Search Results (2,357)

Mei (Prunus mume Sieb. et Zucc.) is a rare woody species that flowers in winter, yet its large-scale propagation is limited by the variable ability of cuttings to form adventitious roots (ARs). In this study, two cultivars were compared: P. mume ‘Xiangxue Gongfen’ (GF), which roots readily, and P. mume ‘Zhusha Wanzhaoshui’ (ZS), which is more recalcitrant. Detailed anatomical observations revealed that following cutting, the basal region expanded within 7 days, callus tissues had appeared by 14 days, and AR primordia emerged between 28 and 35 days. Notably, compared to the recalcitrant cultivar ZS, the experimental cultivar GF exhibited significantly enhanced callus tissue formation and AR primordia differentiation. Physiological analyses showed that the initial IAA concentration was highest at day 0, whereas cytokinin (tZR) and gibberellin (GA1) levels peaked at 14 days, with ABA gradually decreasing over time, resulting in increased IAA/tZR and IAA/GA1 ratios during the rooting process. Transcriptomic profiling across these time points identified significant upregulation of key genes (e.g., PmPIN3, PmLOG2, PmCKX5, PmIAA13, PmLAX2, and PmGA2OX1) and transcription factors (PmWOX4, PmSHR, and PmNAC071) in GF compared to ZS. Moreover, correlation analyses revealed that PmSHR expression is closely associated with IAA and tZR levels. Overexpression of PmSHR in tobacco further validated its role in enhancing lateral root formation. Together, these findings provide comprehensive insights into the temporal, hormonal, and genetic regulation of AR formation in P. mume, offering valuable strategies for improving its propagation. Full article

High-resolution remote sensing imagery (HRRSI) presents significant challenges for building extraction tasks due to its complex terrain structures, multi-scale features, and rich spectral and geometric information. Traditional methods often face limitations in effectively integrating multi-scale features while maintaining a balance between detailed and global semantic information. To address these challenges, this paper proposes an innovative deep learning network, Multi-Source Multi-Scale Residual Attention Network (MMRAD-Net). This model is built upon the classical encoder–decoder framework and introduces two key components: the GCN OA-SWinT Dense Module (GSTDM) and the Res DualAttention Dense Fusion Block (R-DDFB). Additionally, it incorporates Digital Surface Model (DSM) data, presenting a novel feature extraction and fusion strategy. Specifically, the model enhances building extraction accuracy and robustness through hierarchical feature modeling and a refined cross-scale fusion mechanism, while effectively preserving both detail information and global semantic relationships. Furthermore, we propose a Hybrid Loss, which combines Binary Cross-Entropy Loss (BCE Loss), Dice Loss, and an edge-sensitive term to further improve the precision of building edges and foreground reconstruction capabilities. Experiments conducted on the GF-7 and WHU datasets validate the performance of MMRAD-Net, demonstrating its superiority over traditional methods in boundary handling, detail recovery, and adaptability to complex scenes. On the GF-7 Dataset, MMRAD-Net achieved an F1-score of 91.12% and an IoU of 83.01%. On the WHU Building Dataset, the F1-score and IoU were 94.04% and 88.99%, respectively. Ablation studies and transfer learning experiments further confirm the rationality of the model design and its strong generalization ability. These results highlight that innovations in multi-source data fusion, multi-scale feature modeling, and detailed feature fusion mechanisms have enhanced the accuracy and robustness of building extraction. Full article

The degree and speed of deformation are factors that influence microstructure and mechanical properties. Aluminum (99.5%) was used as the test material in this experiment. This material is currently mainly used in the electrical industry to manufacture conductors as a substitute for the more expensive copper. The cylindrical samples were deformed at a strain rate of up to 2500 s⁻¹, and the degree of deformation was up to 85%. At the point place of maximum deformation, usually in the center of the sample, the microhardness was measured under various loads, between 10 gf and 100 gf. The obtained data were used to determine the characteristics or parameters of the indentation size effect (ISE) and the influence of the deformation conditions on the microhardness. The results obtained were processed by linear regression analysis, followed by the creation of deformation maps. Full article

Woody plants serve as crucial ecological barriers surrounding oases in arid and semi-arid regions, playing a vital role in maintaining the stability and supporting sustainable development of oases. However, their sparse distribution makes significant challenges in accurately mapping their spatial extent using medium-resolution remote sensing imagery. In this study, we utilized high-resolution Gaofen (GF-2) and Landsat 5/7/8 satellite images to quantify the relationship between vegetation growth and groundwater table depths (GTD) in a typical inland river basin from 1988 to 2021. Our findings are as follows: (1) Based on the D-LinkNet model, the distribution of woody plants was accurately extracted with an overall accuracy (OA) of 96.06%. (2) Approximately 95.33% of the desert areas had fractional woody plant coverage (FWC) values of less than 10%. (3) The difference between fractional woody plant coverage and fractional vegetation cover proved to be a fine indicator for delineating the range of desert-oasis ecotone. (4) The optimal GTD for Haloxylon ammodendron and Tamarix ramosissima was determined to be 5.51 m and 3.36 m, respectively. Understanding the relationship between woody plant growth and GTD is essential for effective ecological conservation and water resource management in arid and semi-arid regions. Full article

The accurate extraction of cultivated land information is crucial for optimizing regional farmland layouts and enhancing food supply. To address the problem of low accuracy in existing cultivated land products and the poor applicability of cultivated land extraction methods in fragmented, small parcel agricultural landscapes and complex terrain mapping, this study develops an advanced cultivated land extraction model for the western part of Henan Province, China, utilizing Gaofen-2 (GF-2) imagery and an improved U-Net architecture to achieve a 1 m resolution regional mapping in complex terrain. We obtained optimal input data for the U-Net model by fusing spectral features and vegetation index features from remote sensing images. We evaluated and validated the effectiveness of the proposed method from multiple perspectives and conducted a cultivated land change detection and agricultural landscape fragmentation assessment in the study area. The experimental results show that the proposed method achieved an F1 score of 89.55% for the entire study area, with an F1 score ranging from 83.84% to 90.44% in the hilly or transitional zones. Compared to models that solely rely on spectral features, the feature selection-based model demonstrates superior performance in hilly and adjacent mountainous regions, with improvements of 4.5% in Intersection over Union (IoU). Cultivated land mapping results show that 83.84% of the cultivated land parcels are smaller than 0.64 hectares. From 2017 to 2022, the overall cultivated land area decreased by 15.26 km², with the most significant reduction occurring in the adjacent hilly areas, where the land parcels are small and fragmented. This trend highlights the urgent need for effective land management strategies to address fragmentation and prevent further loss of cultivated land in these areas. We anticipate that the findings can contribute to precision agriculture management and agricultural modernization in complex terrains of the world. Full article

The absence of gluten is a technological challenge that requires the addition of components to replace the unique viscoelastic properties of gluten, thus altering the nutritional composition of gluten-free (GF) breads. Moreover, GF flours may have different compositions as compared to gluten-containing (GC) counterparts because of a different origin. This may impact the nutritional quality of GF diets. The aim of the study is to provide updated analytical data on moisture, fat, and fibre contents in GF flour and bread samples, and compare them with their GC counterparts, as well as to analyse ingredients and how they impact nutritional quality. A total of 30 different flours and 24 types of bread were analysed using AOAC methods. GF cereal flours contain more fat than GC flours (3.5 ± 2.1% vs. 2.5 ± 2.1%, p < 0.001), as well as GF flours from pseudocereals, except for wholemeal buckwheat (2.6 ± 0.1%). Fibre content is lower in GF flours (3.6 ± 3.1% vs. 7.1 ± 3.9%, p = 0.03), except for GF pseudocereal and legume flours. GF breads contain almost twice as much fat 6.6 ± 2.3% vs. 1.4 ± 0.2%, p < 0.001, and 4.2 ± 1.2%, p < 0.001) and fibre (7.3 ± 2.4% vs. 2.8 ± 0.5%, p < 0.001, and 4.9 ± 2.1%, p = 0.002) as GC breads. This is due to the raw materials themselves and to the addition of ingredients, such as regular and high oleic sunflower oil, and psyllium. Fibre ingredients and additives are more frequently used in ready-to-eat GF flours and breads, and more GF breads also contain fat-based ingredients, as compared to GC. Amaranth and chickpea flours are good alternatives to produce breads with better nutritional quality. Analysis of GF products for critical nutrients is peremptory because of continuing technological and nutritional innovation. Full article

Filtration bleb (FB) fibrosis represents the primary risk factor for glaucoma filtration surgery (GFS) failure. We reviewed the most recent literature on post-GFS fibrosis in humans, focusing on novel molecular pathways and antifibrotic treatments. Three main literature searches were conducted. First, we performed a narrative review of two models of extra-ocular fibrosis, idiopathic pulmonary fibrosis and skin fibrosis, to improve the comprehension of ocular fibrosis. Second, we conducted a systematic review of failed FB features in the PubMed, Embase, and Cochrane Library databases. Selected studies were screened based on the functional state and morphological features of FB. Third, we carried out a narrative review of novel potential antifibrotic molecules. In the systematic review, 11 studies met the criteria for analysis. Immunohistochemistry and genomics deemed SPARC and transglutaminases to be important for tissue remodeling and attributed pivotal roles to TGFβ and M2c macrophages in promoting FB fibrosis. Four major mechanisms were identified in the FB failure process: inflammation, fibroblast proliferation and myofibroblast conversion, vascularization, and tissue remodeling. On this basis, an updated model of FB fibrosis was described. Among the pharmacological options, particular attention was given to nintedanib, pirfenidone, and rapamycin, which are used in skin and pulmonary fibrosis, since their promising effects are demonstrated in experimental models of FB fibrosis. Based on the most recent literature, modern patho-physiological models of FB fibrosis should consider TGFβ and M2c macrophages as pivotal players and favorite targets for therapy, while research on antifibrotic strategies should clinically investigate medications utilized in the management of extra-ocular fibrosis. Full article

Goldmann–Favre syndrome (GFS) is a rare vitreoretinal degenerative disorder caused by mutations in the NR2E3 gene located on the short arm of chromosome 15. This condition, inherited in an autosomal recessive manner, was first described by Favre in two siblings, with Ricci later confirming its hereditary pattern. In GFS, rod photoreceptors are essentially replaced by S-cone photoreceptors. Enhanced S-Cone Syndrome (ESCS) and Goldmann–Favre syndrome are two distinct entities within the spectrum of retinal degenerative diseases, both caused by mutations in the NR2E3 gene. Despite sharing a common genetic basis, these conditions exhibit significantly different clinical phenotypes. ESCS is characterized by an excessive number of S-cones (blue-sensitive cones) with degeneration of rods and L-/M-cones, leading to increased sensitivity to blue light and early-onset night blindness. In contrast, GFS is considered a more severe form of ESCS, involving additional features such as retinal schisis, vitreous degeneration, and more pronounced visual impairment. GFS typically manifests in the first decade of life as night blindness (nyctalopia) and progressive visual acuity impairment. The clinical features include degenerative vitreous changes such as liquefaction, strands, and bands, along with macular and peripheral retinoschisis, posterior subcapsular cataract, atypical pigmentary dystrophy, and markedly abnormal or nondetectable electroretinograms (ERGs). Although peripheral retinoschisis is more common in GFS, central retinoschisis may also occur. Despite the consistent genetic basis, the phenotype of GFS can vary significantly among individuals. The differential diagnosis should consider diseases within the retinal degenerative spectrum, including retinitis pigmentosa, congenital retinoschisis, and secondary pigmentary retinopathy. Full article

Flooding is among the world’s most destructive natural disasters. From 27 July to 1 August 2023, Zhuozhou City and surrounding areas in Hebei Province experienced extreme rainfall, severely impacting local food security. To swiftly map the spatial and temporal distribution of the floodwaters and assess the damage to major crops, this study proposes a water body identification method with a dual polarization band combination for synthetic-aperture radar (SAR) data to solve the differences in water body feature recognition in SAR due to different polarization modes. Based on the SAR water body extent, the flood inundation extent was mapped with GF-6 optical data. In addition, Landsat-8 data were used to generate information on significant crops in the study area, while Sentinel-2 data and the Google Earth Engine (GEE) platform were used to classify the extent of crop damage. The results indicate that the flood inundated 700.51 km², 14.10% of the study area. Approximately 40,700 hectares (ha) or 8.46% of the main crops were affected, including 33,700 ha of maize, 4300 ha of vegetables, and 2800 ha of beans. Moderate crop damage was the most widespread, affecting 37.62% of the crops, while very extreme damage was the least, affecting 5.10%. Zhuozhou City experienced the most significant impact, with 13,700 ha of crop damage, accounting for 33.70% of the total. This study provides a computational framework for rapid flood monitoring using multi-source remote sensing data, which also serves as a reference for post-disaster recovery, agricultural production, and crop risk assessment. Full article

The continuous use of antibiotics is associated with many complications in the poultry industry. Probiotics have emerged as a viable alternative over the past few decades to counter the adverse effects of antibiotics. No candidate probiotic microorganisms have been fully evaluated in the poultry industry for their effectiveness as potential probiotics in guinea fowls (GFs) compared to chickens. Recently, a metagenome evaluation of GFs in our laboratory revealed a predominance of Lactobacillus reuteri (L. reuteri) and actinobacteria class of bacteria in their gastrointestinal tract. The aim of this study is to evaluate a well-known lactic acid probiotic bacterium (L. reuteri) and a unique probiotic (S. coelicolor) that has not been assessed in any guinea fowl species. In the current study, L. reuteri and Streptomyces coelicolor (S. coelicolor) were selected as probiotic bacteria, encapsulated, and added into French guinea fowl (FGF) feed individually at a concentration of 10⁸ cfu/g or both microorganisms combined each at 10⁴ cfu/g. In an 8-week study, 216-day-old guinea keets were randomly assigned to four dietary treatments as indicated: (1) L. reuteri (10⁸ cfu/g); (2) S. coelicolor (10⁸ cfu/g); (3) mixture of L. reuteri (10⁴ cfu/g) and S. coelicolor (10⁴ cfu/g); and (4) control treatment (no probiotics included). The L. reuteri, S. coelicolor, and L. reuteri + S. coelicolor were added into the feed using wheat middlings as a carrier at a final concentration of 10⁸ cfu/g. The FGFs that were fed diets containing L. reuteri showed improved feed consumption at 3–8 weeks of age (WOA). The guineas fed L. reuteri and S. coelicolor showed a lower feed conversion ratio (FCR), which was significant at 2 and 8 WOA, and a numerically lower 8-week average FCR when compared with other dietary treatments. Differences in body weight gain among all dietary treatments were not significant. This research suggests that L. reuteri and S. coelicolor may have the potential for use as probiotics in FGFs when used in combination or separately. Full article

Pseudoarthrosis—the failure of normal fracture healing—remains a significant orthopedic challenge affecting approximately 10–15% of long bone fractures, and is associated with significant pain, prolonged disability, and repeated surgical interventions. Despite extensive research into the pathophysiological mechanisms of bone healing, diagnostic approaches remain reliant on clinical findings and radiographic evaluations, with little innovation in tools to predict or diagnose non-union. The present review evaluates the current understanding of the genetic and biological basis of pseudoarthrosis and highlights future research directions. Recent studies have highlighted the potential of specific molecules and genetic markers to serve as predictors of unsuccessful fracture healing. Alterations in mesenchymal stromal cell (MSC) function, including diminished osteogenic potential and increased cellular senescence, are central to pseudoarthrosis pathogenesis. Molecular analyses reveal suppressed bone morphogenetic protein (BMP) signaling and elevated levels of its inhibitors, such as Noggin and Gremlin, which impair bone regeneration. Genetic studies have uncovered polymorphisms in BMP, matrix metalloproteinase (MMP), and Wnt signaling pathways, suggesting a genetic predisposition to non-union. Additionally, the biological differences between atrophic and hypertrophic pseudoarthrosis, including variations in vascularity and inflammatory responses, emphasize the need for targeted approaches to management. Emerging biomarkers, such as circulating microRNAs (miRNAs), cytokine profiles, blood-derived MSCs, and other markers (B7-1 and PlGF-1), have the potential to contribute to early detection of at-risk patients and personalized therapeutic approaches. Advancing our understanding of the genetic and biological underpinnings of pseudoarthrosis is essential for the development of innovative diagnostic tools and therapeutic strategies. Full article

Fused filament fabrication (FFF) has recently emerged as a sustainable digital manufacturing technology to fabricate polymer composite parts with complex structures and minimal waste. However, FFF-printed composite parts frequently exhibit heterogeneous structures with low mechanical properties. To manufacture high-end parts with good mechanical properties, advanced predictive tools are required. In this paper, Artificial Neural Network (ANN) models were developed to evaluate the mechanical properties of 3D-printed polyamide 12 (PA) and carbon fiber (CF) and glass fiber (GF) reinforced PA composites. Tensile samples were fabricated by FFF, considering two input parameters, such as printing orientation and infill density, and tested to determine the mechanical properties. Then, single- and multi-target ANN models were trained using the forward propagation Levenberg–Marquardt algorithm. Post-training performance analysis indicated that the ANN models work efficiently and accurately in predicting Young’s modulus and tensile strength of the 3D-printed PA and fiber-reinforced PA composites, with most relative errors being far less than 5%. In terms of mechanical properties, such as Young’s modulus and tensile strength, the 3D-printed composites outperform the unreinforced PA. Printing PA composites with 0° orientation and 100% infill density results in a maximum increase in Young’s modulus (up to 98% for CF/PA and 32% for GF/PA) and tensile strength (up to 36% for CF/PA and 18% for GF/PA) compared to the unreinforced PA. This study underscores the potential of the ANN models to predict the mechanical properties of 3D-printed parts, enhancing the use of 3D-printed PA composite components in structural applications. Full article

The aboveground biomass (AGB) of forests reflects the productivity and carbon-storage capacity of the forest ecosystem. Although AGB estimation techniques have become increasingly sophisticated, the relationships between AGB, spatial distribution, and growth stages still require further exploration. In this study, the Picea schrenkiana (Picea schrenkiana var. tianschanica) forest area in the Kashi River Basin of the Ili River Valley in the western Tianshan Mountains was selected as the research area. Based on forest resources inventory data, Gaofen-1 (GF-1), Gaofen-6 (GF-6), Gaofen-3 (GF-3) Polarimetric Synthetic Aperture Radar (PolSAR), and DEM data, we classified the Picea schrenkiana forests in the study area into three cases: the Whole Forest without vertical zonation and stand age, Vertical Zonality Classification without considering stand age, and Stand-Age Classification without considering vertical zonality. Then, for each case, we used eXtreme Gradient Boosting (XGBoost), Back Propagation Neural Network (BPNN), and Residual Networks (ResNet), respectively, to estimate the AGB of forests in the study area. The results show that: (1) The integration of multi-source remote-sensing data and the ResNet can effectively improve the remote-sensing estimation accuracy of the AGB of Picea schrenkiana. (2) Furthermore, classification by vertical zonality and stand ages can reduce the problems of low-value overestimation and high-value underestimation to a certain extent. Full article

Levansucrases are key enzymes responsible for the synthesis of β-2,6-linked fructans, found in plants and microbes, especially in bacteria. Levansucrases have been applied in the production of levan biopolymer and fructooligosaccharides (FOSs) using sucrose as a substrate as well as in reducing sugar levels in fruit juice. As a result, levansucrases that are active at low temperatures are required for industrial applications to maintain product stability. Therefore, this work firstly reports the novel cold-active levansucrase (SacBPk) isolated from a sucrolytic bacterial strain, P. koreensis HL12. The SacBPk was classified into glycoside hydrolase family 68 subfamily 1 (GH68_1) and comprised a single catalytic domain with the Asp104/Asp267/Glu362 catalytic triad. Interestingly, the recombinant SacBPk demonstrated cold-active levansucrase activity at low temperatures (on ice and 4–40 °C) with the highest specific activity (167.46 U/mg protein) observed at 35 and 40 °C in 50 mM sodium phosphate buffer pH 6.0. SacBPk mainly synthesized levan polymer as the major product (129 g/L, corresponding to 25.8% of total sugar) with a low number of short-chain FOSs (GF2–4) (12.8 g/L, equivalent to 2.5% of total sugar) from 500 g/L sucrose after incubating at 35 °C for 48 h. These results demonstrate the industrial application potential of SacBPk levansucrase for levan and FOSs production. Full article

Currently, there is no publicly available dataset for the classification of potato pest and disease-related queries. Moreover, traditional query classification models generally adopt a single maximum-pooling strategy when performing down-sampling operations. This mechanism only extracts the extreme value responses within the local receptive field, which leads to the degradation of fine-grained feature representation and significantly amplifies text noise. To address these issues, a dataset construction method based on prompt engineering is proposed, along with a question classification method utilizing a gated fusion–convolutional neural network (GF-CNN). By interacting with large language models, prompt words are used to generate potato disease and pest question templates and efficiently construct the Potato Pest and Disease Question Classification Dataset (PDPQCD) by batch importing named entities. The GF-CNN combines outputs from convolutional kernels of varying sizes, and after processing with max-pooling and average-pooling, a gating mechanism is employed to regulate the flow of information, thereby optimizing the text feature extraction process. Experiments using GF-CNN on the PDPQCD, Subj, and THUCNews datasets show F1 scores of 100.00%, 96.70%, and 93.55%, respectively, outperforming other models. The prompt engineering-based method provides a new paradigm for constructing question classification datasets, and the GF-CNN can also be extended for application in other domains. Full article