Search Results (333)

Accurate displacement field measurement by holographic interferometry requires robust analysis of high-density fringe patterns, which is hindered by speckle noise inherent in any interferogram, no matter how perfect. Conventional skeletonization methods, such as edge detection algorithms and active contour models, often fail under these conditions, producing fragmented and unreliable fringe contours. This paper presents a novel skeletonization procedure that simultaneously addresses three fundamental challenges: (1) topology preservation—by representing the fringe family within a physics-informed, finite-dimensional parametric subspace (e.g., Fourier-based contours), ensuring global smoothness, connectivity, and correct nesting of each fringe; (2) extreme noise robustness—through a robust strip integration functional that replaces noisy point sampling with Gaussian-weighted intensity averaging across a narrow strip, effectively suppressing speckle while yielding a smooth objective function suitable for gradient-based optimization; and (3) sub-pixel accuracy without phase extraction—leveraging continuous bicubic interpolation within a recursive quasi-optimization framework that exploits fringe similarity for precise and stable contour localization. The method’s performance is quantitatively validated on synthetic interferograms with controlled noise, demonstrating significantly lower error compared to baseline techniques. Practical utility is confirmed by successful processing of a real interferogram of a bent plate containing over 100 fringes, enabling precise displacement field reconstruction that closely matches independent theoretical modeling. The proposed procedure provides a reliable tool for processing challenging interferograms where traditional methods fail to deliver satisfactory results. Full article

High-quality development of agricultural enterprises is essential for China’s rural revitalization, yet the institutional conditions that support it remain poorly understood. Drawing on institutional logics and configuration theory, this study adopts a holistic systems perspective to examine how government, market, and social institutions interact to shape enterprise performance. Using provincial data (2013–2023) matched with firm-level data for 119 listed agricultural enterprises, we estimate total factor productivity as the core outcome and apply dynamic fuzzy-set Qualitative Comparative Analysis (dynamic fsQCA) to identify equifinal institutional pathways. The results reveal that high-quality development is an emergent property of complex institutional systems; instead, high-quality development emerges from several distinct configurations combining policy support, marketization, financial development, Agricultural Infrastructure Index, market stability, and urban–rural integration. Two contrasting configurations are associated with non-high-quality development, characterized by financial scarcity and infrastructure deficits or by fragmented policy support under weak regulation. Dynamic analysis further reveals clear temporal and spatial heterogeneity: some market–finance driven paths lose robustness over time, while policy–urbanization and regulation–infrastructure based configurations become increasingly stable. These findings extend institutional configuration research to the agricultural sector, demonstrate the value of dynamic fsQCA for capturing temporal effects, and offer differentiated policy implications for optimizing institutional environments to foster the high-quality development of agricultural enterprises. Full article

This study presents an in silico proof of concept exploring whether deep learning models can perform conditional mitochondrial DNA (mtDNA) sequence prediction across species boundaries. A CNN–BiLSTM model was trained under a leave-one-species-out (LOSO) scheme on complete mitochondrial genomes from 21 vertebrate species, primarily archosaurs. Model behavior was evaluated through multiple complementary tests. Under context-conditioned settings, the model performed next-nucleotide prediction using overlapping 200 bp windows to assemble contiguous 2000 bp fragments for held-out species; the resulting high token-level accuracy (>99%) under teacher forcing is reported as a diagnostic of conditional modeling capacity. To assess leakage-free performance, a two-flank masked-span imputation task was conducted as the primary evaluation, requiring free-running reconstruction of 500 bp interior spans using only distal flanking context; in this setting, the model consistently outperformed nearest-neighbor and demonstrated competitive performance relative to flank-copy baselines. Additional robustness analyses examined sensitivity to window placement, genomic region (coding versus D-loop), and random initialization. Biological plausibility was further assessed by comparing predicted fragments to reconstructed ancestral sequences and against composition-matched null models, where observed identities significantly exceeded null expectations. Using the National Center for Biotechnology Information (NCBI) BLAST web interface, BLASTn species identification was performed solely as a biological plausibility check, recovering the correct species as the top hit in all cases. Although limited by dataset size and the absence of ancient DNA damage modeling, these results demonstrate the feasibility of conditional mtDNA sequence prediction as an initial step toward more advanced generative and evolutionary modeling frameworks. Full article

As agricultural labor shifted toward non-farm sectors and the farming population aged, innovative production arrangements became essential to sustain land productivity. While partial agricultural production chain management (PAPM) was widespread, the productivity impact of whole-process agricultural production chain management (WAPM)—a comprehensive model integrating all production stages—remained empirically underexplored. Using nationally representative panel data from the China Labor-force Dynamics Survey (CLDS, 2014–2018) for grain-producing households, this study estimates the differential impacts of WAPM and PAPM with a two-way fixed-effects (TWFE) model, supplemented by propensity score matching (PSM) as a robustness check. The results show that WAPM significantly enhanced land productivity. Notably, the effect size of WAPM (coefficient: 0.486) is substantially larger than that of PAPM (coefficient: 0.214), indicating that systematic integration of service chains offers superior efficiency gains over fragmented outsourcing. Mechanism analysis suggests that WAPM improves productivity primarily by alleviating labor constraints and mitigating the disadvantages of small-scale farming. Furthermore, heterogeneity analysis demonstrated that these benefits are amplified in major grain-producing regions and hilly areas. These findings support policies that facilitate a transition from single-link outsourcing toward whole-process integrated service provision. Full article

Vascular endothelial growth factor 165 (VEGF₁₆₅) stands out as a pivotal isoform of the VEGF-A protein and is critically involved in various angiogenesis-related diseases. Consequently, it has emerged as a promising target for diagnosing and treating such conditions. Structurally, VEGF₁₆₅ forms a homodimer, and each of its constituting monomers comprises a receptor-binding domain (RBD) and a heparin-binding domain (HBD). These two domains are linked by a flexible linker, and thus the overall structure of VEGF₁₆₅ remains incompletely understood. Aptamers are known as potent drugs that interact with VEGF₁₆₅, and dimeric aptamers that can simultaneously interact with two distant domains are frequently adopted to improve the potency. However, designing such aptamer dimers faces challenges in regard to determining the appropriate length of the linker connecting the two aptamer fragments. To gain insight into this distance information, we here employ biased molecular dynamics (MD) simulations with the umbrella sampling method, with the distance between the two HBDs serving as a reaction coordinate. Our simulations reveal an overall preference for compact conformations with HBD-HBD distances below 3 nm, with the minimum of the potential of mean force located at 1.1 nm. We find that VEGF₁₆₅ with the optimal HBD-HBD distance forms hydrogen bonds with its receptor VEGFR-2 that well match experimentally known key hydrogen bonds. We then try to computationally design aptamer homodimers consisting of two del5-1 aptamers connected by various linker lengths to target VEGF₁₆₅. Collectively, our findings may provide quantitative guidelines for rationally designing high-affinity aptamers for targeting VEGF₁₆₅. Full article

Fermentation, one of the oldest food processing techniques, is known to play a pivotal role in improving the nutritional and functional characteristics of cereals, with positive implications for gut health and overall well-being. The present study aims to examine the phenolic acids, peptides, and potential bioactive properties of 2 novel sorghum-based fermented beverages, Niselo and Delishe. A total of 48 phenolic compounds were identified through targeted and untargeted Ultra-High Performance Liquid Chromatography coupled with a Quadrupole Orbitrap High-Resolution Mass Spectrometer (UHPLC–Q-Orbitrap HRMS) analyses, revealing a higher content of phenolic acids in Niselo and a prevalence of flavonoids in Delishe. Niselo exhibited enhanced in vitro cytoprotective and reactive oxygen species (ROS)-scavenging activity and displayed a clear cytoprotective effect against hydrogen peroxide-induced oxidative stress in Caco-2 cells. Proteomic profiling using tryptic digestion revealed that Niselo has a substantial abundance of fragments of peptides matching several stress-related and antioxidant proteins, indicating a superior stress-response and/or defense capability. Overall, these findings highlight the functional potential of sorghum-based fermented beverages, supporting their role as health-promoting products. Full article

Background: Comprehensive and accurate compound composition characterization in natural sources has high relevance in food and nutrition, health and medicine, environmental and agriculture research areas, though profiling of plant metabolites is a challenging task due to the structural complexity of natural products. This study delves into the identification and characterization of compounds within the Plantago genus, leveraging state-of-the-art analytical techniques. Methods: Utilizing an ultra-high-performance liquid chromatography (UHPLC) system in conjunction with Orbitrap™ IQ-X™ Tribrid™ mass spectrometer (MS), we employed a Phenyl-Hexyl HPLC column alongside optimized extraction protocols to analyze both husk and leaf samples. To maximize compound identification, we implemented data-dependent acquisition (DDA) methods including MS² (ddMS2), MS³ (ddMS3), AcquireX™ deep scan, and real-time library search (RTLS). Results: Our results demonstrate a significant increase in the number of putatively yet confidently assigned compounds, with 472 matches in P. lanceolata leaves and 233 in P. ovata husk identified through combined acquisition methods. The inclusion of an additional fragmentation level (MS³) noticeably enhanced the confidence in compound annotation, facilitating the differentiation of isomeric compounds. Furthermore, the application of low-energy fragmentation (10 normalized collision energy (NCE) for higher-energy collisional dissociation (HCD)) improved the detection and grouping of MS¹ fragments by 55% in positive mode and by 16% in negative mode, contributing to a more comprehensive analysis with minimal loss in compound identification. Conclusions: These advancements underscore the potential of our methodologies in expanding the chemical profile of plant materials, offering valuable insights into natural product analysis and dereplication of untargeted data. Full article

Evaluating the effect of recycling Pesticide Packaging Waste (PPW) is essential for improving recycling rates, which plays a crucial role in controlling environmental pollution and optimizing the efficiency of agricultural resources worldwide. Based on the micro-survey data of 1223 farmers in Yunnan and Hainan provinces of China, this study measures the economic effect by the farmers’ annual total household income and the ecological effect by the ecological environment quality of villages. The propensity score matching method (PSM) is employed to empirically test the economic and ecological effects of farmers’ recycling behavior of PPW and their differences. The research findings are as follows: Farmers’ recycling of PPW can generate significant positive economic and ecological effects, which are 116.7% and 4%, respectively. The heterogeneity analysis shows that farmers with a low degree of land fragmentation have a more obvious economic effect from PPW recycling, while farmers with a higher degree of land fragmentation have a more significant ecological effect; farmers with high pesticide costs have more significant economic and ecological effects from PPW recycling. Based on these findings, it is suggested to increase the attention at the policy level, enhance farmers’ environmental awareness and capacity, and focus on the characteristics of different groups. Full article

In this work, the one-pot catalytic reductive fractionation of C-lignin in castor shell powders to efficiently provide catechyl monomers was achieved by tandem metal triflate and Pd/C catalysis. The optimized Pd/C + In(OTf)₃ combination performed best and provided a 66.9 mg·g⁻¹ yield of corresponding aromatic monomers with the catechol selectivity as high as 95.4%. For the promotion effect of the Lewis acid species, the mechanism studied indicated that the introduction of In³⁺ could significantly promote the C–O bond cleavage in the LCC to release the C-lignin fragments from the solid lignocellulose and simultaneously accelerate the cleavage of the critical C_α/β–OAr linkage bond in C-lignin to release catechol monomers. In addition, performance differences highlight the cooperation and function-matching effect between the hydrogenation metals and the Lewis ion species, which can promote the high-value utilization of forestry and agricultural residues in chemical synthesis. Full article

Background: Rare diseases often lead to delayed diagnosis because clinical knowledge is fragmented across unstructured research, individual case reports, and heterogeneous data formats. This study presents ZebraMap, a multimodal knowledge map created to consolidate rare disease information and transform narrative case evidence into structured, machine-readable data. Methods: Using Orphanet as the disease registry, we identified 1727 rare diseases and linked them to PubMed case reports. We retrieved 36,131 full-text case report articles that met predefined inclusion criteria and extracted publication metadata, patient demographics, clinical narratives (cases), and associated images. A central methodological contribution is an automated large language model (LLM) structuring pipeline, in which free-text case reports are parsed into standardized fields, such as symptoms, diagnostic methods, differential diagnoses, treatments, and outcome that produce structured case representations and image metadata matching the schema demonstrated in our extended dataset. In parallel, a retrieval-augmented generation (RAG) component generates concise summaries of epidemiology, etiology, clinical symptoms, and diagnostic techniques by retrieving peer-reviewed research to enhance missing disease-level descriptions. Results: The final dataset contains 69,146 structured patient-level case texts and 98,038 clinical images, each linked to a particular patient ID, disease entry, and publication. Overall cosine similarity between curated and generated text is 94.5% and performance in information extraction and structured data generation is satisfactory. Conclusions: ZebraMap provides the largest openly accessible multimodal resource for rare diseases and enables data-driven research by converting narrative evidence into computable knowledge. Full article

We investigate the leading-power fragmentation of triply heavy $\Omega$ baryons in high-energy hadronic collisions. Extending our previous work on the ${\Omega }_{3c}$ sector, we release the full OMG3Q1.0 family of collinear fragmentation functions by completing the description of the charm channel and delivering novel ${\Omega }_{3b}$ functions. These hadron-structure-oriented functions are constructed from improved proxy-model calculations for heavy-quark and gluon fragmentation, matched to a flavor-aware DGLAP evolution based on the HF-NRevo scheme. For phenomenological applications, we employ the (sym)Jethad multimodular interface to compute and analyze NLL/NLO⁺ semi-inclusive ${\Omega }_{3Q}$ plus jet distributions at the HL-LHC and FCC. This work consolidates the link between hadron structure, rare baryon production, and resummed QCD at the energy frontier. Full article

Background: Acute coronary syndrome (ACS) is a major cardiovascular emergency influenced by environmental and genetic factors. Thrombophilic variants such as prothrombin G20210A (rs1799963) and factor V Leiden G1691A (rs6025) may influence thrombin generation and has been reported to show associations with coronary events. Methods: This case–control study included 100 ACS patients and 131 age and sex-matched healthy controls. Genotyping of rs1799963 and rs6025 was performed using polymerase chain reaction followed by restriction fragment length polymorphism (PCR-RFLP) analysis. Results: The GG genotype was markedly more common among ACS patients for both variants. For rs1799963, carriers of the A allele (GA + AA) were less common in ACS (2.0%) than controls (9.2%; p = 0.039), corresponding to an 8.6-fold higher odds of ACS in GG carriers (OR = 8.624; 95% CI: 1.757–42.345; p = 0.008). For rs6025, A allele carriers (9.0%) were also reduced in ACS versus controls (18.3%; p = 0.049), and GG homozygotes exhibited a 2.6-fold higher risk (OR = 2.635; 95% CI: 1.104–6.290; p = 0.029). Age was independently associated with higher ACS risk (OR = 1.047; 95% CI: 1.029–1.066; p < 0.001). Conclusions: Our findings indicate that the rs1799963 and rs6025 variants were independently associated with ACS, together with advancing age. Both the GG genotype and older age were associated with higher odds of ACS, whereas A-allele carriers appeared less common among ACS cases. Full article

Generative artificial intelligence (GenAI) is rapidly permeating research practices, yet knowledge about its use and topical profile remains fragmented across tools and disciplines. In this study, we present a cross-disciplinary map of GenAI research based on the Web of Science Core Collection (as of 4 November 2025) for the ten tool lines with the largest number of publications. We employed a transparent query protocol in the Title (TI) and Topic (TS) fields, using Boolean and proximity operators together with brand-specific exclusion lists. Thematic similarity was estimated with the Jaccard index for the Top–50, Top–100, and Top–200 sets. In parallel, we computed volume and citation metrics using Python and reconstructed a country-level co-authorship network. The corpus comprises 14,418 deduplicated publications. A strong concentration is evident around ChatGPT, which accounts for approximately 80.6% of the total. The year 2025 shows a marked increase in output across all lines. The Jaccard matrices reveal two stable clusters: general-purpose tools (ChatGPT, Gemini, Claude, Copilot) and open-source/developer-led lines (LLaMA, Mistral, Qwen, DeepSeek). Perplexity serves as a bridge between the clusters, while Grok remains the most distinct. The co-authorship network exhibits a dual-core structure anchored in the United States and China. The study contributes to bibliometric research on GenAI by presenting a perspective that combines publication dynamics, citation structures, thematic profiles, and similarity matrices based on the Jaccard algorithm for different tool lines. In practice, it proposes a comparative framework that can help researchers and institutions match GenAI tools to disciplinary contexts and develop transparent, repeatable assessments of their use in scientific activities. Full article

Accurate identification and monitoring of rice cultivation areas are essential for food security and sustainable agricultural development. However, regions with frequent cloud cover, high rainfall, and fragmented fields often face challenges due to the absence of temporal features caused by cloud and rain interference, as well as spectral confusion from scattered plots, which hampers rice recognition accuracy. To address these issues, this study employs a Satellite Image Time Series Transformer (SITS-Former) model, enhanced with the integration of diverse phenological features to improve rice phenology representation and enable precise rice identification. The methodology constructs a rice phenological feature set that combines temporal, spatial, and spectral information. Through its self-attention mechanism, the model effectively captures growth dynamics, while multi-scale convolutional modules help suppress interference from non-rice land covers. The study utilized Sentinel-2 satellite data to analyze rice distribution in Wuxi City. The results demonstrated an overall classification accuracy of 0.967, with the estimated planting area matching 91.74% of official statistics. Compared to traditional rice distribution analysis methods, such as Random Forest, this approach outperforms in both accuracy and detailed presentation. It effectively addresses the challenge of identifying fragmented rice fields in regions with persistent cloud cover and heavy rainfall, providing accurate mapping of cultivated areas in difficult climatic conditions while offering valuable baseline data for yield assessments. Full article

As rapid urbanisation accelerates in Global South cities, regulatory upzoning is widely promoted as a tool to expand supply and foster compact growth. Yet the interaction between permissive rules, market valuation, and institutional capacity remains underexplored. This article examines Hyderabad’s near two-decade experiment with Floor Space Index deregulation, introduced through Government Order Ms. No. 86 (2006), which eliminated citywide density caps and allowed premium-based flexibility. Using a geocoded dataset of over 9200 projects matched with government circle rate tables, spatial accessibility measures, and a subset of 2500 properties for regression analysis, this study evaluates how rules, price signals, and institutional arrangements shaped development outcomes through a rules–prices–institutions analytical framework. Results reveal that deregulation produced highly selective verticalisation, with high-rise projects concentrated in the western IT corridor while most of the city retained low-to-mid-rise form. Regression estimates demonstrate that FSI and height are capitalised in market prices but remain statistically insignificant in government valuations, reflecting a systematic undervaluation of high-intensity development in official pricing schedules. Institutional fragmentation and valuation inertia thus created a “capture gap,” where fiscal returns failed to match private value increments. The findings underscore that effective densification requires dynamic alignment of regulatory latitude, real-time valuation, and integrated governance. Hyderabad’s case illustrates broader lessons for Global South cities: the analysis demonstrates that the proposed framework is transferable beyond the empirical setting, and that blanket deregulation without fiscal and institutional reform entrenches inequality and weakens the public dividend from urban growth. Full article