Ecotoxicological assessment, oxidative response, and enzyme activity disorder of the rotifer Brachionus asplanchnoidis exposed to a toxic cocktail of spent lithium-ion battery leachate.

Spent lithium-ion batteries (LIBs) have emerged as a major source of waste due to their low recovery rate. The physical disposal of spent LIBs can lead to the leaching of their contents into the surrounding environment. While it is widely agreed that hazardous substances such as nickel and cobalt in the leachate can pose a threat to the environment and human health, the overall composition and toxicity of LIB leachate remain unclear. In this study, a chemical analysis of leachate from spent LIBs was conducted to identify its primary constituents. The ecotoxicological parameters of the model organism, rotifer Brachionus asplanchnoidis, were assessed to elucidate the toxicity of the LIB leachate. Subsequent experiments elucidated the impacts of the LIB leachate and its representative components on the malondialdehyde (MDA) level, antioxidant capacity, and enzyme activity of B. asplanchnoidis. The results indicate that both the LIB leachate and its components are harmful to individual rotifers due to the adverse effects of stress-induced disturbances in biochemical indicators, posing a threat to population development. The intensified poisoning phenomenon under combined stress suggests the presence of complex synergistic effects among the components of LIB leachate. Due to the likely environmental and biological hazards, LIBs should be strictly managed after disposal. Additionally, more economical and eco-friendly recycling and treatment technologies need to be developed and commercialized.

Combining blood pressure variability and heart rate variability to analyze the autonomic nervous function of rotenone induced Parkinson's rat model.

BACKGROUND: Parkinson's patients have significant autonomic dysfunction, early detect the disorder is a major challenge. To assess the autonomic function in the rat model of rotenone induced Parkinson's disease (PD), Blood pressure and ECG signal acquisition are very important. NEW METHOD: We used telemetry to record the electrocardiogram and blood pressure signals from awake rats, with linear and nonlinear analysis techniques calculate the heart rate variability (HRV) and blood pressure variability (BPV). we applied nonlinear analysis methods like sample entropy and detrended fluctuation analysis to analyze blood pressure signals. Particularly, this is the first attempt to apply nonlinear analysis to the blood pressure evaluate in rotenone induced PD model rat. RESULTS: HRV in the time and frequency domains indicated sympathetic-parasympathetic imbalance in PD model rats. Linear BPV analysis didn't reflect changes in vascular function and blood pressure regulation in PD model rats. Nonlinear analysis revealed differences in BPV, with lower sample entropy results and increased detrended fluctuation analysis results in the PD group rats. COMPARISON WITH EXISTING METHODS AND CONCLUSIONS: our experiments demonstrate the ability to evaluate autonomic dysfunction in models of Parkinson's disease by combining the analysis of BPV with HRV, consistent with autonomic impairment in PD patients. Nonlinear analysis by blood pressure signal may help in early detection of the PD. It indicates that the fluctuation of blood pressure in the rats in the rotenone model group tends to be regular and predictable, contributes to understand the PD pathophysiological mechanisms and to find strategies for early diagnosis.

ZBTB7A regulates LncRNA HOTAIR-mediated ELAVL1/SOX17 axis to inhibit malignancy and angiogenesis in endometrial carcinoma.

BACKGROUND: Endometrial cancer (EC) is the sixth most frequent cancer in women worldwide and has higher fatality rates. The pathophysiology of EC is complex, and there are currently no reliable methods for diagnosing and treating the condition. Long non-coding RNA (lncRNA), according to mounting evidence, is vital to the pathophysiology of EC. HOTAIR is regarded as a significant prognostic indicator of EC. ZBTB7A decreased EC proliferation and migration, according to recent studies, however the underlying mechanism still needs to be clarified. METHODS: The research utilized RT-qPCR to measure HOTAIR expression in clinical EC tissues and various EC cell lines. Kaplan-Meier survival analysis was employed to correlate HOTAIR levels with patient prognosis. Additionally, the study examined the interaction between ZBTB7A and HOTAIR using bioinformatics tools and ChIP assays. The experimental approach also involved manipulating the expression levels of HOTAIR and ZBTB7A in EC cell lines and assessing the impact on various cellular processes and gene expression. RESULTS: The study found significantly higher levels of HOTAIR in EC tissues compared to adjacent normal tissues, with high HOTAIR expression correlating with poorer survival rates and advanced cancer characteristics. EC cell lines like HEC-1 A and KLE showed higher HOTAIR levels compared to normal cells. Knockdown of HOTAIR in these cell lines reduced proliferation, angiogenesis, and migration. ZBTB7A was found to be inversely correlated with HOTAIR, and its overexpression led to a decrease in HOTAIR levels and a reduction in malignant cell behaviors. The study also uncovered that HOTAIR interacts with ELAVL1 to regulate SOX17, which in turn activates the Wnt/ß-catenin pathway, promoting malignant behaviors in EC cells. CONCLUSION: HOTAIR is a critical regulator in EC, contributing to tumor growth and poor prognosis. Its interaction with ZBTB7A and regulation of SOX17 via the Wnt/ß-catenin pathway underlines its potential as a therapeutic target.

Fed-MStacking: Heterogeneous Federated Learning with Stacking Misaligned Labels for Abnormal Heart Sound Detection.

Ubiquitous sensing has been widely applied in smart healthcare, providing an opportunity for intelligent heart sound auscultation. However, smart devices contain sensitive information, raising user privacy concerns. To this end, federated learning (FL) has been adopted as an effective solution, enabling decentralised learning without data sharing, thus preserving data privacy in the Internet of Health Things (IoHT). Nevertheless, traditional FL requires the same architectural models to be trained across local clients and global servers, leading to a lack of model heterogeneity and client personalisation. For medical institutions with private data clients, this study proposes Fed-MStacking, a heterogeneous FL framework that incorporates a stacking ensemble learning strategy to support clients in building their own models. The secondary objective of this study is to address scenarios involving local clients with data characterised by inconsistent labelling. Specifically, the local client contains only one case type, and the data cannot be shared within or outside the institution. To train a global multi-class classifier, we aggregate missing class information from all clients at each institution and build meta-data, which then participates in FL training via a meta-learner. We apply the proposed framework to a multi-institutional heart sound database. The experiments utilise random forests (RFs), feedforward neural networks (FNNs), and convolutional neural networks (CNNs) as base classifiers. The results show that the heterogeneous stacking of local models performs better compared to homogeneous stacking.

Efficacy and Molecular Mechanism of Quercetin on Constipation Induced by Berberine via Regulating Gut Microbiota.

Berberine (BBR) is used to treat cancer, inflammatory conditions, and so on. But the side effects of BBR causing constipation should not be ignored. In clinical application, the combination of Amomum villosum Lour. (AVL) and BBR can relieve it. However, the effective ingredients and molecular mechanism of AVL in relieving constipation are not clear. A small intestine propulsion experiment was conducted in constipated mice to screen active ingredients of AVL. We further confirmed the molecular mechanism of action of the active ingredient on BBR-induced constipation. Quercetin (QR) was found to be the effective ingredient of AVL in terms of relieving constipation. QR can efficiently regulate the microbiota in mice suffering from constipation. Moreover, QR significantly raised the levels of substance P and motilin while lowering those of 5-hydroxytryptamine and vasoactive intestinal peptide; furthermore, it also increased the protein expression levels of calmodulin, myosin light-chain kinase, and myosin light chain. The use of QR in combination with BBR has an adverse effect-reducing efficacy. The study provides new ideas and possibilities for the treatment of constipation induced by BBR.

Coagulation and fibrinolytic markers offer utility when distinguishing between benign and malignant gallbladder tumors: A cross-sectional study.

BACKGROUND: The metabolic or proliferative abnormalities that are characteristic of tumor cells can lead to abnormal fibrinolysis or coagulation system activity, with certain tumors exhibiting hypercoagulability or existing in a fibrinolytic state. However, the utility of biomarkers of coagulation and fibrinolysis when seeking to differentiate between benign gallbladder disease and malignant gallbladder tumors remains uncertain. METHODS: This study included a total of 81 patients with benign gallbladder polyps and 94 patients with malignant gallbladder tumors. Pre-biopsy or pretreatment levels of PT, APTT, FIB, D-dimer, FDP, PLT, PIC, TAT, TM, and t-PAIC from these patients were compared using Mann-Whitney tests. The baseline data of the patients were analyzed using chi-square tests, and the diagnostic utility of these biomarkers in distinguishing between benign and malignant gallbladder lesions was evaluated using ROC curves, and Spearman correlation analysis was employed to assess the correlation between these indicators and tumor parameters. RESULTS: The average age of malignant gallbladder tumor group was higher than benign gallbladder polyp group. And the base line analysis showed that there was a statistic difference in age, history of smoking, drinking, biliary tract disease, BMI of over weight between these two groups. In patients with malignant gallbladder tumors, FIB, D-dimer, FDP, PIC, TAT, TM, and t-PAIC levels were significantly elevated relative to those in patients affected by benign gallbladder polyp. The AUC for FIB, D-dimer, and FDP was 0.8469, 0.6514, 0.5950, while for PIC, TAT, TM, t-PAIC and four biomarker combined diagnosed was 0.8455, 0.6554, 0.7130, 0.6806, and 0.8859. Among these, TM was associated with the vascular invasion of tumor patients; TAT and t-PAIC were associated with neural invasion; D-dimer and FDP were related to the maximum tumor diameter; and FDP had a certain correlation with the tumor stage. CONCLUSIONS: In gallbladder tumor patients, conventional coagulation metrics like FIB, D-dimer, and FDP, as well as newer thrombotic indicators such as PIC, TAT, TM, and t-PAIC, were obviously increased. Correlations with tumor parameters suggested their potential as biomarkers to distinguish benign from malignant gallbladder growths.

Optimization of sulfate reduction and methanogenesis via phase separation in a two-phase internal circulation reactor for the treatment of high-sulfate organic wastewater.

To enhance the performance of the internal circulation (IC) reactor when treating high-sulfate organic wastewater, a laboratory-scale two-phase IC reactor with distinct phase separation capabilities was designed, and the sulfate reduction and methanogenesis processes were optimized by segregating the reactor into two specialized reaction zones. The results demonstrated that the first and second reaction areas of the two-phase IC reactor could be maintained at 4.5-6.0 and 7.5-8.5, respectively, turning them into the specialized phase for sulfate reduction and methanogenesis. Through phase separation, the two-phase IC reactor achieved a COD degradation and sulfate reduction efficiency of more than 80% when the influent sulfate concentration exceeded 5,000 mg/L, which were 32.32% and 16.04% higher than that before phase separation. Functional analyses indicated a greater activity of both the dissimilatory and assimilatory sulfate reduction pathways in the acidogenic phase, largely due to a rise in the relative abundance of the genera Desulfovibrio, Bacteroides, and Lacticaseibacillus, the primary carriers of sulfate reduction functional genes. In contrast, all the acetoclastic, hydrogenotrophic, and methylotrophic methanogenesis pathways were inhibited in the acidogenic phase but thrived in the methanogenic phase, coinciding with shifts in the genus Methanothrix, which harbors the mcrA, mcrB, and mcrG genes essential for the final transformation step of all three methanogenesis pathways.

Unveiling the potential of cellulose, chitosan and polylactic acid as precursors for the production of green carbon nanofibers with controlled morphology and diameter.

Carbon nanofibers (CNFs) are very promising materials with application in many fields, such as sensors, filtration systems, and energy storage devices. This study aims to explore the use of eco-friendly biopolymers for CNF production, finding novel, suitable and sustainable precursors and thus prioritising environmentally conscious processes and ecological compatibility. Polymeric nanofibers (PNFs) using cellulose acetate, polylactic acid, and chitosan as precursors were successfully prepared via electrospinning. Rheological testing was performed to determine suitable solution concentrations for the production of PNFs with controlled diameter and appropriate morphology. Their dimensions and structure were found to be significantly influenced by the solution concentration and electrospinning flow rate. Subsequently, the electrospun green nanofibers were subject to stabilisation and carbonisation to convert them into CNFs. Thermal behaviour and chemical/structural changes of the nanofibers during stabilisation were investigated by means of thermogravimetric analysis and Fourier-transform infrared spectroscopy, while the final morphology of the fibers after stabilisation and carbonisation was examined through scanning electron microscopy to determine the optimal stabilisation parameters. The optimal fabrication parameters for cellulose and chitosan-based CNFs with excellent morphology and thermal stability were successfully established, providing valuable insight and methods for the sustainable and environmentally friendly synthesis of these promising materials.

Validating the 2023 FIGO staging system: A nomogram for endometrioid endometrial cancer and adenocarcinoma.

BACKGROUND: To find the factors impacting overall survival (OS) prognosis in patients with endometrioid endometrial carcinoma (EEC) and adenocarcinoma and to establish a nomogram model to validate the 2023 International Federation of Obstetrics and Gynecology (FIGO) staging system for endometrial cancer. METHODS: Data were obtained from the Surveillance, Epidemiology, and End Results (SEER) training cohort. An independent validation cohort was obtained from the First Affiliated Hospital of Anhui Medical University between 2008 and 2023. Cox regression analysis identified independent prognostic factors for OS in EEC and adenocarcinoma patients. A nomogram predicting OS was developed and validated utilizing the C-index, calibration curves, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA). The relationship between the tumor grade and prognosis of EEC and adenocarcinoma was quantified using net reclassification improvement (NRI), propensity score matching (PSM), and Kaplan-Meier curves. RESULTS: Cox regression analysis identified age, race, marital status, tumor grade, tumor stage, tumor size, and chemotherapy as independent prognostic factors for OS. A nomogram for predicting OS was developed based on these factors. The C-indexes for the OS nomogram was 0.743 and 0.720 for the SEER training set and external validation set, respectively. The area under the ROC (AUC) for the OS nomogram was 0.755, 0.757, and 0.741 for the SEER data subsets and 0.844, 0.719, and 0.743 for the external validation subsets. Calibration plots showed high concordance between the nomogram-predicted and observed OS. DCA also demonstrated the clinical utility of the OS nomogram. NRI, PSM, and survival analyses revealed that tumor grade was the most important histopathological factor for EEC and adenocarcinoma prognosis. CONCLUSION: Seven independent prognostic variables for the OS of patients with EEC and adenocarcinoma were identified. The established OS nomogram has good predictive ability and clinical utility and validates the 2023 endometrial cancer FIGO staging system.

Multi-View Time-Series Hypergraph Neural Network for Action Recognition.

Recently, action recognition has attracted considerable attention in the field of computer vision. In dynamic circumstances and complicated backgrounds, there are some problems, such as object occlusion, insufficient light, and weak correlation of human body joints, resulting in skeleton-based human action recognition accuracy being very low. To address this issue, we propose a Multi-View Time-Series Hypergraph Neural Network (MV-TSHGNN) method. The framework is composed of two main parts: the construction of a multi-view time-series hypergraph structure and the learning process of multi-view time-series hypergraph convolutions. Specifically, given the multi-view video sequence frames, we first extract the joint features of actions from different views. Then, limb components and adjacent joints spatial hypergraphs based on the joints of different views at the same time are constructed respectively, temporal hypergraphs are constructed joints of the same view at continuous times, which are established high-order semantic relationships and cooperatively generate complementary action features. After that, we design a multi-view time-series hypergraph neural network to efficiently learn the features of spatial and temporal hypergraphs, and effectively improve the accuracy of skeleton-based action recognition. To evaluate the effectiveness and efficiency of MV-TSHGNN, we conduct experiments on NTU RGB+D, NTU RGB+D 120 and imitating traffic police gestures datasets. The experimental results indicate that our proposed method model achieves the new state-of-the-art performance.

Molecular mechanism of Chang Shen Hua volatile oil modulating brain cAMP-PKA-CREB pathway to improve depression-like behavior in rats.

BACKGROUND: Depression is a common and complex mental illness that manifests as persistent episodes of sadness, loss of interest, and decreased energy, which might lead to self-harm and suicide in severe cases. Reportedly, depression affects 3.8 % of the world's population and has been listed as one of the major global public health concerns. In recent years, aromatherapy has been widely used as an alternative and complementary therapy in the prevention and treatment of depression; people can relieve anxiety and depression by sniffing plant aromatic essential oils. Acorus tatarinowii and Panax ginseng essential oils in Chang Shen Hua volatile oil (CSHVO) are derived from Acorus tatarinowii and Panax ginseng, respectively, the main medicines in the famous Chinese medicine prescription Kai Xin San (KXS), Then, these oils are combined with the essential oil of Albizia julibrissin flower to form a new Chinese medicine inhalation preparation, CSHVO. KXS has been widely used in the treatment of depression; however, whether CSHVO can ameliorate depression-like behavior, its pharmacological effects, and the underlying mechanisms of action are yet to be elucidated. STUDY DESIGN AND METHODS: A rat model of chronic and unpredictable mild stimulation (CUMS) combined with orphan rearing was treated with CSHVO for 4 weeks. Using behavioral tests (sucrose preference, force swimming, tail suspension, and open field), the depression-like degree was evaluated. Concurrently, brain homogenate and serum biochemistry were analyzed to assess the changes in the neurotransmitters and inflammatory and neurotrophic factors. Furthermore, tissue samples were collected for histological and protein analyses. In addition, network pharmacology and molecular docking analyses of the major active compounds, potential therapeutic targets, and intervention pathways predicted a role of CSHVO in depression relief. Subsequently, these predictions were confirmed by in vitro experiments using a corticosterone (CORT)-induced PC12 cell damage model. RESULTS: CSHVO inhalation can effectively improve the weight and depression-like behavior of depressed rats and regulate the expression of inflammatory factors and neurotransmitters. Hematoxylin-eosin, Nissl, and immunofluorescence staining indicated that compared to the model group, the pathological damage to the brain tissues of rats in the CSHVO groups was improved. The network pharmacological analysis revealed that 144 CSHVO active compounds mediate 71 targets relevant to depression treatment, most of which are rich in the cAMP signaling and inflammatory cytokine pathways. Protein-protein interaction analysis showed that TNF, IL6, and AKT are the core anti-depressive targets of CSHVO. Molecular docking analysis showed an adequate binding between the active ingredients and the key targets. In vitro experiments showed that compared to the model group, the survival rate of PC12 cells induced by CSHVO intervention was increased, the apoptosis rate was decreased, and the expression of inflammatory cytokines in the cell supernatant was improved. Western blot analysis and immunofluorescence staining confirmed that CSHVO regulates PC12 cells in the CORT model through the cAMP-PKA-CREB signaling pathway, and pretreatment with PKA blocker H89 eliminates the protective effect of CSHVO on CORT-induced PC12 cells. CONCLUSIONS: CSHVO improves CORT-induced injury in the PC12 cell model and CUMS combined with orphan rearing-induced depression model in rats. The antidepressant mechanism of CSHVO is associated with the modulation of the cAMP-PKA-CREB signaling pathway.

CLCN3 in mediating the proliferation of human ovarian cancer cells.

Background: Chloride channel-3 (CLCN3), a crucial component of the voltage-gated chloride channel family, is implicated in numerous physiological and pathophysiological processes. This study aimed to investigate the characteristics of CLCN3 in pancancer and its influence on the immune response through the use of a range of databases. Concurrently, we assessed the impact of CLCN3 on the proliferation of ovarian cancer (OC) cells and explored its potential mechanisms. Methods: We employed the Tumor Immune Estimation Resource (TIMER) 2.0 and Clinical Proteomic Tumor Analysis Consortium (CPTAC) databases to examine the messenger RNA (mRNA) and the protein expression of CLCN3 across various cancers. The prognostic significance of CLCN3 was evaluated using the Gene Expression Profiling Interactive Analysis 2.0 (GEPIA 2.0) database. The University of Alabama at Birmingham Cancer Data Analysis Portal (UALCAN) facilitated the analysis of CLCN3 promoter methylation levels. The association between CLCN3 expression and tumor-infiltrating immune cells was investigated using various algorithms. The cBioportal database facilitated the analysis of CLCN3 mutations and mutation sites across various cancers. The Tumor-Immune System Interactions Database (TISIDB) database was employed to explore the correlation between CLCN3 expression and immune or molecular subtypes across a variety of cancer types. We collected ovarian tissue samples, encompassing both normal ovarian and OC tissues. The human OC cell lines, SKOV3 cells and OVCAR433 cells, were cultured. CLCN3 expression was determined via reverse-transcription quantitative polymerase chain reaction (RT-qPCR), while phosphatidylinositol 3-kinase/Akt kinase (PI3K/AKT) expression was detected using Western blot. We utilized small interfering RNA (siRNA) technology to suppress CLCN3 expression. The proliferative capacity of SKOV3 and OVCAR433 cells was assessed using the Cell Counting Kit 8 (CCK-8) assay. Results: CLCN3 demonstrated an aberrant expression in a number of cancer types and was markedly reduced in OC tissues. Poor prognosis in cervical squamous cell cancer and myeloid leukemia was linked to excessive expression of CLCN3. The examination of immune cell infiltration, which included CD8+ T cells, B cells, T regulatory cells, and cancer-associated fibroblast cells, showed a strong association with aberrant CLCN3 expression. Following the use of siRNA technology, the ability of the ovarian carcinoma cell line SKOV3 and OVCAR433 to proliferate as well as the expression of PI3K/AKT both increased. Conclusions: CLCN3 is a possible biomarker for immune-related processes and the prognosis of cancer, and the PI3K/AKT signaling pathway may affect OC cells' ability to proliferate.

Multi-Modal Temporal Hypergraph Neural Network for Flotation Condition Recognition.

Efficient flotation beneficiation heavily relies on accurate flotation condition recognition based on monitored froth video. However, the recognition accuracy is hindered by limitations of extracting temporal features from froth videos and establishing correlations between complex multi-modal high-order data. To address the difficulties of inadequate temporal feature extraction, inaccurate online condition detection, and inefficient flotation process operation, this paper proposes a novel flotation condition recognition method named the multi-modal temporal hypergraph neural network (MTHGNN) to extract and fuse multi-modal temporal features. To extract abundant dynamic texture features from froth images, the MTHGNN employs an enhanced version of the local binary pattern algorithm from three orthogonal planes (LBP-TOP) and incorporates additional features from the three-dimensional space as supplements. Furthermore, a novel multi-view temporal feature aggregation network (MVResNet) is introduced to extract temporal aggregation features from the froth image sequence. By constructing a temporal multi-modal hypergraph neural network, we encode complex high-order temporal features, establish robust associations between data structures, and flexibly model the features of froth image sequence, thus enabling accurate flotation condition identification through the fusion of multi-modal temporal features. The experimental results validate the effectiveness of the proposed method for flotation condition recognition, providing a foundation for optimizing flotation operations.

5-Aza-2'-Deoxycytidine Ameliorates Choroidal Neovascularization by Inhibiting the Wnt/ß-Catenin Signaling Pathway.

Purpose: Choroidal neovascularization (CNV) can constitute the final pathology of many ocular diseases and result in severe vision loss. Studies have demonstrated that DNA methylation is critical in retinal development, aging, and disorders. The current work investigated the effects and underlying mechanism of 5-Aza-2'-deoxycytidine (5-aza-dC), a suppressor of DNA methylation, in the pathological progression of CNV. Methods: The DNA methylation profiles of retinal pigment epithelial (RPE)/choroidal complexes in normal and laser-induced CNV mice were assessed by Arraystar Mouse RefSeq Promoter Arrays. The CNV area and blood flow density and intensity were observed by optical coherence tomography angiography, and fluorescence leakage was examined by fundus fluorescein angiography in CNV mice with systemic administration of 5-aza-dC. The effects of 5-aza-dC on the biological functions of bEnd.3 cells were estimated by related assays. Notum gene promoter methylation was measured using bisulfite sequencing PCR. Methyltransferases and Wnt signaling-related genes were detected in animal and cell culture experiments by real-time PCR and immunoblot. Results: Methyltransferases were upregulated, but Notum (a secretion inhibitor of Wnt signaling) was downregulated in the RPE/choroidal complexes of mice with experimental CNV. Intraperitoneal injection of 5-aza-dC inactivated the Wnt pathway and ameliorated the lesion area and the intensity and density of blood flow, as well as the degree of leakage in CNV. In vitro, vascular endothelial growth factor A (VEGFA) stimulation promoted methyltransferases expression and suppressed Notum expression, consequently activating Wnt signaling, whereas exogenous 5-aza-dC reversed VEGFA-induced hyperpermeability, proliferation, migration, and tube formation in bEnd.3 cells via demethylation of Notum promoter. Conclusions: We observed that 5-aza-dC attenuates the growth of CNV by inhibiting the Wnt signaling pathway via promoter demethylation of the Wnt antagonist Notum. These findings provide a theoretical basis for methylation-based treatment with the Notum gene as a potential target for CNV treatment.

Hypergraph Isomorphism Computation.

The isomorphism problem, crucial in network analysis, involves analyzing both low-order and high-order structural information. Graph isomorphism algorithms focus on structural equivalence to simplify solver space, aiding applications like protein design, chemical pathways, and community detection. However, they fall short in capturing complex high-order relationships, unlike hypergraph isomorphism methods. Traditional hypergraph methods face challenges like high memory use and inaccurate identification, leading to poor performance. To overcome these, we introduce a hypergraph Weisfeiler-Lehman (WL) test algorithm, extending the WL test from graphs to hypergraphs, and develop a hypergraph WL kernel framework with two variants: the Hypergraph WL Subtree Kernel and Hypergraph WL Hyperedge Kernel. The Hypergraph WL Subtree Kernel counts different types of rooted subtrees and generates the final feature vector for a given hypergraph by comparing the number of different types of rooted subtrees. The Subtree Kernel identifies different rooted subtrees, while the Hyperedge Kernel focuses on hyperedges' vertex labels, enhancing feature vector generation. In order to fulfill our research objectives, a comprehensive set of experiments was meticulously designed, including seven graph classification datasets and 12 hypergraph classification datasets. Results on graph classification datasets indicate that the Hypergraph WL Subtree Kernel can achieve the same performance compared with the classical Graph Weisfeiler-Lehman Subtree Kernel. Results on hypergraph classification datasets show significant improvements compared to other typical kernel-based methods, which demonstrates the effectiveness of the proposed methods. In our evaluation, our proposed methods outperform the second-best method in terms of runtime, running over 80 times faster when handling complex hypergraph structures. This significant speed advantage highlights the great potential of our methods in real-world applications.

Superconducting and charge-ordered states in the anisotropic t-J-U model.

Motivated by the effect of symmetry breaking in cuprates superconductors YBa[Formula: see text]Cu[Formula: see text]O[Formula: see text], we employ the renormalized mean-field theory to study the presence of uniform superconducting and charge-ordered states in two anisotropic t-J-U models, either with hopping strength anisotropy or antiferromagnetic interaction anisotropy. In the case of uniform superconducting state, compared with the isotropic t-J-U model with only [Formula: see text]-wave superconducting state, there is an additional s-wave superconducting state in the model with hopping strength anisotropy. Meanwhile, the hopping anisotropy may enhance the critical Coulomb interaction [Formula: see text] at the Mott insulator to the Gossamer superconductor transition point, and strong hopping anisotropy may weaken the superconducting state. In the case of a charge-ordered state, hopping anisotropy may suppress the amplitude of the charge density waves and pair density waves, which originate from local Coulomb interactions. These results indicate that the effects of hopping anisotropy and local Coulomb interactions are competitive. Moreover, the antiferromagnetic interaction anisotropy only weakly suppresses the superconducting gap and density wave amplitude. Our results show that the t-J-U model with hopping anisotropy is qualitatively consistent with experimental superconducting pair symmetry and charge density waves in the YBa[Formula: see text]Cu[Formula: see text]O[Formula: see text] system.

Hypergraph-Based Multi-Modal Representation for Open-Set 3D Object Retrieval.

The traditional 3D object retrieval (3DOR) task is under the close-set setting, which assumes the categories of objects in the retrieval stage are all seen in the training stage. Existing methods under this setting may tend to only lazily discriminate their categories, while not learning a generalized 3D object embedding. Under such circumstances, it is still a challenging and open problem in real-world applications due to the existence of various unseen categories. In this paper, we first introduce the open-set 3DOR task to expand the applications of the traditional 3DOR task. Then, we propose the Hypergraph-Based Multi-Modal Representation (HGM 2 R) framework to learn 3D object embeddings from multi-modal representations under the open-set setting. The proposed framework is composed of two modules, i.e., the Multi-Modal 3D Object Embedding (MM3DOE) module and the Structure-Aware and Invariant Knowledge Learning (SAIKL) module. By utilizing the collaborative information of modalities derived from the same 3D object, the MM3DOE module is able to overcome the distinction across different modality representations and generate unified 3D object embeddings. Then, the SAIKL module utilizes the constructed hypergraph structure to model the high-order correlation among 3D objects from both seen and unseen categories. The SAIKL module also includes a memory bank that stores typical representations of 3D objects. By aligning with those memory anchors in the memory bank, the aligned embeddings can integrate the invariant knowledge to exhibit a powerful generalized capacity toward unseen categories. We formally prove that hypergraph modeling has better representative capability on data correlation than graph modeling. We generate four multi-modal datasets for the open-set 3DOR task, i.e., OS-ESB-core, OS-NTU-core, OS-MN40-core, and OS-ABO-core, in which each 3D object contains three modality representations: multi-view, point clouds, and voxel. Experiments on these four datasets show that the proposed method can significantly outperform existing methods. In particular, the proposed method outperforms the state-of-the-art by 12.12%/12.88% in terms of mAP on the OS-MN40-core/OS-ABO-core dataset, respectively. Results and visualizations demonstrate that the proposed method can effectively extract the generalized 3D object embeddings on the open-set 3DOR task and achieve satisfactory performance.

2D Nano-Channeled Molybdenum Compounds for Accelerating Interfacial Polysulfides Catalysis in Li-S Battery.

The shuttle effect, which causes the loss of active sulfur, passivation of lithium anode, and leads to severe capacity attenuation, is currently the main bottleneck for lithium-sulfur batteries. Recent studies have disclosed that molybdenum compounds possess exceptional advantages as a polar substrate to immobilize and catalyze lithium polysulfide such as high conductivity and strong sulfiphilicity. However, these materials show incomplete contact with sulfur/polysulfides, which causes uneven redox conversion of sulfur and results in poor rate performance. Herein, a new type of 2D nano-channeled molybdenum compounds (2D-MoNx) via the 2D organic-polyoxometalate superstructure for accelerating interfacial polysulfide catalysis toward high-performance lithium-sulfur batteries is reported. The 2D-MoNx shows well-interlinked nano-channels, which increase the reactive interface and contact surface with polysulfides. Therefore, the battery equipped with 2D-MoNx displays a high discharge capacity of 912.7 mAh g-1 at 1 C and the highest capacity retention of 523.7 mAh g-1 after 300 cycles. Even at the rate of 2 C, the capacity retention can be maintained at 526.6 mAh g-1 after 300 cycles. This innovative nano-channel and interfacial design of 2D-MoNx provides new nanostructures to optimize the sulfur redox chemistry and eliminate the shuttle effect of polysulfides.

Strategies for the biological production of ectoine by using different chassis strains.

As an amino acid derivative and a typical compatible solute, ectoine can assist microorganisms in resisting high osmotic pressure. Own to its long-term moisturizing effects, ectoine shows extensive applications in cosmetics, medicine and other fields. With the rapid development of synthetic biology and fermentation engineering, many biological strategies have been developed to improve the ectoine production and simplify the production process. Currently, the microbial fermentation has been widely used for large scaling ectoine production. Accordingly, this review will introduce the metabolic pathway for ectoine synthesis and also comprehensively evaluate both wild-type and genetically modified strains for ectoine production. Furthermore, process parameters affecting the ectoine production efficiency and adoption of low cost substrates will be evaluated. Lastly, future prospects on the improvement of ectoine production will be proposed.

Non-line-of-sight imaging at infrared wavelengths using a superconducting nanowire single-photon detector.

Non-line-of-sight (NLOS) imaging can visualize a remote object out of the direct line of sight and can potentially be used in endoscopy, unmanned vehicles, and robotic vision. In an NLOS imaging system, multiple diffusive reflections of light usually induce large optical attenuation, and therefore, a sensitive and efficient photodetector, or, their array, is required. Limited by the spectral sensitivity of the light sensors, up to now, most of the NLOS imaging experiments are performed in the visible bands, and a few at the near-infrared, 1550 nm. Here, to break this spectral limitation, we demonstrate a proof-of-principle NLOS imaging system using a fractal superconducting nanowire single-photon detector, which exhibits intrinsic single-photon sensitivity over an ultra-broad spectral range. We showcase NLOS imaging at 1560- and 1997-nm two wavelengths, both technologically important for specific applications. We develop a de-noising algorithm and combine it with the light-cone-transform algorithm to reconstruct the shape of the hidden objects with significantly enhanced signal-to-noise ratios. We believe that the joint advancement of the hardware and the algorithm presented in this paper could further expand the application spaces of the NLOS imaging systems.