Target Network Research Articles

Proteogenomic verifies targets underlying erythromycin alleviate neutrophil extracellular traps-induced inflammation

BackgroundNeutrophil Extracellular Traps (NETs) are closely related to the progression of inflammation in Chronic Obstructive Pulmonary Disease (COPD). Erythromycin (EM) has been shown to inhibit inflammation in COPD, but its molecular mechanisms is still unclear. The aim of our study is investigate the molecular mechanisms of EM's anti-inflammatory effects in NETs-induced inflammation.MethodsTranscriptomics and proteomics data were obtained from U937 cells treated with NETs and EM. Differentially expressed genes (DEGs) and differentially expressed proteins (DEPs) were identified using R software. Pathway enrichment analyses, were employed to identify inflammation-related pathways. Cytoscape were utilized to construct network of hub targets regulated by EM which related with oxidative stress and inflammation. Additionally, Cytoscape and STRING were used to construct protein–protein interaction (PPI) network of key targets regulated by EM. The expression levels of key targets were further confirmed through WB and PCR experiments.ResultsBoth transcriptomics and proteomics indicate that EM decrease NETs –induced AKT1 expression. Enrichment analysis of DEGs and DEPs reveal multiple common pathways involved in EM’s regulation inflammation, including the PI3K/AKT pathway, response to oxidative stress, IKK/NF-κB signaling and PTEN signaling pathway. Nine key targets in PI3K/AKT-related inflammatory pathways regulated by EM and ten targets of EM-regulated oxidative stress were identified. WB and PCR results confirmed that EM reversing the NETs-induced inflammation by modulating the activity of these targets. Furthermore, clinical samples and vitro experiments confirm that EM alleviates NETs-induced glucocorticoid resistance via inhibiting PI3K/AKT, thereby repressing inflammation.ConclusionsOur study provides a comprehensive proteogenomic characterization of how EM alleviates NET-related inflammation, and identify PI3K/AKT play a critical role in the mechanism by which EM inhibits inflammation.

Accelerated iTBS-Induced Changes in Resting-State Functional Connectivity Correspond with Cognitive Improvement in Amnestic MCI.

Accelerated iTBS-Induced Changes in Resting-State Functional Connectivity Correspond with Cognitive Improvement in Amnestic MCI.

Enhanced DDPG algorithm for latency and energy-efficient task scheduling in MEC systems

This paper presents an improved Deep Deterministic Policy Gradient (DDPG) algorithm for task scheduling in Mobile Edge Computing (MEC) systems, focusing on reducing latency and energy consumption. The enhanced DDPG incorporates a soft update mechanism for target network parameters and an experience replay buffer to improve stability and training efficiency. A composite reward function, considering both latency and energy consumption, is designed and compared in linear and nonlinear forms. Experimental results demonstrate that the nonlinear reward function achieves faster convergence and better system performance. The improved DDPG outperforms traditional algorithms like Shortest Job First (SJF), Round Robin (RR), and Earliest Deadline First (EDF) in terms of latency and energy consumption across different task loads. At a task load of 10, the improved DDPG achieved a latency of 250 ms and energy consumption of 965 J, significantly lower than other algorithms. Ablation experiments further confirm the positive impact of each enhancement, showing that the combination of soft updates and experience replay buffers significantly reduces both latency and energy consumption. The study concludes that the improved DDPG algorithm provides an effective solution for task scheduling in MEC systems, offering robustness and adaptability in complex network environments. Future research will focus on real-world deployment and further optimization of the algorithm for diverse MEC scenarios.

Identification of miRNAs and Their Targets in Cunninghamia lanceolata Under Low Phosphorus Stress Based on Small RNA and Degradome Sequencing

Chinese fir (Cunninghamia lanceolata (Lamb.) Hook) is one of the main afforestation tree species in southern China. Continuous planting for multiple generations has led to a decrease in the content of available phosphorus in the soil. To adapt to low phosphorus stress, plants develop a series of physiological, biochemical, and developmental responses through self-regulation. Recent studies have shown that miRNAs play a regulatory role in plants’ responses to low phosphorus stress. However, the regulatory mechanism of miRNAs in Chinese fir in response to low phosphorus stress is still unclear. Here, we performed small RNA sequencing on the Chinese fir roots treated with normal phosphorus and low phosphorus and identified a total of 321 miRNAs, including 139 known miRNAs and 182 new miRNAs, with 43 differentially expressed miRNAs (DEMs). Integrative analysis combined with degradome sequencing data revealed that 193 miRNAs (98 known and 95 new) targeted 469 genes, among which 23 DEMs targeted 44 genes. Gene enrichment analysis indicated that under low phosphorus stress, transcription and transcriptional regulation, as well as signal transduction, were significantly activated in Chinese fir. Modules in the miRNA–target pathways, such as miR166/HD-ZIP III, miR169/NFYA7, miR529/SPL, and miR399/UBC23, may be the key regulatory factors in the response to low phosphorus stress in Chinese fir. In addition, we found that PC-3p-1033_8666 was significantly downregulated and that PC-5p-3786_2830 was significantly upregulated, which presumably respond to low phosphorus stress by indirectly affecting phosphorus-related hormone signaling or PSR genes. The identified miRNA–target network and significantly activated pathways in this study provide insights into the post-transcriptional regulatory mechanisms of Chinese fir adapting to low phosphorus environments, which can offer theoretical references for the stress resistance and superior variety breeding of Chinese fir.

Open-Set Cross-Network Node Classification via Unknown-Excluded Adversarial Graph Domain Alignment

Existing cross-network node classification methods are mainly proposed for closed-set setting, where the source network and the target network share exactly the same label space. Such a setting is restricted in real-world applications, since the target network might contain additional classes that are not present in the source. In this work, we study a more realistic open-set cross-network node classification (O-CNNC) problem, where the target network contains all the known classes in the source and further contains several target-private classes unseen in the source. Borrowing the concept from open-set domain adaptation, all target-private classes are defined as an additional “unknown” class. To address the challenging O-CNNC problem, we propose an unknown-excluded adversarial graph domain alignment (UAGA) model with a separate-adapt training strategy. Firstly, UAGA roughly separates known classes from unknown class, by training a graph neural network encoder and a neighborhood-aggregation node classifier in an adversarial framework. Then, unknown-excluded adversarial domain alignment is customized to align only target nodes from known classes with the source, while pushing target nodes from unknown class far away from the source, by assigning positive and negative domain adaptation coefficient to known class nodes and unknown class nodes. Extensive experiments on real-world datasets demonstrate significant outperformance of the proposed UAGA over state-of-the-art methods on O-CNNC.

GCD-Sampling: A General Cross-scale Decoupled Sampling for Point Cloud

Sampling strategy (e.g., fixed farthest point sampling) of point cloud has been an essential step for developing practical solutions in 3D computer vision tasks. Previous fixed sampling is simple, but suffer from suboptimal performance for downstream tasks. To adapt to target networks properly, adaptive sampling methods with trainable parameters have been recently developed to enhance the performance. However, existing adaptive sampling methods still suffer from the over-coupling problem of target network, and thus become model-specific, which limits their practical applications. To address this issue, we propose a novel general cross-scale decoupled sampling method (GCD-sampling) for point cloud, which consists of original feature cache, cross-scale feature fusion and convex combination learning for better feature extraction. To reduce the coupling relationship with the target task network, our method only utilizes the point cloud coordinates as the input and output of itself. Besides, we introduce an arbitrary scale structure to enable parameter sharing across multi-scale sampling in point cloud networks. Extensive experiments on different architectures demonstrate the effectiveness of our method over other existing adaptive sampling methods.

Integrating Deep Q-Networks with Rail Transit Systems for Smarter Urban Mobility

With the increasing complexity of rail transit systems, traditional signal priority strategies, which rely on fixed rules, often fail to adapt to dynamic traffic conditions and lack flexibility and scalability. This study introduces a knowledge-driven approach leveraging a deep Q-network (DQN) to optimize signal priority strategies in rail transit systems. By constructing a traffic state representation model, dynamic features are captured and input into a neural network (NN), creating a high-dimensional state space for decision-making. The DQN framework integrates an experience replay mechanism and target network updates to enhance learning stability, ensuring real-time adaptation and continuous optimization. A reward function is defined to balance train delay minimization and road-traffic coordination, achieving system-wide performance improvement. Experimental results demonstrate that, under traffic flow conditions of 300 vehicles/hour, the proposed DQN strategy reduces train delay by 50% and increases road traffic flow by 16.67%.

Unravelling the Role of PANoptosis in Liver Diseases: Mechanisms and Therapeutic Implications.

PANoptosis is a multimodal form of cell death that involves inflammatory, apoptotic, and necroptotic pathways, playing a key role in the development of liver diseases. This article first outlines the definition and characteristics of PANoptosis, and then explores its mechanisms of action in different types of liver diseases, including acute liver injury, liver failure, metabolic dysfunction-associated fatty liver disease, and hepatocellular carcinoma. Furthermore, this article analyses the molecular regulatory network of PANoptosis and potential therapeutic targets. Finally, this article summarises the current research on PANoptosis in liver diseases and future research directions, and it reviews the role of the emerging cell death mechanism of PANoptosis in liver diseases.

Personalized models of Beam/F3 targeting in transcranial magnetic stimulation for depression: Implications for precision clinical translation.

Personalized models of Beam/F3 targeting in transcranial magnetic stimulation for depression: Implications for precision clinical translation.

Research on the anti hyperlipidemia effect and signaling pathway of Polygonum multiflorum based on network pharmacology

Objective: To investigate the mechanism and signaling pathway of Polygonum multiflorum in combating hyperlipidemia based on network pharmacology. Method: Search traditional Chinese medicine databases and literature to screen and integrate the active ingredients and target proteins of Polygonum multiflorum. Retrieve and obtain the active ingredients and targets of Polygonum multiflorum from the TCMSP database and DrugBank database. Construct a compound target network of Polygonum multiflorum and its corresponding protein-protein interaction (PPI) network in Cytoscape software, and perform gene GO function and KEGG pathway enrichment analysis in the DAVID database. Screen out the effective ingredients and targets of Polygonum multiflorum for anti hyperlipidemia, and draw a target pathway network model. Result: It is predicted that the main active ingredients of Polygonum multiflorum for anti hyperlipidemia drugs are omega-hydroxyflavone-8-methyl ether, alfalfa extract, and kaempferol. They bind to three core targets, epidermal growth factor receptor (EGFR), estrogen receptor 1 (ESR1), and matrix metalloproteinase 9 (MMP9), and affect the PI3K Akt, HIF-1, and estrogen signaling pathways to play a key role in anti-hyperlipidemia. Conclusion: Polygonum multiflorum plays a precise role in anti hyperlipidemia by exerting the characteristics of multiple active ingredients, multiple targets, and multiple signaling pathways in order to provide a scientific basis for systematically elucidating the mechanism of Polygonum multiflorum in treating hyperlipidemia.

MiRNA-based regulation in growth plate cartilage: mechanisms, targets, and therapeutic potential.

MicroRNAs (miRNAs) are critical regulators of the skeleton. In the growth plate, these small non-coding RNAs modulate gene networks that drive key stages of chondrogenesis, including proliferation, differentiation, extracellular matrix synthesis and hypertrophy. These processes are orchestrated through the interaction of pivotal pathways including parathyroid hormone-related protein (PTHrP), Indian hedgehog (IHH), and bone morphogenetic protein (BMP) signaling. This review highlights the miRNA-mRNA target networks essential for chondrocyte differentiation. Many miRNAs are differentially expressed in resting, proliferating and hypertrophic cartilage zones. Moreover, differential enrichment of specific miRNAs in matrix vesicles is also observed, providing means for chondrocytes to influence the function and differentiation of their neighbors by via matrix vesicle protein and RNA cargo. Notably, miR-1 and miR-140 emerge as critical modulators of chondrocyte proliferation and hypertrophy by regulating multiple signaling pathways, many of them downstream from their mutual target Hdac4. Demonstration that a human gain-of-function mutation in miR-140 causes skeletal dysplasia underscores the clinical relevance of understanding miRNA-mediated regulation. Further, miRNAs such as miR-26b have emerged as markers for skeletal disorders such as idiopathic short stature, showcasing the translational relevance of miRNAs in skeletal health. This review also highlights some miRNA-based therapeutic strategies, including innovative delivery systems that could target chondrocytes via cartilage affinity peptides, and potential applications related to treatment of physeal bony bridge formation in growing children. By synthesizing current research, this review offers a nuanced understanding of miRNA functions in growth plate biology and their broader implications for skeletal health. It underscores the translational potential of miRNA-based therapies in addressing skeletal disorders and aims to inspire further investigations in this rapidly evolving field.

Exploration of mechanism of Bufalin and Bufalin derivatives in hepatocellular carcinoma.

Exploration of mechanism of Bufalin and Bufalin derivatives in hepatocellular carcinoma.

Elucidating the Molecular Mechanisms of Hederagenin-Regulated Mitophagy in Cervical Cancer SiHa Cells through an Integrative Approach Combining Proteomics and Advanced Network Association Algorithm.

Hederagenin (Hed), a natural triterpenoid, exhibits antitumor potential in cervical cancer. The present study was designed to explore Hed's regulatory mechanisms on mitophagy in SiHa cervical cancer cells, employing tandem mass tag (TMT) proteomics and an advanced network association algorithm (NAA). Our findings revealed that Hed decreased SiHa cell viability, induced apoptosis, and altered mitochondrial membrane potential. Notably, Hed inhibited mitophagic flux under both normoxic and hypoxic conditions. Through TMT proteomics analysis and innovative NAA, we identified a close association between the HIF-1 signaling pathway and mitophagy. Network analysis further suggested that Hed acts on a target network centered on SRC, STAT3, AKT1, and HIF1A. Western blot analysis confirmed the expression and phosphorylation status of these targets in response to Hed. This study elucidates the molecular mechanisms underlying Hed's regulation of mitophagy in SiHa cells, offering novel insights and potential therapeutic targets for cervical cancer treatment.

Exploring the Mechanisms of the Antioxidants BHA, BHT, and TBHQ in Hepatotoxicity, Nephrotoxicity, and Neurotoxicity from the Perspective of Network Toxicology.

The widespread use of food additives, such as butylated hydroxyanisole (BHA), butylated hydroxytoluene (BHT), and tert-butylhydroquinone (TBHQ), has raised concerns about their potential toxicity, especially their hepatotoxicity, nephrotoxicity, and neurotoxicity. This study explores the targets and mechanisms of food additive-induced toxicity using network toxicology. Toxicity predictions of BHA, BHT, and TBHQ were performed using the ProTox-3.0, ADMETlab 3.0, and Xundrug databases, and potential targets were identified using the SwissTargetPrediction, Batman-TCM, SuperPred, and SEA databases. These were integrated with GeneCards-The Human Gene Database (GeneCards) and the Online Mendelian Inheritance in Man (OMIM) database to extract toxicity-related targets for subsequent Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Core-acting genes were further screened through protein-protein interactions (PPIs), and molecular docking was performed to verify the binding free energy between BHA, BHT, and TBHQ and their core targets. Additionally, the mRNA-miRNA-lnRNA interaction regulatory networks of the core targets and potential carcinogenic mechanisms were analyzed. The targets of BHA, BHT, and TBHQ were as follows: ACE, HIF1A, NR1H4, NFKB1, TNF, IL6, IFNG, IL1B, and ESR1 for hepatotoxicity; APP, NFKB1, ACE, FOS, IL10, IL1B, IL6, TNF, and ALB for nephrotoxicity; and GRIN2B, IL1B, and TNF for neurotoxicity. These interactions primarily involved pathways such as interleukin-17 (IL-17) and Janus kinase-signal transducer and activator of transcription (JAK-STAT), as well as various pathways related to non-alcoholic fatty liver disease (NAFLD). This study highlights the potential toxicity of BHA, BHT, and TBHQ to the liver, kidneys, and nerves, providing insights for better safety evaluations.

Research on Underwater Optical Image Target Detection and Network Security Protection Method based on Deep Learning

Research on Underwater Optical Image Target Detection and Network Security Protection Method based on Deep Learning

Investigating the pharmacological mechanisms of clopidogrel for carotid stenosis treatment based on network pharmacology and molecular docking techniques

Carotid artery stenosis is a manifestation of atherosclerosis and is associated with an increased risk of various cardiovascular diseases. Clopidogrel is an antiplatelet drug widely used for the prevention and treatment of atherosclerosis-related diseases. This study explores the potential molecular mechanisms of clopidogrel in the treatment of carotid artery stenosis through network pharmacology and molecular docking techniques. First, network pharmacology methods were used to construct a clopidogrel target network and identify its possible 127 action targets. Secondly, the gene ontology enrichment analysis indicated that clopidogrel for treating carotid stenosis is closely related to inflammatory responses, platelet activation, and angiogenesis. The Kyoto Encyclopedia of Genes and Genomes analysis revealed associations with lipid metabolism and atherosclerosis. Subsequently, molecular docking technology was employed to screen the binding affinity of clopidogrel to these targets. The results revealed that clopidogrel exhibited binding energies less than − 4.20 kcal/mol with multiple targets, including TNF, MMP9, PTGS2, CCL2, TLR4, and IL-10. This indicates that clopidogrel has high binding affinity and stable binding modes with these targets, thereby exerting anti-inflammatory effects. This study reveals the potential molecular mechanisms of clopidogrel in the treatment of carotid artery stenosis through network pharmacology and molecular docking techniques. The experimental results provide a theoretical basis for the application of clopidogrel in the treatment of carotid artery stenosis and offer new ideas for further drug development and personalized treatment.

Differential miRNA expression and regulatory mechanisms in pigmentation and fiber development of white and brown cotton (Gossypium hirsutum).

Cotton (Gossypium hirsutum) is a major global natural fiber crop used in the textile industry. Although white colored cotton remains the most popular form of cultivated cotton, colored varieties could replace chemically dyed fibers and provide more environmental friendly alternatives. In order to investigate the role of miRNAs in fiber color, we selected white and brown cotton varieties for comparative investigations. Through small RNA sequencing, a number of known miRNA families were discovered (74 in white cotton and 61 in brown cotton, with 44 shared) in which 11 miRNA families were significantly elevated in brown cotton variety. Functional enrichment and network analysis of target genes of these miRNAs revealed their regulatory role in secondary metabolite biosynthesis pathway, particularly the flavonoids pathway, which are known to be associated with fiber coloration. Pigmentation and developmental-related miRNA members such as miR396e-5p, miR167l, and miR1446 were also significantly enriched. Real-time PCR results suggest the regulatory role of miRNAs in these two cotton varieties. Furthermore, 30 and 25 novel miRNAs were also identified in white and brown cotton, respectively. Our findings also show miRNAs associated with fiber coloration and development through the intricate networks of miRNA and targets. Understanding these systems may provide novel insights on improving the fiber color and quality.

Natural Antisense Transcript-Mediated Regulation of HOXA10-AS in Oral Squamous Cell Carcinoma.

The oncogenic role of HOXA10-AS and HOXA10 in cancer has been well documented. However, the epigenetic role of HOXA10 and the natural antisense-mediated regulation of HOXA10-AS in oral squamous cell carcinoma progression is not understood. A total of 35 oral squamous cell carcinoma specimens and 35 adjacent normal clinical specimens were collected and categorized on the basis of their lymph node status. HOXA10-AS and HOXA10 expression were analyzed using RT-qPCR. Methyl-capture sequencing was performed using lymph node-negative (n = 6) and lymph node-positive (n = 5) matched cases. The promoter activity of HOXA10 was determined using a luciferase assay. ChIP-qPCR was performed to determine histone mark localization in the distal promoter region of HOXA10. A protein-protein interaction network of genome-wide antisense targets was constructed using StringDB, and functional enrichment was performed using the R package ClusterProfiler. Transient siRNA-mediated transfection was performed to target specific exons of the HOXA10-AS gene, followed by subsequent cell proliferation, cell cycle, and cell migration assays and validation of cancer signaling pathways through western blotting. HOXA10-AS and its antisense target HOXA10 were significantly overexpressed in the lymph node-positive samples. The transcriptionally active distal promoter of HOXA10 consists of a constitutively unmethylated CpG island region (CUR). H3K4me3, H3K27ac, and H3K27me3 histone mark deposition at the adjacent methylated loci of the distal promoter suggest the nature of euchromatin-driven regulation. Genome-wide mapping revealed 11 potential targets of HOXA10-AS. Targeted specific knockdown of HOXA10-AS exons significantly reduced the expression of HOXA10 and deregulated its downstream targets, contributing to decreased cell cycle progression and epithelial-to-mesenchymal transition. HOXA10-AS regulates the expression of HOXA10 through a natural antisense-mediated mechanism and is epigenetically regulated by constitutively unmethylated marks in the distally enhancing promoter of HOXA10.

Autonomous Obstacle Avoidance with Improved Deep Reinforcement Learning Based on Dynamic Huber Loss

In dynamic and unstructured environments, the obstacle avoidance capabilities of Unmanned Aerial Vehicles (UAVs) are crucial for mission success. Traditional methods struggle with adaptability and effectiveness in unknown or changing scenes. In contrast, the commonly used deep reinforcement learning (DRL) ones suffer from slow convergence, reduced accuracy, and inadequate robustness due to the loss of sensitivity to outliers and parameter rigidity. To address these challenges, we propose an enhanced DRL framework that leverages a Dynamic Huber loss function tailored for UAV autonomous obstacle avoidance. By incorporating Soft updates for target network and dynamically tuning the Huber loss, the proposed method facilitates faster model convergence, superior control precision, and improved robustness. Both theoretical analysis and experimental simulation verify its effectiveness with superior planning success rate, shorter average path length, and faster model convergence over traditional approaches. Specifically, in static environments, the Dynamic Huber-loss-based DRL framework achieves a 98.85% success rate with an optimized average path length of 10.73; in dynamic environments, it attains a 74.20% success rate with an average path length of 37.04; adding wind disturbances in a dynamic environment, it attains a 70.95% success rate with an average path length of 40.40, highlighting its enhanced performance and adaptability.

Stochastic resetting mitigates latent gradient bias of SGD from label noise

Abstract Giving up and starting over may seem wasteful in many situations such as searching for a target or training deep neural networks (DNNs). Our study, though, demonstrates that resetting from a checkpoint can significantly improve generalization performance when training DNNs with noisy labels. In the presence of noisy labels, DNNs initially learn the general patterns of the data but then gradually memorize the corrupted data, leading to overfitting. By deconstructing the dynamics of stochastic gradient descent (SGD), we identify the behavior of a latent gradient bias induced by noisy labels, which harms generalization. To mitigate this negative effect, we apply the stochastic resetting method to SGD, inspired by recent developments in the field of statistical physics achieving efficient target searches. We first theoretically identify the conditions where resetting becomes beneficial, and then we empirically validate our theory, confirming the significant improvements achieved by resetting. We further demonstrate that our method is both easy to implement and compatible with other methods for handling noisy labels. Additionally, this work offers insights into the learning dynamics of DNNs from an interpretability perspective, expanding the potential to analyze training methods through the lens of statistical physics.