Heterogeneous Texture Research Articles

Primary scrotal lipoma in a pediatric patient: a case report with current literature review

BackgroundsPrimary scrotal lipomas are benign fatty tumors that develop from adipocytes of the scrotum. The exact cause of their development is unknown. They are typically painless and may exhibit symptoms of heaviness and discomfort.Case presentationA 9-year-old boy presented with a painless scrotal mass that had been present since birth. The mass was located in the left hemiscrotum. Ultrasound showed an enlarged left testicle with an abnormal outline and heterogeneous texture. Magnetic resonance imaging demonstrated a mass arising from the left hemiscrotum with no invasion of the testis. Under general anesthesia and through a scrotal incision, the mass was excised. Histopathological examination revealed a lipoma.ConclusionPrimary scrotal lipomas are benign lesions with an unclear pathogenesis. They are very uncommon in the pediatric age group. Ultrasound is the first-line modality for diagnosing lipomas; however, it may provide ambiguity. MRI provides a more accurate assessment of the mass. Surgical excision of the mass is the standard treatment for primary scrotal lipomas.

Abstract 12928: Deep Learning-Derived Characterization of Lung Tissue Predicts Future Diagnosis of Heart Failure

Background: Cardiovascular and respiratory diseases are intrinsically coupled. Scalable methods for quantifying cardiac and pulmonary structures may improve our understanding of cardiopulmonary disease. Aims: We sought to quantify textural parameters of the lung fields in cardiovascular magnetic resonance imaging (cMRI) from the UK Biobank. We then sought to determine their genetic basis and their relationship with heart failure. Methods: We fine-tuned a convolutional neural network to segment left and right lung on cMRIs performed during expiratory breath hold. A random sample of 300 coronal slices from unique individuals was manually annotated for fine-tuning. The model was applied to 912,320 coronal cMRI images. Segmentation masks were stacked, and lung volume and texture metrics were quantified from the resulting 3D masks using the pyradiomics Python package. Results: After quality control, we computed lung traits in 45,179 participants. Mean lung volume was 2.19 ± 0.49 L in women and 2.87 ± 0.68 L in men. After adjustment for age and sex, lung volume was associated with forced vital capacity (FVC) (β = +0.30 L per SD, p < 1.1 х 10 -308 ). After adjustment for age and sex, body size traits (e.g. standing height and heel bone mineral density) were associated with increased lung volume, whereas adiposity traits (e.g. body mass index and visceral adipose tissue) were associated with decreased lung volume. Greater heterogeneous and coarse lung textures were associated with an increased risk of heart failure (1-SD hazard ratios: 1.21 (95% CI [1.12, 1.31]) and 1.32 (95% CI [1.18, 1.47]), respectively). This association was still significant among non-smokers and after adjustment for left ventricular ejection fraction. Conclusions: Deep-learning derived measures of lung texture are linked to future heart failure. Our findings suggest that heart failure may manifest as changes in lung tissue that are detectable on MRI before changes in cardiac function.

THE FEATURES OF FORMATION OF RARE MINERALS DURING SHALLOW METAMORPHISM AND METASOMATOSIS

The unusual composition of the protolith, its structural and textural heterogeneity, limited mass transfer and high temperatures are the causes of rare mineral formation and mineral associations during metamorphism and metasomatism. Most often, this is observed in shallow conditions (pressure up to 3 kbar) when additional heat is supplied to the rocks by magmatic intrusions. In the article, these issues are discussed on a number of geological complexes, in which the manifestations of metamorphism with the development of rare minerals and mineral associations have been studied in detail.

Unsupervised deep network for image texture transformation: Improving the quality of cross-correlation analysis and mechanical vortex visualisation during cardiac fibrillation

Visualisation of cardiac fibrillation plays a very considerable role in cardiophysiological study and clinical applications. One of the ways to obtain the image of these phenomena is the registration of mechanical displacement fields reflecting the track from electrical activity. In this work, we read these fields using cross-correlation analysis from the video of open pig's epicardium at the start of fibrillation recorded with electrocardiogram. However, the quality of obtained displacement fields remains low due to the weak pixels heterogeneity of the frames. It disables to see more clearly such interesting phenomena as mechanical vortexes that underline the mechanical dysfunction of fibrillation. The applying of chemical or mechanical markers to solve this problem can affect the course of natural processes and falsify the results. Therefore, we developed a novel scheme of an unsupervised deep neural network that is based on the state-of-art positional coding technology for a multilayer perceptron. This network enables to generate a couple of frames with a more heterogeneous pixel texture, that is more suitable for cross-correlation analysis methods, from two consecutive frames. The novel network scheme was tested on synthetic pairs of images with different texture heterogeneity and frequency of displacement fields and also it was compared with different filters on our cardiac tissue image dataset. The testing showed that the displacement fields obtained with our method are closer to the ground truth than those which were computed only with the cross-correlation analysis in low contrast images case where filtering is impossible. Moreover, our model showed the best results comparing with the one of the popular filter CLAHE on our dataset. As a result, using our approach, it was possible to register more clearly a mechanical vortex on the epicardium at the start of fibrillation continuously for several milliseconds for the first time.

Study on the recrystallization microstructure and texture evolution of a rolled 0.075 mm ultra-thin grain-oriented silicon steel

This paper investigated the recrystallization behavior of a rolled 0.075 mm ultra-thin grain-oriented silicon steel, and microstructure and texture evolutions were studied. At the early stage, dominant {hk0}<001> recrystallized grains were ascribed to the preferential {hk0}<001> nucleation, then their growth was reduced by texture pinning effect. In contrast, a few ‘random’ grains grew excessively to consume surrounding {hk0}<001> fine-grained matrix, meanwhile nuclei originating from initial non-Goss grains also showed size advantage and further grew. Despite of quasi-two-dimensional microstructure of ultra-thin sheet, the anisotropic surface energy did not show marked effect on grain growth difference. Those coarse grains, which were 10 times larger than the matrix grains, were surrounded by high energy (HE) boundaries, indicating that their growth was driven by high grain boundary mobility. With the increasing annealing time, the formation of {hk0}<001> oriented assemblies and the growth of non-{hk0}<001> oriented grains contributed to the overall grain size increase. Regarding the relationship between microstructure, texture and magnetic properties, {hk0}<001> texture was weakened and reduced magnetic induction accordingly, and the combination of growing while heterogeneous grain size and changing texture made iron loss a first decrease and then increase tendency. It is essential to control annealing time to obtain suitable combination of microstructure and texture.

TGDAUNet: Transformer and GCNN based dual-branch attention UNet for medical image segmentation

Accurate and automatic segmentation of medical images is a key step in clinical diagnosis and analysis. Currently, the successful application of Transformers’ model in the field of computer vision, researchers have begun to gradually explore the application of Transformers in medical segmentation of images, especially in combination with convolutional neural networks with coding–decoding structure, which have achieved remarkable results in the field of medical segmentation. However, most studies have combined Transformers with CNNs at a single scale or processed only the highest-level semantic feature information, ignoring the rich location information in the lower-level semantic feature information. At the same time, for problems such as blurred structural boundaries and heterogeneous textures in images, most existing methods usually simply connect contour information to capture the boundaries of the target. However, these methods cannot capture the precise outline of the target and ignore the potential relationship between the boundary and the region. In this paper, we propose the TGDAUNet, which consists of a dual-branch backbone network of CNNs and Transformers and a parallel attention mechanism, to achieve accurate segmentation of lesions in medical images. Firstly, high-level semantic feature information of the CNN backbone branches is fused at multiple scales, and the high-level and low-level feature information complement each other’s location and spatial information. We further use the polarised self-attentive (PSA) module to reduce the impact of redundant information caused by multiple scales, to better couple with the feature information extracted from the Transformers backbone branch, and to establish global contextual long-range dependencies at multiple scales. In addition, we have designed the Reverse Graph-reasoned Fusion (RGF) module and the Feature Aggregation (FA) module to jointly guide the global context. The FA module aggregates high-level semantic feature information to generate an original global predictive segmentation map. The RGF module captures non-significant features of the boundaries in the original or secondary global prediction segmentation graph through a reverse attention mechanism, establishing a graph reasoning module to explore the potential semantic relationships between boundaries and regions, further refining the target boundaries. Finally, to validate the effectiveness of our proposed method, we compare our proposed method with the current popular methods in the CVC-ClinicDB, Kvasir-SEG, ETIS, CVC-ColonDB, CVC-300,datasets as well as the skin cancer segmentation datasets ISIC-2016 and ISIC-2017. The large number of experimental results show that our method outperforms the currently popular methods. Source code is released at https://github.com/sd-spf/TGDAUNet.

Fine‐root turnover and leaf litterfall of Salsola passerina facilitate soil restoration in Alxa steppe desert of northwest China

Nutrient cycling in desert ecosystems is heavily reliant on the fine root turnover and litter decomposition processes of dominant plant species. Based on field investigations and laboratory experiments, we explored fine root production (FRP) and turnover of Salsola passerina in Alxa steppe desert, and assessed fine root and leaf litter input and its correlation with soil C and N stocks, and then compared the variability of soil physicochemical properties at the site below and the interspace of S. passerina canopy. Results showed that fine root biomass dynamics of S. passerina were consistent with soil water content. Annual FRP of S. passerina was 243.3 g m−2 yr−1 and the turnover rate was 2.0 per year. S. passerina contributed 172.2 g C m−2 and 6.8 g N m−2 to soil from its fine root senescence and leaf litter input annually. Compared to the canopy interspace, the soil texture and fertility beneath the shrubs significantly improved, while the soil salinity decreased. The results indicated that the activities of desert shrub growth provided the degraded soil with abundant nutrients through fine root turnover and litter decomposition processes, which promoted soil texture and nutrition heterogeneity at the stand scale, and played an important role in degraded dryland restoration.

MDFN: A Multi-level Dynamic Fusion Network with self-calibrated edge enhancement for lung nodule segmentation

Precise segmentation of lung nodules from the surrounding tissues provides radiologists with distinct boundary details, instrumental to the meticulous diagnosis and subsequent treatment of lung cancer. Although extensively applied in practice, the existing deep learning approaches are generally confronted with under-segmentation for intractable nodules with heterogeneous textures, blurred boundaries, adhesive structures and the loss of crucial spatial and edge details during the deep convolutional encoding. Aiming at these issues, we take full advantage of the complementarity between edge and region to enhance the restoration of complex edges and propose a 2D Multi-level Dynamic Fusion Network (MDFN) for precise lung nodule segmentation and fine-grained edge details. Firstly, we innovatively construct a multi-scale spatial and channel feature selection module (MSCFS) to enrich the receptive fields and capture highly correlated multi-scale contexts. Secondly, a self-calibrated encoder with MSCFS is adopted to establish the spatial dependency between long distance and channel to avert the loss of both global and local features. Thirdly, a novel deep feature fusion decoder is designed to separately generate the edge and region features of the same and different levels and dynamically integrate them to restore fine-grained nodule edge morphology. Various experiments on LUNA16 public dataset confirm that MDFN outperforms other advanced methods on the quantitative index appraisal and qualitative visualization analysis, with an average DSC of 89.19% and Hausdorff distance of 2.353 mm. By contrast, with the powerful blend of multi-level edge and region features, MDFN achieves more smooth and consistent boundary details, especially for nodules adhesive to surrounding issues.

Changes in the Heterogeneous Deformation Structure and Texture with Cold Rolling Reduction in {110}&lt;110&gt; Oriented Coarse Grained 3% Silicon Steel

Changes in the Heterogeneous Deformation Structure and Texture with Cold Rolling Reduction in {110}&lt;110&gt; Oriented Coarse Grained 3% Silicon Steel

Heterogeneous Carotid Plaque Predicts Cardiovascular Events after Percutaneous Coronary Intervention.

The relationship between carotid artery ultrasound findings and clinical outcomes in patients who undergo percutaneous coronary intervention (PCI) has not been completely elucidated. This single-center retrospective study investigated 691 patients who underwent PCI and carotid ultrasound testing. Maximum carotid intima-media thickness (CIMT) was defined as the greatest CIMT at the maximally thick point among the common carotid artery, carotid bulb, and internal carotid artery. A carotid plaque was defined as vessel wall thickening with a CIMT ≥ 1.5 mm. The characteristics of carotid plaque (heterogeneity, calcification, or irregular/ulcerated surface) were evaluated visually. Patients were divided into those with and without heterogeneous carotid plaque (maximum CIMT ≥ 1.5 mm and heterogeneous texture). The endpoint was the incidence of a major adverse cardiovascular event (MACE) defined as a composite of cardiovascular (CV) death, myocardial infarction, and ischemic stroke. Among 691 patients, 309 were categorized as having a heterogeneous plaque. Patients with heterogeneous plaques were at a higher risk of MACE than those without (p=0.002). A heterogeneous plaque was independently associated with MACE after adjusting for covariates (hazard ratio [HR], 1.71; 95% confidence interval [CI], 1.01-2.90; p=0.046). Calcified or irregular/ulcerated plaques were correlated with a higher incidence of MACE, but both were not independently associated with MACE (HR, 1.35; 95% CI, 0.69-2.64, p=0.38 and HR, 0.98; 95% CI, 0.57-1.69; p=0.95, respectively). The presence of a heterogeneous carotid plaque in patients who underwent PCI predicted future CV events. These patients may require more aggressive medical therapy and careful follow-up.

The Role of Cyanobacteria In the Microfabric Heterogeneity of Cambrian Leiolite, North China

ABSTRACT Based on their mesostructures, carbonate microbialites are divided into four major groups: laminated stromatolite, clotted thrombolites, structureless leiolites, and dendritic dendrolite. Among the various types of microbialites, leiolites, also known as “cryptomicrobial deposits,” are uncommon in the stratigraphic records. This is exacerbated by leiolites relatively structureless and aphanitic structure, making their identification and characterization more challenging. Previous studies have revealed that leiolites have contrasting fabrics at different scale, heterogeneous microscopic fabrics and structureless mesoscopic to macroscopic fabrics. While it is widely assumed that cyanobacteria play a significant role in the formation of carbonate microbialites in marine environments, the origin and controlling mechanisms of variations in leiolites microstructures and macrostructures remain enigmatic. In the central part of the North China Platform, two excellent exposures of massive, structureless Cambrian bioherms (Furongian) dominated by leiolites were observed (Qijayu section). The Qijayu-section leiolites offer a unique opportunity to investigate the role of cyanobacterial input in governing the formation and textural heterogeneities of such microbialites. In addition, the significance of the Furongian leiolites stems from their association with a period of global development of microbial-dominated carbonate platforms. Our findings suggest that these Furongian leiolites developed during the forced-regressive systems tract. At the microscopic level, these Furongian leiolites exhibit a high degree of heterogeneity which is controlled by the presence of various types of cyanobacteria (Hedstroemia, Subtifioria, Girvanella), sponge mummy, and benthic ooids. The Hedstroemia, Subtifioria and Girvanella excreted extracellular polymeric substances that form multiple biofilms in cyanobacterial microbial mats where Furongian leiolites grew. The role of cyanobacteria in the microscale heterogeneity of Cambrian leiolites is evident, and this provides new insights into the development of microbial-dominated carbonate platforms in similar settings elsewhere. As a result, this study provides not only a reference example for global correlation but also some solid clues for further understanding the growth style of leiolites in the geologic record.

Feasibility study of computed tomography texture analysis for evaluation of canine primary adrenal gland tumors.

This study aimed to investigate the feasibility of computed tomography (CT) texture analysis for distinguishing canine adrenal gland tumors and its usefulness in clinical decision-making. The medical records of 25 dogs with primary adrenal masses who underwent contrast CT and a histopathological examination were retrospectively reviewed, of which 12 had adenomas (AAs), 7 had adenocarcinomas (ACCs), and 6 had pheochromocytomas (PHEOs). Conventional CT evaluation of each adrenal gland tumor included the mean, maximum, and minimum attenuation values in Hounsfield units (HU), heterogeneity of the tumor parenchyma, and contrast enhancement (type, pattern, and degree), respectively, in each phase. In CT texture analysis, precontrast and delayed-phase images of 18 adrenal gland tumors, which could be applied for ComBat harmonization were used, and 93 radiomic features (18 first-order and 75 second-order statistics) were extracted. Then, ComBat harmonization was applied to compensate for the batch effect created by the different CT protocols. The area under the receiver operating characteristic curve (AUC) for each significant feature was used to evaluate the diagnostic performance of CT texture analysis. Among the conventional features, PHEO showed significantly higher mean and maximum precontrast HU values than ACC (p < 0.05). Eight second-order features on the precontrast images showed significant differences between the adrenal gland tumors (p < 0.05). However, none of them were significantly different between AA and PHEO, or between precontrast images and delayed-phase images. This result indicates that ACC exhibited more heterogeneous and complex textures and more variable intensities with lower gray-level values than AA and PHEO. The correlation, maximal correlation coefficient, and gray level non-uniformity normalized were significantly different between AA and ACC, and between ACC and PHEO. These features showed high AUCs in discriminating ACC and PHEO, which were comparable or higher than the precontrast mean and maximum HU (AUC = 0.865 and 0.860, respectively). Canine primary adrenal gland tumor differentiation can be achieved with CT texture analysis on precontrast images and may have a potential role in clinical decision-making. Further prospective studies with larger populations and cross-validation are warranted.

Fluorescent Co/Al-layered double hydroxide intercalated Schiff base-chitosan composite for sensing multiple e-waste metals

An organic-inorganic composite was fabricated by the intercalation of chitosan Schiff base (Chitosan@SB) in the layers of Co/Al layered double hydroxide (Co/Al-LDH) to get the amalgamated properties of parent materials like fluorescent nature and porosity. The structural properties of Chitosan Schiff base intercalated layered double hydroxide (Chitosan@SB-Co/Al-LDH) were explored through FT-IR, PXRD, TGA, BET, HRTEM, and SEM analysis. The composite Chitosan@SB-Co/Al-LDH showed a fluorescence peak pointed at 501 nm (λexc = 370 nm) and a porous surface with heterogeneous texture. It showed an outstanding turn-off signal in the presence of selective metallic species i.e., Cu2+, Fe3+, Al3+, Cr3+, Hg2+, and Pb2+ ions thus acting as a sensing probe for the e-waste metals. Moreover, the Density-Functional Theory supports the procedure of interaction between Chitosan@SB-Co/Al-LDH and metal ions. Furthermore, the composite has been investigated for real life uses in sewage waste water samples.

Transformation of Specific Dispersion Interactions between Cellulose and Polyacrylonitrile in Solutions into Covalent Interactions in Fibers.

Morphological transformations in emulsions of cellulose and polyacrylonitrile (PAN) ternary copolymers containing acrylonitrile, methyl acrylate, and methylsulfonate comonomers in N-methylmorpholine-N-oxide were studied over the entire range of concentrations depending on temperature and intensity of the deformation action. Based on the morphological and rheological features of the system, the temperature-concentration range of spinnability of mixed solutions was determined, and composite fibers were spun. The fibers are characterized by a heterogeneous fibrillar texture. Studies of the structure of the fibers, carried out using X-ray diffraction analysis, revealed a decrease in cellulose crystallinity with an increase in the content of PAN. The study of the thermal properties of the obtained fibers, carried out using DSC, and chemical transformations in them in a wide temperature range by high-temperature diffuse reflection IR spectroscopy made it possible to reveal a new intense exothermic peak on the thermograms at 360 °C, which according to the IR spectra corresponds to the transformation of intermacromolecular physical interactions of the PAN and cellulose into covalent bonds between polymers. In addition, the ester groups found during the thermal treatment of the PAN part of the composite fibers in the pyrolysis zone can have a key effect on the process of their further carbonization.

HyFormer: Hybrid Grouping-Aggregation Transformer and Wide-Spanning CNN for Hyperspectral Image Super-Resolution

Hyperspectral image (HSI) super-resolution is a practical and challenging task as it requires the reconstruction of a large number of spectral bands. Achieving excellent reconstruction results can greatly benefit subsequent downstream tasks. The current mainstream hyperspectral super-resolution methods mainly utilize 3D convolutional neural networks (3D CNN) for design. However, the commonly used small kernel size in 3D CNN limits the model’s receptive field, preventing it from considering a wider range of contextual information. Though the receptive field could be expanded by enlarging the kernel size, it results in a dramatic increase in model parameters. Furthermore, the popular vision transformers designed for natural images are not suitable for processing HSI. This is because HSI exhibits sparsity in the spatial domain, which can lead to significant computational resource waste when using self-attention. In this paper, we design a hybrid architecture called HyFormer, which combines the strengths of CNN and transformer for hyperspectral super-resolution. The transformer branch enables intra-spectra interaction to capture fine-grained contextual details at each specific wavelength. Meanwhile, the CNN branch facilitates efficient inter-spectra feature extraction among different wavelengths while maintaining a large receptive field. Specifically, in the transformer branch, we propose a novel Grouping-Aggregation transformer (GAT), comprising grouping self-attention (GSA) and aggregation self-attention (ASA). The GSA is employed to extract diverse fine-grained features of targets, while the ASA facilitates interaction among heterogeneous textures allocated to different channels. In the CNN branch, we propose a Wide-Spanning Separable 3D Attention (WSSA) to enlarge the receptive field while keeping a low parameter number. Building upon WSSA, we construct a wide-spanning CNN module to efficiently extract inter-spectra features. Extensive experiments demonstrate the superior performance of our HyFormer.

Geochemistry and formation of agate-bearing lithophysae in Lower Permian volcanics of the NW-Saxonian Basin (Germany)

Geochemical and mineralogical investigations of the Lower Permian Kemmlitz rhyolite within the NW-Saxonian Basin (Germany) and associated lithophysae (high-temperature crystallization domains) as well as agates were carried out to constrain the genesis and characteristics of these volcanic rocks and the origin of the agate-bearing lithophysae. The volcanic rocks of rhyolitic composition are dominated by quartz, sanidine, and orthoclase and most likely derive from lava flows. Agate-bearing lithophysae were exclusively formed in a glassy facies (pitchstone) of the rhyolites, which was afterwards altered to illite-smectite mixed-layer clays. The results of this study show that agate formation can be related to the alteration of the volcanic rocks accompanied by the infill of mobilized silica into cavities of lithophysae. Fluid inclusion studies point to temperatures of agate formation above 150 °C, indicating that the mobilization and accumulation of silica started already during a late phase of or soon after the volcanic activities. Remarkable high concentrations of B (29 ppm), Ge (> 18 ppm), and U (> 19 ppm) as well as chondrite-normalized rare earth element (REE) distribution patterns of the agates with pronounced negative Eu-anomalies, slightly positive Ce-anomalies and enriched heavy rare earth elements (HREE) indicate interactions of the host rocks and transport of SiO2 with magmatic volatiles (F/Cl, CO2) and heated meteoric water. Characteristic yellow cathodoluminescence (CL), heterogeneous internal textures as well as high defect density of micro- and macrocrystalline quartz detected by electron paramagnetic resonance (EPR) spectroscopy point to crystallization processes via an amorphous silica precursor under non-equilibrium conditions.

Computer tomography-based shape of tumor contour and texture of tumor heterogeneity are independent prognostic indicators for clinical T1b-T2 renal cell carcinoma.

To evaluate association between computer tomography (CT)-based features of renal cell carcinoma (RCC) and survival outcomes. Data of 958 patients with clinical T1b-T2 RCC who underwent partial/radical nephrectomy from June 2003 to March 2022 were retrospectively evaluated. CT images of patients were reviewed by two radiologists for texture analysis of tumor heterogeneity and shape analysis of tumor contour. Patients were divided into three groups according to patterns of CT-based features: (1) favorable feature group (n = 117); (2) intermediate feature group (n = 606); and (3) unfavorable feature group (n = 235). Kaplan-Meier survival analysis and multivariate Cox regression analysis were performed to evaluate overall survival (OS), cancer-specific survival (CSS), and recurrence-free survival (RFS). RCCs with unfavorable CT-based feature showed larger size on CT, higher nuclear grade, higher rate of histologic necrosis, and higher rate of capsular invasion than those in the other two groups (all p < 0.001). Unfavorable feature was associated with poorer OS (p = 0.001), CSS (p < 0.001), and RFS (p < 0.001) on Kaplan-Meier analysis. In multivariate analysis, intermediate and unfavorable features were independent predictors for recurrence (hazard ratio [HR] 2.51, 95% confidence interval [CI] 1.09-5.79, p = 0.031 and HR 3.71, 95% CI 1.58-8.73, p = 0.003, respectively), but not for overall death or RCC-specific death. A combination of irregular tumor contour feature with heterogeneous tumor texture feature on CT is associated with poor RFS in clinical T1b-T2 RCC preoperatively.

MLAGG-Net: Multi-level aggregation and global guidance network for pancreatic lesion segmentation in histopathological images

Pancreatic cancer is one of the most aggressive and lethal malignancies in the world. Automatic and accurate segmentation of lesion regions from histopathological images is essential for disease diagnosis and analysis. Nevertheless, it remains a challenging task due to the complicated pathological manifestations of the lesion, including the ambiguity of boundaries, the heterogeneity of textures, and the large variations in morphology, size and location. To address these problems, we propose a multi-level aggregation and global guidance network. Specifically, we utilize a multi-level feature fusion and distribution module that aims to alleviate the scale diversity and complex boundary problems in lesions by aggregating fine-grained information of low-level features and semantic information of high-level features. The cross-attention fusion module is proposed to perform the aggregation of hierarchical multi-level features, which can absorb effective information and suppress redundant information from them. This paper also introduces a global information fusion module to guide the network to locate the lesion areas with inconsistent distribution between the internal and boundary regions more accurately for more complete segmentation results. We conduct experiments on our PANC dataset and the public GlaS challenge dataset to verify the effectiveness of the proposed network. The experimental results show that the proposed method can achieve better segmentation performance with 90.02% Dice and 82.07% Jaccard compared to other state-of-the-art methods on our PANC dataset while achieving competitive results on the GlaS challenge dataset.

Estimation of the soil hydraulic properties and parameter characteristics of Benggang erosion in granite in southern China

AbstractBenggang is an erosional landform that forms in granite hills due to coupled hydraulic and gravity erosion, and its unique development makes it difficult to obtain moisture characteristics through direct means. In this study, the soil moisture characteristics were investigated on the collapsing walls with different weathering degrees of two different Benggang types defined as scoop‐shaped Benggang and strip‐shaped Benggang in the typical Benggang erosion areas of southeastern Guangxi. The soil water characteristic curves (SWCCs) of the collapsing wall were measured by the centrifuge method to explore the moisture transport mechanism of Benggang development. The response of moisture characteristics to the Benggang morphology was not significant, but the soil layer changes in the collapsing wall had a significant impact. The Atterberg limits of the surface layer and laterite layer were significantly higher than those of the transition layer and sandy layer, with a liquid limit (LL) value exceeding 55%, indicating high LL clay. The Van Genuchten (VG), Gardner and Fredlund–Xing (FX) models were used to fit the SWCCs, and the results indicated that the VG model had a high fitting accuracy (R2 &gt; 0.984), followed by the FX model (R2 &gt; 0.943); however, the Gardner model had a low fitting accuracy (R2 &gt; 0.891). With the change in matrix suction, the sandy layer had the maximum water release capacity, followed by the transition layer, and finally the surface layer and laterite layer. The variation in equivalent pore size distributions with soil layers showed that the micropores in the laterite layer of the two Benggangs accounted for 44.91% and 44.38% of the total porosity. The macropores of the sandy layer accounted for 35.40% and 36.84% of the total porosity. Both the unsaturated hydraulic conductivity K(θ) and water diffusivity D(θ) exhibited two abrupt changes with increasing soil volumetric water content, and K(θ) and D(θ) of the lower layers were larger than those of the upper layers. The heterogeneity of soil texture and pore conditions among different collapsing wall soil layers can significantly affect the Benggang water conditions. This study provides a database for further investigation of the Benggang development mechanism.

Reconciling fast and slow cooling during planetary formation as recorded in the main group pallasites

Pallasite meteorites contain evidence for vastly different cooling timescales: rapid cooling at high temperatures (K/yrs) and slow cooling at lower temperatures (K/Myrs). Pallasite olivine also shows contrasting textures ranging from well-rounded to angular and fragmental, and some samples record chemical zoning. Previous pallasite formation models have required fortuitous changes to the parent body in order to explain these contrasting timescales and textures, including late addition of a megaregolith layer, impact excavation, or parent body break-up and recombination. We investigate the timescales recorded in Main Group Pallasite meteorites with a coupled multiscale thermal diffusion modelling approach, using a 1D model of the parent body and a 3D model of the metal-olivine intrusion region, to see if these large-scale changes to the parent body are necessary. We test a range of intrusion volumes and aspect ratios, metal-to-olivine ratios, and initial temperatures for both the background mantle and the intruded metal. We find that the contrasting timescales, textural heterogeneity, and preservation of chemical zoning can all occur within one simple ellipsoidal segment of an intrusion complex. These conditions are satisfied in 13% of our randomly generated models (2200 model runs), with small intrusion volumes (with a mean radius ≲100 m) and colder background mantle temperatures (≲1200 K) favourable. Large rounded olivine can be explained by a previous intrusion of metal into a hotter mantle, suggesting possible repeated bombardment of the parent body. We speculate that the formation of pallasitic zones within planetesimals may have been a common occurrence in the early Solar System, as our model shows that favourable pallasite conditions can be accommodated in a wide range of intrusion morphologies, across a wide range of planetesimal mantle temperatures, without the need for large-scale changes to the parent body. We suggest that pallasites represent a late stage of repeated injection of metal into a cooling planetesimal mantle, and that heterogeneity observed in micro-scale rounding or chemical zoning preservation in pallasite olivine can be explained by diverse cooling rates in different regions of a small intrusion.