Network Clustering Research Articles

Combining deep neural network and spatio-temporal clustering to automatically assess rockburst and seismic hazard – Case study from Marcel coal mine in Upper Silesian Basin, Poland

Mine induced seismic events are a major safety concern in mining and require careful monitoring and management to reduce their effects. Therefore, an essential step in assessing seismic and rock burst hazards is the analysis of mine seismicity. Recently, deep neural networks have been used to automatically determine seismic wave arrival times, surpassing human performance and allowing their use in seismic data analysis such as seismic event location and seismic energy calculation. In order to properly automate the rockburst and seismic hazard assessment deep neural network phase picker and a spatio-temporal clustering method were utilized. Seismic and rockburst hazards were statistically quantified using two-way contingency tables for two categorical variables: seismic energy level of mine tremors and number of clusters. Correlations between several spatio-temporal clusters and a statistical association between two categorical variables: seismic energy level and cluster number indicate an increase of seismic hazard in the Marcel hard coal mine in Poland. A new automated tool has been elaborated to automatically identify high-stress areas in mines in the form of spatio-temporal clusters.

Detection and Classification of Banana Leaf Diseases: Systematic Literature Review

Bananas, a staple fruit globally, are essential for sustenance, employment, and income. However, diseases like Sigatoka, Bacterial Wilt, Bunchy Top, and Fusarium Wilt pose a threat to their cultivation, affecting both small-scale and large-scale production. This survey investigates methods for the early identification and classification of these banana leaf diseases using deep learning and machine learning techniques. A systematic review of 15 studies revealed that the majority of research concentrates on binary classification, which distinguishes healthy from diseased leaves. Common preprocessing steps include image resizing, color space conversion, and background removal to improve model accuracy. We utilize techniques such as ensemble approaches, support vector machines (SVM), random forests, K-means clustering, and convolutional neural networks (CNNs), with CNNs demonstrating superior performance, achieving accuracy rates ranging from 85% to 98.97%. CNNs excel in hierarchical feature extraction but require significant computational power. Traditional machine learning methods offer simplicity and resistance to overfitting but need careful parameter tuning. Advanced deep learning architectures, such as DenseNet and Inception V3, achieve high accuracy but with greater computational demands. Lightweight models like SqueezeNet balance performance and size, but ensemble methods, while improving generalization, add complexity. The choice of method depends on dataset characteristics, available computational resources, and desired trade-offs between performance and complexity. This study provides an overview of current research in banana leaf disease classification, discussing the strengths and limitations of various approaches and suggesting directions for future research to improve detection accuracy and robustness.

Literature Survey on Revolutionizing Fake Currency Detection: CNN-Based Approach for Indian Rupee Notes

The proliferation of counterfeit currency poses a significant challenge in various economies, including India. To address this issue, several studies have proposed innovative image processing and machine learning techniques for detecting counterfeit coins and banknotes. Leveraging digital image processing, these studies aim to enhance the security measures against counterfeit currency through accurate and efficient recognition systems. Techniques such as preprocessing, segmentation, feature extraction, and clustering are employed to identify fraudulent currency. The use of Support Vector Machines (SVM), k-means clustering, and Convolutional Neural Networks (CNN) facilitates the recognition and classification of genuine and counterfeit currency. Moreover, these studies explore the application of spatial coding, component-based recognition, and deep learning algorithms to improve the accuracy and robustness of counterfeit detection systems. By developing real-time recognition systems capable of identifying counterfeit currency, these research efforts contribute to combating financial fraud and safeguarding economic integrity.

Unequal-radius clustering in WSN-based IoT networks: energy optimization and load balancing in UDCOPA protocol

Unequal-radius clustering in WSN-based IoT networks: energy optimization and load balancing in UDCOPA protocol

Retraction Note: Enhancement of network lifetime using fuzzy clustering and multidirectional routing for wireless sensor networks

Retraction Note: Enhancement of network lifetime using fuzzy clustering and multidirectional routing for wireless sensor networks

Single-cell and spatial transcriptome assays reveal heterogeneity in gliomas through stress responses and pathway alterations.

Glioma is a highly heterogeneous malignancy of the central nervous system. This heterogeneity is driven by various molecular processes, including neoplastic transformation, cell cycle dysregulation, and angiogenesis. Among these biomolecular events, inflammation and stress pathways in the development and driving factors of glioma heterogeneity have been reported. However, the mechanisms of glioma heterogeneity under stress response remain unclear, especially from a spatial aspect. This study employed single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) to explore the impact of oxidative stress response genes in oligodendrocyte precursor cells (OPCs). Our analysis identified distinct pathways activated by oxidative stress in two different types of gliomas: high- and low- grade (HG and LG) gliomas. In HG gliomas, oxidative stress induced a metabolic shift from oxidative phosphorylation to glycolysis, promoting cell survival by preventing apoptosis. This metabolic reprogramming was accompanied by epithelial-to-mesenchymal transition (EMT) and an upregulation of stress response genes. Furthermore, SCENIC (Single-Cell rEgulatory Network Inference and Clustering) analysis revealed that oxidative stress activated the AP1 transcription factor in HG gliomas, thereby enhancing tumor cell survival and proliferation. Our findings provide a novel perspective on the mechanisms of oxidative stress responses across various grades of gliomas. This insight enhances our comprehension of the evolutionary processes and heterogeneity within gliomas, potentially guiding future research and therapeutic strategies.

Molecular transmission network analysis reveals the challenge of HIV-1 in ageing patients in China: elderly people play a crucial role in the transmission of subtypes and high pretreatment drug resistance in developed Eastern China, 2019–2023

BackgroundThe number and proportion of HIV/AIDS patients among older people are continuously and rapidly increasing in China. We conducted a detailed molecular epidemiological analysis of HIV-1 epidemic strains in a developed city in eastern China and found that elderly people play a crucial role in the transmission of subtypes and high pretreatment drug resistance (PDR).MethodsA total of 1048 samples were obtained from 1129 (92.8%) newly confirmed HIV-1-positive and treatment-naive patients between 2019 and 2023. The 1316 bp target fragment of the pol gene was amplified by reverse transcription polymerase chain reaction (RT‒PCR) and nested PCR, and Maximum-likelihood (ML) phylogenetic trees and molecular transmission network were constructed to analyse the subtypes and transmission clusters. Molecular transmission network was visualized using Cytoscape with the distance threshold of 0.0075. PDR-associated mutations were determined according to the Stanford University HIV Drug Resistance Database.ResultsA total of 933 pol sequences (89.0%, 933/1048) were successfully obtained, and twelve HIV-1 subtypes were detected. CRF07_BC was the predominant subtype, accounting for 48.1% (449/933) of sequences, followed by CRF01_AE (29.4%, 274/933). A total of 398 individuals (42.7%, 398/933) formed 89 clusters in the network. Multivariable logistic regression analysis revealed that age, nationality, subtype, and PDR were the most significant factors associated with clustering in the transmission network. The prevalence of PDR was 14.6% (136/933).PDR associated with non-nucleoside reverse transcriptase inhibitors (10.0%, 93/933) was much more common than that associated with nucleoside reverse transcriptase inhibitors (1.8%, 17/933) and protease inhibitors (3.2%, 30/933) (χ2 = 77.961, p < 0.001). The most frequent NNRTI mutations were K103N/S/KN/NS (52.2%, 71/136), which led to the highest proportion of high-level resistance to nevirapine and efavirenz (52.2%).ConclusionsOur study revealed the important influence of elderly people on CRF07_BC transmission and the high prevalence of PDR. The clustering of drug-resistant cases was significant, which suggested the potential for localized widespread transmission of drug-resistant strains. HIV screening and the determination of PDR are recommended for older patients to improve early detection and reduce treatment failure and second-generation transmission.

Slow ramping emerges from spontaneous fluctuations in spiking neural networks

The capacity to initiate actions endogenously is critical for goal-directed behavior. Spontaneous voluntary actions are typically preceded by slow-ramping activity in medial frontal cortex that begins around two seconds before movement, which may reflect spontaneous fluctuations that influence action timing. However, the mechanisms by which these slow ramping signals emerge from single-neuron and network dynamics remain poorly understood. Here, we developed a spiking neural-network model that produces spontaneous slow ramping activity in single neurons and population activity with onsets ~2 s before threshold crossings. A key prediction of our model is that neurons that ramp together have correlated firing patterns before ramping onset. We confirmed this model-derived hypothesis in a dataset of human single neuron recordings from medial frontal cortex. Our results suggest that slow ramping signals reflect bounded spontaneous fluctuations that emerge from quasi-winner-take-all dynamics in clustered networks that are temporally stabilized by slow-acting synapses.

Deciphering early responsive signature genes in rice blast disease: an integrated temporal transcriptomic study.

Rice blast disease, caused by Magnaporthe oryzae, reigns as the top-most cereal killer, jeopardizing global food security. This necessitates the timely scouting of pathogen stress-responsive genes during the early infection stages. Thus, we integrated time-series microarray (GSE95394) and RNA-Seq (GSE131641) datasets to decipher rice transcriptome responses at 12- and 24-h post-infection (Hpi). Our analysis revealed 1580 differentially expressed genes (DEGs) overlapped between datasets. We constructed a protein-protein interaction (PPI) network for these DEGs and identified significant subnetworks using the MCODE plugin. Further analysis with CytoHubba highlighted eight plausible hub genes for pathogenesis: RPL8 (upregulated) and RPL27, OsPRPL3, RPL21, RPL9, RPS5, OsRPS9, and RPL17 (downregulated). We validated the expression levels of these hub genes in response to infection, finding that RPL8 exhibited significantly higher expression compared with other downregulated genes. Remarkably, RPL8 formed a distinct cluster in the co-expression network, whereas other hub genes were interconnected, with RPL9 playing a central role, indicating its pivotal role in coordinating gene expression during infection. Gene Ontology highlighted the enrichment of hub genes in the ribosome and protein translation processes. Prior studies suggested that plant immune defence activation diminishes the energy pool by suppressing ribosomes. Intriguingly, our study aligns with this phenomenon, as the identified ribosomal proteins (RPs) were suppressed, while RPL8 expression was activated. We anticipate that these RPs could be targeted to develop new stress-resistant rice varieties, beyond their housekeeping role. Overall, integrating transcriptomic data revealed more common DEGs, enhancing the reliability of our analysis and providing deeper insights into rice blast disease mechanisms.

Cross-biome microbial networks reveal functional redundancy and suggest genome reduction through functional complementarity

The structure of microbial communities arises from a multitude of factors, including the interactions of microorganisms with each other and with the environment. In this work, we sought to disentangle those drivers by performing a cross-study, cross-biome meta-analysis of microbial occurrence data in more than 5000 samples, applying a novel network clustering algorithm aimed to capture conditional taxa co-occurrences. We then examined the phylogenetic and functional composition of the resulting clusters, and searched for global patterns of assembly both at the community level and in the presence/absence of individual metabolic pathways.Our analysis highlighted the prevalence of functional redundancy in microbial communities, particularly between taxa that co-occur in more than one environment, pointing to a relationship between functional redundancy and environmental adaptation. In spite of this, certain pathways were observed in fewer taxa than expected by chance, suggesting the presence of auxotrophy, and presumably cooperation among community members. This hypothetical cooperation may play a role in genome reduction, since we observed a negative relationship between the size of bacterial genomes and the size of the community they belong to.Overall, our results suggest the microbial community assembly is driven by universal principles that operate consistently across different biomes and taxonomic groups.

CONSORT article: Single-cell sequencing analysis revealed CMKLR1+ macrophage as a subpopulation of macrophage with tumor-suppressive characteristics in oral squamous cell carcinoma.

Oral squamous cell carcinoma (OSCC) is an aggressive oral malignancy. Metastasis and postoperative recurrence are major causes of a high mortality of OSCC. The landscape of immune cells in OSCC remained to be comprehensively explored. Tumor tissues of OSCC patients were collected from the Gene Expression Omnibus database, based on which single-cell sequencing analysis was performed to identify subtypes of macrophages and to annotate the subpopulations according to the expression levels of cell marker genes. Functional enrichment analysis was performed to explore the biological processes involved in each cell subcluster. Gene regulatory networks driven by SPECIFIC transcription factors (TFs) were developed applying single-cell regulatory network inference and clustering (SCENIC) analysis. Binding strength between receptors and ligands between different cells was analyzed using cell communication analysis. A single-cell landscape in OSCC was successfully developed and a total of 11 cell clusters were identified. Specifically, CD163 + macrophages were a crucial type of macrophage with 4 cell subpopulations, namely, SAT1 + macrophages, IDO1 + macrophages, TRIM29 + macrophages, and CMKLR1 + macrophages. IDO1 + macrophages and CMKLR1 + macrophages mainly had the characteristics of M1-type macrophages. CMKLR1 + macrophages fulfilled the function of M1-type macrophages to inhibit OSCC progression. IDO1 + macrophages and CMKLR1 + macrophages were both involved in the activation response of T cells. CMLKR1 + macrophages had a stronger activating effect on T cells. CMKLR1 + macrophages directly regulated the proliferation of epithelial cells and inhibited the progression of OSCC. CMKLR1 + macrophages in OSCC were identified as a crucial cell subpopulation of macrophages in inhibiting tumor progression. Adjusting the infiltration abundance and cell activity of CMKLR1 + macrophages may be a novel therapeutic direction to improve OSCC prognosis.

Dimers and 2D Networks of Adamantane-Related Ternary Organosilicon Coinage Metal Sulfide Clusters.

Adamantane-type organotin sulfide clusters were recently shown to react with coinage metal phosphine complexes under replacement of an organic substituent by a metal-phosphine unit. An extension of such studies involving the silicon-based congener [(PhSi)4S6] (A) revealed that the cluster core will be partly disassembled and a {PhSi} moiety is replaced by a coinage metal phosphine complex to form [(Et3PAg)3(PhSi)3S6] (B) and [Na2(thf)2.33][(Me3PCu)(PhSi)3S6] (C). Herein, we present an extension of this work upon variation of the reactants and reaction conditions. Besides the isolation of crystalline precursor complexes [CuCl(PMe2Ph)3] (1) and [AgCl(PMe2Ph)2]2 (2), the study addresses reactions of A with AgCl and a phosphine ligand in CH2Cl2, upon which A is completely disassembled to form [(Ph3P)3Ag(μ-S)SiCl2Ph] (3). In another case a CH2 group, most likely stemming from CH2Cl2, was attached to the ligand, thus generating [{PhCl(S)SiSCH2P(Ph2)CH2CH2}2] (4). Upon using CuCl and 1,4-bis(diphenylphosphino)butane (dppb) we isolated the phosphine-bridged analog of B, [{(dppbCu2)CuP(Ph2)(CH2CH2)(PhSi)3S6}2] (5). In order to receive the yet elusive silver homolog of C, we used PMe2Ph as a bulkier ligand. This way we generated a 2D coordination polymer of the desired composition, [Na2(thf)1.5][(Me2PhPAg)(PhSi)3S6] (6). UV-visible spectra of 6 indicated a bandgap of 3.89 eV, thus blue-shifted in regards to B and C.

Bioinformatics deciphers the thebaine biosynthesis pathway in opium poppy: Hub genes, network analysis, and miRNA regulation

Thebaine, a vital precursor in the codeine and morphine pathway, shows promise in addiction treatment. We conducted a comprehensive study on the thebaine biosynthesis pathway in opium poppy, utilizing bioinformatics tools. The dataset comprising the thirteen genes associated with the thebaine biosynthesis pathway was compiled from an extensive review of published literature and validated using the NCBI BLAST tool. Utilizing STRING and Cytoscape, we analyzed gene interactions and visualized the molecular interaction network, respectively. To identify hub proteins, CytoHubba was administered. The Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) at STRING were used for the enrichment analysis of the hub genes. CytoCluster was used to analyze the network in clusters. Promoter regions of hub genes and potential miRNAs were explored using MEME and the psRNATarget database. Hub genes crucial to thebaine biosynthesis were identified, contributing to essential cellular functions like growth, development, stress response, and signal transduction. Metabolic processes emerged as pivotal for thebaine production, indicating a broader role for the thebaine pathway gene network beyond primary metabolite production. Cell component subnetwork genes demonstrated associations with anatomical units, indicating involvement in plant defense responses. Dominant molecular functions drove plant defense responses. KEGG pathway analysis highlighted the significance of metabolic pathways and biosynthesis of secondary metabolites. Cluster analysis emphasized the relevance of the biosynthesis of amino acids, confirming the link between primary and secondary metabolites. Promoter analysis suggested the potential involvement of signal transduction in thebaine production. Hub genes were targeted by 40 miRNAs, suggesting potential novel biomarkers or target genes within the thebaine biosynthesis pathway. Based on the role of miRNAs identified in connection with the hub genes of the thebaine production process, the secondary metabolite pathway of thebaine appears to be associated with several key plant pathways, e.g. growth, development and stress response. However, these findings, based on bioinformatics analysis, warrant further experimental validation and promise to advance our understanding of the biosynthesis of thebaine and its interactions with other genes and metabolic pathways that influence the production of metabolites.

Nanostructured Molecular-Network Arsenoselenides from the Border of a Glass-Forming Region: A Disproportionality Analysis Using Complementary Characterization Probes.

Binary AsxSe100-x alloys from the border of a glass-forming region (65 < x < 70) subjected to nanomilling in dry and dry-wet modes are characterized by the XRPD, micro-Raman scattering (micro-RS) and revised positron annihilation lifetime (PAL) methods complemented by a disproportionality analysis using the quantum-chemical cluster modeling approach. These alloys are examined with respect to tetra-arsenic biselenide As4Se2 stoichiometry, realized in glassy g-As65Se35, glassy-crystalline g/c-As67Se33 and glassy-crystalline g/c-As70Se30. From the XRPD results, the number of rhombohedral As and cubic arsenolite As2O3 phases in As-Se alloys increases after nanomilling, especially in the wet mode realized in a PVP water solution. Nanomilling-driven amorphization and reamorphization transformations in these alloys are identified by an analysis of diffuse peak halos in their XRPD patterning, showing the interplay between the levels of a medium-range structure (disruption of the intermediate-range ordering at the cost of an extended-range one). From the micro-RS spectroscopy results, these alloys are stabilized by molecular thioarsenides As4Sen (n = 3, 4), regardless of their phase composition, remnants of thioarsenide molecules destructed under nanomilling being reincorporated into a glass network undergoing a polyamorphic transition. From the PAL spectroscopy results, volumetric changes in the wet-milled alloys with respect to the dry-milled ones are identified as resulting from a direct conversion of the bound positron-electron (Ps, positronium) states in the positron traps. Ps-hosting holes in the PVP medium appear instead of positron traps, with ~0.36-0.38 ns lifetimes ascribed to multivacancies in the As-Se matrix. The superposition of PAL spectrum peaks and tails for pelletized PVP, unmilled, dry-milled, and dry-wet-milled As-Se samples shows a spectacular smoothly decaying trend. The microstructure scenarios of the spontaneous (under quenching) and activated (under nanomilling) decomposition of principal network clusters in As4Se2-bearing arsenoselenides are recognized. Over-constrained As6·(2/3) ring-like network clusters acting as pre-cursors of the rhombohedral As phase are the main products of this decomposition. Two spontaneous processes for creating thioarsenides with crystalline counterparts explain the location of the glass-forming border in an As-Se system near the As4Se2 composition, while an activated decomposition process for creating layered As2Se3 structures is responsible for the nanomilling-driven molecular-to-network transition.

Boundary-aware semantic clustering network for segmentation of prostate zones from T2-weighted MRI

Objective. Automatic segmentation of prostatic zones from MRI can improve clinical diagnosis of prostate cancer as lesions in the peripheral zone (PZ) and central gland (CG) exhibit different characteristics. Existing approaches are limited in their accuracy in localizing the edges of PZ and CG. The proposed boundary-aware semantic clustering network (BASC-Net) improves segmentation performance by learning features in the vicinity of the prostate zonal boundaries, instead of only focusing on manually segmented boundaries. Approach. BASC-Net consists of two major components: the semantic clustering attention (SCA) module and the boundary-aware contrastive (BAC) loss. The SCA module implements a self-attention mechanism that extracts feature bases representing essential features of the inner body and boundary subregions and constructs attention maps highlighting each subregion. SCA is the first self-attention algorithm that utilizes ground truth masks to supervise the feature basis construction process. The features extracted from the inner body and boundary subregions of the same zone were integrated by BAC loss, which promotes the similarity of features extracted in the two subregions of the same zone. The BAC loss further promotes the difference between features extracted from different zones. Main results. BASC-Net was evaluated on the NCI-ISBI 2013 Challenge and Prostate158 datasets. An inter-dataset evaluation was conducted to evaluate the generalizability of the proposed method. BASC-Net outperformed nine state-of-the-art methods in all three experimental settings, attaining Dice similarity coefficients of 79.9% and 88.6% for PZ and CG, respectively, in the NCI-ISBI dataset, 80.5% and 89.2% for PZ and CG, respectively, in Prostate158 dataset, and 73.2% and 87.4% for PZ and CG, respectively, in the inter-dataset evaluation. Significance. As prostate lesions in PZ and CG have different characteristics, the zonal boundaries segmented by BASC-Net will facilitate prostate lesion detection.

Revealing Cellular Heterogeneity and Key Regulatory Factors of Triple-Negative Breast Cancer through Single-Cell RNA Sequencing.

Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer (BC). TNBC has a poor prognosis due to high intratumoral heterogeneity and metastasis, pointing to the need to explore distinct molecular subtypes and gene regulatory networks. The scRNA-seq data of five primary BC samples were downloaded from the Gene Expression Omnibus (GEO) database. Clustering was performed based on filtered and normalized data using the Seurat R package to identify marker genes, which were subsequently annotated to each subset using the CellMarker database. AUCell R package was applied to calculate the hallmark score for each epithelial cell. Marker genes of each subset were screened with FindAllMarkers and their biological functions were analyzed using the Database for Annotation Visualization and Integrated Discovery (DAVID) database. Next, cell-cell communication was performed with the CellChat R package. To identify the key regulatory genes, single-cell regulatory network inference and clustering (SCENIC) analysis was conducted. Finally, the expression and potential biological functions of the key regulatory factors were verified through cellular experiments. A total of 29,101 cells were classified into nine cell subsets, namely, Fibroblasts, Fibroepithelial cells, Epithelial cells 1, Epithelial cells 2, Epithelial cells 3, Endothelial cells, T cells, Plasma B cells and Macrophages. Particularly, the epithelial cells had a higher proportion and higher transforming growth factor-β (TGF-β) activity in the TNBC pathotype as compared to the non-TNBC pathotype. Furthermore, four epithelial cell subsets (marked as Stearoyl-CoA Desaturase (SCD1), marker of proliferation Ki67 (MKI67), Annexin A3 (ANXA3) and aquaporin 5 (AQP5)) were identified as having the greatest impact on the TNBC pathotype. Cell-cell interaction analysis revealed that ANXA3-epithelial cell subset suppressed the T cell function through different mechanisms. C-fos gene (FOS) and X-box binding protein 1 (XBP1) were considered critical regulons involved in TNBC progression. Notably, cellular experiments demonstrated that silencing XBP1 and overexpressing FOS inhibited cancer cell invasion. The four epithelial cell subsets and two critical regulons identified based on the scRNA-seq data could help explore the underlying intratumoral heterogeneity molecular mechanism and develop effective therapies for TNBC.

Identification of energy metabolism anomalies and serum biomarkers in the progression of premature ovarian failure via extracellular vesicles’ proteomic and metabolomic profiles

BackgroundPremature ovarian failure (POF) is a clinical condition characterized by the cessation of ovarian function, leading to infertility. The underlying molecular mechanisms remain unclear, and no predictable biomarkers have been identified. This study aimed to investigate the protein and metabolite contents of serum extracellular vesicles to investigate underlying molecular mechanisms and explore potential biomarkers.MethodsThis study was conducted on a cohort consisting of 14 POF patients and 16 healthy controls. The extracellular vesicles extracted from the serum of each group were subjected to label-free proteomic and unbiased metabolomic analysis. Differentially expressed proteins and metabolites were annotated. Pathway network clustering was conducted with further correlation analysis. The biomarkers were confirmed by ROC analysis and random forest machine learning.ResultsThe proteomic and metabolomic profiles of POF patients and healthy controls were compared. Two subgroups of POF patients, Pre-POF and Pro-POF, were identified based on the proteomic profile, while all patients displayed a distinguishable metabolomic profile. Proteomic analysis suggested that inflammation serves as an early factor contributing to the infertility of POF patients. For the metabolomic analysis, despite the dysfunction of metabolism, oxidative stress and hormone imbalance were other key factors appearing in POF patients. Signaling pathway clustering of proteomic and metabolomic profiles revealed the progression of dysfunctional energy metabolism during the development of POF. Moreover, correlation analysis identified that differentially expressed proteins and metabolites were highly associated, with six of them being selected as potential biomarkers. ROC curve analysis, together with random forest machine learning, suggested that AFM combined with 2-oxoarginine was the best diagnostic biomarker for POF.ConclusionsOmics analysis revealed that inflammation, oxidative stress, and hormone imbalance are factors that damage ovarian tissue, but the progressive dysfunction of energy metabolism might be the critical pathogenic pathway contributing to the development of POF. AFM combined with 2-oxoarginine serves as a precise biomarker for clinical POF diagnosis.

Mobility‐compatible cache controlled cluster networking protocol

Mobility‐compatible cache controlled cluster networking protocol

Mental health training for physicians supervising resident physicians: a cluster randomised controlled trial.

To evaluate an online training program for physician supervisors with the aim of promoting a mentally healthy workplace by improving their use of both responsive and preventive mental health support strategies. Cluster randomised, waitlist-controlled trial. Royal Australasian College of Physicians fellows who were supervising at least one resident physician in any of the 31 primary health networks in Australia and 20 district health boards in New Zealand (health network clusters). A brief online skills-based mental health training program, comprising twelve modules grouped into three topics: common mental illnesses; helping trainees you are concerned about (responsive strategies); and minimising mental health risks at work (preventive strategies). Change between baseline and the 3-month assessment in self-reported recommended supervisor behaviours; differences between intervention and control groups in recommended behaviour scores three weeks, three months, and six months after the program. Ninety physicians from 20 health network clusters were allocated to the intervention group, 88 physicians from 22 clusters to the control group. Intervention group participants reported greater positive change in behaviour across the study period than those in the control group (mixed model repeated measures analysis, group × time interaction: P < 0.001). The mean change in self-reported supervisory behaviour score was higher for the intervention than the control group at the 3-week (mean difference in score, 1.6; 95% confidence interval [CI], 0.8-2.4), 3-month (0.9; 95% CI, 0.2-1.6), and 6-month assessments (1.9; 95% CI, 1.1-2.7). The mean change in self-reported responsive behaviour score was also greater for the intervention group at the 3-week (mean difference, 2.3; 95% CI, 1.5-3.1), 3-month (1.0; 95% CI, 0.2-1.9), and 6-month assessments (2.0; 95% CI, 1.1-2.9); differences in the mean change in preventive behaviour scores were statistically significant at the 3-week (mean difference, 1.1; 95% CI, 0.1-2.2) and 6-month assessments (1.8; 95% CI, 0.8-2.8), but not the 3-month assessment (0.8; 95% CI, -0.1 to 1.7). Brief online mental health training for senior physicians can lead to changes in their self-reported behaviour for supporting the mental health needs of resident physicians. Whether this leads to better mental health for resident physicians should be investigated. Australian New Zealand Clinical Trials Registry, ACTRN12619001496101 (prospective).

A novel BiGRU multi-step wind power forecasting approach based on multi-label integration random forest feature selection and neural network clustering

Accurate wind power forecasting helps to carry out effective scheduling and scientific management of wind power, and improve the security and reliability of the power grid. However, the intermittency, volatility and instability of wind energy make wind power forecasting challenging. Therefore, in order to improve the accuracy and stability of wind power forecasting, this paper proposes a bidirectional gated recurrent unit (BiGRU) multi-step wind power forecasting approach based on multi-label integration random forest (MLRF) feature selection and neural network clustering (NNClustering). The proposed MLRF method extends the applicability of random forest through multiple criteria and enables feature selection for multi-step forecasting tasks of multi-factor time series to obtain optimal input features and time steps, which reduces the computational cost and improves the generalization ability of the model. The proposed NNClustering method establishes a novel convolution-based clustering structure and adjusts the parameters by gradient descent method to obtain the optimal clustering centers, and the robust data applicability of the method is validated in multiple seasonal experiments. The WOA-BiGRU forecasting model is constructed separately for each cluster, which reduces the modeling difficulty and better extracts the characteristics. The BiGRU model extracts more efficient characteristics by processing sequences in both directions and the important parameters of BiGRU are optimized by the whale optimization algorithm (WOA) to obtain the optimal forecasting model. Experimental results over multiple seasons show that the proposed hybrid approach has good forecasting performance and robustness, which provides a novel and efficient solution for wind power forecasting.