Dynamic Communication Networks Research Articles

DNA Reaction Circuits to Establish Designated Biological Functions in Multicellular Community.

In multicellular organisms, individual cells are coordinated through complex communication networks to accomplish various physiological tasks. Aiming to establish new biological functions in the multicellular community, we used DNA as the building block to develop a cascade of nongenetic reaction circuits to establish a dynamic cell-cell communication network. Utilizing membrane-anchored amphiphilic DNA tetrahedra (TDN) as the nanoscaffold, reaction circuits were incorporated into three unrelated cells in order to uniquely regulate their sense-and-response behaviors. As a proof-of-concept, this step enabled these cells to simulate significant biological events involved in T cell-mediated anticancer immunity. Such events included cancer-associated antigen recognition and the presentation of antigen-presenting cells (APCs), APC-facilitated T cell activation and dissociation, and T cell-mediated cancer targeting and killing. By combining the excellent programmability and molecular recognition ability of DNA, our cell-surface reaction circuits hold promise for mimicking and manipulating many biological processes.

A Distributed Multi-Agent Deep Reinforcement Learning-Aided Transmission Design for Dynamic Vehicular Communication Networks

A Distributed Multi-Agent Deep Reinforcement Learning-Aided Transmission Design for Dynamic Vehicular Communication Networks

Multi-Objective Optimization of Dynamic Communication Network for Multi-UAVs System

Multi-Objective Optimization of Dynamic Communication Network for Multi-UAVs System

Reinforcement Learning-Based Resource Allocation for Coverage Continuity in High Dynamic UAV Communication Networks

Reinforcement Learning-Based Resource Allocation for Coverage Continuity in High Dynamic UAV Communication Networks

Machine-learning based integrating bulk and single-cell RNA sequencing reveals the SLC38A5-CCL5 signaling as a promising target for clear cell renal cell carcinoma treatment

Cancer-associated fibroblasts paly critical roles in regulating cancer cell biological properties by intricate and dynamic communication networks. But the mechanism of CAFs in clear cell renal cell carcinoma (ccRCC) is not clear. In our study, we identified CAFs and malignant cells from the integrated scRNA-seq datasets and establish a CAF-derived communication signature based on the highly activated regulons ETS1 and MEF2C. We stratified the ccRCC TME into two molecular subtypes with distinct prognoses, immune cell infiltration landscapes, and immune-related characteristics. The model derived from signature demonstrated high accuracy and robustness in predicting prognosis and ICIs therapy responses. Subsequently, the SLC38A5 of the model was found upregulated in CAFs and was related to decreased survival probabilities, inflamed TME, and upregulated inhibitory checkpoints. SLC38A5 inhibition could attenuate the pro-tumoral abilities of CAFs in terms of proliferation, migration, and invasion. Mechanically, CCL5 could restore these properties induced by SLC38A5 inhibition. In conclusion, our communication signature and its derived model enabled a more precise selection of ccRCC patients who were potential beneficiaries of ICIs. Besides, the SLC38A5-CCL5 axis may serve as a promising target for ccRCC treatment.

Evaluating the Performance of Proposed Switched Beam Antenna Systems in Dynamic V2V Communication Networks.

This paper develops a novel approach for reliable vehicle-to-vehicle (V2V) communication in various environments. A switched beam antenna is deployed at the transmitting and receiving points, with a beam management system that concentrates the power in each beam using a low-computation algorithm and a potential mathematical model. The algorithm is designed to be flexible for various environments faced by vehicles. Additionally, an anti-failure system is proposed in case the intelligent transportation system (ITS) system fails to retrieve real-time Packet Delivery Ratio (PDR) values related to traffic density. Performance metrics include the time to collision in seconds, the bit error rate (BER), the packet error rate (PER), the average throughput (Mbps), the beam selection probability, and computational complexity factors. The proposed system is compared with traditional systems. Extensive experiments, simulations, and comparisons show that the proposed approach is excellent and reliable for vehicular systems. The proposed study demonstrates an average throughput of 1.7 Mbps, surpassing conventional methods' typical throughput of 1.35 Mbps. Moreover, the bit error rate (BER) of the proposed study is reduced by a factor of 0.1. Additionally, the proposed framework achieves a beam power efficiency of touching to 100% at computational factor of 34. These metrics indicate that the proposed method is both efficient and sufficiently robust.

Moving-Landmark Assisted Distributed Learning Based Decentralized Cooperative Localization (DL-DCL) with Fault Tolerance

This paper considers the problem of cooperative localization of multiple robots under uncertainty, communicating over a partially connected, dynamic communication network and assisted by an agile landmark. Each robot owns an IMU and a relative pose sensing suite, which can get faulty due to system or environmental uncertainty, and therefore exhibit large bias in their estimation output. For the robots to localize accurately under sensor failure and system or environmental uncertainty, a novel Distributed Learning based Decentralized Cooperative Localization (DL-DCL) algorithm is proposed that involves real-time learning of an information fusion strategy by each robot for combining pose estimates from its own sensors as well as from those of its neighboring robots, and utilizing the moving landmark's pose information as a feedback to the learning process. Convergence analysis shows that the learning process converges exponentially under certain reasonable assumptions. Simulations involving sensor failures inducing around 40-60 times increase in the nominal bias show DL-DCL's estimation performance to be approximately 40% better than the well-known covariance-based estimate fusion methods. For the evaluation of DL-DCL's implementability and fault-tolerance capability in practice, a high-fidelity simulation is carried out in Gazebo with ROS2.

Energy Minimization of Dynamic Multi-UAV Communication Network for Cooperative MultiHop Data Gathering

This study proposes a minimum-energy operational planning problem for multiple unmanned aerial vehicles (UAVs) conducting a cooperative multi-hop data-gathering mission. A multi-UAV ad-hoc network collects data from multiple sources and transmits them to a ground base station. The proposed mixed-integer nonlinear programming (MINLP) formulation simultaneously determines cooperative task allocation, data gathering schedule, dynamic aerial data flow, and UAV trajectories to minimize operational energy consumption. A hierarchical two-phase solution procedure that decomposes the original problem into a series of less complex problems and solves them efficiently is developed. The effectiveness of the proposed formulation and the solution procedure is demonstrated through numerical experiments and comparison with benchmarking operational schemes.

A Hansel & Gretel breadcrumb-style dynamically deployed communication network paradigm using mesh topology for planetary subsurface exploration

We introduce a dynamically deployed communication network (DDCN) paradigm using mesh topology in support of a distributed robotic multi-agent approach for the autonomous exploration of subsurface environments, i.e., caves, lava tube caves, lakes, and oceans, etc. The DDCN, comprising wireless communication beacons autonomously deployed via a rover or submersible in a Hansel & Gretel-inspired breadcrumb style, allows for the longest and most robust communication link between subterranean robotic agent(s) within, e.g., a lave tube cave or a subsurface ocean, and associated surface-borne robotic agent(s). Moreover, we briefly touch on the development of a robotic testbed and wired/wireless communication beacons in support of such astrobiological surface/subsurface exploration scenarios. Candidate lava tube caves have been identified on the Moon and Mars, raising possibilities for planetary exploration, astrobiology, habitat construction for future astronauts, and potential mining operations. Subterranean caverns, and in particular relatively deep lava tube caves, provide a possible refuge for life under otherwise challenging planetary surface conditions, and, as such, are of prime astrobiological relevance. Lava tube caves or other subsurface environments may also be suitable habitats for astronauts and subsequent human settlement but are yet to be explored in part due to difficulties ensuring continued communication with a robotic probe inside these environments. Moreover, the existence of subsurface oceans on ocean worlds, such as Europa, Enceladus, and Titan, has been backed by varying levels of evidence since the 1980s, though there has been no confirmation, i.e., direct observation, thus far. Such environments are also shielded from radiation, and, in combination with the hypothesized presence of water, are additional candidate environments for finding extant or fossilized life. The DDCN paradigm introduced herein directly addresses NASA’s Space Technology Grand Challenges – “All Access Mobility” by enabling the most unconstrained exploration of subsurface environments through a dynamic communication network which ensures transmission of data from and possibly commands to the subsurface robotic probe.

Dynamic allosteric networks drive adenosine A1 receptor activation and G-protein coupling.

Biological receptors respond to environmental changes through allosteric regulation. In G-protein coupled receptors (GPCRs), the binding of ligands establishes chemical and dynamic communication networks resulting in key structural changes of a transmembrane helix that promotes the coupling and activation of G-proteins. The activation pathways and allosteric mechanisms of GPCRs present system-specific profiles among subtypes. Hence, the development of novel allosteric drugs clearly benefits from a complete characterization of the structural dynamics of the target receptor. In this work, we focus on the adenosine A1 receptor (A1R), for which regardless the progress made in understanding its allosteric modulation though experimental and computational studies, essential knowledge of the allosteric mechanism and communication networks that activates the receptor remains unknown. In particular, we have developed a computational strategy that combines molecular dynamics simulations, enhanced sampling techniques, network theory and pocket detection in order to decipher the interplay between the activation pathway of A1R, the allosteric networks and the transient pockets. Our simulations reveal hidden intermediate and pre-active states together with the inactive and fully-active states observed experimentally. The protein energy networks computed throughout these conformational states successfully unravel the extra and intracellular allosteric centers together with the communication pathways that couples them. We observe that the allosteric networks are dynamic, being increased along activation and fine-tuned in presence of the trimeric G-proteins. Overlap of the transient pockets detected and energy networks uncover how the allosteric coupling between pockets and distinct functional regions of the receptor is altered along activation. This complete dynamic picture provides essential information to ease the design of efficient allosteric modulators for A1R. Our computational workflow can be also applied to other GPCRs and related receptors.

Unpacking the black box: An investigation of online crisis communication patterns among stakeholders in the NIMBY conflict

Conflicts caused by NIMBY facilities pose significant threats to urban harmony and sustainability. Ubiquitous online access undoubtedly exacerbates social tensions, and discerning “who is doing what?” is critical for urban managers during crisis communication. However, communication patterns among online stakeholders in NIMBY conflicts have not been comprehensively investigated. Based on Social-Mediated Crisis Communication theory, this research crawls 66,831 Sina blogs to reveal the evolution of online messages and the interaction patterns of diverse stakeholders. Findings show that stakeholders release messages with different emphases and play different roles. Specifically, government functions as the “leader”, who plays a dominant role in online communication; news organizations function as the “bridge”, receiving and spreading information released by governments to other stakeholders with an efficient transmission function; media practitioners function as the “regulator”, mainly posting rumor management messages to clarify misinformation and alleviate social tensions; and citizen agents function as both “receiver” and “promulgator”, who receive information from other stakeholders and also disseminate opinions to other netizens. These findings can advance the existing knowledge body of NIMBY crisis communication and yield valuable inputs for evidence-based policymaking in conflict management in China and beyond. • Social-Mediated Crisis Communication was applied to investigate online NIMBY crisis communication patterns. • The dynamic communication networks among stakeholders were investigated. • The influential actors in different stages of online crisis communication in NIMBY conflict were identified.

Epigenetics in depression and gut-brain axis: A molecular crosstalk.

Gut-brain axis is a dynamic, complex, and bidirectional communication network between the gut and brain. Changes in the microbiota-gut-brain axis are responsible for developing various metabolic, neurodegenerative, and neuropsychiatric disorders. According to clinical and preclinical findings, the gut microbiota is a significant regulator of the gut-brain axis. In addition to interacting with intestinal cells and the enteric nervous system, it has been discovered that microbes in the gut can modify the central nervous system through metabolic and neuroendocrine pathways. The metabolites of the gut microbiome can modulate a number of diseases by inducing epigenetic alteration through DNA methylation, histone modification, and non-coding RNA-associated gene silencing. Short-chain fatty acids, especially butyrate, are well-known histone deacetylases inhibitors. Similarly, other microbial metabolites such as folate, choline, and trimethylamine-N-oxide also regulate epigenetics mechanisms. Furthermore, various studies have revealed the potential role of microbiome dysbiosis and epigenetics in the pathophysiology of depression. Hence, in this review, we have highlighted the role of gut dysbiosis in epigenetic regulation, causal interaction between host epigenetic modification and the gut microbiome in depression and suggest microbiome and epigenome as a possible target for diagnosis, prevention, and treatment of depression.

An efficient federated learning framework for multi-channeled mobile edge network with layered gradient compression

A fundamental issue for federated learning (FL) is how to achieve efficient training performance under complex dynamic communication environments. This issue can be alleviated by the fact that modern edge devices usually can connect to the edge server via multiple communication channels (e.g., 4G, LTE, and 5G) because multi-channel communication can increase the communication bandwidth and has lower communication costs and energy consumption than a single high-speed communication channel. However, if the communication data cannot be properly allocated to multiple channels in a complex dynamic communication network, multi-channel communication will still waste resources (e.g., bandwidth, battery life, and monetary cost). In this paper, we propose an efficient FL framework called, which consists of two parts, the layered gradient compression (LGC), and a learning-driven control algorithm. Specifically, with LGC, local gradients from a device are coded into several layers, and each layer is sent to the server along a different channel. The FL server aggregates the received layers of local gradients from devices to update the global model and sends the result back to the devices. Furthermore, we prove the convergence of LGC and formally define the problem of resource-efficient with LGC. We then propose a learning-driven algorithm for each device to dynamically adjust its local computation (i.e., the number of local stochastic descent) and communication decisions (i.e., the compression level of different layers and the layer-to-channel mapping) in each iteration. Results from extensive experiments show that significantly reduces the training time and improves the resource utilization (energy consumption and money cost) while achieving a similar test accuracy compared with well-known FL baselines.

Joining Policy Forums Together to Develop Ki-no-Eki, a Community Currency System for Forest Management in Japan: Dynamics of Policy Communication Networks

In some mountainous areas of Japan, the Ki-no-Eki system, in which wood is collected to thin the forest and is exchanged for community currency, has been specifically designed and implemented as a solution to current and emerging forest governance issues. This study aimed to capture the evolutionary processes of a complete communication network consisting of organizations that joined policy forums to help develop the Ki-no-Eki system. A total of 26 policy forums were held from 2011 to 2019 to discuss the adoption and implementation of the Ki-no-Eki system across Japan, and coattendance and the resultant policy discourses among 62 participating Ki-no-Eki organizations in these forums were regarded as dynamic communication network processes. We analyzed how policy communication networks formed and evolved to understand the underlying network dynamics driven by not only endogenous network processes—bonding and bridging social capital—but also exogenous effects defined by actors’ attributes. We employed the stochastic actor-oriented model for network dynamics to manage the collected longitudinal undirected network data. We found (i) the emergence of bonding social capital and (ii) homophilic and heterophilic connections in communication networks, which provided insightful explanations of the driving forces of social cohesion among Ki-no-Eki organizations engaged in forest management in Japan.

Microbiota- Brain-Gut-Axis Relevance to Parkinson's Disease: Potential Therapeutic Effects of Probiotics.

Parkinson's disease (PD) is the second most common type of neurogenerative disease among middleaged and older people, characterized by aggregation of alpha-synuclein and dopaminergic neuron loss. The microbiota- gut-brain axis is a dynamic bidirectional communication network and is involved in the pathogenesis of PD. The aggregation of misfolded protein alpha-synuclein is a neuropathological characteristic of PD, originates in the gut and migrates to the central nervous system (CNS) through the vagus nerve and olfactory bulb. The change in the architecture of gut microbiota increases the level short-chain fatty acids (SCFAs) and other metabolites, acting on the neuroendocrine system and modulating the concentrations of gamma-Aminobutyric acid (GABA), serotonin, and other neurotransmitters. It also alters the vagus and intestinal signalling, influencing the brain and behaviour by activating microglia and systemic cytokines. Both experimental and clinical reports indicate the role of intestinal dysbiosis and microbiota host interaction in neurodegeneration. Probiotics are live microorganisms that modify the gut microbiota in the small intestine to avoid neurological diseases. Probiotics have been shown in clinical and preclinical studies to be effective in the treatment of PD by balancing the gut microbiota. In this article, we described the role of gut-microbiota in the pathogenesis of PD. The article aims to explore the mechanistic strategy of the gut-brain axis and its relation with motor impairment and the use of probiotics to maintain gut microbial flora and prevent PD-like symptoms.

Bases biologiques de l’immunothérapie anti-cancéreuse

Malignant tumors can be recognized as different from self identity by the immunological system, and the basis of immunotherapy thus relies on the use of immune system to fight cancer. Modern immunotherapy is actually a targeted therapy, since targeting proteins and biological mechanisms identified as favoring the anergy of immune system towards cancer. Complex cell interactions occur within the cancer microenvironment, between immune cells (T-lymphocytes CD4 and CD8, B-lymphocytes and plasma cells), between immune cells and cancer cells, between immune cells and stromal cells (cancer-associated fibroblasts), or between stromal cells and cancer cells. Such interactions consist of dynamic inter-cell communication networks, varying during the course of tumor evolution, but still sensitive to therapeutic interventions. A family of receptors named «immune checkpoint controls» has the physiological role to avoid a spontaneous unregulated hyper-activation of T-lymphocytes that could lead to self-immune diseases otherwise. However, such receptors are also used by cancer cells for inactivating T-lymphocytes, and thus escape to the anti-cancer immune system response. The aim of immunotherapy is therefore to restore an efficient T-cell response to cancer cells, either by inhibition of negative immune checkpoints, with blocking antibodies (immune checkpoints inhibitors), or to activate positive immune checkpoints using agonist antibodies.1877-1203/© 2022 SPLF. Published by Elsevier Masson SAS. All rights reserved.

The aging of ER-mitochondria communication: A journey from undifferentiated to aged cells.

The complex physiology of eukaryotic cells requires that a variety of subcellular organelles perform unique tasks, even though they form highly dynamic communication networks. In the case of the endoplasmic reticulum (ER) and mitochondria, their functional coupling relies on the physical interaction between their membranes, mediated by domains known as mitochondria-ER contacts (MERCs). MERCs act as shuttles for calcium and lipid transfer between organelles, and for the nucleation of other subcellular processes. Of note, mounting evidence shows that they are heterogeneous structures, which display divergent behaviors depending on the cell type. Furthermore, MERCs are plastic structures that remodel according to intra- and extracellular cues, thereby adjusting the function of both organelles to the cellular needs. In consonance with this notion, the malfunction of MERCs reportedly contributes to the development of several age-related disorders. Here, we integrate current literature to describe how MERCs change, starting from undifferentiated cells, and their transit through specialization, malignant transformation (i.e., dedifferentiation), and aging/senescence. Along this journey, we will review the function of MERCs and their relevance for pivotal cell types, such as stem and cancer cells, cardiac, skeletal, and smooth myocytes, neurons, leukocytes, and hepatocytes, which intervene in the progression of chronic diseases related to age.

Efficient Wireless Traffic Prediction at the Edge: A Federated Meta-Learning Approach

Wireless traffic prediction plays a vital role in managing high dynamic and low latency communication networks, especially in 6G wireless networks. Regarding data and computing resources constraints in edge devices, federated wireless traffic prediction has attracted considerable interest. However, federated learning is limited to dealing with heterogeneous scenarios and unbalanced data availability. Along this line, we propose an efficient federated meta-learning approach to learn a sensitive global model with knowledge collected from different regions. The global model can efficiently adapt to the heterogeneous local scenarios by processing only one or a few steps of fine-tuning on the local data sets. Additionally, distance-based weighted model aggregation is designed to capture the dependencies among different regions for better spatial-temporal prediction. We evaluate the performance of the proposed scheme by comparing it with the conventional federated learning approaches and other commonly used benchmarks for traffic prediction. The extensive simulation results reveal that the proposed scheme outperforms the benchmarks.

Overview of Distributed Machine Learning Techniques for 6G Networks

The main goal of this paper is to survey the influential research of distributed learning technologies playing a key role in the 6G world. Upcoming 6G technology is expected to create an intelligent, highly scalable, dynamic, and programable wireless communication network able to serve many heterogeneous wireless devices. Various machine learning (ML) techniques are expected to be deployed over the intelligent 6G wireless network that provide solutions to highly complex networking problems. In order to do this, various 6G nodes and devices are expected to generate tons of data through external sensors, and data analysis will be needed. With such massive and distributed data, and various innovations in computing hardware, distributed ML techniques are expected to play an important role in 6G. Though they have several advantages over the centralized ML techniques, implementing the distributed ML algorithms over resource-constrained wireless environments can be challenging. Therefore, it is important to select a proper ML algorithm based upon the characteristics of the wireless environment and the resource requirements of the learning process. In this work, we survey the recently introduced distributed ML techniques with their characteristics and possible benefits by focusing our attention on the most influential papers in the area. We finally give our perspective on the main challenges and advantages for telecommunication networks, along with the main scenarios that could eventuate.

Dynamic coupling of residues within proteins as a mechanistic foundation of many enigmatic pathogenic missense variants.

Many pathogenic missense mutations are found in protein positions that are neither well-conserved nor fall in any known functional domains. Consequently, we lack any mechanistic underpinning of dysfunction caused by such mutations. We explored the disruption of allosteric dynamic coupling between these positions and the known functional sites as a possible mechanism for pathogenesis. In this study, we present an analysis of 591 pathogenic missense variants in 144 human enzymes that suggests that allosteric dynamic coupling of mutated positions with known active sites is a plausible biophysical mechanism and evidence of their functional importance. We illustrate this mechanism in a case study of β-Glucocerebrosidase (GCase) in which a vast majority of 94 sites harboring Gaucher disease-associated missense variants are located some distance away from the active site. An analysis of the conformational dynamics of GCase suggests that mutations on these distal sites cause changes in the flexibility of active site residues despite their distance, indicating a dynamic communication network throughout the protein. The disruption of the long-distance dynamic coupling caused by missense mutations may provide a plausible general mechanistic explanation for biological dysfunction and disease.