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ABSTRACTThe Medium Resolution Spectral Image (MERSI) is a MODIS-like sensor aboard Fengyun-3 satellite. The first version of MERSI aerosol algorithm has been developed based on MODIS dark target (DT) algorithm, with modified models for estimating surface reflectance and an adjusted inland water masking method to release haze aerosols. This study applies MERSI DT algorithm to the global observations from the upgraded MERSI sensor (MERSI-II) on Fengyun-3D. And then, the Aerosol Optical Depth (AOD) results from the year of 2019–2020 are validated against the Aerosol Robotic Network (AERONET) data. In addition, analyses of the spatial distribution and error characteristics of MODIS and MERSI-II retrievals are presented. The overall validation demonstrates that MERSI-II retrievals perform well globally, with a correlation coefficient of 0.877 and 67.1% of matchups within the Expected Error envelope of ± (0.05 + 0.2τ), which are close to the statistic metrics of MODIS products. In addition, MERSI-II and MODIS AODs exhibit similar error trends and error dependence. Moreover, the similar global distribution characteristics of the two AODs are revealed in the retrieval performance at site and regional scales, as well as in the analysis of monthly averages. These findings indicate the success of the ported MERSI algorithm.

Abstract Hair follicle (HF) regeneration during wound healing continues to present a significant clinical challenge. Dermal papilla cell-derived exosomes (DPC-Exos) hold immense potential for inducing HF neogenesis. However, the accurate role and underlying mechanisms of DPC-Exos in HF regeneration in wound healing remain to be fully explained. This study, represents the first analysis into the effects of DPC-Exos on fibroblasts during wound healing. Our findings demonstrated that DPC-Exos could stimulate the proliferation and migration of fibroblasts, more importantly, enhance the hair-inducing capacity of fibroblasts. Fibroblasts treated with DPC-Exos were capable of inducing HF neogenesis in nude mice when combined with neonatal mice epidermal cells. In addition, DPC-Exos accelerated wound re-epithelialization and promoted HF regeneration during the healing process. Treatment with DPC-Exos led to increased expression levels of the Wnt pathway transcription factors β-catenin and Lef1 in both fibroblasts and the dermis of skin wounds. Specifically, the application of a Wnt pathway inhibitor reduced the effects of DPC-Exos on fibroblasts and wound healing. Accordingly, these results offer evidence that DPC-Exos promote HF regeneration during wound healing by enhancing the hair-inducing capacity of fibroblasts and activating the Wnt/β-catenin signaling pathway. This suggests that DPC-Exos may represent a promising therapeutic strategy for achieving regenerative wound healing.

Under the influence of global climate change, the climatic conditions of China's major agricultural regions have changed significantly over the last half-century, affecting regional grain production levels. With its favorable conditions for agricultural activities, China's central region has been a strategic location for grain production since ancient times and has assumed an essential responsibility for maintaining national grain security. However, the key concerns of this study are whether the national grain security pattern is stable and whether it might be affected by global climate change (especially climate instability and increased risks in recent years). Therefore, the present study collected grain production data and used descriptive statistical and geospatial analyses to reveal the trend and spatiotemporal pattern of grain production in China's central region from 2010 to 2020. Then, a further analysis was conducted by combining meteorological data with a geographically weighted regression (GWR) model to investigate the relationship between spatial differences in the output per unit of the grain sown area (OPUGSA). The findings were as follows: (1) The overall development trend of grain production in China's central region from 2010 to 2020 revealed a positive overall trend in grain production, with notable differences in growth rates between northern and southern provinces. (2) Most regions in the southern part of the central region from 2015 to 2020 showed varying degrees of total output of grain (TOG) and OPUGSA reduction, possibly affected by the effects of the anomalies for global climate change and a strong El Niño effect in 2015. (3) Low-low (L-L) clusters of TOG and OPUGSA indicators were consistently in the northwest part (Shanxi) of the central region, and high-high (H-H) clusters of TOG were consistently in the central part (Henan and Anhui) of the central region, but H-H clusters of OPUGSA were not stably distributed. (4) The fitting results of the GWR model showed a better fit compared to the ordinary least squares (OLS) model; it was found that the annual average temperature (AAT) had the greatest impact on OPUGSA, followed by annual sunshine hours (ASH) and annual precipitation (AP) last. The spatiotemporal analysis identified distinct clusters of productivity indicators. It suggested an expanding range of climate impact possibilities, particularly in exploring climate-resilient models of grain production, emphasizing the need for targeted adaptation strategies to bolster resilience and ensure agricultural security.

The ever-increasing couplings between electricity and gas systems highlight the coordinated restoration decisions for interdependent electricity and gas system (IEGS) to enhance resilience. However, the distinct timescales of power and gas flow rates as well as information privacy concern cause additional complexities in making restoration decisions, and large calculation scale and nonconvexity also results in computational obstacles. To address these issues, this paper proposes a hybrid spatio-temporal scale decentralized restoration strategy for IEGS to enhance restoration efficiency and security. First, in the spatial scale, the network sectionalizing and the restoration processes in sectionalized electricity and gas systems are coordinated in a decentralized manner with limited boundary information interactions to respect information privacy. Next, the impacts of distinct timescales of power and gas flow rates are explored to accelerate restoration, and a linear gas flow dynamics model is also proposed to capture the gas transmission dynamics with high accuracy, enhancing restoration security. Furthermore, a decomposition-based alternating direction method of multipliers algorithm is proposed to efficiently solve the proposed IEGS restoration strategy, showing good calculation time and convergence performance. Finally, the effectiveness of the proposed restoration strategy is validated in a T118N20 test system and a real-world system, demonstrating the enhanced restoration efficiency, security alongside commendable computational performance.

Abstract Soil alkalization is an adverse factor limiting plant growth and yield. As a signaling molecule and secondary metabolite, γ‐aminobutyric acid (GABA) responds rapidly to alkaline stress and enhances the alkaline resistance of plants. However, the molecular mechanisms by which the GABA pathway adapts to alkaline stress remain unclear. In this study, a transcription factor, MdNAC104 is identified, from the transcriptome of the alkaline‐stressed roots of apple, which effectively reduces GABA levels and negatively regulates alkaline resistance. Nevertheless, applying exogenous GABA compensates the negative regulatory mechanism of overexpressed MdNAC104 on alkaline resistance. Further research confirms that MdNAC104 repressed the GABA biosynthetic gene MdGAD1/3 and the GABA transporter gene MdALMT13 by binding to their promoters. Here, MdGAD1/3 actively regulates alkaline resistance by increasing GABA synthesis, while MdALMT13 promotes GABA accumulation and efflux in roots, resulting in an improved resistance to alkaline stress. This subsequent assays reveal that MdSINA2 interacts with MdNAC104 and positively regulates root GABA content and alkaline resistance by ubiquitinating and degrading MdNAC104 via the 26S proteasome pathway. Thus, the study reveals the regulation of alkaline resistance and GABA homeostasis via the MdSINA2‐MdNAC104‐MdGAD1/3/MdALMT13 module in apple. These findings provide novel insight into the molecular mechanisms of alkaline resistance in plants.

Robot control in complex and unpredictable scenarios presents challenges such as adaptability, robustness, and human-robot interaction. These scenarios often require robots to perform tasks that involve unknown objects in unstructured environments with high levels of uncertainty. Traditional control methods, such as automatic control, may not be suitable due to their limited adaptability and reliance on prior knowledge. Human-in-the-loop method faces issues such as insufficient feedback, increased failure rates due to noise and delays, and lack of operator immersion, preventing the achievement of human-level performance. This study proposed a shared control framework to achieve a trade-off between efficiency and adaptability by combing the advantages of both teleoperation and automatic control method. The proposed approach combines the advantages of both human and automatic control methods to achieve a balance between performance and adaptability. We developed a linear model to compare three control methods and analyzed the impact of position noise and communication delays on performance. The real-world implementation of the shared control system demonstrates its effectiveness in object grasping and manipulation tasks. The results suggest that shared control can significantly improve grasping efficiency while maintaining adaptability in task execution for practical robotics applications.

Abstract Background We describe a case in which bilateral optic nerve infiltration and leukemic retinopathy were the initial signs of disease relapse in a patient with Philadelphia chromosome-positive acute lymphoblastic leukemia (Ph+-ALL) with central nervous system (CNS) involvement. Case presentation A 65-year-old Asian female with Ph+-ALL in complete remission presented at our institution with symptoms of visual disturbance, central scotoma and pain with eye movement in both eyes for a 1-month duration. Ophthalmic examination revealed remarkable optic disc swelling with multiple flame-shaped peripapillary hemorrhages, retinal venous dilation and retinal hemorrhages in both eyes. She was subsequently referred to the treating oncologist and diagnosed with Ph+-ALL relapse with multiple relapsed diseases involving the bone marrow and CNS. After intrathecal (IT) therapy, her visual acuity dramatically improved, and her leukemic infiltrates decreased. Conclusions To the best of our knowledge, this is the first case report of ALL relapse with CNS involvement presenting as bilateral optic nerve infiltration and leukemic retinopathy in an adult. Hence, we highlight the priority and sensitivity of ophthalmic examinations, as they are noninvasive methods for detecting leukemia relapse.

Abstract The Golden2-like (GLK) transcription factor family is a significant group of transcription factors in plantae. The currently available studies have shown that GLK transcription factors have been studied mainly in chloroplast growth and development, with fewer studies in abiotic stress regulation. In this study, all tea plant GLK transcription factors were identified for the first time in tea plants, and genome-wide identification, phylogenetic analysis, and thematic characterization were performed to identify 66 GLK transcription factors in tea plants. These genes are categorized into seven groups, and an amino acid sequence comparison analysis is performed. This study revealed that the structure of GLK genes in tea plants is highly conserved and that these genes are distributed across 14 chromosomes. Collinearity analysis revealed 17 pairs of genes with fragment duplications and one pair of genes with tandem duplications, and the analysis of Ka/Ks ratios indicated that most of the genes underwent negative purifying selection. Analysis of promoter cis-elements revealed that the promoters of tea plant GLK genes contain a large number of cis-acting elements related to phytohormones and stress tolerance. In addition, a large number of genes contain LTR elements, suggesting that tea plant GLK genes are involved in low-temperature stress. qRT‒PCR analysis revealed that the expression of CsGLK17, CsGLK38, CsGLK54, CsGLK11 and CsGLK60 significantly increased and that the expression of CsGLK7 and CsGLK13 decreased in response to low-temperature induction. Taken together, the results of the transcription profile analysis suggested that CsGLK54 may play an important regulatory role under low-temperature stress. The subcellular localization of CsGLK54 was in the nucleus. Furthermore, CsGLK54 positively regulated the transcription levels of the NbPOD and NbSOD genes under low-temperature stress, which led to an increase in POD and SOD enzyme activities and a decrease in MDA content. These findings provide valuable insights into the regulatory mechanism of low-temperature stress in tea plants.

Abstract Elastic stability is the basis for understanding structural responses to external stimuli in crystalline solids, including melting, incipient plasticity and fracture. In this work, elastic stability is investigated in a series of high-entropy alloys (HEAs) using in situ mechanical tests and atomic-resolution characterization in transmission electron microscopy. Under tensile loading, the HEA lattices are observed to undergo a sudden loss of ordering as the elastic strain reached ∽ 10%. Such elastic strain-induced amorphization stands in intrinsic contrast to previously reported dislocation-mediated elastic instability and defect accumulation-mediated amorphization, introducing a form of elastic instability. Together with the first principle calculations and atomic-resolution chemical mapping, we identify that the elastic strain-induced amorphization is closely related to the depressed dislocation nucleation due to the local atomic environment inhomogeneity of HEAs. Our findings provide insights for the understanding of the fundamental nature of physical mechanical phenomena like elastic instability and incipient plasticity.

Abstract Background Ventricular tachycardia (VT) is the primary cause of sudden cardiac death in patients with hypertrophic cardiomyopathy (HCM). However, the strategy for VT treatment in HCM patients remains unclear. This study is aimed to compare the effectiveness of catheter ablation versus antiarrhythmic drug (AAD) therapy for sustained VT in patients with HCM. Methods A total of 28 HCM patients with sustained VT at 4 different centers between December 2012 and December 2021 were enrolled. Twelve underwent catheter ablation (ablation group) and sixteen received AAD therapy (AAD group). The primary outcome was VT recurrence during follow-up. Results Baseline characteristics were comparable between two groups. After a mean follow-up of 31.4 ± 17.5 months, the primary outcome occurred in 35.7% of the ablation group and 90.6% of the AAD group (hazard ratio [HR], 0.29 [95%CI, 0.10–0.89]; P = 0.021). No differences in hospital admission due to cardiovascular cause (25.0% vs. 71.0%; P = 0.138) and cardiovascular cause-related mortality/heart transplantation (9.1% vs. 50.6%; P = 0.551) were observed. However, there was a significant reduction in the composite endpoint of VT recurrence, hospital admission due to cardiovascular cause, cardiovascular cause-related mortality, or heart transplantation in ablation group as compared to that of AAD group (42.9% vs. 93.7%; HR, 0.34 [95% CI, 0.12–0.95]; P = 0.029). Conclusions In HCM patients with sustained VT, catheter ablation reduced the VT recurrence, and the composite endpoint of VT recurrence, hospital admission due to cardiovascular cause, cardiovascular cause-related mortality, or heart transplantation as compared to AAD.

Abstract Background Mycobacterium tuberculosis heat-resistant antigen (Mtb-HAg) is a peptide antigen released from the mycobacterial cytoplasm into the supernatant of Mycobacterium tuberculosis (Mtb) attenuated H37Ra strain after autoclaving at 121 °C for 20 min. Mtb-HAg can specifically induce γδ T-cell proliferation in vitro. However, the exact composition of Mtb-HAg and the protein antigens that are responsible for its function are currently unknown. Methods Mtb-HAg extracted from the Mtb H37Ra strain was subjected to LC‒MS mass spectrometry. Twelve of the identified protein fractions were recombinantly expressed in Escherichia coli by genetic engineering technology using pET-28a as a plasmid and purified by Ni–NTA agarose resin to stimulate peripheral blood mononuclear cells (PBMCs) from different healthy individuals. The proliferation of γδ T cells and major γδ T-cell subset types as well as the production of TNF-α and IFN-γ were determined by flow cytometry. Their proliferating γδ T cells were isolated and purified using MACS separation columns, and Mtb H37Ra-infected THP-1 was co-cultured with isolated and purified γδ T cells to quantify Mycobacterium viability by counting CFUs. Results In this study, Mtb-HAg from the attenuated Mtb H37Ra strain was analysed by LC‒MS mass spectrometry, and a total of 564 proteins were identified. Analysis of the identified protein fractions revealed that the major protein components included heat shock proteins and Mtb-specific antigenic proteins. Recombinant expression of 10 of these proteins in by Escherichia coli genetic engineering technology was used to successfully stimulate PBMCs from different healthy individuals, but 2 of the proteins, EsxJ and EsxA, were not expressed. Flow cytometry results showed that, compared with the IL-2 control, HspX, GroEL1, and GroES specifically induced γδ T-cell expansion, with Vγ2δ2 T cells as the main subset, and the secretion of the antimicrobial cytokines TNF-α and IFN-γ. In contrast, HtpG, DnaK, GroEL2, HbhA, Mpt63, EsxB, and EsxN were unable to promote γδ T-cell proliferation and the secretion of TNF-α and IFN-γ. None of the above recombinant proteins were able to induce the secretion of TNF-α and IFN-γ by αβ T cells. In addition, TNF-α, IFN-γ-producing γδ T cells inhibit the growth of intracellular Mtb. Conclusion Activated γδ T cells induced by Mtb-HAg components HspX, GroES, GroEL1 to produce TNF-α, IFN-γ modulate macrophages to inhibit intracellular Mtb growth. These data lay the foundation for subsequent studies on the mechanism by which Mtb-HAg induces γδ T-cell proliferation in vitro, as well as the development of preventive and therapeutic vaccines and rapid diagnostic reagents.

Abstract Caustics occur in diverse physical systems, spanning the nano-scale in electron microscopy to astronomical-scale in gravitational lensing. As envelopes of rays, optical caustics result in sharp edges or extended networks. Caustics in structured light, characterized by complex-amplitude distributions, have innovated numerous applications including particle manipulation, high-resolution imaging techniques, and optical communication. However, these applications have encountered limitations due to a major challenge in engineering caustic fields with customizable propagation trajectories and in-plane intensity profiles. Here, we introduce the “compensation phase” via 3D-printed metasurfaces to shape caustic fields with curved trajectories in free space. The in-plane caustic patterns can be preserved or morphed from one structure to another during propagation. Large-scale fabrication of these metasurfaces is enabled by the fast-prototyping and cost-effective two-photon polymerization lithography. Our optical elements with the ultra-thin profile and sub-millimeter extension offer a compact solution to generating caustic structured light for beam shaping, high-resolution microscopy, and light-matter-interaction studies.

Animal proteins are mainly derived from meat, eggs and milk. They are usually rich in nutritional value, whereas their functional properties usually cannot fully meet industrial requirements. As a kind of non-thermal physical treatments, ultrasound can lead to changes in the physicochemical and structural properties of animal proteins, therefore, improving their functional properties. However, relationships between the effects of ultrasound on various animal proteins and changes in their properties are still unclear and need to be studied. In order to clarify the effects of ultrasonic treatment on the structure and properties of animal proteins, the physicochemical properties, microstructure, interfacial properties and functional properties of different kinds of animal proteins are reviewed under different ultrasonic power and ultrasonic time in this study. Relationships between their changes are also analyzed. At last, in order to provide theoretical reference for the application and promotion of ultrasonic treatment on animal proteins, their application is discussed and prospected.

Abstract Understanding flower developmental processes is a prerequisite for improving flowering ‘plants’ production. Adonis amurensis is a fascinating spring ephemeral plant that develops its flower organs underground. Nevertheless, knowledge of the molecular mechanisms driving this particular process is scarce. Herein, we examined transcriptional changes during underground flower differentiation in A. amurensis and unveiled key differently regulated genes and pathways. High-throughput RNA sequencing of meristems at different flower developmental stages, including flower primordium (FP), sepal stage (SE), perianth primordium (PE), stamen stage (ST), and pistil stage (PI), identified 303,234 unigenes that showed 44.79% similarity with sequences in Aquilegia coerulea. Correlations, principal component, and differentially expressed genes (DEGs) analyses revealed that few molecular changes occurred during the transition from PE to ST. Many DEGs exhibited stage-specific regulations. Transcription factor (TF) and phytohormone family genes are critical regulators of the floral differentiation process in A. amurensis. The most differentially regulated TFs were MADS, FAR1, MYBs, AP2/ERF, B3, C2H2, and LOBs. We filtered out 186 candidate genes for future functional studies, including 18 flowering/circadian-related, 32 phytohormone-related, and TF family genes. Our findings deepen our understanding of the underground flower differentiation process and offer critical resources to dissect its regulatory network in A. amurensis. These findings establish a foundational platform for researchers dedicated to exploring the unique phenotypic characteristics of this specific flowering modality and delving into the intricate molecular mechanisms underpinning its regulation and expression.

Abstract Background Familial hypercholesterolemia (FH) is an autosomal semi-dominant disease, characterized by markedly elevated levels of low-density lipoprotein cholesterol (LDL-c) from conception and accelerated atherosclerotic cardiovascular disease, often resulting in early death. The aim of this study was to evaluate the prevalence of clinically defined FH in Chinese Han patients with acute coronary syndrome (ACS) and compare the long-term prognosis of ACS patients with and without FH receiving lipid-lowering therapy containing statins after a coronary event. Methods All ACS patients were screened at the Second Affiliated Hospital of Xi’an Jiaotong University between Jan 2019 and Sep 2020, and 531 participants were enrolled. All were examined for FH under the Dutch Lipid Clinical Network (DLCN) criteria, and those patients were divided into definite/probable FH, possible FH and unlikely FH. The severity of coronary artery disease was evaluated by the Gensini scoring system. Plasma levels of total cholesterol (TC), triacylglycerol (TG), HDL-cholesterol (HDL-c), LDL-cholesterol (LDL-c), very low-density lipoproteins-cholesterol (VLDL-c), apolipoprotein A1 (apoA1), apolipoprotein B (apoB) and lipoprotein (a) (Lp(a)) were determined centrally at baseline and the last follow-up visit in the fasting state. The non-high-density lipoprotein cholesterol (non-HDL-c) concentration, the TC/HDL-c and apoB/apoA1 ratios were calculated. After FH patients received lipid-lowering treatment containing statin, the target LDL-c levels recommended by the guidelines (LDL-c 50% from baseline) were evaluated, and the occurrence of major adverse cardiovascular and cerebrovascular events (MACCE) during the 12-month follow-up was recorded. Results The prevalence of clinically definite or probable FH was 4.3%, and the prevalence of possible FH was 10.6%. Compared with the unlikely FH patients with ACS, the FH patients had higher levels of TC, LDL-c, apoB, Lp(a), non-HDL-c, TC/HDL-c and apoB/apoA1 ratio, more severe coronary artery diseases and greater prevalence of left main and triple or multiple vessel lesions. After lipid-lowering therapy containing statins, a minority of FH patients reached the target LDL-c levels defined by the guidelines (χ2 = 33.527, P 0.05). Conclusions FH was an independent risk factor for MACCE in adult patients after a coronary event during long-term follow-up. However, there was inadequate high-intensity statins prescriptions for high-risk patients in this current study. It is important for FH patients to optimize lipid-lowering treatment strategies to reach the target LDL-c level to improve the long-term prognosis of clinical outcomes.

Abstract Background Acute kidney injury (AKI) is a common clinical disorder with complex etiology and poor prognosis, and currently lacks specific and effective treatment options. Mitochondrial dynamics dysfunction is a prominent feature in AKI, and modulation of mitochondrial morphology may serve as a potential therapeutic approach for AKI. Methods We induced ischemia–reperfusion injury (IRI) in mice (bilateral) and Bama pigs (unilateral) by occluding the renal arteries. ATP depletion and recovery (ATP-DR) was performed on proximal renal tubular cells to simulate in vitro IRI. Renal function was evaluated using creatinine and urea nitrogen levels, while renal structural damage was assessed through histopathological staining. The role of Drp1 was investigated using immunoblotting, immunohistochemistry, immunofluorescence, and immunoprecipitation techniques. Mitochondrial morphology was evaluated using confocal microscopy. Results Renal IRI induced significant mitochondrial fragmentation, accompanied by Dynamin-related protein 1 (Drp1) translocation to the mitochondria and Drp1 phosphorylation at Ser616 in the early stages (30 min after reperfusion), when there was no apparent structural damage to the kidney. The use of the Drp1 inhibitor P110 significantly improved kidney function and structural damage. P110 reduced Drp1 mitochondrial translocation, disrupted the interaction between Drp1 and Fis1, without affecting the binding of Drp1 to other mitochondrial receptors such as MFF and Mid51. High-dose administration had no apparent toxic side effects. Furthermore, ATP-DR induced mitochondrial fission in renal tubular cells, accompanied by a decrease in mitochondrial membrane potential and an increase in the translocation of the pro-apoptotic protein Bax. This process facilitated the release of dsDNA, triggering the activation of the cGAS-STING pathway and promoting inflammation. P110 attenuated mitochondrial fission, suppressed Bax mitochondrial translocation, prevented dsDNA release, and reduced the activation of the cGAS-STING pathway. Furthermore, these protective effects of P110 were also observed renal IRI model in the Bama pig and folic acid-induced nephropathy in mice. Conclusions Dysfunction of mitochondrial dynamics mediated by Drp1 contributes to renal IRI. The specific inhibitor of Drp1, P110, demonstrated protective effects in both in vivo and in vitro models of AKI.

Aluminum foam, as a typical multifunctional porous metal material, has demonstrated significant potential for application in modern industries. However, the addition of stabilizers to enhance foaming stability resulted in a brittle failure mode of aluminum foam, which limited its practical applications. Therefore, this paper improved the non-thickening foaming technology for fabricating closed-cell aluminum foam, where a slight amount of CaCO3 served as an internal oxide film self-repair agent was introduced into the foaming mixtures. The addition of CaCO3 caused a shift in the temperature at which the 1st endothermic peak was observed, from 522.8 °C to 576.9 °C, and contributed to the occurrence of an exothermic reaction within a temperature range of 670 °C–690 °C. The gradual release of CO2 altered the composition of oxidizing gas within the enclosed bubbles, leading to the self-repair of the ruptured oxide film caused by the growth of bubbles. Consequently, the microtopography of the bubble surface was modified. However, the excessive CaCO3 led to the deterioration of pore structures and an increased bubble-free layer. The results of the three-point bending test indicated that sample fabricated using 0.3 g CaCO3 exhibited excellent load-bearing capacity, with an approximate enhancement of 60.4 % in bending capacity observed under the same deflection of 10 mm. The predominant failure mode of non-thickened aluminum foam was characterized by ductile fracture.

AbstractPurpose This study aimed to improve the accuracy of brain-computer interface (BCI) systems based on motor imagery (MI) and mental arithmetic (MA) by utilizing functional near-infrared spectroscopy (fNIRS) and an improved dilation CapsuleNet (ID-CapsuleNet) model.Methods The study focused on the characteristics of fNIRS and employed large-kernel dilation convolution to extract hemodynamic features from fNIRS data. Inspired by CapsuleNet’s success in image classification, an ID-CapsuleNet model was designed, combining large-kernel dilation convolution and CapsuleNet. Four publicly available datasets (A, B, C, and D) were utilized for evaluating the proposed model. Datasets A and B were MA type, while datasets C and D were MI type. Ablation experiments were conducted to assess the usefulness of large-kernel convolution, dynamic routing, and dilation convolution.Results The average accuracies for each dataset were 95.01%, 76.88%, 74.03%, and 80.29% respectively. Cross-subject average accuracies were 88.72%, 75.80%, 75.78%, and 80.34%. Ablation experiments confirmed the importance of large-kernel convolution, dynamic routing, and dilation convolution in the ID-CapsuleNet model.Conclusion The developed ID-CapsuleNet model demonstrated promising potential for enhancing the performance of BCI systems based on MI and MA. The findings contribute to the advancement of BCI technology, offering improved assistive tools for disabled individuals.

ABSTRACTDeep learning has been extensively utilized in the assessment of building damage after disasters. However, the field of building damage segmentation faces challenges, such as misjudged regions, high network complexity, and long running times. Hence, this paper proposes a two-stage building damage assessment network called the Efficient Channel Attention and Depthwise Separable Convolutional Neural Network (ECADS-CNN). It aims to quickly detect the types of disaster damage in buildings. Deep object segmentation and deep damage classification networks were integrated into a unified building damage detection network. In this study, the efficient channel attention (ECA) module was used to enhance the performance of building semantic segmentation, and a depthwise separable (DS) module was added to the dimension upscaling process. Finally, untrained disaster dataset images were used to test the robustness of the proposed model by comparing the evaluation results of each disaster. The experiments involve testing a total of five common deep learning models, and the results indicate that the ECADS-CNN model improves the speed by 7.4% and the overall F1 score by 5.2% compared with the baseline model. The comprehensive performance is better than that of mainstream deep learning models.

Desert steppe is widely distributed in wind erosion regions, and is also an important potential dust emission source in the regions. Recognition of dust emission characteristics of desert steppe is the foundation for accurate simulation of dust emission in wind erosion regions; however, few related studies have been conducted in desert steppe areas. In this study, we selected four sites in a region of desert steppe to conducting dust emission observations, analyzing the characteristics of horizontal saltation flux and vertical dust flux, identifying the suitable models for desert steppe, and examining the relationship between saltation bombardment efficiency and wind friction speed (u*). The results revealed the following: the supply of saltation particles in most desert steppe is unlimited, the intensity of saltation is reasonably high, and the modified Owen-type model is suitable for predicting sediment transport above desert steppe. Dust emission intensity in desert steppe is lower than that in other land use types under similar wind speeds. Schemes based on saltation bombardment theory (i.e., the MB95, Shao93, and Kok12 schemes) were found suitable for predicting vertical dust flux above desert steppe, the Kok12 dust emission scheme performed slightly better than the other two schemes. Saltation bombardment is the main mechanism for dust emission when u*s exceeds a threshold u*s (u*ts), but aerodynamic entrainment also plays an important role in dust emission, especially when u*s

Abstract Fruit ripening is associated with the degreening process (loss of chlorophyll) that occurs in most fruit species. Kiwifruit is one of the special species whose fruits may maintain green flesh by accumulating a large amount of chlorophyll even after ripening. However, little is known about the genetic variations related to the fruit degreening process. Here, a graph‐based kiwifruit pangenome by analyzing 14 chromosome‐scale haplotype‐resolved genome assemblies from seven representative cultivars or lines in Actinidia chinensis is built. A total of 49,770 non‐redundant gene families are identified, with core genes constituting 46.6%, and dispensable genes constituting 53.4%. A total of 84,591 non‐redundant structural variations (SVs) are identified. The pangenome graph integrating both reference genome sequences and variant information facilitates the identification of SVs related to fruit color. The SV in the promoter of the AcBCM gene determines its high expression in the late developmental stage of fruits, which causes chlorophyll accumulation in the green‐flesh fruits by post‐translationally regulating AcSGR2, a key enzyme of chlorophyll catabolism. Taken together, a high‐quality pangenome is constructed, unraveled numerous genetic variations, and identified a novel SV mediating fruit coloration and fruit quality, providing valuable information for further investigating genome evolution and domestication, QTL genes function, and genomics‐assisted breeding.

Excessive nitrogen and phosphorus (N/P) input is the important reason for lake eutrophication, a widespread environmental problem that harms freshwater ecosystems worldwide. Land use land cover changes (LULCCs) strongly affect the migration and transformation process on land of N/P. Aiming to improve the accuracy of N/P estimation and deeply understand the spatiotemporal impact of LULCCs on N/P distribution, this study proposed an optimized framework, InVEST-UM, by integrating an unmixing algorithm with the conventional InVEST model. A comprehensive and intuitive index LPI (LULCCs purification capacity index) is developed for the impact assessment and spatialization. Validation shows a strong correlation (0.94) between the LPI and eutrophication degree of Dianshan Lake. This suggests the LPI effectively captures the spatiotemporal influence of LULCCs on lake eutrophication. Based on the sub-pixel results of N/P estimation, dramatic changes of LPI with an average rate of 46.182% were explored. There is a strong positive correlation (0.91) between vegetation area and LULCCs purification capacity for N/P. Conversely, negative relationships are found between LULCCs purification capacity with bare lands (−0.86), and artificial surfaces (−0.75). Changes in the water bodies have weak influence (−0.33) on LULCCs purification capacity. The increase in vegetation area can not only enhance the lake purification capacity, but also help maintain low ecological sensitivity. By employing this comprehensive framework, this study provides valuable insights into the long-term dynamics of LULCCs changes and their implications for lake ecosystems, and could support the protection and land use planning for the sustainability and ecological balance of the lake.

Mechanical stirring can promote uniform distribution of crude oil temperature and increase the heating rate, but the effect is different for different heating methods in crude oil storage tank. In this paper, jet heating and tubular heating are used as the heating methods in the tank, the physical and mathematical models under the synergy of two heating methods and mechanical stirring are established, FVM and SIMPLE algorithm are used to numerical calculate. The results show that mechanical stirring can enhance the flow of crude oil in the tank, affect greatly the temperature and velocity fields of crude oil and improve uniformity of the temperature field. Mechanical stirring has more significant effect on tubular heating for the 1000 m3 actual oil storage tank. For tubular heating, mechanical stirring can increase the crude oil average velocity 13.8 times, heating rate by 0.123 °C/h, heating efficiency by 8.37 % and reduce the temperature variance by 92.03 % during 2h of heating. At the same time, the indicators of economic benefits for different heating methods are put forward, and the order of energy consumption per unit temperature rise is obtained: Tubular heating > Jet heating > Jet heating and stirring > Tubular heating and stirring.

Abstract Actinidia arguta, known as hardy kiwifruit, is a widely cultivated species with distinct botanical characteristics such as small and smooth-fruited, rich in beneficial nutrients, rapid softening and tolerant to extremely low temperatures. It contains the most diverse ploidy types, including diploid, tetraploid, hexaploid, octoploid, and decaploid. Here we report a haplotype-resolved tetraploid genome (A. arguta cv. ‘Longcheng No.2’) containing four haplotypes, each with 40,859, 41,377, 39,833 and 39,222 protein-coding genes. We described the phased genome structure, synteny, and evolutionary analyses to identify and date possible WGD events. K s calculations for both allelic and paralogous genes pairs throughout the assembled haplotypic individuals showed its tetraploidization is estimated to have formed ~ 1.03 Mya following Ad-α event occurred ~ 18.7 Mya. Detailed annotations of NBS-LRRs or CBFs highlight the importance of genetic variations coming about after polyploidization in underpinning ability of immune responses or environmental adaptability. WGCNA analysis of postharvest quality indicators in combination with transcriptome revealed several transcription factors were involved in regulating ripening kiwi berry texture. Taking together, the assembly of an A. arguta tetraploid genome provides valuable resources in deciphering complex genome structure and facilitating functional genomics studies and genetic improvement for kiwifruit and other crops. Graphical Abstract

[Objective] In water-rich rock stratum regions, preventing confined aquifer inrush accidents is a key focus and challenge in the design and construction of deep foundation pit projects. Accurately and effectively determining the critical thickness of foundation pit bottom plate is crucial in preventing confined aquifer inrush accidents. Therefore, it is necessary to conduct stability study on foundation pit bottom plate inrush damage problem in confined aquifer. [Method] Based on the nonlinear Mohr-Coulomb failure criterion and considering the influence of various strength parameters and the heterogeneity characteristics of soil layers, a mechanical analysis model for foundation pit bottom plate inrush damage is established. Using the limit equilibrium method, a safety factor calculation formula for bottom plate inrush stability considering the inherent strength of soil layers is proposed. The calculation method for inrush damage in the current technical specifications for foundation pit support is modified. Taking the foundation pit of a utility tunnel project in soft soil layers in Jinan as example, the effectiveness of the proposed modified calculation formula for pit bottom plate inrush stability is validated. Additionally, variations in the safety factor of bottom plate inrush stability under the influence of different soil strength parameters, impermeable layer thickness, and confined water pressure are analyzed. [Result & Conclusion] With the increase in soil cohesion, heterogeneity constant, density, and impermeable layer thickness, the safety factor of pit bottom plate inrush stability continuously increases. Conversely, as the soil nonlinear coefficient, confined water pressure, and foundation pit width increase, the safety factor of pit bottom plate inrush stability continuously decreases. The calculated values of the inrush safety factors considering the influence of soil inherent strength parameters, are generally greater than the values obtained using the current calculation method in the technical specifications for foundation support. The verification using the proposed modified inrush stability calculation formula is more economical.

Visual-based brain-computer interface (BCI) enables people to communicate with others by spelling words from the brain and helps professionals recognize targets in large numbers of images. P300 signals evoked by different types of stimuli, such as words or images, may vary significantly in terms of both amplitude and latency. A unified approach is required to detect variable P300 signals, which facilitates BCI applications, as well as deepens the understanding of the P300 generation mechanism. In this study, our proposed approach involves a cascade network structure that combines xDAWN and classical EEGNet techniques. This network is designed to classify target and non-target stimuli in both P300 speller and rapid serial visual presentation (RSVP) paradigms. The proposed approach is capable of recognizing more symbols with fewer repetitions (up to 5 rounds) compared to other models while possessing a better information transfer rate (ITR) as demonstrated on Dataset II (17.22 bits/min in the second repetition round) of BCI Competition III. Additionally, our approach has the highest unweighted average recall (UAR) performance for both 5 Hz ( $0.8134\pm 0.0259$ ) and 20 Hz ( $0.6527\pm 0.0321$ ) RSVP. The results show that the cascade network structure has better performance between both the P300 Speller and RSVP paradigms, manifesting that such a cascade structure is robust enough for dealing with P300-related signals (source code is available at https://github.com/embneural/Cascade-xDAWN-EEGNet-for-ERP-Detection).

Affective brain-computer interfaces (aBCIs) have garnered widespread applications, with remarkable advancements in utilizing electroencephalogram (EEG) technology for emotion recognition. However, the time-consuming process of annotating EEG data, inherent individual differences, non-stationary characteristics of EEG data, and noise artifacts in EEG data collection pose formidable challenges in developing subject-specific cross-session emotion recognition models. To simultaneously address these challenges, we propose a unified pre-training framework based on multi-scale masked autoencoders (MSMAE), which utilizes large-scale unlabeled EEG signals from multiple subjects and sessions to extract noise-robust, subject-invariant, and temporal-invariant features. We subsequently fine-tune the obtained generalized features with only a small amount of labeled data from a specific subject for personalization and enable cross-session emotion recognition. Our framework emphasizes: 1) multi-scale representation to capture diverse aspects of EEG signals, obtaining comprehensive information; 2) an improved masking mechanism for robust channel-level representation learning, addressing missing channel issues while preserving inter-channel relationships; and 3) invariance learning for regional correlations in spatial-level representation, minimizing inter-subject and inter-session variances. Under these elaborate designs, the proposed MSMAE exhibits a remarkable ability to decode emotional states from a different session of EEG data during the testing phase. Extensive experiments conducted on the two publicly available datasets, i.e., SEED and SEED-IV, demonstrate that the proposed MSMAE consistently achieves stable results and outperforms competitive baseline methods in cross-session emotion recognition.

Calcium influx into neurons triggers neuronal death during cerebral ischemia/reperfusion injury. Various calcium channels are involved in cerebral ischemia/reperfusion injury. Cav3.2 channel is a main subtype of T-type calcium channels. T-type calcium channel blockers, such as pimozide and mibefradil, have been shown to prevent cerebral ischemia/reperfusion injury-induced brain injury. However, the role of Cav3.2 channels in cerebral ischemia/reperfusion injury remains unclear. Here, in vitro and in vivo models of cerebral ischemia/reperfusion injury were established using middle cerebral artery occlusion in mice and high glucose hypoxia/reoxygenation exposure in primary hippocampal neurons. The results showed that Cav3.2 expression was significantly upregulated in injured hippocampal tissue and primary hippocampal neurons. We further established a Cav3.2 gene-knockout mouse model of cerebral ischemia/reperfusion injury. Cav3.2 knockout markedly reduced infarct volume and brain water content, and alleviated neurological dysfunction after cerebral ischemia/reperfusion injury. Additionally, Cav3.2 knockout attenuated cerebral ischemia/reperfusion injury-induced oxidative stress, inflammatory response, and neuronal apoptosis. In the hippocampus of Cav3.2-knockout mice, calcineurin overexpression offset the beneficial effect of Cav3.2 knockout after cerebral ischemia/reperfusion injury. These findings suggest that the neuroprotective function of Cav3.2 knockout is mediated by calcineurin/nuclear factor of activated T cells 3 signaling. Findings from this study suggest that Cav3.2 could be a promising target for treatment of cerebral ischemia/reperfusion injury.

Abstract Background The association between breast cancer (BC) and thyroid cancer (TC) has been studied in several epidemiological studies. However, the underlying causal relationship between them is not yet clear. Methods The data from the latest large-sample genome-wide association studies (GWAS) of BC and TC were searched in the public GWAS database. The BC GWAS data included estrogen receptor (ER)-positive and negative subgroups. Two-way two-sample Mendelian Randomization (MR) was used to explore the potential causal relationship between BC and TC. Inverse variance weighting (IVW) and the MR-Egger method were used to combine the estimation of each single nucleotide variation (previous single nucleotide polymorphism). BC was taken as the result, and the effect of TC exposure was analyzed. Then, the effect of BC exposure on the result of TC was analyzed. Results Both IVW and MR-Egger results indicated that gene-driven thyroid cancer does not cause estrogen receptor-positive breast cancer and is a protective factor (β = -1.203, SE = 4.663*10–4, P = 0.010). However, gene-driven estrogen receptor-positive breast cancer can lead to the development of thyroid cancer (β = 0.516, SE = 0.220, P = 0.019). Conclusion From the perspective of gene drive, people with TC are less likely to have ER-positive BC. In contrast, people with ER-positive BC are more likely to have TC. Therefore, it is recommended that patients with BC be screened regularly for TC.

Abstract Backgrounds Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease characterized by synovial hyperplasia. Maintaining a balance between the proliferation and apoptosis of rheumatoid arthritis synovial fibroblasts (RASFs) is crucial for preventing the erosion of bone and cartilage and, ultimately, mitigating the progression of RA. We found that the lncRNA LEF1-AS1 was expressed at low levels in the RASFs and inhibited their abnormal proliferation by targeting PIK3R2 protein and regulating the PI3K/AKT signal pathway through its interaction with miR-30-5p. In this study, we fabricated a nano-drug delivery system for LEF1-AS1 using Zn-Adenine nanoparticles (NPs) as a novel therapeutic strategy against RA. Methods The expression levels of LEF1-AS1, miR-30-5p, PIK3R2, p-PI3K, and p-AKT were detected in the primary RASFs and a human fibroblast-like synovial cell line (HFLS). Zn-Adenine nanoparticles (NPs) were functionalized with anti-CD305 antibody to construct (Zn-Adenine)@Ab. These NPs were then loaded with LEF1-AS1 to form (Zn-Adenine)@Ab@lncRNA LEF1-AS1. Finally, the (Zn-Adenine)@Ab@lncRNA LEF1-AS1 NPs were locally injected into a rat model with collagen-induced arthritis (CIA). The arthritic injuries in each group were evaluated by HE staining and other methods. Results LEF1-AS1 was expressed at low levels in the primary RASFs. High expression levels of LEF1-AS1 were detected in the HFLS cells, which corresponded to a significant downregulation of miR-30-5p. In addition, the expression level of PIK3R2 was significantly increased, and that of p-PI3K and p-AKT were significantly downregulated in these cells. The (Zn-Adenine)@Ab@lncRNA LEF1-AS1 NPs significantly inhibited the proliferation of RASFs and decreased the production of inflammatory cytokines (IL-1β, IL-6, TNF-α). Intra-articular injection (IAI) of (Zn-Adenine)@Ab@lncRNA LEF1-AS1 NPs significantly alleviated cartilage destruction and joint injury in the CIA-modeled rats. Conclusions LEF1-AS1 interacts with miR-30-5p to inhibit the abnormal proliferation of RASFs by regulating the PI3K/AKT signal pathway. The (Zn-Adenine)@Ab NPs achieved targeted delivery of the loaded LEF1-AS1 into the RASFs, which improved the cellular internalization rate and therapeutic effects. Thus, LEF1-AS1 is a potential target for the treatment of RA.

The ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2) can collect earth surface elevation data with high precision on a global scale. However, the collected photon data contains a large amount of background noise due to the influence of sunlight, cloud reflection, and other factors. For photon data of different scenes, how to effectively denoise and achieve accurate classification of photon point clouds is crucial for subsequent applications. This study proposes a random forest based method for denoising and classifying ICESat-2 photon data in urban areas by fusing spectral features from Sentinel-2 images and spatial distribution features from photon data. The experimental results show that the method can effectively identify various types of photons. Compared with the reference data, the overall accuracy of photon denoising and classification is 95.97% on average, and the average kappa coefficient is 94.18%. Further analysis demonstrates that the addition of sentinel-2 spectral information can effectively improve the classification accuracy of photon point clouds in urban areas, and the photon classification method of combining photon lidar data and optical images can be a promising solution to improve classification accuracy.

The growth of vegetation directly maintains the ecological security of coal mining areas. It is of great significance to monitor the dynamic changes in vegetation in mining areas and study the driving factors of vegetation spatial division. This study focuses on the Yima mining area in Henan Province. Utilizing MODIS and multi-dimensional explanatory variable data, the Theil–Sen Median + Mann–Kendall trend analysis, variation index, Hurst index, and optimal-parameter-based geographical detector model (OPGD) are employed to analyze the spatiotemporal changes and future trends in the EVI (enhanced vegetation index) from 2000 to 2020. This study further investigates the underlying factors that contribute to the spatial variation in vegetation. The results indicate the following: (1) During the period studied, the Yima mining area was primarily characterized by a moderate-to-low vegetation cover. The area exhibited significant spatial variation, with a notable pattern of “western improvement and eastern degradation”. This pattern indicated that the areas that experienced improvement greatly outnumbered the areas that underwent degradation. Moreover, there was an inclination towards a deterioration in vegetation in the future. (2) Based on the optimal parameter geographic detector, it was found that 2 km was the optimal spatial scale for the analysis of the driving factors of vegetation change in this area. The optimal parameter combination was determined by employing five spatial data discretization methods and selecting an interval classification range of 5–10. This approach effectively addresses the subjective bias in spatial scales and data discretization, leading to enhanced accuracy in vegetation change analysis and the identification of its driving factors. (3) The spatial heterogeneity of vegetation is influenced by various factors, such as topography, socio-economic conditions, climate, etc. Among these factors, population density and mean annual temperature were the primary driving forces in the study area, with Q > 0.29 and elevation being the strongest explanatory factor (Q = 0.326). The interaction between temperature and night light was the most powerful explanation (Q = 0.541), and the average Q value of the interaction between the average annual temperature and other driving factors was 0.478, which was the strongest cofactor among the interactions. The interactions between any two factors enhanced their impact on the vegetation’s spatial changes, and each driving factor had its suitable range for affecting vegetative growth within this region. This research provides scientific support for conserving vegetation and restoring the ecological system.

Abstract Background Long non-coding RNAs (lncRNAs) are critical regulators in the initiation and progression of breast cancer. Our study aims to characterize the functions of LINC02086 which few published in breast cancer and decipher the downstream molecular mechanisms. Methods LINC02086 expression is tested in RNA-seq data from GEPIA database, tumor tissue samples from hospital patients and breast cancer cell lines. LINC02086 was silenced or overexpressed by lenti-virus-mediated shRNAs, or pLVX-Puro plasmids. Luciferase reporter assay and RNA pull-down assay were applied to study interactions between LINC02086, miR-6757-5p and ephrin type-A receptor 2 (EPHA2). LINC02086-silencing MCF-7 cells were injected into mice to establish xenograft animal models. Results Using RNA-seq data, tumor tissue samples and breast cancer cells, LINC02086 was consistently found to be up-regulated in breast cancer, and correlated with poorer prognosis. LINC02086 knockdown decreased cell viability, promoted cell apoptosis and suppressed tumor growth. LINC02086 interacted with miR-6757-5p that interacted with EPHA2.LINC02086 expression was negatively correlated with miR-6757-5p expression (r = -0.5698, P

Objective To investigate the effects and possible mechanisms of intervening P2X7 receptors on ischemia/reperfusion (I/R)-induced renal fibrosis. Methods Ten male wild-type C57BL/6 mice were randomly assigned to the sham operation group (WT-Sham) and the renal ischemia-reperfusion injury(IRI) group (WT-I/R). Ten P2X7 receptors gene knockout mice were also randomly assigned to the sham operation group (KO-Sham) or the renal IRI group (KO-I/R), with five mice in each group. The renal IRI model was established by unilateral nephrectomy and contralateral renal pedicle clamping. In the sham operation group, mice underwent renal pedicle localization without nephrectomy or clamping. Renal tissue specimens were collected after 42 days of reperfusion or sham operation. PAS staining was used to observe changes in renal tissue structure, Picrosirius Red staining was used to assess the degree of renal fibrosis, Western blot was performed to detect the expression of renal calcium-adhering protein E-cadherin and α-smooth muscle actin （α-SMA）, and immunohistochemical staining was used to observe the infiltration of F4/80-positive macrophages in the kidneys. Results After 42 days of renal IRI, PAS staining showed tubular atrophy and significant infiltration of inflammatory cells in the interstitium, while Picrosirius Red staining indicated obvious renal fibrosis. Compared with wild-type mice, P2X7 receptors gene knockout mice showed reduced tubular atrophy, decreased infiltration of inflammatory cells, and improved renal fibrosis. Western blot results showed that compared to the sham operation group, WT-I/R mice had decreased expression of the tubular marker E-cadherin and increased expression of the fibrosis marker α-SMA (P＜0.05). Compared to WT-I/R mice, KO-I/R mice showed improvement in E-cadherin decrease and α-SMA increase (P＜0.05). Immunohistochemical results revealed increased infiltration of macrophages in the kidneys after 42 days of renal IRI, while P2X7 receptors gene knockout mice showed reduced macrophage infiltration. Conclusion Renal IRI can lead to macrophage infiltration and fibrosis in renal tissue. Intervention of P2X7 receptors can reduce macrophage infiltration caused by IRI, improve renal tissue fibrosis, and delay the progression of chronic kidney disease.

Abstract Background The defect of B cell self-tolerance and the continuous antigen presentation by T cells (TCs) mediated by autoreactive B cells (BCs) play a key role in the occurrence and development of systemic lupus erythematosus (SLE). PD-1/PD-L1 signaling axis negatively regulates the immune response of TCs after activation and maintains immune tolerance. However, the effect of PD-1/PD-L1 signaling axis on the interaction between CD19+B/CD4+TCs in the peripheral blood of patients with SLE has not been studied in detail. Methods PD-1/PD-L1 and Ki-67 levels in peripheral blood (PB) of 50 SLE patients and 41 healthy controls (HCs) were detected through flow cytometry, and then the expression of PD-1+/−cells and PD-L1+/−cells Ki-67 was further analyzed. CD19+B/CD4+TCs were separated for cell culture and the supernatant was collected to determine proliferation and differentiation of TCs. IL-10 and IFN-γ secretion in the supernatant was also determined using ELISA. Results The PD-1, PD-L1, and Ki-67 levels on CD19+B/CD4+TCs in patients with SLE were higher than HCs. In CD19+B/CD4+TCs of SLE patients, the proliferative activity of PD-L1+ cells was higher than that of PD-L1− cells, and the proliferative activity of PD-1+ cells was higher than that of PD-1− cells. In the system co-culturing CD19+B/CD4+TCs from HCs/SLE patients, activated BCs promoted TCs proliferation and PD-L1 expression among TCs. Addition of anti-PD-L1 to co-culture system restored the proliferation of TCs, and inhibited IL-10/IFN-γ level. The addition of anti-PD-L1 to co-culture system also restored Tfh and downregulated Treg in HCs. Conclusions Axis of PD-1/PD-L1 on CD19+B/CD4+TCs in PB of SLE patients is abnormal, and cell proliferation is abnormal. In CD19+B/CD4+TCs of SLE patients, the proliferative activity of PD-L1+ and PD-1+ cells compared with PD-L1− and PD-1− cells in SLE patients, respectively. CD19+B/CD4+TCs in SLE patients can interact through PD-1/PD-L1.

The integration of multi-source remote sensing data, bolstered by advancements in deep learning, has emerged as a pivotal strategy for enhancing land use and land cover (LULC) classification accuracy. However, current methods often fail to consider the numerous prior knowledge of remote sensing images and the characteristics of heterogeneous remote sensing data, resulting in data loss between different modalities and the loss of a significant amount of useful information, thus affecting classification accuracy. To tackle these challenges, this paper proposes a LULC classification method based on remote sensing data that combines a Transformer and cross-pseudo-siamese learning deep neural network (TCPSNet). It first conducts shallow feature extraction in a dynamic multi-scale manner, fully leveraging the prior information of remote sensing data. Then, it further models deep features through the multimodal cross-attention module (MCAM) and cross-pseudo-siamese learning module (CPSLM). Finally, it achieves comprehensive fusion of local and global features through feature-level fusion and decision-level fusion combinations. Extensive experiments on datasets such as Trento, Houston 2013, Augsburg, MUUFL and Berlin demonstrate the superior performance of the proposed TCPSNet. The overall accuracy (OA) of the network on the Trento, Houston 2013 and Augsburg datasets is of 99.76%, 99.92%, 97.41%, 87.97% and 97.96%, respectively.

Mapping wall-to-wall forest aboveground biomass (AGB) at large scales is critical for understanding global climate change and the carbon cycle. In previous studies, a regression-based method was commonly used to map the spatially continuous distribution of forest AGB with the aid of optical images, which may suffer from the saturation effect. The Global Ecosystem Dynamics Investigation (GEDI) can collect forest vertical structure information with high precision on a global scale. In this study, we proposed a collaborative kriging (co-kriging) interpolation-based method for mapping spatially continuous forest AGB by integrating GEDI and Sentinel-2 data. First, by fusing spectral features from Sentinel-2 images with vertical structure features from GEDI, the optimal estimation model for footprint-level AGB was determined by comparing different machine-learning algorithms. Second, footprint-level predicted AGB was used as the main variable, with rh95 and B12 as covariates, to build a co-kriging guided interpolation model. Finally, the interpolation model was employed to map wall-to-wall forest AGB. The results showed the following: (1) For footprint-level AGB, CatBoost achieved the highest accuracy by fusing features from GEDI and Sentinel-2 data (R2 = 0.87, RMSE = 49.56 Mg/ha, rRMSE = 27.06%). (2) The mapping results based on the interpolation method exhibited relatively high accuracy and mitigated the saturation effect in areas with higher forest AGB (R2 = 0.69, RMSE = 81.56 Mg/ha, rRMSE = 40.98%, bias = −3.236 Mg/ha). The mapping result demonstrates that the proposed method based on interpolation combined with multi-source data can be a promising solution for monitoring spatially continuous forest AGB.

Vegetation plays a crucial role in terrestrial ecosystems, and the FVC (Fractional Vegetation Coverage) is a key indicator reflecting the growth status of vegetation. The accurate quantification of FVC dynamics and underlying driving factors has become a hot topic. However, the scale effect on FVC changes and driving factors has received less attention in previous studies. In this study, the changes and driving factors of FVC at multiple scales were analyzed to reveal the spatial and temporal change in vegetation in the Henan section of the Yellow River basin. Firstly, based on the pixel dichotomy model, the FVC at different times and spatial scales was calculated using Landsat-8 data. Then, the characteristics of spatial and temporal FVC changes were analyzed using simple linear regression and CV (Coefficient of Variation). Finally, a GD (Geographic Detector) was used to quantitatively analyze the driving factors of FVC at different scales. The results of this study revealed that (1) FVC showed an upward trend at all spatial scales, increasing by an average of 0.55% yr−1 from 2014 to 2022. The areas with an increasing trend in FVC were 10.83% more than those with a decreasing trend. (2) As the spatial scale decreased, the explanatory power of the topography factors (aspect, elevation, and slope) for changes in FVC was gradually strengthened, while the explanatory power of climate factors (evapotranspiration, temperature, and rainfall) and anthropogenic activities (night light) for changes in FVC decreased. (3) The q value of evapotranspiration was always the highest across different scales, peaking notably at a spatial scale of 1000 m (q = 0.48).

The purpose of this study is to discover and excavate more key factors and signaling pathways that regulate the formation intensity of biofilms and to fully reveal the possible models affecting biofilm formation. By using gene homologous recombination and bioinformatics technology, a MutL protein-directed deletion mutant strain was successfully constructed. The growth status of the mutant strain was observed, and it was confirmed that, except for the change in cell morphology, there were no significant differences in growth and reproduction between the mutant strain and the wild-type strain. By using the induced biofilm formation technique, the significant decrease in biofilm formation in the MutL mutant strain was successfully verified. The plate confrontation test confirmed that the inhibitory ability of the mutant strain against rice blast fungus was not significantly different from that of the WT strain. The colonization ability of the mutant strain on rice stems was tested, and it was confirmed that the colonization ability of the mutant strain was significantly lower than that of the WT strain. In terms of the prevention and control effect of rice blast disease, the mutant strain showed a significant decrease. By using transcriptomic big data, the gene and pathway expression differences between the mutant strain and the WT strain during biofilm formation were analyzed. The analysis revealed no significant correlation with the previously reported spo0A and tapA-sipW-tasA pathways. The key factor capB of the polyglutamic acid signaling pathway, which affects the formation of the biological model, was found to have a significant decrease in expression. A mechanical hypothesis was proposed: MutL may participate in regulating the formation intensity of Bacillus biofilms by regulating the formation of glutamic acid to polyglutamic acid.

Septic encephalopathy (SE) represents a severe inflammatory syndrome linked to elevated septic mortality rates, lacking specific therapeutic interventions, and often resulting in enduring neurological sequelae. The present investigation endeavors to elucidate the involvement of C-X-C Motif Chemokine Receptor 2 (CXCR2) in the pathogenesis of SE and to explore the potential of CXCR2 modulation as a therapeutic avenue for SE. Employing a murine SE model induced by lipopolysaccharide (LPS) administration, CXCR2 knockout mice and the CXCR2 inhibitor SB225002 were utilized to assess neutrophil recruitment, endothelial integrity, and transendothelial migration. Our findings substantiate that either CXCR2 deficiency or its inhibition curtails neutrophil recruitment without impacting their adhesion to cerebral endothelial cells. This phenomenon is contingent upon endothelial CXCR2 expression rather than CXCR2’s presence on neutrophils. Furthermore, the CXCR2 blockade preserves the integrity of tight junction protein ZO-1 and mitigates F-actin stress fiber formation in cerebral endothelial cells following septic challenge. Mechanistically, CXCL1-mediated CXCR2 activation triggers cerebral endothelial actin contraction via Rho signaling, thereby facilitating neutrophil transmigration in SE. These observations advocate for the potential therapeutic efficacy of CXCR2 inhibition in managing SE.

Epinephrine (EP) is a very important chemical transmitter in the transmission of nerve impulses in the central nervous system of mammals. Ascorbic acid (AA) is considered to be the most important extracellular fluid antioxidant and has important antioxidant properties in the cell. In this study, a series of transition metal–polyhistidine-carboxylated multi-wall carbon nanotube nanocomposites were synthesized, and their simultaneous catalytic effects on epinephrine and ascorbic acid were investigated. The results showed that nanocomposites based on iron ions had the highest catalytic activity. The prepared biosensor expressed high selectivity toward EP and AA with LOD values of 0.1 μΜ (AA) and 0.01 μΜ (EP), and sensitivity values of 4.18 μA mM−1 with a range of 0.001–5 mM (AA), 50.98 μA mM−1 with a range of 0.2–100 μM (EP), and 265.75 μA mM−1 with a range of 0.1–1.0 mM (EP). Moreover, it showed good stability, good repeatability and high selectivity in real sample detection. This work is a reference for the design of new electrochemical enzyme-free biosensors and the detection of biomarkers.

The utilization efficiency of conventional pesticides is relatively low in agricultural production, resulting in excessive application and environmental pollution. The efficient utilization of pesticides is crucial for promoting sustainable agriculture, and the development of nanopesticides presents a promising solution to the challenges associated with traditional pesticides. In order to explore an efficient application method for indendicarb (IDC), a star polymer nanocarrier (SPc) was employed to design and construct an efficient nanodelivery system for IDC. In this study, the morphology and physicochemical properties of the complex were determined, and its bioactivity and control efficacy were assessed using leaf-dipping and field spraying methods. The results show that IDC could be spontaneously incorporated into the hydrophobic core of SPc via hydrophobic association. This assembly disrupted the self-aggregated structure of IDC and significantly reduced its particle size to nanoscale. Furthermore, IDC emulsifiable concentrate (IDC EC) demonstrated improved adhesion to plant leaves with the aid of SPc, increasing retention from 8.083 to 10.418 mg/cm2. The LC50 (1d) of IDC EC against Plutella xylostella (Linnaeus) and Pieris rapae (Linnaeus) decreased by 6.784 and 1.931 times, respectively, with the addition of SPc. The inclusion of SPc increased the control effect of IDC EC by up to 8.28% (7d, 3000×) for P. xylostella and 12.53% (3d, 8000×) for P. rapae. This reveals that the IDC EC + SPc formulation exhibits superior insecticidal activity against these two highly destructive insect pests. This study successfully developed a novel nanodelivery system for the efficient application of IDC, which has the potential to reduce over-application and promote sustainable agricultural practices.

Accurate estimation of forest canopy height is crucial for biomass inversion, carbon storage assessment, and forestry management. However, deep learning methods are underutilized compared to machine learning. This paper introduces the convolutional neural network–bidirectional long short-term memory (CNN-BiLSTM) model and proposes a Convolutional Neural network–spatial channel attention–bidirectional long short-term memory (CNN-SCA-BiLSTM) model, incorporating dual attention mechanisms for richer feature extraction. A dataset comprising vegetation indices and canopy height data from forest regions in Luoyang, specifically within the 8–20 m range, is used for a comparative analysis of multiple models, with accuracy evaluated based on the mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). The results demonstrate that (1) the CNN-BiLSTM model exhibits strong potential (MAE = 1.6554 m, RMSE = 2.2393 m, R2 = 0.9115) and (2) the CNN-SCA-BiLSTM model, while slightly less efficient (2 value by 0.0495. Furthermore, the model is utilized to generate a canopy height map (MAE = 5.2332 m, RMSE = 7.0426 m) for Henan in the Yellow River Basin for the year 2022. The canopy height is primarily distributed around 5–20 m, approaching the accuracy levels of global maps (MAE = 4.0 m, RMSE = 6.0 m).

By analyzing students’ understanding of a certain subject’s knowledge and learning process, and evaluating their learning level, we can formulate students’ learning plans and teachers’ curricula. However, the large amount of data processing consumes a lot of manpower and time resources, which increases the burden on educators. Therefore, this study aims to use a machine learning model to build a model to evaluate students’ learning levels for art education. To improve the prediction accuracy of the model, SVM was adopted as the basic model in this study, and was combined with SSA, ISSA, and KPCA-ISSA algorithms in turn to form a composite model. Through the experimental analysis of prediction accuracy, we found that the prediction accuracy of the KPCA-ISSA-SVMM model reached the highest, at 96.7213%, while that of the SVM model was only 91.8033%. Moreover, by putting the prediction results of the four models into the confusion matrix, it can be found that with an increase in the complexity of the composite model, the probability of classification errors in model prediction gradually decreases. It can be seen from the importance experiment that the students’ achievements in target subjects (PEG) have the greatest influence on the model prediction effect, and the importance score is 9.5958. Therefore, we should pay more attention to this characteristic value when evaluating students’ learning levels.