Search Results (7,303)

Analyzing events in dynamic environments poses a fundamental challenge in the development of intelligent agents and robots capable of interacting with humans. Current approaches predominantly rely on visual–text models; however, these methods often capture information implicitly from images, lacking interpretable and structured spatio-temporal object representations and their relationships. To address this issue, we introduce DyGEnc—a novel method for dynamic graph encoding. This method integrates compressed spatio-temporal representation with the cognitive capabilities of large language models. The purpose of this integration is to enable advanced question answering based on sequences of textual scene graphs. Extensive evaluations on the STAR and AGQA datasets demonstrate that DyGEnc improves large language model performance when addressing queries related to the history of human–object interactions. Furthermore, the proposed method can be extended to process input images by leveraging foundation models to extract explicit textual scene graphs, as validated by the evaluation results. We expect these findings to contribute to the development of robust and compact graph-based memory for long-horizon reasoning in real-world applications, as demonstrated in a robotic experiment conducted using a wheeled manipulator platform. Full article

Background: Urothelial carcinoma (UC) is characterized by high recurrence rates and the need for long-term surveillance. Cystoscopy remains the diagnostic gold standard but is invasive, costly, and burdensome for patients. Urine, as a tumor-proximal and non-invasive biospecimen, represents an attractive source for biomarkers enabling screening, diagnosis, risk stratification, and follow-up. Objective: This review summarizes current and emerging urine-based diagnostic approaches for UC, ranging from conventional cytology to advanced molecular technologies, and discusses their clinical utility, limitations, and future perspectives. Methods: A narrative review of the literature was conducted focusing on urine-based diagnostics for UC, including urinary cytology, FDA-approved and investigational protein and DNA/RNA biomarkers, next-generation sequencing (NGS), cell-free DNA (cfDNA), exosomes, and microRNAs. Evidence from clinical validation studies, meta-analyses, and translational research was evaluated. Results: Urinary cytology remains highly specific for high-grade disease but has limited sensitivity for low-grade tumors. Protein- and DNA-based biomarkers have improved sensitivity but often lack sufficient specificity for standalone use. Recent advances in NGS-based assays enable comprehensive detection of tumor-specific genomic alterations in urinary cfDNA, offering high sensitivity for both initial diagnosis and disease monitoring. Exosomes and microRNAs represent promising biomarkers reflecting tumor biology, though standardization and large-scale validation are ongoing challenges. Overall, multimodal approaches combining cytology with molecular assays appear most promising for clinical implementation. Conclusions: Urine-based diagnostics are rapidly evolving toward integrated liquid biopsy platforms capable of transforming UC management. While several assays show strong potential to reduce reliance on cystoscopy, robust prospective validation, cost-effectiveness analyses, and clinical integration strategies are required before widespread adoption. Full article

High-salinity water environments, e.g., saline lacustrine basins and lagoons, represent significant sedimentary settings on Earth. They serve not only as crucial archives of paleoclimate and paleoenvironmental evolution but also as favorable realms for the development of high-quality hydrocarbon source rocks. Although traditional views suggested that high salinity inhibits biological activity and is thus detrimental to source rock formation; recent hydrocarbon discoveries in formations such as the Leikoupo Formation (Sichuan Basin) and Majiagou Formation (Ordos Basin) in China have confirmed the exceptional hydrocarbon generation potential of source rocks in such settings. Focusing on major sedimentary basins in China, this review synthesizes how high-salinity settings critically control the integrated “generation-storage” sequence of hydrocarbon source rocks. Research indicates that moderate salinity can promote blooms of halophilic microorganisms, e.g., algae, cyanobacteria, resulting in high primary productivity. Concurrently, salinity-driven stable water stratification creates a strongly reducing bottom water environment, which greatly facilitates the preservation of organic matter, establishing a synergistic enrichment model of “high productivity—excellent preservation.” Products of high-salinity environments, such as evaporites, e.g., gypsum, halite, can act as catalysts, lowering the activation energy for hydrocarbon generation and enhancing hydrocarbon yield. Additionally, associated organic salts provide supplementary material for hydrocarbon generation. Regarding reservoir quality, the laminated structures formed in high-salinity settings, combined with organic–inorganic synergistic diagenesis, e.g., dolomitization, organic acid dissolution, and hydrocarbon-generation overpressure, collectively shape high-quality reservoirs with significant heterogeneity. Despite important progress, challenges remain, including the quantitative analysis of primary factors controlling organic matter enrichment, the threshold of salinity inhibiting biological communities, and the prediction of strongly heterogeneous reservoirs. Saline settings serve as critical carbon sinks in the geological carbon cycle through high primary productivity, enhanced preservation conditions, and distinctive mineral assemblages, playing a particularly important role in the formation of hydrocarbon source rocks and long-term carbon sequestration. Future research should integrate modern saline lake observations with high-resolution characterization techniques to deepen the understanding of the formation mechanisms of high-salinity source rocks, aiming to provide theoretical guidance and exploration targets for petroleum systems in similar geological settings worldwide. Full article

Monoculture and mixed sowing are common practices for restoring degraded alpine meadow grasslands. To investigate the effects of different sowing patterns on soil bacterial community characteristics in alpine artificial grasslands, this study examined a 20-year-old established artificial grassland, systematically analyzing plant community attributes, soil physicochemical properties, and the diversity and functional structure of soil bacterial communities under various monoculture and mixed-sowing treatments. The results showed that: (1) Mixed-sowing treatments significantly improved soil physicochemical properties and plant community characteristics. The P4 (Elymus nutans + Poa pratensis + Festuca sinensis + Poa crymophila) mixed-sowing treatment notably enhanced vegetation performance and soil conditions. Compared with the monoculture P1 (Elymus nutans) treatment, aboveground biomass (AGB) and soil organic matter (SOM) content increased by 57.23% and 68.25%, respectively, indicating that perennial grass mixtures improve soil water and nutrient retention, thereby promoting plant growth. (2) Microbiome analysis revealed that mixed sowing significantly optimized the structure of rhizosphere bacterial communities. Operational Taxonomic Units (OTUs), which represent sequence-based taxonomic units and their abundance information, were most abundant in the P4 mixed-sowing treatment, reaching a total of 5685 OTUs. In terms of bacterial diversity indices, the OTU richness, Ace index, and Chao1 index in the P4 mixed-sowing treatment were 26.12%, 25.81%, and 24.34% higher, respectively, than those in the monoculture P1 treatment, with all differences being statistically significant (p < 0.05). (3) Mantel test and redundancy analysis (RDA) revealed that soil electrical conductivity (SEC) and pH were negatively correlated with bacterial diversity indices, while soil organic matter (SOM) was identified as the key environmental driver shaping bacterial community assembly. In summary, appropriate grass mixtures effectively enhance “plant–soil–microbe” interactions, leading to improved soil fertility and optimized bacterial communities, representing a viable strategy for long-term ecological restoration and sustainability of alpine artificial grassland ecosystems. The P4 treatment—comprising a four-species mixture of Elymus nutans, Poa pratensis, Poa crymophila, and Festuca sinensis—achieved the best overall performance. Full article

In the field of ocean observation, we often face the challenge of the contradiction between the vast ocean environment and limited ocean sensor observations, resulting in significant sparsity in the acquired ocean sensor data. This sparse ocean sensor data typically exhibits characteristics such as discrete spatial distribution, discontinuous observation time, and vertical stratification with water depth variations. Current methods primarily employ rule-based quality control, time series modeling, or traditional graph neural networks for processing. This paper addresses the characteristics of sparse ocean sensor data, building upon these methods by further utilizing topological correlation and hierarchical feature modeling on a topological basis. It proposes a depth-aware heterogeneous spatiotemporal graph neural network (DAHSGNN) to achieve efficient anomaly detection and data correction for this type of data. DAHSGNN integrates discrete observation data along the depth axis using a local graph construction method. It employs hierarchical feature engineering to characterize the vertical stratification of the ocean. A Gaussian Hidden Markov Model is used to segment the water layers, and intra- and inter-layer trend features are extracted using a water layer probability-guided Transformer encoder. Then, a bidirectional long short-term memory deep sequence encoder captures the local dynamic context, thereby achieving fine-grained modeling of the ocean’s vertical stratification features. Finally, a heterogeneous graph autoencoder is used to reconstruct the site-level data distribution. Experiments were conducted using multiple environmental variables from the International Seabed Authority (ISA) DeepData database. Results show that DAHSGNN exhibits good cross-variable generalization ability, achieves higher reconstruction accuracy than baseline methods, and significantly improves anomaly detection performance. Full article

Accurate gold price forecasting is crucial for economic stability and investment decision-making. In order to improve the accuracy of gold price prediction and quantify the uncertainty of gold price fluctuation, this paper proposes a hybrid model (CNN-QRLSTM) that integrates convolutional neural network (CNN) and quantile regression long- and short-term memory network (QRLSTM) and innovatively introduces news text data to quantify the media sentiment. We combine EEMD with the Hurst index to remove white noise from the original signal, and the processed data is used as the input layer of the prediction model. Furthermore, to demonstrate the impact of news sentiment on gold prices, this paper employs entropy measurement methods based on information theory to quantify the uncertainty and information content embedded within processed gold price sequences and derived sentiment indicators. The mutual information (MI) algorithm, based on information entropy, captures the nonlinear correlations between financial keywords and market sentiment. It constructs a financial sentiment lexicon (covering keywords such as economic policies and geopolitical conflicts), combines semantic rules with context-weighted strategies, calculates sentiment scores for news texts, and generates daily aggregated media sentiment indicators. This entropy-based perception method not only enhances the interpretability of emotion-driven fluctuations but also provides a theoretical foundation for reducing prediction uncertainty through multi-source data fusion. The experiment uses 2022–2025 daily London gold spot price data, Shanghai Gold Exchange gold price data, and the same period of Gold Investment Network gold market news to carry out the study. The empirical study shows that the synergy of multi-source data fusion and the quantile regression mechanism can improve the accuracy of gold price prediction and the new paradigm of risk interpretation while providing theoretical support for the formulation of quantitative investment strategies. Full article

Malicious traffic detection in the Industrial Internet of Things (IIoT) faces significant challenges, primarily due to the scarcity of labeled data, high inference latency on resource-constrained edge devices, and the lack of comprehensibility in deep learning models. To overcome these limitations, this paper proposes MeeDet, a novel detection framework that integrates Mamba-based state-space modeling, a dynamic early-exit mechanism, and Large Language Model (LLM)-driven comprehensibility. The proposed MeeDet operates through a four-stage pipeline. First, raw packet captures are preprocessed into header-only, standardized stride-based sequences. Second, a 12-layer unidirectional Mamba backbone is pretrained on unlabeled data using two complementary tasks: Masked Byte Modeling for byte-level semantics and Next-Flow Prediction for long-range flow-level temporal coherence. Third, the model is fine-tuned by attaching lightweight binary heads to each Mamba layer, allowing for the early termination of high-confidence benign samples and adaptive routing of ambiguous flows to deeper layers. Finally, for detected malicious samples, structured prompts containing key network traffic features are processed by an LLM to generate human-readable diagnostic reports, without affecting real-time detection latency. Extensive experiments on five public IIoT datasets demonstrate the superiority of MeeDet over existing baselines. MeeDet achieves F1-scores exceeding 0.98 on key benchmarks while significantly reducing computational overhead. Specifically, at a 1% malicious traffic ratio, MeeDet requires only 1.7 MFLOPs and 1.58 ms of average inference latency, representing a reduction of over 70% in computational cost compared to strong pretrained baselines. Full article

The genus Capsicum comprises several species that are vital vegetable and spice crops cultivated worldwide, possessing significant economic, nutritional, and ornamental value due to their diverse fruit morphologies, colors, spiciness levels, and stress resistance. Historically, the large genome size (approximately 3 Gb) and high proportion of repetitive sequences (over 80% transposable elements) have constrained in-depth analysis of structural variations and functional genes within Capsicum species. However, recent advances in long-read sequencing, Hi-C scaffolding, and genome assembly have enabled the production of multiple high-quality and telomere-to-telomere (T2T) Capsicum genomes, which have ushered in a new era of research at the nuclear, organellar, and pan-genome levels. The publication of these omics resources has greatly expanded our understanding of the evolution of agronomically and environmentally relevant traits in peppers and their wild relatives. This review systematically summarizes recent progress in reference genomes, pan-genomes, and organellar genomes of the genus Capsicum, highlighting the enhancement of key breeding trait analyses through omics data, and outlines future integrated breeding strategies to provide theoretical and methodological references for genetic improvement and molecular breeding in pepper. Full article

Ascorbate peroxidase (APX) is a heme-containing enzyme involved in hydrogen peroxide (H₂O₂) detoxification within the ascorbate–glutathione (AsA–GSH) cycle. In this study, the full-length genomic DNA and cDNA of an APX1 gene (VsAPX1) were cloned and characterized from Vicia sativa. The genomic sequence of VsAPX1 is 2425 bp in length and comprises 10 exons separated by nine introns, with the first intron located within the 5′ untranslated region (5′UTR). The corresponding cDNA is 1010 bp long and includes a 61 bp 5′UTR, a 753 bp open reading frame, and a 196 bp 3′UTR. VsAPX1 encodes a predicted cytosolic APX protein of 250 amino acids, with a molecular weight of 27.1 kDa and a theoretical isoelectric point (pI) of 5.60. Bioinformatics analysis revealed that the deduced VsAPX1 protein shares high sequence similarity with cytosolic APX1 proteins from other plant species, contains conserved APX domains, and clusters within the cytosolic APX clade in phylogenetic analysis. Quantitative real-time PCR analysis showed that VsAPX1 expression exhibits transient and moderate changes in response to abiotic stress and phytohormone treatments. Transcript levels increased at early time points following heat stress (42 °C), abscisic acid, and salicylic acid treatments, and after 4 h of jasmonic acid exposure, whereas hydrogen peroxide treatment resulted in a gradual down-regulation of expression. Overall, this study provides the first molecular and expression characterization of a cytosolic APX1 gene from Vicia sativa and establishes a foundation for future functional analyses of antioxidant genes in this species. Full article

Dinophysis acuminata, the main agent of diarrhetic shellfish poisoning (DSP) worldwide, shows a high variability in morphology and toxin content between strains from contrasting habitats. Most frequent uncertainties in morphological discrimination are within the “D. acuminata complex”, but confusion with other species (e.g., D. norvegica, D. fortii) also occurs. Here we describe a unique PTX2-containing population of Dinophysis cf acuminata observed during opportunistic samplings in San Rafael Lagoon (Chilean Patagonia), the only tidewater glacier lagoon remaining in the glacier with the world’s lowest latitude. Dinophysis acuminata was the only Dinophysis species observed during three seasonal surveys in the well-mixed cold (4–7° C) and brackish (salinity 14–15) waters of the lagoon. Cell densities ranged from 500 cells L⁻¹ (winter) to 2800 cells L⁻¹ (summer). Partial sequences of their ITS rDNA aligned them with D. acuminata strains from Europe and North America, and sequences of their stolen plastids 23S rDNA confirmed ciliates of the Mesodinium rubrum + major complex as their prey and plastid source. All these reasons make this lagoon a highly sensitive area and natural laboratory for climate change-related topics and Dinophysis issues related to (i) the effect of long-term exposure of marine fauna to pectenotoxins and (ii) the adaptations of D. cf acuminata to persist in a unique ecosystem with austral water characteristics located in a warm temperate latitude light regime. Results here add knowledge to the biogeography and habitat ranges of D. acuminata and the problems faced to monitor and provide early warning of its distribution. Full article

Efficient and clean separation of residual plastic mulch film is the primary bottleneck hindering its resource-oriented reutilization. Currently, the field faces critical technical challenges, most notably the elusive motion mechanisms of flexible materials and the inherent difficulty of film–impurity separation. To address these issues, this study investigates a drum-type mulch-film impurity-removal unit by modeling the throw-off motion mechanism of the material stream, followed by comprehensive multiphysics simulation and optimization. First, to overcome the simulation hurdles typical of flexible materials, “Meta-particles” and the “Bonding V2” contact model were implemented on the EDEM platform to establish a discrete element method (DEM) framework. The resulting analysis revealed a non-linear transport trajectory and morphological evolution within the drum flow field, characterized by a “wall-adhering–slipping–throwing” sequence. These findings were further quantified through MATLAB-based numerical calculations to determine collision frequency and axial residence behavior. Second, ANSYS modal analysis verified the dynamic stability of the frame structure, confirming that the operating frequency (2.37 Hz) remains well below the first natural frequency (6.77 Hz). Furthermore, Box–Behnken response surface methodology (RSM) was employed to elucidate the coupled effects of key process parameters. The results demonstrated that separation efficiency and impurity-removal mass are predominantly governed by the quadratic terms of the inclination angle and rotational speed, respectively. After multi-objective optimization and engineering refinement, the optimal operating parameters were established: a film length of 220 mm, an inclination angle of 3°, and a drum rotational speed of 25 r/min. Bench tests indicated that, under these optimal conditions, the impurity-removal rate stabilized between 71.5% and 72.4%, satisfying the design requirement (≥70%). By elucidating the drum’s throw-off screening mechanism, this study achieves a coordinated improvement in both impurity-removal mass and separation efficiency, resolving long-standing engineering uncertainties regarding film–impurity trajectories and providing a theoretical foundation for the clean treatment of waste mulch film. Full article

Visual SLAM algorithms rely on image sequences to achieve autonomous localization and mapping, where line features act as crucial structural information to enhance system robustness in weakly textured or structured environments. However, conventional line feature-based methods, such as the Line Segment Detector (LSD) algorithm, are prone to over-segmentation during line segment extraction, resulting in a large number of redundant short segments and fragmented line pieces. This phenomenon increases the false matching rate, which in turn degrades the accuracy of pose estimation and the overall stability of the Visual SLAM system. To address the above issues, we perform comparative experiments on multiple public datasets between the proposed improved line feature algorithm and classical counterparts from dimensions of time overhead, line feature number and detection accuracy. The results show that the proposed algorithm incurs a 20% increase in overall time for line feature extraction and matching, yet achieves a 14% higher proportion of long line segments, an 8% improvement in Average Precision (AP) and a 15% rise in Average Recall (AR). It is thus verified that the proposed method retains real-time performance while remarkably improving its line segment matching success rate, with its localization accuracy and system robustness maintained or even enhanced. Full article

Saussurea involucrata is a rare perennial herb endemic to the alpine zone of the Tianshan Mountains, possessing significant medicinal value yet facing severe threats from overharvesting and habitat fragmentation. The core distribution area and recognized genetic differentiation center of this species are located in the Bayinbuluke region of the Western Tianshan Mountains. In contrast, the genetic distinctiveness and conservation status of populations in the Eastern Tianshan Mountains have remained unclear. To clarify the genetic relationships, we conducted an integrated analysis using nuclear microsatellite (SSR) markers as well as chloroplast (cpDNA) and nuclear ribosomal DNA (nrDNA) sequences on 16 populations (5 from the Eastern Tianshan Mountains and 11 from the Bayinbuluke region). The results showed that the Eastern Tianshan Mountains populations exhibited higher genetic diversity (mean He = 0.5568). Bayesian clustering and principal coordinate analysis (PCoA) clearly separated all populations into two genetic groups corresponding to the two geographical regions. Notably, private haplotypes (cpDNA H1 and nrDNA H7) were identified exclusively in the Eastern Tianshan populations, and no recent genetic bottleneck was detected, indicating historical demographic stability. These findings demonstrate significant genetic differentiation and a unique evolutionary trajectory in the Eastern Tianshan Mountains populations, likely resulting from long-term geographical isolation and local adaptation to arid environments. Therefore, we propose that these populations be managed as an independent Management Unit (MU) to preserve their unique genetic legacy. This study provides critical genetic evidence for refining conservation strategies and promoting the sustainable use of this endangered species, while the established molecular marker system also offers a reliable framework for its geographical traceability. Full article

Background/Objectives: Cervical cancer remains a leading cause of cancer morbidity and mortality worldwide. Although chemoradiation followed by brachytherapy is the curative-intent standard for locally advanced disease, outcomes remain heterogeneous and recurrence and distant metastasis persist. In parallel, immune checkpoint inhibitors (ICIs) and antibody–drug conjugates (ADCs) have expanded systemic options in recurrent or metastatic settings and created new opportunities for multimodality. This review aims to integrate treatment-relevant cervical cancer biology and biomarkers to clarify how chemoradiation, immunotherapy, and ADCs can be optimally selected, sequenced, and combined across disease states. Methods: We conducted a structured narrative, evidence-based literature synthesis focusing on cervical cancer management. The review encompassed: (i) the molecular and immune mechanisms underlying human papillomavirus (HPV)-driven carcinogenesis; (ii) contemporary diagnostic and staging approaches, including advanced imaging modalities and histopathological evaluation; and (iii) clinical and translational evidence supporting the optimization of chemoradiation, immune checkpoint inhibition, and antibody–drug conjugates, with emphasis on clinically validated or emerging biomarkers that are relevant to patient stratification and mechanistically rational combination or sequencing strategies. A systematic search of PubMed/MEDLINE, Embase, and major oncology conference proceedings was performed. Priority was given to peer-reviewed original research articles, high-impact clinical trials (Phase II–III), meta-analyses, and consensus guidelines published within the past 10 years to ensure contemporary relevance. Articles published prior to this period were generally excluded to maintain clinical currency; however, seminal studies that established foundational therapeutic standards, mechanistic paradigms, or landmark treatment milestones were intentionally retained due to their enduring influence on current practice. Exclusion criteria included non-peer-reviewed sources, case reports with limited generalizability, non-English publications, and studies lacking methodological rigor or clinical relevance to cervical cancer management. Preclinical studies were included selectively when directly informing therapeutic mechanisms, biomarker development, or translational rationale. This approach was designed to balance historical context with up-to-date clinical applicability, ensuring both scientific rigor and contemporary relevance. Results: Chemoradiation and brachytherapy remain essential for local control, while ICIs can restore antitumor T-cell activity in biomarker-enriched contexts. ADCs enable target-directed delivery of potent cytotoxins and may promote immunogenic cell death, supporting immunotherapy and radiation. However, key challenges include resistance mechanisms, toxicity management, and patient identification for the most beneficial combined multimodality. Conclusions: A biology- and biomarker-informed framework can guide more rational integration of multimodality therapy in cervical cancer. Future progress will depend on validated predictive biomarkers, optimized sequencing/combination strategies, and trials that balance efficacy with short- and long-term toxicity. Full article

Achieving completeness of multipartite bacterial genomes has been a difficult task, especially in rhizobia. In this study, we performed a deep bioinformatic analysis of the newly re-sequenced genome of Rhizobium favelukesii LPU83^T. This strain was isolated from acid soils in Argentina and is capable of nodulating several leguminous plants, although it is unable to fix nitrogen efficiently in any of them. Oxford Nanopore sequencing allowed us to completely assemble the symbiotic plasmid of the strain, pRfaLPU83b, and we discovered that it harbors three intact prophages and a high density of insertion sequences (ISs). These characteristics show why it is often so difficult to complete the symbiotic plasmids of rhizobial strains and the importance of having long-read sequencing methods. Upon detailed analysis of this replicon, we identified a complete conjugation system with gene structure consistent with quorum sensing-associated systems that may have contributed to the genetic mosaic structure of the strain. Furthermore, we identified in the symbiotic plasmid of R. favelukesii LPU83^T a large proportion of the symbiotic genes previously identified as essential for Biological Nitrogen Fixation (BNF) in symbiosis with alfalfa, with a high percentage of identity with respect to those of Sinorhizobium meliloti 2011. Among the determinants related to BNF, we found genes encoding the HrrP and SapA peptidases in the LPU83 genome, previously described and related to the degradation of nodule-specific cysteine-rich peptides. These peptides are essential for bacteroid differentiation and, therefore, efficient BNF. Our results show that despite having these genes, they are not directly responsible for the inefficient BNF phenotype of LPU83. Full article