Search Results (24,693)

The genus Lasiodiplodia includes numerous plant-pathogenic species whose delimitation is complicated by overlapping morphological traits and limited resolution of common genetic markers. Lasiodiplodia brasiliensis was described as a species closely related to L. theobromae; however, its taxonomic status remains controversial. In this study, we re-evaluated the species boundaries between L. theobromae and L. brasiliensis using an integrative approach that combined multilocus and genome-scale phylogenetic analyses with morphological comparisons. Multilocus phylogenetic analyses based on ITS, tef1-α, tub2, and rpb2 revealed an unresolved relationship between the two taxa. The L. theobromae clade had low bootstrap support, whereas the ancestral node connecting both species had high support. In contrast, genome-scale phylogenetic analysis using hundreds of single-copy orthologous genes strongly supported a single monophyletic clade encompassing isolates assigned to both L. theobromae and L. brasiliensis. Morphological analyses further revealed that conidial dimensions and other diagnostic characteristics largely overlapped between the two taxa, rendering them unreliable criteria for species separation. Considering the combined molecular and morphological evidence, our results support treating L. brasiliensis as a synonym of L. theobromae. Clarifying species boundaries within this group helps stabilize the taxonomy of Lasiodiplodia and provides a reliable foundation for accurate pathogen identification and disease management. Full article

Wireless sensor networks using battery-powered, low-cost sensors, due to their non-rechargeability and strictly limited energy resources, are more sensitive to energy efficiency than other networks of this type. Clustered wireless sensor networks address this problem. In these networks, the most energy-intensive communication, i.e., a long-range one, is carried out via designated nodes, called cluster head nodes, while other cluster nodes communicate with their cluster heads. Cluster head node selection is handled by appropriate routing protocols, and newly designed protocols are first tested in simulations. Among the simulators of cluster head selection protocols, those implemented in a MATLAB environment play an important role, and among these, those implementing a first-order radio model to estimate the energy cost of transmission, both at the transmitter and at the receiver, play a particularly important role. This paper presents and discusses the energy aspects of MATLAB-based open-source wireless sensor network environments that employ the first-order radio model for the simulation of cluster head selection protocols. Current MATLAB-based open-source simulators of cluster head selection protocols were inventoried and analyzed. The review results showed that the first-order radio model had been used in its classic form for years, with the same default parameters. Although the simulators were written using different programming paradigms, precluding simple copy-and-paste, the first-order radio model was generally similar. However, there were exceptions to this rule. A hard exception is the simulator for a body-area wireless sensor network, which only implements a version of the first-order radio model specific to that environment. Soft exceptions are two simulators of the popular cluster head selection protocol, which implemented only half the functionality of the classic first-order radio model. On the one hand, this demonstrates both the widespread use of a conservative approach to the model, which ensures relatively easy repeatability of simulation results, and, on the other hand, the flexibility of the model, which allows its extension to other environments. Finally, the limitations of the model are presented and directions for future research are indicated. Full article

Excessive planting density and heavy rainfall weather threatens global agriculture, particularly affecting maize. Biochar is an environmentally friendly soil amendment that has a yield-increasing effect. However, the regulatory mechanism of biochar frequency on crop internode development and photosystem II photosynthetic efficiency remains unknown. A total of nine treatments were followed in this experiment. Three applications of biochar were as follows: no biochar application (B0); biochar application at 4.2 t ha⁻¹ year⁻¹ (B1); and biochar application at 8.4 t ha⁻¹ 2 year⁻¹ (B2), alongside three nitrogen (N) fertilizer rates (0, N0; 180 kg ha⁻¹, N1 and 225 kg ha⁻¹, N2). The results showed that the internode thickness of the 2nd to 5th nodes under N2B2 treatment increased by 17.7%, 16.0%, 19.7%, and 21.7%, respectively, compared to N0B0. Annual biochar application had a higher stem diameter coefficient for the 1st to 3rd nodes than no biochar (B0) and treatments applied every two years (B2). Annual biochar application had the highest dry weight of internodes and plant height compared with B0 and B2. The relative chlorophyll content of leaves was significantly increased by biochar combined with N fertilizer or by N fertilizer alone. Biochar combined with N fertilizer significantly reduced NPQt and ΦNPQ, which were reduced by 59% and 50%, respectively, under N2B1 treatment compared with N0B0. The N2B1 treatment increased ΦII by 30% compared to N0B0. Stem diameter coefficient was significantly negatively correlated with NPQt and ΦNPQ and significantly positively correlated with ΦII and Fv/Fm. Compared to B1, B2 increased the maize yield. Annual biochar application combined with N fertilizer reduced stem collapse and enhanced post-flowering photosynthesis. Overall, considering the yield traits, 8.4 t ha⁻¹ biochar application combined with 180 kg ha⁻¹ N fertilizer treatment was the best. This study will provide reference data for cultivation regulation to enhance maize’s resistance to collapse and maintain photosynthetic capacity. Full article

Transmission towers, as critical infrastructure in power systems, are frequently threatened by multiple hazards such as strong winds and flood scour. Traditional structural health monitoring methods face limitations in data feedback timeliness and mechanical interpretation, making real-time condition awareness and early warning under disaster scenarios challenging. To address these issues, this paper proposes a digital twin framework for transmission tower structures, integrating Building Information Modeling (BIM), Internet of Things (IoT) technology, and the Finite Element Method (FEM) for structural health monitoring and visual warning under wind loads and flood scour effects. The framework achieves cross-platform collaboration through the FEM Open Application Programming Interface (OAPI) and Python scripts. In the physical domain, fluctuating wind loads are simulated based on the Davenport spectrum, flood scour depth is modeled using the HEC-18 formulation, and foundation constraint degradation is represented through nonlinear spring stiffness reduction. In the FEM domain, dynamic time-history analyses are conducted to obtain structural responses. In the BIM domain, a three-level warning mechanism based on stress change rate (ΔR) is established to achieve intuitive rendering and dynamic feedback of structural damage. A 44.4 m high latticed angle steel tower is employed as the case study for validation. Results demonstrate that the simulated wind spectrum closely matches the theoretical target spectrum, confirming the validity of the load input. A critical scour evolution threshold of 40% is identified, beyond which the first two natural frequencies exhibit nonlinear decay with a maximum reduction of 80.9%. Non-uniform scour induces significant load transfer, with axial forces at leeside nodes increasing from 27 kN to 54 kN. During the 0–60 s wind loading process, BIM visualization accurately captures the full stress evolution from the tower base to the upper structure, showing excellent agreement with FEM results. The proposed framework establishes a closed-loop interaction mechanism of “physical sensing–digital simulation–visual warning”, effectively enhancing the timeliness and interpretability of structural health monitoring for transmission towers under multiple hazards, providing an innovative approach for intelligent disaster prevention in power infrastructure. Full article

This study presents a nanosensor network system for autonomous microclimate optimization in precision horticulture, leveraging a field-programmable gate array (FPGA)-based control architecture that is integrated with an edge-level machine learning inference. Unlike the conventional greenhouse automation systems, which exhibit thermal and hygroscopic hysteresis often exceeding 32 °C and 78% relative humidity, the proposed framework embeds a random forest regression (RFR) model directly within the Altera DE2-115 FPGA fabric to enable predictive environmental regulation. The model achieved an R² of 0.985 and root mean square error (RMSE) of 0.28 °C, allowing proactive compensation for the thermodynamic disturbances from the high-intensity light-emitting diode (LED) lighting with a 120 s predictive horizon. The real-time monitoring and remote supervision were supported via a NodeMCU-based IoT gateway, achieving a 140 ms mean communication latency and a 99.8% packet delivery reliability. The preliminary validation using lettuce (Lactuca sativa) optimized the environmental parameters, while the subsequent experiments with pepper (Capsicum annuum), a commercially important and environmentally sensitive crop, demonstrated system performance under real-world conditions. The control system maintained a temperature and humidity within ±0.3 °C and ±1.2% of the setpoints, respectively, and outperformed the baseline rule-based control with a 28% increase in fresh biomass, a 22% improvement in dry matter accumulation, a 25% reduction in actuator duty-cycle switching, and an 18% decrease in overall energy consumption. These results highlight the efficacy of FPGA-integrated edge intelligence combined with low-latency IoT telemetry as a scalable, energy-efficient, and high-fidelity solution for sub-degree environmental control in next-generation, controlled-environment, and vertical farming systems. Full article

We investigate whether an ontology-typed knowledge graph (KG) can improve SOAP note generation from clinician–patient encounter transcripts by serving as a structured intermediate representation that organizes clinically salient content while preserving provenance. We introduce Knowledge graph Ontology Supported Medical Output System (KOSMOS), which extracts typed clinical entities with attributes and relationships, grounds entities to UMLS concepts and a schema, and retains links to supporting transcript turns. The resulting graph is provided as context for large language model (LLM)-based SOAP generation either alone (KG-only) or combined with the original transcript (Transcript + Nodes, Transcript + KG). We evaluate these conditions against DocLens and Ambient Clinical Intelligence Benchmark (ACI-BENCH) baselines on their benchmark, claim, and citation analyses. Across all three test sets, transcript-inclusive KOSMOS variants achieve the highest raw scores, numerically exceeding the transcript-only baselines. Claim-level evaluation shows modest, non-significant recall gains for Transcript + Nodes and low hallucination under transcript-conditioned GPT-5.2, while citation analysis shows about a 3% accuracy gain for KOSMOS (Transcript + KG) over DocLens GPT-5.2. Overall, ontology-guided KG structure appears most beneficial as a complementary scaffold paired with transcript access, while relationships provide limited additional benefit under current extraction quality. Full article

Ecological zoning is a critical instrument for coordinating economic development with environmental conservation, ensuring regional ecological security, and fostering sustainable development. Using the Western Sichuan Plateau (WSP) as a case study and taking 2000, 2010, and 2020 as the time nodes, this research employed an optimized landscape ecological risk assessment model to comprehensively evaluate the spatiotemporal evolution of regional landscape ecological risk (LER) and ecosystem services (ESs). By analyzing the spatial correlation between LER and ESs, we constructed an ecological zoning framework, identified key driving factors, and proposed differentiated management strategies. The results showed the following: (1) The LER generally declined from 2000 to 2020, with the high-risk areas mainly distributed in the high-elevation meadow belt in the west and north, and the low and lowest-risk areas concentrated in the eastern part of the plateau continued to expand in area. (2) The overall level of ESs showed a downward and then upward trend, with a spatial pattern of “high in the east and low in the west”. (3) LER was significantly negatively correlated with most ESs (except WY), and localized agglomeration was clearly characterized. (4) Based on the four-quadrant model, the study area was categorized into four types of ecological zones, high LER–high ES, low LER–high ES, low LER–low ES and high LER–low ES, whose spatial patterns were mainly significantly influenced by factors such as elevation, per capita GDP and precipitation (PRE). The “risk-service” synergistic zoning framework proposed in this study provides a spatially explicit decision-making basis for ecological protection and restoration on the WSP, and is useful for the sustainable management of similar ecologically fragile areas. Full article

We present a systematic evaluation of the positional and rotational tracking accuracy of the Meta Quest 3 mixed-reality headset using a TECHMAN TM5-900 collaborative robot (±0.05 mm repeatability) as a highly repeatable robot-driven reference. The headset was rigidly attached to the robot’s tool flange and subjected to single-axis translational motions (200 mm along X, Y, and Z) and rotational motions (Roll ± 65°, Pitch ± 85°, and Yaw ± 85°). Each test was repeated three times, and the resulting trajectories were averaged to improve statistical robustness. Both data sources were integrated into a single Python-based application running on the same computer. The headset streamed its data via UDP, while the robot, implemented as an ROS2 node, published its data to the same host. This configuration enabled simultaneous acquisition of both streams, ensuring temporal consistency without the need for offline interpolation. All comparisons were performed in a relative reference frame, thereby avoiding the need for absolute hand–eye calibration. Coordinate-frame alignment was achieved using Singular Value Decomposition (SVD)-based rigid-body Procrustes analysis. Over 2848 synchronized samples spanning 151.46 s, the Meta Quest 3 achieved a mean translational RMSE of 0.346 mm (3D RMSE = 0.621 mm) and a mean rotational RMSE of 0.143°, with Pearson correlation coefficients greater than 0.9999 on all axes. These results show sub-millimeter positional tracking and sub-degree rotational tracking under controlled conditions, supporting the potential of the Meta Quest 3 for precision-oriented mixed-reality applications in industrial and research settings. Full article

Cannabinoid receptors occupy strategic control nodes within motor circuitry, making them potential targets for modulating different motor manifestations. They are positioned both within basal ganglia circuits that regulate movement and within spinal circuits that control skeletal muscle tone. Consequently, cannabinoids have been studied across diverse motor disorders, most notably in movement disorders and tone disorders, particularly those resulting in spasticity. Because motor control spans multiple anatomically and functionally distinct levels, relating cannabinoid signaling to effects on motor function is not straightforward. Limited understanding of cannabinoid receptor distribution has led to cannabinoids being tested even in disorders where receptor localization would predict little or no benefit. Mapping receptor distribution within individual motor circuits and integrating them with their pharmacological effects can help anticipate how cannabinoid signaling shapes motor output. Combined with characteristic motor manifestations, one can identify motor disorders in which cannabinoids may have therapeutic value. In this review, we integrate existing evidence to place cannabinoid receptors within key motor pathways, ranging from basal ganglia circuits controlling movement to peripheral mechanisms governing muscle tone. We consider both cannabinoid 1 receptor (CB₁R) and cannabinoid 2 receptor (CB₂R), with CB₂R gaining attention only recently for its potential relevance within the central nervous system. Building on this framework, we infer how cannabinoids acting at these sites may modulate motor control, and consequently, influence motor manifestations across major motor disorders. Finally, we examine how these distribution-based expectations align with available clinical observations. Full article

WSN node energy forecasting contributes to improving network efficiency, extending network lifespan, and providing energy management strategies. In this study, a deep-learning-based wireless sensor network (WSN) node energy forecasting model based on Long Short-Term Memory (LSTM) and stacked-LSTM was developed across different wireless communication technologies in both static and dynamic WSN setups. The performance of the deep-learning-based models was compared with traditional forecasting techniques such as Exponential Smoothing and Prophet. The results showed the superiority of LSTM and stacked-LSTM in terms of root mean square error and mean absolute error, with consistently lower values compared with the traditional forecasting techniques. The results also show that the models perform best with Long Range technology. The deep learning-based model also demonstrates its ability to perform better in both static and dynamic WSN scenarios. These results demonstrate the potential of deep-learning-based models in WSN node energy management, which can result in an optimal energy efficiency and prolong the network lifetime. Future research is needed to explore hybrid approaches to further improve the prediction performance of deep learning-based models by combining their strengths with statistical or traditional forecasting techniques. Full article

Background: The clinical efficacy of the BCL-2 inhibitor venetoclax in acute myeloid leukemia (AML) is significantly undermined by the frequent emergence of drug resistance, which precipitates disease progression and poor patient outcomes. However, the molecular landscape of this resistance remains insufficiently understood. Methods: To address this, we developed venetoclax-resistant AML cell models and utilized transcriptomic profiling integrated with comprehensive in vitro and in vivo functional assays. Results: Resistant cells demonstrated sustained proliferation even under the suppression of BCL-2, MCL-1, and key intrinsic apoptotic markers, including cleaved PARP and caspase-9, indicating a bypass mechanism independent of classical BCL-2 signaling. Compared to their sensitive counterparts, resistant Kasumi-1 (VENK) and MV4-11 (VENM) cells exhibit aggressive growth phenotypes in vitro and in vivo, characterized by larger, more numerous spheroids and colonies, alongside heightened tumorigenicity in murine models. Transcriptomic profiling and KEGG analysis identified the neuroactive ligand–receptor interaction (NLRI) pathway as a significant signaling node shared between these resistant lines. While multiple NLRI-associated genes were altered, CHRNB4 was consistently and significantly downregulated in both VENK and VENM cells and tumors. Re-expression of CHRNB4 in resistant cells, a primary gain-of-function approach, significantly impaired colony formation, and tumor growth in vivo. Clinically, CHRNB4 downregulation correlates with shortened overall survival and diminished response to venetoclax. Conclusions: Our findings implicate the NLRI pathway in venetoclax resistance and identify CHRNB4 as a robust prognostic indicator and a promising therapeutic target for developing next-generation AML strategies. Full article

Background/Objectives: Intrapulmonary metastasis (IPM) after an initial single primary lung cancer (SPLC) is an adverse follow-up pattern; however, when studying population-based longitudinal records, the determinants remain unclear. We aimed to identify factors associated with subsequent IPM after initial SPLC using artificial intelligence (AI)-driven analytical approaches. Methods: We used Surveillance, Epidemiology, and End Results (SEER) lung cancer records from 2000 to 2019. Adults with at least two records were restricted to those with SPLC at the first record. Outcome at the second record was registry-classified IPM versus persistent SPLC. A machine learning framework based on random forest models was developed using baseline variables, first record characteristics, and the interval between records. Temporal validation was performed by training on cases from 2000 to 2013 and testing on cases from 2014 to 2019. A dynamic Bayesian network (DBN) supported simulated intervention (SI) analyses to estimate model-implied risk ratios (RRs) with 95% confidence intervals (CIs). Results: Among 3450 patients, 361 had registry-classified IPM at the second record. The random forest model achieved an area under the curve (AUC) of 0.852 in internal validation and 0.929 in temporal validation. Surgery and record timing were the leading predictors. The DBN retained surgery as the only direct parent and achieved an AUC of 0.779. SI analyses showed higher IPM probability for pleural invasion level (PL) 3 versus PL 0, RR 1.378 (95% CI, 1.080–1.657). Lobectomy with mediastinal lymph node dissection versus wedge resection lowered the IPM probability, RR 0.378 (95% CI, 0.219–0.636). Conclusions: AI-based time-sequence modeling integrating machine learning and a DBN allowed for the identification of surgery, pleural invasion, and record timing as key factors associated with subsequent IPM classification after initial SPLC. This framework demonstrates the potential of combining predictive and probabilistic dependency modeling to investigate registry-based disease classification patterns, and may support hypothesis generation for future prospective studies. Full article

Histopathological evaluation of tissue sections remains the gold standard for the diagnosis, classification, and grading of breast cancer (BC). The widespread adoption of whole-slide imaging (WSI) has enabled the digitization of histological slides and facilitated the development of artificial intelligence (AI) approaches for computational pathology. In recent years, machine learning and deep learning (DL) algorithms have been increasingly investigated for the analysis of hematoxylin and eosin (H&E)-stained images, with potential applications in tumor detection, histological classification, prognostic stratification, and prediction of treatment response. This narrative review summarizes recent developments in AI-driven models applied to BC histopathology and discusses their potential role in supporting diagnostic and prognostic assessment. Several studies have demonstrated the promising performance of DL algorithms in tasks such as the detection of lymph node metastases, assessment of residual tumor after neoadjuvant therapy, and prediction of clinical outcomes from histopathological images. Emerging research has also explored the possibility of inferring molecular and biomarker information from histology images, although these approaches currently identify statistical associations rather than direct molecular measurements. Despite the rapid expansion of this research field, significant barriers remain before routine clinical implementation can be achieved. Key challenges include dataset bias, variability in staining and image acquisition, limited external validation across institutions, and the need for transparent and reproducible model development. In addition, the translation of AI-based systems into clinical practice requires compliance with regulatory frameworks governing software used for medical purposes, such as those established by the U.S. Food and Drug Administration. Overall, AI represents a promising research direction in computational pathology and may contribute to decision-support tools capable of assisting pathologists in the analysis of digital slides. Continued efforts toward methodological rigor, large multicenter datasets, and prospective validation studies will be essential to determine the future role of AI in BC histopathology. Full article

The Western Songnen Plain, a critical yet ecologically fragile grain-producing area, is facing sustainability risks arising from rapid land use changes, which demand scientific assessment and regulation. From an ecological security standpoint, this study synthesizes multiple data sources, including GlobeLand30 data, climate, topography, and soil data. Based on the assessment of water conservation, soil conservation and biodiversity maintenance, combined with minimum cumulative resistance model (MCR) and the CLUMondo model, this study comprehensively reveals the dynamic evolutionary patterns of land use in the Western Songnen Plain over the past two decades, concurrently analyzed the spatial heterogeneity pattern of ecosystem services, and further simulated land use changes under natural growth, farmland protection, and ecological security scenarios. According to the results, the grassland area decreased significantly, while cropland and construction land continued to expand. Water conservation, soil conservation, and habitat quality displayed remarkable regional differences, with high values predominantly situated in wetlands, grasslands, and mountainous regions. In contrast, low values exhibited strong spatial correspondence with regions of heightened anthropogenic disturbance. Although the cropland protection scenario promoted agricultural intensification, it reduced ecological heterogeneity. In contrast, the ecological security scenario achieved a higher patch density (0.408) and landscape diversity (1.142) compared to the natural growth scenario, with moderate increases in aggregation. This study identified 27 ecological pinch points, 24 ecological barrier points, and 97 ecological corridors, which provide direct support for regional water and soil resource protection and further underpin the constructed ecological security pattern of “two belts, three zones, and multiple nodes”. These findings have important reference significance for optimizing regional land use structure and maintaining the stability of terrestrial ecosystems in the Western Songnen Plain. Full article

Traditional VANET authentication schemes often face challenges such as centralization bottlenecks and the updating of vehicle keys or pseudonyms. This paper proposes a layered approach that divides VANET into regions, utilizing dual-blockchain to enable anonymous message authentication between vehicles and RSUs, as well as between vehicles within the VANET. Compared to traditional blockchain authentication methods, this paper introduces an approach that enhances authentication efficiency and ensures information security by establishing secure connections between private and consortium chains through a trusted authority (TA). By leveraging third-party public parameter updates, the automatic updating of private and public keys for VANET nodes is achieved without the need for certificate issuance and updates. This approach facilitates long-term anonymous authentication and communication between VANET nodes, reduces the frequency of authentication interactions, simplifies authentication processes, and lowers computational and communication costs. The proposed scheme is well-suited for practical VANET environments that require low authentication latency and robust large-scale privacy protection. Full article