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Diego Raimondo, Antonio Raffone, Paolo Salucci, Ivano Raimondo, Giampiero Capobianco, Federico Andrea Galatolo, Mario Giovanni Cosimo Antonio Cimino, Antonio Travaglino, Manuela Maletta, Stefano Ferla, Agnese Virgilio, Daniele Neola, Paolo Casadio and Renato Seracchioli
Background: Although hysteroscopy with endometrial biopsy is the gold standard in the diagnosis of endometrial pathology, the gynecologist experience is crucial for a correct diagnosis. Deep learning (DL), as an artificial intelligence method, might help to overcome this limitation. Unfortunately, only preliminary findings
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Background: Although hysteroscopy with endometrial biopsy is the gold standard in the diagnosis of endometrial pathology, the gynecologist experience is crucial for a correct diagnosis. Deep learning (DL), as an artificial intelligence method, might help to overcome this limitation. Unfortunately, only preliminary findings are available, with the absence of studies evaluating the performance of DL models in identifying intrauterine lesions and the possible aid related to the inclusion of clinical factors in the model. Aim: To develop a DL model as an automated tool for detecting and classifying endometrial pathologies from hysteroscopic images. Methods: A monocentric observational retrospective cohort study was performed by reviewing clinical records, electronic databases, and stored videos of hysteroscopies from consecutive patients with pathologically confirmed intrauterine lesions at our Center from January 2021 to May 2021. Retrieved hysteroscopic images were used to build a DL model for the classification and identification of intracavitary uterine lesions with or without the aid of clinical factors. Study outcomes were DL model diagnostic metrics in the classification and identification of intracavitary uterine lesions with and without the aid of clinical factors. Results: We reviewed 1500 images from 266 patients: 186 patients had benign focal lesions, 25 benign diffuse lesions, and 55 preneoplastic/neoplastic lesions. For both the classification and identification tasks, the best performance was achieved with the aid of clinical factors, with an overall precision of 80.11%, recall of 80.11%, specificity of 90.06%, F1 score of 80.11%, and accuracy of 86.74 for the classification task, and overall detection of 85.82%, precision of 93.12%, recall of 91.63%, and an F1 score of 92.37% for the identification task. Conclusion: Our DL model achieved a low diagnostic performance in the detection and classification of intracavitary uterine lesions from hysteroscopic images. Although the best diagnostic performance was obtained with the aid of clinical data, such an improvement was slight.
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The impact of goals-of-care programs on acute hospitalization costs is unclear. We compared the hospitalization cost in an 8-month period before implementation of a multimodal interdisciplinary goals-of-care program (1 May 2019 to 31 December 2019) to an 8-month period after program implementation (1
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The impact of goals-of-care programs on acute hospitalization costs is unclear. We compared the hospitalization cost in an 8-month period before implementation of a multimodal interdisciplinary goals-of-care program (1 May 2019 to 31 December 2019) to an 8-month period after program implementation (1 May 2020 to 31 December 2020). Propensity score weighting was used to adjust for differences in potential covariates. The primary outcome was total direct cost during the hospital stay for each index hospitalization. This analysis included 6977 patients in 2019 and 5964 patients in 2020. The total direct cost decreased by 3% in 2020 but was not statistically significant (ratio 0.97, 95% CI 0.92, 1.03). Under individual categories, there was a significant decrease in medical oncology (ratio 0.58, 95% CI 0.50, 0.68) and pharmacy costs (ratio 0.86, 95% CI 0.79, 0.96), and an increase in room and board (ratio 1.06, 95% CI 1.01, 1.10). In subgroup analysis, ICU patients had a significant reduction in total direct cost after program implementation (ratio 0.83, 95% CI 0.72, 0.94). After accounting for the length of ICU admission, we found that the total direct cost per hospital day was no longer different between 2019 and 2020 (ratio 0.986, 95% CI 0.92, 1.05), suggesting that shorter ICU admissions likely explained much of the observed cost savings. This study provides real-world data on how “in-the-moment” GOC conversations may contribute to reduced hospitalization costs among ICU patients.
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Sebastian Halskov, Felix Krenzien, Laura Segger, Dominik Geisel, Bernd Hamm, Uwe Pelzer, Jana Ihlow, Wenzel Schöning, Timo Alexander Auer and Uli Fehrenbach
Objective: To investigate the prognostic value of enhancement patterns of intrahepatic mass-forming cholangiocarcinomas (IMCCs) during the hepatobiliary phase (HBP) in gadoxetic acid (Gd-EOB)-enhanced MRI. Methods: We retrospectively identified 66 consecutive patients with histopathologically proven IMCCs (reference standard: resection) and preoperative Gd-EOB-enhanced MRI. Gd-EOB
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Objective: To investigate the prognostic value of enhancement patterns of intrahepatic mass-forming cholangiocarcinomas (IMCCs) during the hepatobiliary phase (HBP) in gadoxetic acid (Gd-EOB)-enhanced MRI. Methods: We retrospectively identified 66 consecutive patients with histopathologically proven IMCCs (reference standard: resection) and preoperative Gd-EOB-enhanced MRI. Gd-EOB retention area was subjectively rated based on areas of intermediate signal intensity. Lesions were classified as either hypointense (0–25% retention area) or significantly-retaining (>25% retention area). Clinical, radiological, and prognostic features were compared between these groups. The primary endpoints were recurrence-free survival (RFS) and overall survival (OS) after primary surgical resection. Results: 73% (48/66) of lesions were rated as hypointense and 29% (19/66) as significantly-retaining. While the hypointense subgroup more frequently featured local and distant intrahepatic metastases (p = 0.039 and p = 0.022) and an infiltrative growth pattern (p = 0.005), RFS, OS, and clinical features did not differ significantly with estimated Gd-EOB retention area or quantitatively measured HBP enhancement ratios. Lymph node metastasis was an independent predictor of poor RFS (p = 0.001). Conclusions: Gd-EOB-enhanced MRI revealed two subtypes of IMCC in the HBP: hypointense and signal-retaining. The hypointense subtype is associated with more frequent intrahepatic metastases and an infiltrative growth pattern, indicating potential tumor aggressiveness. However, this did not result in a significant difference in survival after the primary resection of IMCC.
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Metabolic plasticity is recognised as a hallmark of cancer cells, enabling adaptation to microenvironmental changes throughout tumour progression. A dysregulated lipid metabolism plays a pivotal role in promoting oncogenesis. Oncogenic signalling pathways, such as PI3K/AKT/mTOR, JAK/STAT, Hippo, and NF-kB, intersect with the lipid
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Metabolic plasticity is recognised as a hallmark of cancer cells, enabling adaptation to microenvironmental changes throughout tumour progression. A dysregulated lipid metabolism plays a pivotal role in promoting oncogenesis. Oncogenic signalling pathways, such as PI3K/AKT/mTOR, JAK/STAT, Hippo, and NF-kB, intersect with the lipid metabolism to drive tumour progression. Furthermore, altered lipid signalling in the tumour microenvironment contributes to immune dysfunction, exacerbating oncogenesis. This review examines the role of lipid metabolism in tumour initiation, invasion, metastasis, and cancer stem cell maintenance. We highlight cybernetic networks in lipid metabolism to uncover avenues for cancer diagnostics, prognostics, and therapeutics.
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Endemic nasopharyngeal carcinoma (NPC) is closely associated with the Epstein–Barr virus (EBV), which contributes to tumor development and influences the tumor immune microenvironment (TIME) in NPC. Natural killer (NK) cells, as part of the innate immune system, play a crucial role in responding
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Endemic nasopharyngeal carcinoma (NPC) is closely associated with the Epstein–Barr virus (EBV), which contributes to tumor development and influences the tumor immune microenvironment (TIME) in NPC. Natural killer (NK) cells, as part of the innate immune system, play a crucial role in responding to viral infections and malignant cell transformations. Notably, NK cells possess a unique ability to target tumor cells independent of major histocompatibility complex class I (MHC I) expression. This means that MHC I-deficient tumor cells, which can escape from effective T cell attack, are susceptible to NK-cell-mediated killing. The activation of NK cells is determined by the signals generated through inhibitory and activating receptors expressed on their surface. Understanding the role of NK cells in the complex TIME of EBV+ NPC is of utmost importance. In this review, we provide a comprehensive summary of the current understanding of NK cells in NPC, focusing on their subpopulations, interactions, and cytotoxicity within the TIME. Moreover, we discuss the potential translational therapeutic applications of NK cells in NPC. This review aims to enhance our knowledge of the role of NK cells in NPC and provide valuable insights for future investigations.
Full article
Inhibition of menin in acute myeloid leukemia (AML) harboring histone-lysine-N-methyltransferase 2A rearrangement (KMT2Ar) or the mutated Nucleophosmin gene (NPM1c) is considered a novel and effective treatment approach in these patients. However, rapid acquisition of resistance mutations can impair treatment success. In
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Inhibition of menin in acute myeloid leukemia (AML) harboring histone-lysine-N-methyltransferase 2A rearrangement (KMT2Ar) or the mutated Nucleophosmin gene (NPM1c) is considered a novel and effective treatment approach in these patients. However, rapid acquisition of resistance mutations can impair treatment success. In patients with elevated retinoic acid receptor alpha (RARA) expression levels, promising effects are demonstrated by the next-generation RARalpha agonist tamibarotene, which restores differentiation or induces apoptosis. In this study, the combination of revumenib and tamibarotene was investigated in various KMT2Ar or NPM1c AML cell lines and patient-derived blasts, focusing on the potential synergistic induction of differentiation or apoptosis. Both effects were analyzed by flow cytometry and validated by Western blot analysis. Synergy calculations were performed using viability assays. Regulation of the relevant key mediators for the MLL complex were quantified by RT-qPCR. In MV4:11 cells characterized by the highest relative mRNA levels of RARA, highly synergistic induction of apoptosis is demonstrated upon combination treatment. Induction of apoptosis by combined treatment of MV4:11 cells is accompanied by pronounced induction of the pro-apoptotic protein BAX and a synergistic reduction in CDK6 mRNA levels. In MOLM13 and OCI-AML3 cells, an increase in differentiation markers like PU.1 or a decreased ratio of phosphorylated to total CEBPA is demonstrated. In parts, corresponding effects were observed in patient-derived AML cells carrying either KMT2Ar or NPM1c. The impact of revumenib on KMT2Ar or NPM1c AML cells was significantly enhanced when combined with tamibarotene, demonstrating synergistic differentiation or apoptosis initiation. These findings propose promising strategies for relapsed/refractory AML patients with defined molecular characteristics.
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Chiara Castellana, Leonardo Henry Eusebi, Elton Dajti, Veronica Iascone, Amanda Vestito, Pietro Fusaroli, Lorenzo Fuccio, Antonietta D’Errico and Rocco Maurizio Zagari
Autoimmune atrophic gastritis (AAG) is a chronic condition characterized by the presence of atrophy in the oxyntic mucosa due to anti-parietal cell antibodies. This review provides a comprehensive and up-to-date overview of autoimmune atrophic gastritis, reporting recent evidence on epidemiology, pathogenesis, diagnosis, clinical
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Autoimmune atrophic gastritis (AAG) is a chronic condition characterized by the presence of atrophy in the oxyntic mucosa due to anti-parietal cell antibodies. This review provides a comprehensive and up-to-date overview of autoimmune atrophic gastritis, reporting recent evidence on epidemiology, pathogenesis, diagnosis, clinical presentation, risk of malignancies, and management. The prevalence of AAG has been estimated at between 0.3% and 2.7% in the general population. The diagnosis of AAG is based on a combination of the serologic profile and the histological examination of gastric biopsies. Patients with AAG are often asymptomatic but can also have dyspeptic or reflux symptoms. The atrophy of the oxyntic mucosa leads to iron and vitamin B12 malabsorption, which may result in anemia and neurological affections. Autoimmune atrophic gastritis is associated with an increased risk of type I neuroendocrine tumors (NETs) and gastric cancer, with an incidence rate of 2.8% and 0.5% per person/year, respectively. Management is directed to reinstate vitamins and iron and to prevent malignancies with endoscopic surveillance. In conclusion, atrophic autoimmune gastritis is an infrequent condition, often asymptomatic and misdiagnosed, that requires an early diagnosis for appropriate vitamin supplementation and endoscopic follow-up for the early diagnosis of NETs and gastric cancer.
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Treatment of penile cancer (PC) focuses on organ preservation, employing various surgical and non-surgical approaches. These interventions may lead to disfigurement, impacting patients’ functional outcomes and psychosocial well-being. We reviewed studies related to penile health and PC up to February 2024, limited to
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Treatment of penile cancer (PC) focuses on organ preservation, employing various surgical and non-surgical approaches. These interventions may lead to disfigurement, impacting patients’ functional outcomes and psychosocial well-being. We reviewed studies related to penile health and PC up to February 2024, limited to studies published in English. Studies employing health-related quality of life (HRQoL) assessments have identified a detrimental association between aggressive treatment and overall health status, physical functioning, and relationships. In contrast, organ-sparing demonstrates improved measures related to HRQoL and sexual function. Assessment through validated questionnaires reveals diverse voiding outcomes, and varying impacts on QoL and sexual activity, emphasizing the necessity for multidisciplinary personalized care. Studies highlight substantial variations in sexual function, with patients reporting adaptations, reduced satisfaction, and concerns about body image and sexual well-being. Furthermore, unmet needs include challenges in patient–clinician communication, obtaining information, and accessing psychosocial support. Patient experiences underscore the importance of timely diagnosis, treatment access, and addressing psychological consequences. Organ-sparing approaches have higher QoL preservation and sexual function. Individualized support, including sexual therapy, support groups, and family counseling, is essential for post-treatment rehabilitation. Timely diagnosis and comprehensive care are paramount in addressing the multifaceted impact of PC on patients and families.
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Accurate perception is crucial for autonomous vehicles (AVs) to navigate safely, especially in adverse weather and lighting conditions where single-sensor networks (e.g., cameras or radar) struggle with reduced maneuverability and unrecognizable targets. Deep camera–radar fusion neural networks offer a promising solution for reliable
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Accurate perception is crucial for autonomous vehicles (AVs) to navigate safely, especially in adverse weather and lighting conditions where single-sensor networks (e.g., cameras or radar) struggle with reduced maneuverability and unrecognizable targets. Deep camera–radar fusion neural networks offer a promising solution for reliable AV perception under any weather and lighting conditions. Cameras provide rich semantic information, while radars act like an X-ray vision, piercing through fog and darkness. This work proposes a novel, efficient camera–radar fusion network called NeXtFusion for robust AV perception with an improvement in object detection accuracy and tracking. Our proposed approach of utilizing an attention module enhances crucial feature representation for object detection while minimizing information loss from multi-modal data. Extensive experiments on the challenging nuScenes dataset demonstrate NeXtFusion’s superior performance in detecting small and distant objects compared to other methods. Notably, NeXtFusion achieves the highest mAP score (0.473) on the nuScenes validation set, outperforming competitors like OFT (35.1% improvement) and MonoDIS (9.5% improvement). Additionally, NeXtFusion demonstrates strong performance in other metrics like mATE (0.449) and mAOE (0.534), highlighting its overall effectiveness in 3D object detection. Furthermore, visualizations of nuScenes data processed by NeXtFusion further demonstrate its capability to handle diverse real-world scenarios. These results suggest that NeXtFusion is a promising deep fusion network for improving AV perception and safety for autonomous driving.
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To address the challenge of balancing privacy protection with regulatory oversight in blockchain transactions, we propose a regulatable privacy protection scheme for blockchain transactions. Our scheme utilizes probabilistic public-key encryption to obscure the true identities of blockchain transaction participants. By integrating commitment schemes
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To address the challenge of balancing privacy protection with regulatory oversight in blockchain transactions, we propose a regulatable privacy protection scheme for blockchain transactions. Our scheme utilizes probabilistic public-key encryption to obscure the true identities of blockchain transaction participants. By integrating commitment schemes and zero-knowledge proof techniques with deep learning graph neural network technology, it provides privacy protection and regulatory analysis of blockchain transaction data. This approach not only prevents the leakage of sensitive transaction information, but also achieves regulatory capabilities at both macro and micro levels, ensuring the verification of the legality of transactions. By adopting an identity-based encryption system, regulatory bodies can conduct personalized supervision of blockchain transactions without storing users’ actual identities and key data, significantly reducing storage computation and key management burdens. Our scheme is independent of any particular consensus mechanism and can be applied to current blockchain technologies. Simulation experiments and complexity analysis demonstrate the practicality of the scheme.
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Byers Peninsula is considered one of the largest ice-free areas in maritime Antarctica. Since 2006, the Spanish Polar Program has taken part in a large number of environmental studies involving the effects of climate change on biological life cycles, limnology, and microbiology. Soils
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Byers Peninsula is considered one of the largest ice-free areas in maritime Antarctica. Since 2006, the Spanish Polar Program has taken part in a large number of environmental studies involving the effects of climate change on biological life cycles, limnology, and microbiology. Soils from maritime Antarctica are generally weakly developed and have chemical, physical, and morphological characteristics that are strongly influenced by the parent material. However, biological activity during the short Antarctic summer promotes intense transference of nutrients and organic matter in areas occupied by different species of birds and marine mammals. Mapping and monitoring those areas that are highly occupied by various species could be very useful to create models prepared from satellite images of the edaphic properties. In this approach, deep learning and linear regression models of the soil properties and spectral indexes, which were considered as explicative variables, were used. We trained the models on soil properties closely related to biological activity such as dissolved organic carbon (DOC) and the iron fraction associated with the organic matter (Fe). We tested the best approach to model the spatial distribution of DOC, Fe, and pH by training the linear regression and deep learning models on Sentinel-2 and WorldView-2 images. The most robust models, the pH model built with the deep learning approach on Sentinel images (MAE of 0.51, RMSE of 0.70, and R2 with a residual of −0.49), the DOC model built with linear regression on Sentinel images (MAE of 189.39, RMSE of 342.23, and R2 with a residual of 0.0), and the organic Fe model built with deep learning (MAE of 116.20, RMSE of 209.93, and R2 of −0.05), were used to track possible areas with ornithogenic soils, as well as areas of Byers Peninsula that could be supporting the highest biological development.
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Soil moisture (SM) is an important parameter in water cycle research. Rapid and accurate monitoring of SM is critical for hydrological and agricultural applications, such as flood detection and drought characterization. The Global Navigation Satellite System (GNSS) uses L-band microwave signals as carriers,
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Soil moisture (SM) is an important parameter in water cycle research. Rapid and accurate monitoring of SM is critical for hydrological and agricultural applications, such as flood detection and drought characterization. The Global Navigation Satellite System (GNSS) uses L-band microwave signals as carriers, which are particularly sensitive to SM and suitable for monitoring it. In recent years, with the development of Global Navigation Satellite System–Reflectometry (GNSS-R) technology and data analysis methods, many studies have been conducted on GNSS-R SM monitoring, which has further enriched the research content. However, current GNSS-R SM inversion methods mainly rely on auxiliary data to reduce the impact of non-target parameters on the accuracy of inversion results, which limits the practical application and widespread promotion of GNSS-R SM monitoring. In order to promote further development in GNSS-R SM inversion research, this paper aims to comprehensively review the current status and principles of GNSS-R SM inversion methods. It also aims to identify the problems and future research directions of existing research, providing a reference for researchers. Firstly, it introduces the characteristics, usage scenarios, and research status of different GNSS-R SM observation platforms. Then, it explains the mechanisms and modeling methods of various GNSS-R SM inversion research methods. Finally, it highlights the shortcomings of existing research and proposes future research directions, including the introduction of transfer learning (TL), construction of small models based on spatiotemporal analysis and spatial feature fusion, and further promoting downscaling research.
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One of the most promising applications of satellite data is providing users in charge of land and emergency management with information and data to support decision making for geohazard mapping, monitoring and early warning. In this work, we consider ground displacement data obtained
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One of the most promising applications of satellite data is providing users in charge of land and emergency management with information and data to support decision making for geohazard mapping, monitoring and early warning. In this work, we consider ground displacement data obtained via interferometric processing of satellite radar imagery, and we provide a novel post-processing approach based on a Functional Data Analysis paradigm capable of detecting precursors in displacement time series. The proposed approach appropriately accounts for the spatial and temporal dependencies of the data and does not require prior assumptions on the deformation trend. As an illustrative case, we apply the developed method to the identification of precursors to a mud volcano eruption in the Santa Barbara village in Sicily, southern Italy, showing the advantages of using a Functional Data Analysis framework for anticipating the warning signal. Indeed, the proposed approach is able to detect precursors of the paroxysmal event in the time series of the locations close to the eruption vent and provides a warning signal months before a scalar approach would. The method presented can potentially be applied to a wide range of geological events, thus representing a valuable and far-reaching monitoring tool.
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In contemporary warfare, radar countermeasures have become multifunctional and intelligent,rendering the conventional jamming method and platform unsuitable for the modern radar countermeasures battlefield due to their limited efficiency. Reinforcement learning has been proven to be a practical solution for cognitive jamming decision-making in
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In contemporary warfare, radar countermeasures have become multifunctional and intelligent,rendering the conventional jamming method and platform unsuitable for the modern radar countermeasures battlefield due to their limited efficiency. Reinforcement learning has been proven to be a practical solution for cognitive jamming decision-making in the cognitive electronic warfare. In this paper, we proposed a radar-jamming decision-making algorithm based on an improved Q-Learning algorithm. This improved Q-Learning algorithm ameliorated the problem of overestimating the Q-value that exists in the Q-Learning algorithm by introducing a second Q-table. At the same time, we performed a comprehensive design and implementation based on the classical Q-Learning algorithm, deploying it to a Field Programmable Gate Array (FPGA) hardware. We decomposed the implementation of the reinforcement learning algorithm into individual steps and described each step using a hardware description language. Then, the reinforcement learning algorithm can be computed on FPGA by linking the logic modules with valid signals. Experiments show that the proposed Q-Learning algorithm obtains considerable improvement in performance over the classical Q-Learning algorithm. Additionally, they confirm that the FPGA hardware can achieve great efficiency improvement on the radar-jamming decision-making algorithm implementation.
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The Earth’s center of mass (CM) is defined in satellite orbit dynamics as the center of mass of the entire Earth system, including the solid Earth, oceans, cryosphere, and atmosphere. The CM can be realized using the vector from the origin of the
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The Earth’s center of mass (CM) is defined in satellite orbit dynamics as the center of mass of the entire Earth system, including the solid Earth, oceans, cryosphere, and atmosphere. The CM can be realized using the vector from the origin of the International Terrestrial Reference Frame (ITRF) to the CM, and directly estimated from satellite laser ranging (SLR) data. In previous studies and ITRF translations, SLR observations were assumed to contain only a constant, systematic, station-dependent bias. This treatment leads to a difference of a few mm between the SLR results and other estimates, such as GPS-based global inversions. We show that the difference cannot be attributed to the deficiency of the distribution of SLR tracking stations but is due to the impact of a significant surface-loading-induced seasonal signal captured in the laser range measurement (appearing in station range bias) during the traveling of the laser light pulse. The errors in the modeling of the troposphere zenith delay considerably impact the determination of geocenter motion from SLR data. The SLR-data-derived geocenter motion becomes comparable to the global inversion results when the range biases and thermosphere delay for SLR tracking stations in the SLR network are adjusted as part of the monthly solution.
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In this contribution we evaluate the 3D geometry reconstructed by Neural Radiance Fields (NeRFs) of an object’s occluded parts behind obstacles through a point cloud comparison in 3D space against traditional Multi-View Stereo (MVS), addressing the accuracy and completeness. The key challenge lies
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In this contribution we evaluate the 3D geometry reconstructed by Neural Radiance Fields (NeRFs) of an object’s occluded parts behind obstacles through a point cloud comparison in 3D space against traditional Multi-View Stereo (MVS), addressing the accuracy and completeness. The key challenge lies in recovering the underlying geometry, completing the occluded parts of the object and investigating if NeRFs can compete against traditional MVS for scenarios where the latter falls short. In addition, we introduce a new “obSTaclE, occLusion and visibiLity constrAints” dataset named STELLA concerning transparent and non-transparent obstacles in real-world scenarios since there is no existing dataset dedicated to this problem setting to date. Considering that the density field represents the 3D geometry of NeRFs and is solely position-dependent, we propose an effective approach for extracting the geometry in the form of a point cloud. We voxelize the whole density field and apply a 3D density-gradient based Canny edge detection filter to better represent the object’s geometric features. The qualitative and quantitative results demonstrate NeRFs’ ability to capture geometric details of the occluded parts in all scenarios, thus outperforming in completeness, as our voxel-based point cloud extraction approach achieves point coverage up to 93%. However, MVS remains a more accurate image-based 3D reconstruction method, deviating from the ground truth 2.26 mm and 3.36 mm for each obstacle scenario respectively.
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The gravity and gradient anomalies contain valuable information about the underground geological structures at various depths. Deep and shallow buried source bodies are able to be identified through multi-scale field separation processes, and visual comprehensions of geological structures can be obtained via 3D
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The gravity and gradient anomalies contain valuable information about the underground geological structures at various depths. Deep and shallow buried source bodies are able to be identified through multi-scale field separation processes, and visual comprehensions of geological structures can be obtained via 3D density inversion techniques. In this study, we propose an improved 3D imaging strategy based on gravitational field separation using the preferential continuation filter. This strategy incorporates the relationship between spectral features and buried depths of source bodies, allowing for a one-step transformation from planar gravity and full-tensor gradient field observations to a 3D density structure in the wave-number domain. Synthetic tests validate the effectiveness and robustness of the gravity and gradient imaging approaches, highlighting their advantages in high vertical resolution and low computational requirements. Nonetheless, it should be noted that the imaging effects of horizontal gradients and are unsatisfactory due to their weak noise resistance. Thus, they are not suitable for real data applications. The other imaging approaches are further applied to recover the subsurface 3D density structure beneath the Weishan cone in Wudalianchi Volcanic Field, Northeastern China. Our results provide insights into the possible location and shape of the low-density magma chamber. Also, the potential presence of partial melts is inferred and supported from a gravity perspective. The primary advantage of these approaches is their ability to generate a reasonable geological model in scenarios with limited prior information and physical property constraints. As a result, they have significant practical value in the field of applied geophysics, including mineral exploration and volcanology studies.
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Satellite precipitation products (SPPs) have emerged as an alternative to estimate rainfall erosivity. However, prior studies showed that SPPs tend to underestimate rainfall erosivity but without reported bias-correction methods. This study evaluated the efficacy of two SPPs, namely, GPM_3IMERGHH (30-min and 0.1°) and
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Satellite precipitation products (SPPs) have emerged as an alternative to estimate rainfall erosivity. However, prior studies showed that SPPs tend to underestimate rainfall erosivity but without reported bias-correction methods. This study evaluated the efficacy of two SPPs, namely, GPM_3IMERGHH (30-min and 0.1°) and GPM_3IMERGDF (daily and 0.1°), in estimating two erosivity indices in mainland China: the average annual rainfall erosivity (R-factor) and the 10-year event rainfall erosivity (10-yr storm EI), by comparing with that derived from gauge-observed hourly precipitation (Gauge-H). Results indicate that GPM_3IMERGDF yields higher accuracy than GPM_3IMERGHH, though both products generally underestimate these indices. The Percent Bias (PBIAS) is −55.48% for the R-factor and −56.38% for the 10-yr storm EI using GPM_3IMERGHH, which reduces to −10.86% and −32.99% with GPM_3IMERGDF. A bias-correction method was developed based on the systematic difference between SSPs and Gauge-H. A five-fold cross validation shows that with bias-correction, the accuracy of the R-factor and 10-yr storm EI for both SPPs improve considerably, and the difference between two SSPs is reduced. The PBIAS using GPM_3IMERGHH decreases to −0.06% and 0.01%, and that using GPM_3IMERGDF decreases to −0.33% and 0.14%, respectively, for the R-factor and 10-yr storm EI. The rainfall erosivity estimated with SPPs with bias-correction shows comparable accuracy to that obtained through Kriging interpolation using Gauge-H and is better than that interpolated from gauge-observed daily precipitation. Given their high temporal and spatial resolution, and timely updates, GPM_3IMERGHH and GPM_3IMERGDF are viable data products for rainfall erosivity estimation with bias correction.
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Although rock glaciers (RGs) are prevalent in the southwestern Pamirs, systematic studies on them are scarce. This article introduces the first inventory of RGs in the southwestern Pamirs, situated at the western edge of the High Mountain Asia region. The inventory, established through
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Although rock glaciers (RGs) are prevalent in the southwestern Pamirs, systematic studies on them are scarce. This article introduces the first inventory of RGs in the southwestern Pamirs, situated at the western edge of the High Mountain Asia region. The inventory, established through a combination of Google Earth optical imagery and Interferometric Synthetic Aperture Radar (InSAR) techniques, encompasses details on the locations, geomorphological parameters, and kinematic attributes of RGs. A total of 275 RGs were cataloged in an area of 55.52 km2 from 3620 to 5210 m in altitude. Our inventory shows that most RGs in this region are talus-connected (213 landforms), with the highest frequency facing northeast (23%). The distribution of RGs thins from west to east and is more abundant in higher altitudes. The Shakhdara range to the south hosts a denser and more active population of RGs than the Shughnon range to the north, highlighting the influence of topography and precipitation. Overall, RGs in the southwestern Pamirs exhibit high activity levels, with active RGs predominating (58%). A comparison between active and transitional RGs showed no significant differences in elevation, temperature, and slope. Glacier-connected and glacier forefield-connected RGs demonstrated higher line-of-sight (LOS) velocities than talus-connected and debris-mantled slope-connected RGs, underscoring the significant impact of precipitation and meltwater on their activity.
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Road extraction is a crucial aspect of remote sensing imagery processing that plays a significant role in various remote sensing applications, including automatic driving, urban planning, and path navigation. However, accurate road extraction is a challenging task due to factors such as high
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Road extraction is a crucial aspect of remote sensing imagery processing that plays a significant role in various remote sensing applications, including automatic driving, urban planning, and path navigation. However, accurate road extraction is a challenging task due to factors such as high road density, building occlusion, and complex traffic environments. In this study, a Spatial Attention Swin Transformer (SASwin Transformer) architecture is proposed to create a robust encoder capable of extracting roads from remote sensing imagery. In this architecture, we have developed a spatial self-attention (SSA) module that captures efficient and rich spatial information through spatial self-attention to reconstruct the feature map. Following this, the module performs residual connections with the input, which helps reduce interference from unrelated regions. Additionally, we designed a Spatial MLP (SMLP) module to aggregate spatial feature information from multiple branches while simultaneously reducing computational complexity. Two public road datasets, the Massachusetts dataset and the DeepGlobe dataset, were used for extensive experiments. The results show that our proposed model has an improved overall performance compared to several state-of-the-art algorithms. In particular, on the two datasets, our model outperforms D-LinkNet with an increase in Intersection over Union (IoU) metrics of 1.88% and 1.84%, respectively.
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Although there is extensive research demonstrating the significant loss and fragmentation of urban spaces caused by rapid urbanization, to date, no empirical research in Shanghai has investigated the spatiotemporal dynamics of urban open spaces using a comprehensive set of integrated geospatial techniques based
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Although there is extensive research demonstrating the significant loss and fragmentation of urban spaces caused by rapid urbanization, to date, no empirical research in Shanghai has investigated the spatiotemporal dynamics of urban open spaces using a comprehensive set of integrated geospatial techniques based on long-sequence time series. Based on the Google Earth Engine (GEE) platform and using the Random Forest (RF) classifier, multiple techniques, namely landscape metrics, trend analysis, open space ratio, transition matrix, Normalized Difference Vegetation Index (NDVI), and fractal dimension analysis, were applied to analyze the Landsat satellite data. Next, Geographic Detector (GeoDetector) methods were used to investigate the driving forces of such spatial variations. The results showed that (1) the RF classification algorithm, supported by the GEE, can accurately and quickly obtain a research object dataset, and that calculating the optimal spatial grain size for open space pattern was 70 m; (2) open spaces exhibited declining and contracting trends; and open spaces in the city experienced a decline from 91.83% in 1980 to 69.63% in 2020. Meanwhile, the degree of open spaces in each district increased to different extents, whilst connectivity markedly decreased. Furthermore, the open space of city center districts showed the lowest rate of decrease, with open space patterns fragmenting due to encroaching urbanization; (3) the contribution of socioeconomic factors to the spatial–temporal changes in open space continually has increased over the past 40 years, and were also higher than natural geographic factors to some extent. Apart from offering policy insights guiding the future spatial planning and development of the city, this paper has contributions from both methodological and empirical perspectives. Based on integrated remote sensing and geographic information science (GIS) techniques, this paper provides updated evidence and a clearer understanding of the spatiotemporal variations in urban spaces and their influencing mechanisms in Shanghai.
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Real-time reconstruction of ocean surface currents is a challenge due to the complex, non-linear dynamics of the ocean, the small number of in situ measurements, and the spatio-temporal heterogeneity of satellite altimetry observations. To address this challenge, we introduce HIRES-CURRENTS-Net, an operational real-time
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Real-time reconstruction of ocean surface currents is a challenge due to the complex, non-linear dynamics of the ocean, the small number of in situ measurements, and the spatio-temporal heterogeneity of satellite altimetry observations. To address this challenge, we introduce HIRES-CURRENTS-Net, an operational real-time convolutional neural network (CNN) model for daily ocean current reconstruction. This study focuses on the Mediterranean Sea, a region where operational models have great difficulty predicting surface currents. Notably, our model showcases higher accuracy compared to commonly used alternative methods. HIRES-CURRENTS-Net integrates high-resolution measurements from the infrared or visible spectrum—high resolution Sea Surface Temperature (SST) or chlorophyll (CHL) images—in addition to the low-resolution Sea Surface Height (SSH) maps derived from satellite altimeters. In the first stage, we apply a transfer learning method which uses a high-resolution numerical model to pre-train our CNN model on simulated SSH and SST data with synthetic clouds. The observation of System Simulation Experiments (OSSEs) offers us a sufficient training dataset with reference surface currents at very high resolution, and a model trained on this data can then be applied to real data. In the second stage, to enhance the real-time operational performance of our model over previous methods, we fine-tune the CNN model on real satellite data using a novel pseudo-labeling strategy. We validate HIRES-CURRENTS-Net on real data from drifters and demonstrate that our data-driven approach proves effective for real-time sea surface current reconstruction with potential operational applications such as ship routing.
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Inland water level and its dynamics are key components in the global water cycle and land surface hydrology, significantly influencing climate variability and water resource management. Satellite observations, in particular altimetry missions, provide inland water level time series for nearly three decades. Space-based
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Inland water level and its dynamics are key components in the global water cycle and land surface hydrology, significantly influencing climate variability and water resource management. Satellite observations, in particular altimetry missions, provide inland water level time series for nearly three decades. Space-based remote sensing is regarded as a cost-effective technique that provides measurements of global coverage and homogeneous accuracy in contrast to in-situ sensors. The advent of Open-Loop Tracking Command (OLTC), and Synthetic Aperture Radar (SAR) mode strengthened the use of altimetry missions for inland water level monitoring. However, it is still very challenging to obtain accurate measurements of water level over narrow rivers and small lakes. This scoping systematic literature review summarizes and disseminates the research findings, highlights major results, and presents the limitations regarding inland water level monitoring from satellite observations between 2018 and 2022. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline and through a double screening process, 48 scientific publications were selected meeting the eligibility criteria. To summarize the achievements of the previous 5 years, we present fundamental statistical results of the publications, such as the annual number of publications, scientific journals, keywords, and study regions per continent and type of inland water body. Also, publications associated with specific satellite missions were analyzed. The findings show that Sentinel-3 is the dominant satellite mission, while the ICESat-2 laser altimetry mission has exhibited a high growth trend. Furthermore, publications including radar altimetry missions were charted based on the retracking algorithms, presenting the novel and improved methods of the last five years. Moreover, this review confirms that there is a lack of research on the collaboration of altimetry data with machine learning techniques.
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