Primary Features Research Articles

Primordial Odontogenic Tumor: A Decade Post-Description Systematic Review.

Primordial odontogenic tumor (POT) is a rare benign tumor arising from odontogenic epithelium and ectomesenchyme. It typically presents in children and young adults. POT is often found in the posterior mandible and frequently presents as asymptomatic swelling. A systematic review of the literature was conducted to comprehensively analyze the clinicopathologic features of this rare entity over the past ten years. A systematic review of POT case series and case reports following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) statement guidelines was performed. Data on demographics and clinical characteristics, including age, sex, clinical presentation, duration of the lesion, location, and radiographic and histological features, were extracted. A quantitative description of immunohistochemical studies reported in the literature was also performed. Treatment, a follow-up period, and recurrence were collected for analysis. This review included 26 studies involving 36 POT cases. Patients often presented with asymptomatic swelling at a median age of 12 years, with a male-to-female ratio of 1.18:1. The posterior mandible was the most commonly affected site, while three cases were noted in the anterior region exclusively in the maxilla. Most lesions appeared as unilocular radiolucencies with well-defined borders; however, five cases exhibited fine trabeculation or radiopacities. The primary histological features observed in POT included ectomesenchymal stroma lined by columnar cells with nuclear reverse polarity. Most cases were treated through enucleation and curettage (50.0%), followed by tumor excision (36.1%). Only one case demonstrated recurrence among the 29 cases with known follow-up information. This study offers comprehensive and current descriptive data on POT, enhancing the ability of clinicians and pathologists to accurately identify these rare lesions and thereby avoid misdiagnosis and inappropriate management.

Classification of Current Experimental Models of Epilepsy.

This article provides an overview of several experimental models, including in vivo, genetics, chemical, knock-in, knock-out, electrical, in vitro, and optogenetics models, that have been employed to investigate epileptogenesis. The present review introduces a novel categorization of these models, taking into account the fact that the most recent classification that gained widespread acceptance was established by Fisher in 1989. A significant number of such models have become virtually outdated. This paper specifically examines the models that have contributed to the investigation of partial seizures, generalized seizures, and status epilepticus. A description is provided of the primary features associated with the processes that produce and regulate the symptoms of various epileptogenesis models. Numerous experimental epilepsy models in animals have made substantial contributions to the investigation of particular brain regions that are capable of inducing seizures. Experimental models of epilepsy have also enabled the investigation of the therapeutic mechanisms of anti-epileptic medications. Typically, animals are selected for the development and study of experimental animal models of epilepsy based on the specific form of epilepsy being investigated. Currently, it is established that specific animal species can undergo epileptic seizures that resemble those described in humans. Nevertheless, it is crucial to acknowledge that a comprehensive assessment of all forms of human epilepsy has not been feasible. However, these experimental models, both those derived from channelopathies and others, have provided a limited comprehension of the fundamental mechanisms of this disease.

Trust, Contextual Impartiality, and Open-Mindedness in Investor-State Dispute Settlement

Abstract This article analyses impartiality in Investor-State Dispute Settlement (ISDS) by identifying the way that the parties’ trust in arbitrators is constructed. Drawing on the findings of a large-scale empirical project, it questions the applicability of an orthodox judicial doctrine of impartiality to ISDS on the grounds that trust in arbitrators is constructed on a fundamentally different basis from that of trust in judges. The primary feature of a judicial doctrine of impartiality is that trust is founded on an absolutist approach to impartiality which is intended to ensure that judges have no predispositions to parties. In contrast, trust in ISDS is founded on the method of party appointment which is based on a very different assumption—that arbitrators’ predispositions can be valuable and appropriate in the decision-making process. Drawing on empirical findings, moral philosophy, and psychology, this article proposes the idea of contextual impartiality. Under this approach, the question is not whether an arbitrator can meet universal standards of impartiality irrespective of the context within which the arbitrator operates. Rather, the critical distinction is between permissible and impermissible partiality which depends on whether the individual is reasonably expected to act partially because of their circumstances. In this respect, the article identifies open-mindedness as a fundamental feature of a contextual approach to impartiality and a bright line between permissible and impermissible partiality.

Application of CRISPR/Cas9 gene-editing technology in Alzheimers disease

Alzheimer's disease (AD) is a prevalent neurodegenerative disorder. It triggers metabolic disorders within the body due to genetic mutations, thus leading to irreversible damage to one's memory and brain. The primary pathogenic features of AD are intracellular neurofibrillary tangles caused by hyperphosphorylated Tau and extracellular amyloid plaques created by the buildup of -amyloid -protein (A). However, up to now, there has been no effective treatment program for Alzheimer's disease. And after a long time of development, many gene editing technologies have been perfected and utilized in practice. Among them, CRISPR-Cas9 technology is the most efficient and practical. As a result, an increasing number of scientists have examined and debated the potential use of CRISPR-Cas9 technology in the management of Alzheimer's disease. This review addresses the use of the CRISPR/Cas9 system to treat Alzheimer's disease and presents the disease's pathophysiology, including the establishment of AD models, genetic screening for the cause of AD, and targeted therapy for AD.

A Quick Look at the Atmospheric Circulation Leading to Extreme Weather Phenomena on a Continental Scale

The study delves into the primary large-scale atmospheric features contributing to extreme weather events across Europe during early September 2023. The period was examined using a dataset composed by the European Centre for Medium-Range Weather Forecasts (ECMWF) analysis and satellite imagery. In early September 2023, an omega blocking pattern led to the development of a low-pressure system over the Iberian Peninsula producing heavy precipitation and flooding over Spain and acting as a mechanism for a mineral dust outbreak. A second low-pressure system developed over Greece. Extreme precipitation was recorded across Greece, Turkey, and Bulgaria as the system gradually shifted southward over the Mediterranean. The system earned the name “Storm Daniel” as it acquired subtropical characteristics. It caused floods over Libya and its associated circulation favoured the transport of mineral dust over Northern Egypt as it moved eastward. Meanwhile, the high-pressure blocking system associated with the omega pattern induced heatwave temperatures in countries further north. This period was compared with the large-scale circulation observed in mid-September 2020, when severe weather also affected the Mediterranean region. However, the weather systems were not directly connected by the large-scale circulation, as shown in September 2023. Although mesoscale conditions are relevant to formation and intensification of some atmospheric phenomena, the establishment of an omega blocking pattern in early September 2023 showed how large-scale atmospheric dynamics can produce abnormal weather conditions on a continental scale over several days.

Cognitive Control.

Humans and other primates have a remarkable ability to perform a wide range of tasks and behaviors, even novel ones, in order to achieve their goals. Further, they are able to shift flexibly among these behaviors as the contexts demands. Cognitive control is the function at the base of this remarkable behavioral generativity and flexibility. The present review provides a survey of current research on cognitive control focusing on two of its primary features within a control systems framework: (a) the ability to select new behaviors based on context and (b) the ability to monitor ongoing behavior and adjust accordingly. Throughout, the review places an emphasis on how differences in the content and structure of task representations affect these core features of cognitive control.

Heterogeneous afferent arteriolopathy: a key concept for understanding blood pressure-dependent renal damage.

Hypertension, aging, and other factors are associated with arteriosclerosis and arteriolosclerosis, primary morphological features of nephrosclerosis. Although such pathological changes are not invariably linked with renal decline but are prevalent across chronic kidney disease (CKD), understanding kidney damage progression is more pragmatic than precisely diagnosing nephrosclerosis itself. Hyalinosis and medial thickening of the afferent arteriole, along with intimal thickening of small arteries, can disrupt the autoregulatory system, jeopardizing glomerular perfusion pressure given systemic blood pressure (BP) fluctuations. Consequently, such vascular lesions cause glomerular damage by inducing glomerular hypertension and ischemia at the single nephron level. Thus, the interaction between systemic BP and afferent arteriolopathy markedly influences BP-dependent renal damage progression in nephrosclerosis. Both dilated and narrowed types of afferent arteriolopathy coexist throughout the kidney, with varying proportions among patients. Therefore, optimizing antihypertensive therapy to target either glomerular hypertension or ischemia is imperative. In recent years, clinical trials have indicated that combining renin-angiotensin system inhibitors (RASis) and sodium-glucose transporter 2 inhibitors (SGLT2is) is superior to using RASis alone in slowing renal function decline, despite comparable reductions in albuminuria. The superior efficacy of SGLT2is may arise from their beneficial effects on both glomerular hypertension and renal ischemia. A comprehensive understanding of the interaction between systemic BP and heterogeneous afferent arteriolopathy is pivotal for optimizing therapy and mitigating renal decline in patients with CKD of any etiology. Therefore, in this comprehensive review, we explore the role of afferent arteriolopathy in BP-dependent renal damage.

Influence of Tibial Component Design Features and Interference Fit on Implant-Bone Micromotion in Total Ankle Replacement: A Finite Element Study.

Implant survivorship in uncemented total ankle replacement (TAR) is dependent on achieving initial stability. This is because early micromotion between the implant and bone can disrupt the process of osseointegration, leading to poor long-term outcomes. Tibial implant fixation features are designed to resist micromotion, aided by bony sidewall retention and interference fit. The goal of this study was to investigate design-specific factors influencing implant-bone micromotion in TAR tibial components with interference fit. Three implant designs with fixation features representative of current TAR tibial components (ARC, SPIKES, KEEL) were virtually inserted into models of the distal tibias of 2 patients with end-stage ankle arthritis. Tibia models were generated from deidentified patient computed tomography scans, with material properties for modeling bone behavior and compaction during press-fit. Finite element analysis (FEA) was used to simulate 2 fixation configurations: (1) no sidewalls or interference fit, and (2) sidewalls with interference fit. Load profiles representing the stance phase of gait were applied to the models, and implant-bone micromotions were computed from FEA output. Sidewalls and interference fit substantially influenced implant-bone micromotions across all designs studied. When sidewalls and interference fit were modeled, average micromotions were less than 11 µm, consistent across the stance phase of gait. Without sidewalls or interference fit, micromotions were largest near either heel strike or toe-off. In the absence of sidewalls and interference fit, the amount of micromotion generally aligned inversely with the size of implant fixation features; the ARC design had the largest micromotion (~540 µm average), whereas the KEEL design had the smallest micromotion (~15 µm). This study presents new insights into the effect of TAR fixation features on implant-bone micromotion. With sidewalls and interference fit, micromotion is predicted to be minimal for implants, whereas with no sidewalls and no interference fit, micromotion depended primarily on the implant design. This study presents new insights into the effect of TAR primary fixation features on implant-bone micromotion. Although design features heavily influenced implant stability in the model, their influence was greatly diminished when interference fit was introduced. The results of this study show the relative importance of design features and interference fit in the predicted initial stability of uncemented TAR, potentially a key factor in implant survivorship.

Memory-related brain potentials for visual objects in early AD show impairment and compensatory mechanisms.

Impaired episodic memory is the primary feature of early Alzheimer's disease (AD), but not all memories are equally affected. Patients with AD and amnestic Mild Cognitive Impairment (aMCI) remember pictures better than words, to a greater extent than healthy elderly. We investigated neural mechanisms for visual object recognition in 30 patients (14AD, 16 aMCI) and 36 cognitively unimpaired healthy (19 in the "preclinical" stage of AD). Event-related brain potentials (ERPs) were recorded while participants performed a visual object recognition task. Hippocampal occupancy (integrity), amyloid (florbetapir) PET, and neuropsychological measures of verbal & visual memory, executive function were also collected. A right-frontal ERP recognition effect (500-700ms post-stimulus) was seen in cognitively unimpaired participants only, and significantly correlated with memory and executive function abilities. A later right-posterior negative ERP effect (700-900ms) correlated with visual memory abilities across participants with low verbal memory ability, and may reflect a compensatory mechanism. A correlation of this retrieval-related negativity with right hippocampal occupancy (r = 0.55), implicates the hippocampus in the engagement of compensatory perceptual retrieval mechanisms. Our results suggest that early AD patients are impaired in goal-directed retrieval processing, but may engage compensatory perceptual mechanisms which rely on hippocampal function.

Towards the development of a ‘land-river-lake’ two-stage deep learning model for water quality prediction and its application in a large plateau lake

Accurate prediction of water quality that is essential for effective lake management requires a comprehensive understanding of influential factors in the ’land-river-lake’ continuum. We develop a novel deep learning model employing a Long Short-Term Memory (LSTM) model for predicting water quality, and apply it for a large plateau lake, Lake Dianchi. The model comprises two stages: the first establishes a correlation between land and rivers by predicting nutrient loads, incorporating socioeconomic and meteorological features. The second establishes a correlation between feeding rivers and the lake by predicting nutrient concentrations, taking water quality, meteorological conditions, riverine import, and agricultural Non-Point Source (NPS) loads into consideration. Compared to single-stage deep learning models developed using classical methods, the two-stage model reduces Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE), indicating enhanced prediction accuracy. Shapley Additive Explanation (SHAP) analysis is further employed to identify key contributing features. Precipitation emerges as the primary feature in the first stage, while in the second stage, disparities in the most contributing features for two zones of the lake are revealed due to dam isolation and water diversion projects. In the main zone, the primary predicting features are temperature and agricultural NPS features, while in the dam-isolated zone, Secchi depth is the key predictor, highlighting spatial–temporal variations in driving features. Our model contributes a novel deep learning framework and valuable insights for water quality management in large lakes, emphasizing the importance of considering the dynamic interplay of features or factors in the ’land-river-lake’ system.

Large-Eddy Simulation of Supersonic Combustion in a Mach 2 Cavity Model Scramjet Combustor

In this study, we report on reactive large-eddy simulations (LESs) of flow and combustion in the U.S. Air Force Research Laboratory Research Cell 19 cavity-based scramjet combustor. This case involves the combustion of ethylene that is injected into a cavity. It has previously been studied experimentally with multiple techniques, including particle image velocimetry, hyperspectral imagining, and laser-induced breakdown spectroscopy, as well as numerically using hybrid Reynolds-averaged Navier–Stokes/LES. Here, we build on the existing knowledge and provide an in-depth investigation of the flow and combustion using a pure LES approach. A range of fuel injection rates are studied, as well as the nonreacting case. This way we can disseminate how different amounts of combustion and the associated volumetric expansion affect the shear layer above the cavity, the recirculation zone, and the downstream shock train. For example, an additional shock train emerges from the start of the cavity when combustion is present, and it grows in intensity with increasing fueling rate from 50 to 110 slpm. For the cases studied, good agreement is found between the LES and experiments. The primary flow features, such as the shock train, primary and secondary cavity recirculation regions, and shear-layer lift due to exothermicity, are found to evolve similarly with increasing fueling rate in the experiments and the LES. This enables the use of the more complete LES results to further investigate the flow and combustion physics.

Impact of the Fujiwhara effect from tropical cyclones Seroja and Odette on ocean dynamic in Southern Indonesia: Insights from argo data and model analysis

Tropical cyclone (TC) disturbances can cause changes in ocean dynamics before, during, and afterwards. We employed an Argo data and ocean model product of Copernicus Marine Environment Monitoring Service (CMEMS) to address the gap in observations of the ocean response from the severe TC Seroja and its interactions with the tropical low TC Odette, known as the rare Fujiwhara effect phenomenon. Our study in southern Indonesia seawater was conducted between March and April 2021, beginning with reconciling the deviation of the CMEMS ocean model product by assessing the model accuracy to the Argo float data to build confidence in using the model for further analysis. Comparison revealed that the temperature model datasets have a higher correlation coefficient (∼0.95) with the Argo observation than the salinity model datasets. Further, we investigated the temporal evolution of vertical profiles and upper ocean responses both inside and beyond the TCs route zone. We discovered that the upper ocean response results in a colder and saltier surface with a recovery time of more than 15 days. Furthermore, as a result of the strong surface cooling, mixed layers inside the Seroja track were rapidly thickening following the passage of TC. Our findings provide insights into how the TCs Seroja and Odette affect the ocean layers, especially the primary ocean features and behaviours (temperature and salinity), using the Argo and model datasets.

Perceptual metric for face image quality with pixel-level interpretability

This paper tackles the shortcomings of image evaluation metrics in evaluating facial image quality. Conventional metrics do neither accurately reflect the unique attributes of facial images nor correspond with human visual perception. To address these issues, we introduce a novel metric designed specifically for faces, utilizing a learning-based adversarial framework. This framework comprises a generator for simulating face restoration and a discriminator for quality evaluation. Drawing inspiration from facial neuroscience studies, our metric emphasizes the importance of primary facial features, acknowledging that minor changes in the eyes, nose, and mouth can significantly impact perception. Another key limitation of existing image evaluation metrics is their focus on numerical values at the image level, without providing insight into how different areas of the image contribute to the overall assessment. Our proposed metric offers interpretability regarding how each region of the image is evaluated. Comprehensive experimental results confirm that our face-specific metric surpasses traditional general image quality assessment metrics for facial images, including both full-reference and no-reference methods. The code and models are available at https://github.com/AIM-SKKU/IFQA.

MSAACNN for intense noise suppression in DAS-VSP records

Distributed optical fiber sensor (DAS) is an emerging acquisition technique and has begun to be widely applied in seismic exploration owing to its advantages in acquisition and deployment. Nonetheless, DAS record has a low signal-to-noise ratio (SNR) due to the intense background noise. How to suppress the DAS background noise and increase the SNR of the DAS records has gradually become one of the hot issues in the field of seismic data processing. To solve the challenging tasks in intense DAS noise suppression, a multiscale sparse asymmetric attention convolutional neural network (MSAACNN) is proposed. The network uses dilated convolutions to expand the receptive field and map more feature information. Moreover, asymmetric convolutions are introduced to form an asymmetric unit, aiming to strengthen the feature extraction ability and realize the interaction between feature information of different scales. Finally, a pyramid attention module is used to enhance the primary features and improve the network denoising performance. The experimental results show that MSAACNN can effectively suppress the complex background noise in DAS records, compared with the traditional denoising methods and typical convolutional neural network (CNN) architecture. Additionally, the recovered signal components in the processing results are clear and complete, with significantly improved SNR.

Optimal MMSE and MoCA cutoffs for cognitive diagnoses in Parkinson's disease: A data-driven decision tree model

BackgroundDetecting cognitive impairment in Parkinson's disease (PD) is challenging due to diverse manifestations and outdated diagnostic criteria. Cognitive screening tools, as Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA), are adopted worldwide, but despite several cutoffs has been proposed for PD, no consensus has been reached, hindered by limited sample sizes, lack of validation, and inconsistent age- and education-adjustments. ObjectivesDetermine the optimal MMSE and MoCA cutoffs in a large PD cohort, spanning from normal cognition (PD-NC), mild cognitive impairment (PD-MCI) to dementia (PDD), and develop a decision tree model to assist physicians in cognitive workups. MethodsOur retrospective Italian multicenter study involves 1780 PD, cognitively diagnosed with a level-II assessment: PD-NC(n = 700), PD-MCI(n = 706), and PDD(n = 374). Optimal cutoffs (for raw scores) were determined through ROC analysis. Then, a machine learning approach—decision trees—was adopted to validate and analyze the possible inclusion of other relevant clinical features. ResultsThe decision tree model selected as primary feature a MMSE cutoff ≤24 to predict dementia, and a score ≤ 27 for PD-MCI. To enhance PD-MCIvs.PD-NC accuracy, it also recommends including a MoCA score ≤ 22 for PD-MCI, and > 22 for PD-NC. Age and education were not selected as relevant features for the cognitive workup. Both MoCA and MMSE cutoffs exhibited high sensitivity and specificity in detecting PD cognitive statues. ConclusionsFor the first time, a clinical decision tree model based on robust MMSE and MoCA cutoffs has been developed, allowing to diagnose PD-MCI and/or PDD with a high accuracy and short administration time.

Novel age constraints on offshore islets around Taiwan with implications for the Northern Taiwan Volcanic Zone

Determining the exact age of volcanic activity in the Northern Taiwan Volcanic Zone (NTVZ) is essential for comprehending Taiwan's tectonic evolution. The timing and duration of the volcanic processes that formed the NTVZ remain uncertain despite the availability of geochronological, geochemical, and geophysical data. While extensive efforts have been directed towards investigating the age of the Tatun Volcanic Group, the primary feature of the NTVZ, many other aspects require further examination. This study concentrates on two offshore islets in the northern region of the NTVZ. We present new 40Ar/39Ar ages of 0.252 ± 0.018 Ma for a lava flow, representing an early stage of volcanic activity, and 0.118 ± 0.012 Ma for a dike, representing the latest stage of volcanic activity around Pengjia Islet. A sample collected from Mianhua Islet did not yield a reasonable age due to high uncertainty, likely attributable to young and low radiogenic argon. Field observations support the interpretation that volcanic activity on both islets is considerably younger than previously estimated. This study corroborates the hypothesis from geophysical studies that submarine NTVZ features to record a potentially active magmatic reservoir offshore of northern Taiwan. By refining our understanding of spatio-temporal patterns in volcanic activity, this study contributes to volcanic hazard assessment in Taiwan and surrounding areas.

Hydrogel-Integrated Heart-on-a-Chip Platform for Assessment of Myocardial Ischemia Markers.

Organ-on-a-chip platform scans offer a controllable environment and a physiological similarity to mimic human pathophysiology. In this study, a single-channel PDMS microchip was fabricated, characterized, and optimized to obtain a heart-on-a-chip platform, which is integrated with a hydrogel scaffold suitable for cardiomyocyte growth inside its channel. Single-channel chips with a size of 20 × 12 mm and a channel height ranging from 60 to 100 μm were produced using photolithography and soft lithography techniques. A gelatin-embedded alginate network-based hydrogel was further augmented with 3% (v/v) collagen type I. Pore sizes were in the range of 74-153 μm for H9C2 implantation and biomimicry. The hydrogels are characterized both on PDMS surfaces and in capillaries. The primary feature distinguishing this study from previous microchip studies is that it mimics the cell microenvironment much better using different hydrogel formulations instead of creating a 2D cell culture by passing fluids, such as fibronectin, for cell adhesion. Instead of using complex microchip designs, the chip system we created intends to provide a physiologically relevant copy by using a 3D cell culture to its advantage and a simple, single-channel architecture. The microchip study was combined with cardiomyocytes to create the heart-on-a-chip system and tested under normoxic and hypoxic conditions to create a myocardial ischemia model inside this channel. As a result, this heart-on-a-chip platform was shown to be utilized for the detection of several small-size biomarkers such as adenosine, ADP, lactic acid, l-isoleucine, l-glutamic acid, and oxidized glutathione via LC-MS/MS from control conditions and a myocardial ischemia model. Cell-embedded and hydrogel matrix-supported versions of this heart-on-a-chip system were successfully prepared and shown to provide powerful outputs with myocardial ischemia markers. In light of this research, these outputs aim to develop simple and biologically effective organ-on-a-chip systems for future research.

Building Resilience through Territorial Planning: Water Management Infrastructure and Settlement Design in the Coastal Wetlands of Northern Apulia (Salpia vetus-Salapia) from the Hellenistic Period to Late Antiquity

This Gulf of Manfredonia has, for millennia, been the primary water feature of the coastal wetland of Northern Apulia, Italy, although modern reclamation works make writing its long-term history challenging. Our recent paleoenvironmental research has reconstructed the evolution of the southern half of this lagoon since the Neolithic period. Here, we write a history of water management and environmental change in this landscape from the perspective of two key urban sites: pre-Roman Salpia vetus and Roman Salapia. The Roman architectural historian Vitruvius recounts the abandonment of Salpia vetus and the refoundation of Salapia. We employ his narrative as a frame for a more complex environmental history, starting from a historiography of this landscape’s study and a summary of our interdisciplinary research agenda, which unifies environmental, topographical, remote sensing, and archaeological approaches. Resilience in this changeable wetland environment was only possible through an integrated and intentional management of water among rivers, the lagoon, and the Adriatic Sea. While Salpia vetus exploited this wetland and thrived for centuries, the settlement eventually collapsed due to human and environmentally impelled factors. Roman Salapia subsequently emerged with a different approach, new infrastructure, and a new location. This blueprint would sustain urban life in this wetland for six centuries and lay the groundwork for the Medieval town.

PGNN-Net: Parallel Graph Neural Networks for Hyperspectral Image Classification Using Multiple Spatial-Spectral Features

Hyperspectral image (HSI) shows great potential for application in remote sensing due to its rich spectral information and fine spatial resolution. However, the high dimensionality, nonlinearity, and complex relationship between spectral and spatial features of HSI pose challenges to its accurate classification. Traditional convolutional neural network (CNN)-based methods suffer from detail loss in feature extraction; Transformer-based methods rely too much on the quantity and quality of HSI; and graph neural network (GNN)-based methods provide a new impetus for HSI classification by virtue of their excellent ability to handle irregular data. To address these challenges and take advantage of GNN, we propose a network of parallel GNNs called PGNN-Net. The network first extracts the key spatial-spectral features of HSI using principal component analysis, followed by preprocessing to obtain two primary features and a normalized adjacency matrix. Then, a parallel architecture is constructed using improved GCN and ChebNet to extract local and global spatial-spectral features, respectively. Finally, the discriminative features obtained through the fusion strategy are input into the classifier to obtain the classification results. In addition, to alleviate the over-fitting problem, the label smoothing technique is embedded in the cross-entropy loss function. The experimental results show that the average overall accuracy obtained by our method on Indian Pines, Kennedy Space Center, Pavia University Scene, and Botswana reaches 97.35%, 99.40%, 99.64%, and 98.46%, respectively, which are better compared to some state-of-the-art methods.

The First Discovery of Archean Dolerite Dikes in the Western Part of the Aldan Shield

Dolerite dikes were studied in the western part of the Aldan terrane, in the middle reaches of the Tokko River. These dolerite dikes form a swarm of submeridional trend about 1 km wide. The dolerites of the thickest dike preserve their primary textural and structural features and mineral composition: plagioclase + pigeonite + augite + titanomagnetite. Dolerite in the chilled margins and central parts of the dike are homogeneous in composition, corresponds to low-Mg tholeiites, has low contents of Ti and other HFSE, with weak enrichment in light REE and small negative Nb anomalies. Sm–Nd isotope data on magmatic minerals of dolerite from the central part of the dike yield a good linear regression in an isochron diagram that gives to an age of 2510 ± 64 Ma, which probably corresponds to the crystallization age of the basalt. Metadolerites in a thin dike retain plagioclase porphyritic structures, but the pyroxenes are completely replaced by amphibole and chlorite. The metadolerites are contrastingly different in low contents of MgO, Cr, and Ni and in higher contents of TiO2, Fe2O3, P2O5, Nb, and all REE. The differences in the composition of the dikes may be explained by the longterm (about 65%) crystallization differentiation of the initial melt and the emplacement of the residual melt from a shallow intermediate magma chamber via opening cracks. Such conditions probably may have existed in tectonically stable intraplate settings. The age of the dolerites of the dike swarm is comparable to that of the anorogenic granites of the Nelyuki complex (~2.4–2.5 Ga), which are widespread in the western part of Aldan granulite–gneiss terrane. Our data bridge some gaps in characteristics of intraplate anorogenic magmatism that occurred in the western Aldan Shield in the Late Archean and marked the final consolidation of a large block of Archean crust in the Chara–Olekma granite–greenstone area.