Variety Of Textures Research Articles

NIMG-79. A RADIOMIC-RISK SCORE FOR SURVIVAL PREDICTION IN GLIOMA: A LARGE MULTI-INSTITUTIONAL RETROSPECTIVE VALIDATION STUDY

Abstract Gliomas have dismal survival. Recently, radiomics has demonstrated success in developing non-invasive image-based biomarkers for survival prediction in gliomas. However, clinical applicability of radiomics-based biomarkers will ultimately require rigorous validation on large, multi-institutional data. This work attempts to evaluate a radiomic-based survival risk-assessment prognostic score on a multi-institutional cohort of Glioma patients. Our rationale is that optimizing and validating radiomic features that capture intensity-based textural variations and morphology-based changes (e.g., local curvature and global contour) on large multi-institutional cohorts can allow for robust risk assessment in Glioma. n=668 pre-operative T1w MRI scans for Glioma patients from 4 cohorts: University of California San Francisco “UCSF” (n=495), LUMIERE (n=86)), xCures (n=13), and Cleveland Clinic (CCF) (n=74) were analyzed. Subjects from UCSF, CCF, and xCures were used for training, whereas the LUMIERE cohort was used for hold-out testing. Following pre-processing, expert-vetted tumor segmentation into Edema (ED) and Enhancing tumor (ET) regions was performed. A total of n=1558 radiomic features were extracted from the tumor regions (779 per region), namely, 31 global contour, 20 curvature-based, 624 gradient co-occurrence statistics, and 104 Collage features. Cox regression model was employed to create a radiomic risk score (RRS) for every subject, which included radiomics features as well as on clinical and demographic variables (IDH, MGMT, sex, age), for survival analysis. RRS incorporated a total of 43 radiomic features, and the associated clinical and demographic variables (CDS), yielding significant differences between high and low risk groups in the training (RRS: p=0.043, HR = 2, CSD: p=0.0031, HR = 2.8) and testing sets (RRS: p=0.0096, HR = 1.8, CSD: p=0.0071, HR = 1.8). Combining RRS and CDS improved the OS prediction at group level (p=3.6e-6, 0.0011, HR=5.4, 2, for training and testing, respectively). Radiomics-based risk-stratification may be promising for reliable risk-stratification in glioma.

Nucleation delay controlling the formation of mafic enclaves and banded pumice

The presence of mafic enclaves and banded pumice reveals key physical processes associated with volcanic eruptions. Here, through the textural and geochemical analyses of the 3550 B.P. Waimihia deposits in Taupō, New Zealand, we demonstrate how disequilibrium crystallization controls the way magmas mix. Andesitic enclaves in pyroclastic deposits from this predominantly rhyolitic eruption consist of microlites that crystallized rapidly during mafic injection into rhyolitic host magma. The variation of microlite textures depends on enclave size, implying that mafic enclaves crystallized as discrete blobs within a host rhyolitic magma. However, gray pumice and dark bands in banded pumice are characterized by a lack of or less plagioclase microlites that should be present if equilibrium crystallization occurred. Our textural and chemical data suggest that the lack of plagioclase in gray pumice and dark bands resulted from the nucleation delay arising from the mixing with rhyolitic magma. After mafic magma broke up in a magma chamber as discrete mafic blobs, the plagioclase-free rim of the blobs was disaggregated by shear flow. The eroded mafic blobs form a hybrid magma by mixing with rhyolitic magma, which further delays the plagioclase nucleation. This hybrid magma eventually erupted as gray pumice or banded pumice, depending on the intensity of magma mingling in the conduit. We use a plagioclase nucleation delay model to calculate residence times of hours to tens of hours prior to eruption. Our mixing model with nucleation delay enables small volumes of mafic magma to mix with large amounts of silicic magma.

Remote Sensing Identification and Information Extraction Method of Glacial Debris Flow Based on Texture Variation Characteristics

The formation mechanism of glacial debris flows in alpine gorge mountain areas is complex, with varying characteristics across different regions. Due to the influence of mountain shadows and the accumulation and ablation of ice and snow, accurately identifying and rapidly extracting glacial debris flows using optical images remains challenging. This study utilizes the Random Forest method to develop a multi-feature spatiotemporal information extraction model based on Landsat-8 images and a glacial debris flow gully identification model. These models were applied to the Songzong–Tongmai section of the Sichuan–Tibet Highway to identify glacial debris flows. The results showed that (1) the multi-feature spatiotemporal extraction model effectively eliminated the interference of mountain shadows and ice–snow phase changes, resulting in a higher accuracy for identifying and extracting glacial debris flows in areas with significant information loss due to deep shadows. The total accuracy was 93.6%, which was 8.9% and 4.2% higher than that of the Neural Network and Support Vector Machine methods, respectively. (2) The accuracy of the glacial debris flow gully identification model achieved 92.6%. The proposed method can accurately and rapidly identify glacial debris flows in alpine gorge mountain areas, facilitating remote sensing dynamic monitoring. This approach reduces the damage caused by debris flows to both transportation and the environment, ensuring the safe passage of highways and promoting the sustainable development of the region.

MUCM-Net: a Mamba powered UCM-Net for skin lesion segmentation

Aim: Skin lesion segmentation is critical for early skin cancer detection. Challenges in automatic segmentation from dermoscopic images include variations in color, texture, and artifacts of indistinct lesion boundaries. This study aims to develop and evaluate MUCM-Net, a lightweight and efficient model for skin lesion segmentation, leveraging Mamba state-space models integrated with UCM-Net architecture optimized for mobile deployment and early skin cancer detection. Methods: MUCM-Net combines Convolutional Neural Networks (CNNs), multi-layer perceptions (MLPs), and Mamba elements into a hybrid feature learning module. Results: The model was trained and tested on the International Skin Imaging Collaboration (ISIC) 2017 and ISIC2018 datasets, consisting of 2,000 and 2,594 dermoscopic images, respectively. Critical metrics for evaluation included Dice Similarity Coefficient (DSC), sensitivity (SE), specificity (SP), and accuracy (ACC). The model’s computational efficiency was also assessed by measuring Giga Floating-point Operations Per Second (GFLOPS) and the number of parameters. MUCM-Net demonstrated superior performance in skin lesion segmentation with an average DSC of 0.91 on the ISIC2017 dataset and 0.89 on the ISIC2018 dataset, outperforming existing models. It achieved high SE (0.93), SP (0.95), and ACC (0.92) with low computational demands (0.055–0.064 GFLOPS). Conclusions: The model’s innovative Mamba-UCM layer significantly enhanced feature learning while maintaining efficiency that is suitable for mobile devices. MUCM-Net establishes a new standard in lightweight skin lesion segmentation, balancing exceptional ACC with efficient computational performance. Its ability to perform well on mobile devices makes it a scalable tool for early skin cancer detection in resource-limited settings. The open-source availability of MUCM-Net supports further research and collaboration, promoting advances in mobile health diagnostics and the fight against skin cancer. MUCM-Net source code will be posted on https://github.com/chunyuyuan/MUCM-Net.

Addressing Challenges in Skin Cancer Diagnosis: A Convolutional Swin Transformer Approach.

Skin cancer is one of the top three hazardous cancer types, and it is caused by the abnormal proliferation of tumor cells. Diagnosing skin cancer accurately and early is crucial for saving patients' lives. However, it is a challenging task due to various significant issues, including lesion variations in texture, shape, color, and size; artifacts (hairs); uneven lesion boundaries; and poor contrast. To solve these issues, this research proposes a novel Convolutional Swin Transformer (CSwinformer) method for segmenting and classifying skin lesions accurately. The framework involves phases such as data preprocessing, segmentation, and classification. In the first phase, Gaussian filtering, Z-score normalization, and augmentation processes are executed to remove unnecessary noise, re-organize the data, and increase data diversity. In the phase of segmentation, we design a new model "Swinformer-Net" integrating Swin Transformer and U-Net frameworks, to accurately define a region of interest. At the final phase of classification, the segmented outcome is input into the newly proposed module "Multi-Scale Dilated Convolutional Neural Network meets Transformer (MD-CNNFormer)," where the data samples are classified into respective classes. We use four benchmark datasets-HAM10000, ISBI 2016, PH2, and Skin Cancer ISIC for evaluation. The results demonstrated the designed framework's better efficiency against the traditional approaches. The proposed method provided classification accuracy of 98.72%, pixel accuracy of 98.06%, and dice coefficient of 97.67%, respectively. The proposed method offered a promising solution in skin lesion segmentation and classification, supporting clinicians to accurately diagnose skin cancer.

Predicting Invasiveness in Lepidic Pattern Adenocarcinoma of Lung: Analysis of Visual Semantic and Radiomic Features.

To differentiate invasive lepidic predominant adenocarcinoma (iLPA) from adenocarcinoma in situ (AIS)/minimally invasive adenocarcinoma (MIA) of lung utilizing visual semantic and computer-aided detection (CAD)-based texture features on subjects initially diagnosed as AIS or MIA with CT-guided biopsy. From 2011 to 2017, all patients with CT-guided biopsy results of AIS or MIA who subsequently underwent resection were identified. CT scan before the biopsy was used to assess visual semantic and CAD texture features, totaling 23 semantic and 95 CAD-based quantitative texture variables. The least absolute shrinkage and selection operator (LASSO) method or forward selection was used to select the most predictive feature and combination of semantic and texture features for detection of invasive lung adenocarcinoma. Among the 33 core needle-biopsied patients with AIS/MIA pathology, 24 (72.7%) had invasive LPA and 9 (27.3%) had AIS/MIA on resection. On CT, visual semantic features included 21 (63.6%) part-solid, 5 (15.2%) pure ground glass, and 7 (21.2%) solid nodules. LASSO selected seven variables for the model, but all were not statistically significant. "Volume" was found to be statistically significant when assessing the correlation between independent variables using the backward selection technique. The LASSO selected "tumor_Perc95", "nodule surround", "small cyst-like spaces", and "volume" when assessing the correlation between independent variables. Lung biopsy results showing noninvasive LPA underestimate invasiveness. Although statistically non-significant, some semantic features showed potential for predicting invasiveness, with septal stretching absent in all noninvasive cases, and solid consistency present in a significant portion of invasive cases.

Compositional and textural evidences of Na-bentonite authigenesis in the Pellegrini lake deposit (Neuquen basin, Argentina)

Textural and compositional variations of Na-bentonites from the Pellegrini Lake deposit (Neuquén Basin) have been established, with special emphasis on petrographic features in order to better understand the genetic pathways in their formation. In this region, two bentonite beds named “white bentonite” and “green bentonite” are mined mainly for the oil and foundry industries. The genesis of these beds was studied using petrological (petrography, SEM and TEM), mineralogical (DRX, FT-IR, DTA-TGA, 27Al MAS NMR) and geochemical (ICP-AES) data from two quarries named COR and CA. The bentonites mainly consist of Na-montmorillonite, with lesser amounts of quartz, plagioclase, potassium feldspar, illite, zeolite, barite and gypsum. Mineralogical and petrographical evidence indicates that the authigenic smectite was formed by devitrification of unstable glass shards in a shallow coastal marine environment. Green bentonite would be the result of several episodes of volcanic ash-fall input from multiple eruptions with a higher degree of alteration uppermost in the bed. In this case, the alteration would have occurred in a reducing environment as evidenced by the greenish colouration of these bentonites, the Ce anomaly and the presence of pyrite. Meanwhile, white bentonite would have formed in a less restricted environment than green bentonite, as evidenced by the existence of intercalations of silt-sized grains and a higher amount of detrital grains in the clay groundmass transported probably by fresher waters. The presence of celestine, barite, halite and gypsum indicates evaporitic conditions after smectite formation. The observed secondary gypsum and iron staining of surfaces are much later processes. Green bentonite samples indicate a trachyandesite composition of the parent material, while the white bentonite suggests a parent material most related to a rhyodacite/dacite composition showing that the characteristics of the volcanism might have changed with time.

Deep and shallow feature fusion framework for remote sensing open pit coal mine scene recognition

Understanding land use and damage in open-pit coal mining areas is crucial for effective scientific oversight and management. Current recognition methods exhibit limitations: traditional approaches depend on manually designed features, which offer limited expressiveness, whereas deep learning techniques are heavily reliant on sample data. In order to overcome the aforementioned limitations, a three-branch feature extraction framework was proposed in the present study. The proposed framework effectively fuses deep features (DF) and shallow features (SF), and can accomplish scene recognition tasks with high accuracy and fewer samples. Deep features are enhanced through a neighbouring feature attention module and a Graph Convolutional Network (GCN) module, which capture both neighbouring features and the correlation between local scene information. Shallow features are extracted using the Gray-Level Co-occurrence Matrix (GLCM) and Gabor filters, which respectively capture local and overall texture variations. Evaluation results on the AID and RSSCN7 datasets demonstrate that the proposed deep feature extraction model achieved classification accuracies of 97.53% and 96.73%, respectively, indicating superior performance in deep feature extraction tasks. Finally, the two kinds of features were fused and input into the particle swarm algorithm optimised support vector machine (PSO-SVM) to classify the scenes of remote sensing images, and the classification accuracy reached 92.78%, outperforming four other classification methods.

Deep coupled registration and segmentation of multimodal whole-brain images.

Recent brain mapping efforts are producing large-scale whole-brain images using different imaging modalities. Accurate alignment and delineation of anatomical structures in these images are essential for numerous studies. These requirements are typically modeled as two distinct tasks: Registration and segmentation. However, prevailing methods, fail to fully explore and utilize the inherent correlation and complementarity between the two tasks. Furthermore, variations in brain anatomy, brightness and texture pose another formidable challenge in designing multi-modal similarity metrics. A high-throughput approach capable of overcomingthe bottleneck of multi-modal similarity metric design, while effective leveraging the highly correlated and complementary nature of two tasks is highly desirable. We introduce a deep learning framework for joint registration and segmentation of multi-modal brain images. Under this framework, registration and segmentation tasks are deeply coupled and collaborated at two hierarchical layers. In the inner layer, we establish a strong feature-level coupling between the two tasks by learning a unified common latent feature representation. In the outer layer, we introduce a mutually supervised dual-branch network to decouple latent features and facilitate task-level collaboration between registration and segmentation. Since the latent features we designed are also modality-independent, the bottleneck of designing multi-modal similarity metric is essentially addressed. Another merit offered by this framework is the interpretability of latent features, which allows intuitive manipulation of feature learning, thereby further enhancing network training efficiency and the performance of both tasks. Extensive experiments conducted on both multi-modal and mono-modal datasets of mouse and human brains demonstrate the superiority of our method. The code is available at https://github.com/tingtingup/DCRS. Supplementary data are available at Bioinformaticsonline.

Microexpression Recognition Method Based on ADP-DSTN Feature Fusion and Convolutional Block Attention Module

Microexpressions are subtle facial movements that occur within an extremely brief time frame, often revealing suppressed emotions. These expressions hold significant importance across various fields, including security monitoring and human–computer interaction. However, the accuracy of microexpression recognition is severely constrained by the inherent characteristics of these expressions. To address the issue of low detection accuracy regarding the subtle features present in microexpressions’ facial action units, this paper proposes a microexpression action unit detection algorithm, Attention-embedded Dual Path and Shallow Three-stream Networks (ADP-DSTN), that incorporates an attention-embedded dual path and a shallow three-stream network. First, an attention mechanism was embedded after each Bottleneck layer in the foundational Dual Path Networks to extract static features representing subtle texture variations that have significant weights in the action units. Subsequently, a shallow three-stream 3D convolutional neural network was employed to extract optical flow features that were particularly sensitive to temporal and discriminative characteristics specific to microexpression action units. Finally, the acquired static facial feature vectors and optical flow feature vectors were concatenated to form a fused feature vector that encompassed more effective information for recognition. Each facial action unit was then trained individually to address the issue of weak correlations among the facial action units, thereby facilitating the classification of microexpression emotions. The experimental results demonstrated that the proposed method achieved great performance across several microexpression datasets. The unweighted average recall (UAR) values were 80.71%, 89.55%, 44.64%, 80.59%, and 88.32% for the SAMM, CASME II, CAS(ME)3, SMIC, and MEGC2019 datasets, respectively. The unweighted F1 scores (UF1) were 79.32%, 88.30%, 43.03%, 81.12%, and 88.95%, respectively. Furthermore, when compared to the benchmark model, our proposed model achieved better performance with lower computational complexity, characterized by a Floating Point Operations (FLOPs) value of 1087.350 M and a total of 6.356 × 106 model parameters.

Geology and geochronology of the El Peñón granitic pluton: A contribution to the famatinian magmatic evolution in the Sierra de San Luis, Argentina

The El Peñón pluton is a peraluminous granite located in the northeastern sector of the Sierra de San Luis. According to its textural and mineral variations two endmember facies have been distinguished along it: a coarse-grained muscovite-bearing facies, and a fine-grained biotite-bearing facies. Field relationships aligned with mineralogical and geochemical compositions, allow us to establish an anatectic origin for this pluton. Regarding the inherited zircon ages, the provenance area of the metasedimentary source could be linked to the orogenic systems of Western Gondwana. The analysis of the internal structure of the intrusive body related with the structural arrangement observed in the country rocks indicates a syn-kinematic emplacement of the igneous body with respect to the main deformational event described in the Sierra de San Luis, which has been assigned to the closure of the Famatinian arc. New U-Pb (LA-ICP-MS) zircon ages constrain the emplacement/crystallization age of the El Peñón granite within the time lapse between ∼470 and 460 Ma. The geochronological data presented here, together with geochemical and structural analysis, enables us to correlate the origin of the El Peñón pluton and the deformation that occurred during its emplacement with the Famatinian Orogeny. Thus, these results allow us to rule out the presence of Pampean magmatism in the Sierra de San Luis but also establish a new absolute age for the main deformational event of the range.

Constraints of in-situ elemental compositions and U–Pb ages of cassiterite on the origin of the Cretaceous Gejiu and Dulong tin deposits, SW China: Implications for the linkage of tin belts in SE Asia

The western Yunnan tin belts in SW China and the southern Myanmar tin belt in SE Asia are traditionally thought to be part of the SE Asian tin metallogenic province. However, the tectonic setting of the Youjiang tin belt in SW China and its genetic relationship to the other tin belts are poorly documented. In this belt, the giant Gejiu and Dulong tin deposits are typical of skarn-type deposits and both contain contact and distal skarn orebodies. Tin ores of distal orebodies in these two deposits are composed of cassiterite, sulfide and calcite. In both deposits, most cassiterite grains from these ores are rich in Fe and W and depleted in Nb and Ta. They have 238U/206Pb ages between 81.4 and 84.1 Ma, similar to hosting granitic plutons. Two generations of cassiterite with distinctly different microtextures and trace elemental compositions can be recognized. The first generation (Cst-I) is compositionally and texturally homogeneous, but the second generation (Cst-II) displays distinctly oscillatory zoning. Cst-II contains Nb, W, and U lower, and Fe, Ta, Zr and chalcophile elements (Ga, Ge, In, and Sb) higher than Cst-I. These textural and compositional variations reflect the potential involvement of meteoric water in the magmatic-hydrothermal system. In this system, ore-forming fluids were evolved to more alkaline and oxidized to facilitate the precipitation of cassiterite. Our study indicates that these two deposits in the Youjiang tin belt have mineralization styles similar to those in the adjacent western Yunnan and southern Myanmar tin belts and all these tin belts belong to the SE Asian metallogenic provinces. A westward-younging trend of tin mineralization in these three belts can be explained by the rollback of the subducted Neo-Tethyan oceanic slab. This setting is different from the one related to the subduction of the Paleo-Pacific plate.

Organoleptic profiling for demand led breeding of eggplant (Solanum melongena L.) suitable for Kerala market

In order to develop eggplant hybrids with consumer acceptance, an organoleptic evaluation of fruits in two different sets of eggplant genotypes was conducted by a panel of 15 judges with 21 descriptors, following a quantitative descriptive analysis (QDA). In 2023, twenty genotypes were evaluated as the first set to identify suitable parents for hybridization. The results showed significant variation in odour, appearance, texture, flavor and taste, and overall quality attributes. From the first set, five genotypes (Ponni, Vengeri, IC618016, IC636521, and IC624240) were selected to produce ten F1 hybrids, and they were further evaluated, along with parents and three popular varieties as check in 2024. In the second set, many descriptors showed no significant variation. F1 hybrid, Vengeri x IC636521, showed higher yield and overall quality attributes, and most of the other hybrids (seven) were high yielders over popular checks with on par for overall quality. The findings suggest the role of organoleptic evaluation in selection and development of quality breeding materials. Keywords: Odour, Appearance, Texture, Flavour, Taste, Overall quality

A Modified Deep Semantic Segmentation Model for Analysis of Whole Slide Skin Images

Automated segmentation of biomedical image has been recognized as an important step in computer-aided diagnosis systems for detection of abnormalities. Despite its importance, the segmentation process remains an open challenge due to variations in color, texture, shape diversity and boundaries. Semantic segmentation often requires deeper neural networks to achieve higher accuracy, making the segmentation model more complex and slower. Due to the need to process a large number of biomedical images, more efficient and cheaper image processing techniques for accurate segmentation are needed. In this article, we present a modified deep semantic segmentation model that utilizes the backbone of EfficientNet-B3 along with UNet for reliable segmentation. We trained our model on Non-melanoma skin cancer segmentation for histopathology dataset to divide the image in 12 different classes for segmentation. Our method outperforms the existing literature with an increase in average class accuracy from 79 to 83%. Our approach also shows an increase in overall accuracy from 85 to 94%.

Plant production and community structure in a mesic semi-natural grassland: Moderate soil textural variation has a much stronger influence than experimentally increased atmospheric nitrogen deposition

Plant production and community structure in a mesic semi-natural grassland: Moderate soil textural variation has a much stronger influence than experimentally increased atmospheric nitrogen deposition

Diverse non-homogeneous texture synthesis from a single exemplar

Capturing non-local, long range features present in non-homogeneous textures is difficult to achieve with existing techniques. We introduce a new training method and architecture for single-exemplar texture synthesis that combines a Generative Adversarial Network (GAN) and a Variational Autoencoder (VAE). In the proposed architecture, the combined networks share information during training via structurally identical, independent blocks, facilitating highly diverse texture variations from a single image exemplar. Supporting this training method, we also include a similarity loss term that further encourages diverse output while also improving the overall quality. Using our approach, it is possible to produce diverse results over the entire sample size taken from a single model that can be trained in approximately 15 min. We show that our approach obtains superior performance when compared to SOTA texture synthesis methods and single image GAN methods using standard diversity and quality metrics.

A synthesis of the REE-Fe-polymetallic mineral system of the REE-line, Bergslagen, Sweden: New mineralogical and textural-paragenetic constraints

Rare earth elements (REE) have gained increasing significance for numerous technologies, particularly in today’s rapidly expanding “green transition” applications such as wind generators and electric vehicle traction engines. Among the more well-known REE mineralisation types in Sweden, together with alkaline intrusions and apatite-iron oxide ores, is the classic yet enigmatic REE-Fe-polymetallic mineral system of Bastnäs-type. The mineralisation type is regional in context and occurs in a discontinuous SW-NE-striking belt (the REE-line) in the west-central part of the Palaeoproterozoic Bergslagen ore province, Sweden. This contribution is aimed at integrating and synthesising existing geological mineralogical, and textural features with new observations from both well-known and several lesser-known, underexplored or previously unrecognised REE-enriched occurrences within this belt, and to discuss key features within the context of mineral systems modelling. A considerable diversity in both the style and abundance of REE mineralisation as well as in discrete REE mineralogy is evident both regionally across the entire REE-line and locally within different ore districts or mine fields. These variations also extend laterally within or across different stratigraphic levels, and within different host rocks, primarily skarn-altered metacarbonates but also variably altered felsic metavolcanic rocks. Many of the mineralisations share similar textural features, which record a protracted evolution with multiple stages of formation or replacement of REE-minerals. The earliest recognised REE assemblages feature fine-grained cerite-(CeCa) with minor bastnäsite-(Ce) – bastnäsite-(La) or fluorbritholite-(Ce) – fluorbritholite-(Y) or locally britholite-(Ce) – britholite-(Y) minerals. Such assemblages typically display anhedral-granoblastic textures appearing in folded assemblages, all suggesting recrystallisation and ductile deformation during regional metamorphism of REE-minerals that had formed during an early stage of the Svecokarelian orogeny. Overprinting overgrowths and cross-cutting vein-like features of allanite-group minerals likely represent different stage(s) of REE mineralisation and (re)-mobilisation during this orogenic evolution. Several of the REE-enriched occurrences contain variably abundant and diverse polymetallic Cu-Mo-Bi-(Co) sulphide mineralisation that typically occur in late paragenetic positions and show a prevalence to REE-rich assemblages, often dominated by different allanite-group minerals. Sulphide and REE mineralisation are locally strongly associated with metamorphic minerals formed during metamorphism of variably Mg-(Fe)-altered metavolcanic rocks. The diversity in style and intensity of the REE mineralisations, along with variations in textures and specific mineralogy, suggest slight differences in the ore-forming conditions or environment at the time of mineralisation. Additionally, these differences may also reflect variations in the preservation or modification processes that operated later in the evolution of the Svecokarelian orogeny, also featuring remobilisation of REEs and sulphidic minerals. The insights gained from all available evidence synthesised herein, from micro-scale to province-scale, help define key proxies for prospectivity mapping. The combined evidence supports that the primary ore-forming stages in the mineral system coincided with felsic volcanism and associated sub-volcanic to plutonic processes at around 1.9 Ga.

Rb enrichment processes in highly evolved granites: Insights from mica and K-feldspar from the Baishitouquan pluton, Eastern Tianshan

Highly evolved granites are closely related to rare metal mineralization. The mechanisms controlling rare metal enrichment, however, remain poorly constrained. The highly evolved Baishitouquan (BST) pluton in the Eastern Tianshan, which hosts the Zhangbaoshan Rb deposit, has been well characterized and provides an excellent opportunity to study Rb enrichment processes in granitic systems. The BST pluton has five lithological zones, which, from bottom to top, are leucogranite (Zone-a), amazonite-bearing granite (Zone-b), amazonite granite (Zone-c), topaz-bearing amazonite granite (Zone-d), and topaz albite granite (Zone-e). To investigate Rb enrichment processes in the BST pluton, compositional variations in K(–Rb)-rich minerals from the different lithological zones are characterized and interpreted in the context of textural variations. The main K- and Rb-bearing minerals in the pluton are mica and K-feldspar, which are mainly represented by phengite and microcline, respectively. Phengite in Zone-e occurs as euhedral grains with irregular boundaries, indicating that they crystalized from the Baishitouquan magma and were overprinted by hydrothermal fluids. Phengite is generally enriched in F (1.05–9.94 wt%), Li (780–10171 ppm), and Rb (3133–6657 ppm), and is characterized by low K/Rb and Nb/Ta ratios. Most microcline grains occur as elongate, euhedral–subhedral laths, with tartan and Carlsbad twinning. These microcline grains are enriched in Rb (1547–2927 ppm), Pb (15–120 ppm), and Cs (14–100 ppm), and have low K/Rb and Al/Ga ratios. In the BST pluton, the concentrations of Li, F, and Rb in phengite increase from Zone-a to Zone-c, then decrease in Zone-d and Zone-e. Conversely, the K/Rb and Nb/Ta ratios decrease from Zone-a to Zone-c, then increase in Zone-d and Zone-e. The Rb concentration and K/Rb ratio of microcline exhibit a similar trend. Considering the gradual changes in lithology among the zones, the variations of mineral chemical from Zone-a to Zone-c implies continuous evolution of the magma dominated by crystal fractionation. The occurrence of secondary mica, and the abrupt geochemical changes in phengite and microcline in Zone-d and Zone-e are indicative of hydrothermal activity. The gradual decrease in Al/Ga ratio of microcline from Zone-d to Zone-e and the presence of secondary mica that is compositionally similar to primary mica suggests that the fluids were magmatic in origin. During magmatic evolution, volatile-rich (e.g., F) melts have lower viscosities and solidus temperatures than volatile-poor melts, allowing them to undergo extreme degrees of fractional crystallization. This significantly enhances the solubility of Rb, leading to continuous enrichment of Rb in the residual melt. As the BST system evolved from one dominated by magmatic processes to one dominated by hydrothermal activity, the crystallization of topaz consumed F, reducing the solubility of Rb in the melt, and leading to a decrease in the concentration of Rb in phengite and microcline. Hydrothermal alteration of phengite would have liberated Rb into the fluids, resulting in lower Rb concentrations in secondary mica compared to primary mica. Therefore, the enrichment of Rb in the BST pluton was mainly controlled by extreme degrees of fractional crystallization of a F-rich granitic magma.

Lithofacies and diagenetic controls on thrombolitic dolomite reservoir development from the Precambrian Dengying Formation in the central Sichuan Basin, SW China

Microbialite reservoirs are of great importance in oil and gas exploration. However, there is still a lack of comprehensive studies on the formation mechanisms of thrombolitic reservoirs, a specific type of microbialite. This research focuses on the oldest thrombolitic dolomite reservoir located within the Precambrian Dengying Formation in the central Sichuan Basin, southwestern China. A multi-disciplinary approach was employed to characterize different thrombolite facies and elucidate the formation mechanism of thrombolitic dolomite reservoirs and their controlling factors, involving core observation, thin-section analysis, cathodoluminescence, scanning electron microscope (SEM) microscopy, elemental analysis using LA-ICP-MS, and carbon and oxygen stable isotope analysis. Based on variations in texture, four types of thrombolite were identified: 1) distinct clotted thrombolite, 2) diffuse and regular clotted thrombolite, 3) diffuse and irregular clotted thrombolite, and 4) composite clotted thrombolite. Notably, the diffuse clotted thrombolitic dolomite is the prevalent lithology in the reservoir. Through modification by meteoric water, organic acid fluids, and hydrothermal fluids, a reservoir with dominant porosity in the form of primary growth-framework pores, dissolution pores, and vugs was formed. This resulted in the development of two high-quality reservoir intervals within the Second Member and at the top of the Fourth Member of the Dengying Formation. The growth-framework porosity of the thrombolites, epigenetic karstification, and tectonic fracturing were mainly conducive to reservoir development. However, various types of cementation have reduced porosity and connectivity within the reservoir. Overall, this study is a valuable example of the methodology required to understand meso- and microstructures of deep-buried thrombolitic dolomite reservoirs, including their heterogeneities and diagenesis, as the original structures influence diagenesis.

PROBLEMA DE DESPLACAMENTO CERÂMICO EM OBRAS VERTICAIS

Before the arrival of the Europeans, the Brazilian indigenous peoples already practiced the use of ceramic production in an advanced way. Ceramic tiles can be found in different forms, such as floors, tiles, and tiles, and are available in a variety of colors, textures, and patterns, allowing for extensive customization of spaces. These materials are especially popular in areas such as kitchens, bathrooms, and outdoor spaces, where water and wear resistance are crucial. In addition, ceramic coating is a good thermal and acoustic insulator, contributing to the comfort of environments. Thus, this article aims to show the reader the importance of quality in the process of performing the service to avoid the inconvenience caused by the fact of the detachment. The research began through a literature review of the main authors on the subject of displacement. Based on studies carried out through articles, monographs, dissertations, theses, among others, it is clear that the peeling of tiles or ceramics is considered the most common and serious problem in civil construction and its cause is the most varied. Therefore, it is concluded that the main causes of ceramic peeling is nothing more than a defect in the system that did not allow the correct adhesion between the plates, the mortar and the wall or floor, causing the pieces to detach.