medium-band and broadband photometry from the Southern Photometric Local Universe Survey (S-PLUS) by leveraging two approaches: an improved Gaia XP synthetic photometry (XPSP) method with corrected Gaia XP spectra, and the stellar color regression (SCR) method with corrected Gaia Early Data Release 3 photometric data and spectroscopic data from LAMOST Data Release 7. Through the use of millions of stars as standards per band, we demonstrate the existence of position-dependent systematic errors, up to 23 mmag for the main survey region, in the S-PLUS iDR4 photometric data. A comparison between the XPSP and SCR methods reveals minor differences in zero-point offsets, typically within the range of 1–6 mmag, indicating the accuracy of the recalibration, and a twofold to threefold improvement in the zero-point precision. During this process, we also verify and correct for systematic errors related to CCD position. The corrected S-PLUS iDR4 photometric data will provide a solid data foundation for conducting scientific research that relies on high-precision calibration. Our results underscore the power of the XPSP method in combination with the SCR method, showcasing their effectiveness in enhancing calibration precision for wide-field surveys when combined with Gaia photometry and XP spectra, to be applied for other S-PLUS subsurveys.

The upcoming Chinese Space Station Telescope (CSST) slitless spectroscopic survey poses a challenge of flux calibration, which requires a large number of flux-standard stars. In this work, we design an uncertainty-aware residual attention network, the UaRA-net, to derive the CSST spectral energy distributions (SEDs) with a resolution of R = 200 over the wavelength range of 2500–10000 Å using LAMOST normalized spectra with a resolution of R = 2000 over the wavelength range of 4000–7000 Å. With the special structure and training strategy, the proposed model provides accurate predictions not only of SEDs, but also of their corresponding errors. The precision of the predicted SEDs depends on the effective temperature ( T _eff ), wavelength, and the LAMOST spectral signal-to-noise ratios (S/Ns), particularly in the GU band. For stars with T _eff = 6000 K, the typical SED precisions in the GU band are 4.2%, 2.1%, and 1.5% at S/N values of 20, 40, and 80, respectively. As T _eff increases to 8000 K, the precision increases to 1.2%, 0.6%, and 0.5%, respectively. The precision is higher at redder wavelengths. In the GI band, the typical SED precisions for stars with T _eff = 6000 K increase to 0.3%, 0.1%, and 0.1% at S/N values of 20, 40, and 80, respectively. We further verify our model using empirical MILES spectra and find a good performance. The proposed method will open up new possibilities for the optimal use of slitless spectra of the CSST and other surveys.

We combine photometric data from GALEX GR6+7 All-Sky Imaging Survey and Gaia Early Data Release 3 with stellar parameters from the SAGA and PASTEL catalogs to construct high-quality training samples for dwarfs (0.4 < BP − RP < 1.6) and giants (0.6 < BP − RP < 1.6). We apply careful reddening corrections using empirical temperature- and extinction-dependent extinction coefficients. Using the two samples, we establish a relationship between stellar loci (near-ultraviolet (NUV)−BP versus BP − RP colors), metallicity, and M _G . For a given BP − RP color, a 1 dex change in [Fe/H] corresponds to an approximately 1 magnitude change in NUV − BP color for solar-type stars. These relationships are employed to estimate metallicities based on NUV − BP, BP − RP, and M _G . Thanks to the strong metallicity dependence in the GALEX NUV band, our models enable a typical photometric-metallicity precision of approximately σ _[Fe/H] = 0.11 dex for dwarfs and σ _[Fe/H] = 0.17 dex for giants, with an effective metallicity range extending down to [Fe/H] = −3.0 for dwarfs and [Fe/H] = −4.0 for giants. We also find that the NUV-band-based photometric-metallicity estimate is not as strongly affected by carbon enhancement as previous photometric techniques. With the GALEX and Gaia data, we have estimated metallicities for about 5 million stars across almost the entire sky, including approximately 4.5 million dwarfs and 0.5 million giants. This work demonstrates the potential of the NUV band for estimating photometric metallicities, and sets the groundwork for utilizing the NUV data from space telescopes such as the upcoming Chinese Space Station Telescope.

By combining spectra from the CALSPEC and NGSL, as well as spectroscopic data from the LAMOST Data Release 7 (DR7), we have analyzed and corrected the systematic errors of the Gaia DR3 BP/RP (XP) spectra. The errors depend on the normalized spectral energy distribution (simplified by two independent “colors”) and G magnitude. Our corrections are applicable in the range of approximately −0.5 < BP − RP < 2, 3 < G < 17.5, and E ( B − V ) < 0.8. To validate our correction, we conduct independent tests by comparisons with the MILES and LEMONY spectra. The results demonstrate that the systematic errors of BP − RP and G have been effectively corrected, especially in the near-ultraviolet. The consistency between the corrected Gaia XP spectra and the MILES and LEMONY is better than 2% in the wavelength range of 336–400 nm and 1% in redder wavelengths. A global absolute calibration is also carried out by comparing the synthetic Gaia photometry from the corrected XP spectra with the corrected Gaia DR3 photometry. Our study opens up new possibilities for using XP spectra in many fields. A Python package is publicly available to do the corrections (doi: https://doi.org/10.12149/101375 or https://github.com/HiromonGON/GaiaXPcorrection ).

Three-dimensional object detection is a pivotal research topic in computer vision, aiming to identify and locate objects in three-dimensional space. It has wide applications in various fields such as geoscience, autonomous driving, and drone navigation. The rapid development of deep learning techniques has led to significant advancements in 3D object detection. However, with the increasing complexity of applications, 3D object detection faces a series of challenges such as data imbalance and the effectiveness of network models. Specifically, in an experiment, our investigation revealed a notable discrepancy in the LiDAR reflection intensity within a point cloud scene, with stronger intensities observed in proximity and weaker intensities observed at a distance. Furthermore, we have also noted a substantial disparity in the number of foreground points compared to the number of background points. Especially in 3D object detection, the foreground point is more important than the background point, but it is usually downsampled without discrimination in the subsequent processing. With the objective of tackling these challenges, we work from both data and network perspectives, designing a feature alignment filtering algorithm and a two-stage 3D object detection network. Firstly, in order to achieve feature alignment, we introduce a correction equation to decouple the relationship between distance and intensity and eliminate the attenuation effect of intensity caused by distance. Then, a background point filtering algorithm is designed by using the aligned data to alleviate the problem of data imbalance. At the same time, we take into consideration the fact that the accuracy of semantic segmentation plays a crucial role in 3D object detection. Therefore, we propose a two-stage deep learning network that integrates spatial and spectral information, in which a feature fusion branch is designed and embedded in the semantic segmentation backbone. Through a series of experiments on the KITTI dataset, it is proven that the proposed method achieves the following average precision (AP_R40) values for easy, moderate, and hard difficulties, respectively: car (Iou 0.7)—89.23%, 80.14%, and 77.89%; pedestrian (Iou 0.5)—52.32%, 45.47%, and 38.78%; and cyclist (Iou 0.5)—76.41%, 61.92%, and 56.39%. By emphasizing both data quality optimization and efficient network architecture, the performance of the proposed method is made comparable to other state-of-the-art methods.