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Intelligent analysis of 3D point clouds has become a frontier in emerging fields such as autonomous driving, digital twins, and the metaverse. Precise segmentation of 3D point clouds is particularly important within these domains; however, it faces several challenges: (1) point cloud data inherently lacks structured topological information; (2) point cloud shapes are complex and highly variable, making it difficult to utilize semantic priors; and (3) the sampling process of point clouds may result in sparse and uneven data. To address these issues, this paper proposes a novel Point Cloud Segmentation Network based on multi-scale feature fusion and Transformer architecture (MFFTNet). MFFTNet enhances the performance of existing segmentation methods by globally modeling the overall point cloud shape and embedding local point cloud details. Specifically, MFFTNet divides the segmentation task into encoding and decoding stages. The encoder is designed as a hierarchical pyramid structure that extracts relatively sparse local center points and fuses local features during progressive downsampling. It also utilizes a Transformer for global feature modeling to establish multi-scale topological and semantic information of the point cloud. Subsequently, multi-scale feature fusion further enhances the network’s perception of local features and global structure. The decoder progressively upsamples to restore the original point cloud and injects multi-scale feature information to achieve precise segmentation. Based on the aforementioned encoding-decoding structure and multi-scale feature fusion, MFFTNet outperforms existing methods on the point cloud semantic segmentation datasets ShapeNetPart and S3DIS.

Abstract The rapid development of renewable energy has made hydropower’s role as a flexible resource increasingly important in power systems. However, hydropower generation capability highly depends on water inflows, particularly during dry seasons, making it difficult to independently meet growing load demands. The application of hydro-wind-photovoltaic-storage systems offers a promising solution, yet faces challenges from the high-dimensional uncertainties in natural conditions. This paper proposes a capacity planning method that considers high-dimensional uncertainties characterized by spatiotemporal correlations of natural factors. Firstly, a scenario generation method based on the transition probability matrix and C-Vine Copula model is developed. The constructed scenario sets capture the temporal correlations of natural conditions and spatial correlations between different parameters. Secondly, a bi-level optimization model for capacity planning is established. The upper level minimizes the deviation of operational cost and grid supply revenue to determine optimal capacity allocation, while the lower level optimizes both economic and safe objectives for operational dispatch. The normal boundary intersection method is employed to obtain Pareto front solutions that balance economy and safety. Different case studies are conducted to validate the effectiveness of the proposed method. Compared with the fixed ratio and variable ratio capacity allocation strategies without uncertainty, the optimal total system cost is reduced by 2.90% and 3.88%, respectively.

The large-diameter and long-distance drilling in coal mines can serve as a transportation channel to replace traditional underground tunnels. Compared with conventional large-diameter drilling, the main differences and difficulties lie in “point-to-point” precise drilling, efficient and rapid reaming of large-diameter and long-distance classification, and full hole section casing laying and grouting for pipe fixation. This article introduces the key technology for drilling holes through large-diameter and long-distance boreholes in coal mines. We will conduct in-depth research on five aspects, including RMRS obstacle avoidance and docking technology, graded hole reaming technology, slag removal process technology, full hole section casing laying technology, and grouting and pipe fixing technology, in order to achieve efficient drilling of large-diameter and long-distance through holes. In the guide straight holes drilled at multiple key points of the drilling design direction track, RMRS technology is used for advanced guidance to achieve directional obstacle avoidance and precise point-to-point penetration; reasonably optimize the drilling sequence and adopt the back pull drilling process to improve the efficiency of drilling; utilizing the combined slag removal process of “intra hole circulation + mechanical assistance” to improve the slag removal efficiency of borehole drilling; optional ball centralizer, using a combination of “forward pull + backward push” to achieve long-distance full hole section casing laying; adopting the “annular pressure grouting method” to ensure the quality of grouting and pipe fixation. Based on the above key technologies, a large-diameter long-distance water transmission pipeline was constructed between Daliuta Well and Huojitu Well, with a total length of 1 932 m, a final borehole diameter of 330 mm, and a water transmission casing size for the entire borehole section ϕ219 mm×8.94 mm; the pilot hole successfully passed diagonally and safely between two bridge piers. The horizontal projection displacement deviation between the actual penetration point Bs and the design penetration point B was 0.4 m, the vertical profile displacement deviation was 0.25 m, and the spatial displacement deviation was 0.47 m, basically achieving point-to-point connectivity; the qualification rate of acoustic amplitude logging for grouting pipes is 95%, and the actual water delivery capacity of the pipeline exceeds 150 m3/h.

Boundary identification of field sources is an indispensable task for interpreting field data. Initially, people used the distribution characteristics of data to obtain boundary information of field sources, making it difficult to identify weak anomalies amidst strong background anomalies. To address this issue, automatic control filters based on a certain window size were employed to identify the distribution of field sources. However, this method's results heavily relied on the window size and were not well applicable to complex anomalies. In recent years, features reflecting the boundary information of field sources have mainly been derived from the derivatives of scalar field data. Then, the correspondence between imaging results and boundaries is utilized to identify the horizontal boundaries of field sources. Specifically, extreme values of magnetic anomaly horizontal derivatives and zero values of vertical derivatives correspond to geological body boundaries. Existing boundary identification methods mainly utilize a balanced boundary identification filter composed of the ratio of first-order horizontal and vertical derivatives to delineate the positions of geological bodies, but the method has lower resolution and generality. Therefore, this paper proposes combining boundary detection filters based on ratios of derivatives of different orders with multiscale unsupervised deep learning. This approach utilizes different orders of derivative ratios to obtain higher-resolution edge imaging results. Additionally, a combination of Deep Image Prior (DIP) and Generative Adversarial Network-None Local (GAN-NL) networks for multiscale unsupervised deep learning is established to determine the horizontal position of sources based on extreme values of edge imaging results. The multiscale DIP network is used to identify the source position, and a self-attention mechanism neural network is added to the DIP network to enhance its learning ability, which can remove noise without requiring a large amount of data label.

The development of coal seam wind oxidation zone has a huge impact on the production organization and safe mining of coal mines. This paper focuses on the technical difficulties in delineating the coal seam wind oxidation zone in the eastern uplift zone of Junggar Coalfield, and studies the feasibility of using transient electromagnetic method to explore the coal seam wind oxidation zone. Firstly, the electrical resistivity logging curves of normal strata and coal seam wind oxidation areas in the research area were analyzed, and the differences in electrical properties between normal areas and coal seam wind oxidation areas were summarized. A geoelectric model of coal seam wind oxidation areas was constructed based on the electrical resistivity logging curves; then, MAXWELL software was used to numerically simulate the transient electromagnetic response of the theoretical model. It was found that the resistivity cross-section of the transient electromagnetic inversion in the coal seam wind oxidation zone exhibited abnormal characteristics of shallow low resistance layer thickening and reduced low resistance anomaly amplitude, which can be used to delineate the distribution range of the coal seam wind oxidation zone; finally, based on numerical simulation, on-site experiments were conducted in the Chuancao Gedan Coal Mine. Combined with drilling conditions, the experimental data were analyzed from the perspectives of transient electromagnetic inversion resistivity cross-section and plane. The results showed that the transient electromagnetic method is feasible for detecting and dividing wind oxidation zones.

Abstract Lung adenocarcinoma (LUAD) is a prevalent form of non-small cell lung cancer with a rising incidence in recent years. Understanding the mutation characteristics of LUAD is crucial for effective treatment and prediction of this disease. Among the various mutations observed in LUAD, KRAS mutations are particularly common. Different subtypes of KRAS mutations can activate the Ras signaling pathway to varying degrees, potentially influencing the pathogenesis and prognosis of LUAD. This study aims to investigate the relationship between different KRAS mutation subtypes and the pathogenesis and prognosis of LUAD. A total of 63 clinical samples of LUAD were collected for this study. The samples were analyzed using targeted gene sequencing panels to obtain sequencing data. To complement the dataset, additional clinical and sequencing data were obtained from TCGA and MSK. The analysis revealed significantly higher Ki67 immunohistochemical scores in patients with missense mutations compared to controls. Moreover, the expression level of KRAS was found to be significantly correlated with Ki67 expression. Enrichment analysis indicated that KRAS missense mutations activated the SWEET_LUNG_CANCER_KRAS_DN and CREIGHTON_ENDOCRINE_THERAPY_RESISTANCE_2 pathways. Additionally, patients with KRAS missense mutations and high Ki67 IHC scores exhibited significantly higher tumor mutational burden levels compared to other groups, which suggests they are more likely to be responsive to ICIs. Based on the data from MSK and TCGA, it was observed that patients with KRAS missense mutations had shorter survival compared to controls, and Ki67 expression level could more accurately predict patient prognosis. In conclusion, when utilizing KRAS mutations as biomarkers for the treatment and prediction of LUAD, it is important to consider the specific KRAS mutant subtypes and Ki67 expression levels. These findings contribute to a better understanding of LUAD and have implications for personalized therapeutic approaches in the management of this disease.

In the context of digital construction, responsibility management in smart city building information modeling (BIM) projects spans the entire building lifecycle. The involvement of numerous BIM designers in project management and frequent data exchanges pose significant challenges for the traceability, immutability, and responsibility attribution of BIM models. To address these issues, this study proposes a blockchain-based responsibility management and collaboration framework for BIM projects using non-fungible tokens (NFTs), aiming to enhance the management of responsibilities and accountability in BIM projects. This research adopts a design science methodology, strictly adhering to scientific research procedures to ensure rigor. First, NFTs based on blockchain technology were developed to generate corresponding digital signatures for BIM model files. This approach ensures that each BIM model file has a unique digital identity, enhancing transparency and traceability in responsibility management. Next, the interplanetary file system (IPFS) was used to generate digital fingerprints, with the content identifier generated by IPFS uploaded to the blockchain to ensure the immutability of BIM model files. This method guarantees the integrity and security of BIM model files throughout their lifecycle. Finally, the proposed methods were validated through a blockchain network. The experimental results indicate that the proposed framework is theoretically highly feasible and demonstrates good applicability and efficiency in practical production. The constructed blockchain network meets the actual needs of responsibility management in smart city BIM projects, enhancing the transparency and reliability of project management.

The construction of group foundation pits near subway stations often leads to environmental pollution, thereby causing certain damage to urban ecology. By optimizing the excavation sequence of group foundation pits, the adverse effects on surrounding underground structures and soil during excavation can be effectively mitigated, contributing to the sustainable development of cities. Taking a group foundation pit project in Changzhou as an example, this study utilized the finite element software PLAXIS 3D to simulate various working conditions under different excavation sequences, comparing the deformation of the subway station, shield tunnel, and surrounding soil. The results show that, influenced by the excavation of group foundation pits, the difference between maximum deformation and minimum deformation of shield tunnel is 25.85%, and the difference between the maximum deformation and minimum deformation of the subway envelope is 19.44%. The subway envelope is least affected by the change in excavation sequence. Both the displacement of the subway station and the surrounding soil exhibit a significant “cumulative effect”, with displacement changes closely related to the distance from the pit to the station and the ground, as well as the amount of soil unloaded in each excavation. Therefore, it is advisable to adhere to the principle of “far before near, shallow before deep, small before large” during excavation, which facilitates the coordinated development of urban infrastructure construction and the urban ecological environment, providing valuable reference and guidance for the sustainable development of cities.

In plants, a family of terpene synthases (TPSs) is responsible for the biosynthesis of terpenes and contributes to species-specific diversity of volatile organic compounds, which play essential roles in fitness of plants. However, little is known about the TPS gene family in peach and/or nectarine (Prunus persica L.). In this study, we identified 40 PpTPS genes in peach genome v2.0. Although these PpTPSs could be clustered into five classes, they distribute in several gene clusters of three chromosomes, share conserved exon-intron organizations, and code similar protein motifs. Thirty-five PpTPSs, especially PpTPS2, PpTPS23, PpTPS17, PpTPS18, and PpTPS19, altered their transcript levels after inoculation with Botryosphaeria dothidea, a cause of peach gummosis, compared to the mock treatments, which might further affect the contents of 133 terpenoids at 48 hours and/or 84 hours post inoculations in the current-year shoots of ‘Huyou018’, a highly susceptible nectarine cultivar. Moreover, about fifteen PpTPSs, such as PpTPS1, PpTPS2, PpTPS3, and PpTPS5, showed distinct expression patterns during fruit development and ripening in two peach cultivars, yellow-fleshed ‘Jinchun’ and white-fleshed ‘Hikawa Hakuho’. Among them, the transcription level of chloroplast-localized PpTPS3 was obviously related to the content of linalool in fruit pulps. In addition, elevated concentrations (0.1 g/L to 1.0 g/L) of linalool showed antifungal activities in PDA medium. These results improve our understanding of peach PpTPS genes and their potential roles in defense responses against pathogens.

Peach fruits are known to be highly susceptible to chilling injury (CI) during low-temperature storage, which has been linked to the level of sugar concentration in the fruit. In order to better understand the relationship between sugar metabolism and CI, we conducted a study examining the concentration of sucrose, fructose, and glucose in peach fruit with different sugar concentrations and examined their relationship with CI. Through transcriptome sequencing, we screened the functional genes and transcription factors (TFs) involved in the sugar metabolism pathway that may cause CI in peach fruit. Our results identified five key functional genes (PpSS, PpINV, PpMGAM, PpFRK, and PpHXK) and eight TFs (PpMYB1/3, PpMYB-related1, PpWRKY4, PpbZIP1/2/3, and PpbHLH2) that are associated with sugar metabolism and CI development. The analysis of co-expression network mapping and binding site prediction identified the most likely associations between these TFs and functional genes. This study provides insights into the metabolic and molecular mechanisms regulating sugar changes in peach fruit with different sugar concentrations and presents potential targets for breeding high-sugar and cold-tolerant peach varieties.

SUMMARY: Axillary web syndrome (AWS) is a self-limiting disease that can occur as an early or possibly late postoperative complication post-axillary surgery. This syndrome is characterized by the spread of the cords of subcutaneous tissue extending from the axilla into the arm and is clinically associated with pain and limited movement of the shoulder in the affected limb. Although its pathophysiology is not well established, the most common cause is surgery-related axillary lymphatic injury. Both the echography and magnetic resonance imaging results support the lymphatic hypothesis. The diagnosis of AWS is based on physical examination. Risk factors may include extensiveness of surgery, younger age, hypertension, lower body mass index, ethnicity, and healing complications. Effective clinical intervention shortens the natural course of AWS and improves the quality of life of patients with AWS. Treatments may include physical therapy, drug therapy, manual drainage, instrument-assisted soft tissue mobilization (IASTM), thoracic manipulation and stretching, manual axial distraction, percutaneous needle cord disruption with fat grafting and Xiaflex injection, and surgical intervention. Routine surgical treatment for AWS may not be recommended. Further research is needed to provide more comprehensive improvements in the diagnosis and treatment of AWS.

Peach (Prunus persica L.) is an economically important fruit crop worldwide due to its pleasant flavor. Volatile organic compounds (VOCs) are vital factors for assessing fruit quality. Here, we constructed the VOC profiles for the top eight popular commercial peach cultivars produced in Shanghai by combining gas chromatography-mass spectrometry (GC-MS), odor activity value and gas chromatograph-ion mobility spectrometry (GC-IMS). Seventy VOCs were detected using GC-MS, of which twenty-three were commonly found in eight peach cultivars, including hexanal, nonanal, benzaldehyde, 2-hexenal, butyl acetate, hexyl acetate, (Z)-3-hexen-1-yl acetate, linalool, β-myrcene, D-limonene, 1-hexanol, 3-hexenol, 2-hexenol, 2-ethyl-1-hexanol, γ-octalactone, δ-decalactone, γ-hexalactone, γ-decalactone, γ-dodecalactone, β-ionone, 2-octanone, 2-ethyl furan and 2,4-ditert-butyl phenol. A total of 17 VOCs were screened on the basis of OAV ≥ 1 and the top 5 of this contribution were γ-decalactone, β-ionone, hexanal, 2-hexenal and linalool. Lactones had the highest OAV in HJML and terpenoids had the highest OAV in JC. JXIU had the lowest OAV of lactones and terpenoids. Based on the range of their OAV values, the flavor evaluation standard of Shanghai high-quality peach cultivars can be established, which is also a reference for breeding excellent offspring. Twenty-six VOCs were detected using GC-IMS, and the largest proportion were aldehydes. Principal component analysis (PCA) showed that Hikawa Hakuho (HH) and Jinchun (JC) were distant from the other samples, indicating that their volatiles were more distinct. These results provide a foundation for improving our understanding of aroma compositions in these high-quality peach cultivars, which might also provide a reference for future design breeding to improve fruit flavor.

Peach gummosis is one of the most widespread and destructive diseases. It causes growth stunting, yield loss, branch, trunk, and tree death, and is becoming a restrictive factor in healthy and sustainable development of peach production. Although a locus has been identified based on bi-parental quantitative trait locus (QTL) mapping, selection of gummosis-resistant cultivars remains challenging due to the lack of resistant parents and of the complexity of an inducing factor. In this study, an integrated approach of genome-wide association study (GWAS) and comparative transcriptome was used to elucidate the genetic architecture associated with the disease using 195 accessions and 145,456 genome-wide single nucleotide polymorphisms (SNPs). The broad-sense and narrow-sense heritabilities were estimated using 2-year phenotypic data and genotypic data, which gave high values of 70 and 73%, respectively. Evaluation of population structure by neighbor-joining and principal components analysis (PCA) clustered all accessions into three major groups and six subgroups, mainly according to fruit shape, hairy vs. glabrous fruit skin, pedigree, geographic origin, and domestication history. Five SNPs were found to be significantly associated with gummosis disease resistance, of which SNPrs285957, located on chromosome6 across 28 Mb, was detected by both the BLINK and the FarmCPU model. Six candidate genes flanked by or harboring the significant SNPs, previously implicated in biotic stress tolerance, were significantly associated with this resistance. Two highly resistant accessions were identified with low disease severity, which could be potential sources of resistance genes for breeding. Our results provide a fresh insight into the genetic control of peach gummosis disease.

Unmanned helicopters (UH) can evade radar detection by flying at ultralow altitudes, so as to conduct raids on targets. Path planning is one of the key technologies to realize UH’s autonomous completion of raid missions. Since the probability of UH being detected by radar varies with height, how to accurately identify the radar coverage area to avoid crossing has become a difficult problem in UH path planning. Aiming at this problem, a heuristic deep Q-network (H-DQN) algorithm is proposed. First, as part of the comprehensive reward function, a heuristic reward function is designed. The function can generate dynamic rewards in real time according to the environmental information, so as to guide the UH to move closer to the target and at the same time promote the convergence of the algorithm. Second, in order to smooth the flight path, a smoothing reward function is proposed. This function can evaluate the pros and cons of UH’s actions, so as to prompt UH to choose a smoother path for flight. Finally, the heuristic reward function, the smooth reward function, the collision penalty, and the completion reward are weighted and summed to obtain the heuristic comprehensive reward function. Simulation experiments show that the H-DQN algorithm can help UH to effectively avoid the radar coverage area and successfully complete the raid mission.

This paper investigates the path planning problem of an unmanned aerial vehicle (UAV) for completing a raid mission through ultra-low altitude flight in complex environments. The UAV needs to avoid radar detection areas, low-altitude static obstacles, and low-altitude dynamic obstacles during the flight process. Due to the uncertainty of low-altitude dynamic obstacle movement, this can slow down the convergence of existing algorithm models and also reduce the mission success rate of UAVs. In order to solve this problem, this paper designs a state detection method to encode the environmental state of the UAV’s direction of travel and compress the environmental state space. In considering the continuity of the state space and action space, the SD-TD3 algorithm is proposed in combination with the double-delayed deep deterministic policy gradient algorithm (TD3), which can accelerate the training convergence speed and improve the obstacle avoidance capability of the algorithm model. Further, to address the sparse reward problem of traditional reinforcement learning, a heuristic dynamic reward function is designed to give real-time rewards and guide the UAV to complete the task. The simulation results show that the training results of the SD-TD3 algorithm converge faster than the TD3 algorithm, and the actual results of the converged model are better.

Abstract Background Pru p 1 is a major allergen in peach and nectarine, and the different content in varieties may affect the degree of allergic reactions. This study aimed to quantify Pru p 1 levels in representative peach varieties and select hypoallergenic Pru p 1 varieties. Methods To obtain monoclonal and polyclonal antibodies, mice and rabbits, respectively, were immunized with recombinant Pru p 1.01 and Pru p 1.02. The Pru p 1 levels in fruits from 83 representative peach varieties was quantified by sandwich enzyme‐linked immunosorbent assay (sELISA). nPru p 1 was obtained through specific monoclonal antibody affinity purification and confirmed by Western blot and mass spectrometry. The variable Pru p 1 content of selected varieties was evaluated by Western blot and the expression level of encoding Pru p 1 genes by quantitative polymerase chain reaction. Results A sELISA method with monoclonal and polyclonal antibodies was built for quantifying Pru p 1 levels in peach. Pru p 1 was mainly concentrated in the peel (0.20–73.44 μg/g, fresh weight), being very low in the pulp (0.05–9.62 μg/g) and not detected in wild peach. For the 78 peach and nectarine varieties, Pru p 1 content varied widely from 0.12 to 6.45 μg/g in whole fruit. We verified that natural Pru p 1 is composed of 1.01 and 1.02 isoallergens, and the Pru p 1 expression level and Pru p 1 band intensity in the immunoblots were in agreement with protein quantity determined by ELISA for some tested varieties. In some cases, the reduced levels of Pru p 1 did not coincide with low Pru p 3 in the same variety in whole fruit, while some ancient wild peach and nectarines contained low levels of both allergens, and late‐ripening yellow flesh varieties were usually highly allergenic. Conclusion Pru p 1 content is generally low in peach compared to Pru p 3. Several hypoallergenic Pru p 1 and Pru p 3 varieties, “Zi Xue Tao,” “Wu Yue Xian,” and “May Fire,” were identified, which could be useful in trials for peach allergy patients.

Peach (Prunus persica (L.) Batsch) is a typical shallow-rooted fruit plant with a high respiratory intensity and oxygen demand, which makes it highly susceptible to oxygen-deficient soil conditions resulting from waterlogging. Rootstock waterlogging resistance is essential to the performance of cultivated peaches under waterlogging stress. In comparison to Prunus persica var. persica (‘Maotao’, M) and Prunus davidiana (Carr.) C. de Vos (‘Shantao’, S), Prunus persica f. Hossu (‘Hossu’, H) exhibited superior leaf photosynthetic electron transfer efficiency, a higher rate of mycorrhizal fungi infection in both fine roots and mesophyll palisade cells, as well as earlier air cavity formation in both leaf midvein and fine roots under waterlogging stress. Furthermore, under non-waterlogging conditions, Hossu had greater leaf superoxide dismutase (SOD) activity, higher proline content, and a greater content of starch granules in the pith and xylem ray cells of stems and roots than rootstocks M and S. As a result, Hossu’s tolerance to waterlogging may be due to its higher photosynthetic efficiency, improved tissue oxygen permeability, higher energy metabolism, and increased intracellular mycorrhizal fungus infection rates in both root parenchyma cells and mesophyll palisade cells.

Unmanned helicopter (UH) is often utilized for raid missions because it can evade radar detection by flying at ultra-low altitudes. Path planning is the key technology to realizing the autonomous action of UH. On the one hand, the dynamically changing radar coverage area and the mountains in the low airspace environment will seriously affect the flight safety of UH. On the other hand, the huge state space of the three-dimensional (3D) environment will also make traditional algorithms difficult to converge. To address the above problems, a memory-enhanced dueling deep Q-network (ME-dueling DQN) algorithm was proposed. First, a comprehensive reward function was designed, which can guide the algorithm to converge quickly and effectively improve the sparse reward problem. Then, we introduced a dual memory pool structure and proposed a memory-enhanced mechanism, which can reduce invalid exploration, further improve the learning efficiency of the algorithm, and make the algorithm more stable. Finally, the path planning ability of the proposed algorithm in multiple experimental environments was verified. Experiments showed that the proposed algorithm has good environmental adaptability and can help UH to accurately identify dangerous areas and plan a safe and reliable flight path.

In order to fully understand the variation of the fruit alpha-glucosidase inhibitory activity-related phytochemical basis in the Chinese peach [Prunus persica (L.) Batsch], mature fruit from 33 cultivars was used for the investigation of fruit phenolic phytochemical attributes, including total phenolics, flavonoids, anthocyanins, and procyanidins, as well as the alpha-glucosidase inhibitory activity in vitro. Alpha-glucosidase inhibitory activity varied significantly among tested peach cultivars and was strongly correlated with total phenolics, total procyanidins, and total flavonoids. Untargeted UPLC-Q-TOF/MS-based metabolomics were used to comprehensively discriminate between peaches with different inhibitory activity on alpha-glucosidase. Principal component analysis (PCA) and orthogonal partial least squares discrimination analysis (OPLS-DA) were used for this process. Twenty-three differential compounds were identified between peach cultivars with high and low alpha-glucosidase inhibitory activity, and nine, including procyanidin C1, procyanidin trimer isomer 1, procyanidin trimer isomer 2, procyanidin B1, procyanidin dimer, epicatechin-epicatechin-epicatechin, phloridzin, kaempferol 3-(2’’,6’’-di-(E)-p-coumarylglucoside), and luteolin 3’-methyl ether 7-malonylglucoside, were identified as marker compounds responsible for the discrimination. Overall, variations in metabolites in peach pulp reflect the diversity in peach germplasm, and these nine compounds are good candidate markers for future genetic breeding of peach fruit with high alpha-glucosidase inhibitory activity.

Abstract Hydraulic fracturing, which forms complex fracture networks, is a common technique for efficiently exploiting low-permeability conglomerate reservoirs. However, the presence of gravel makes conglomerate highly heterogeneous, endowing the deformation, failure, and internal micro-scale fracture expansion mechanisms with uniqueness. The mechanism of fracture expansion when encountering gravel in conglomerate reservoirs remains unclear, challenging the design and effective implementation of hydraulic fracturing. This paper utilizes the Abaqus platform and adopts the maximum circumferential strain fracture criterion to establish a two-dimensional fluid-solid-structure coupled fracture expansion model. The study investigates the impact of permeability, Young’s modulus, in-situ stress difference, and incidence angle on the fracture expansion behavior in the presence of gravel. The results indicate that low-permeability gravel induces pressure changes near the fracture tip, like 13% pore pressure increase and stress reductions. Shear stress on the gravel surface rises by 28.3%, and choosing layers with a smaller permeability gap between matrix and gravel can reduce complex near-well fractures. High Young’s modulus gravel has a stress shielding effect around fractures, which is direction-dependent, with over tenfold stress variations. It causes a 60.7% increase in peak shear stress on the gravel surface, exacerbating fracture complexity and reducing extension distance, and a greater strength disparity between gravel and matrix enhances this effect. A high in-situ stress difference (over 8 MPa) weakens gravel-caused stress shielding, leading to stable fracture propagation with a single morphology and increased extension distance, though not favoring complex network formation. As the incidence angle rises, the gravel’s impact on stress and seepage fields weakens, but the risk of shear failure on the cementation interface increases. The findings provide a basis for optimizing operations in such reservoirs considering the various properties of gravel and their effects on fluid flow and fracture behavior.

In this study, a novel (α+β) dual-phase Ti–19Nb–0.6O was developed and processed via additive manufacturing, featuring ultra-low Young’s modulus of 42 GPa approximating that of human bone, coupled with high strength (919 MPa), and 15% elongation. By utilizing post-heat treatment to adjust the distribution and content of the precipitated α phase, the elemental distribution of Nb and O between α and β phase was effectively manipulated, meanwhile, the β matrix stability was regulated, enabling the attainment of lower modulus and higher strength. These findings advance the development of biomedical titanium alloys produced using additive manufacturing, characterized by excellent mechanical performance.

Combined application of organic fertilizer is an effective measure to improve the productivity and ecological effect of newly added soil. However, the effect of organic fertilizer application in newly added orchard soil is not dear. In this project, the soil of newly built peach orchard in the Longquan Mountain of Chengdu was applied with 45 t/hm2 of organic fertilizer. After 9 months of planting 6 varieties of honey peach (Wanhujing, Baifeng, Zhongtao 13, Huangjinmitao 1, Zhongpan 101, Zhongpan 104), the locally well developed peach orchard was taken as the control. The physical and chemical properties of soil from four topography (top slope, middle slope, lower slope and flat land) of newly built peach orchard were analyzed, and the effect of organic fertilizer on soil was evaluated. The results showed that combined application of organic fertilizer had different effects on soils from orchards with different varieties of peach and from different terrain parts of the same peach variety. Specifically, it had the best effect on soil improvement in Baifeng (local variety), Zhongpan 101 and Zhongpan 104 (introduced varieties). Meanwhile, the best effect of soil improvement was found on top slope. Cluster analysis divided newly built peach orchards, uncultivated soil, and locally well developed peach orchard into four groups, indicating that the selected amount of organic fertilizer application in this study has improved the soil of peach orchards to some extent, but it was still necessary to increase the application amount. It was better for 54 t/hm2 amount of organic fertilization on the part of top and middle slope, and 60 t/hm2 on the lower slope and flat land. The comprehensive ecological benefit assessment of organic fertilizer should be conducted based on long-term monitoring of peach orchard ecological environment, fruit tree growth, fruit yield and quality, which would provide scientific basis for peach orchard production and management. [ABSTRACT FROM AUTHOR]

Asparagus cochinchinensis is a medicinal plant in China, which has gained attention owing its protective effect in human health. However, there are seldom studies to systematically reveal the rhizosphere bacterial community of A. cochinchinensis. In this study, we employed metagenomics and culturomics to analyze the bacterial community composition and diversity in continuous rhizosphere soil of A. cochinchinensis. Meanwhile, we assessed the effect of soil physicochemical properties on the bacterial community. Results showed that the most abundant TAXA is a taxon belonging to the family Streptomycetaceae, the genus Mycobacterium and the species Oligotropha carboxidovorans. The bacterial communities across various areas were similar. Significant differences of exchangeable magnesium and available phosphorus level were observed between three groups. Furthermore, bacterial community structure correlated closely with soil physicochemical properties. Additionally, a total of 103 strains were isolated and identified, representing 28 species. Based on this study, the rhizosphere bacterial community of A. cochinchinensis might influence its growth and development. The rhizosphere strains were isolated and their function request further investigation. This study firstly revealed the bacterial community in the A. cochinchinensis rhizosphere soil, providing valuable references for its quality improvement in practical cultivation process.