Multilayer Interaction Research Articles

Complex Task Planning Method of Space-Aeronautics Cooperative Observation Based on Multi-Layer Interaction

Complex Task Planning Method of Space-Aeronautics Cooperative Observation Based on Multi-Layer Interaction

Preparation of Rumex abyssinicus based biosorbent for the removal of methyl orange from aqueous solution

Methyl orange is abundantly present in wastewater generated from textile industries causing serious human health and environmental problems. Therefore, this study was aimed at preparing a low cost and effective biosorbent from the stem of Rumex abyssinicus plant for the removal of methyl orange from aqueous solution. Characterization of the prepared adsorbent material was carried out using a pH point of zero charge, Scanning Electron microscope (SEM), Fourier Transform Infrared (FTIR) spectroscopy and X-Ray Diffraction (XRD). The design and optimization of methyl orange batch adsorption was carried out using the Box Behnken approach to Response Surface methodology aiming to reduce the experimental runs and time with adequate results. The characterization of the adsorbent revealed 7.9 (pHpzc), porous and heterogonous surface (SEM), presence of multiple functional groups (FTIR) and amorphous structure (XRD). The maximum removal efficiency of 98.5 % was found at pH, contact time, Methyl orange concentration and adsorbent dosage of 6, 60 min, 20 mg/L and 0.2 g/100 mL respectively. The isotherm studies were carried out using Langmuir, Freundlich, Toth and Koble Corrigan models in which Freundlich isotherm with a maximum R2 of 0.95 was found to fit data best showing heterogeneous and multilayer surface interaction. On the other hand, a kinetics study revealed that pseudo-second-order fitted the data best. Moreover, the thermodynamics analysis showed the nature of the adsorption to be endothermic, spontaneous and feasible. Generally, this work proved that the low-cost, environmentally friendly and easily prepared Rumex abyssinicus-based material could be an alternative adsorbent for dye detoxification at an industrial scale.

Image and Text Aspect Level Multimodal Sentiment Classification Model Using Transformer and Multilayer Attention Interaction

Many existing image and text sentiment analysis methods only consider the interaction between image and text modalities, while ignoring the inconsistency and correlation of image and text data, to address this issue, an image and text aspect level multimodal sentiment analysis model using transformer and multi-layer attention interaction is proposed. Firstly, ResNet50 is used to extract image features, and RoBERTa-BiLSTM is used to extract text and aspect level features. Then, through the aspect direct interaction mechanism and deep attention interaction mechanism, multi-level fusion of aspect information and graphic information is carried out to remove text and images unrelated to the given aspect. The emotional representations of text data, image data, and aspect type sentiments are concatenated, fused, and fully connected. Finally, the designed sentiment classifier is used to achieve sentiment analysis in terms of images and texts. This effectively has improved the performance of sentiment discrimination in terms of graphics and text.

Explainable Multilayer Graph Neural Network for Cancer Gene Prediction.

The identification of cancer genes is a critical yet challenging problem in cancer genomics research. Existing computational methods, including deep graph neural networks, fail to exploit the multilayered gene-gene interactions or provide limited explanations for their predictions. These methods are restricted to a single biological network, which cannot capture the full complexity of tumorigenesis. Models trained on different biological networks often yield different and even opposite cancer gene predictions, hindering their trustworthy adaptation. Here, we introduce an Explainable Multilayer Graph Neural Network (EMGNN) approach to identify cancer genes by leveraging multiple gene-gene interaction networks and pan-cancer multi-omics data. Unlike conventional graph learning on a single biological network, EMGNN uses a multilayered graph neural network to learn from multiple biological networks for accurate cancer gene prediction. Our method consistently outperforms all existing methods, with an average 7.15% improvement in area under the precision-recall curve (AUPR) over the current state-of-the-art method. Importantly, EMGNN integrated multiple graphs to prioritize newly predicted cancer genes with conflicting predictions from single biological networks. For each prediction, EMGNN provided valuable biological insights via both model-level feature importance explanations and molecular-level gene set enrichment analysis. Overall, EMGNN offers a powerful new paradigm of graph learning through modeling the multilayered topological gene relationships and provides a valuable tool for cancer genomics research. Our code is publicly available at https://github.com/zhanglab-aim/EMGNN.

Improvement of the specific surface area of biochar by calcium-precipitated nanoparticles synthesized by microbial induction as a template skeleton: Removal mechanism of tetracycline in water

The template method is an effective means to improve the specific surface area and porosity of biochar, but the synthesis of template agents and the way they are integrated with biomass materials still need further development. Therefore, the free Pseudomonas sp. Y1 was used to synthesize calcium-precipitated nanoparticles (CPN) on sludge as a fused template skeleton to enlarge the surface area of sludge biochar facilitating the adsorption of tetracycline (TC) in this work. The modified biochar (FBC) showed excellent specific surface area (448.55 m2 g−1) and porosity (0.0053 cm³ g−1), stable morphological structure, abundant active functional groups, and appreciable adsorption capacity (65.43 mg g−1) based on several characterization and adsorption experiments. Moreover, the adsorption model postulated that the removal of TC is mainly a chemisorption-based heat-trapping, disordered multilayer interaction. In detail, this process involved the joint contribution from electrostatic interactions, ligand exchange, hydrogen bonding, π-π bonding, complexation, and pore filling. Meanwhile, the adaptability and stability of FBC were examined by pH and coexisting substances. This template skeleton induced by microorganisms can provide new insight into the modification of biochar with the template method.

A Narrative Inquiry into the Cultivation of Professional Identity Among Medical Students Through an Online Knowledge Community

Background: Cultivating medical talents with professional identity is the ultimate goal of medical education. Nonetheless, the pre-service stage does not create sufficient opportunities to develop their professional identities. Also, there is a lack of long-term empirical data for this process. This longitudinal narrative study involved a dynamic online knowledge community in which students shared confusion, anxiety, and transformation about professional identity. Through capturing their reflections and interactions, the study explored how their professional identities developed over time through the online community in medical school and how this community best supported professional identity development. Method: We collected data from chat records and telephone interviews, using the narrative inquiry method and its interpretative tools to analyze professional identity development. Quantitative analysis was carried out to reveal some influencing factors. Results: Fine-grained analysis showed that medical students experienced a contextual change from individual identity to physician community and a collaborative process to create a supportive environment to develop a professional identity through multi-layered interactions. The analysis also identified that in a virtual community that formed group resonance, medical students balanced their self-identities and professional identity through changed perspectives to engage in selfreflection, with mentor coordination, blended interaction, and facilitating strategies as influencing factors. Our study uniquely presents online learning patterns and identity development trajectories. Conclusions: The study complements the literature about online communities and the professional identity of medical students and has important implications for online informal learning

Identification of herpesvirus transcripts from genomic regions around the replication origins

Long-read sequencing (LRS) techniques enable the identification of full-length RNA molecules in a single run eliminating the need for additional assembly steps. LRS research has exposed unanticipated transcriptomic complexity in various organisms, including viruses. Herpesviruses are known to produce a range of transcripts, either close to or overlapping replication origins (Oris) and neighboring genes related to transcription or replication, which possess confirmed or potential regulatory roles. In our research, we employed both new and previously published LRS and short-read sequencing datasets to uncover additional Ori-proximal transcripts in nine herpesviruses from all three subfamilies (alpha, beta and gamma). We discovered novel long non-coding RNAs, as well as splice and length isoforms of mRNAs. Moreover, our analysis uncovered an intricate network of transcriptional overlaps within the examined genomic regions. We demonstrated that herpesviruses display distinct patterns of transcriptional overlaps in the vicinity of or at the Oris. Our findings suggest the existence of a ‘super regulatory center’ in the genome of alphaherpesviruses that governs the initiation of both DNA replication and global transcription through multilayered interactions among the molecular machineries.

Modeling host–microbiome interactions to improve mechanistic understanding of aphid vectored plant pathogens

Insect transmission of plant pathogens involves multi-layered interactions between vectors, viruses, host plants and environmental factors. Adding to the complexity of vector–virus relationships are diverse microbial communities, which are hypothesized to influence pathogen transmission. Although vector–virus interaction research has flourished, the role played by microbes in vector competence and disease epidemiology remains unclear in many pathosystems. We therefore aimed to develop a novel ecological modeling approach to identify environmental drivers of complex vector–virus–microbiome interactions, particularly differences in the abundance of microbial symbionts within vector microbiomes and relationships between symbionts and the probability of virus acquisition. Our approach combines established molecular tools for profiling microbial communities with underutilized Bayesian hierarchical modeling and data integration techniques. We used a globally relevant aphid–virus pathosystem to develop custom vector–microbiome models that incorporate environmental covariates (e.g., temperature, landcover) and applied them to individual vector symbionts to identify the extent to which environmental factors drive changes in microbial communities that then influence virus acquisition by the host aphid. Specifically, we focus on the aphid obligate symbiont (Buchnera) and a wide-spread facultative symbiont (Serratia) as proof of concept to develop models for two major vector species that include a single environmental covariate (i.e., temperature). Overall, we demonstrate how community-level modeling and microbiome profiling can identify candidate microbes and environmental variables associated with vector competence. Our modeling framework can accommodate a range of microbial symbionts with different abundances, overcome spatial misalignment of data streams, and is robust to varying levels of disease incidence. Results show Buchnera relative abundance is strongly negatively associated with virus acquisition by the vector S.avenae, but not R. padi. Although Serratia was not associated with R. padi vector competence, relative abundance was influenced by differences in spring temperatures. This work lays the foundation for developing a broader modeling framework for predicting disease dynamics in agroecosystems and deploying microbiome-targeted pest management tactics.

Research on the Development and Dissemination of Film Based on the Geopolitical Perspective

With the increasing integration of geopolitics in the film field, the development and dissemination of early films are undergoing new changes. This paper explores this transformation from a geopolitical perspective and delves into the multilayered interactions between geopolitics and film. First of all, geopolitics has brought new opportunities for film creation through innovative content and production and dissemination methods to meet the diverse needs of audiences. Then, this paper reveals the impact of geopolitical intervention in film production, distribution, and dissemination and discusses user portraits, market trends, and changes in the industrial chain, including research and development, live marketing, and digitalization. However, geopolitics has also brought cultural challenges, resulting in an imbalance in the film system, resulting in problems such as homogenization and value conflicts. At the same time, transnational operations in geopolitics have also led to algorithmic decision-making errors, cultural grievances, and security risks. Based on this, the paper proposes countermeasures, including improving film quality, strengthening geopolitical regulation, optimizing collaborative governance, and restoring the cultural value of films. Through these strategies, the film industry can achieve sustainable development and effectively respond to the challenges posed by geopolitics.

Do teams need their employees to work remotely? A simulation analysis based on a multi-layer interactive system

Organizations have widely begun to adopt remote working since the COVID-19 pandemic. However, the effect of remote work on team performance remains unknown. A multi-layer interaction system based on organizational systems theory was designed to assess how remote working affects team performance. Individual performance was computed using a positively skewed stochastic performance model and a modified NK model was used to simulate the team performance under specialized and collaborative conditions. The results showed a complex relationship between task complexity and remote rate and that collaborative teams require a higher remote rate when the probability of employees benefiting from remote work is low to avoid potential detriments from excessive competition. Further results considering agent heterogeneity suggest that individual-level gains are magnified or reduced at the team level and that assessing individual heterogeneity and task complexity is significant for designing remote strategies. In addition, differential mechanisms in team structure and the hierarchy of authority are discussed. This study presents the design and application of a novel business system that helps teams make optimal remote decisions in addition to responding to conflicting discussions in the literature and in practice and providing new insights into decision-making systems in a digital context.

Multi-Layer Stochastic Block Interaction driven by Logistic Regression (MLSBI-LR) for Efficient Link Recommendation in Intra-Layer Linkage Graphs

Link Recommendation (LR) in complex networks has attracted huge interest in the social and computer science communities. Numerous networks, such as recommendation systems and social networks (which facilitate user contact), are probabilistic rather than deterministic due to the uncertainty surrounding the presence of links. Evaluating the various characteristics of such networks has frequently been tricky as the Intra-layer linkage graph requires at least two nodes to be in the same layer. Moreover, many existing LR methods mainly operate well on Single-Layer Graphs (SLGs) compared to Multi-Layer Graphs (MLGs) when nodes traverse multi-layers in a network of Intra-layer linkages. Considering this drawback, this paper proposes a Multi-Layer Stochastic Block Interaction method driven by Logistic Regression (MLSBI-LR) to exploit the bi-directional resources associated with Intra-layer linkages. Its inherent dependence on knowledge-based systems uses multi-criteria recommender systems to accommodate additional criteria and can modify neighborhood-based approaches. A multi-criteria network with relations over the same set of nodes is used since the modified neighbor-based method can exhibit rich dependence between entities and have available experimental data sets in MLGs to recommend links that would efficiently enrich users' experience. The accuracy and robustness of the proposed MLSBI-LR method compared to existing LR methods were extensively investigated using three distinct benchmark data sets and four evaluation metrics. Based on our experimental results across the databases and metrics, the proposed MLSBI-LR method performed significantly better (recording up to 17% increment in accuracy), recommending potential links in MLGs. Consequently, the proposed method may revolutionize link recommendation tasks in social networks by improving users' overall experience.

Integration of feedforward and feedback control in the neuromechanics of vertebrate locomotion: a review of experimental, simulation and robotic studies.

Animal locomotion is the result of complex and multi-layered interactions between the nervous system, the musculo-skeletal system and the environment. Decoding the underlying mechanisms requires an integrative approach. Comparative experimental biology has allowed researchers to study the underlying components and some of their interactions across diverse animals. These studies have shown that locomotor neural circuits are distributed in the spinal cord, the midbrain and higher brain regions in vertebrates. The spinal cord plays a key role in locomotor control because it contains central pattern generators (CPGs) - systems of coupled neuronal oscillators that provide coordinated rhythmic control of muscle activation that can be viewed as feedforward controllers - and multiple reflex loops that provide feedback mechanisms. These circuits are activated and modulated by descending pathways from the brain. The relative contributions of CPGs, feedback loops and descending modulation, and how these vary between species and locomotor conditions, remain poorly understood. Robots and neuromechanical simulations can complement experimental approaches by testing specific hypotheses and performing what-if scenarios. This Review will give an overview of key knowledge gained from comparative vertebrate experiments, and insights obtained from neuromechanical simulations and robotic approaches. We suggest that the roles of CPGs, feedback loops and descending modulation vary among animals depending on body size, intrinsic mechanical stability, time required to reach locomotor maturity and speed effects. We also hypothesize that distal joints rely more on feedback control compared with proximal joints. Finally, we highlight important opportunities to address fundamental biological questions through continued collaboration between experimentalists and engineers.

Removal of oxytetracycline from wastewater by biochar modified with biosynthesized iron oxide nanoparticles and carbon nanotubes: Modification performance and adsorption mechanism

The pollution problem of oxytetracycline (OTC) from wastewater becomes more serious, so an efficient, economical, and green adsorption material is urgently explored. In this study, the multilayer porous biochar (OBC) was prepared by coupling carbon nanotubes with iron oxide nanoparticles synthesized by Aquabacterium sp. XL4 to modify corncobs under medium temperature (600 °C) conditions. The adsorption capacity of OBC could reach 72.59 mg g−1 after preparation and operation parameters were optimized. In addition, various adsorption models suggested that OTC removal resulted from the combined effect of chemisorption, multilayer interaction, and disordered diffusion. Meanwhile, the OBC was fully characterized and exhibited a large specific surface area (237.51 m2 g−1), abundant functional groups, stable crystal structure, high graphitization, and mild magnetic properties (0.8 emu g−1). The OTC removal mechanisms mainly included electrostatic interactions, ligand exchange, π-π bonding reactions, hydrogen bonds, and complexation. pH and coexistence substance experiments revealed that the OBC possesses a wide pH adaptation range and excellent anti-interference ability. Finally, the safety and reusability of OBC were confirmed by repeated experiments. In summary, OBC as a biosynthetic material shows considerable potential for application in the field of purifying new pollution from wastewater.

Exchange and Dzyaloshinskii-Moriya interaction in Rh/Co/Fe/Ir multilayers: Towards skyrmions in exchange-frustrated multilayers

We study the magnetic interactions in transition-metal multilayers composed of Co layers and Co/Fe bilayers sandwiched between Ir and Rh layers based on density functional theory (DFT). By mapping our total energy DFT calculations of collinear and noncollinear spin states including spin-orbit coupling to an atomistic spin model we extract the intra- and interlayer exchange constants, the intra- and interlayer Dzyaloshinskii-Moriya interaction constants and the magnetocrystalline anisotropy energies of several multilayers. We demonstrate that the exchange frustration, which stabilizes zero-field sub-10 nm magnetic skyrmions in Rh/Co films on the Ir(111) surface [S. Meyer et al., Nat. Commun. 10, 3823 (2019)], can be transferred to multilayers formed by a repetition of Rh/Co/Ir trilayers. Increasing the number of Co layers reduces the exchange frustration whereas with adding Fe, a sufficient exchange frustration and strength of the Dzyaloshinskii-Moriya interaction can be obtained to stabilize topological spin structures such as skyrmions in Rh/Co/Fe/Ir multilayers. We show that the exchange interaction in Rh/Co/Fe/Ir multilayers results from the interplay of strong frustration of intralayer exchange in the Fe layer, weak ferromagnetic intralayer exchange in the Co layer, and ferromagnetic interlayer Fe-Co exchange. We analyze the Dzyaloshinskii-Moriya interaction in terms of inter- and intralayer contributions as well as its sign based on the electronic structure of Co/Fe based multilayers with different stacking sequences.

THCANet: Two-layer hop cascaded asymptotic network for robot-driving road-scene semantic segmentation in RGB-D images

In several existing red–green–blue and depth (RGB-D) semantic segmentation algorithms, schemes are used to supplement contextual information through multilayer feature interactions. However, these approaches ignore the complementation of the contextual information and the introduction of noise interfering with the segmentation process. To minimize noise interference during this process, we introduce a two-layer hop cascaded asymptotic network (THCANet) for robot-driving road-scene semantic segmentation in RGB-D images. To exploit the depth map and supervision to strengthen semantic segmentation, we propose an attention cross-fusion module for the interactive combination of RGB-D features through multimodality weighting. Notably, the information of the two modalities reduces noise during fusion. After fusing features from the RGB-D modalities, we also use a novel multiscale context module to fuse features at multiple scales and employ a jump cascade architecture between the modules to recover lost context information and suppress irrelevant noise. Moreover, multiple supervision is performed at different segmentation stages to improve accuracy. The proposed THCANet system demonstrates the best performance on a robot-driving road dataset compared with similar methods, and its generalization ability is demonstrated using the NYU-Depth V2 dataset.

Image Emotion Classification Network Based on Multilayer Attentional Interaction, Adaptive Feature Aggregation

The image emotion classification task aims to use the model to automatically predict the emotional response of people when they see the image. Studies have shown that certain local regions are more likely to inspire an emotional response than the whole image. However, existing methods perform poorly in predicting the details of emotional regions and are prone to overfitting during training due to the small size of the dataset. Therefore, this study proposes an image emotion classification network based on multilayer attentional interaction and adaptive feature aggregation. To perform more accurate emotional region prediction, this study designs a multilayer attentional interaction module. The module calculates spatial attention maps for higher-layer semantic features and fusion features through a multilayer shuffle attention module. Through layer-by-layer up-sampling and gating operations, the higher-layer features guide the lower-layer features to learn, eventually achieving sentiment region prediction at the optimal scale. To complement the important information lost by layer-by-layer fusion, this study not only adds an intra-layer fusion to the multilayer attention interaction module but also designs an adaptive feature aggregation module. The module uses global average pooling to compress spatial information and connect channel information from all layers. Then, the module adaptively generates a set of aggregated weights through two fully connected layers to augment the original features of each layer. Eventually, the semantics and details of the different layers are aggregated through gating operations and residual connectivity to complement the lost information. To reduce overfitting on small datasets, the network is pre-trained on the FI dataset, and further weight fine-tuning is performed on the small dataset. The experimental results on the FI, Twitter I and Emotion ROI (Region of Interest) datasets show that the proposed network exceeds existing image emotion classification methods, with accuracies of 90.27%, 84.66% and 84.96%.

Evolutionary aspects of gastrointestinal tract microbiome-host interaction underlying gastrointestinal barrier integrity

In the host sustenance and homeostasis, the microbiome is a key component in the functional system. Throughout ontogenetic development, microbiome including that of the gastrointestinal tract (GIT) is the vital factor that ensures not only host functioning, but also its interaction with environment. To uncover the mechanisms underlying GIT microbiome showing a decisive influence on host organism, a systematic approach is needed, because diverse microorganisms are predominantly localized in different parts of the GIT. Recently, a new interdisciplinary direction of science, nanobioinformatics that has been extensively developed considers gene networks as the major object of study representing a coordinated group of genes that functionally account for formation and phenotypic disclosure of various host traits. Here, an important place should be provided to the genetically determined level of the gastrointestinal tract microbiome, its interaction at the level of the host food systems. There have been increasing evidence indicating that the microbiome is directly involved in the pathogenesis of host diseases showing a multi-layered interaction with host metabolic and immune systems. At the same time, the microbial community is unevenly distributed throughout the gastrointestinal tract, and its different portions are variously active while interacting with the host immune system. The architecture of interaction between the microbiome and host cells is extremely complex, and the interaction of individual cells, at the same time, varies greatly. Bacteria colonizing the crypts of the small intestine regulate enterocyte proliferation by affecting DNA replication and gene expression, while bacteria at the tip of the intestinal villi mediate gene expression responsible for metabolism and immune response. Enterocytes and Paneth cells, in turn, regulate the vital activity of the community of microorganisms through the production of polysaccharides (carbohydrates) and antibacterial factors on their surface. Thus, the integrity of the gastrointestinal barrier (GIB) is maintained, which protects the body from infections and inflammation, while violation of its integrity leads to a number of diseases. It has been shown that depending on the dominance of certain types of bacteria the microbiome can maintain or disrupt GIB integrity. The structural and functional GIB integrity is important for body homeostasis. To date, at least 50 proteins have been characterized as being involved in the structural and functional integrability of tight junctions between gastrointestinal tract epithelial cells. The current review comprehensively discusses such issues and presents original research carried out at various facilities of translational biomedicine.

Development of multilayer interacted characteristic melting region for large-scale horizontal latent heat storage investigation

The development of thermal energy storage (TES) technology has aroused extensive attentions due to its considerable energy-saving benefits. In the current work, the strategy of multilayer interacted characteristic melting region (MCR) was established to characterize large-scale horizontal latent heat thermal energy storage (LHTES). Based on the shell-and-tube configuration, the influence of multilayer interaction was numerically investigated and the single-tube approach was found to be inapplicable to characterize the melting performance of horizontal LHTES devices. Furthermore, the characterization strategy with MCR was applied to reexamine typical eccentric design and quite different rules were concluded. The melting performance of cases with intersection angles ranging from 60° to 120° and the tube eccentricity ranging from 0 to 0.6 were established and compared. For the design of eccentric side fins, horizontally spreading heat transfer structures was found to be a better choice, which extended the area dominated by the natural convection above the tubes. The full melting time of the case with the intersection angle of 90° was reduced by 2.9 % and 6.2 % compared with the case with intersection angles of 60° and 120°, respectively. The eccentric tube arrangement was found to have negative influence on the melting performance for large-scale horizontal LHTES devices. The full melting time of the case with the fin-tubes eccentricity of 0.6 was increased by 23.3 % compared with the concentric design.

The role of P2Y12 in cardiovascular disease beyond atherothrombosis: P2Y12 signaling promotes emergency hematopoiesis after myocardial infarction

Abstract Background Adenosine diphosphate (ADP) plays a pivotal role in platelet activation. The purinergic ADP-receptor P2Y12 has therefore been targeted in the treatment of cardiovascular disease (CVD) to prevent atherothrombosis (1). Beyond P2Y12 expression on platelets, purinergic receptors have also been described on hematopoietic stem and progenitor cells (LSK) (2). After myocardial infarction (MI), accelerated LSK proliferation launches emergency hematopoiesis as the driving force behind the inflammatory response to MI, increasing inflammatory cell production in the bone marrow (BM) and providing leukocyte resupply for local cell recruitment to the infarct (3). The inflammatory cascade after MI covers intricate multilayered interactions between the injured myocardium and the hematopoietic BM that still remain to be fully elucidated and may unearth novel therapeutic strategies. Whereas P2X receptors have recently been found to be involved in cell trafficking (4), the role of P2Y receptors in the hematopoietic BM have not yet been characterized. Purpose This study aims to characterize the influence of P2Y12 signaling on emergency hematopoiesis and cardiac remodeling after MI. Methods Permanent coronary ligation was performed for MI to assess BM activation, inflammatory cell composition, cardiac remodeling and function in murine global and platelet-specific P2Y12 knockout models and under pharmacological P2Y12 inhibition with prasugrel using flow cytometry, qPCR, immunohistochemistry and echocardiography. In vitro studies including colony forming unit (CFU) assays and flow cytometry allowed for investigation of ADP-dependent effects on LSK cells and intracellular pathway analysis. Results We identified ADP as a danger signal for the hematopoietic BM, fueling emergency hematopoiesis by promoting Akt phosphorylation and cell cycle progression. Detection of P2Y12 expression in LSK implicated a direct effect of ADP on LSK via P2Y12 signaling. P2Y12 deficiency and P2Y12 inhibition with prasugrel decelerated emergency hematopoiesis and consecutively reduced the excessive inflammatory response to MI, translating to lower numbers of hematopoietic progenitors and inflammatory cells in the blood and infarct. Ultimately, P2Y12 inhibition ameliorated chronic adverse cardiac remodeling and preserved cardiac function after MI. Conclusion ADP-dependent P2Y12-mediated activation of hematopoietic stem and progenitor cells in the BM promotes emergency hematopoiesis after MI and fuels post-ischemic inflammation, proposing a novel role of P2Y12 antagonists in CVD beyond atherothrombosis. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Deutsche Forschungsgemeinschaft (DFG)

A Multilayer General Type-2 Fuzzy Community Detection Model in Large-Scale Social Networks

In this article, a novel general type-2 fuzzy community detection model is presented based on different interactions in multilayer graphs to detect communities in a graph with an interaction between nodes, called a multiplex graph considering both structural and attribute similarities. The focus of this model is mostly on the uncertainty related to the interactions between nodes. This model regards a set of layers as the variety of relations among a set of nodes. Based on multilayers, the membership functions of the communities are delineated as “general type-2 fuzzy sets” comprising “primary and secondary variables.” The former indicate the degree of belonging to communities in the multiplex graph based on different interactions in multilayers, and the latter indicate the degree of belonging of the multilayers to the multiplex graph. In the suggested community detection algorithm, no type-reduction or defuzzification exists to update the community prototypes. Moreover, an index, in accordance with the suggested model, is defined to validate the suggested model and its results; and several experimental results on different network sizes including large-scale social networks are given to determine the efficiency of the suggested model compared to the other models.