Search Results (94,399)

The melon fly, Zeugodacus cucurbitae (Coquillett), is recognized as a globally significant quarantine pest, and it ranks among the most destructive insect species infesting cucurbit and solanaceous crops. However, the molecular mechanisms governing reproductive regulation in female Z. cucurbitae remain poorly characterized, particularly those underlying the reproductive processes mediated by microRNAs (miRNAs). In this study, we firstly identified the ovary-specific gene ZcCTL-S1 in Z. cucurbitae via transcriptomic analysis, and subsequently predicted its targeted miRNAs using bioinformatics approaches. Among these miRNAs, overexpression or inhibition of miR-971-1 and miR-let-7 led to corresponding inverse changes in the transcriptional level of ZcCTL-S1. Notably, only miR-let-7 displayed markedly elevated expression levels in Z. cucurbitae ovaries. Further analyses confirmed that miR-let-7 exhibited a direct targeting relationship with ZcCTL-S1, via a combinatorial approach involving in vivo RNA immunoprecipitation, in vitro dual-luciferase reporter assays, and site-directed mutagenesis techniques. Phenotypic analyses showed that both knockdown of ZcCTL-S1 and overexpression of miR-let-7 significantly inhibited egg hatchability, ultimately compromising the female reproductive capacity of Z. cucurbitae. Collectively, these findings identify a novel miRNA-gene regulatory module in the reproductive development of Z. cucurbitae, and provide novel insights for the development of gene- or miRNA-based pest control strategies. Full article

Cyberbullying behaviors manifest uniquely in different regions, shaped strongly by local slang, dialectal expressions, and cultural context. Code-mixed Chinese–English colloquial language (Cantonese) is commonly used in Hong Kong, Macau, and parts of southern China. Code-mixing is the use of multiple languages concurrently, and Cantonese text includes distinct phonetic, lexical, and syntactic features that are not exhibited in datasets developed for either Chinese or English applications. In this study, a privacy-aware code-mixed cyberbullying dataset (PCCD), containing 14,115 annotated tweets organized into 1668 sessions, was developed. Personally identifiable information and well-known identifiers, such as the names of famous celebrities, politicians, and organizations, were replaced with randomly generated dummy names. The anonymized data empirically demonstrated improved performance in terms of precision, recall, and F₁ score, indicating a greater generalization ability when handling unseen participants. To the best of our knowledge, the PCCD is the first code-mixed Chinese–English dataset that includes abuser and victim identity annotation. Our dataset facilitates the development of robust cyberbullying detection tools that researchers and developers can use to accurately measure aggressiveness, attack frequency, and abuser–victim power imbalance in a dialogue session. Full article

Prostate cancer frequently progresses to lethal, drug-resistant castration-resistant prostate cancer (CRPC), where conventional therapies often fail due to intrinsic and acquired resistance mechanisms. This resistance creates a critical therapeutic impasse, leaving patients with limited options and poor prognoses. Immunotherapy has emerged as a promising strategy to harness the immune system against these treatment-refractory tumors, offering a potential avenue to overcome the immunosuppressive barriers that underlie CRPC drug resistance. This review synthesizes findings from a structured search of PubMed, Web of Science, and Embase (2020–2025), revealing significant clinical progress: 4 vaccine trials, 5 immune checkpoint inhibitor trials, 18 combination therapy trials (≥2 agents), and 6 targeted drug trials have been conducted. Preliminary efficacy was observed in novel approaches like bispecific antibodies (e.g., Xaluritamig achieving 59% PSA50 response), PSMA-CAR-T (P-PSMA-101), and oncolytic viruses (Ad5 PSA/MUC-1/brachyury). Basic research identified four targeted resistance mechanisms (e.g., AR-LLT1, Pygo2, and HnRNP L) and one nanoparticle-mediated triple-combination therapy (CM-AMS@AD NPs integrating photothermal, chemotherapy, and immunotherapy), which enhanced cytotoxic T-cell infiltration and suppressed CRPC growth preclinically. These collective findings suggest the potential of immunotherapy for CRPC in overcoming resistance barriers and improving patient outcomes, with bispecific T cell engagers (Xaluritamig, 59% PSA50) and PSMA-directed CAR-T therapy (P-PSMA-101, >50% PSA reduction) emerging as the most promising near-term candidates and biomarker-stratified combinations (nivolumab plus rucaparib, 84.6% PSA50, in HRR-deficient patients) illustrating the transformative power of precision patient selection; however, these findings require validation in larger, biomarker-stratified trials before definitive conclusions can be drawn. Translating this potential into clinical reality requires optimized patient selection through predictive biomarkers and rigorously validated Phase III trials to confirm durable clinical responses and long-term survival benefits. Full article

Understanding how climate extremes affect crop growth in humid–subtropical hilly regions is essential for climate-smart agriculture, yet phenology-resolved evidence remains limited. We combined 20 ETCCDI extreme climate indices (1960–2024) with field records of wheat and maize production (2005–2024) from the hilly area of southwest China, and quantified climate–crop linkages using Mantel tests and generalized additive models; persistence and prospective tendencies were evaluated using Hurst (H) and Mann–Kendall statistics. Warming extremes intensified, with significant increases in TXx (0.22 °C decade⁻¹), SU25 (2.48 days decade⁻¹), and DTR (0.47 °C decade⁻¹), while TNx and TNn declined and frost days increased; most precipitation intensity indices showed no significant trends except CDD, which increased by 1.73 days decade⁻¹. Seasonally, warm extremes and CDD strengthened during the maize season, whereas climatic conditions during the wheat season were comparatively more favorable. Climate impacts on crop growth were stage-dependent, typically lagging by 1–2 months: wheat biomass was positively associated with TXx/TNx (strongest near heading), whereas maize production was more sensitive to temperature extremes (negative) and precipitation frequency indices; CDD significantly affected both crops. These findings suggest that compound heat–drought risks for maize could increase under the persistence and trend signals observed in the historical record, while modest warming may benefit wheat but cold extremes could remain a constraint for management. Full article

This study investigated the effects of inoculating peanuts with two Aspergillus flavus strains (Aspergillus flavus CGMCC 3.4408 and A. flavus LNZW 23) on plant growth and the rhizosphere bacterial community. Infection significantly inhibited peanut growth. By 60 days post-inoculation (dpi), plant height in inoculated groups (CGMCC 3.4408, 26.4 cm; LNZW 23, 25.5 cm) was significantly lower than in the non-inoculated control (CK, 32.3 cm), with concomitant significant reductions in shoot and root biomass. Analysis of rhizosphere microbiota revealed that early infection (7 dpi) reduced bacterial species richness and phylogenetic diversity. Beta diversity analysis (PCoA) confirmed a significant divergence in microbial community structure between inoculated and control groups over time, with a statistically significant difference also observed between the two inoculated strains (p = 0.016). In terms of community composition, Proteobacteria, Acidobacteriota, and Actinobacteria were the three dominant phyla. At the genus level, infection altered the relative abundance of key taxa; genera such as KD4-96, Vicinamibacteraceae, and RB41 decreased at 7 dpi, while Sphingomonas remained relatively stable. By 60 dpi, community dominance increased, marked by rising abundances of Actinobacteria and Proteobacteria. In conclusion, A. flavus infection not only suppresses peanut growth but also persistently alters its rhizosphere microbial community, with effects demonstrating both time-dependency and strain-specificity. Full article

Building height is a fundamental parameter for characterizing urban three-dimensional structure and supporting applications such as urban planning, population estimation, and energy assessment. However, traditional shadow-based height inversion methods often suffer from occlusion, shadow overlap, and orientation inconsistencies when applied to heterogeneous urban environments. This study proposes a single-image building height estimation method that explicitly incorporates spatial distribution characteristics to enhance robustness and estimation accuracy. Shadow lengths are first robustly extracted using a fishnet–Pauta strategy, followed by a multi-scenario scaling coefficient model accommodating different sun–sensor geometric configurations. Urban areas are then subdivided into high-rise, mid-to-high-rise mixed, and dense low-rise zones using DBSCAN clustering and a composite indicator system. For each spatial type, tailored optimization strategies—including neighborhood-weighted correction, similarity-constrained local regression, and median smoothing—are applied to suppress systematic biases and local outliers. Experiments on 11,168 buildings across 13 Chinese cities demonstrate strong overall performance, achieving an MAE of 2.07 m, an RMSE of 2.56 m, and an R² of 0.99. The proposed method outperforms existing approaches and remains highly stable across diverse urban morphologies, providing a scalable solution for large-area building height mapping from single high-resolution imagery. Full article

The quantity and quality of arable land are the basic prerequisites for food security; the arable land balance policy is a key measure to strictly protect farmland, and plays an important role in ensuring arable land use control, sustainable land use and reducing the contradiction between people and land. Drawing on panel data from 26 Chinese provinces spanning 2004 to 2017, this study employs the Nerlove supply response model to empirically examine the impact mechanism and regional heterogeneity of the arable land balance policy on the structure of grain crop cultivation, considering variations in land use following farmland supplementation. The findings reveal that the policy has induced fluctuations in grain crop structure, oscillating between “grain-oriented” and “non-grain-oriented” patterns. These shifts are primarily driven by the heterogeneous technological effects associated with farmland supplementation, which influence farmers’ planting decisions. Nonetheless, the policy has helped mitigate the adverse effects of farmland development on grain production, with the mitigation effect being more pronounced in non-major grain-producing regions. Furthermore, supporting measures such as land consolidation, outsourcing of agricultural services, and cross-regional mechanized operations have contributed to maintaining grain crop cultivation after land supplementation. Based on these findings, optimizing the arable land balance policy requires greater alignment with crop-specific production characteristics and regional farming practices. This includes refining the farmland supplementation coefficient and enhancing the policy’s differentiation mechanisms. Policy adjustments should also reflect the economic development levels and natural resource endowments of both major and non-major grain-producing regions, to promote a functional equilibrium in farmland utilization. Additionally, efforts to improve soil fertility and mechanization capabilities following land supplementation are essential to sustaining stable grain production. This study provides decision-making information support for optimizing the arable land balance policy and improving crop planting structure. Full article

Despite extensive pore-scale studies on oil–water displacement, quantitative understanding of the dynamic evolution of residual oil morphology and waterflooding efficiency in geologically heterogeneous sandstones remains limited, particularly under large water-injection multiples. To better understand pore-scale oil–water distribution and its influence on enhanced oil recovery, this study utilized Micro-CT combined with SEM-EDS to examine the 3D pore structure and oil–water phase evolution in a heterogeneous sandstone sample from the Xiayang Formation, Wushi Sag, Zhanjiang. Mineralogical analyses reveal that dolomite cementation and vermicular kaolinite infilling introduce strong pore-scale heterogeneity by selectively reducing pore connectivity and permeability, posing challenges for uniform fluid displacement. A 30% KI solution was used to enhance X-ray attenuation of the aqueous phase, enabling clear discrimination between oil and water. Micro-CT reconstructions reveal a relatively uniform pore network dominated by medium-to-large intergranular pores. As the water-injection multiple increases, water progressively invades larger pores, while residual oil is immobilized by capillary forces within micro-throats, forming isolated clusters. The oil-droplet size distribution broadens from a narrow range (50–100 µm) to a wider one (200–300 µm), indicating interfacial destabilization and droplet coalescence. Quantitative analysis indicates that oil saturation decreases from approximately 90% to 36%, while waterflooding efficiency increases rapidly to ~45% at 1 PV and gradually approaches a plateau of ~60% beyond 500–1000 PV. This waterflooding plateau is attributed to capillary trapping and pore-scale connectivity limitations imposed by mineral-induced heterogeneity, which prevent further mobilization of residual oil despite continued water injection. This study advances pore-scale waterflooding research by combining mineralogical heterogeneity with long-term micro-CT imaging, revealing the pore-scale mechanisms controlling residual oil evolution and ultimate waterflooding limits in realistic sandstone. Full article

Environmental challenges, such as climate change, resource scarcity, and pollution, increasingly demand organizational strategies that integrate artificial intelligence (AI) into sustainable innovation. This study examines how employee-level artificial intelligence capabilities (AIC) enable digital green innovation, a strategic approach that leverages AI-powered digital technologies to enhance green product development, green processes, and sustainable supply chains. Drawing on knowledge-based view (KBV) and the dynamic capability view (DCV), this study develops a theoretical framework linking AIC, knowledge-based dynamic capabilities (KBDC), and digital green innovation. Using survey data from 299 employees in Chinese High-Tech firms, results show that higher employee AIC strengthens KBDC, which in turn facilitates effective digital green innovation. The findings contribute theoretically by extending the antecedents of digital green innovation to the individual level and clarifying the multilevel mechanism through which AIC translates into organizational environmental performance, thereby enhancing both theories’ explanatory power in digital environments. Practically, the study highlights the importance for environmental managers of strengthening employee AIC and organizational KBDC to implement AI-driven sustainability strategies more effectively. Full article

Sugarcane (Saccharum spp.) is a globally vital sugar crop, yet its productivity faces severe challenges from infestation by Chilo sacchariphagus. To decipher the plant’s molecular and metabolic defense mechanisms, this study applied an integrated transcriptomic and metabolomic analysis to three field-grown sugarcane cultivars (Zhongtang 4, 5, and 6) under natural borer stress. The transcriptomic analysis identified a total of 34,004 differentially expressed genes (DEGs), of which 18,674 were up-regulated, and 15,330 were down-regulated. The three cultivars exhibited distinct transcriptional regulatory patterns: Z4 and Z5 showed a global suppression-type response and a strong activation-type response, respectively, and Z6 presented a balanced-type response. A functional enrichment analysis revealed that the DEGs were significantly involved in metabolic processes, stress response, plant hormone signal transduction, phenylpropanoid biosynthesis, and plant-pathogen interaction pathways. Metabolomic analysis detected 963 differentially accumulated metabolites (DAMs), primarily including flavonoids, phenolic acids, amino acids and their derivatives, and lipids. These metabolites were significantly enriched in pathways such as amino acid metabolism, biosynthesis of secondary metabolites, and glutathione metabolism. Integrated multi-omics analysis further revealed strong synergistic regulatory relationships between gene expression and metabolite accumulation, particularly in defense-related secondary metabolic pathways, such as phenylpropanoid and flavonoid biosynthesis. Several key regulatory hubs were identified, including novel transcripts and D-xylulose-5-phosphate. Sugarcane employs a genetic background-dependent, multi-layered transcriptional reprogramming and metabolic restructuring to cope with borer stress. Cultivars Z4 and Z6 tend to activate and accumulate defensive compounds, while Z5 exhibits a different pattern of metabolic resource allocation. This research provides a systematic elucidation of the molecular mechanisms underlying insect resistance in sugarcane and offers important candidate genes and metabolites for breeding resistant varieties. Full article

The equate-to-differentiate (ETD) model posits that individuals tend to equate a less significant difference between options on one dimension and thus leave the greater one-dimensional difference to be differentiated as the determinant for the preferred option. However, when confronted with an ostensibly “simple” choice between two risky options with single-nonzero outcomes or between two intertemporal options with single-dated outcomes, we face an insurmountable barrier against the ETD model’s explanation and prediction of these choices. The reason is that determining which intra-dimensional difference (∆Payoff_A,B or ∆Probability_A,B/∆Delay_A,B) between Option A and Option B is greater is meaningless and is considered to be a challenge in the physical world. To address this challenge and evaluate whether such decisions are indeed governed by the ETD process, the present study developed a visual analogue scale designed to capture individuals’ subjective comparisons across dimensions of different units. Across two studies, we demonstrate that the analogically measured intra-dimensional comparison reliably and consistently predicts choice patterns attributed to separate anomalies: the common difference effect and unit effect in intertemporal decisions, and subproportionality and the peanuts effect in risky decisions. These findings suggest that both types of decisions may share a common cognitive mechanism based on dimensional evaluation, despite involving distinct informational metrics (time vs. probability). By enabling direct measurement of dimension-wise comparisons, our analogue scale—though unconventional—offers a novel methodological tool for exploring the underlying structure of seemingly “simple” decisions. The implications of this work extend to the development of unified models capable of integrating intertemporal and risky decision-making under a shared explanatory framework. Full article

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) employs a 532 nm laser with strong water-penetration capability, making it well suited for satellite-derived bathymetry in shallow waters; however, the effective denoising of photon-counting data remains essential due to strong solar background and intrinsic instrument noise. To address this challenge, this study proposes a novel photon denoising method, termed the Directional Nearest Neighbor Distance-based Algorithm (DNNDA), for robust extraction of signal photons from shallow-water ICESat-2 data. Unlike existing methods that rely heavily on density or terrain features and often degrade under high-noise conditions, DNNDA systematically exploits both scale-corrected spatial relationships and directional distribution characteristics of photons. By quantitatively characterizing the directional features of photon distributions and embedding this information into a density representation, DNNDA amplifies the density contrast between signal and noise photons, rendering the seafloor signal photons more distinct and easier to extract. An evaluation index was further designed to automate optimal parameter determination. Validation using multiple global ICESat-2 datasets demonstrates that DNNDA achieves superior seafloor photon extraction performance, with F1-scores exceeding 95%. Further regression analysis against high-precision CUDEM data in the Puerto Rico region yields root-mean-square errors below 0.57 m. By jointly correcting scale anisotropy and incorporating directional information, DNNDA enables reliable and adaptive signal photon extraction across local and global scales, providing a robust solution for shallow-water bathymetry in complex, high-noise environments. Full article

Autonomous mobile robots require robust localization in complex and dynamic environments, where single-sensor solutions often fail due to accumulated drift or signal degradation. LiDAR–inertial odometry provides accurate short-term motion estimation, but suffers from long-term error accumulation, whereas RTK-GNSS offers absolute positioning that becomes unreliable under occlusion or multipath effects. To solve the above problems, this paper proposes an adaptive multi-sensor fusion positioning framework that dynamically fuses LiDAR, IMU, and RTK-GNSS data based on the real-time quality evaluation of sensors. The system uses the front-end tightly coupled LiDAR–IMU iterative extension Kalman filter (IEKF) as the core estimator and combines loop detection with incremental factor graph optimization to suppress long-term drift. In addition, a degradation detection method based on the minimum eigenvalue of the Jacobian matrix is proposed to identify unreliable matching constraints in real time. In order to avoid abrupt changes in positioning results caused by fluctuations in sensor data quality, the system adopts a smooth fusion strategy based on covariance weighting. Experiments on the KITTI benchmark and self-collected datasets demonstrate that the proposed method significantly improves localization accuracy and robustness compared with pure LiDAR-based approaches, achieving stable centimeter-level performance while maintaining real-time capability on embedded platforms. Full article

Background: Perineural invasion (PNI) is associated with aggressive tumor behavior in several malignancies, but its independent prognostic value in early-stage cervical cancer remains uncertain. We evaluated the clinical significance of PNI and explored molecular and immune features associated with PNI. Methods: We retrospectively analyzed 499 patients with FIGO 2009 stage IB–IIA cervical cancer treated with radical hysterectomy and pelvic lymphadenectomy. Associations between PNI, clinicopathological variables, recurrence-free survival, and overall survival were assessed using Kaplan–Meier methods and Cox regression. An independent cohort of 286 cervical cancers from The Cancer Genome Atlas (TCGA) was analyzed to characterize PNI-associated transcriptomic patterns, pathway enrichment, immune cell composition, and microRNA profiles. Results: PNI was identified in 11.6% of cases and was associated with larger tumor size, deep stromal invasion, and lymphovascular space invasion. PNI was not an independent prognostic factor in the overall cohort; however, it was associated with increased recurrence risk in the subgroup without high-risk factors and not meeting Sedlis criteria, with a modest improvement in 5-year recurrence discrimination when incorporated into Sedlis-based models. In TCGA, PNI was associated with differential gene expression and enrichment of oncogenic and immune-related pathways, an increased estimated abundance of resting mast cells, and six differentially expressed microRNAs. Conclusions: In early-stage cervical cancer, PNI is strongly correlated with established adverse pathological features and shows a subgroup-specific association with recurrence in an otherwise low-risk postoperative population. The multi-omics findings are exploratory and support biological hypotheses regarding tumor–nerve–immune interactions; external validation is needed before PNI can be used to guide postoperative management. Full article

NiO electrochromic films have significant potential for applications in smart windows, displays, energy-efficient buildings, and portable electronics, owing to their excellent electrochemical stability, favorable optical modulation performance, and environmental friendliness. However, several challenges remain, such as limited long-term durability, stability under extreme environmental conditions, and the cost-effectiveness of large-scale production. Future research efforts should focus on enhancing the cyclic stability and environmental adaptability of NiO films, developing low-cost fabrication techniques, and advancing multifunctional composite materials for smart devices. This review summarizes recent advances in the preparation and performance optimization of NiO electrochromic films. Several key fabrication methods—including magnetron sputtering, hydrothermal synthesis, electrodeposition, chemical bath deposition, sol–gel processing, and spray pyrolysis—are highlighted, and their effects on film structure, thickness uniformity, and optical properties are analyzed. Furthermore, the critical role of different electrolytes (inorganic, organic, and gel-based) in the electrochromic process is discussed, with a comparative evaluation of their influence on the electrochromic performance of NiO films. This article offers a comprehensive overview of the progress in high-performance NiO electrochromic films and provides theoretical insights and technical support for their broader application in renewable energy and smart home technologies. Full article