Specific Modulation Research Articles

Ontology Supported Domain Knowledge Module for E-Tutoring System

E-tutoring Systems are computer applications that provide direct customized education to learners. The E-tutoring system usually includes four modules: Teaching Module, Knowledge Module, Learner Module, and Learner Interface. This investigation focuses on Knowledge Module, considering design and implementation for a selected domain. One of the useful mechanisms of Artificial Intelligence (AI) that is commonly used to formalize domain knowledge module concepts and their relation in an E-tutoring systems is ontology. The term ontology is described as a representation of the entities in a domain and the way those entities connect to each other. This paper introduces a domain knowledge module for an E-tutoring system that allows knowledge stored in a well-defined form to support reusability, shareability, flexibility, and standardability and to assist the storage of transfer and prerequisite knowledge relationships. The introduced knowledge domain module is designed in two ways the general concepts domain knowledge module and a specific domain knowledge module ontology. This innovative technique is helpful for students in enhancing their learning progress. Combining the proposed ontology domain knowledge module with an E-tutoring system can enhance the quality of intelligent problem-solving. Also, it will be possible to reuse the knowledge domains. As a result, the proposal of the domain knowledge module for the E-tutoring system can enhance the teaching and learning process, support recommendations, and generate hints. In the future, the suggested module can be improved by adding some functionalities and automatically supporting the generation of problems and their solutions.

An exploratory framework to identify dust on photovoltaic panels in offshore floating solar power stations

Offshore floating solar power stations represent a new frontier in energy development. These stations maximize solar energy use, reduce land consumption and promote algae growth. However, moist marine air makes airborne particles like sand adhere more easily to PV panels, reducing photoelectric conversion efficiency and causing overheating and potential damage. Additionally, drones and fixed cameras face challenges due to complex maritime factors and complicating image analysis. To address these issues, this paper proposes an exploratory framework for identifying dust regions on photovoltaic panels specifically for offshore floating solar power stations. The framework aims to address how to accurately and reliably identify dust accumulation on PV panels, even in images with complex background interference. The framework is designed to be compatible with existing image recognition models and incorporates specialized enhancement modules to further improve recognition accuracy and efficiency. In this study, we utilized a dust feature enhancement module based on the HLS color space, combined with Mask R-CNN, to demonstrate the framework's feasibility and effectiveness. Experimental results show that, compared to a single Mask R-CNN, the framework significantly reduces misidentification and omission of dust areas, boosting the confidence level and effectively meeting the operational needs of offshore floating solar power stations.

Integrated Long Noncoding RNA and Messenger RNA Expression Analysis Identifies Molecules Specifically Associated With Resiliency and Susceptibility to Depression and Antidepressant Response

BackgroundDepression involves maladaptive processes impairing an individual’s ability to interface with the environment appropriately. Long noncoding RNAs (lncRNAs) are gaining traction for their role in higher-order brain functioning. Recently, we reported that lncRNA coexpression modules may underlie abnormal responses to stress in rats showing depression-like behavior. The current study explored the global expression regulation of lncRNAs and messenger RNAs (mRNAs) in the hippocampus of rats showing susceptibility (learned helplessness [LH]) or resiliency (non-LH) to depression and fluoxetine response to LH (LH+FLX). MethodsMultiple comparison analysis was performed with an analysis of variance via the aov and summary function in the R platform to identify the differential expression of mRNAs and lncRNAs among LH, non-LH, tested control, and LH+FLX groups. Weighted gene coexpression network analysis was used to identify distinctive modules and pathways associated with each phenotype. A machine learning analysis was conducted to screen the critical target genes. Based on the combined analysis, the regulatory effects of lncRNAs on mRNA expression were explored. ResultsMultiple comparison analyses revealed differentially expressed mRNAs and lncRNAs with each phenotype. Integrated bioinformatics analysis identified novel transcripts, specific modules, and regulatory pairs of mRNA-lncRNA in each phenotype. In addition, the machine learning approach predicted lncRNA-regulated Spp2 and Olr25 genes in developing LH behavior, whereas joint analysis of mRNA-lncRNA pairs identified Mboat7, Lmod1, Il18, and Rfx5 genes in depression-like behavior and Adam6 and Tpra1 in antidepressant response. ConclusionsThe study shows a novel role for lncRNAs in the development of specific depression phenotypes and in identifying newer targets for therapeutic development.

Inductive biases of neural network modularity in spatial navigation.

The brain may have evolved a modular architecture for daily tasks, with circuits featuring functionally specialized modules that match the task structure. We hypothesize that this architecture enables better learning and generalization than architectures with less specialized modules. To test this, we trained reinforcement learning agents with various neural architectures on a naturalistic navigation task. We found that the modular agent, with an architecture that segregates computations of state representation, value, and action into specialized modules, achieved better learning and generalization. Its learned state representation combines prediction and observation, weighted by their relative uncertainty, akin to recursive Bayesian estimation. This agent's behavior also resembles macaques' behavior more closely. Our results shed light on the possible rationale for the brain's modularity and suggest that artificial systems can use this insight from neuroscience to improve learning and generalization in natural tasks.

An interdisciplinary treatment to reshape upper anterior displaced teeth by using dynamic occlusal recording with an intraoral scanner.

This article describes an interdisciplinary treatment that helped a patient with displaced upper anterior teeth. A gingivectomy, root canal therapies, digital smile design, digital wax-up, and guided tooth preparations were applied. A patient with pathologically migrated teeth asked for treatment without orthodontic involvement due to a primary failed orthodontic treatment history. A smile photo was taken and superimposed with the dentition in a CAD software to accomplish a digital smile design. The jaw movements were recorded with two different methods, a mechanical articulator and an intraoral scanner with Patient-Specific-Motion function. The occlusal contacts during protrusive and lateral movements were compared and the digital wax-up was designed according to the later occlusal data. An aesthetic crown lengthening and pre-op root canal treatment were carried out in advance accordingly. After guided tooth preparation with a silicone index, the final fixed restorations were manufactured and cemented. A 2-year follow-up showed that our prosthesis functions well. This clinical report revealed that an intraoral scanner with Patient-Specific-Motion function can effectively record individual dynamic occlusal patterns and these data can be integrated into the CAD/CAM process to enhance the fulfillment of clinical requirements. This clinical procedure with a 2-year follow-up demonstrated that a prosthodontic-based interdisciplinary treatment of pathologically migrated teeth using dynamic occlusal recording with an intraoral scanner could achieve satisfactory esthetics in a relatively short treatment period. The Patient Specific Motion module may be used to record a personalized functional movement and the data can be integrated into the design process of the final restorations.

Broader anti-EBV TCR repertoire in multiple sclerosis: disease specificity and treatment modulation.

Epstein-Barr virus (EBV) infection has long been associated with the development of multiple sclerosis (MS). MS patients have elevated titers of EBV-specific antibodies in serum and show signs of CNS damage only after EBV infection. Regarding CD8+ T-cells, an elevated but ineffective response to EBV was suggested in MS patients, who present with a broader MHC-I-restricted EBV-specific T-cell receptor beta chain (TRB) repertoire compared to controls. It is not known whether this altered EBV response could be subject to dynamic changes, e.g., by approved MS therapies, and whether it is specific for MS. 1317 peripheral blood TRB repertoire samples of healthy donors (n=409), patients with MS (n=710) before and after treatment, patients with neuromyelitis optica spectrum disorder (n=87), myelin-oligodendrocyte-glycoprotein antibody-associated disease (n=64) and Susac's syndrome (n=47) were analyzed. Apart from MS, none of the evaluated diseases presented with a broader anti-EBV TRB repertoire. In MS patients undergoing autologous hematopoietic stem-cell transplantation, EBV reactivation coincided with elevated MHC-I-restricted EBV-specific TRB sequence matches. Therapy with ocrelizumab, teriflunomide or dimethyl fumarate reduced EBV-specific, but not CMV-specific MHC-I-restricted TRB sequence matches. Together, this data suggests that the aberrant MHC-I-restricted T-cell response directed against EBV is specific to MS with regard to NMO, MOGAD and Susac's Syndrome and that it is specifically modified by MS treatments interfering with EBV host cells or activated lymphocytes.

Rapid analysis of salbutamol residues in animal-derived food based on the fluorescence of MOFs/MIPs paper-based chips

Mimetic recognition materials for rapid and selective analysis of foodborne contamination from complex foodstuff becomes a hot research topic in the field of food safety. In this work, borate-affinity-functionalized MOF/molecularly imprinted polymer paper-based microfluidic chips (FSU-BA@MIP) were prepared by growing functionalized MOFs on paper surface with 3-carboxyphenylboronic acid (3-CPBA) as the ligands, followed by coating of a MIP layer of salbutamol (SAL) through a borate-affinity surface imprinting strategy. Using the obtained paper-based chip as a mimetic recognition and specific enrichment module, the fluorescence sensing platform achieved visual rapid quantitative analysis of SAL with a linear range of 0.1 × 10−2–5.0 mg L−1. The limits of detection and quantification were calculated to be 0.095 and 0.290 μg kg−1, respectively, and the sample loading via lateral flow aid could be completed within 30 min. The obtained MOFs/MIPs paper chips provide an effective way for the rapid analysis of harmful contaminants from different animal-derived foods.

Childcare restrictions and gender gap in labor outcomes

Purpose This study aims to contribute to the literature by examining the gender gap effects of childcare restrictions. Specifically, not using professional childcare services due to issues like access, quality or costs. Additionally, we explore the long-run consequences of extended work interruptions for childcare. Design/methodology/approach Using a specialized cross-sectional module from the 2018 Spanish Labor Force Survey, we estimated a set of linear regression models to capture the short and long run effects of childcare restrictions in labor market outcomes. Findings We identify substantial gender gaps in labor force, employment, full-time employment and hours worked among parents facing childcare constraints. In contrast, parents without such restrictions experience much lower gender gaps. The long-run analysis reveals that mainly career breaks lasting 2 years or more significantly diminish the labor supply and employment rates of mothers. Originality/value Our study goes beyond examining the effects of childcare restrictions on mothers’ labor market behavior and explicitly studies the gender disparities related to these restrictions. Moreover, our database includes information on work flexibility for childcare, allowing us to explore whether such flexibility can help mitigate these gender gaps. Additionally, we assess the long-term effects of work interruptions due to childcare responsibilities on women’s labor outcomes.

Mitigating thermal runaway propagation in high specific energy lithium-ion battery modules through nanofiber aerogel composite material

Thermal runaway and its propagation within lithium-ion battery systems pose significant challenges to widespread adoption in electric vehicles and energy storage systems. Deploying a thermal barrier between adjacent batteries is a common and effective strategy to prevent thermal propagation. This experimental study evaluates the inhibitory effect of nanofiber aerogel on thermal propagation within high-energy-density lithium-ion battery modules. The results indicate that increasing the thickness of nanofiber aerogel prolongs the average time interval between thermal runaway propagation events between adjacent batteries and increases their peak temperature difference, while the maximum surface temperature of each battery exhibits an overall downward trend.Specifically, compared to no nanofiber aerogel, a 0.5 mm nanofiber aerogel extends the average propagation time by 2 times, and a 1.0 mm nanofiber aerogel successfully prevents thermal propagation from the third to the fourth battery, with an average time extension of nearly 6 times. Furthermore, it is found that thermal runaway propagation can be effectively prevented when the aerogel thickness exceeds 2.0 mm. The microstructure of both fresh and damaged nanofiber aerogels was examined using Scanning Electron Microscopy to validate and analyze their robust durability. This study provides valuable insights for designing safer high-energy-density battery systems.

Human–Robot Collaborative Manufacturing Cell with Learning-Based Interaction Abilities

This paper presents a collaborative manufacturing cell implemented in a laboratory setting, focusing on developing learning-based interaction abilities to enhance versatility and ease of use. The key components of the system include 3D real-time volumetric monitoring for safety, visual recognition of hand gestures for human-to-robot communication, classification of physical-contact-based interaction primitives during handover operations, and detection of hand–object interactions to anticipate human intentions. Due to the nature and complexity of perception, deep-learning-based techniques were used to enhance robustness and adaptability. The main components are integrated in a system containing multiple functionalities, coordinated through a dedicated state machine. This ensures appropriate actions and reactions based on events, enabling the execution of specific modules to complete a given multi-step task. An ROS-based architecture supports the software infrastructure among sensor interfacing, data processing, and robot and gripper controllers nodes. The result is demonstrated by a functional use case that involves multiple tasks and behaviors, paving the way for the deployment of more advanced collaborative cells in manufacturing contexts.

The immune modules conserved across the tree of life: Towards a definition of ancestral immunity.

Immune defence mechanisms exist across the tree of life in such diversity that prokaryotic antiviral responses have historically been considered unrelated to eukaryotic immunity. Mechanisms of defence in divergent eukaryotes were similarly believed to be largely clade specific. However, recent data indicate that a subset of modules (domains and proteins) from prokaryote defence systems are conserved in eukaryotes and populate many stages of innate immune pathways. In this Essay, we propose the notion of ancestral immunity, which corresponds to the set of immune modules conserved between prokaryotes and eukaryotes. After offering a typology of ancestral immunity, we speculate on the selective pressures that could have led to the differential conservation of specific immune modules across domains of life. The exploration of ancestral immunity is in its infancy and appears full of promises to illuminate immune evolution, and also to identify and decipher immune mechanisms of economic, ecological, and therapeutic importance.

Smart Medical Evacuation Support System for the Military.

Medical support in crisis situations is a major challenge. Efficient implementation of the medical evacuation process especially in operations with limited human resources that may occur during armed conflicts can limit the loss of these resources. Proper evacuation of wounded soldiers from the battlefield can increase the chances of their survival and rapid return to further military operations. This paper presents the technical details of the decision support system for medical evacuation to support this process. The basis for the functioning of this system is the continuous measurement of vital signs of soldiers via a specialized measurement module with a set of medical sensors. Vital signs values are then transmitted via the communication module to the analysis and inference module, which automatically determines the color of medical triage and the soldier's chance of survival. This paper presents the results of tests of our system to validate it, which were carried out using test vectors of soldiers' vital signs, as well as the results of the system's performance on a group of volunteers who performed typical activities of tactical operations. The results of this study showed the usefulness of the developed system for supporting military medical services in military operations.

Identification of volatile metabolites produced from levodopa metabolism by different bacteria strains of the gut microbiome

Interspecies pathways in the gut microbiome have been shown to metabolize levodopa, the primary treatment for Parkinson’s disease, and reduce its bioavailability. While the enzymatic reactions have been identified, the ability to establish the resulting macromolecules as biomarkers of microbial metabolism remains technically challenging. In this study, we leveraged an untargeted mass spectrometry-based approach to investigate volatile organic compounds (VOCs) produced during levodopa metabolism by Enterococcus faecalis, Clostridium sporogenes, and Eggerthella lenta. We cultured these organisms with and without their respective bioactive metabolites and detected levodopa-induced shifts in VOC profiles. We then utilized bioinformatics to identify significant differences in 2,6-dimethylpyrazine, 4,6-dimethylpyrimidine, and 4,5-dimethylpyrimidine associated with its biotransformation. Supplementing cultures with inhibitors of levodopa-metabolizing enzymes revealed specific modulation of levodopa-associated diazines, verifying their relationship to its metabolism. Furthermore, functional group analysis depicts strain-specific VOC profiles that reflect interspecies differences in metabolic activity that can be leveraged to assess microbiome functionality in individual patients. Collectively, this work identifies previously uncharacterized metabolites of microbe-mediated levodopa metabolism to determine potential indicators of this activity and further elucidate the metabolic capabilities of different gut bacteria.

Time- and Region-specific Effect of Vortioxetine on Central LPS-induced Transcriptional Regulation of NLRP3 Inflammasome.

Inflammasome overactivation, multiprotein complexes that trigger inflammatory responses, plays a critical role in Major Depressive Disorder (MDD) pathogenesis and treatment responses. Indeed, different antidepressants alleviate depression-related behaviours by specifically counteracting the NLRP3 inflammasome signalling pathway. The immunomodulatory effects of vortioxetine (VTX), a multimodal antidepressant with cognitive benefits, were recently revealed to counter memory impairment induced by a peripheral lipopolysaccharide (LPS) injection 24 hours (h) postchallenge. The potential link between VTX and NLRP3, along with other inflammasomes, remains unexplored. Hence, adult C57BL/6J male mice (n = 73) were fed with a standard or VTX-enriched diet (600 mg/kg of food, 28 days), injected with LPS (830 μg/kg) or saline, and sacrificed 6/24 h post-LPS. At these time-points, transcriptional effects of LPS and VTX's on NLRP3, NLRP1, NLRC4, AIM2 (inflammasomes), ASC and CASP1 (related subunits) and NEK7 mediator (NLRP3 regulator) were assessed in dorsal and ventral hippocampal subregions, frontal-prefrontal cortex and hypothalamus, brain regions serving behavioural-cognitive functions impaired in MDD. Varied expression patterns of inflammasomes were revealed, with long-term NLRP3 and ASC transcriptional changes observed in response to LPS. It was discovered that VTX counteracted the LPS-mediated NLRP3 and ASC upregulation in memory-related brain areas like the dorsal hippocampus at 24 h time-point, potentially via regulating NEK7 expression. No VTX-mediated transcriptional effects were observed on other inflammasomes, reinforcing a potentially specific modulation on the NLRP3 inflammasome signalling pathway. Thus, a novel VTX's molecular mechanism in modulating the NLRP3 inflammasome in a time- and area-specific manner in the brain was highlighted, with significant clinical implications in treating depression and cognitive impairments.</p>.

Co-regulatory network analysis of the main secondary metabolite (SM) biosynthesis in Crocus sativus L.

Saffron (Crocus sativus L.) is being embraced as the most important medicinal plant and the commercial source of saffron spice. Despite the beneficial economic and medicinal properties of saffron, the regulatory mechanism of the correlation of TFs and genes related to the biosynthesis of the apocarotenoids pathway is less obvious. Realizing these regulatory hierarchies of gene expression networks related to secondary metabolites production events is the main challenge owing to the complex and extensive interactions between the genetic behaviors. Recently, high throughput expression data have been highly feasible for constructing co-regulation networks to reveal the regulated processes and identifying novel candidate hub genes in response to complex processes of the biosynthesis of secondary metabolites. Herein, we performed Weighted Gene Co-expression Network Analysis (WGCNA), a systems biology method, to identify 11 regulated modules and hub TFs related to secondary metabolites. Three specialized modules were found in the apocarotenoids pathway. Several hub TFs were identified in notable modules, including MADS, C2H2, ERF, bZIP, HD-ZIP, and zinc finger protein MYB and HB, which were potentially associated with apocarotenoid biosynthesis. Furthermore, the expression levels of six hub TFs and six co-regulated genes of apocarotenoids were validated with RT-qPCR. The results confirmed that hub TFs specially MADS, C2H2, and ERF had a high correlation (P < 0.05) and a positive effect on genes under their control in apocarotenoid biosynthesis (CCD2, GLT2, and ADH) among different C. sativus ecotypes in which the metabolite contents were assayed. Promoter analysis of the co-expressed genes of the modules involved in apocarotenoids biosynthesis pathway suggested that not only are the genes co-expressed, but also share common regulatory motifs specially related to hub TFs of each module and that they may describe their common regulation. The result can be used to engineer valuable secondary metabolites of C. sativus by manipulating the hub regulatory TFs.

Liver transcriptome changes in pearl gentian grouper in response to acute high-temperature stress

Pearl gentian grouper is the main cultivated grouper variety along the southeast coast of China. However, the coastal waters often experience prolonged of high temperatures during summer due to global climate warming and frequent extreme high-temperature events near the coast. As a result, the high-temperature stress seriously hinders the development and growth of grouper aquaculture. In this study, SMRT-Seq and RNA-Seq sequencing techniques were used to analyze the gene expression changes in the pearl gentian grouper liver under room temperature (25 °C) and acute high temperatures (30, 34, 38, and 38 °C for 6 h). Based on the differential expression analysis, Short Time-series Expression Miner (STEM), and Weighted Gene Co-expression Network Analysis (WGCNA), 4144 differentially expressed genes (DEGs), two significantly expressed gene profiles (Profile0 and Profile19), and two high-temperature specific modules (Blue and Turquoise) were identified. The results revealed that the grouper responded to acute high-temperature stress by regulating genes enriched in biological processes, such as protein homeostasis, oxidative defense, apoptosis, and glucose and lipid metabolisms. Furthermore, 34 °C and 38 °C were confirmed as the key temperature points for the significant alterations in the physiological and metabolic processes of pearl gentian grouper under high-temperature stress, corresponding to the critical suitable growth temperature and high-temperature tolerance, respectively. The findings in this study provide a solid theoretical basis for understanding the high-temperature adaptation mechanism of pearl gentian grouper. They also provide important guidelines for promoting the development of the grouper aquaculture industry and molecular breeding high-temperature resistant grouper varieties.

Learning feature relationships in CNN model via relational embedding convolution layer

Establishing the relationships among hierarchical visual attributes of objects in the visual world is crucial for human cognition. The classic convolution neural network (CNN) can successfully extract hierarchical features but ignore the relationships among features, resulting in shortcomings compared to humans in areas like interpretability and domain generalization. Recently, algorithms have introduced feature relationships by external prior knowledge and special auxiliary modules, which have been proven to bring multiple improvements in many computer vision tasks. However, prior knowledge is often difficult to obtain, and auxiliary modules bring additional consumption of computing and storage resources, which limits the flexibility and practicality of the algorithm. In this paper, we aim to drive the CNN model to learn the relationships among hierarchical deep features without prior knowledge and consumption increasing, while enhancing the fundamental performance of some aspects. Firstly, the task of learning the relationships among hierarchical features in CNN is defined and three key problems related to this task are pointed out, including the quantitative metric of connection intensity, the threshold of useless connections, and the updating strategy of relation graph. Secondly, Relational Embedding Convolution (RE-Conv) layer is proposed for the representation of feature relationships in convolution layer, followed by a scheme called use & disuse strategy which aims to address the three problems of feature relation learning. Finally, the improvements brought by the proposed feature relation learning scheme have been demonstrated through numerous experiments, including interpretability, domain generalization, noise robustness, and inference efficiency. In particular, the proposed scheme outperforms many state-of-the-art methods in the domain generalization community and can be seamlessly integrated with existing methods for further improvement. Meanwhile, it maintains comparable precision to the original CNN model while reducing floating point operations (FLOPs) by approximately 50%.

Integrated Transcriptomic and Metabolomic Analysis Revealed Abscisic Acid-Induced Regulation of Monoterpene Biosynthesis in Grape Berries.

Monoterpenes are a class of volatile organic compounds that play crucial roles in imparting floral and fruity aromas to Muscat-type grapes. However, our understanding of the regulatory mechanisms underpinning monoterpene biosynthesis in grapes, particularly following abscisic acid (ABA) treatment, remains elusive. This study aimed to explore the impact of exogenous ABA on monoterpene biosynthesis in Ruiduhongyu grape berries by employing Headspace Solid-Phase Micro-Extraction Gas Chromatography-Mass Spectrometry (HS-SPME/GC-MS) analysis and transcriptome sequencing. The results suggested significant differences in total soluble solids (TSS), pH, and total acid content. ABA treatment resulted in a remarkable increase in endogenous ABA levels, with concentrations declining from veraison to ripening stages. ABA treatment notably enhanced monoterpene concentrations, particularly at the E_L37 and E_L38 stages, elevating the overall floral aroma of grape berries. According to the variable gene expression patterns across four developmental stages in response to ABA treatment, the E_L37 stage had the largest number of differential expressed genes (DEGs), which was correlated with a considerable change in free monoterpenes. Furthermore, functional annotation indicated that the DEGs were significantly enriched in primary and secondary metabolic pathways, underlining the relationship between ABA, sugar accumulation, and monoterpene biosynthesis. ABA treatment upregulated key genes involved in the methylerythritol phosphate (MEP) pathway, enhancing carbon allocation and subsequently impacting terpene synthesis. This study also identified transcription factors, including MYB and AP2/ERF families, potentially modulating monoterpene and aroma-related genes. Weighted gene co-expression network analysis (WGCNA) linked ABA-induced gene expression to monoterpene accumulation, highlighting specific modules enriched with genes associated with monoterpene biosynthesis; one of these modules (darkgreen) contained genes highly correlated with most monoterpenes, emphasizing the role of ABA in enhancing grape quality during berry maturation. Together, these findings provide valuable insights into the multifaceted effects of exogenous ABA on monoterpene compounds and grape berry flavor development, offering potential applications in viticulture and enology.

Central nervous system-associated macrophages modulate the immune response following stroke in aged mice.

Age is a major nonmodifiable risk factor for ischemic stroke. Central nervous system-associated macrophages (CAMs) are resident immune cells located along the brain vasculature at the interface between the blood circulation and the parenchyma. By using a clinically relevant thromboembolic stroke model in young and aged male mice and corresponding human tissue samples, we show that during aging, CAMs acquire a central role in orchestrating immune cell trafficking after stroke through the specific modulation of adhesion molecules by endothelial cells. The absence of CAMs provokes increased leukocyte infiltration (neutrophils and CD4+ and CD8+ T lymphocytes) and neurological dysfunction after stroke exclusively in aged mice. Major histocompatibility complex class II, overexpressed by CAMs during aging, plays a significant role in the modulation of immune responses to stroke. We demonstrate that during aging, CAMs become central coordinators of the neuroimmune response that ensure a long-term fine-tuning of the immune responses triggered by stroke.

Meta-Path Semantic and Global-Local Representation Learning Enhanced Graph Convolutional Model for Disease-Related miRNA Prediction.

Dysregulation of miRNAs is closely related to the progression of various diseases, so identifying disease-related miRNAs is crucial. Most recently proposed methods are based on graph reasoning, while they did not completely exploit the topological structure composed of the higher-order neighbor nodes and the global and local features of miRNA and disease nodes. We proposed a prediction method, MDAP, to learn semantic features of miRNA and disease nodes based on various meta-paths, as well as node features from the entire heterogeneous network perspective, and node pair attributes. Firstly, for both the miRNA and disease nodes, node category-wise meta-paths were constructed to integrate the similarity and association connection relationships. Each target node has its specific neighbor nodes for each meta-path, and the neighbors of longer meta-paths constitute its higher-order neighbor topological structure. Secondly, we constructed a meta-path specific graph convolutional network module to integrate the features of higher-order neighbors and their topology, and then learned the semantic representations of nodes. Thirdly, for the entire miRNA-disease heterogeneous network, a global-aware graph convolutional autoencoder was built to learn the network-view feature representations of nodes. We also designed semantic-level and representation-level attentions to obtain informative semantic features and node representations. Finally, the strategy based on the parallel convolutional-deconvolutional neural networks were designed to enhance the local feature learning for a pair of miRNA and disease nodes. The experiment results showed that MDAP outperformed other state-of-the-art methods, and the ablation experiments demonstrated the effectiveness of MDAP's major innovations. MDAP's ability in discovering potential disease-related miRNAs was further analyzed by the case studies over three diseases.