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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.

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.

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.

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.

Mosquito‐borne viruses (MBVs) are a large class of viruses transmitted mainly through mosquito bites, including dengue virus, Zika virus, Japanese encephalitis virus, West Nile virus, and chikungunya virus, which pose a major threat to the health of people around the world. With global warming and extended human activities, the incidence of many MBVs has increased significantly. Mosquito saliva contains a variety of bioactive protein components. These not only enable blood feeding but also play a crucial role in regulating local infection at the bite site and the remote dissemination of MBVs as well as in remodeling the innate and adaptive immune responses of host vertebrates. Here, we review the physiological functions of mosquito salivary proteins (MSPs) in detail, the influence and the underlying mechanism of MSPs on the transmission of MBVs, and the current progress and issues that urgently need to be addressed in the research and development of MSP‐based MBV transmission blocking vaccines. [ABSTRACT FROM AUTHOR]

Strategies beyond hormone-related therapy need to be developed to improve prostate cancer mortality. Here, we show that FUBP1 and its methylation were essential for prostate cancer progression, and a competitive peptide interfering with FUBP1 methylation suppressed the development of prostate cancer. FUBP1 accelerated prostate cancer development in various preclinical models. PRMT5-mediated FUBP1 methylation, regulated by BRD4, was crucial for its oncogenic effect and correlated with earlier biochemical recurrence in our patient cohort. Suppressed prostate cancer progression was observed in various genetic mouse models expressing the FUBP1 mutant deficient in PRMT5-mediated methylation. A competitive peptide, which was delivered through nanocomplexes, disrupted the interaction of FUBP1 with PRMT5, blocked FUBP1 methylation, and inhibited prostate cancer development in various preclinical models. Overall, our findings suggest that targeting FUBP1 methylation provides a potential therapeutic strategy for prostate cancer management. [ABSTRACT FROM AUTHOR]

Objective To evaluate the effects of remimazolam versus propofol on the quality of postoperative recovery in patients undergoing vocal cord polypectomy with supportive laryngoscopy by the 15-item quality of recovery scale (QoR-15). Methods A total of 90 patients who underwent vocal cord polypectomy with supported laryngoscopy from the Fourth Clinical College of Xinxiang Medical University from December 2022 to October 2023 were divided into remimazolam combined with mivacurium group (group R) and propofol combined with mivacurium group (group P) by random block design method, with 45 cases in each group. By intravenous administration, patients in group R and group P were given remiazolam 0.2 to 0.3 mg/kg and propofol 1.5 to 2.0 mg/kg, respectively. After the disappearance of eyelash reflex, sufentanil 0.3 g/kg and mivacurium 0.25 mg/kg were given to all patients. The quality of postoperative recovery was evaluated by the QoR-15 scale 1 day before, 1 day and 2 days after surgery. Mean arterial pressure, heart rate, pulse oxygen saturation, and Modified Observers Assessment of Alertness/Sedation Scale (MOAA/S) scores of the two groups were recorded at different time points. The time required including surgery, recovery of spontaneous breathing, endotracheal tube removal, and post anesthesia care unit (PACU) stay after surgery,and the occurrence of intra- and post-operative adverse reactions were recorded. Results The QoR-15 scale score of group R was lower than that of group P (P＜0.05) on the first day after surgery, but it was not clinically significant because the minimum clinically significant change value of QoR-15 scale score was 6 points. The recovery time of spontaneous breathing and extubation time in group R were longer than those in group P (P＜0.05), but there was no significant difference in PACU stay time between the two groups (P＞0.05). The incidences of hypotension and injection site pain in group R were lower than those in group P (24.4% vs 64.4%, χ2=14.580, P＜0.01; 6.7% vs 84.4%, χ2=54.878, P＜0.01), and no significant difference was found in the incidence of other adverse reactions (P＞0.05). ConclusionIn the anesthesia of vocal cord polypectomy under supported laryngoscopy, the quality of patients recovery using remimazolam is comparable to that of propofol. While meeting perioperative needs, it can maintain more stable hemodynamics, without significant injection site pain, and can improve perioperative patients comfort."