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Abstract Despite the impressive progress in visual localisation, 6‐DoF cross‐view localisation is still a challenging task in the computer vision community due to the huge appearance changes. To address this issue, the authors propose the CLaSP, a coarse‐to‐fine framework, which leverages a synthetic panorama to facilitate cross‐view 6‐DoF localisation in a large‐scale scene. The authors first leverage a segmentation map to correct the prior pose, followed by a synthetic panorama on the ground to enable coarse pose estimation combined with a template matching method. The authors finally formulate the refine localisation process as feature matching and pose refinement to obtain the final result. The authors evaluate the performance of the CLaSP and several state‐of‐the‐art baselines on the Airloc dataset, which demonstrates the effectiveness of our proposed framework.

Ophiocordyceps sinensis (Berk.) is a complex is formed by Hepialidae larvae and Hirsutella sinensis. Infestation by H. sinensis, interaction with host larvae, and fruiting body development are three crucial processes affecting the formation of O. sinensis. However, research on the molecular mechanism of O. sinensis formation has been hindered by the lack of effective genetic transformation protocols. Therefore, Agrobacterium tumefaciens-mediated transformation (ATMT) was adopted to genetically transform two H. sinensis strains and optimize the transformation conditions. The results revealed that the most suitable Agrobacterium strain for H. sinensis transformation was AGL1, and that the surfactant Triton X-100 could also induce ATMT, although less effectively than acetosyringone (AS). In addition, the endogenous promoters of H. sinensis genes had a stronger ability to drive the expression of the target gene than did the exogenous promoter. The optimal transformation conditions were as follows: AS and hygromycin B concentrations of 100 μM and 50 μg/mL, respectively; A. tumefaciens OD600 of 0.4; cocultivation at 18 °C for 24 h; and H. sinensis used within three passages. The results lay a foundation for the functional study of key regulatory genes involved in the formation of O. sinensis.

Ceramic fiber sponge with low density，high specific surface area，high porosity，good thermal stability and good thermal insulation performance is expected to become one of the most promising commercial ceramic materials in the fields of heat insulation，flame retardant，water-oil absorption and energy conversion. This paper summarizes the direct assembly methods such as three-dimensional electrospinning，solution blowing spinning and centrifugal spinning，reviews the research progress in the production of ceramic fiber sponge by direct spinning method，analyses the problems of low production efficiency of ceramic fiber sponge，and proposes the future development directions of ceramic fiber sponge：（1）improve the production efficiency，reduce the production cost，and mass produce ceramic fibre sponge with controllable shape；（2）improve the high-temperature thermal insulation performance and promote the application of ceramic fibre sponge in the field of heat insulation；（3）improve the structural stability and produce ceramic fibre sponge with high elasticity，flexibility，and fatigue resistance；（4）research and develop ceramic fibre sponge materials with special functions such as light and electromagnetism，and expand the application range of ceramic fibre sponge.

Societal Impact Statement Rice wild relatives (RWR) provide valuable genetic resources for modern rice breeding, yet knowledge gaps constrain their conservation and further utilization. To address these gaps, the potential distributions of 22 RWR taxa were modeled, and their conservation statuses were assessed. Most taxa were identified as medium priority for further conservation. Further ex‐situ collecting hotspots are in Southeast and South Asia, Northern Australia, West Africa, and tropical Americas, while habitat protection lies in Southeast and South Asia, Northern Australia, and West Africa. Climate change may shift equatorial habitats to higher latitudes and mountain habitats to higher altitudes. Summary Rice wild relatives (RWR) provide valuable genetic resources for modern rice breeding. However, knowledge gaps on their geographic distributions and conservation status constrain their conservation and further utilization. To fill these critical gaps, we modeled the potential distributions of 22 RWR taxa under current climate scenarios, assessed their conservation status, both ex situ (in genebanks or botanical gardens) and in situ (in protected areas), and examined changes in taxa richness of RWR in predicted areas under future climate scenarios. The RWR were primarily distributed in tropical Asia to tropical Australia, tropical Africa, and South and Central America. We identified 4 out of 22 taxa as high priority (HP) for further conservation action and 18 taxa as medium priority (MP). Hotspots requiring further collecting for ex situ conservation are concentrated in Southeast and South Asia, Northern Australia, West Africa, and tropical Americas. Meanwhile, habitat protection should be enhanced in Southeast and South Asia, Northern Australia, and West Africa. Under future climate change, suitable habitats near the equator are expected to shift toward higher latitudes and some in lower‐latitude basins may become unsuitable due to excessive heat, resulting in decreased taxa richness in these areas. Additionally, suitable habitats in high mountain areas may shift to higher altitudes, potentially augmenting taxa richness in the highlands. Our findings provide vital insights to guide future rescue conservation efforts for RWR.

This study explores the reduction of carbamates (CAs) and pyrethroids (PYs) − commonly used pesticides − in lettuce using various immersion solutions and ultrasonic processing. It also examines the role of machine learning and molecular docking in understanding the mechanisms of pesticide reduction. The results revealed that the highest reduction of both CAs and PYs exceeded 80 % on lettuce leaves. In most samples, the reduction increased with the power of ultrasonic processing and processing time. The results of machine learning models (XGBoost and SHAP) showed that during the immersion cleaning of CAs and PYs, as well as during both immersion cleaning and ultrasonic processing of CAs + PYs, the reduction was most influenced by the initial pesticide levels and immersion time. Gas Chromatography-Mass Spectrometry (GC–MS) analysis of lettuce’s wax layer identified 24 compounds, including fatty alcohols, fatty acids, fatty acid esters, and triterpenoids. Despite the absence of active sites, the lipophilic nature of long-chain aliphatic compounds aids in pesticide binding, while triterpenoids form strong hydrogen bonds with pesticides, indicating a robust adsorption on the lettuce surface. This study aims to offer insights into the efficient removal of chemical pesticide residues from fruits and vegetables, addressing critical concerns for food safety and human health.

Pyrethroids (PYs) are widely applied pesticides whose residues pose potential health risks. This review describes current knowledge on PY chemical properties, usage patterns, environmental and food contamination, and human exposure models. It evaluates life cycle assessment (LCA), chemical alternatives assessment (CAA), and high-throughput screening (HTS) as tools for pesticide policy. Despite efforts to mitigate PY presence, their pervasive residues in the environment and food persist. And the highest concentrations ranged from 54,360 to 80,500 ng/L in water samples from agricultural fields. Food processing techniques variably reduce PY levels, yet no method guarantees complete elimination. This review provides insights into the fates and exposure pathways of PY residues in agriculture and food, and highlights the necessity for improved PY management and alternative practices to safeguard health and environment.

"Five-tone figurative hypnosis technique" is a mind-body therapy technique of traditional Chinese medicine based on the five-tone theory,combining the "figurative thinking" of traditional Chinese medicine and modern psychological hypnosis technique. Following the principle of "scientific,practical and normative" and combining theory and clinical practice, the formulation of "Operation specification of Five-tone figurative hypnosis technology" can provide reference and evidence-based basis for workers in related industries to implement "five-tone figurative hypnosis technology". This technical operation code is formulated from the aspects of technical scope, normative reference documents, terms and definitions,indications and contraindications, operation steps and requirements,operation process, treatment time and frequency, precautions and efficacy evaluation, forming a relatively standardized, scientific and unified operation guide. The terms and definitions mainly include five-tone therapy, figurative thinking, hypnosis, finger point, ideomotor response, suggestion and synesthetic effect. The indications mainly include non-organic sleep disorders, psychological and emotional disorders, and psychosomatic diseases. Contraindications mainly included schizophrenia and panic attack. The operation steps and requirements mainly include [music preparation (five-element music selection,audio and video selection, instrument selection and music intensity), environmental requirements, disinfection preparation, instrument preparation, patient preparation and practitioner preparation]; The operation process mainly includes first diagnosis interview, hypnotic introduction, concrete thinking establishment, hypnotic deepening, post-hypnotic suggestion and recall. This standard can provide correct operation guidance for Chinese medicine doctors, psychotherapists, psychological counselors,rehabilitation therapists, music therapists, health massage therapists,specialist nurses and other relevant practitioners to carry out this technology, and has good clinical applicability, safety and effectiveness.

Abstract Knowledge bases have been instrumental in advancing biological research, facilitating pathway analysis and data visualization, which are now widely employed in the scientific community. Despite the establishment of several prominent knowledge bases focusing on signaling, metabolic networks, or both, integrating these networks into a unified topological network has proven to be challenging. The intricacy of molecular interactions and the diverse formats employed to store and display them contribute to the complexity of this task. In a prior study, we addressed this challenge by introducing a “meta-pathway” structure that integrated the advantages of the Simple Interaction Format (SIF) while accommodating reaction information. Nevertheless, the earlier Global Integrative Network (GIN) was limited to reliance on KEGG alone. Here, we present GIN version 2.0, which incorporates human molecular interaction data from ten distinct knowledge bases, including KEGG, Reactome, and HumanCyc, among others. We standardized the data structure, gene IDs, and chemical IDs, and conducted a comprehensive analysis of the consistency among the ten knowledge bases before combining all unified interactions into GINv2.0. Utilizing GINv2.0, we investigated the glycolysis process and its regulatory proteins, revealing coordinated regulations on glycolysis and autophagy, particularly under glucose starvation. The expanded scope and enhanced capabilities of GINv2.0 provide a valuable resource for comprehensive systems-level analyses in the field of biological research. GINv2.0 can be accessed at: https://github.com/BIGchix/GINv2.0 .

The entomopathogenic fungus Beauveria majiangensis strain MJ1015, recently isolated from white grubs on a blueberry farm in Guizhou, China, could be used as a biocontrol agent. As a first step toward determining the effect of different solid culture media, temperature, and pH on colony growth rate and sporulation, we evaluated the optimum solid medium for mycelial growth and conidia production on a commercial scale. Subsequently, we also used single-factor analysis and response surface optimization to optimize the composition of the solid culture medium. On potato dextrose agar (PDA) medium, MJ1015 grew fastest and produced the highest spore yield at 29°C and pH 5. The best solid medium for the growth and sporulation of strain MJ1015 comprised 64.70 g/l of rice, 13.00 g/l of wheat, 0.30 g/l of NaNO3, 0.36 g/l of K2HPO4 · 3H2O, and 1.00 g/l of CaCO3. Rice, NaNO3, and K2HPO4 · 3H2O were the main influencing factors. The predicted value of cultured spores using the optimal medium was 4.56 x 1010 conidia/l. The validation test results showed that the average growth rate of strain MJ1015 on the optimal medium was 85% and 96% faster than that on Sabouraud dextrose agar with yeast extracts medium (SDAY) and PDA, respectively. Sporulation was 43.90 times and 9.65 times of that produced on SDAY and PDA, respectively. Our findings provide a theoretical basis for the commercial production of B. majiangensis to control white grubs.

This study developed an automated language learning teaching assessment system based on electroencephalography (EEG) and differential language large models (LLMs), aimed at enhancing language instruction effectiveness by monitoring learners’ cognitive states in real time and personalizing teaching content accordingly. Through detailed experimental design, the paper validated the system’s application in various teaching tasks. The results indicate that the system exhibited high precision, recall, and accuracy in teaching effectiveness tests. Specifically, the method integrating differential LLMs with the EEG fusion module achieved a precision of 0.96, recall of 0.95, accuracy of 0.96, and an F1-score of 0.95, outperforming other automated teaching models. Additionally, ablation experiments further confirmed the critical role of the EEG fusion module in enhancing teaching quality and accuracy, providing valuable data support and theoretical basis for future improvements in teaching methods and system design.

Abstract Background Single-cell RNA sequencing (scRNA-seq) measurements of gene expression show great promise for studying the cellular heterogeneity of rice roots. How precisely annotating cell identity is a major unresolved problem in plant scRNA-seq analysis due to the inherent high dimensionality and sparsity. Results To address this challenge, we present NRTPredictor, an ensemble-learning system, to predict rice root cell stage and mine biomarkers through complete model interpretability. The performance of NRTPredictor was evaluated using a test dataset, with 98.01% accuracy and 95.45% recall. With the power of interpretability provided by NRTPredictor, our model recognizes 110 marker genes partially involved in phenylpropanoid biosynthesis. Expression patterns of rice root could be mapped by the above-mentioned candidate genes, showing the superiority of NRTPredictor. Integrated analysis of scRNA and bulk RNA-seq data revealed aberrant expression of Epidermis cell subpopulations in flooding, Pi, and salt stresses. Conclusion Taken together, our results demonstrate that NRTPredictor is a useful tool for automated prediction of rice root cell stage and provides a valuable resource for deciphering the rice root cellular heterogeneity and the molecular mechanisms of flooding, Pi, and salt stresses. Based on the proposed model, a free webserver has been established, which is available at https://www.cgris.net/nrtp .

The carbon nanotube (CNT)-polyetherimide (PEI) was successfully grafted onto the surface of carbon fibers (CF) through chemical grafting method for constructing a CF-g-CNT reinforcing phase. Results show the construction of CF-g-CNT fiber can effectively improve the interface, impact resistance and bending properties of the composites. Considering the effect of strengthening phase on the mechanical properties of composites, the interfacial shear strength, impact strength, flexural modulus and flexural strength of CF-g-CNT/BMI composites respectively increase by 48.8%, 10.8%, 5.2% and 14.6%, companed with those of CF/BMI. In addition, the interfacial shear strength, impact strength, flexural modulus and flexural strength of CF-g-CNT/BMI-PEI-CNT composites are 56.9%, 27.1%, 12.5% and 16.1% higher than those of CF/BMI-PEI-CNT, respectively. The interfacial shear strength, impact strength, flexural modulus and flexural strength of CF-g-CNT/BMI-PEI-CNT composites increase by 57.7%, 33.7%, 20.0% and 23.7% compared with CF/BMI. The CNT-PEI in CF-g-CNT can effectively change the interface composition and change the expansion path of horizontal stress or vertical stress at the interface, which is beneficial to disperse the stress concentration as well as improve the mechanical properties of the composites.

Plasmonic waveguides have attracted tremendous interest due to efficiently confining photons on the subwavelength spatial scale to be beating the propagation diffraction limit. Transition metal molybdenum (Mo) exhibits outstanding properties in light trapping and electromagnetic field confining, making it potentially valuable in 1.55 μm plasmonic waveguide applications. However, the reliable fabrication of high-quality Mo plasmonic waveguides is a significant challenge. A real-space micro-imaging study of the surface plasmon on Mo structures is still absent. In this study, we successfully prepared a single-crystalline Mo microrod waveguide structure and fabricated subwavelength gratings on it. The diffraction gratings were designed, optimized, and etched to excite the surface plasmon polariton behaviour of Mo for the first time. The grating-optimized Mo microrod single-crystal reveals highly efficient waveguide performance around near-infrared spectroscopy, exhibiting a long propagation length of 32 μm and a low transmission loss of 0.067 dB μm−1. The results provide an alternative to advanced materials research and optical device applications of plasmonic waveguide systems.

This paper introduces a component model for analysing embedded column bases to predict rotational stiffness, moment resistance, and the full-range moment–rotation response. The key components identified include the embedded column, concrete in compression on the column side, concrete in compression beneath the base plate, concrete in punching shear above the base plate, and anchor bolts. The embedded column is modelled as a Timoshenko beam, considering both shear and flexural deformations, while other components are represented by springs. Methods are provided for determining their uniaxial constitutive behaviour. A simplified iterative solution method is proposed, where the embedded column is further simplified into three rigid segments to specifically address shear and bending deformations. A corresponding simplified finite element model is developed for accurate numerical solutions. The validity of the component model is confirmed through comparisons with the results of existing tests and refined solid finite element analysis for H-steel column bases. The simplified iterative solution method effectively predicts strength but underestimates the stiffness of deeply embedded column bases. This is due to the trilinear deformation pattern simplification, which concentrates flexural deformation at the upper bearing stress resultant force point, leading to an overestimation of steel column rotation on the foundation surface.

KASP marker technology has been used in molecular marker-assisted breeding because of its high efficiency and flexibility, and an intelligent evaluation model of KASP marker primer typing results is essential to improve the efficiency of marker development on a large scale. To this end, this paper proposes a gene population delineation method based on NTC identification module and data distribution judgment module to improve the accuracy of K-Means clustering, and introduces a decision tree to construct the KASP-IEva primer typing evaluation model. The model firstly designs the NTC identification module and data distribution judgment module to extract four types of data, grouping and categorizing to achieve the improvement of the distinguishability of amplification product signals; secondly, the K-Means algorithm is used to aggregate and classify the data, to visualize the five aggregated clusters and to obtain the morphology location eigenvalues; lastly, the evaluation criteria for the typing effect level are constructed, and the logical decision tree is used to make conditional discrimination on the eigenvalues in order to realize the score prediction. The performance of the model was tested by the KASP marker typing test results of 2519 groups of cotton varieties, and the following conclusions were obtained: the model is able to visualize the aggregation and classification effects of the amplification products of NTC, pure genotypes, heterozygous genotypes, and untyped genotypes, enabling rapid and accurate KASP marker typing evaluation. Comparing and analyzing the model evaluation results with the expert evaluation results, the average accuracy rate of the four grades evaluated by the model was 87%, and the overall evaluation results showed an uneven distribution of the grades with significant differential characteristics. When evaluating 2519 KASP fractal maps, the expert evaluation consumes 15 hours, and the model evaluation only uses 8min27.45s, which makes the model intelligent evaluation significantly better than the expert evaluation from the perspective of time. The establishment of the model will further enhance the application of KASP markers in molecular marker-assisted breeding and provide technical support for the large-scale screening and identification of excellent genotypes.

Cross sections carry information on the spatial distribution of rock strata and the development of geological structures, and it is an important data source for three-dimensional (3D) geological modeling. However, the interpretation and mapping of geological structures in sections by means of manual interpretation are inefficient and costly, and the performance varies greatly with the experts’ ability and experience. The objective of this article is to develop an automatic recognition and mapping method for folds in cross sections. This method mainly includes identifying folds based on stratigraphic sequence characteristics (symmetrical and repetitive), classifying fold types based on geometric attributes of folds (interval scheduling, strike, and section morphology), optimizing strata based on the superposition principle and area conservation principle, and constructing the polygon features of folds. Based on experiments in the Parallel Fold Belt of Eastern Sichuan and the central Appalachian fold-thrust belt in the Appalachian Mountains, the method presented in this article can effectively be used for automatic recognition and high-quality mapping of folds in the cross sections. The method provides a good source of geological cross-sectional data for the 3D modeling of geologic bodies.

Macrophage polarization is an essential process involved in immune regulation. In response to different microenvironmental stimulation, macrophages polarize into cells with different phenotypes and functions, most typically M1 (pro-inflammatory) and M2 (anti-inflammatory) macrophages. Iron-based nanoparticles have been widely explored and reported to regulate macrophage polarization for various biomedical applications. However, the influence factors and modulation mechanisms behind are complicated and not clear. In this review, we systemically summarized different iron-based nanoparticles that regulate macrophage polarization and function and discussed the influence factors and mechanisms underlying the modulation process. This review aims to deepen the understanding of the modulation of macrophage polarization by iron-based nanoparticles and expects to provide evidence and guidance for subsequent design and application of iron-based nanoparticles with specific macrophage modulation functions.

With the continuous advancement of the construction of smart cities, the availability of large-scale and semantically enriched datasets is essential for enhancing the machine’s ability to understand urban scenes. Mesh data have a distinct advantage over point cloud data for large-scale scenes, as they can provide inherent geometric topology information and consume less memory space. However, existing publicly available large-scale scene mesh datasets are limited in scale and semantic richness and do not cover a wide range of urban semantic information. The development of 3D semantic segmentation algorithms depends on the availability of datasets. Moreover, existing large-scale 3D datasets lack various types of official annotation data, which hinders the widespread applicability of benchmark applications and may cause label errors during data conversion. To address these issues, we present a comprehensive urban-scale semantic segmentation benchmark dataset. It is suitable for various research pursuits on semantic segmentation methodologies. This dataset contains finely annotated point cloud and mesh data types for 3D, as well as high-resolution original 2D images with detailed 2D semantic annotations. It is constructed from a 3D reconstruction of 10,840 UVA aerial images and spans a vast area of approximately 2.85 square kilometers that covers both urban and rural scenes. The dataset is composed of 152,298,756 3D points and 289,404,088 triangles. Each 3D point, triangular mesh, and the original 2D image in the dataset are carefully labeled with one of the ten semantic categories. Six typical 3D semantic segmentation methods were compared on the CUS3D dataset, with KPConv demonstrating the highest overall performance. The mIoU is 59.72%, OA is 89.42%, and mAcc is 97.88%. Furthermore, the experimental results on the impact of color information on semantic segmentation suggest that incorporating both coordinate and color features can enhance the performance of semantic segmentation. The current limitations of the CUS3D dataset, particularly in class imbalance, will be the primary target for future dataset enhancements.

Generative Deep Learning holds promise for mining the unexplored “chemical universe” for new drugs. Here, the authors demonstrate the de novo design of phosphoinositide 3-kinase gamma (PI3Kγ) inhibitors for the PI3K/Akt pathway in human tumor cells.

Loose layers are the locus of human activities. The high-quality 3D modeling of loose layers has essential research significance and applicability in engineering geology, hydraulic and hydroelectric engineering, and urban underground space design. To address the shortcomings of traditional 3D loose-layer modeling based on borehole data, such as the lack of bedrock surface constraints, simple strata pinch-out processing, and the higher fitting error of the strata surface, a 3D loose-layer modeling method based on the stratum development law is proposed. The method mainly uses three different virtual boreholes, bedrock-boundary virtual boreholes, pinch-out virtual boreholes, and densified virtual boreholes, to control the stratigraphic distribution. Case studies demonstrate the effectiveness of this 3D loose-layer modeling method in the Qinhuai District of Nanjing and Hangkonggang District of Zhengzhou. Compared to the previous methods that interpolated stratigraphic surfaces with elevation information, the method proposed in this article interpolates the stratum thickness based on stacking, which could improve the interpolation accuracy. In the area where the loose layers and exposed bedrock are alternately distributed, stratigraphic thickness errors’ mean and standard deviation decreased by 2.11 and 2.13 m. In the pure loose-layer area, they dropped by 0.96 and 0.33 m. In addition, the proposed approach allows us to infer the different stratigraphic distribution patterns accurately and complete 3D loose-layer model construction with higher accuracy and a good visualization effect.

The Atmospheric Boundary Layer (ABL) links the free atmosphere and the earth surface (land and ocean), and plays a significant role in the initiation and development of rainfall. The ABL height is a key parameter of ABL, and usually applied to parameterizations of the ABL processes in numerical weather prediction and climate models. Obtaining accurate ABL height data can contribute to improving prediction skills of numerical weather prediction and climate models. The present paper introduces main methods in evaluating the ABL heights from a variety of profile data, such as conventional radiosonde data, satellite remote sensing data, aerosol backscattering intensity data, and global navigation satellite system (GNSS) radio occultation (RO) sounding data. Additionally, some progresses are presented. Three rules are suggested for dealing with the ABL with different ABL height estimates. Particular attention is paid to the application of the parameterization schemes of the ABL processes in numerical weather (or climate) prediction models, such as Weather Research and Forecasting (WRF), which includes application of ABL height data in solving the equation of turbulent eddy dispersion. And some prospects are presented finally.

The increase in carbon dioxide emissions is a key factor leading to the warming of the Earth’s climate system and the intensification of the greenhouse effect. Internal combustion engines have a significant impact on carbon emissions. For internal combustion engines, friction is the main cause of energy loss, which reduces fuel utilization and increases carbon emissions. Therefore, it is necessary to reduce friction work of internal combustion engine. This work focuses on the friction pairs of piston pins and liners in internal combustion engines, and optimizes the surface technology of piston pins by comparing carburized and nitriding processes. The results show that the friction work of the nitrided piston pin is 8.9% lower than that of the carburized piston pin, and the wear of the liner is reduced by 10%. Therefore, surface nitriding process of piston pin is an effective way to reduce friction work and carbon emissions of internal combustion engines.

In this paper, the kinematics and dynamics model of the ship’s six-degree-of-freedom simulation swing platform were studied. Then, a random wave and ship motion response model was established, and a six-degree-of-freedom motion model of DP ship in four-level sea state was established and studied by using the open source MSS toolbox, which lays a foundation for the subsequent simulation and control strategy research. The ordinary PID controller and fuzzy PID controller were designed and simulated, and the results were compared to verify the effect of fuzzy PID controller.

Abstract View graph construction aims to effectively organise disordered image dataset through image retrieval technique before structure from motion (SfM). Existing view graph construction methods usually fail to handle scenes with duplicate structure, because these methods solely treat the construction of view graph as a process of image‐pair‐wise matching and lack in exploiting images' topological details in dataset. In this paper, we handle this problem from a novel perspective to construct view graph in a global paradigm by introducing an end‐to‐end graph convolutional network (GCN). First, a location‐aware embedding module is introduced to encode images into a feature space that takes into account the feature's location by using Vision Transformer architecture, improving the distinction between features of duplicate structure. Second, graph convolutional network that consists of topological relationship preserving module and feature metric learning module is proposed. Topological relationship preserving network is proposed to help nodes maintain their connected neighbourhood features. By merging the topological connected information into images' embedding, our method can process image matching in a global mode, thus improving the disambiguation ability for images with duplicate scenes. Then a feature metric learning network is embedded into GCN to dynamically compute the linkage prediction among nodes based on their features. Finally, our method combines these three parts to jointly optimise nodes' features and linkage prediction in an end‐to‐end paradigm. We make qualitative and quantitative comparisons based on three public benchmark datasets and demonstrate that our proposed method performs favourably against other state‐of‐the‐art methods.

In view of the actual situation of agricultural science data sharing in China, the characteristics of various existing licensing agreements at home and abroad and their applicability in the situation of agricultural science data sharing in China were analyzed.Combined with the actual situation of China’s current intellectual property system, legal system, scientific data management mechanism, scientific development level and so on, the scientific data sharing agreements: Weigong Village scientific data academic/general license draft were proposed, which is suitable for China’s national conditions.And some suggestions for future application were put forward.

Detecting and eliminating sprouted potatoes is a basic measure before potato storage, which can effectively improve the quality of potatoes before storage and reduce economic losses due to potato spoilage and decay. In this paper, we propose an improved YOLOv5-based sprouted potato detection model for detecting and grading sprouted potatoes in complex scenarios. By replacing Conv with CrossConv in the C3 module, the feature similarity loss problem of the fusion process is improved, and the feature representation is enhanced. SPP is improved using fast spatial pyramid pooling to reduce feature fusion parameters and speed up feature fusion. The 9-Mosaic data augmentation algorithm improves the model generalization ability; the anchor points are reconstructed using the genetic algorithm $k$ -means to enhance small target features, and then multi-scale training and hyperparameter evolution mechanisms are used to improve the accuracy. The experimental results show that the improved model has 90.14% recognition accuracy and 88.1% mAP, and the mAP is 4.6%, 7.5%, and 12.4% higher compared with SSD, YOLOv5, and YOLOv4, respectively. In summary, the improved YOLOv5 model, with good detection accuracy and effectiveness, can meet the requirements of rapid grading in automatic potato sorting lines.

Execution time, reliability, and energy consumption are the three main performance parameters of processor scheduling for real-time embedded systems. It is very meaningful to optimize the energy optimization of processor scheduling to satisfy the requirements on time limit and reliability. This paper tries to optimize the energy consumption of the processor under three constraints: the partial ordering relations between task modules, the time limit, and the reliability. Based on directed acrylic graph (DAG) and quantum particle swarm optimization (QPSO), two scheduling algorithms were developed for the problem, namely, DAG_QPSO_I and DAG_QPSO_II. The two algorithms were compared with each other, and with other intelligent algorithms. The results show that the proposed algorithms are superior in optimization effect and efficiency, DAG_QPSO_I makes energy consumption more efficient than DAG_QPSO_II, and DAG_QPSO_II meets stricter requirements on time limit and reliability than DAG_QPSO_I.

Abstract Background The aim of this study was to investigate the biological functions and underlying mechanisms of SIRT5 in clear cell renal cell carcinoma (ccRCC). Methods SIRT5 expression data in The Cancer Genome Atlas Kidney Clear Cell Carcinoma (TCGA-KIRC) were selected, and the correlations between SIRT5 expression and various clinicopathological parameters were analysed. SIRT5 expression in ccRCC tissues was examined using immunohistochemistry. Stable cell lines with SIRT5 knockdown were established. In vitro and in vivo experiments were conducted to investigate the functional roles of SIRT5 in the cellular biology of ccRCC, including cell viability assays, wound healing assays, soft agar colony formation assays, Transwell invasion assays, qRT–PCR, and Western blotting. In addition, microarrays, rescue experiments and Western blotting were used to investigate the molecular mechanisms underlying SIRT5 functions. Results SIRT5 expression was downregulated in ccRCC compared with normal tissues, which correlated with a poor prognosis of ccRCC. SIRT5 knockdown significantly increased cell proliferation, migration and invasion in vitro. In vivo experiments revealed that SIRT5 knockdown promoted ccRCC tumorigenesis and metastasis. Mechanistically, SIRT5 deglycosylated PDHA1 at K351 and increased PDC activity, thereby altering the metabolic crosstalk with the TCA cycle and inhibiting the Warburg effect. SIRT5 overexpression was related to low succinylation of PDHA1. Conclusions Downregulated SIRT5 expression in ccRCC accelerated the Warburg effect through PDHA1 hypersuccinylation and induced tumorigenesis and progression, indicating that SIRT5 may become a potential target for ccRCC therapy.

The finely tuned integration of intra- and extracellular cues by components of the mitogen-activated protein kinase (MAPK) signaling pathways controls the mutually exclusive phenotypic manifestations of uncontrolled growth and tumor cell dissemination. The Ser/Thr kinase MAP4K4 is an upstream integrator of extracellular cues involved in both proliferation and cell motility control. Initially identified as an activator of the c-Jun N-terminal kinase (JNK), the discovery of diverse functions and additional effectors of MAP4K4 beyond JNK signaling has considerably broadened our understanding of this complex kinase. The implication of MAP4K4 in the regulation of cytoskeleton dynamics and cell motility provided essential insights into its role as a pro-metastatic kinase in cancer. However, the more recently revealed role of MAP4K4 as an activator of the Hippo tumor suppressor pathway has complicated the understanding of MAP4K4 as an oncogenic driver kinase. To develop a better understanding of the diverse functions of MAP4K4 and their potential significance in oncogenesis and tumor progression, we have collected and assessed the current evidence of MAP4K4 implication in molecular mechanisms that control proliferation and promote cell motility. A better understanding of these mechanisms is particularly relevant in the brain, where MAP4K4 is highly expressed and under pathological conditions either drives neuronal cell death in neurodegenerative diseases or cell dissemination in malignant tumors. We review established effectors and present novel interactors of MAP4K4, which offer mechanistic insights into MAP4K4 function and may inspire novel intervention strategies. We discuss possible implications of novel interactors in tumor growth and dissemination and evaluate potential therapeutic strategies to selectively repress pro-oncogenic functions of MAP4K4.

Abstract The heterocharge accumulated in the oil‐paper insulation system of the converter transformer will enhance the local electric field and cause insulation deterioration. Numerical simulation is an effective method to analyze heterocharge. Currently, the numerical simulation of heterocharge is mainly based on the bipolar charge transport (BCT) model and the impurity ion transport (IIT) model. However, the effects of temperature, conductivity, and thickness of oil‐impregnated paper on charge transport have not been considered in these models. In view of this, a modified charge transport (MCT) model by introducing the Maxwell–Wagner model and heat conduction equation is proposed in this paper based on the BCT model and IIT model. Then, the influence of multi‐factors (temperature gradient, moisture content, and oil‐impregnated paper thickness) on heterocharge accumulation is investigated by using the MCT model. The simulation results obtained by the MCT model are more consistent with the experimental results than the BCT model, which proves the accuracy and superiority of the MCT model. Moreover, the increase in temperature gradient, moisture content, and oil‐impregnated paper thickness will enlarge the ion dissociation rate, conductivity, and trap density, and thus promote the generation of heterocharge. In that respect, the MCT model is expected to optimize the insulation design and provide a reference for space charge dynamic simulation.

Rhizosphere bacteria play important role in soil nutrient cycling and plant growth, and their richness and diversity are influenced by soil management systems. However, the specific changes in tobacco rhizosphere bacterial community structure in continuous and tobacco-rice rotation cropping systems remain uninvestigated. In this study, soil properties and the composition of the rhizosphere bacterial community in tobacco monocropping and tobacco-rice rotation cropping systems were analyzed. Moreover, the comparison of rhizosphere bacterial community structure between tobacco continuous and tobacco-rice rotation cropping systems was performed via high-throughput sequencing. The changes in the composition of the rhizosphere bacterial community were investigated at different tobacco growth stages. The results showed that continuous tobacco cropping increased the soil soluble organic carbon (SOC), total nitrogen (TN), and the content of other nutrients (e.g., available phosphorus and available potassium) compared to tobacco-rice rotation cropping. However, monocropping decreased bacterial alpha-diversity and altered the community composition when compared to the rotation cropping system. At the phylum level, the relative abundance of Proteobacteria, Gemmatimonadetes, and Bacteroidetes increased in the continuous cropping soil, while that of Acidobacteria, Firmicutes, and Actinobacteria decreased. At the genera level, the average abundance of the dominant genus Bacillus varied from 12.96% in continuous cropping libraries to 6.33% in the rotation cropping libraries (p < 0.05). Additionally, several other taxa, such as o_Acidobacteriales and Candidatus_Solibacter decreased from 7.63 to 6.62% (p < 0.05) and 4.52 to 2.91% (p < 0.05), respectively. However, the relative abundance of f_Gemmatimonadaceae and c_Subgroup_6 showed an increase of 1.46% (p < 0.05) and 1.63% (p < 0.05) in the tobacco-rice rotation cropping system, respectively. The results of NMDS indicated that the rhizobacteria community structure differed in the two cropping systems. In tobacco, the rhizosphere bacterial community structure showed no significant changes in the prosperous long-term stage and topping stage, but the composition changed significantly in the mature stage.

This article proposes an progressive-collapse mechanism for suspended-dome structures subjected to cable rupture, based on experimental and finite element results. The anti-collapse mechanism can be succinctly described as a node-buckling mechanism: the potential for node buckling in a local arch-like spatial grid centered on unsupported node directly determines whether progressive collapse will occur in the overall structure. Subsequently, based on this anti-collapse mechanism, a node-buckling model is further proposed, and the factors affecting the anti-collapse bearing capacity of suspended domes are quantitatively expressed through the construction of a resistance index, which can be used to judge the sensitivity of hoop cables. Further, using Ribbed and Lamella suspended domes as examples, extensive calculations demonstrate the applicability and accuracy of the node-buckling model and resistance index to other types of suspended domes. Finally, the resistance index is used to analyze two important but easily overlooked factors that affect the anti-collapse bearing capacity of suspended domes. Initial geometric imperfections result in a rise–span ratio too small for the local arch-like spatial grid, while the lack of lateral stiffness at the supports will weaken the axial stiffness of the outermost radial or diagonal members. Both of these factors significantly reduce the stability of the local arch-like spatial grid, making it more likely to trigger progressive collapse in suspended-dome structures.

By using 4,7-dibromo-2,1,3-benzothiadiazole as photoactive group, the Sonogashira coupling between 4,7-dibromo-2,1,3-benzothiadiazole and 1,3,5-triethynylbenzene results in benzothiadiazole functionalized conjugated microporous polymer (CMP), named CMP-3-BT. The obtained CMP-3-BT shows hierarchical structures, which consists of microspheres and curved nanobelts. Moreover, CMP-3-BT is semiconductive porous materials with band gap of 2.24 eV. By using 425 nm LED (3 W) as light source, assisted by H2O2, CMP-3-BT enables the oxidation of thioanisole bearing various substituents to their corresponding sulfoxides under O2 atmospheres at room temperature with high efficiency and excellent selectivity. EPR spectra indicate that the 1O2 and O·- 2 are the active oxygen species for the photo-oxidation of thioanisole. Importantly, the catalytic activity of CMP-3-BT remains unchanged after 6 cycles, indicating the excellent structural stability and catalytic activity of CMP-3-BT serving as a reusable green catalyst.

As the main insulation material of the power electronic transformer (PET), polyimide (PI) is easy to accumulate space charge under the continuous high-frequency pulse voltage, which will threaten the operation of PET and cause electric field distortion. At present, the microscopic mechanism of charge transport under high-frequency voltage is unclear. Numerical simulation is an effective method to explore the transport characteristics of space charge. The simulation of charge transport under DC voltage is mainly based on the bipolar charge transport (BCT) model. However, the charge transport behaviors under high-frequency pulse voltage cannot be simulated by the traditional models accurately. Given this, based on the BCT model, the High-Frequency Voltage Charge Transport (HFCT) model is proposed, considering nonlinear charge mobility of the polymer and the effect of the frequency. Finally, the space charge and electric field distribution under square pulse wave voltages with different rising times are simulated based on the HVCT model, concluding that the reduction of rising time will promote the accumulation of space charge. In view of this, the HVCT model is expected to optimize the insulation design of PET and provide a reference for the dynamic simulation of space charge.

Despite the fact that point cloud registration under noisy conditions has recently begun to be tackled by several non-correspondence algorithms, they neither struggle to fuse the global features nor abandon early state estimation during the iterative alignment. To solve the problem, we propose a novel method named R-PCR (recurrent point cloud registration). R-PCR employs a lightweight cross-concatenation module and large receptive network to improve global feature performance. More importantly, it treats the point registration procedure as a high-order Markov decision process and introduces a recurrent neural network for end-to-end optimization. The experiments on indoor and outdoor benchmarks show that R-PCR outperforms state-of-the-art counterparts. The mean average error of rotation and translation of the aligned point cloud pairs are, respectively, reduced by 75% and 66% on the indoor benchmark (ScanObjectNN), and simultaneously by 50% and 37.5% on the outdoor benchmark (AirLoc).

Abstract Vacuum ultraviolet (VUV) lasers have demonstrated great potential as the light source for various spectroscopies, which, if they can be focused into a small beam spot, will not only allow investigation of mesoscopic materials and structures but also find application in the manufacture of nano-objects with excellent precision. In this work, we report the construction of a 177 nm VUV laser that can achieve a record-small (~0.76 μm) focal spot at a long focal length (~45 mm) by using a flat lens without spherical aberration. The size of the beam spot of this VUV laser was tested using a metal grating and exfoliated graphene flakes, and we demonstrated its application in a fluorescence spectroscopy study on pure and Tm3+-doped NaYF4 microcrystals, revealing a new emission band that cannot be observed in the traditional up-conversion process. In addition, this laser system would be an ideal light source for spatially and angle-resolved photoemission spectroscopy.

The aim of this study is to examine the clinical features of patients with Behçet’s disease (BD) in the presence or absence of latent tuberculosis infection (LTBI).

Abstract Background Pathway analysis is widely applied in transcriptome analysis. Given certain transcriptomic changes, current pathway analysis tools tend to search for the most impacted pathways, which provides insight into underlying biological mechanisms. Further refining of the enriched pathways and extracting functional modules by “crosstalk” analysis have been proposed. However, the upstream/downstream relationships between the modules, which may provide extra biological insights such as the coordination of different functional modules and the signal transduction flow have been ignored. Results To quantitatively analyse the upstream/downstream relationships between functional modules, we developed a novel GEne Set Topological Impact Analysis (GESTIA), which could be used to assemble the enriched pathways and functional modules into a super-module with a topological structure. We showed the advantages of this analysis in the exploration of extra biological insight in addition to the individual enriched pathways and functional modules. Conclusions GESTIA can be applied to a broad range of pathway/module analysis result. We hope that GESTIA may help researchers to get one additional step closer to understanding the molecular mechanism from the pathway/module analysis results.

Most of the widely used multi-view 3D reconstruction algorithms assume that object appearance is predominantly diffuse and full of good texture. For the objects that violate this restriction, the surface can hardly be reconstructed because such area lacks sufficient support from dense point clouds. To tackle this problem, we introduce a novel two-stage prior-guided method based on point clouds enhancement to enable the application of multi-view reconstruction approaches in such scenes. In the first stage, we optimize the original PlaneNet plane segmentation priors by taking advantage of the estimated depth map and confidence map from multi-view stereo. In the second stage, we correct and supply 3D point clouds for the weakly supported plane surface on the basis of the upgraded priors. Furthermore, we utilize a slight disturbance of the enhanced point clouds to facilitate the subsequent mesh reconstruction. The proposed point cloud enhancement approach is evaluated on the large-scale $DTU$ dataset. Our method significantly outperforms previous multi-view stereo state-of-the-arts. We also demonstrate weakly supported plane surface reconstruction results from real-world photos that are unachievable with either the methods aiming at preserving weakly supported surfaces or the traditional state-of-the-art 3D reconstruction systems.

This paper is concerned with the co-design problem of event-triggered scheme and H static output control of linear Markov jump systems with deception attacks. To save the previous communication resources, a mode-dependent event-triggered scheme is utilized based on system output. To describe the deception attacks, a random variable satisfying Bernoulli distribution is employed. By using a separation approach, sufficient linear matrix inequality conditions for the existence of event-triggered output controllers that ensure the stochastic stability with prescribed H∞ are obtained. Then, a co-design algorithm is proposed to obtain the trade-off between the communication cost and H∞ performance. Lastly, the validity of the developed method is verified by a numerical example and a practical single-link robot arm system.

Image-to-point cloud registration refers to finding relative transformation between the camera and the reference frame of the 3D point cloud, which is critical for autonomous driving. Recently, a two-stage “frustum point cloud classification + camera pose optimization” pipeline has shown impressive results on this task. This paper focuses on the second stage and reformulates the optimization procedure as a Markov decision process. An initial pose is modified incrementally, sequentially aligning a virtual 3D point observation towards a previous classification solution. We consider such an iterative update process as a reinforcement learning task and, to this end, propose a novel agent (AgentI2P) to conduct decision making. To guide AgentI2P, we employ behaviour cloning (BC) and reinforcement learning (RL) techniques: cloning an expert to learn accurate pose movement and reinforcing an alignment reward to improve the policy further. [We demonstrate the effectiveness and efficiency of our approach on Oxford Robotcar and KITTI datasets. The (RTE, RRE) metrics are (1.34m,1.46∘) on Oxford Robotcar and (3.90m,5.94∘) on KITTI, and the inference time is 60 ms, both achieving state-of-the-art performance]. The source code will be publicly available upon publication of the paper.

Cross-spectral local feature matching between visual and thermal images benefits many vision tasks in low-light environments, including image-to-image fusion and camera re-localization. An essential prerequisite for unleashing the potential of supervised deep learning algorithms in the area of visible–thermal matching is the availability of large-scale and high-quality annotated datasets. However, publicly available datasets are either in relative small quantity scales or have limited pose annotations due to the expensive cost of data acquisition and annotation, which severely hinders the development of this field. In this paper, we proposed a multi-view thermal–visible image dataset for large-scale cross-spectral matching. We first recovered a 3D reference model from a group of collected RGB images, in which a certain image (bridge) shares almost the same pose as the thermal query. We then effectively registered the thermal image to the model based on manually annotating a 2D-2D tie point between the bridge and the thermal. In this way, through simply annotating one same viewpoint image pair, numerous overlapping image pairs between thermal and visible could be available. We also proposed a semi-automatic approach for generating accurate supervision for training multi-view cross-spectral matching. Specifically, our dataset consists of 40,644 cross-modal pairs with well supervision, covering multiple complex scenes. In addition, we also provided the camera metadata, 3D reference model, depth map of the visible images and 6-DoF pose of all images. We extensively evaluated the performance of state-of-the-art algorithms on our dataset and provided a comprehensive analysis of the results. We will publish our dataset and pre-processing code.

Illegal, unreported and unregulated (IUU) fishing activities threaten marine biodiversity, livelihoods, food security, and human rights across the globe. Often occurring in waters that are difficult to control, and across multi-sector, transboundary, value chains that are hard to regulate, such a complex and heterogeneous problem requires multiple strategies beyond sovereign nations’ legislation alone. Here we explore the mechanisms through which eco-certification, by fostering private-public and cross-jurisdiction cooperation, can incentivize fishers to adopt best practices in harvesting and ecosystem impacts mitigation, increase the transparency of fishery operations and accountability to suppliers. The Marine Stewardship Council (MSC) sets globally recognized standards for fisheries sustainability and supply chain assurance, based on the FAO Code of Conduct for Responsible Fisheries. Building on the MSC experience of over 400 certified fisheries representing 18% of global wild marine catch, we analyze examples and available information on the changes achieved by the seafood industry through engagement with the program, with particular focus on the elimination or reduction of illegal, unreported or unregulated fishing practices. We propose here that different, interlinked mechanisms come into play: the Standards provide best practice guidelines for improved catch documentation, monitoring, control and surveillance (MCS), and strengthening regulations. These lead to change either through (1) direct improvements required for fisheries to achieve the certificate (e.g., in Fishery Improvement Projects) or, (2) once certified, to maintain the certificate, or (3) as an emergent effect of the engagement process itself, requiring stakeholder cooperation and transparent information-sharing leading to a greater culture of compliance, and (4), as an effect of strengthening chain of custody documentation and standardizing it across jurisdictions. We also discuss limitations, such as the capacity for fisheries in low-income regions to embark on the management and social reform required, and evolving challenges in seafood sustainability, such as ethical concerns for forced and child labor and shark finning. While not the single silver bullet against such a complex problem, we argue that certification is an important tool in addressing IUU fishing.

The friction and wear behavior of NM400 and NM500 steels in the temperature range from room temperature to 300℃ were investigated, including the formation of interface oxide, wear surface morphology, and microstructures. A high-temperature sliding friction tester was used to study the behavior of sliding friction between wear-resistant steel and Al2O3 ceramic balls under different loads of 200-300 N and speeds of 100-400 r·min-1. A ball-disc friction pair containing mix-sintered Al2O3 ceramic balls with a diameter of 5 mm was mounted on the holding tool and steel plate. The friction coefficients of the two materials from room temperature to 300℃ are determined to be in a range of 0.27-0.40, whereas the average friction coefficients of NM400 and NM500 steels are found to decrease gradually from 0.337 to 0.296 and from 0.323 to 0.288. The generation of oxides is the primary reason for slight decrease in the friction coefficient at a high temperature of 300℃. The friction behavior is controlled by the abrasive wear mechanism, and then the phenomenon of pressure-into-peeling-oxidation of oxide gradually occurs at a higher temperature, which slightly reduces the wear rate. Larger amount of oxides are produced on the interface as the temperature increases, but this is not sufficient to form a continuous oxide layer. The main wear pattern at this time is still abrasive wear, although the wear rate and friction coefficient are affected by oxides. The main factors influencing the wear behavior are the hardness, oxide volume fraction, and oxidation activation energy of the wear-resistant steel, as found through the analysis of high-temperature frictional wear behavior and micro-oxidation model. In conclusion, the wear mechanisms of NM400 and NM500 steels from room temperature to 300℃ are influenced by the combined effect of abrasive wear, extrusion deformation wear, and trace oxide wear. NM500 steel exhibites better wear resistance than NM400 steel, and this can be mainly attributed to higher level of its hardness. A small amount of additional alloying elements in the high-strength microalloyed martensitic wear-resistant steel can reduce the wear rate to some extent, due to the formation of a certain amount of stable attached oxides that are produced during the high-temperature friction process.

This work was aimed to develop levodopa (L-dopa) nasal powder to achieve controllable drug release and high nasal deposition efficiency. A series of uniform microparticles, composed of amorphous L-dopa and excipients of hydroxypropyl methyl cellulose (HPMC), polyvinylpyrrolidone (PVP), or hydroxypropyl-β-cyclodextrin (CD), were fabricated by a self-designed micro-fluidic spray dryer. The effects of excipient type and drug/excipient mass ratio on the particle size, morphology, density, and crystal property, as well as the in vitro performance of drug release, mucoadhesion, and nasal deposition, were investigated. Increased amounts of added excipient, regardless of its type, could accelerate the L-dopa release to different extent. The addition of CD showed the most obvious effect, i.e., ~83% of L-dopa released in 60 min for SD-L1CD2, compared to 37% for raw L-dopa. HPMC could more apparently improve the particle mucoadhesion than PVP and CD, with respective adhesive forces of ~269, 111, and 26 nN for SD-L1H2, -L1P2, and -L1CD2. Nevertheless, the deposition fractions in the olfactory region for such samples were almost the same (~14%), probably ascribable to their quite similar particle aerodynamic diameter (~30 μm). This work demonstrates a feasible methodology for the development of nasal powder.

Weaning causes the generation of excessive reactive oxygen species in the body, which could lead to oxidative stress. Polyphenols, for which blueberries are an important dietary source, are known for various health benefits including antioxidant properties. Here, we sought to elucidate the effects of blueberry polyphenol extracts (BPE) on intestinal antioxidant capacity and possible underlying mechanisms in weaned rats. Ninety-six rats were assigned to two groups and fed either a standard diet or a standard diet supplemented with BPE (200 mg/kg). The results showed that BPE supplementation increased (P < 0.05) catalase and superoxide dismutase activities and decreased (P < 0.05) interleukin-1 and interferon-γ contents in the jejunum and ileum. The abundances of mammalian target of rapamycin, ribosomal p70 S6 kinase and eukaryotic initiation factor 4E-binding protein 1 mRNA were elevated in the jejunum and ileum (P < 0.05) after BPE supplementation. Additionally, BPE supplementation decreased (P < 0.05) Kelch-like ECH-associated protein 1 (Keap1) gene transcription and enhanced (P < 0.05) NF-E2-related factor 2 (Nrf2) gene transcription in the jejunum and ileum. According to our results, BPE-induced protective effects against oxidative stress appear through the promotion of the jejunal and ileal antioxidant defense system in weaned rats, which was associated with the Nrf2–Keap1 signaling pathway.