Clinicopathologic Characteristics of MYC Copy Number Amplification in Breast Cancer.

Introduction. MYC overexpression is a known phenomenon in breast cancer. This study investigates the correlation of MYC gene copy number amplification and MYC protein overexpression with coexisting genetic abnormalities and associated clinicopathologic features in breast cancer patients. Methods. The study analyzed data from 81 patients with localized or metastatic breast cancers using targeted next-generation sequencing and MYC immunohistochemical studies, along with pathological and clinical data. Results. Applying the criteria of MYC/chromosome 8 ratio ≥5, MYC copy number amplified tumors (n = 11, 14%) were associated with invasive ductal carcinoma (91% vs 68%, P = .048), poorly differentiated (grade 3, 64% vs 30%, P = .032), mitotically active (Nottingham mitotic score 3, 71% vs 20%, P = .004), estrogen receptor (ER)-negative (45% vs 12%, P = .008), and triple-negative (56% vs 12%, P = .013) compared to MYC non-amplified tumors. Among MYC-amplified breast cancer patients, those with triple-negative status showed significantly shorter disease-free survival time than non-triple negative MYC-amplified patients (median survival month: 25.5 vs 127.6, P = .049). MYC amplification is significantly associated with TP53 mutation (P = .007). The majority (10 of 11; 91%) of MYC-amplified tumors showed positive c-MYC immunostaining. Conclusion. Breast cancers with MYC copy number amplication display distinct clinicopathologic characteristics indicative of more aggressive behavior.

H-Net: Heterogeneous Neural Network for Multi-Classification of Neuropsychiatric Disorders.

Clinical studies have proved that both structural magnetic resonance imaging (sMRI) and functional magnetic resonance imaging (fMRI) are implicitly associated with neuropsychiatric disorders (NDs), and integrating multi-modal to the binary classification of NDs has been thoroughly explored. However, accurately classifying multiple classes of NDs remains a challenge due to the complexity of disease subclass. In our study, we develop a heterogeneous neural network (H-Net) that integrates sMRI and fMRI modes for classifying multi-class NDs. To account for the differences between the two modes, H-Net adopts a heterogeneous neural network strategy to extract information from each mode. Specifically, H-Net includes an multi-layer perceptron based (MLP-based) encoder, a graph attention network based (GAT-based) encoder, and a cross-modality transformer block. The MLP-based and GAT-based encoders extract semantic features from sMRI and features from fMRI, respectively, while the cross-modality transformer block models the attention of two types of features. In H-Net, the proposed MLP-mixer block and cross-modality alignment are powerful tools for improving the multi-classification performance of NDs. H-Net is validate on the public dataset (CNP), where H-Net achieves 90% classification accuracy in diagnosing multi-class NDs. Furthermore, we demonstrate the complementarity of the two MRI modalities in improving the identification of multi-class NDs. Both visual and statistical analyses show the differences between ND subclasses.

A multi-scale feature fusion neural network for multi-class disease classification on the maize leaf images.

Maize is a globally important cereal crop, however, maize leaf disease is one of the most common and devastating diseases that afflict it. Artificial intelligence methods face challenges in identifying and classifying maize leaf diseases due to variations in image quality, similarity among diseases, disease severity, limited dataset availability, and limited interpretability. To address these challenges, we propose a residual-based multi-scale network (MResNet) for classifying multi-type maize leaf diseases from maize images. MResNet consists of two residual subnets with different scales, enabling the model to detect diseases in maize leaf images at different scales. We further utilize a hybrid feature weight optimization method to optimize and fuse the feature mapping weights of two subnets. We validate MResNet on a maize leaf diseases dataset. MResNet achieves 97.45% accuracy. The performance of MResNet surpasses other state-of-the-art methods. Various experiments and two additional datasets confirm the generalization performance of our model. Furthermore, thermodynamic diagram analysis increases the interpretability of the model. This study provides technical support for the disease classification of agricultural plants.

The Effects of Tremella fuciformis Polysaccharide on the Physicochemical, Multiscale Structure and Digestive Properties of Cyperus esculentus Starch.

In this study, we have investigated the effects of Tremella fuciformis polysaccharide (TP) on the pasting, rheological, structural and in vitro digestive properties of Cyperus esculentus starch (CS). The results showed that the addition of TP significantly changed the pasting characteristics of CS, increased the pasting temperature and pasting viscosity, inhibited pasting, reduced the exudation of straight-chain starch and was positively correlated with the amount of TP added. The addition of the appropriate amount of TP could increase its apparent viscosity and enhance its viscoelasticity. The composite system of CS/TP exhibited higher short-range ordered structure and solid dense structure, which protected the crystal structure of CS, but was related to the amount of TP added. In addition, the introduction of TP not only decreased the in vitro digestion rate of CS and increased the content of slow-digestible starch (SDS) and resistant starch (RS), but also reduced the degree of digestion. Correlation studies established that TP could improve the viscoelasticity, relative crystallinity and short-range order of the CS/TP composite gel, maintain the integrity of the starch granule and crystalline structure, reduce the degree of starch pasting and strengthen the gel network structure of CS, which could help to lower the digestibility of CS.

Solid-liquid redistribution and degradation of antibiotics during hydrothermal treatment of sewage sludge: Interaction between biopolymers and antibiotics.

Waste activated sludge serves an important reservoir for antibiotics within wastewater treatment plants, and understanding the occurrence and evolution of antibiotics during sludge treatment is crucial to mitigate the potential risks of subsequent resource utilization of sludge. This study explores the degradation and transformation mechanisms of three typical antibiotics, oxytetracycline (OTC), ofloxacin (OFL), and azithromycin (AZI) during sludge hydrothermal treatment (HT), and investigates the influence of biopolymers transformation on the fate of these antibiotics. The findings indicate that HT induces a shift of antibiotics from solid-phase adsorption to liquid-phase dissolution in the initial temperature range of 25-90 °C, underscoring this phase's critical role in preparing antibiotics for subsequent degradation phases. Proteins (PN) and humic acids emerge as crucial for antibiotic binding, facilitating their redistribution within sludge. Specifically, the binding capacity sequence of biopolymers to antibiotics is as follows: OFL>OTC>AZI, highlighting that OFL-biopolymers display stronger electrostatic attraction, more available adsorption sites, and more stable binding strength. Furthermore, antibiotic degradation mainly occurs above 90 °C, with AZI being the most temperature-sensitive, degrading 92.97% at 180 °C, followed by OTC (91.26%) and OFL (52.51%). Concurrently, the degradation products of biopolymers compete for active sites to form novel amino acid-antibiotic conjugates, which inhibits the further degradation of antibiotics. These findings illuminate the effects of biopolymers evolution on intricate dynamics of antibiotics fate in sludge HT and are helpful to optimize the sludge HT process for effective antibiotics abatement.

Response mechanisms of resistance in L-form bacteria to different target antibiotics: Implications from oxidative stress to metabolism.

Due to the specific action on bacterial cell wall, ß-lactam antibiotics have gained widespread usage as they exhibit a high degree of specificity in targeting bacteria, but causing minimal toxicity to host cells. Under antibiotic pressure, bacteria may opt to shed their cell walls and transform into L-form state as a means to evade the antibiotic effects. In this study, we explored and identified diverse optimal conditions for both Gram-negative bacteria (E. coli DH5α (CTX)) and Gram-positive bacteria (B. subtilis ATCC6633), which were induced to L-form bacteria using lysozyme (0.5 ppm) and meropenem (64 ppm). Notably, when bacteria transformed into L-form state, both bacterial strains showed varying degrees of increased resistance to antibiotics polymyxin E, meropenem, rifampicin, and tetracycline. E. coli DH5α (CTX) exhibited the most significant enhancement in resistance to tetracycline, with a 128-fold increase, while B. subtilis ATCC6633 showed a 32-fold increase in resistance to tetracycline and polymyxin E. Furthermore, L-form bacteria maintained their normal metabolic activity, combined with enhanced oxidative stress, served as an adaptive strategy promoting the sustained survival of L-form bacteria. This study provided a theoretical basis for comprehending antibiotic resistance mechanisms, developing innovative treatment strategies, and confronting global antibiotic resistance challenges.

Mechanism of antibiotic resistance spread during sub-lethal ozonation of antibiotic-resistant bacteria with different resistance targets.

The increase and spread of antibiotic-resistant bacteria (ARB) in aquatic environments and the dissemination of antibiotic resistance genes (ARGs) greatly impact environmental and human health. It is necessary to understand the mechanism of action of ARB and ARGs to formulate measures to solve this problem. This study aimed to determine the mechanism of antibiotic resistance spread during sub-lethal ozonation of ARB with different antibiotic resistance targets, including proteins, cell walls, and cell membranes. ARB conjugation and transformation frequencies increased after exposure to 0-1.0 mg/L ozone for 10 min. During sub-lethal ozonation, compared with control groups not stimulated by ozone, the conjugative transfer frequencies of E. coli DH5α (CTX), E. coli DH5α (MCR), and E. coli DH5α (GEN) increased by 1.35-2.02, 1.13-1.58, and 1.32-2.12 times, respectively; the transformation frequencies of E. coli DH5α (MCR) and E. coli DH5α (GEN) increased by 1.49-3.02 and 1.45-1.92 times, respectively. When target inhibitors were added, the conjugative transfer frequencies of antibiotics targeting cell wall and membrane synthesis decreased 0.59-0.75 and 0.43-0.76 times, respectively, while that for those targeting protein synthesis increased by 1-1.38 times. After inhibitor addition, the transformation frequencies of bacteria resistant to antibiotics targeting the cell membrane and proteins decreased by 0.76-0.89 and 0.69-0.78 times, respectively. Cell morphology, cell membrane permeability, reactive oxygen species, and antioxidant enzymes changed with different ozone concentrations. Expression of most genes related to regulating different antibiotic resistance targets was up-regulated when bacteria were exposed to sub-lethal ozonation, further confirming the target genes playing a crucial role in the inactivation of different target bacteria. These results will help guide the careful utilization of ozonation for bacterial inactivation, providing more detailed reference information for ozonation oxidation treatment of ARB and ARGs in aquatic environments.

Effects of post-saccadic oscillations on visual processing times.

Saccadic eye movements enable us to search for the target of interest in a crowded scene or, in the case of goal-directed saccades, to simply bring the image of the peripheral target to the very centre of the fovea. This mechanism extends the use of the superior image processing performance of the fovea over a large visual field. We know that visual information is processed quickly at the end of each saccade but estimates of the times involved remain controversial. This study aims to investigate the processing of visual information during post fixation oscillations of the eyeball. A new psychophysical test measures the combined eye movement response latencies, including fixation duration and visual processing times. When the test is used in conjunction with an eye tracker, each component that makes up the 'integrated saccade latency' time, from the onset of the peripheral stimulus to the correct interpretation of the information carried by the stimulus, can be measured and the discrete components delineated. The results show that the time required to process and encode the stimulus attribute of interest at the end of a saccade is longer than the time needed to carry out the same task in the absence of an eye movement. We propose two principal hypotheses, each of which can account for this finding. 1. The known inhibition of afferent retinal signals during fast eye movements extends beyond the end point of the saccade. 2. The extended visual processing times measured when saccades are involved are caused by the transient loss of spatial resolution due to eyeball instability during post-saccadic oscillations. The latter can best be described as retinal image smear with greater loss of spatial resolution expected for stimuli of low luminance contrast.

Leaching hazards of tire wear particles in hydrothermal treatment of sludge: Exploring molecular composition, transformation mechanism, and ecological effects of tire wear particle-derived compounds.

Tire wear particles (TWPs) are considered a significant contributor of microplastics (MPs) in the sludge during heavy rainfall events. Numerous studies have shown that hydrothermal treatment (HT) of sludge can accelerate the leaching of MP-derived compound into hydrothermal liquid, thus impairing the performance of subsequent anaerobic digestion and the quality of the hydrothermal liquid fertilizer. However, the leaching behavior of TWPs in the HT of sludge remains inadequately explored. This study examined the molecular composition of TWP-derived compounds and transformation pathways of representative tire-related additives under different hydrothermal temperatures using liquid chromatography-tandem mass spectrometry (LC-MS/MS) combined with mass difference analysis. The acute toxicity and phytotoxicity of TWP leachates were assessed using Vibrio qinghaiensis Q67 and rice hydroponics experiments. The results indicated that elevating the hydrothermal temperature not only amplified the leaching behavior of TWPs but also enhanced the chemical complexity of the TWP leachate. Utilizing both suspect and non-target screenings, a total of 144 compounds were identified as additives, including N-(1,3-dimethylbutyl)-N'-phenyl-p-phenylenediamine (6-PDD), hexa(methoxymethyl)melamine (HMMM), dibutyl phthalate (DBP). These additives underwent various reactions, such as desaturation, acetylation, and other reactions, leading to the formation of different transformation products (TPs). Moreover, certain additives, including caprolactam and 2,2,6,6-tetramethyl-4-piperidinol, demonstrated the potential to form conjugate products with amino acids or Maillard products. Meanwhile, TWP-derived compounds showed significant acute toxicity and detrimental effects on plant growth. This study systematically investigated the environmental fate of TWPs and their derived compounds during the HT of sludge, offering novel insights into the intricate interactions between the micropollutants and dissolved organic matter (DOM) in sludge.

Research on domain ontology construction based on the content features of online rumors.

Online rumors are widespread and difficult to identify, which bring serious harm to society and individuals. To effectively detect and govern online rumors, it is necessary to conduct in-depth semantic analysis and understand the content features of rumors. This paper proposes a TFI domain ontology construction method, which aims to achieve semantic parsing and reasoning of the rumor text content. This paper starts from the term layer, the frame layer, and the instance layer, and based on the reuse of the top-level ontology, the extraction of core literature content features, and the discovery of new concepts in the real corpus, obtains the core classes (five parent classes and 88 subclasses) of the rumor domain ontology and defines their concept hierarchy. Object properties and data properties are designed to describe relationships between entities or their features, and the instance layer is created according to the real rumor datasets. OWL language is used to encode the ontology, Protégé is used to visualize it, and SWRL rules and pellet reasoner are used to mine and verify implicit knowledge of the ontology, and judge the category of rumor text. This paper constructs a rumor domain ontology with high consistency and reliability.

A comparative study on urban waterlogging susceptibility assessment based on multiple data-driven models.

Accurate identification of urban waterlogging areas and assessing waterlogging susceptibility are crucial for preventing and controlling hazards. Data-driven models are utilized to forecast waterlogging areas by establishing intricate relationships between explanatory variables and waterlogging states. This approach tackles the constraints of mechanistic models, which are frequently complex and unable to incorporate socio-economic factors. Previous research predominantly employed single-type data-driven models to predict waterlogging locations and evaluation of their effectiveness. There is a scarcity of comprehensive performance comparisons and uncertainty analyses of different types of models, as well as a lack of interpretability analysis. The chosen study area was the central area of Beijing, which is prone to waterlogging. Given the high manpower, time, and economic costs associated with collecting waterlogging information, the waterlogging point distribution map released by the Beijing Water Affairs Bureau was selected as labeled samples. Twelve factors affecting waterlogging susceptibility were chosen as explanatory variables to construct Random Forest (RF), Support Vector Machine with Radial Basis Function (SVM-RBF), Particle Swarm Optimization-Weakly Labeled Support Vector Machine (PSO-WELLSVM), and Maximum Entropy (MaxEnt). The utilization of diverse single evaluation indicators (such as F-score, Kappa, AUC, etc.) to assess the model performance may yield conflicting results. The Distance between Indices of Simulation and Observation (DISO) was chosen as a comprehensive measure to assess the model's performance in predicting waterlogging points. PSO-WELLSVM exhibited the highest performance with a DISOtest value of 0.63, outperforming MaxEnt (0.78), which excelled in identifying areas highly susceptible to waterlogging, including extremely high susceptibility zones. The SVM-RBF and RF models demonstrated suboptimal performance and exhibited overfitting. The examination of waterlogging susceptibility distribution maps predicted by the four models revealed significant spatial differences due to variations in computational principles and input parameter complexities. The integration of four WSAMs based on logistic regression has been shown to significantly decrease the uncertainty of a single data-driven model and identify the most flood-prone areas. To improve the interpretability of the data model, a geographical detector was incorporated to demonstrate the explanatory capacity of 12 variables and the process of waterlogging. Building Density (BD) exhibits the highest explanatory power in relation to explain waterlogging susceptibility (Q value = 0.202), followed by Distance to Road, Frequency of Heavy Rainstorms (FHR), DEM, etc. The interaction between BD and FHR results in a nonlinear increase in the explanatory power of waterlogging susceptibility. The presence of waterlogging susceptibility risk in the research area can be attributed to the interactions of multiple factors.

Mariculture waters as yet another hotbed for the creation and transfer of new antibiotic-resistant pathogenome.

With the rapid growth of aquaculture globally, large amounts of antibiotics have been used to treat aquatic disease, which may accelerate induction and spread of antibiotic-resistant bacteria (ARB) and antibiotic resistance genes (ARGs) in aquaculture environments. Herein, metagenomic and 16S rRNA analyses were used to analyze the potentials and co-occurrence patterns of pathogenome (culturable and unculturable pathogens), antibiotic resistome (ARGs), and mobilome (mobile genetic elements (MGEs)) from mariculture waters near 5000 km coast of South China. Total 207 species of pathogens were identified, with only 10 culturable species. Furthermore, more pathogen species were detected in mariculture waters than those in coastal waters, and mariculture waters were prone to become reservoirs of unculturable pathogens. In addition, 913 subtypes of 21 ARG types were also identified, with multidrug resistance genes as the majority. MGEs including plasmids, integrons, transposons, and insertion sequences were abundantly present in mariculture waters. The co-occurrence network pattern between pathogenome, antibiotic resistome, and mobilome suggested that most of pathogens may be potential multidrug resistant hosts, possibly due to high frequency of horizontal gene transfer. These findings increase our understanding of mariculture waters as reservoirs of antibiotic resistome and mobilome, and as yet another hotbed for creation and transfer of new antibiotic-resistant pathogenome.

Surface Adsorption and Photoinduced Degradation: A Study of Spinel Ferrite Nanomaterials for Removal of a Model Organic Pollutant from Water.

Spinel oxide nanocrystals are attractive materials for photoinduced advanced oxidation processes that degrade organic pollutants in water due to their chemical stability and tunability, visible light absorption, and magnetic recoverability. However, a systematic understanding of the structural and chemical factors that control the reactivity of specific spinel oxide nanocrystal materials toward photoinduced degradation processes is lacking. This Perspective illustrates these knowledge gaps through an investigation into the impacts of surface chemistry and composition of spinel ferrite nanocrystals of formula MFe2O4 (M = Mg, Fe, Co, Ni, Cu, Zn) on their ability to remove a model organic pollutant (methyl orange (MO)) from water. We identify two mechanisms by which the nanocrystals remove MO from water: (i) surface adsorption and (ii) photoinduced degradation under visible light irradiation in the presence of hydrogen peroxide via the photo-Fenton reaction. Nanocrystals that do not contain any surface ligands are more effective at removing MO from water than nanocrystals that contain surface ligands, despite our observation that the ligand-less nanocrystals do not form stable colloidal dispersions in water, while ligand-coated nanocrystals are colloidally stable. For many of the spinel ferrite compositions studied here, the fraction of methyl orange removal via adsorption to the nanocrystal surface in the absence of photoexcitation is larger than the fraction removed under irradiation. Our data indicate that the composition-dependent surface charge of the nanocrystals controls the degree of surface adsorption of the charged MO molecule. Overall, these results demonstrate that careful consideration of the impacts of surface chemistry on the behavior of spinel ferrite nanocrystals is required to accurately assess and subsequently understand their activity toward the photoinduced degradation of organic molecules.

Polypeptide N-Acetylgalactosaminyl transferase 14 is a novel mediator in pancreatic ß-cell function and growth.

Polypeptide N-Acetylgalactosaminyl transferase 14 (GALNT14) plays important roles in cancer progression and chemotherapy response. Here, we show that GALNT14 is highly expressed in pancreatic ß cells and regulates ß cell function and growth. We found that the expression level of Ganlt14 was significantly decreased in the primary islets from three rodent type-2 diabetic models. Single-Cell sequencing defined that Galnt14 was mainly expressed in ß cells of mouse islets. Galnt14 knockout (G14KO) INS-1 cell line, constructed by using CRISPR/Cas9 technology were growth normal, but showed blunt shape, and increased basal insulin secretion. Combined proteomics and glycoproteomics demonstrated that G14KO altered cell-to-cell junctions, communication, and adhesion. Insulin receptor (IR) and IGF1-1R were indirectly confirmed for GALNT14 substrates, contributed to diminished IGF1-induced p-AKT levels and cell growth in G14KO cells. Overall, this study uncovers that GALNT14 is a novel modulator in regulating ß cells biology, providing a missing link of ß cells O-glycosylation to diabetes development.

Joint global and local interpretation method for CIN status classification in breast cancer.

Breast cancer is among the cancer types with the highest numbers of new cases. The study of this disease from a microscopic perspective has been a prominent research topic. Previous studies have shown that microRNAs (miRNAs) are closely linked to chromosomal instability (CIN). Correctly predicting CIN status from miRNAs can help to improve the survival of breast cancer patients. In this study, a joint global and local interpretation method called GL_XGBoost is proposed for predicting CIN status in breast cancer. GL_XGBoost integrates the eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanation (SHAP) methods. XGBoost is used to predict CIN status from miRNA data, whereas SHAP is used to select miRNA features that have strong relationships with CIN. Furthermore, SHAP's rich visualization strategies enhance the interpretability of the entire model at the global and local levels. The performance of GL_XGBoost is validated on the TCGA-BRCA dataset, and it is shown to have an accuracy of 78.57% and an area under the curve value of 0.87. Rich visual analysis is used to explain the relationships between miRNAs and CIN status from different perspectives. Our study demonstrates an intuitive way of exploring the relationship between CIN and cancer from a microscopic perspective.

Low-abundance proteins-based label-free SERS approach for high precision detection of liver cancer with different stages.

BACKGROUND: Surface-enhanced Raman scattering (SERS) technology have unique advantages of rapid, simple, and highly sensitive in the detection of serum, it can be used for the detection of liver cancer. However, some protein biomarkers in body fluids are often present at ultra-low concentrations and severely interfered with by the high-abundance proteins (HAPs), which will affect the detection of specificity and accuracy in cancer screening based on the SERS immunoassay. Clearly, there is a need for an unlabeled SERS method based on low abundance proteins, which is rapid, noninvasive, and capable of high precision detection and screening of liver cancer. RESULTS: Serum samples were collected from 60 patients with liver cancer (27 patients with stage T1 and T2 liver cancer, 33 patients with stage T3 and T4 liver cancer) and 40 healthy volunteers. Herein, immunoglobulin and albumin were separated by immune sorption and Cohn ethanol fractionation. Then, the low abundance protein (LAPs) was enriched, and high-quality SERS spectral signals were detected and obtained. Finally, combined with the principal component analysis-linear discriminant analysis (PCA-LDA) algorithm, the SERS spectrum of early liver cancer (T1-T2) and advanced liver cancer (T3-T4) could be well distinguished from normal people, and the accuracy rate was 98.5% and 100%, respectively. Moreover, SERS technology based on serum LAPs extraction combined with the partial least square-support vector machine (PLS-SVM) successfully realized the classification and prediction of normal volunteers and liver cancer patients with different tumor (T) stages, and the diagnostic accuracy of PLS-SVM reached 87.5% in the unknown testing set. SIGNIFICANCE: The experimental results show that the serum LAPs SERS detection combined with multivariate statistical algorithms can be used for effectively distinguishing liver cancer patients from healthy volunteers, and even achieved the screening of early liver cancer with high accuracy (T1 and T2 stage). These results showed that serum LAPs SERS detection combined with a multivariate statistical diagnostic algorithm has certain application potential in early cancer screening.

Na-Ru bimetallic functional sites promote photo-driven CO2 directed conversion into CH4.

Recently, there has been a significant increase in interest in using photocatalysis for the energy conversion of polluting gases. In this research, sodium and ruthenium bimetallic functional sites co-modified bismuth tungstate (Ru/Na-Bi2WO6) nanoflower photocatalyst was synthesized via the hydrothermal method. The CO2 reduction products on the Bi2WO6 substrate were CO (1.66 µmol/g/h, 68 %) and CH4 (0.78 µmol/g/h, 32 %). After optimization, a significant change in the CO2 products of the Bi2WO6-based composite material was observed, with CO (0.61 µmol/g/h, 3.6 %) and CH4 (16.1 µmol/g/h, 96.4 %). Results showed that the dominance of CH4 as the main product in the Ru/Na-BWO system is attributed to the effective doping of Na, which generates impurity energy levels composed of oxygen vacancies, lowering the conduction band position of Bi2WO6, thereby suppressing CO generation, and enhancing CH4 selectivity by changing the CO2 activation pathway. The remarkable performance is ascribed to the synergized adsorption and activation of CO2 by the tandem Na+ sites and Ru0 sites. Specifically, the doped Na+ sites play a major role in promoting the adsorption CO2 molecules, while the Ru0 sites play a dominant role in facilitating the activation of the intermediates.

A multivariate analysis of microplastics in soils along the headwaters of Yangtze river on the Tibetan Plateau.

Microplastics (MPs) are among the most widespread anthropogenic pollutants of natural environments, while limited research has focused on the fate of MPs in soils along the Plateau rivers. In this study, we investigated MPs in soils along the source areas of the Yangtze River on the Qinghai-Tibet Plateau. The results showed mean MP abundance values of (89.4 ± 51.0) and (64.4 ± 24.5) items/kg of dry soils around the tributary and mainstream areas, respectively. Film, transparent colors, and polyethylene were common shape, color, and compositions, respectively. The correlation analysis and PCA revealed that MP abundance was related to soil heavy metals (Cr and Ni) and nutrients (TOC and TP) (p < 0.05). Structural equation modeling also revealed that population density was the dominant driving factor contributing to MPs, with a total effect coefficient of 0.45. In addition, the conditional fragmentation model further distinguished the differences in MP sources from upstream to downstream along the Jinsha River. The significant sources of MPs in the bare land and grasslands from the upper reaches of the Jinsha River included traffic, tourism, and atmospheric transport. In contrast, MP transport during farming activities mainly contributed to MPs in the agricultural soil in the lower reaches.