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Abstract Background There has been a concerning rise in the incidence of major adverse cardiovascular and cerebrovascular events (MACCE) following noncardiac surgeries (NCS), significantly impacting surgical outcomes and patient prognosis. Glucose metabolism abnormalities induced by stress response under acute medical conditions may be a risk factor for postoperative MACCE. This study aims to explore the association between stress hyperglycemia ratio (SHR) and postoperative MACCE in patients undergoing general anesthesia for NCS. Methods There were 12,899 patients in this perioperative cohort study. The primary outcome was MACCE within 30 days postoperatively, defined as angina, acute myocardial infarction, cardiac arrest, arrhythmia, heart failure, stroke, or in-hospital all-cause mortality. Kaplan-Meier curves visualized the cumulative incidence of MACCE. Cox proportional hazard models were utilized to assess the association between the risk of MACCE and different SHR groups. Restricted cubic spline analyses were conducted to explore potential nonlinear relationships. Additionally, exploratory subgroup analyses and sensitivity analyses were performed. Results A total of 592 (4.59%) participants experienced MACCE within 30 days after surgery, and 1,045 (8.10%) within 90 days. After adjusting for confounding factors, compared to the SHR T2 group, the risk of MACCE within 30 days after surgery increased by 1.34 times (95% CI 1.08–1.66) in the T3 group and by 1.35 times (95% CI 1.08–1.68) in the T1 group respectively. In the non-diabetes group, the risk of MACCE within 30 days after surgery increased by 1.60 times (95% CI 1.21–2.12) in the T3 group and by 1.61 times (95% CI 1.21–2.14) in the T1 group respectively, while no statistically significant increase in risk was observed in the diabetes group. Similar results were observed within 90 days after surgery in the non-diabetes group. Additionally, a statistically significant U-shaped nonlinear relationship was observed in the non-diabetes group (30 days: P for nonlinear = 0.010; 90 days: P for nonlinear = 0.008). Conclusion In this large perioperative cohort study, we observed that both higher and lower SHR were associated with an increased risk of MACCE within 30 and 90 days after NCS, especially in patients without diabetes. These findings suggest that SHR potentially plays a key role in stratifying cardiovascular and cerebrovascular risk after NCS.

Abstract Point cloud semantic segmentation is a key step in the scan-to-HBIM process. In order to reduce the information in the process of DGCNN, this paper proposes a Mix Pooling Dynamic Graph Convolutional Neural Network (MP-DGCNN) for the segmentation of ancient architecture point clouds. The proposed MP-DGCNN differs from DGCNN mainly in two aspects: (1) to more comprehensively characterize the local topological structure of points, the edge features are redefined, and distance and neighboring points are added to the original edge features; (2) based on a Multilayer Perceptron (MLP), an internal feature adjustment mechanism is established, and a learnable mix pooling operator is designed by fusing adaptive pooling, max pooling, average pooling, and aggregation pooling, to learn local graph features from the point cloud topology. To verify the proposed algorithm, experiments are conducted on the Qutan Temple point cloud dataset, and the results show that compared with PointNet, PointNet++, DGCNN, GACNet and LDGCNN, the MP-DGCNN segmentation network achieves the highest OA, mIOU and mAcc, reaching 90.19%,65.34% and 79.41%, respectively.

Uncertainty quantification (UQ) in polynomial chaos expansion (PCE) suffers from the curse of dimensionality, which is fundamentally reflected in that the number of the PCE coefficients to be found grows rapidly as the number of random inputs increases, and thus the number of samples required increases dramatically. Regardless of the approach taken to alleviate this disaster, we are usually faced with the technical challenge of automatically and programmatically determining the basis functions during the PCE implementation, which are composed of a large number of multivariate polynomials. To address this problem, this paper proposes an algorithm based on the recursive idea for automatically determining the basis functions, and then combines this algorithm with the latest weight-based regression called least squares polynomial chaos regression (LSPCR), develops the six algorithms for the LSPCR in the medium- and high-dimensional cases, and evaluates their performance in the electromagnetic coupling problem of the transmission lines (TLs) in this case. Subsequently, the performance of six algorithms for LSPCR are compared in the medium- and high-dimensional, low-order cases, each algorithm consisting of a sampling strategy selected from asymptotic sampling (AS), standard sampling (SS), and coherence-optimal sampling (COS), paired with a norm problem chosen from either least squares optimization (LSO) or $\ell _{\mathbf {1}}$ -minimization ( $\ell _{\mathbf {1}}$ -M) problems. Numerical experiments demonstrate the excellence of the proposed algorithm. Furthermore, the results also show that only the algorithms that employ the SS (SSs) and the algorithms based on the COS (COSs) require no more than 1.4% of the total Monte Carlo (MC) computation time when producing similarly accurate results to the MC, regardless of the chosen norm problem.

Raman spectroscopy with the advantages of the in situ and simultaneous detection of multi-components has been widely used in the identification and quantitative detection of gas. As a type of scattering spectroscopy, the detection sensitivity of Raman spectroscopy is relatively lower, mainly due to the low signal collection efficiency. This paper presents the design and assembly of a multi-channel cavity-enhanced Raman spectroscopy system, optimizing the structure of the sample pool to reduce the loss of the laser and increase the excitation intensity of the Raman signals. Moreover, three channels are used to collect Raman signals to increase the signal collection efficiency for improving the detection sensitivity. The results showed that the limits of detection for the CH4, H2, CO2, O2, and N2 gases were calculated to be 3.1, 34.9, 17.9, 27, and 35.2 ppm, respectively. The established calibration curves showed that the correlation coefficients were all greater than 0.999, indicating an excellent linear correlation and high level of reliability. Meanwhile, under long-time integration detection, the Raman signals of CH4, H2, and CO2 could be clearly distinguished at the concentrations of 10, 10, and 50 ppm, respectively. The results indicated that the designed Raman system possesses broad application prospects in complex field environments.

Hydro-thermal coupling is the essence of the freeze-thaw process, and theoretical studies of this coupled process have been hot topics in the field of frozen soil. Darcy's law of unsaturated soil water flow, heat conduction theory, and relative saturation and solid-liquid ratio are based on this paper. According to the principle that the cumulative curve of particle gradation of canal foundation soil is similar to soil-water properties. A soil-water characteristic curve is derived using the cumulative particle gradation curve. VG model is then used to fit soil-water characteristic curves to obtain the canal foundation soil's hydraulic characteristic parameters, and the established hydro-thermal coupling model is modified to reflect canal foundation soil hydro-thermal evolution more objectively. A closed system one-way freezing test method is used to verify the feasibility of the proposed method in this part. The results show that the optimal parameters of the VG model of the subsoil are a = 0.06, n = 1.2, and m = 0.17, and the temperature and water fields obtained from the simulation are in good agreement with the measured data, showing the utility of the hydro-thermal coupling model in predicting hydraulic parameters. Analysis of the multi-field interaction mechanism and dynamic coupling process of the canal foundation soil during freezing and thawing. This has great importance for preventing freezing damage in canals and protecting agricultural safety.

Abstract The ridge beast is a beast placed on the ridge of the roof of ancient Chinese buildings, not only has a decorative function, and has a strict hierarchical meaning, the number and form of the ridge beast placed on different levels of buildings are strictly limited. The detection technology of ridge beast decorative parts has important application value in the fields of fine 3D reconstruction of ancient buildings, historical dating and cultural and tourism services. Aiming at the problem of poor detection performance of traditional detection algorithms due to high texture similarity and poor discrimination of ridge beast, this paper proposed an improved YOLOv3 based detection algorithm for ridge beast decorative pieces. In terms of basic network improvement, local features are aggregated to the deep separable convolution internal embedding summation layer, and point convolution is used to connect the channel information of original features and aggregated features, so as to expand the receptive field and learn more diverse features. The residual structure of the feature extraction network was constructed by using the convolution, and the extraction effect of the model on the fine-grained features of the ridge beast was optimized, so that the detection accuracy was improved. In the prediction head improvement of the model, the original linear structure was reconstructed, and the extrusion and excitation modules were introduced to model the channel relationship of multi-scale feature map, which suppressed the response of interference signals and made the feature more directivity. The parallel 1 × 1 and 3 × 3 convolution are used to construct a multi-size convolution structure, which enhances the semantic information extraction ability of the model and further improves the detection effect. Experiments were conducted on the constructed ridge-beast dataset, and the results showed that the mAP of the improved algorithm can reach 86.48%, which is 3.05% higher than YOLO-v3, and the model parameters are reduced by 70%, which has a better detection performance and can provide a reference for the automated detection of ancient building components.

A coverage prediction model helps network operators find coverage gaps, plan base station locations, evaluate quality of service, and build radio maps for spectrum sharing, interference management, localization, etc. Existing coverage prediction models rely on the height and transmission power of the base station, or the assistance of a path loss model. All of these increase the complexity of large-scale coverage predictions. In this paper, we propose a multi-modal model, DNN-SS, which combines a DNN (deep neural network) and SS (semantic segmentation) to perform coverage prediction for mobile networks. Firstly, DNN-SS filters the samples with a geospatial-temporal moving average filter algorithm, and then uses a DNN to extract numerical features. Secondly, a pre-trained model is used to perform semantic segmentation of satellite images of the measurement area. Thirdly, a DNN is used to extract features from the results after semantic segmentation to form environmental features. Finally, the prediction model is trained on the dataset consisting of numerical features and environmental features. The experimental results on campus show that for random location prediction, the model achieves a RMSE (Root Mean Square Error) of 1.97 dB and a MAE (Mean Absolute Error) of 1.41 dB, which is an improvement of 10.86% and 10.2%, respectively, compared with existing models. For the prediction of a test area, the RMSE and MAE of the model are 4.32 dB and 3.45 dB, respectively, and the RMSE is only 0.22 dB lower than that of existing models. However, the DNN-SS model does not need the height, transmission power, and antenna gain of the base station, or a path loss model, which makes it more suitable for large-scale coverage prediction.

This paper presents two sensitivity analyses, one of which is for the time-domain transmission line (TL) model using the latest weight-based algorithm that is a combination of the coherence-optimal sampling (COS) and the l1-minimum problem (l1-M) and the other is for the high-frequency field line coupling model based on the algorithm that combines the Latin hypercube sampling (LHS) with the l1-M for the first time, where the COS and the LHS are employed to draw samples and the polynomial chaos expansion (PCE) coefficients are obtained by solving the l1-M. Both algorithms are within the PCE framework. The results show that for the time-domain TL model, the capacitance has the greatest impact on the voltage response, followed by the resistance. The height and the radius have the least impact, while there is little difference between them. For the high-frequency field line coupling model, the azimuth angle has the greatest impact on the current in the TL, followed by the elevation angle. The terminal load, the conductivity, the field amplitude, the relative permittivity, and the TL radius have the least effect, and there is little difference among them. Additionally, both algorithms only require no more than 0.60% of the total Monte Carlo (MC) computation time when yielding solutions with almost identical accuracy. Compared to performing a sensitivity analysis using the MC method, the use of two algorithms significantly improves efficiency.

Central nervous system (CNS) embryonal tumors (WHO grade IV) are a heterogeneous group of rare, poorly differentiated neuroepithelial malignant neoplasms that commonly occur in children, and they have a poor prognosis. The 2016 WHO (World Health Organization) classification of CNS tumors created a major shift in paradigm of the classification of embryonal tumors. However, some cases were still difficult to classify. Further integrative genomic analysis is needed to improve the precise classification, diagnosis and treatment of CNS embryonal tumors. Herein, we firstly report a case of CNS embryonal tumor harboring the pathogenic CIC–LEUTX gene fusion. A 2-year-old male infant presented with a solid cystic mass in the left temporal lobe-basal ganglia and left parietal lobe (maximum diameter, 75 mm) and underwent gross tumor resection. The tumor was classified as a poorly differentiated embryonal neoplasm of neuroectodermal origin that lacked specific features and rosettes. By immunohistochemistry, the tumor cells were strongly positive for synaptophysin, and the Ki67 proliferation index was high (>50%). FISH (Fluorescence in situ hybridization) results indicated no change in the copy number at the 19q13.42 C19MC locus. Next generation sequencing showed a CIC–LEUTX gene fusion, a somatic TSC2 c.G2714A mutation, and a heterozygous germline NBN c.C127T mutation. One month after surgery, there was recurrence of the intracranial tumor (maximum diameter, 55 mm) as well as spinal cord implantation metastasis. The patient received chemotherapy (CTX+CBP+VCR/DDP+VP-16), radiotherapy, and a drug targeting the TSC2 gene (everolimus). At the time of this writing, the patient is alive without evidence of disease for 11 months. This is the first report of the CIC–LEUTX gene fusion in a case of CNS embryonal tumor.

Despite the wide and long-term applications of ultrasonic (US) wire bonding and other US metal joining technologies, the mechanism of microweld changes during the bonding process, including formation, deformation and breakage, is rarely known as it is very difficult to be investigated by experiments. In this work, this mechanism under different surface topographies and displacement patterns is studied by molecular dynamics simulation. It is found that microwelds can be formed or broken instantly. Due to the relative motion between the local wire part and the local substrate part, microwelds can be largely deformed or even broken. The impacts of material, surface topography, approaching distance and vibration amplitude on the microweld changes are investigated via the quantification of the shear stress and the equivalent bonded area. It is shown that these four factors significantly influence the final connection and the interface structure. The analysis of the scale influence on the microweld changes shows that the simulation results at a small-scale are able to represent those at a large-scale which is close to the range of the commonly used surface roughness. This deeper understanding on the microweld changes leads to a better control strategy and an enhancement of the bonding process.

The Ming and Qing Dynasty type of official-style architecture roof can provide plenty of prior knowledge relating to the structure and size of these works of architecture, and plays an important role in the fields of 3D modeling, semantic recognition and culture inheriting. In this paper, we take the 3D point cloud as the data source, and an automatic classification method for the roof type of Ming and Qing Dynasty official-style architecture based on the hierarchical semantic network is illustrated. To classify the roofs into the correct categories, the characteristics of different roof types are analyzed and features including SoRs, DfFtR, DoPP and NoREs are first selected; subsequently, the corresponding feature extraction methods are proposed; thirdly, aiming at the structure of the ridges, a matching graph relying on the attributed relational graph of the ridges is given; based on the former work, a hierarchical semantic network is proposed and the thresholds are determined with the help of the construction rules of the Ming and Qing Dynasty official-style architecture. In order to fully verify the efficiency of our proposed method, various types of Ming and Qing Dynasty official-style architecture roof are identified, and the experimental results show that all structures are classified correctly.

The ancient roof decorative components of the official-style architectures from the Ming and Qing dynasties in China hold both physical and symbolic significance. These roof structures are the essential objects in three-dimensional (3D) modeling of ancient architectures for traditional Chinese cultural preservation. Although ancient architectures can be surveyed by a 3D laser scanner, the complex geometry and diverse pattern of their roof decorative components make the 3D point cloud reconstruction challenging, or at some points, nearly impossible in a fully automated manner. In this paper, we propose a method to ensure that the 3D shape of each roof decorative component is accurately modeled. First, we establish a decorative components template library (or “template library” in short hereafter), which is the first of its kind for the roofs of Ming and Qing official-style architectures. The process of establishing the decorative components template library begins with a remote collection of survey data using a terrestrial laser scanner and digital camera. The next stage involves the design and construction of different 3D decorative components in the template library with reference to the manuscripts written in the Ming and Qing dynasties’ architectural pattern books. With the point cloud data collected on any Ming and Qing official-style architecture, we further propose a geo-registration mechanism to search for an optimal fitting of the decorative components from the template library on the collected point cloud automatically. Based on the experimental results, the accuracy of point cloud registration yields less than 0.02 m, which meets the accuracy of the 3D model at LoD 300 level. Time consumption is less than 5s and stable, for large volume computing capacity has good robustness. The proposed strategy provides a new way for the 3D modeling of large and clustered historical architectures, particularly with complex structures.

The posterior intestinal microbiota plays a vital role in the growth and health of Holstein dairy calves. However, its establishment and dynamic changes during early development remain unclear. The aim of this study was to investigate microbial colonization and development in the rectum of calves within the first 70 d after birth. Here, 96 rectal content samples were collected from 8 Holstein dairy calves at 12 time points and analyzed using 16S rRNA gene sequencing. The microbial alpha diversity increased with age. The bacterial community displayed a distinct dynamic distribution. The phylum Proteobacteria was replaced by Firmicutes and Bacteroidetes after d 3. The colonization process of bacterial genera in the rectum of neonatal calves can be divided into 2 periods: the colonization period (stage 1: d 1 and stage 2: d 3) and the stable period (stage 3: d 7–14, stage 4: d 21–42, and stage 5: d 49–70). The fermentation pattern and metabolic function changed from propionate fermentation dominated by Shigella to lactic acid fermentation dominated by Lactobacillus, Blautia, and Oscillospira. The stable period was more comprehensive and complete than the colonization period. This study revealed the dynamic changes in the posterior intestinal microbiota of Holstein dairy calves during early development. The transition period (d 7–14) was identified as a key stage for early nutritional intervention, as the abundance of Lactobacillus increased and the abundance of harmful bacteria (such as Proteobacteria and Shigella) decreased. This study provides a framework for understanding early-life gut health and offers theoretical guidance for future research on host–microbe interactions and early nutritional interventions. It is suggested that nutritional interventions based on microbial characteristics at different stages be implemented to improve calf growth performance and immune function, which may contribute to the reduction of diarrhea and other gastrointestinal disorders during dairy production.

Water temperature is one of the most important ecological environmental factors affecting fish survival and growth. When the water temperature is higher than the optimal temperature for fish survival, fish homeostasis may be disrupted. The heart plays a crucial role in maintaining vertebrate homeostasis. In this study, TMT and LC-MS/MS were performed to analyze differentially expressed protein (DEP) profiles in the hearts of turbot under high-temperature stress to explore the key proteins and metabolic pathways related to the high-temperature tolerance traits of turbot. Ninety turbot (mean weight 23.01±3.63 g, mean length 10.34±0.76 cm) were used in experiments. Three temperature points for heat stress experiments were designed: control=(14.0±0.5)℃, HT1=(20.0±0.5)℃, and HT2=(28.0±0.5)℃. Three parallel groups were established for each temperature group, with 10 turbots placed in each group. Fold Change≥1.2 or < 0.83, P < 0.05 was used as a criterion to screen for DEPs in turbot. Through mass spectrometry analysis of heart tissues from turbot, 85, 731 spectra were identified, with 35, 434 peptides, of which 31, 704 were unique, and 4, 765 proteins were identified. The HT1/control group in the heart of turbot had a total of 87 differentially expressed proteins, including 77 upregulated and 10 down-regulated proteins; the HT2/control group in the heart of turbot had 171 DEPs, including 149 upregulated and 22 down-regulated proteins; the HT2/HT1 group in the heart of turbot had a total of 188 DEPs, including 130 upregulated and 58 downregulated proteins. Through bioinformatics analysis, the DEPs between the 28℃ group and the 14℃ group, as well as the 28℃ group and the 20℃ group, were mainly enriched in KEGG pathways related to fatty acid metabolism, inflammation, and immune defense. Multiple proteins in the heat shock protein family (HSP90B, HSP70, etc.) were significantly upregulated in the 28℃ group compared to the 14℃ group. The protein interaction network revealed that HSP90B exhibited the highest correlation. A new discovery is that the expression levels of fibrinogen-related proteins (FGA, FGB, and FGG) increased, and their role in the thermal response requires further research. These results indicate that extremely high temperatures may cause fatty acid metabolic disorders in turbot, leading to inflammatory reactions. The upregulation of heat shock protein family proteins may have a positive effect on alleviating stress symptoms under heat stress. This study identified DEPs, such as HSP90B, HSPA5, and HYOU1, which were significantly upregulated under heat stress, as candidate proteins for high-temperature tolerance breeding in turbot and provided a theoretical basis for further elucidating the molecular mechanism of heat stress in turbot.

Abstract A novel technique that couples microbially induced calcite precipitation (MICP) and calcium carbide residue (CCR) is proposed for immobilizing Cd2+ in contaminated soil. The properties and mechanism of CCR-enhanced MICP were investigated through a series of experimental analyses considering factors such as heavy metal concentration, curing time, and the effect of Ca2+. The unconfined compressive strength (UCS) increased with increasing curing time and reached a maximum value at 28 d, and the leaching concentration of Cd2+ decreased and tended to level off with increasing curing time. The addition of CCR enhanced the immobilization performance of Cd2+ through the MICP method, resulting in UCSs that were 3.8–4.2 times those of samples without CCR and leaching concentrations of Cd2+ that were 38.9–69.2% lower at a curing time of 28 d. The addition of Ca2+ to cementation solutions further improved the immobilization effectiveness, resulting in the UCSs of the samples increasing by 18.7–49.8% and the leaching concentrations of Cd2+ decreasing by 11–40% CaCO3 and its hydration products can immobilize Cd2+ through coprecipitation, reducing its toxicity by converting weak acid-extractable cadmium into residual cadmium. Consequently, Sporosarcina pasteurii combined with CCR improved the UCS of the treated contaminated soil and greatly decreased cadmium migration.

Abstract Pair density wave (PDW) is a distinct superconducting state characterized by a periodic modulation of its order parameter in real space. Its intricate interplay with the charge density wave (CDW) state is a continuing topic of interest in condensed matter physics. While PDW states have been discovered in cuprates and other unconventional superconductors, the understanding of diverse PDWs and their interactions with different types of CDWs remains limited. Here, utilizing scanning tunneling microscopy, we unveil the subtle correlations between PDW ground states and two distinct CDW phases — namely, anion-centered-CDW (AC-CDW) and hollow-centered-CDW (HC-CDW) — in 2H-NbSe2. In both CDW regions, we observe coexisting PDWs with a commensurate structure that aligns with the underlying CDW phase. The superconducting gap size, Δ(r), related to the pairing order parameter is in phase with the charge density in both CDW regions. Meanwhile, the coherence peak height, H(r), qualitatively reflecting the electron-pair density, exhibits a phase difference of approximately 2π/3 relative to the CDW. The three-fold rotational symmetry is preserved in the HC-CDW region but is spontaneously broken in the AC-CDW region due to the PDW state, leading to the emergence of nematic superconductivity.

Abstract In van der Waals heterostructures (vdWHs), the manipulation of interlayer stacking/coupling allows for the construction of customizable quantum systems exhibiting exotic physics. An illustrative example is the diverse range of states of matter achieved through varying the proximity coupling between two-dimensional (2D) quantum spin liquid (QSL) and superconductors within the TaS2 family. This study presents a demonstration of the intertwined physics of spontaneous rotational symmetry breaking, hidden magnetism, and Ising superconductivity (SC) in the three-fold rotationally symmetric, non-magnetic natural vdWHs 6R-TaS2. A distinctive phase emerges in 6R-TaS2 below a characteristic temperature (T *) of approximately 30 K, which is characterized by a remarkable set of features, including a giant extrinsic anomalous Hall effect (AHE), Kondo screening, magnetic field-tunable thermal hysteresis, and nematic magneto-resistance. At lower temperatures, a coexistence of nematicity and Kondo screening with Ising superconductivity is observed, providing compelling evidence of hidden magnetism within a superconductor. This research not only sheds light on unexpected emergent physics resulting from the coupling of itinerant electrons and localized/correlated electrons in natural vdWHs but also emphasizes the potential for tailoring exotic quantum states through the manipulation of interlayer interactions.

The swarming confrontation problem of unmanned aerial vehicles (UAVs) has been a focal point and challenge in the field of complex systems research in recent years, with significant application value in the military, network security, and artificial intelligence industries. In real-world confrontations, the uncertainty of the environment and the diversity of intelligent agent behaviors render the problem difficult to model, and operational environments necessitate intelligent agents to provide real-time, efficient responses to changes in the situation. To address these challenges, this paper proposes a research framework for the swarming confrontation problem of UAVs. First, an adversarial game model is developed based on the improved Lanchester equation to solve this problem toward the red and blue swarms. The adversarial game model focuses on describing the dynamic quantity change process of the red and blue UAV swarms. Second, based on the above confrontation model, a multiple-task assignment problem is investigated, which is derived from the above confrontation process. A new assignment strategy of these UAVs for strike tasks is proposed by adaptive improvement based on the traditional Kuhn–Munkres algorithm. This strategy is suitable for the red and blue parties under the adversarial environment, which can effectively complete the strike tasks and improve the confrontation ability of these UAVs. Third, a swarming confrontation algorithm is proposed to improve the environmental suitability of each UAV, especially when dealing with the influences of a series of uncertain factors generated by the real-time process of swarming offensive and defensive confrontations. This algorithm is based on the dynamic Bayesian network structure and focuses on predicting and evaluating the uncertainty generated during the confrontation process of the red and blue UAV swarms and performing corresponding reasoning and prediction through the dynamic Bayesian network, which can effectively reduce the complexity and calculation of the confrontation model and widely improve the accuracy and speed of decision-making. Finally, a Python-based real-time simulation platform is built on the above-mentioned confrontation model to illustrate the evolutionary process of the red and blue UAV swarms and to verify the effectiveness of the proposed algorithm by comparison with the most classic artificial potential field method. The simulation results reveal that the above framework can demonstrate the real-time offensive and defensive confrontation process of red and blue UAV swarms according to the designed penetration mission, effectively solve the problem of task assignment conflicts between red and blue swarms, properly predict and evaluate the uncertainty issues derived from the swarm confrontation process, and improve the combat capabilities of UAV swarms.

As a core molecular breeding tool, whole genome sequencing (WGS) can improve the speed of selection and breeding of superior variety and promote the progress of biotechnology-based breeding. However, the high cost of WGS limits its wide application for most aquaculture species. Low-coverage whole genome sequencing is a cost-effective alternative to genotype imputation to obtain a high density of genome-wide SNPs and ultimately Genomic selection (GS) and Genome-wide Association Studies (GWAS). In this study, we constructed the first haplotype reference panel for turbot (Scophthalmus maximus), which is an economically important marine farmed fish in China. We performed WGS of fish from major turbot aquaculture areas. We used 183 fish from which six populations as the reference group and 30 individual turbots from a seventh population as the validation group. In total, more than 239 million single nucleotide polymorphisms (SNPs) were identified in the reference group, and 4092,338 of them were screened for the construction of the haplotype reference panel. The reference panels were evaluated based on several factors that affect the accuracy of reference panel imputation. Twenty-two chromosomes had imputation accuracies ranging from 0.96 to 0.99, and there were no significant differences among them (p > 0.05). As the sequencing depth increased, the imputation accuracy increased and stabilized at 0.1 × , ultimately reaching a imputation accuracy of 0.96. As the minor allele frequency (MAF) or genotype probability (GP) increased, the imputation accuracy increased accordingly and eventually stabilized. The results indicated that the depth of coverage could range from 0.1 × to 1.0 × and that the imputation accuracies were all as high as 0.96, which would result in high-quality sequencing data. In conclusion, we constructed a reference panel of turbot haplotypes and determined its depth of coverage for low-coverage whole genome sequencing. This is a cost-effective alternative to whole genome sequencing to provide new ideas for fish breeding.

In orchard environments, compared with picking robotic arms, improving the efficiency and safety of the fruit-picking unmanned aerial vehicle (UAV) becomes more challenging. In this paper, an autonomous obstacle avoidance and path planning method based on LiDAR data is proposed for the self-built fruit-picking UAV. First, a LiDAR static-dynamic dual map construction scheme is designed. Using the original point cloud data from LiDAR, a time-accumulated local point cloud map is generated to provide orchard obstacle information for path planning. Then, an improved hybrid A* algorithm based on the B-spline curve is proposed. This algorithm not only comprehensively takes into account the impact of surrounding branches on the flight of the picking UAV near the target fruit bunch, but also ensures that the planned path meets the specific action requirements of the picking UAV when picking the target fruit bunch. The experimental results demonstrate that the proposed map construction scheme significantly reduces the computational power requirements and collision detection time. Moreover, the path planning algorithm effectively guides the UAV and its attached picking actuator to successfully navigate around obstacles, enabling efficient picking of the target fruit bunch. Indicating that the proposed method provides a feasible solution for task execution of the fruit-picking UAV in complex orchard environments.

ContextIntercropping in agriculture is crucial for addressing challenges in intensive tea farming. Forage legumes reduce fertilizer dependence and significantly boost productivity. Currently, intercropping with legumes enhances the environmental conditions of tea plantations and improves tea quality.ObjectiveHowever, the comprehension of the rhizosphere’s impact on the associated microbes and the community structure of tea plants is still somewhat constrained.MethodsHence, four distinct planting methodologies were examined: Monoculture cultivation of Tieguanyin tea plants (MT), Laredo forage soybean (Glycine max Linn.) without partitioning in conjunction with tea (IT), intercropping with tea using plastic partitions (PPIT), and intercropping with tea facilitated by net partitions (NPIT). An absolute quantitative analysis of soil phospholipid fatty acids, labeled with the rhizosphere microbial characteristics of tea plants, was conducted through multi-ion reaction monitoring (MRM). The bacterial and fungal communities were anticipated utilizing the FAPROTAX and FUNG databases, respectively. Gas chromatography was employed to ascertain greenhouse gas emissions across diverse root interaction cultivation systems.Results and conclusionThe rhizospheric influence culminated in a 44.6% increase in total phospholipid fatty acids (PLFAs) and a remarkable 100.9% escalation in the ratio of unsaturated to saturated fatty acids. This rhizospheric enhancement has significantly potentiated the ecological functionalities within the bacterial community, including xylanolysis, ureolysis, nitrogen respiration, nitrogen fixation, nitrite respiration, nitrite ammonification, and nitrate reduction. Mycorrhizomonas, encompassing both ectomycorrhizal and arbuscular forms, has notably colonized the rhizosphere. The interspecific mutualistic interactions within the rhizosphere have resulted in a significant enhancement of plant growth-promoting bacteria, including allorhizobium, bradyrhizobium, rhizobium, burkholderia, gluconacetobacter, and gluconobacter, while concurrently reducing the prevalence of pathogenic microorganisms such as xanthomonas, ralstonia, fusarium, and opportunistic fungi responsible for white and soft rot. The intercropping system showed lower total greenhouse gas emissions than monocultured tea plants, particularly reducing soil CO2 emissions due to complex interspecific rhizosphere interactions. This tea/legume intercropping approach promotes a sustainable ecosystem, enhancing microbial biomass and vitality, which helps suppress rhizospheric pathogens.SignificanceThese findings are instrumental in enhancing our comprehension of the pivotal practical implications of rhizosphere intercropping, thereby optimizing the structure of rhizosphere communities and alleviating the impact of greenhouse gases within croplands.

Accurate wetland extraction using remote sensing technology poses significant challenges due to the complex hydrological dynamics, diverse landscapes, and varied wetland types. Constructing a reliable sample set is a critical first step in overcoming these challenges for large-scale wetland mapping. To meet the demand for global wetland mapping, this study (1) proposes a multi-level wetland classification system suitable for remote sensing, incorporating the soil moisture, vegetation cover and temporal dynamic characteristics of wetlands; (2) introduces a theoretically plausible wetland sample identification method based on the ecological, geographical and temporal dynamic characteristics of wetland ecosystems; (3) develops an approach that combines the Inundation-Frequency and Ecological Remote Sensing Indicators for global wetland sampling based on global climatic zones. The global wetland sample set was finally produced with 64,486 samples. The dataset revealed that seasonal marsh, swamp, mangrove, floodplain, salt marsh, tidal flat and permanent marsh accounted for 22.99%, 20.05%, 18.06%, 14.58%, 12.38%, 10.62% and 1.29% of the total sample set, respectively. Furthermore, the water body sample set comprised 13,402 samples, distributed among permanent (45.50%), seasonal (31.35%) and temporary (23.15%) water bodies. The proposed knowledge-based method, which makes use of big earth-observing data and the Google Earth Engine platform, has been demonstrated to have the capability to generate reliable wetland samples with a high degree of accuracy. This represents the first effort to create a global wetland sample set, which has the potential to offer critical support for comprehensive wetland mapping initiatives

IntroductionPorcine deltacoronavirus (PDCoV) is a zoonotic pathogen with a global distribution, capable of infecting both pigs and humans. To mitigate the risk of cross-species transmission and potential outbreaks, it is crucial to characterize novel antiviral genes, particularly those from human hosts.MethodsThis research used HIEC-6 to investigate PDCoV infection. HIEC-6 cells were infected with PDCoV. Samples were collected 48 h postinfection for proteomic analysis.ResultsWe discovered differential expression of MRPS6 gene at 48 h postinfection with PDCoV in HIEC-6 cells. The gene expression initially increased but then decreased. To further explore the role of MRPS6 in PDCoV infection, we conducted experiments involving the overexpression and knockdown of this gene in HIEC-6 and Caco2 cells, respectively. Our findings revealed that overexpression of MRPS6 significantly inhibited PDCoV infection in HIEC-6 cells, while knockdown of MRPS6 in Caco2 cells led to a significant increase of virus titer. Furthermore, we investigated the correlation between PDCoV infection and the expression of MRPS6. Subsequent investigations demonstrated that MRPS6 exerted an augmentative effect on the production of IFN-β through interferon pathway activation, consequently impeding the progression of PDCoV infection in cellular systems. In conclusion, this study utilized proteomic analysis to investigate the differential protein expression in PDCoV-infected HIEC-6 cells, providing evidence for the first time that the MRPS6 gene plays a restrictive role in PDCoV virus infection.DiscussionOur findings initially provide the validation of MRPS6 as an upstream component of IFN-β pathway, in the promotion of IRF3, IRF7, STAT1, STAT2 and IFN-β production of HIEC-6 via dual-activation from interferon pathway.

ObjectiveFocusing on the problem of underactuated surface vehicle (USV) swarm trajectory tracking control with unmodeled dynamics in the context of external environment disturbance and reference signals unknown, this study explores the USV swarm motion decision-making strategy and proposes a finite-time trajectory tracking control scheme. MethodsFirst, using the Lyapunov function and artificial potential function, combined with the position information of the swarm virtual reference vehicle (SVRV), a swarm motion decision-making strategy with velocity guidance is constructed. Second, a finite-time lumped uncertainty observer (FLUO) is designed to compensate for the unknown information contained in the velocity error equation. Furthermore, a non-singular terminal sliding mode (NTSM) swarm trajectory tracking control method based on FLUO is adopted to design the control law. Result Based on the theoretical analysis and simulation experiments, it is proven that the system is stable. ConclusionBased on the proposed swarm motion decision-making strategy and FLUO−NTSM tracking control scheme, USVs can maintain their swarm and achieve accurate path tracking.

Air pollutants have both acute and chronic impacts on human health, affecting multiple systems and organs. While PM2.5 exposure is commonly assumed to be strongly associated with all respiratory diseases, this relationship has not been systematically analyzed. This study employed a two-sample Mendelian randomization approach to investigate the effects of PM2.5 on eight common lung diseases, using data from GWAS. Additionally, multivariable Mendelian randomization was applied to assess the direct effects of various air pollutants and the mediating roles of common factors such as BMI and smoking. At a significance threshold of 5×10−8, PM2.5 showed a significant causal relationship with both asthma and COPD. When the screening threshold was relaxed to 5× 10−6, this exposure continued to demonstrate significant associations not only with asthma and COPD, but also with other respiratory diseases, including pneumonia, emphysema/chronic bronchitis, and lung cancer. In the multivariable Mendelian randomization analysis, which controlled for smoking and bacterial infections, the association with pneumonia became non-significant, while the relationships with the other four diseases persisted. This study provides a systematic exploration of the relationship between PM2.5 and eight pulmonary diseases from a new perspective, deepening our understanding of the impact of air pollution on health and laying the foundation for future efforts to mitigate these effects.

This article aims to explore the existence and stability of solutions to differential equations involving a ψ-Hilfer fractional derivative in the Caputo sense, which, compared to classical ψ-Hilfer fractional derivatives (in the Riemann–Liouville sense), provide a clear physical interpretation when dealing with initial conditions. We discovered that the ψ-Hilfer fractional derivative in the Caputo sense can be represented as the inverse operation of the ψ-Riemann–Liouville fractional integral, and used this property to prove the existence of solutions for linear differential equations with a ψ-Hilfer fractional derivative in the Caputo sense. Additionally, we applied Mönch’s fixed-point theorem and knowledge of non-compactness measures to demonstrate the existence of solutions for nonlinear differential equations with a ψ-Hilfer fractional derivative in the Caputo sense, and further discussed the Ulam–Hyers–Rassias stability and semi-Ulam–Hyers–Rassias stability of these solutions. Finally, we illustrated our results through case studies.

Abstract Background Brain extraction is an essential prerequisite for the automated diagnosis of intracranial lesions and determines, to a certain extent, the accuracy of subsequent lesion recognition, location, and segmentation. Segmentation using a fully convolutional neural network (FCN) yields high accuracy but a relatively slow extraction speed. Methods This paper proposes an integrated algorithm, FABEM, to address the above issues. This method first uses threshold segmentation, closed operation, convolutional neural network (CNN), and image filling to generate a specific mask. Then, it detects the number of connected regions of the mask. If the number of connected regions equals 1, the extraction is done by directly multiplying with the original image. Otherwise, the mask was further segmented using the region growth method for original images with single-region brain distribution. Conversely, for images with multi-region brain distribution, Deeplabv3 + is used to adjust the mask. Finally, the mask is multiplied with the original image to complete the extraction. Results The algorithm and 5 FCN models were tested on 24 datasets containing different lesions, and the algorithm’s performance showed MPA = 0.9968, MIoU = 0.9936, and MBF = 0.9963, comparable to the Deeplabv3+. Still, its extraction speed is much faster than the Deeplabv3+. It can complete the brain extraction of a head CT image in about 0.43 s, about 3.8 times that of the Deeplabv3+. Conclusion Thus, this method can achieve accurate brain extraction from head CT images faster, creating a good basis for subsequent brain volume measurement and feature extraction of intracranial lesions.

IntroductionOolong tea, celebrated for its significance in Chinese tea culture, was the subject of investigation in this study.MethodsFive varieties of Minnan oolong tea were sampled, each cultivated under two distinct management approaches: conventional management and natural growth methods. The study aimed to discern variations in sensory attributes, encompassing appearance and liquor color, alongside the analysis of chemical composition.Results and discussionThe results indicated that oolong tea cultivated through conventional manual management generally exhibited qualities in terms of shape and foliage appearance, in contrast to those grown naturally. However, naturally grown oolong tea tended to exhibit more favorable aroma and taste profiles compared to conventionally managed counterparts. Furthermore, the content of water extract, amino acids, polyphenols, caffeine, and other pivotal chemical constituents were typically higher in naturally grown tea varieties compared to conventionally managed ones. Conversely, catechin content was found to be more abundant in traditionally managed bushes than in those grown naturally. These findings emphasize the significance of implementing appropriate natural growth management practices to enhance the quality of Minnan oolong tea and maintain ecological sustainability.

COVID-19 has globally spread to burden the medical system. Even with a massive vaccination, a mucosal vaccine offering more comprehensive and convenient protection is imminent. Here, a micro-sized vaccine based on recombinant Lactiplantibacillus plantarum (rLP) displaying spike or receptor-binding domain (RBD) was characterized as microparticles, and its safety and protective effects against SARS-CoV-2 were evaluated. We found a 66.7% mortality reduction and 100% protection with rLP against SARS-CoV-2 in a mouse model. The histological analysis showed decreased hemorrhage symptoms and increased leukocyte infiltration in the lung. Especially, rLP:RBD significantly decreased pulmonary viral loads. For the first time, our study provides a L. plantarum-vectored vaccine to prevent COVID-19 progress and transmission via intranasal vaccination.