Search Results (120)

The Ras/PI3K/ERK signaling network is frequently mutated and overactivated in various human cancers. Focal adhesion kinase (FAK) is commonly overexpressed in several cancer types and has been implicated in treatment resistance mechanisms. A positive feedback loop between Ras, PI3K, the cytoskeleton, and FAK was previously shown to drive Ras signaling excitability. In this study, we investigated the effectiveness of targeting Ras signaling excitability by concurrently inhibiting FAK and PI3K in cervical and pancreatic cancer cells, which depend on activation Ras/PI3K signaling. We found that the combination of FAK and PI3K inhibitors synergistically suppressed the growth of cervical and pancreatic cancer cell lines through increased apoptosis and decreased mitosis. PI3K inhibitors alone caused only a transient suppression of downstream AKT activity and paradoxically increased FAK signaling in cancer cells. The addition of an FAK inhibitor effectively counteracted this PI3K-inhibitor-induced FAK activation. Furthermore, PI3K inhibitors were found to activate multiple receptor tyrosine kinases (RTKs), including insulin receptor, IGF-1R, EGFR, HER2, HER3, AXL, and EphA2. Taken together, our results suggest that FAK inhibition is necessary to counteract the compensatory RTK activation induced by PI3K inhibitors, thereby achieving more effective suppression of cancer cell growth. These findings highlight the therapeutic potential of combined FAK and PI3K inhibition in cancer treatment. Full article

This article examines the combined use of UAV (Unmanned Aerial Vehicle) photogrammetry and TLS (Terrestrial Laser Scanning) to detect changes in coastal cliffs in the Strunjan Nature Reserve. Coastal cliffs present unique surveying challenges, including limited access, unstable reference points due to erosion, GNSS (Global Navigation Satellite System) signal obstruction, dense vegetation, private property restrictions and weak mobile data. To overcome these limitations, UAV and TLS techniques are used with the help of GNSS and TPS (Total Positioning Station) surveying to establish a network of GCPs (Ground Control Points) for georeferencing. The methodology includes several epochs of data collection between 2019 and 2024, using a DJI Phantom 4 RTK for UAV surveys and a Riegl VZ-400 scanner for TLS. The data processing includes point cloud filtering, mesh comparison and a DoD (DEM of difference) analysis to quantify cliff surface changes. This study addresses the effects of vegetation by focusing on vegetation-free regions of interest distributed across the cliff face. The results aim to demonstrate the effectiveness and limitations of both methods for detecting and monitoring cliff erosion and provide valuable insights for coastal management and risk assessment. Full article

In the conventional PPP-RTK mode, the platform and users act only as the generator and the utilizer of ionospheric corrections, respectively. In sparse reference station networks or regions with an active ionosphere, high-precision modeling still faces challenges. This study utilizes the concept of crowdsourcing and treats users as dynamic reference stations. By continuously feeding back ionospheric information to the platform, high-spatial-resolution modeling is achieved. Additionally, weight factors related to user positions are incorporated into conventional polynomial models to transform the regional ionosphere model from a common model into customized models, thereby providing more personalized services for different users. Validation was conducted with a sparse reference network with an average inter-station distance of approximately 391 km. While increasing the number of crowdsourcing users generally improves modeling performance, the enhancement also depends on their spatial distribution; that is, crowdsourcing users primarily provide localized improvements in their vicinity. Therefore, crowdsourcing users should ideally be uniformly distributed across the whole network. Compared with the conventional common model, the proposed customized model can more effectively characterize the irregular physical characteristics of the ionosphere, and the modeling accuracy is improved by about 12% to 41% in different scenarios. Furthermore, the performance of single-frequency PPP-RTK was verified on the terminal. In general, both crowdsourcing enhancement and the customized model can accelerate the convergence speed of the float solutions and improve positioning accuracy to varying degrees, and the epoch fix rate of the fixed solutions is also significantly improved. Full article

The Global Navigation Satellite System (GNSS), renowned for its high precision and automation, has shone brightly in the deformation monitoring of offshore facilities and sea-crossing bridges. However, antennas placed in these locations are often subject to signal interference from various reflective surfaces, such as rivers and oceans, which significantly compromises observation accuracy and reliability. Synthesizing previous research, we first propose a method for multipath dataset construction, which involves GNSS observation linear combinations, detailed mapping of the near-field reflector, and employed static solution residuals as reference. Subsequently, we construct and train a corresponding para-TCN (parametric Temporal Convolution Network) to enable real-time prediction of multipath prediction. Through time domain and frequency domain analysis, it has been demonstrated that the trained network can capture the main features of multipath models and suppress those components in both the data distribution and frequency band, effectively mitigating the interference of multipath errors in observations. Full article

Chlorophyll content is an essential parameter for evaluating the growth condition of winter wheat, and its accurate monitoring through remote sensing is of great significance for early warnings about winter wheat growth. In order to investigate unmanned aerial vehicle (UAV) multispectral technology’s capability to estimate the chlorophyll content of winter wheat, this study proposes a method for estimating the relative canopy chlorophyll content (RCCC) of winter wheat based on UAV multispectral images. Concretely, an M350RTK UAV with an MS600 Pro multispectral camera was utilized to collect data, immediately followed by ground chlorophyll measurements with a Dualex handheld instrument. Then, the band information and texture features were extracted by image preprocessing to calculate the vegetation indices (VIs) and the texture indices (TIs). Univariate and multivariate regression models were constructed using random forest (RF), backpropagation neural network (BPNN), kernel extremum learning machine (KELM), and convolutional neural network (CNN), respectively. Finally, the optimal model was utilized for spatial mapping. The results provided the following indications: (1) Red-edge vegetation indices (RIs) and TIs were key to estimating RCCC. Univariate regression models were tolerable during the flowering and filling stages, while the superior multivariate models, incorporating multiple features, revealed more complex relationships, improving R² by 0.35% to 69.55% over the optimal univariate models. (2) The RF model showed notable performance in both univariate and multivariate regressions, with the RF model incorporating RIS and TIS during the flowering stage achieving the best results (R²_train = 0.93, RMSE_train = 1.36, RPD_train = 3.74, R²_test = 0.79, RMSE_test = 3.01, RPD_test = 2.20). With more variables, BPNN, KELM, and CNN models effectively leveraged neural network advantages, improving training performance. (3) Compared to using single-feature indices for RCCC estimation, the combination of vegetation indices and texture indices increased from 0.16% to 40.70% in the R² values of some models. Integrating UAV multispectral spectral and texture data allows effective RCCC estimation for winter wheat, aiding wheatland management, though further work is needed to extend the applicability of the developed estimation models. Full article

The MET oncogene, encoding the hepatocyte growth factor (HGF) receptor, plays a key role in tumorigenesis, invasion, and resistance to therapy, yet its full biological functions and activation mechanisms remain incompletely understood. A feature of MET is its extensive interaction network, encompassing the following: (i) receptor tyrosine kinases (RTKs); (ii) co-receptors (e.g., CDCP1, Neuropilin1); (iii) adhesion molecules (e.g., integrins, tetraspanins); (iv) proteases (e.g., ADAM10); and (v) other receptors (e.g., CD44, plexins, GPCRs, and NMDAR). These interactions dynamically modulate MET’s activation, signaling, intracellular trafficking, and degradation, enhancing its functional versatility and oncogenic potential. This review offers current knowledge on MET’s partnerships, focusing on their functional impact on signaling output, therapeutic resistance, and cellular behavior. Finally, we evaluate emerging combination therapies targeting MET and its interactors, highlighting their potential to overcome resistance and improve clinical outcomes. By exploring the complex interplay within the MET network of interacting cell surface proteins, this review provides insights into advancing anti-cancer strategies and understanding the broader implications of RTK crosstalk in oncology. Full article

Forests are crucial for biodiversity, climate regulation, and hydrological cycles, requiring sustainable management due to threats like deforestation and climate change. Traditional forest monitoring methods are labor-intensive and limited, whereas UAV LiDAR offers detailed three-dimensional data on forest structure and extensive coverage. This study primarily assesses individual tree segmentation algorithms in two forest ecosystems with different levels of complexity using high-density LiDAR data captured by the Zenmuse L1 sensor on a DJI Matrice 300RTK platform. The processing methodology for LiDAR data includes preliminary preprocessing steps to create Digital Elevation Models, Digital Surface Models, and Canopy Height Models. A comprehensive evaluation of the most effective techniques for classifying ground points in the LiDAR point cloud and deriving accurate models was performed, concluding that the Triangular Irregular Network method is a suitable choice. Subsequently, the segmentation step is applied to enable the analysis of forests at the individual tree level. Segmentation is crucial for monitoring forest health, estimating biomass, and understanding species composition and diversity. However, the selection of the most appropriate segmentation technique remains a hot research topic with a lack of consensus on the optimal approach and metrics to be employed. Therefore, after the review of the state of the art, a comparative assessment of four common segmentation algorithms (Dalponte2016, Silva2016, Watershed, and Li2012) was conducted. Results demonstrated that the Li2012 algorithm, applied to the normalized 3D point cloud, achieved the best performance with an F1-score of 91% and an IoU of 83%. Full article

Accurate, continuous, and reliable positioning is critical to achieving autonomous driving. However, in complex urban canyon environments, the vulnerability of stand-alone sensors and non-line-of-sight (NLOS) caused by high buildings, trees, and elevated structures seriously affect positioning results. To address these challenges, a sky-view image segmentation algorithm based on a fully convolutional network (FCN) is proposed for NLOS detection in global navigation satellite systems (GNSSs). Building upon this, a novel NLOS detection and mitigation algorithm (named S−NDM) uses a tightly coupled GNSS, inertial measurement units (IMUs), and a visual feature system called Sky−GVIO with the aim of achieving continuous and accurate positioning in urban canyon environments. Furthermore, the system combines single-point positioning (SPP) with real-time kinematic (RTK) methodologies to bolster its operational versatility and resilience. In urban canyon environments, the positioning performance of the S−NDM algorithm proposed in this paper is evaluated under different tightly coupled SPP−related and RTK−related models. The results exhibit that the Sky−GVIO system achieves meter-level accuracy under the SPP mode and sub-decimeter precision with RTK positioning, surpassing the performance of GNSS/INS/Vision frameworks devoid of S−NDM. Additionally, the sky-view image dataset, inclusive of training and evaluation subsets, has been made publicly accessible for scholarly exploration. Full article

The geometric correction of thermal infrared (TIR) orthophotos generated by unmanned aerial vehicles (UAVs) presents significant challenges due to low resolution and the difficulty of identifying ground control points (GCPs). This study addresses the limitations of real-time kinematic (RTK) UAV data acquisition, such as network instability and the inability to detect GCPs in TIR images, by proposing a method that utilizes RGB orthophotos as a reference for geometric correction. The accelerated-KAZE (AKAZE) method was applied to extract feature points between RGB and TIR orthophotos, integrating binary descriptors and absolute coordinate-based matching techniques. Geometric correction results demonstrated a significant improvement in regions with stable and changing environmental conditions. Invariant regions exhibited an accuracy of 0.7~2 px (0.01~0.04), while areas with temporal and spatial changes saw corrections within 5~7 px (0.10~0.14 m). This method reduces reliance on GCP measurements and provides an effective supplementary technique for cases where GCP detection is limited or unavailable. Additionally, this approach enhances time and economic efficiency, offering a reliable alternative for precise orthophoto generation across various sensor data. Full article

The Real-Time Kinematic (RTK) method is currently the most widely used method for positioning using Global Navigation Satellite Systems (GNSSs) due to its accuracy, efficiency and ease of use. In forestry, position is a critical factor for numerous applications, with GNSS currently being the preferred solution for obtaining such data. However, the decreased performance of GNSS observations in challenging environments, such as under the forest canopy, must be considered. This paper analyzes the performance of a survey-grade GNSS receiver under coniferous/deciduous tree cover. Unlike most previous research concerning this topic, the focus here is on employing a methodology that is as close as possible to real working conditions in the field of forestry. To achieve this, short observation times of 30 s were used, with corrections received directly in the field from a Continuously Operating Reference Station (CORS) of the national RTK network in Romania. In total, 84 test points were determined, randomly distributed under the canopy, with reference data collected by topographical surveys using total station equipment. In terms of the overall horizontal accuracy, an RMSE of 2.03 m and MAE of 1.63 m are found. Meanwhile, the overall vertical accuracy is lower, as expected, with an RMSE of 4.85 m and MAE of 4.01 m. The variation in GNSS performance under the different forest compositions was found to be statistically significant, while GNSS-specific factors such as DOP values only influenced the precision and not the accuracy of observations. We established that this methodology offers sufficient accuracy, which is application-dependent, even if the majority of GNSS solutions were code-based, rather than carrier-phase-based, due to strong interference from the vegetation. Full article

Forest inspection is a crucial component of forest monitoring in China. The current methods for detecting changes in forest patches primarily rely on remote sensing imagery and manual visual interpretation, which are time-consuming and labor-intensive approaches. This study aims to automate the extraction of changed forest patches using UAVs and artificial intelligence technologies, thereby saving time while ensuring detection accuracy. The research first utilizes position and orientation system (POS) data to perform geometric correction on the acquired UAV imagery. Then, a convolutional neural network (CNN) is used to extract forest boundaries and compare them with the previous vector data of forest boundaries to initially detect patches of forest reduction. The average boundary distance algorithm (ABDA) is applied to eliminate misclassified patches, ultimately generating precise maps of reduced forest patches. The results indicate that using POS data with RTK positioning for correcting UAV imagery results in a central area correction error of approximately 4 m and an edge area error of approximately 12 m. The TernausNet model achieved a maximum accuracy of 0.98 in identifying forest areas, effectively eliminating the influence of shrubs and grasslands. When the UAV flying height is 380 m and the distance threshold is set to 8 m, the ABDA successfully filters out misclassified patches, achieving an identification accuracy of 0.95 for reduced forest patches, a precision of 0.91, and a kappa coefficient of 0.89, fully meeting the needs of forest inspection work in China. Select urban forests with complex scenarios in the research area can be used to better promote them to other regions. This study ultimately developed a fully automated forest change detection system. Full article

This paper presents a new design for a multimode fusion communication system, aimed at tackling the complexities of modern aeronautical communication. The system integrates multiple communication technologies, such as ad hoc networking, 5G, BeiDou satellite, RTK positioning, and ADS-B broadcasting. This integration effectively solves the problem of increasing the size and weight of aviation communication equipment while also improving the efficiency and security of data communication. The study demonstrates that the implementation of this fusion communication system can lead to the development of more efficient and intelligent avionics equipment in the future, thereby offering robust technical support for flight safety. Full article

Unmanned aerial vehicles (UAVs) are now widely preferred systems that are capable of rapid mapping and generating topographic models with relatively high positional accuracy. Since the integrated GNSS receivers of UAVs do not allow for sufficiently accurate outcomes either horizontally or vertically, a conventional method is to use ground control points (GCPs) to perform bundle block adjustment (BBA) of the outcomes. Since the number of GCPs to be installed limits the process in UAV operations, there is an important research question whether the precise point positioning (PPP) method can be an alternative when the real-time kinematic (RTK), network RTK, and post-process kinematic (PPK) techniques cannot be used to measure GCPs. This study introduces a novel approach using precise point positioning with ambiguity resolution (PPP-AR) for ground control point (GCP) positioning in UAV photogrammetry. For this purpose, the results are evaluated by comparing the horizontal and vertical coordinates obtained from the 24 h GNSS sessions of six calibration pillars in the field and the horizontal length differences obtained by electronic distance measurement (EDM). Bartlett’s test is applied to statistically determine the accuracy of the results. The results indicate that the coordinates obtained from a two-hour PPP-AR session show no significant difference from those acquired in a 30 min session, demonstrating PPP-AR to be a viable alternative for GCP positioning. Therefore, the PPP technique can be used for the BBA of GCPs to be established for UAVs in large-scale map generation. However, the number of GCPs to be selected should be four or more, which should be homogeneously distributed over the study area. Full article

The positioning performance of the Global Navigation Satellite System (GNSS) network real-time kinematic (NRTK) depends on regional atmospheric error modeling. Under normal atmospheric conditions, NRTK positioning provides high accuracy and rapid initialization. However, fluctuations in atmospheric conditions can lead to poor atmospheric error modeling, resulting in significant atmospheric biases that affect the positioning accuracy, initialization speed, and reliability of NRTK positioning. Consequently, this decreases the efficiency of NRTK operations. In response to these challenges, this paper proposes a fast and reliable NRTK positioning method based on sequential ambiguity resolution (SAR) of multi-frequency combined observations. This method processes observations from extra-wide-lane (EWL), wide-lane (WL), and narrow-lane (NL) measurements; performs sequential AR using the LAMBDA algorithm; and subsequently constrains other parameters using fixed ambiguities. Ultimately, this method achieves high precision, rapid initialization, and reliable positioning. Experimental analysis was conducted using Continuous Operating Reference Station (CORS) data, with baseline lengths ranging from 88 km to 110 km. The results showed that the proposed algorithm offers positioning accuracy comparable to conventional algorithms in conventional NRTK positioning and has higher fixed rate and positioning accuracy in single-epoch positioning. On two datasets, the proposed algorithm demonstrated over 30% improvement in time to first fix (TTFF) compared to conventional algorithms. It provides higher precision in suboptimal positioning solutions when conventional NRTK algorithms fail to achieve fixed solutions during the initialization phase. These experiments highlight the advantages of the proposed algorithm in terms of initialization speed and positioning reliability. Full article

Currently, network real-time kinematic (NRTK) technology is one of the primary approaches used to achieve real-time dynamic high-precision positioning, and virtual reference station (VRS) technology, with its high accuracy and compatibility, has become the most important type of network RTK solution. The key to its successful implementation lies in correctly fixing integer ambiguities and extracting spatially correlated errors. This paper first introduces real-time data processing flow on the VRS server side. Subsequently, an improved ionosphere-weighted VRS approach is proposed based on single-differenced observations of GPS, GAL, and BDS. With the prerequisite of ensuring estimable integer properties of ambiguities, it directly estimates the single-differenced ionospheric delay and tropospheric delay between reference stations, reducing the double-differenced (DD) observation noise introduced by conventional models and accelerating the system initialization speed. Based on this, we provide an equation for generating virtual observations directly based on single-differenced atmospheric corrections without specifying the pivot satellite. This further simplifies the calculation process and enhances the efficiency of the solution. Using Australian CORS data for testing and analysis, and employing the approach proposed in this paper, the average initialization time on the server side was 40 epochs, and the average number of available satellites reached 23 (with an elevation greater than 20°). Two positioning modes, ‘Continuous’ (CONT) and ‘Instantaneous’ (INST), were employed to evaluate VRS user positioning accuracy, and the distance covered between the user and the master station was between 20 and 50 km. In CONT mode, the average positioning errors in the E/N/U directions were 0.67/0.82/1.98 cm, respectively, with an average success fixed rate of 98.76% (errors in all three directions were within 10 cm). In INST mode, the average positioning errors in the E/N/U directions were 1.29/1.29/2.13 cm, respectively, with an average success fixed rate of 89.56%. The experiments in this study demonstrate that the proposed approach facilitates efficient ambiguity resolution (AR) and atmospheric parameter extraction on the server side, thus enabling users to achieve centimeter-level positioning accuracy instantly. Full article