Deeper Layers Research Articles

Literature Review on Stages of Colorectal Cancer

Colorectal cancer, primarily originating from polyps in the colon or rectum, represents a significant health challenge due to its potential progression to malignancy. Polyps, abnormal tissue growths in the inner lining of the colon or rectum, often start as benign adenomas. Over time, these adenomas may undergo cellular mutations, transforming into cancerous tumors. Early detection and removal of polyps during screening significantly reduce the likelihood of cancer development. If left untreated, colorectal cancer can invade deeper layers of the colon wall, metastasize to nearby lymph nodes, and spread to distant organs, commonly the liver, peritoneum, or lungs. Colon cancer typically originates in the mucosa, the innermost lining of the colon, and progresses through the tissue and muscle layers over a span of approximately 10 years. The anatomy of the colon and rectum, consisting of the cecum, ascending colon, transverse colon, descending colon, sigmoid colon, and rectum, plays a critical role in understanding the disease's progression. Advances in screening methods and treatments have significantly improved outcomes, emphasizing the importance of early intervention in reducing colorectal cancer mortality.

DMFR-YOLO: an infrared small hotspot detection algorithm based on double multi-scale feature fusion

Abstract Hotspot automatic detection is an effective strategy to realize intelligent maintenance of photovoltaic (PV) modules. However, it is challenging to detect small hotspots accurately using deep learning-based object detection methods due to the weak information and small area of the target. This study presents a double multi-scale feature reconstruction (DMFR)-YOLO to detect small hotspots in PV modules. In the proposed DMFR-YOLO, the backbone of YOLOv8n is improved by eliminating redundant deep layers and introducing a shallow detection head to enhance its perception and recognition ability for small objects. Moreover, a weighted multi-layer feature reconstruction (MLFR) module is introduced to fuse the features of different depths and a multi-receptive field feature reconstruction (MRFR) module is designed to fuse the information of different receptive fields. Finally, ablation and comparison experiments are conducted to evaluate the performance of the proposed method. The model with both MLFR and MRFR can achieve a recall rate of 88.6%, with a mean average precision (mAP@0.5) of 93.3%. Experimental results demonstrate that the proposed DMFR-YOLO is able to achieve small hotspot detection in IR images with high accuracy.

Geo-Net: Geometry-Guided Pretraining for Tooth Point Cloud Segmentation.

Accurately delineating individual teeth in 3-dimensional tooth point clouds is an important orthodontic application. Learning-based segmentation methods rely on labeled datasets, which are typically limited in scale due to the labor-intensive process of annotating each tooth. In this article, we propose a self-supervised pretraining framework, named Geo-Net, to boost segmentation performance by leveraging large-scale unlabeled data. The framework is based on the scalable masked autoencoders, and 2 geometry-guided designs, curvature-aware patching algorithm (CPA) and scale-aware reconstruction (SCR), are proposed to enhance the masked pretraining for tooth point cloud segmentation. In particular, CPA is designed to assemble informative patches as the reconstruction unit, guided by the estimated pointwise curvatures. Aimed at equipping the pretrained encoder with scale-aware modeling capacity, we also propose SCR to perform multiple reconstructions across shallow and deep layers. In vitro experiments reveal that after pretraining with large-scale unlabeled data, the proposed Geo-Net can outperform the supervised counterparts in mean Intersection of Union (mIoU) with the same amount of annotated labeled data. The code and data are available at https://github.com/yifliu3/Geo-Net.

A Review on Formulation and Evaluation of Herbal Face Serum

Facial wrinkles and skin aging are often caused by sun damage and UV rays, and there are currently no effective methods to slow down the aging process. Aloe Vera, glycerin, and honeybee venom are key ingredients in a concentrated face serum that helps improve skin health. Aloe Vera provides quick cosmetic benefits and also has psychological effects. The serum is easily absorbed and can penetrate deeper layers of the skin. Aloe Vera is known for treating skin issues like sunburns, minor cuts, and insect bites, and it has healing, anti-inflammatory, antibacterial, and antifungal properties. Bee venom, a toxin produced by bees, is believed to help improve blood flow and increase collagen production. The face serum was tested for its quality, including pH, consistency, and size of the droplets. Stability tests showed that the serum maintained its appearance, texture, and consistency without any changesg

Giant scalp dermatofibrosarcoma protuberans at mid-occipital scalp: A rare case report

Background: Dermatofibrosarcoma protuberans (DFSP) is a rare type of skin cancer that arises in the deeper layers of the skin, most commonly on the trunk and limbs. The presentation of DFSP on the scalp is worth documentation due to its extremely rare occurrence in the literature. Case Description: We describe a case of a 41-year-old female presented with a lump on the back of the head with gradual enlargement. It was solitary with a size 19 × 12 × 10 cm with purulent discharge and intermittent pain. A computed tomography-scan showed an isodense mass at mid occipital without intracranial involvement. Tumor extirpation and the free flap were performed collaborated with plastic surgery. The patient was discharged unremarkably on the 7th postoperative day without any neurological deficits. Conclusion: Our case is distinct because, after 24 months without any adjuvant therapy and without any recurrence, the patient is still doing well. This is a very rare clinical entity.

Transplanted deep-layer cortical neuroblasts integrate into host neural circuits and alleviate motor defects in hypoxic-ischemic encephalopathy injured mice.

Hypoxic-ischemic encephalopathy (HIE) is a major cause of neonatal disability and mortality. Although intensive studies and therapeutic approaches, there are limited restorative treatments till now. Human embryonic stem cell (hESCs)-derived cortical neural progenitors have shown great potentials in ischemic stroke in adult brain. However, it is unclear whether they are feasible for cortical reconstruction in immature brain with hypoxic-ischemic encephalopathy. By using embryonic body (EB) neural differentiation method combined with DAPT pre-treatment and quantitative cell transplantation, human cortical neuroblasts were obtained and transplanted into the cortex of hypoxic-ischemic injured brain with different dosages 2 weeks after surgery. Then, immunostaining, whole-cell patch clamp recordings and behavioral testing were applied to explore the graft survival and proliferation, fate commitment of cortical neuroblasts in vitro, neural circuit reconstruction and the therapeutic effects of cortical neuroblasts in HIE brain. Transplantation of human cortical neural progenitor cells (hCNPs) in HIE-injured cortex exhibited long-term graft overgrowth. DAPT pre-treatment successfully synchronized hCNPs from different developmental stages (day 17, day 21, day 28) to deep layer cortical neuroblasts which survived well in HIE injured brain and greatly prevented graft overgrowth after transplantation. Importantly, the cortical neuroblasts primarily differentiated into deep-layer cortical neurons and extended long axons to their projection targets, such as the cortex, striatum, thalamus, and internal capsule in both ipsilateral and contralateral HIE-injured brain. The transplanted cortical neurons established synapses with host cortical neurons and exhibited spontaneous excitatory or inhibitory post-synaptic currents (sEPSCs or sIPSCs) five months post-transplantation. Rotarod and open field tests showed greatly improved animal behavior by intra-cortex transplantation of deep layer cortical neuroblasts in HIE injured brain. Transplanted hESCs derived cortical neuroblasts survive, project to endogenous targets, and integrate into host cortical neural circuits to rescue animal behavior in the HIE-injured brain without graft overgrowth, providing a novel and safe cell replacement strategy for the future treatment of HIE.

CNSC-65. ELECTROPHYSIOLOGICAL AND SPATIAL TRANSCRIPTOMIC PROFILING OF GLIOMA-INFILTRATED CORTEX REVEAL LAYER SPECIFIC ALTERATIONS

Abstract Specialized neocortical circuits selectively organize neuronal signals and encode features of cognitive processing via local and long-range connections. Since glioma-infiltrated cortex is excitable and can participate in cognitive processing, the underlying laminar structure and functionality may still be preserved despite infiltration. Moreover, since glioma cells remodel existing neural circuits and microenvironmental factors drive invasion and proliferation, neuron-glioma interactions may demonstrate layer specificity. As glioma-infiltrated cortex remains poorly understood, we sought to characterize these regions using electrophysiological, structural, and genomic approaches. We assessed the power spectra of normal-appearing and glioma-infiltrated cortex using magnetoencephalography and subdural high-density electrode arrays. Immunohistochemistry of formalin-fixed paraffin-embedded (FFPE) samples of infiltrated cortex enabled protein-level neuronal and glioma identification to determine laminar preservation and spatial invasion patterns. Spatial transcriptomics of FFPE tissues and single-nucleus RNA-sequencing were used to identify cell populations, genomic alterations, and cell-cell communication within and across samples at testing and validation sites across the US and Europe. Increased delta range (1-4 Hz) power and decreased power in the beta range (12-20 Hz), activity thought to originate from deep layers, were identified as robust features of infiltrated cortex which were maintained across glioma subtypes (n = 139 patients) and preserved in a validation cohort (n = 8 patients). Immunohistochemistry revealed greater tumor burden within deep laminae regardless of glioma subtype (n = 20 samples). Tissue proteomics and spatial genomics analyses performed using glioma-infiltrated cortical samples (n = 10 samples; 29,011 spots) confirmed routine preservation of laminar organization, greater tumor burden in deep layers, as well as layer specific differences in glioma-related expression programs. Cell-cell communication analyses demonstrated increased interactions in glioma-infiltrated cortex across layers. These findings suggest that laminar structure may be preserved in glioma-infiltrated cortex while remodeling of neural circuits alters spatiotemporal activity in a predictable manner and support a deep to superficial invasion pattern. Thus, investigating glioma infiltration using layer-centric approaches may facilitate novel insights into neuron-glioma interactions.

Neuroimage-Based Stroke Identification: A Machine Learning Approach

Stroke diagnosis is a time-critical process that requires rapid and accurate identification to ensure timely treatment. This study proposes a machine learning-based diagnostic model for stroke identification using neuro images. Early identification and timely intervention are critical to improving outcomes for stroke patients, but current diagnostic techniques, such as CT and MRI scans, often require time-consuming expert analysis. These delays can limit the effectiveness of treatment, particularly in acute cases where every minute counts. The problem lies in the need for faster, more reliable diagnostic tools that can analyze neuroimaging data with high accuracy and minimal human intervention. Machine learning, specifically deep learning, offers a promising solution to address this gap by automating the process of stroke detection. We employed a comprehensive approach, utilizing Inceptionv3, MobileNet, Convolutional Neural Network (CNN) algorithms to analyze neuroimages and predict stroke occurrence. This research proposes a machine learning-based diagnostic model for stroke identification using neuroimages, leveraging the power of Convolutional Neural Networks (CNN), with Inception V3 and MobileNet architectures. Inception V3, known for its ability to capture intricate image features through deep convolutional layers, and MobileNet, optimized for efficiency and speed, were employed to process large datasets of brain scans. The model was trained on these neuroimaging datasets to distinguish between healthy brain tissues and those affected by stroke. The combination of these two architectures allows for both detailed analysis and fast processing, making the model adaptable to clinical settings. The results showed that the model achieved a high accuracy rate in stroke identification, demonstrating its potential to assist healthcare professionals in diagnosing stroke faster and more accurately. By integrating this machine learning model into existing diagnostic workflows, it could significantly reduce the time to diagnosis, enabling earlier treatment and ultimately improving patient outcomes. Our model has the potential to enhance patient outcomes and reduce the economic burden of stroke. By leveraging the power of these advanced machine learning techniques, the model aims to enhance the efficiency and accuracy of stroke diagnosis compared to traditional methods

Deep-learning models reveal how context and listener attention shape electrophysiological correlates of speech-to-language transformation.

To transform continuous speech into words, the human brain must resolve variability across utterances in intonation, speech rate, volume, accents and so on. A promising approach to explaining this process has been to model electroencephalogram (EEG) recordings of brain responses to speech. Contemporary models typically invoke context invariant speech categories (e.g. phonemes) as an intermediary representational stage between sounds and words. However, such models may not capture the complete picture because they do not model the brain mechanism that categorizes sounds and consequently may overlook associated neural representations. By providing end-to-end accounts of speech-to-text transformation, new deep-learning systems could enable more complete brain models. We model EEG recordings of audiobook comprehension with the deep-learning speech recognition system Whisper. We find that (1) Whisper provides a self-contained EEG model of an intermediary representational stage that reflects elements of prelexical and lexical representation and prediction; (2) EEG modeling is more accurate when informed by 5-10s of speech context, which traditional context invariant categorical models do not encode; (3) Deep Whisper layers encoding linguistic structure were more accurate EEG models of selectively attended speech in two-speaker "cocktail party" listening conditions than early layers encoding acoustics. No such layer depth advantage was observed for unattended speech, consistent with a more superficial level of linguistic processing in the brain.

Dakar Niño under global warming investigated by a high-resolution regionally coupled model

Abstract. In this study, we investigated interannual variability in sea surface temperature (SST) along the northwestern African coast, focusing on strong Dakar Niño and Niña events and their potential alterations under the RCP8.5 emission scenario for global warming, using a high-resolution regional coupled model. Our model accurately reproduces the SST seasonal cycle along the northwestern African coast, including its interannual variability in terms of amplitude, timing, and the position of maximum variability. Comparing Dakar Niño variability between the 1980–2010 and 2069–2099 periods, we found that it intensifies under a warmer climate without changing its location and timing. The intensification is more pronounced during Dakar Niñas (cold SST events) than during Dakar Niños (warm SST events). In the future, SST variability will be correlated with ocean temperature and vertical motion at deeper layers. The increase in Dakar Niño variability can be explained by the larger variability in meridional wind stresses, which is likely to be amplified in the future by enhanced land–sea thermal contrast and associated sea-level-pressure anomalies extending from the Iberian Mediterranean area. A heat budget analysis of the mixed layer suggests that surface heat flux and horizontal-advection anomalies are comparably important for Dakar Niño and Niña events in the present climate. However, the future intensification of Dakar Niños and Niñas is likely to be driven by surface heat flux (latent heat flux and shortwave radiation). While horizontal- and vertical-advection anomalies also contribute to the intensification, their roles are secondary.

Double-Layer Cogged Thread Lifting Technique: A Superficial- and Deep-Layer Lifting Method.

Although thread lifting is a widely performed method, the degree of initial lifting and sustainability varies based on how the threads are inserted into the skin layers. This study aimed to perform thread lifting on 43 patients to achieve maximum lifting effects using thicker threads in the deeper layers of the subcutaneous fat layer and thinner threads in the shallower layers. Immediately after the procedure, we measured the depth of the inserted threads using ultrasonography and evaluated the results based on patient satisfaction and the global aesthetic improvement scale (GAIS). At the end of the procedure, an upright position was employed to check for dimples or irregularities, and intraoral observation was also conducted. Patient satisfaction significantly increased immediately after the procedure compared to that before the procedure. High GAIS scores were maintained even after 6months, indicating high satisfaction with the procedure. Inserting threads into multiple layers was deemed more effective than inserting them into a single layer. Thread lifting on 43 patients was used to maximize lifting effects by utilizing thicker threads in deeper subcutaneous fat layers and thinner threads in shallower ones. Our procedure maintained high global aesthetic improvement scale (GAIS) scores even after 6months, indicating patient satisfaction. Multiple layers were shown to be more effective than a single layer for thread insertion. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Vertical fungal community distribution patterns along a stratified soil profile in subalpine Larix principis-rupprechtii plantations on China's Luya mountain

Soil microorganisms play important roles in the biogeochemical cycling of terrestrial ecosystems. Recent studies found that soil fungal composition and diversity varied significantly with soil depth. However, little is known about the vertical distribution patterns of soil fungal communities and their associated drivers. For this study, we collected soil samples from six soil layers (i.e., litter layer (P), humus layer (P0), 0–10 cm layer (P1), 10–20 cm layer (P2), 20–40 cm layer (P3), and 40–80 cm layer (P4)) within Larix principis-rupprechtii plantations to investigate fungal community composition, diversity, and associated drivers using Illumina MiSeq high-throughput sequencing. Results showed that the top 10 dominant genera with the highest relative abundance belonged to Ascomycota, Basidiomycota, and Mortierellomycota. A decreasing α-diversity trend was observed along with soil depth. Moreover, correlation analysis showed that ammonia nitrogen (NH4+-N), pH, total carbon (TC), and total nitrogen (TN) content markedly correlated with fungal α-diversity. Significant β-diversity differences were found in soil fungal communities. Additionally, TN and total phosphorus (TP) content were the main environmental drivers that influenced the spatial distribution pattern of fungal communities. The βNTI showed a gradual increase with soil depth. In the surface layers of soil, the dominant factor of fungal community assembly was homogeneous selection, while in the deep layers of soil, it was variable selection. Co-occurrence network analysis showed that fungal community interactions in the deepest soil layer (40–80 cm) were more complex and were more positive. Results from this study provide a theoretical basis and data support to understand the mechanisms of soil fungal community assembly processes more deeply in L. principis-rupprechtii plantations.

Topography Dominates the Spatial and Temporal Variability of Soil Bulk Density in Typical Arid Zones

Soil bulk density is a crucial indicator for assessing soil matter storage and soil quality. Due to the complexity of sampling soil bulk density, particularly in deeper layers, it is essential to study the spatial distribution patterns of soil bulk density and their influencing factors. To address the gap in large-scale studies of vertical (from surface to deeper layers) and horizontal (across a broad area) variations in soil bulk density in arid regions, this study focuses on Changji Prefecture, located in the central northern slope of the Tianshan Mountains and characterized by typical vertical zonation. By integrating classical statistics, geostatistics, and geographic information systems (GISs), this study investigates the spatial distribution patterns and driving factors of soil bulk density. The results indicate that soil bulk density in Changji Prefecture increases with soil depth, with significantly lower values in the surface layer than in deeper layers. Spatially, despite minimal variation in latitude, there is considerable elevation difference within the study area, with the lowest elevations in the central region. Soil bulk density exhibits a spatial distribution pattern of higher values in the northeast (desert areas) and lower values in the southwest (forest areas). The nugget effect in the surface layer (0–20 cm) is substantial at 44.9%, while the deeper layers (20–100 cm) show nugget effects below 25%, suggesting that the influence of both natural and anthropogenic factors on deep soil bulk density is limited and mainly affects the surface layer. Stepwise regression analysis indicates that among topographic factors, slope and elevation are the primary controls of spatial variability in soil bulk density across layers. This research demonstrates that, in arid regions, soil bulk density is influenced primarily by natural factors, with limited impact from human activities. These findings provide valuable data support and theoretical guidance for soil management, agricultural planning, and sustainable ecosystem development in arid regions.

Neuronal fate resulting from indirect neurogenesis in the mouse neocortex.

Excitatory cortical neurons originate from cortical radial glial cells (RGCs). Initially, these neurons were thought to derive directly from RGCs (direct neurogenesis) and be distributed in an inside-out fashion. However, the discovery of indirect neurogenesis, whereby intermediate neuronal progenitors (INPs) generate neurons, challenged this view. To investigate the integration of neurons via these two modes, we developed a method to identify INP progeny and analyze their fate using transgenic mice expressing tamoxifen-inducible Cre recombinase under the neurogenin-2 promoter, alongside thymidine analog incorporation. Their fate was further analyzed using mosaic analysis with double markers in mice. Indirect neurogenesis was prominent during early neurogenesis, generating neuron types that would emerge slightly later than those produced via direct neurogenesis. Despite the timing difference, both neurogenic modes produced fundamentally similar neuron types, as evidenced by marker expression and cortical-depth location. Furthermore, INPs generated pairs of similar phenotype neurons. These findings suggest that indirect neurogenesis, like direct neurogenesis, generates neuron types in a temporally ordered sequence and increases the number of similar neuron types, particularly in deep layers. Thus, both neurogenic modes cooperatively generate a diverse array of neuron types in a similar order, and their progeny populate together to form a coherent cortical structure.

Simultaneous 2-photon and 3-photon excitation with a red fluorescent protein-cyanine dye probe pair in the 1700-nm excitation window for deep in vivo neurovascular imaging

In vivo imaging of the neurovascular network is considered to be one of the most powerful approaches for understanding brain functionality. Nevertheless, simultaneously imaging the biological neural network and blood vessels in deep brain layers in a non-invasive manner remains to a major challenge due to the lack of appropriate labeling fluorescence probe pairs. Herein, we proposed a 2-photon and 3-photon fluorescence probe pair for neurovascular imaging. Specifically, the red fluorescence protein (RFP) with an absorption maximum of around 550 nm is used as a 3-photon excited probe to label neurons, and a cyanine derivative dye Q820@BSA has a NIR absorption maximum of 825 nm as a 2-photon excited probe to label the vasculature, enabling single wavelength excitation at 1650 nm for neurovascular imaging with high emission spectral separation (>250 nm). In particular, the 2-photon action cross-section of Q820@BSA was found to be about 2-fold larger than that of indocyanine green (ICG), a commonly used red 2-photon fluorescence labeling agent, at the same excitation wavelength. Benefiting from the long wavelength advantage in reducing scattering in both 2 and 3-photon excitation of the fluorescence pairs, we demonstrated in vivo neurovascular imaging in intact adult mouse brains through white matter and deep into the hippocampus in the somatosensory cortex.

Advanced Mineral Deposit Mapping via Deep Learning and SVM Integration With Remote Sensing Imaging Data

ABSTRACTAutomating mineral delineation and rock type analysis using remote sensing imaging data is a critical application of machine learning. Traditional machine learning methods often struggle with accuracy and precise map generation. This study aims to enhance performance through a refined deep learning model. In this work, we present a deep learning pipeline to map the mineral deposits in the study area. Initially, we apply a deep convolutional neural network (CNN) to a specialized mineral dataset to map mineral deposits within the study area. Subsequently, we build a hybrid model combining deep CNN layers with a support vector machine (SVM). This merger significantly improves classification accuracy from an initial 92.7% to 95.3%. In our approach, CNN layers function as feature extractors while the SVM serves as the classification model. Moreover, we conduct an evaluation of the SVM using polynomial kernels of degrees 3, 6, 9, and 12. The results indicate that the SVM with a degree of 12 achieved the highest classification accuracy, followed by degrees 9, 6, and 3. Experimental results demonstrate the effectiveness of our proposed method for classifying remote sensing imaging data, showcasing its potential for advancing mineral delineation and rock type analysis.

Gelatin-Modified Bioactive Glass for Treatment of Dentin Hypersensitivity

When dentin is directly exposed to the oral cavity for various reasons, such as a lack of enamel on the tooth surface, external stimuli to the dentin often cause transient discomfort known as dentin hypersensitivity. In order to block the incoming stimulus signal, an ideal treatment is to induce the production of minerals to block the dentinal tubules. In this work, a dentin-desensitizing plugging material was prepared by modifying mesoporous bioactive glass with gelatin, the mineralization and desensitization effects of which were compared with Gluma in in vitro experiments. These experiments confirmed that gelatin-modified bioactive glass (MBG@PDA@Gel) is more effective than traditional desensitizing agents at blocking dentin tubules. Following the successful synthesis of MBG@PDA@Gel, as confirmed by scanning electron microscopy, transmission electron microscopy, and other tests, the treatment of demineralized dentin with MBG@PDA@Gel demonstrated that the dentinal tubules were tightly blocked under scanning electron microscopy. MBG@PDA@Gel induces minerals in deeper layers of dentinal tubules, promoting remineralization and forming a unified structure with the tubule blockage. Animal studies showed that MBG@PDA@Gel can remineralize demineralized dentin, and it is stable in the oral cavity and does not fall out. MBG@PDA@Gel not only enhances the biocompatibility of the nanoparticle but also results in an overall uniform and rapid remineralization of the demineralized dentin.

Multi-scale Information Aggregation for Spoofing Detection

Synthesis artifacts that span scales from small to large are important cues for spoofing detection. However, few spoofing detection models leverage artifacts across different scales together. In this paper, we propose a spoofing detection system built on SincNet and Deep Layer Aggregation (DLA), which leverages speech representations at different levels to distinguish synthetic speech. DLA is totally convolutional with an iterative tree-like structure. The unique topology of DLA makes possible compounding of speech features from convolution layers at different depths, and therefore the local and the global speech representations can be incorporated simultaneously. Moreover, SincNet is employed as the frontend feature extractor to circumvent manual feature extraction and selection. SincNet can learn fine-grained features directly from the input speech waveform, thus making the proposed spoofing detection system end-to-end. The proposed system outperforms the baselines when tested on ASVspoof LA and DF datasets. Notably, our single model surpasses all competing systems in ASVspoof DF competition with an equal error rate (EER) of 13.99%, which demonstrates the importance of multi-scale information aggregation for synthetic speech detection.

Taxon-specific contributions of microeukaryotes to biological carbon pump in the Oyashio region

Abstract Microeukaryotes are critical components of sinking particles contributing to carbon export from the surface to deep oceans. However, the knowledge of the sinking microeukaryotic communities and their dynamics is currently limited. In this study, we applied 18S rRNA gene metabarcoding to investigate the microeukaryotic communities in sinking and suspended particles distinguished by marine snow catchers (MSC) during spring in the Oyashio region. Sinking particles displayed distinct communities and lower diversity than suspended particles. The community compositions of the sinking particles varied with depth, suggesting that microeukaryotes were selectively disaggregated or decomposed during settling. Prymnesiophyceae and diatoms were effectively removed, as indicated by their decreased abundance in sinking particles at increasing depths. Conversely, Dinophyceae maintained a higher abundance in sinking particles across depths, indicating resistance to disaggregation and decomposition. Spirotrichea and heterotrophic Dinophyceae were enriched in sinking particles, while marine stramenopiles (MAST) groups were enriched in suspended particles. The heterotrophs in the deeper layers were mainly transported from the surface layers by increasing their relative abundance towards deep layers, indicating that they contributed to the transformation processes of sinking particles. Overall, our results demonstrate the functional differences among microeukaryotes in the biological carbon pump.

Pollution risk assessment and spatial distribution of potentially hazardous elements in selenium-rich soils in the Menyuan Basin of the northeastern Tibetan Plateau

The scientific utilization of selenium (Se)-rich soils necessitates the evaluation of the contents of potentially hazardous elements (PHEs). The Menyuan Basin, a representative Se-rich region located on the northern periphery of the Qinghai–Tibet Plateau, was chosen as the research area. Pollution risk and spatial source analysis was conducted for eight PHEs (As, Cr, Cu, Pb, Zn, Cd, Ni and Hg) using 5297 topsoil samples and 480 deep-soil samples. The results show that more than 50% of the total area has a Se content >0.24 mg kg –1 . According to both single and comprehensive pollution indices, there are extremely few sampling sites with PHEs at risk of contamination. Low ecological risk was seen in 93.34% of the topsoil samples. The results show that although the overall level of surface soil pollution in the study area may be deemed negligible, on the surface or in deeper layers, the concentration centres of As, Cr, Ni, Zn, Pb and other elements show consistency, indicating that underlying parent rocks are the primary source of these elements. The findings presented in this study establish a scientific foundation for the secure development and utilization of Se-rich soils.