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Abstract Background Hypertension (HT) is one of the most common manifestations in patients with catecholamine-secreting neuroendocrine tumors. Although the cardiovascular manifestations of these tumors have been described, there have been no large-scale investigations of the profile of HT and changes in cardiac structure and function that occur in patients with pheochromocytomas and paragangliomas (PPGL). Materials and methods In this study, we investigated the prevalence of HT and left ventricular remodeling (LVR) in a cohort of 598 patients who underwent surgery for PPGL at our center between January 2001 and April 2022. Information on demographics, reason for hospitalization, medical history, biochemical parameters, findings on echocardiography, and tumor characteristics were recorded. The LVR index was compared according to whether or not there was a history of HT. Results The average age was 47.07 ± 15.07 years, and 277 (46.32%) of the patients were male. A history of HT was found in 423 (70.74%) of the 598 patients. Paraganglioma was significantly more common in the group with HT (26.00% vs. 17.71%, P = 0.030) and significantly less likely to be found incidentally during a health check-up in this group (22.93% vs. 59.43%, P

Highlights A unique approach is proposed: constructing a polymer fiber rigid network with high glass transition temperature. Frozen bulk heterojunction morphology impeded deterioration of exciton quenching, charge transport, and charge extraction properties during thermal aging. The strategy is universal and can be further optimized for enhanced thermal stability and improved mechanical resilience.

Abstract Engineering natural microbiomes for biotechnological applications remains challenging, as metabolic interactions within microbiomes are largely unknown, and practical principles and tools for microbiome engineering are still lacking. Here, we present a combinatory top-down and bottom-up framework to engineer natural microbiomes for the construction of function-enhanced synthetic microbiomes. We show that application of herbicide and herbicide-degrader inoculation drives a convergent succession of different natural microbiomes toward functional microbiomes (e.g., enhanced bioremediation of herbicide-contaminated soils). We develop a metabolic modeling pipeline, SuperCC, that can be used to document metabolic interactions within microbiomes and to simulate the performances of different microbiomes. Using SuperCC, we construct bioremediation-enhanced synthetic microbiomes based on 18 keystone species identified from natural microbiomes. Our results highlight the importance of metabolic interactions in shaping microbiome functions and provide practical guidance for engineering natural microbiomes.

Abstract Objective To evaluate the antibacterial effectiveness of a combination of ε-poly-L-lysine (ε-PL), funme peptide (FP) as well as domiphen against oral pathogens, and assess the efficacy of a BOP® mouthwash supplemented with this combination in reducing halitosis and supragingival plaque in a clinical trial. Materials and methods The minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) of the compound against Fusobacterium nucleatum, Porphyromonas gingivalis, Streptococcus mutans, and Aggregatibacter actinomycetemcomitans were determined by the gradient dilution method. Subsequently, the CCK-8 assay was used to detect the toxicity of mouthwash on human gingival fibroblastst, and the effectiveness in reducing halitosis and supragingival plaque of the mouthwash supplemented with the combination was analyzed by a randomized, double-blind, parallel-controlled clinical trial. Results The combination exhibited significant inhibitory effects on tested oral pathogens with the MIC

Abstract Graph neural networks (GNNs) have shown powerful capabilities in modeling and representing graph structural data across various graph learning tasks as an emerging deep learning approach. However, most existing GNNs focus on single-relational graphs and fail to fully utilize the rich and diverse relational information present in real-world graph data. In addition, deeper GNNs tend to suffer from overfitting and oversmoothing issues, leading to degraded model performance. To deeply excavate the multi-relational features in graph data and strengthen the modeling and representation abilities of GNNs, this paper proposes a multi-channel deep graph convolutional neural network method called DeepMCGCN. It constructs multiple relational subgraphs and adopts multiple GCN channels to learn the characteristics of different relational subgraphs separately. Cross-channel connections are utilized to obtain interactions between different relational subgraphs, which can learn node embeddings richer and more discriminative than single-channel GNNs. Meanwhile, it alleviates overfitting issues of deep models by optimizing convolution functions and adding residual connections between and within channels. The DeepMCGCN method is evaluated on three real-world datasets, and the experimental results show that its node classification performance outperforms that of single-channel GCN and other benchmark models, which improves the modeling and representation capabilities of the model.

Abstract Background m6A modification is currently recognized as a major driver of RNA function that maintains cancer cell homeostasis. Long non-coding (Lnc) RNAs control cell proliferation and play an important role in the occurrence and progression of colorectal cancer (CRC). ZCCHC4 is a newly discovered m6A methyltransferase whose role and mechanism in tumors have not yet been elucidated. Methods The EpiQuik m6A RNA methylation kit was used to detect the level of total RNA m6A in six types of digestive tract tumors. The Kaplan-Meier method and receiver operating characteristic curve were used to evaluate the prognostic and diagnostic value of the newly discovered m6A methyltransferase, ZCCHC4, in CRC. The effects on CRC growth in vitro and in vivo were studied using gain- and loss-of-function experiments. The epigenetic mechanisms underlying ZCCHC4 upregulation in CRC were studied using RIP, MeRIP-seq, RNA pull-down, and animal experiments. Results We reported that the ZCCHC4-LncRNAGHRLOS-KDM5D axis regulates the growth of CRC in vitro and in vivo. We found that ZCCHC4 was upregulated in primary CRC samples and could predict adverse clinical outcomes in patients with CRC. Mechanistically, ZCCHC4 downregulated LncRNAGHRLOS to promote CRC tumorigenesis. As a downstream molecule of LncRNAGHRLOS, KDM5D directly controls CRC cell proliferation, migration, and invasion. Conclusion This study suggests that the ZCCHC4 axis contributes to the tumorigenesis and progression of CRC and that ZCCHC4 may be a potential biomarker for this malignancy.

Graph Neural Networks (GNNs) have demonstrated significant potential as powerful tools for handling graph data in various fields. However, traditional GNNs often encounter limitations in information capture and generalization when dealing with complex and high-order graph structures. Concurrently, the sparse labeling phenomenon in graph data poses challenges in practical applications. To address these issues, we propose a novel graph contrastive learning method, TP-GCL, based on a tensor perspective. The objective is to overcome the limitations of traditional GNNs in modeling complex structures and addressing the issue of sparse labels. Firstly, we transform ordinary graphs into hypergraphs through clique expansion and employ high-order adjacency tensors to represent hypergraphs, aiming to comprehensively capture their complex structural information. Secondly, we introduce a contrastive learning framework, using the original graph as the anchor, to further explore the differences and similarities between the anchor graph and the tensorized hypergraph. This process effectively extracts crucial structural features from graph data. Experimental results demonstrate that TP-GCL achieves significant performance improvements compared to baseline methods across multiple public datasets, particularly showcasing enhanced generalization capabilities and effectiveness in handling complex graph structures and sparse labeled data.

The distributed permutation flowshop scheduling is a critical issue in various industries, involving jobs allocation and scheduling among multiple flowshops. This paper extends the research to explore the Distributed Mixed No-Wait Permutation Flowshop Scheduling Problems (DMNWPFSP) with minimizing makespan. The innovation lies in an optimized mathematical model, hybrid heuristic algorithms, an improved iterated greedy algorithm (IIG), and high-quality solutions. Extensive experimental results demonstrate the effectiveness and superiority of the proposed IIG in terms of scheduling quality, computational efficiency, and robustness compared to existing approaches. The outcomes of this work contribute to the field of distributed flowshop scheduling, providing valuable insights for practitioners seeking to enhance production efficiency and competitiveness.

Abstract Series of giant molecule acceptors DY, TY and QY with two, three and four small molecule acceptor subunits are synthesized by a stepwise synthetic method and used for systematically investigating the influence of subunit numbers on the structure-property relationship from small molecule acceptor YDT to giant molecule acceptors and to polymerized small molecule acceptor PY-IT. Among these acceptors-based devices, the TY-based film shows proper donor/acceptor phase separation, higher charge transfer state yield and longer charge transfer state lifetime. Combining with the highest electron mobility, more efficient exciton dissociation and lower charge carrier recombination properties, the TY-based device exhibits the highest power conversion efficiency of 16.32%. These results indicate that the subunit number in these acceptors has significant influence on their photovoltaic properties. This stepwise synthetic method of giant molecule acceptors will be beneficial to diversify their structures and promote their applications in high-efficiency and stable organic solar cells.

Abstract The present-day globalized economy and diverse market demands have compelled an increasing number of manufacturing enterprises to move toward the distributed manufacturing pattern and the model of multi-variety and small-lot. Taking these two factors into account, this study investigates an extension of the distributed hybrid flowshop scheduling problem (DHFSP), called the distributed hybrid flowshop scheduling problem with consistent sublots (DHFSP_CS). To tackle this problem, a mixed integer linear programming (MILP) model is developed as a preliminary step. The NP-hard nature of the problem necessitates the use of the iterated F-Race (I/F-Race) as the automated algorithm design (AAD) to compose a metaheuristic that requires minimal user intervention. The I/F-Race enables identifying the ideal values of numerical and categorical parameters within a promising algorithm framework. An extension of the collaborative variable neighborhood descent algorithm (ECVND) is utilized as the algorithm framework, which is modified by intensifying efforts on the critical factories. In consideration of the problem-specific characteristics and the solution encoding, the configurable solution initializations, configurable solution decoding strategies, and configurable collaborative operators are designed. Additionally, several neighborhood structures are specially designed. Extensive computational results on simulation instances and a real-world instance demonstrate that the automated algorithm conceived by the AAD outperforms the CPLEX and other state-of-the-art metaheuristics in addressing the DHFSP_CS.

The electromagnetic transient (EMT)-type impedance scanning methods conducted in positive sequence domain and dq-frame (p-scan and dq-scan, respectively) have been widely used for identifying inverter-based resource (IBR) instabilities. They obtain the sequence domain single-input single-output (sequence-SISO) and dq-frame multi-input multi-output (dq-MIMO) impedance models of an IBR, respectively. The dq-MIMO impedance-based stability assessment (IBSA) is more accurate compared to the sequence-SISO IBSA as it considers the mirror frequency effect (MFE). However, unlike the sequence-SISO, the dq-MIMO IBSA cannot differentiate the resonance frequency from its mirror frequency, because the dynamics in dq-frame incorporate both the positive and negative sequence impedances of IBR. This paper proposes a new IBSA scheme to eliminate this shortcoming. First, a dq-MIMO IBSA is carried out to obtain the accurate dq-frame resonance frequency and stability margin. Then a sequence-SISO IBSA is conducted to determine the phase domain resonance frequency. The sequence-SISO impedance models are transformed from the dq-MIMO model. The proposed scheme has lower computational costs than the existing dq-MIMO IBSA methods. The effectiveness of the proposed scheme is validated on a test system incorporating full-size converter-based wind park (FSC-based WP). This paper also demonstrates the possibility of weak grid instability occurrence in supersynchronous frequency range.

AIM: To investigate the effect of modified Chufeng Yisun Decoction on ocular surface inflammation after pterygium surgery.METHODS: A total of 60 patients(60 eyes)with primary pterygium who underwent pterygium surgery were randomly divided into control group and study group, with 30 cases(30 eyes)in each group. In the control group, patients were treated with pranoprofen eye drops, tobramycin dexamethasone eye drops, and deproteinized calf blood extract eye gel after the surgery. In the study group, patients were treated by oral modified Chufeng Yisun Decoction in addition to the treatments in the control group. The changes of ocular irritation symptoms, ocular inflammatory signs, tear interleukin 6(IL-6)levels, and tear ferning test(TFT)of patients in the two groups were assessed.RESULTS: The visual analogue scale(VAS)in patients of both groups was significantly lower at 2d and 1wk after the surgery than that at 1d after the surgery(all P

This paper uses approximate analytical formulas and numerical results with the bino-dominated dark matter (DM) as an example to analyze the impact of the LUX-ZEPLIN (LZ) experiment on the DM phenomenology and naturalness in the Minimal Supersymmetric Standard Model (MSSM). We conclude that the limitation of the latest LZ experiment worsens the naturalness of the MSSM, as the predictions of the Z-boson mass and DM relic density demonstrate, particularly in the regions where the correct DM relic density is obtained by the Z- or h-mediated resonant annihilations.

IntroductionStatistical methods such as empirical orthogonal functions (EOFs) are often used to model the sound speed profile (SSP). However, their statistical nature often leads to the sample dependence and physical fuzziness.MethodThis study proposes a technique for modeling the SSP from the perspective of ocean dynamics. It employs the ocean normal mode, which is the mode of fluid particles motion, to deduce perturbations in the SSP, which is called the ocean mode basis (OMB).ResultThe results of SSP reconstruction of in-situ samples showed that a few leading orders of the OMB can provide a compact representation of the SSP. Oscillations of the contours and gradient of the sound speed in thermocline were analyzed by using the first two orders of the projection coefficients of the relationship between the OMB and the baroclinic mode. As a physical model, this technique can also be used to characterize the dynamics of internal solitary waves. Furthermore, the OMB derived from archival date was used for SSP inversion. The results showed that the OMB can reconstruct SSP of a reasonable resolution without requiring in-situ samples.DiscussionCompared with statistical models, the OMB can better explain the ocean dynamics underlying variations in the SSP while requiring fewer samples.

Existing graph contrastive learning methods often rely on differences in node features within subgraphs, lacking effective capture of the global structural information of the graph. To address this issue, we propose a novel graph contrastive learning method, Line Graph Contrastive Learning (LineGCL), aimed at overcoming the deficiencies in understanding the overall characteristics and topological structures of graphs found in current methods. LineGCL utilizes the characteristics of line graphs to transform the original graph into corresponding line graphs, presenting edge information in the form of node features, effectively capturing the global structural information of the graph. Additionally, LineGCL adopts a novel multi-view contrastive learning approach, characterizing the similarity and differences between the original graph and line graph comprehensively from the perspectives of node features and spectral features, further enhancing the model's understanding and learning capabilities of the global structure of graphs. Experimental results on public datasets demonstrate that LineGCL outperforms baseline models, achieving performance improvements ranging from 0.5% to 28.5%. These results validate the effectiveness and superiority of LineGCL in capturing and understanding global structural information, surpassing the limitations of baseline methods in graph contrastive learning.

IntroductionExisting methods primarily focus on earth acoustic parameters inversion under specific layered structures. However, they face challenges with experimental data from unknown seabed stratification, hindering accurate parameter inversion.MethodsTo address this, a novel algorithm combines Back Propagation Neural Network (BPNN) for distinguishing seabed stratification and inverting acoustic parameters. Simulated sound pressure data disturb seabed parameters as input, enabling feature recognition for training the neural network inversion model. Acoustic parameters are then estimated under identified stratification using the sound field model.ResultsThe inversion model is validated using simulation and pool shrinkage data. Results show the neural network model effectively stratifies simulation and experimental data, providing accurate inversion results for acoustic parameters corresponding to distinct layers.DiscussionThe neural network model's accuracy and practicality are confirmed through hierarchical judgment of scale test data and acoustic parameter inversion. This approach introduces a new perspective for shallow sea acoustic parameter inversion, offering a promising application scenario.

Copper (Cu) is a key cofactor in ammonia monooxygenase functioning responsible for the first step of nitrification, but its excess availability impairs soil microbial functions and plant growth. Yet, the impact of Cu on nitrogen (N) cycling and process-related variables in cropland soils remains unexplored globally. Through a meta-analysis of 1209-paired and 319-single observations from 94 publications, we found that Cu (Cu addition or Cu-polluted soil) reduced soil potential nitrification by 33.8% and nitrite content by 73.5% due to reduced soil enzyme activities of nitrification and urease, microbial biomass content, and ammonia oxidizing archaea abundance. The response ratio of potential nitrification decreased with increasing Cu concentration, soil total N, and clay content. We further noted that soil potential nitrification inhibited by 46.5% only when Cu concentration was higher than 150 mg kg−1, while low Cu concentration (less than 150 mg kg−1) stimulated soil nitrate by 99.0%. Increasing initial soil Cu content stimulated gross N mineralization rate due to increased soil organic carbon and total N, but inhibited gross nitrification rate, which ultimately stimulated gross N immobilization rate as a result of increased the residence time of ammonium. This resulted in a lower ratio of gross nitrification rate to gross N immobilization rate, implying a lower potential risk of N loss as evidenced by decreased nitrous oxide emissions with increasing initial soil Cu content. Our analysis offers initial global evidence that Cu has an important role in controlling soil N availability and loss through its effect on N production and consumption.

The carbon nanotube (CNT)-polyetherimide (PEI) was successfully grafted onto the surface of carbon fibers (CF) through chemical grafting method for constructing a CF-g-CNT reinforcing phase. Results show the construction of CF-g-CNT fiber can effectively improve the interface, impact resistance and bending properties of the composites. Considering the effect of strengthening phase on the mechanical properties of composites, the interfacial shear strength, impact strength, flexural modulus and flexural strength of CF-g-CNT/BMI composites respectively increase by 48.8%, 10.8%, 5.2% and 14.6%, companed with those of CF/BMI. In addition, the interfacial shear strength, impact strength, flexural modulus and flexural strength of CF-g-CNT/BMI-PEI-CNT composites are 56.9%, 27.1%, 12.5% and 16.1% higher than those of CF/BMI-PEI-CNT, respectively. The interfacial shear strength, impact strength, flexural modulus and flexural strength of CF-g-CNT/BMI-PEI-CNT composites increase by 57.7%, 33.7%, 20.0% and 23.7% compared with CF/BMI. The CNT-PEI in CF-g-CNT can effectively change the interface composition and change the expansion path of horizontal stress or vertical stress at the interface, which is beneficial to disperse the stress concentration as well as improve the mechanical properties of the composites.

Abstract Recent studies have shown that the high incidence and low cure rate of hepatocellular carcinoma (HCC) have not improved significantly. Surgery and liver transplantation are the mainstays of prolonging the survival of HCC patients. However, the surgical resection rate of HCC patients is very low, and even after radical surgical resection, the recurrence rate at 5 years postoperatively remains high and the prognosis is very poor, so more treatment options are urgently needed. Increasing evidence suggests that cellular senescence is not only related to cancer development but may also be one of its primary driving factors. We aimed to establish a prognostic signature of senescence-associated genes to predict the prognosis and therapeutic response of HCC patients. The aim of this study was to develop a risk model associated with cellular senescence and to search for potential strategies to treat HCC. We divided HCC patients into two clusters and identified differentially expressed genes (DEGs) between clusters. In this study, low-risk patients had a better prognosis, higher levels of immune cell infiltration, and better efficacy to fluorouracil, Paclitaxel and Cytarabine chemotherapy compared to high-risk patients. To further identify potential biomarkers for HCC, we further validated the expression levels of the four signature genes in HCC and neighbouring normal tissues by in vitro experiments. In conclusion, we identified and constructed a relevant prognostic signature, which performed well in predicting the survival and treatment response of HCC patients. This helps to differentiate between low-score and high-risk HCC, and the results may contribute to precise treatment protocols in clinical practice.

Abstract Background Drug resistance is a main factor affecting the chemotherapy efficacy of gastric cancer (GC), in which meiosis plays an important role. Therefore, it is urgent to explore the effect of meiosis related genes on chemotherapy resistance. Methods The expression of meiotic nuclear divisions 1 (MND1) in GC was detected by using TCGA and clinical specimens. In vitro and in vivo assays were used to investigate the effects of MND1. The molecular mechanism was determined using luciferase reporter assay, CO-IP and mass spectrometry (MS). Results Through bioinformatics, we found that MND1 was highly expressed in platinum-resistant samples. In vitro experiments showed that interference of MND1 significantly inhibited the progression of GC and increased the sensitivity to oxaliplatin. MND1 was significantly higher in 159 GC tissues in comparison with the matched adjacent normal tissues. In addition, overexpression of MND1 was associated with worse survival, advanced TNM stage, and lower pathological grade in patients with GC. Further investigation revealed that forkhead box protein A1 (FOXA1) directly binds to the promoter of MND1 to inhibit its transcription. CO-IP and MS assays showed that MND1 was coexpressed with transketolase (TKT). In addition,TKT activated the PI3K/AKT signaling axis and enhanced the glucose uptake and lactate production in GC cells. Conclusions Our results confirm that FOXA1 inhibits the expression of MND1, which can directly bind to TKT to promote GC progression and reduce oxaliplatin sensitivity through the PI3K/AKT signaling pathway.

Impedance-based stability analysis (IBSA) is an effective method to identify subsynchronous interaction (SSI) problem between the inverter-based resources (IBRs) and series compensated or weakly tied AC grids. The electromagnetic transient (EMT) level positive sequence and dq-frame frequency scanning methods (p-scan and dq-scan, respectively) are used to obtain the sequence single-input single-output (SISO) and dq multi-input multi-output (MIMO) impedance of IBRs, respectively. The dq MIMO impedance usage in IBSA provides more accurate results compared to the sequence SISO impedance. This paper proposes an EMT-level αβ-frame frequency scanning method (αβ-scan) to obtain the αβMIMO impedance and its usage in IBSA. The αβ-scan requires significantly less time compared to dq-scan for the convergence of MIMO IBR impedance representation and offers similar accuracy with dq-scan in IBSA. The accuracy of the proposed αβMIMO IBSA is validated by comparing with dq MIMO IBSA and EMT simulations on series capacitor SSI cases with different type IBRs (Doubly-fed induction generator (DFIG)-based wind park (WP) and full-scale converter (FSC)-based WP). This paper also uses a multivariable structure function(MSF)-based method for the first time in IBSA of SSI to achieve Bode plot-based analysis for providing better visualized presentation of resonance frequency and stability margins compared to generalized Nyquist criterion.

Abstract Link prediction is a widely adopted method for extracting valuable data insights from graphs, primarily aimed at predicting interactions between two nodes. However, there are not only pairwise interactions but also multivariate interactions in real life. For example, reactions between multiple proteins, multiple compounds, and multiple metabolites cannot be mined effectively using link prediction. A hypergraph is a higher-order network composed of nodes and hyperedges, where hyperedges can be composed of multiple nodes, and can be used to depict multivariate interactions. The interactions between multiple nodes can be predicted by hyperlink prediction methods. Since hyperlink prediction requires predicting the interactions between multiple nodes, it makes the study of hyperlink prediction much more complicated than that of other complex networks, thus resulting in relatively limited attention being devoted to this field. The existing hyperlink prediction can only predict potential hyperlinks in uniform hypergraphs, or need to predict hyperlinks based on the candidate hyperlink sets, or only study hyperlink prediction for undirected hypergraphs. Therefore, a hyperlink prediction framework for predicting multivariate interactions based on graph representation learning is proposed to solve the above problems, and then the framework is extended to directed hyperlink prediction (e.g., directed metabolic reaction networks). Furthermore, any size of hyperedges can be predicted by the proposed hyperlink prediction algorithm framework, whose performance is not affected by the number of nodes or the number of hyperedges. Finally, the proposed framework is applied to both the biological metabolic reaction network and the organic chemical reaction network, and experimental analysis has demonstrated that the hyperlinks can be predicted efficiently by the proposed hyperlink prediction framework with relatively low time complexity, and the prediction performance has been improved by up to 40% compared with the baselines.

Abstract Cardiovascular diseases (CVDs) continue to exert a significant impact on global mortality rates, encompassing conditions like pulmonary arterial hypertension (PAH), atherosclerosis (AS), and myocardial infarction (MI). Oxidative stress (OS) plays a crucial role in the pathogenesis and advancement of CVDs, highlighting its significance as a contributing factor. Maintaining an equilibrium between reactive oxygen species (ROS) and antioxidant systems not only aids in mitigating oxidative stress but also confers protective benefits on cardiac health. Herbal monomers can inhibit OS in CVDs by activating multiple signaling pathways, such as increasing the activity of endogenous antioxidant systems and decreasing the level of ROS expression. Given the actions of herbal monomers to significantly protect the normal function of the heart and reduce the damage caused by OS to the organism. Hence, it is imperative to recognize the significance of herbal monomers as prospective therapeutic interventions for mitigating oxidative damage in CVDs. This paper aims to comprehensively review the origins and mechanisms underlying OS, elucidate the intricate association between CVDs and OS, and explore the therapeutic potential of antioxidant treatment utilizing herbal monomers. Furthermore, particular emphasis will be placed on examining the cardioprotective effects of herbal monomers by evaluating their impact on cardiac signaling pathways subsequent to treatment. Graphical Abstract