Heterogeneous Content Research Articles

Recent trends in neuromorphic systems for non-von Neumann in materia computing and cognitive functionalities

In the era of artificial intelligence and smart automated systems, the quest for efficient data processing has driven exploration into neuromorphic systems, aiming to replicate brain functionality and complex cognitive actions. This review assesses, based on recent literature, the challenges and progress in developing basic neuromorphic systems, focusing on “material-neuron” concepts, that integrate structural similarities, analog memory, retention, and Hebbian learning of the brain, contrasting with conventional von Neumann architecture and spiking circuits. We categorize these devices into filamentary and non-filamentary types, highlighting their ability to mimic synaptic plasticity through external stimuli manipulation. Additionally, we emphasize the importance of heterogeneous neural content to support conductance linearity, plasticity, and volatility, enabling effective processing and storage of various types of information. Our comprehensive approach categorizes fundamentally different devices under a generalized pattern dictated by the driving parameters, namely, the pulse number, amplitude, duration, interval, as well as the current compliance employed to contain the conducting pathways. We also discuss the importance of hybridization protocols in fabricating neuromorphic systems making use of existing complementary metal oxide semiconductor technologies being practiced in the silicon foundries, which perhaps ensures a smooth translation and user interfacing of these new generation devices. The review concludes by outlining insights into developing cognitive systems, current challenges, and future directions in realizing deployable neuromorphic systems in the field of artificial intelligence.

Heterogeneous Volatility Information Content for the Realized GARCH Modeling and Forecasting Volatility

Abstract As the demand for accuracy in volatility modeling and forecasting increases, the literature tends to incorporate different volatility measures with heterogeneous information content to construct the hybrid volatility model. This study focuses on one of the popular hybrid volatility models: the Realized Generalized Autoregressive Heteroskedasticity (Realized GARCH) and embeds various volatility measures, including the CBOE VIX, VIX1D, Realized Volatility, and Daily Range to examine their heterogeneous impact on the conditional volatility estimation and forecasting. To evaluate the impact of the volatility measures, we first construct a volatility response function. This involves calculating the difference in one-step-ahead conditional volatility forecasts that incorporate information from both return and volatility measures against the forecasts based on return innovations only. Subsequently, the variance share is calculated to evaluate its role in explaining future variations in the Realized GARCH. Our results show that among these four volatility measures, VIX is the most informative volatility. Although VIX1D is overemphasized by the literature, its significance in volatility forecasting remains substantial, confirming that risk-neutral volatility measures are generally more informative than physical measures. Finally, we also find that incorporating multiple risk-neutral volatility measures does not improve forecasting performance compared to using a single measure due to overlapping information.

Smartphone video imaging: A versatile, low-cost technology for food authentication

This study presents a low-cost smartphone-based imaging technique called smartphone video imaging (SVI) to capture short videos of samples that are illuminated by a colour-changing screen. Assisted by artificial intelligence, the study develops new capabilities to make SVI a versatile imaging technique such as the hyperspectral imaging (HSI). SVI enables classification of samples with heterogeneous contents, spatial representation of analyte contents and reconstruction of hyperspectral images from videos. When integrated with a residual neural network, SVI outperforms traditional computer vision methods for ginseng classification. Moreover, the technique effectively maps the spatial distribution of saffron purity in powder mixtures with predictive performance that is comparable to that of HSI. In addition, SVI combined with the U-Net deep learning module can produce high-quality images that closely resemble the target images acquired by HSI. These results suggest that SVI can serve as a consumer-oriented solution for food authentication.

Does Anyone Care about the Opinion of People on Participating in a “Social” Metaverse? A Review and a Draft Proposal for a Surveying Tool

In recent years, the attention paid to the metaverse in the scientific world has increased; the hottest topics include system architecture and enabling technologies, as well as business, privacy, ethical, and security issues. On the other side, at the mainstream level, it is well known that the company Meta (formerly Facebook) is striving to realize its interpretation of a “social” metaverse. As Meta is a big leader of social media, it is reasonable to guess that, in the future, users will participate in a new social platform, such as that which the company is building by depicting unlimited and engaging opportunities. Regardless of Meta, we ask what the opinion of people is about this possible future scenario. A literature search of previous works about this topic has been done; the few results we found were not properly on topic and showed heterogeneous content. A survey on interpretations of the metaverse of major information and communication technologies (ICT) companies that impact the consumer world was undertaken; the results show that Meta is the most prominent company with the mission of building a ”social” metaverse worldwide. Finally, a draft of a tool for assessing the predilection of people for a “social” metaverse, based on various facets of the future social platform, is proposed.

Electron Microscopic Mapping of Mitochondrial Morphology in the Cochlear Nerve Fibers.

To enable nervous system function, neurons are powered in a use-dependent manner by mitochondria undergoing morphological-functional adaptation. In a well-studied model system-the mammalian cochlea, auditory nerve fibers (ANFs) display distinct electrophysiological properties, which is essential for collectively sampling acoustic information of a large dynamic range. How exactly the associated mitochondrial networks are deployed in functionally differentiated ANFs remains scarcely interrogated. Here, we leverage volume electron microscopy and machine-learning-assisted image analysis to phenotype mitochondrial morphology and distribution along ANFs of full-length in the mouse cochlea inner spiral bundle. This reveals greater variance in mitochondrial size with increased ANF habenula to terminal path length. Particularly, we analyzed the ANF terminal-residing mitochondria, which are critical for local calcium uptake during sustained afferent activities. Our results suggest that terminal-specific enrichment of mitochondria, in addition to terminal size and overall mitochondrial abundance of the ANF, correlates with heterogenous mitochondrial contents of the terminal.

Diagnostic Tool for Early Detection of Rheumatic Disorders Using Machine Learning Algorithm and Predictive Models

Background: Rheumatic diseases are chronic diseases that affect joints, tendons, ligaments, bones, muscles, and other vital organs. Detection of rheumatic diseases is a complex process that requires careful analysis of heterogeneous content from clinical examinations, patient history, and laboratory investigations. Machine learning techniques have made it possible to integrate such techniques into the complex diagnostic process to identify inherent features that lead to disease formation, development, and progression for remedial measures. Methods: An automated diagnostic tool using a multilayer neural network computational engine is presented to detect rheumatic disorders and the type of underlying disorder for therapeutic strategies. Rheumatic disorders considered are rheumatoid arthritis, osteoarthritis, and systemic lupus erythematosus. The detection system was trained and tested using 70% and 30% respectively of labelled synthetic dataset of 100,000 records containing both single and multiple disorders. Results: The detection system was able to detect and predict underlying disorders with accuracy of 97.48%, sensitivity of 96.80%, and specificity of 97.50%. Conclusion: The good performance suggests that this solution is robust enough and can be implemented for screening patients for intervention measures. This is a much-needed solution in environments with limited specialists, as the solution promotes task-shifting from the specialist level to the primary healthcare physicians.

Unprecedented variation pattern of plastid genomes and the potential role in adaptive evolution in Poales

BackgroundThe plastid is the photosynthetic organelle in plant cell, and the plastid genomes (plastomes) are generally conserved in evolution. As one of the most economically and ecologically important order of angiosperms, Poales was previously documented to exhibit great plastomic variation as an order of photoautotrophic plants.ResultsWe acquired 93 plastomes, representing all the 16 families and 5 major clades of Poales to reveal the extent of their variation and evolutionary pattern. Extensive variation including the largest one in monocots with 225,293 bp in size, heterogeneous GC content, and a wide variety of gene duplication and loss were revealed. Moreover, rare occurrences of three inverted repeat (IR) copies in angiosperms and one IR loss were observed, accompanied by short IR (sIR) and small direct repeat (DR). Widespread structural heteroplasmy, diversified inversions, and unusual genomic rearrangements all appeared in Poales, occasionally within a single species. Extensive repeats in the plastomes were found to be positively correlated with the observed inversions and rearrangements. The variation all showed a “small-large-moderate” trend along the evolution of Poales, as well as for the sequence substitution rate. Finally, we found some positively selected genes, mainly in C4 lineages, while the closely related lineages of those experiencing gene loss tended to have undergone more relaxed purifying selection.ConclusionsThe variation of plastomes in Poales may be related to its successful diversification into diverse habitats and multiple photosynthetic pathway transitions. Our order-scale analyses revealed unusual evolutionary scenarios for plastomes in the photoautotrophic order of Poales and provided new insights into the plastome evolution in angiosperms as a whole.

Inositol Hexaphosphate as an Inhibitor and Potential Regulator of p47phox Membrane Anchoring.

As a key component for NADPH oxidase 2 (NOX2) activation, the peripheral membrane protein p47phox translocates a cytosolic activating complex to the membrane through its PX domain. This study elucidates a potential regulatory mechanism of p47phox recruitment and NOX2 activation by inositol hexaphosphate (IP6). Through NMR, fluorescence polarization, and FRET experimental results, IP6 is shown to be capable of breaking the lipid binding and membrane anchoring events of p47phox-PX with low micromolar potency. Other phosphorylated inositol species such as IP5(1,3,4,5,6), IP4(1,3,4,5), and IP3(1,3,4) show weaker binding and no ability to inhibit lipid interactions in physiological concentration ranges. The low micromolar potency of IP6 inhibition of the p47phox membrane anchoring suggests that physiologically relevant concentrations of IP6 serve as regulators, as seen in other membrane anchoring domains. The PX domain of p47phox is known to be promiscuous to a variety of phosphatidylinositol phosphate (PIP) lipids, and this regulation may help target the domain only to the membranes most highly enriched with the highest affinity PIPs, such as the phagosomal membrane, while preventing aberrant binding to other membranes with high and heterogeneous PIP content, such as the plasma membrane. This study provides insight into a potential novel regulatory mechanism behind NOX2 activation and reveals a role for small-molecule regulation in this important NOX2 activator.

Magnetic Nanoparticle-Based Microfluidic Platform for Automated Enrichment of High-Purity Extracellular Vesicles.

Extracellular vesicles (EVs) are available in various biological fluids and have highly heterogeneous sizes, origins, contents, and functions. Rapid enrichment of high-purity EVs remains crucial for enhancing research on EVs in tumors. In this work, we present a magnetic nanoparticle-based microfluidic platform (ExoCPR) for on-chip isolation, purification, and mild recovery of EVs from cell culture supernatant and plasma within 29 min. The ExoCPR chip integrates bubble-driven micromixers and immiscible filtration assisted by surface tension (IFAST) technology. The bubble-driven micromixer enhances the mixing between immunomagnetic beads and EVs, eliminating the need for manual pipetting or off-chip oscillatory incubation. The high-purity EVs were obtained after passing through the immiscible phase interface where hydrophilic or hydrophobic impurities nonspecifically bound to SIMI were removed. The ExoCPR chip had a capture efficiency of 75.8% and a release efficiency of 62.7% for model EVs. We also demonstrated the powerful performance of the ExoCPR in isolating EVs from biological samples (>90% purity). This chip was further employed in clinical plasma samples and showed that the number of GPC3-positive EVs isolated from hepatocellular carcinoma patients was significantly higher than that of healthy individuals. This ExoCPR chip may provide a promising tool for EV-based liquid biopsy and other fundamental research.

Object-Based Semi-Supervised Spatial Attention Residual UNet for Urban High-Resolution Remote Sensing Image Classification

Accurate urban land cover information is crucial for effective urban planning and management. While convolutional neural networks (CNNs) demonstrate superior feature learning and prediction capabilities using image-level annotations, the inherent mixed-category nature of input image patches leads to classification errors along object boundaries. Fully convolutional neural networks (FCNs) excel at pixel-wise fine segmentation, making them less susceptible to heterogeneous content, but they require fully annotated dense image patches, which may not be readily available in real-world scenarios. This paper proposes an object-based semi-supervised spatial attention residual UNet (OS-ARU) model. First, multiscale segmentation is performed to obtain segments from a remote sensing image, and segments containing sample points are assigned the categories of the corresponding points, which are used to train the model. Then, the trained model predicts class probabilities for all segments. Each unlabeled segment’s probability distribution is compared against those of labeled segments for similarity matching under a threshold constraint. Through label propagation, pseudo-labels are assigned to unlabeled segments exhibiting high similarity to labeled ones. Finally, the model is retrained using the augmented training set incorporating the pseudo-labeled segments. Comprehensive experiments on aerial image benchmarks for Vaihingen and Potsdam demonstrate that the proposed OS-ARU achieves higher classification accuracy than state-of-the-art models, including OCNN, 2OCNN, and standard OS-U, reaching an overall accuracy (OA) of 87.83% and 86.71%, respectively. The performance improvements over the baseline methods are statistically significant according to the Wilcoxon Signed-Rank Test. Despite using significantly fewer sparse annotations, this semi-supervised approach still achieves comparable accuracy to the same model under full supervision. The proposed method thus makes a step forward in substantially alleviating the heavy sampling burden of FCNs (densely sampled deep learning models) to effectively handle the complex issue of land cover information identification and classification.

CONTRIBUTION OF CT ANGIOS IN THE DIAGNOSIS OF SUBRENAL ABDOMINAL AORTA ANEURYSM AT THE LUXEMBOURG MOTHER-CHILD HOSPITAL CENTER

Objective: Our study aimed to study the value of CT angiography in the diagnosis of subrenal abdominal aortic aneurysm at the Mother-Child University Hospital Center Le Luxembourg Methodology : This was a retrospective, descriptive and analytical study carried out over a period of three years (from January 1, 2019 to December 31, 2021) in the radiology departments of CHME Mère et Enfant Le Luxembourg. It concerned all patients who had an angio-CT scan in the radiology department of the CHU-ME Le Luxembourg in whom a subrenal abdominal aortic aneurysm was diagnosed. Excluded from our study were aneurysms of the thoracic aorta and vessels of the neck as well as the limbs. Results : We retained 22 cases of AAA meeting our inclusion criteria. The male gender was the majority (66.7%) with a sex ratio (M/F) of 1.5. The most represented age group was those over 50 with 66.7%. Hypertension was the most represented risk factor with a rate of 33.3%. Abdominal pain was the most frequent functional sign with 77.77%. More than 66.7% of our patients had an abdominal mass. The majority of our patients had a saccular type of aneurysm (67%). The average anteroposterior diameter of the AAA was 56 mm. The average transverse diameter was 63 mm. The average length of the aneurysm was 77 mm. All our patients had heterogeneous aneurysm contents with mural thrombus. The edge of the aneurysm was irregular with loss of parallelism in all our patients. More than half (55.6%) of our patients had aortic calcifications. Only one of our patients presented with fissure syndrome. 33.3% of our patients had the sign of extension to the renal arteries. The majority of our patients had visceral branch patency with a rate of 88.9%. The biological assessment was dominated by anemia with a rate of 44.4%. The echocardium was pathological in 11.1%. Conclusion: Abdominal aortic aneurysms are becoming more and more common. More than 90% of abdominal aortic aneurysms develop at the expense of the infrarenal aorta. The most common vascular risk factors are hypertension and smoking. Aneurysms are generally asymptomatic, making their clinical diagnosis difficult. CT angiography remains the complementary examination of choice in the diagnosis of thoraco-abdominal arterial lesions.

Anatomy and histology of the male reproductive tract of the parasitoid wasp Hymenoepimecis bicolor (Brullé, 1846) (Hymenoptera: Ichneumonidae)

AbstractHymenoepimecis wasps (Hymenoptera: Ichneumonidae) are ectoparasitoids of spiders that modify the hosts’ behaviour by inducing changes in webs. Variations in the male reproductive tract contribute to enhancing our understanding of the reproductive behaviour of these insects. This study describes the morphology of the male reproductive tract of Hymenoepimecis bicolor (Brullé, 1846) using light microscope. The male reproductive tract of H. bicolor consists of a pair of testes, each containing a single follicle, two vasa deferentia, one ejaculatory duct and two accessory glands. The testis follicle has cysts at various stages of spermatogenesis, indicating ongoing sperm cell development and maturation. The vasa deferentia are elongated, with a coiled distal portion, whereas the proximal regions are laterally associated with a pair of accessory glands. The wall of the vasa deferentia has simple cuboidal epithelium. The accessory glands had a simple columnar epithelium with the lumen filled with heterogeneous content. The seminal vesicle is absent. The similarity in the anatomy and histology of the male reproductive tract of H. bicolor with other Hymenoptera suggests that the parasitoid behaviour does not result in significant alteration to their reproductive organs, indicating that the reproductive tract is conserved in Hymenoptera, regardless of their behaviours.

Heterogeneous Graph Neural Network with Personalized and Adaptive Diversity for News Recommendation

The emergence of online media has facilitated the dissemination of news, but has also introduced the problem of information overload. To address this issue, providing users with accurate and diverse news recommendations has become increasingly important. News possesses rich and heterogeneous content, and the factors that attract users to news reading are varied. Consequently, accurate news recommendation requires modeling of both the heterogeneous content of news and the heterogeneous user-news relationships. Furthermore, users’ news consumption is highly dynamic, which is reflected in the differences in topic concentration among different users and in the real-time changes in user interests. To this end, we propose a Heterogeneous Graph Neural Network with Personalized and Adaptive Diversity for News Recommendation (DivHGNN). DivHGNN first represents the heterogeneous content of news and the heterogeneous user-news relationships as an attributed heterogeneous graph. Then, through a heterogeneous node content adapter, it models the heterogeneous node attributes into aligned and fused node representations. With the proposed attributed heterogeneous graph neural network, DivHGNN integrates the heterogeneous relationships to enhance node representation for accurate news recommendations. We also discuss relation pruning, model deployment, and cold-start issues to further improve model efficiency. In terms of diversity, DivHGNN simultaneously models the variance of nodes through variational representation learning for providing personalized diversity. Additionally, a time-continuous exponentially decaying distribution cache is proposed to model the temporal dynamics of user real-time interests for providing adaptive diversity. Extensive experiments on real-world news datasets demonstrate the effectiveness of the proposed method.

Ten-year survival after resection of a huge rhabdomyosarcoma in the pericardial cavity

BackgroundPrimary malignant cardiac tumors are exceedingly rare and cardiac rhabdomyosarcoma among them is an exceptional rarity characterized by a dismal poor prognosis.Case presentationA 48-year-old man had suffered from a persistent cough lasting for more than 6 months and computed tomography showed a huge mass in the pericardial cavity with heterogeneous content. Following referral to our department for suspected cardiac malignancy, cardiac magnetic resonance imaging revealed a lucent layer on the boundary around the mass, suggesting the feasibility of surgical resection. A baby-head-sized mass in the pericardial cavity was entirely resected and the pathological examination confirmed a tumor as a spindle cell type rhabdomyosarcoma. The patient underwent adjuvant chemotherapy and has well survived over a decade after surgery without any sign of recurrence.ConclusionRadical resection with adjuvant chemotherapy may achieve a favorable outcome for patients with massive rhabdomyosarcomas inside the pericardial cavity.

Working out measurement overlap in the assessment of maladaptive exercise.

Although exercise is generally considered healthy, many individuals engage in maladaptive exercise (e.g., compulsive in nature). Several definitions of maladaptive exercise exist, leading to multiple, varied assessment tools; assuming homogeneity across these assessments contributes to low consensus in etiological models. We used a Jaccard Index to quantify content overlap among 15 commonly-used self-report instruments measuring maladaptive exercise, with 31 features identified across 224 items. The most common features were exercise to control weight/shape and to avoid negative affect (both included in 9/15 instruments), or compensate for calories consumed (8/15 instruments). Overlap among instruments was low (.206) and no features were common across all instruments. Findings generally support theoretical models of exercise in eating pathology. However, instruments most commonly used to assess maladaptive exercise measure heterogenous content. Careful consideration should be taken when comparing findings derived from differing instruments, when synthesizing literature on maladaptive exercise, and when selecting instruments to measure specific maladaptive exercise features. Many, varied, tools exist for the assessment of maladaptive exercise (e.g., compulsive or compensatory) in the context of eating disorders. Assuming homogeneity across tools contributes to low consensus in the field. We used a Jaccard Index to quantify content overlap among 15 self-report instruments measuring maladaptive exercise. The most commonly used instruments measure heterogenous content. Careful consideration should be taken when synthesizing literature and selecting instruments to use in research.

Traumatic gallbladder rupture in a 9-year-old child

We present gallbladder rupture following trauma. A 9-year-old boy admitted in 1.5 hours after injury. Considering clinical and ultrasound data, we diagnosed traumatic damage to the spleen and hemoperitoneum, biliary dyskinesia, cholestasis, sludge. Hemostatic therapy was carried out. After 3 days, signs of peritonitis appeared. Follow-up ultrasound revealed gallbladder enlargement with heterogeneous content, fluid in all parts of abdominal cavity. Intraoperatively, the gallbladder was enveloped in omentum soaked in bile. After mobilization of the gallbladder, we found longitudinal linear tear up to 3 cm clogged with omentum. Cholecystectomy was performed. Thus, we present a patient with combined injury and damage to the spleen. However, gallbladder wall thickening and heterogeneous content were interpreted as concomitant pathology. Delayed manifestation of peritonitis was due to gallbladder enveloped in omentum. The last one soaked in bile partially entered the gallbladder through perforation and prevented bile leakage into abdominal cavity. Timely diagnosis of gallbladder damage presents certain difficulties, especially in case of combined injury. Ultrasound signs of traumatic gallbladder rupture in this case were wall thickening, heterogeneous content and gradual gallbladder enlargement. It is necessary to analyze all organs at the damage site including computed tomography in patients with combined trauma.

Exosomes Isolation, Purification, and Characterization.

Exosomes are double-layered lipid membranous nanovesicles that are endosomal in origin and secreted by almost all cells. They are 30-130nm in size and contain various molecular signatures such as miRNAs, mRNAs, DNA, lipids, and proteins. Due to their highly heterogeneous content, exosomes have a major role in influencing cellular physiology and pathology. Although exosome research has been in progress for a long time, its biomedical applications have recently been expanding due to its bio-friendly nature. However, the most challenging part is its isolation to obtain quality exosomes with good yield. Therefore, in this chapter, we have described appropriate protocols for exosome isolation and characterization along with alternative purification methods.

Germanium-rich nanoparticles in Cu-poor sphalerite: A new mechanism for Ge enrichment

Abstract Germanium (Ge) is a critical raw material used in high-technology industry (i.e., optical industry) applications, and it is predominantly concentrated in coals and Zn-rich deposits. Previous studies on Zn-rich deposits have documented a correlation between Ge enrichment and the Cu, Ag, and/or Pb-Mn contents in the sphalerite crystal lattice. In this study, we observed Ge-rich nanoparticles hosted in Cu-poor sphalerite from the Banbianjie Zn-Ge deposit (>800 t graded at ~100 ppm Ge), located in southwest China. Laser-ablation–inductively coupled plasma–mass spectroscopy (LA-ICP-MS) analyses revealed that sphalerite contains very heterogeneous Ge contents (172–1553 ppm). Germanium contents showed positive correlations with Fe, Mn, and Pb contents and negative correlations with Cd contents. Higher Ge contents were detected in the darker zones, whereas the lighter zones showed systematically low Ge contents and were enriched in Cd. Using transmission electron microscopy (TEM), Zn-Ge-Pb-S nanoparticles were identified in the darker zones of sphalerite. These nanoparticles exhibited Ge/Pb ratios (0.48–1.96) very similar to those measured in sphalerite (0.36–2.04), suggesting that Ge could be essentially hosted within the nanoparticles. We propose that the amounts of Zn-Ge-Pb-S nanoparticles are related to a self-organization model induced by rapid crystal growth. This self-organization processes may control the fluctuations of element concentrations in the boundary layer. This study highlights the importance of studying the nanoscale expression of critical elements to understand their incorporation mechanisms into natural materials.

Fractal thermal conductivity of unsaturated soils considering pore heterogeneity

Accurately predicting the thermal conductivity of unsaturated soils is crucial for assessing and simulating the long-term environmental impact of subterranean engineering structures. This study presents a fractal thermal conductivity model for unsaturated soils that considers the influence of heterogeneous pore topology, water content and volumetric deformation. Based on the results from the scanning electron microscopy (SEM) and mercury intrusion-extrusion cyclic porosimetry (MIECP) tests on Shanghai clay and Kaolin, we introduce the pore-throat structure to describe the soil heterogeneity and deal with the coupling of solid-pore thermal properties through the mixing law at the pore scale. Together with the self-similar fractal law, the thermal conductivity model is upscaled from the pore scale to the representative element volume (REV) scale. During the process, heterogeneity factors are introduced to describe differences between local and bulk properties, such as porosity and saturation. Thermal property tests of Shanghai clay and Kaolin with varying porosities are then conducted to validate the proposed thermal conductivity model. Additionally, nineteen published datasets on thermal conductivity are also utilized to confirm the adaptability and robustness of the model. The results show that the proposed model predicts well the thermal conductivity of unsaturated soils. Moreover, when compared to existing models, the proposed model offers superior performance in describing the thermal properties of unsaturated soils.

Ore genesis of the Laguhe Au deposit, West Qinling, China: Evidence from sulfide geochemistry and machine learning

The precious metal Au has a wide range of utilization in various fields, and the Au deposits in China are hosted mostly within the West Qinling Orogen and Jiaodong Peninsula. The Laguhe deposit is a newly discovered medium–size Au deposit in the West Qinling Orogen. Previous studies focused mainly on the nature of Au mineralization in this deposit during different exploration stages, so its genesis and ore–forming physico–chemical conditions and processes remain enigmatic. Here, we present major and trace element analyses of Au–bearing pyrite and arsenopyrite from the Laguhe Au deposit, in combination with machine learning to explore its origin and ore–forming processes. The early and late stages of arsenopyrite show As contents of 40.69–46.22 wt% and 39.12–43.22 wt%, respectively, which constrain corresponding ore–forming temperatures at 320–370 °C and 270–340 °C, respectively. The sulfur fugacities in the two stages range from 10–13.2 to 10–8.4. The arsenopyrite has high Co and Ni contents of 0.02–0.28 wt% and 0.02–0.27 wt%, respectively, which suggest that a deep source mafic magmatic water participated in the Au mineralization. The Fe/(S + As) ratios in pyrite vary from 0.838 to 0.869, which indicate that the Laguhe deposit formed at a moderate to deep ore–forming depth (1.5–8.0 km). Most of the Co/Ni ratios (1–5) and the negative correlation (–0.47) between Fe and As contents in pyrite suggest that the ore–forming hydrothermal fluids in the Laguhe deposit were moderately acidic. Based on the heterogeneous Au contents and the lack of correlation between Fe and Au in pyrite and arsenopyrite, most of Au are suggested to likely exist as nanoparticles, with few as lattice Au in pyrite and arsenopyrite. Machine learning results based on the geochemistry of arsenopyrite and pyrite and geochemical features of arsenopyrite and pyrite classified the Laguhe deposit as a Carlin–like type Au deposit. The Au in the Laguhe deposit migrated mainly in the forms of Au(HS)2– and AuH3SiO4 in hydrothermal fluids, which then precipitated as a result of water–rock reaction, fluid immiscibility and fluid boiling at and around plutons and along faults.