Dynamic Cluster Formation Research Articles

Dcaro: Dynamic cluster formation and AUV-aided routing optimization for energy-efficient UASNs

Dcaro: Dynamic cluster formation and AUV-aided routing optimization for energy-efficient UASNs

Analysis of Normal Plasma Cell Distribution Across Distinct Age Cohorts Reveals Age-Dependent Changes.

Hematopoietic and stromal cells within the bone marrow (BM) provide membrane-bound and/or soluble factors that are vital for the survival of plasma cells (PCs). Recent reports in murine BM demonstrated the dynamic formation and dispersion of PC clusters. To date, PC clustering in normal human BM has yet to be thoroughly examined. The goal of this study was to determine whether PC clusters are present in human BM and whether clustering changes as a function of age. Quantification of PCs and clustering in BM sections across six different age groups revealed that fewer PCs and PC clusters were observed in the youngest and oldest age groups. PC clustering increased with age until the sixth decade and then began to decrease. A positive correlation between the number of PCs and PC clusters was observed across all age groups. PC clusters were typically heterogeneous for immunoglobulin heavy- and light-chain expression. Taken together, these data demonstrate that PC clusters are present in human BM and that PC clustering increases until middle adulthood and then begins to diminish. These results suggest the spatial distribution of BM PC-supportive stromal cells changes with age.

An innovative approach for cluster head selection and Energy Optimization in wireless sensor networks using Zebra Fish and Sea Horse Optimization techniques

In recent times, Wireless Sensor Networks (WSNs) have become an indispensable technology across various industries, offering diverse applications and services. Among the crucial performance metrics for WSNs, optimal cluster head (CH) selection and energy efficiency are paramount for cost-effective network operations. This paper proposes a novel approach for WSNs that tackles both challenges using Zebra Fish Optimization (ZFO) and Sea Horse Optimization (SHO) algorithms. The proposed approach focuses on dynamic cluster formation and CH selection. The ZFO algorithm, enhanced with a new multi-level threading technique, dynamically selects the most suitable CH based on a fitness function. Subsequently, the SHO algorithm, equipped with an innovative adaptive parameter tuning mechanism, optimizes energy consumption within the network. This two-phased approach ensures balanced performance. Performance evaluation is validated using key metrics like packet delivery ratio (PDR), throughput, network lifetime, and residual energy. Experimental results and statistical analysis demonstrate that the proposed hybrid scheme outperforms existing popular algorithms in all these metrics. The improvements range from 1.8 % to 6.9 % for PDR, 6.7 % to 24 % for throughput, 1.86 % to 7.40 % for network lifetime, and 9.65 % to 37.95 % for residual energy. These advancements are attributed to the innovative modifications introduced in both ZFO and SHO algorithms, ultimately contributing to the enhanced performance of the entire system.

Zinc Cations Uniquely Stabilize Cell Membrane for Cell Cryopreservation.

We report, for the first time, merely using a small amount of (0.039% w/w) Zn(II) instead of very high concentration (25%-50% w/w) of conventional cryoprotective agents (CPAs), i.e., glycerol, during the cryopreservation of red blood cells (RBCs) can lead to a comparable post-thaw recovery rate of ∼95% while avoiding the tedious gradient washout process for the removal of CPA afterward. The result is remarkable, since Zn(II) does not have the ice-controlling ability reported to be critical for CPA. It benefits from its moderate interaction with lipid molecules, facilitating the formation of small and dynamic lipid clusters. Consequently, the membrane fluidity is maintained, and the cells are resilient to osmotic and mechanical stresses during cryopreservation. This study first reports the ion-specific effect on stabilizing the cell membrane; meanwhile, reversibly tuning the structure of biological samples against injuries during the cooling and rewarming provides a new strategy for cryopreservation.

Temporally resolved SMLM (with large PAR shift) enabled visualization of dynamic HA cluster formation and migration in a live cell

The blinking properties of a single molecule are critical for single-molecule localization microscopy (SMLM). Typically, SMLM techniques involve recording several frames of diffraction-limited bright spots of single-molecules with a detector exposure time close to the blinking period. This sets a limit on the temporal resolution of SMLM to a few tens of milliseconds. Realizing that a substantial fraction of single molecules emit photons for time scales much shorter than the average blinking period, we propose accelerating data collection to capture these fast emitters. Here, we put forward a short exposure-based SMLM (shortSMLM) method powered by sCMOS detector for understanding dynamical events (both at single molecule and ensemble level). The technique is demonstrated on an Influenza-A disease model, where NIH3T3 cells (both fixed and live cells) were transfected by Dendra2-HA plasmid DNA. Analysis shows a 2.76-fold improvement in the temporal resolution that comes with a sacrifice in spatial resolution, and a particle resolution shift PAR-shift (in terms of localization precision) of sim 11.82 nm compared to standard SMLM. We visualized dynamic HA cluster formation in transfected cells post 24 h of DNA transfection. It is noted that a reduction in spatial resolution does not substantially alter cluster characteristics (cluster density, # molecules/cluster, cluster spread, etc.) and, indeed, preserves critical features. Moreover, the time-lapse imaging reveals the dynamic formation and migration of Hemagglutinin (HA) clusters in a live cell. This suggests that short-SMLM using a synchronized high QE sCMOS detector (operated at short exposure times) is excellent for studying temporal dynamics in cellular system.

The shape of water – How cluster formation provides a unifying explanation of water’s anomalous properties

Water, essential to life on Earth, has many anomalous properties, that are not yet fully understood. We used density functional theory, the COSMO-RS implicit solvent model and statistical thermodynamics to calculate the dynamic equilibrium of formation of water clusters in water. Clusters of certain shape are predicted to be thermodynamically stable at ambient down to supercooled conditions and their presence almost quantitatively explains water’s anomalous properties as a function of temperature and pressure. The dodecahedron is the dominant cluster below ∼235 K and below ∼150 MPa and forms a miscibility gap with water below a critical point at ∼230 K. The hexagonal torus is the dominant cluster above ∼235 K and below ∼75 MPa. Both the dodecahedron and the torus are low-density clusters and their presence explains the density maximum of pure water at 277 K (+4 °C). The waters in these clusters are tetrahedrally coordinated, their structures are consistent with experimental data and their presence explains the observed tetrahedral fraction as a function of temperature. A significant part of the overall thermodynamic stability comes from the configurational entropy of the hydrogen bond network in the clusters, so dynamic cluster formation and reformation would be observed as fluctuations in the water structure, rather than stable clusters. At pressures above ∼100 MPa, many of water’s anomalous properties diminish or vanish, which is also the pressure above which high-density water clusters become more stable and prevalent. Our simple unifying 3-cluster theory reproduces water’s anomalous properties from supercooled to ambient temperatures and from ambient pressure up to at least 400 MPa, including density anomalies, two-liquid behavior, compressibility, and heat capacity.

Modulated Collective Motions and Condensation of Bacteria

Bacteria can spontaneously develop collective motions by aligning their motions in dense systems. Here we show that bacteria can also respond collectively to an alternating electrical field and form dynamic clusters oscillating at the same frequency of the field. As the dynamic clusters go beyond a critical size, they split into smaller ones spontaneously. The critical size for splitting depends on the frequency of electric field and the concentration of bacteria. We show that, instead of their biological activity, the physical properties of bacteria as charged particles are responsible for the formation of dynamic clusters. Electroconvective flows across the system play the key role in stabilizing the clusters. However, to form clusters, collective hydrodynamic cooperation between bacteria is important such that no aggregation occurs in dilute suspensions. The findings in this study illustrate that bio-systems can respond collectively to an external field, promising an effective way to control and modulate the behavior of organisms. Moreover, the controlled aggregation and condensation of bacteria offer a robust approach to improve the local concentration of bacteria for early and rapid detection, which has wide applications in clinics.

Ion-cluster-mediated ultrafast self-healable ionoconductors for reconfigurable electronics

Implementing self-healing capabilities in a deformable platform is one of the critical challenges for achieving future wearable electronics with high durability and reliability. Conventional systems are mostly based on polymeric materials, so their self-healing usually proceeds at elevated temperatures to promote chain flexibility and reduce healing time. Here, we propose an ion-cluster-driven self-healable ionoconductor composed of rationally designed copolymers and ionic liquids. After complete cleavage, the ionoconductor can be repaired with high efficiency (∼90.3%) within 1 min even at 25 °C, which is mainly attributed to the dynamic formation of ion clusters between the charged moieties in copolymers and ionic liquids. By taking advantages of the superior self-healing performance, stretchability (∼1130%), non-volatility (over 6 months), and ability to be easily shaped as desired through cutting and re-assembly protocol, reconfigurable, deformable light-emitting electroluminescent displays are successfully demonstrated as promising electronic platforms for future applications.

Measurements of _{Λ}^{3}H and _{Λ}^{4}H Lifetimes and Yields in Au+Au Collisions in the High Baryon Density Region.

We report precision measurements of hypernuclei _{Λ}^{3}H and _{Λ}^{4}H lifetimes obtained from Au+Au collisions at sqrt[s_{NN}]=3.0 GeV and 7.2GeV collected by the STAR experiment at the Relativistic Heavy Ion Collider, and the first measurement of _{Λ}^{3}H and _{Λ}^{4}H midrapidity yields in Au+Au collisions at sqrt[s_{NN}]=3.0 GeV. _{Λ}^{3}H and _{Λ}^{4}H, being the two simplest bound states composed of hyperons and nucleons, are cornerstones in the field of hypernuclear physics. Their lifetimes are measured to be 221±15(stat)±19(syst) ps for _{Λ}^{3}H and 218±6(stat)±13(syst) ps for _{Λ}^{4}H. The p_{T}-integrated yields of _{Λ}^{3}H and _{Λ}^{4}H are presented in different centrality and rapidity intervals. It is observed that the shape of the rapidity distribution of _{Λ}^{4}H is different for 0%-10% and 10%-50% centrality collisions. Thermal model calculations, using the canonical ensemble for strangeness, describes the _{Λ}^{3}H yield well, while underestimating the _{Λ}^{4}H yield. Transport models, combining baryonic mean-field and coalescence (jam) or utilizing dynamical cluster formation via baryonic interactions (phqmd) for light nuclei and hypernuclei production, approximately describe the measured _{Λ}^{3}H and _{Λ}^{4}H yields. Our measurements provide means to precisely assess our understanding of the fundamental baryonic interactions with strange quarks, which can impact our understanding of more complicated systems involving hyperons, such as the interior of neutron stars or exotic hypernuclei.

SHIP164 is a Chorein Motif Lipid Transfer Protein that Controls Endosome‐Golgi Membrane Traffic

Recent studies have shown that proteins with an N‐terminal chorein motif are lipid transport proteins and further suggested that they mediate lipid transfer between closely apposed membranes by a novel bridge‐like mechanism. Within these proteins, the chorein motif represents the beginning of a rod‐like structure that harbors a hydrophobic groove running along its entire length. Lipids are thought to transfer between bilayers by sliding along this groove (PMID: 34216812). VPS13 and ATG2 are founding members of this family. Another protein with a predicted N‐terminal chorein motif is SHIP164 (UHRF1BP1L), previously identified as a Syntaxin 6‐binding protein (PMID: 20163565) and as a Parkinson’s Disease (PD) susceptibility candidate gene (PMID: 28137300). Here, we first explored whether the similarity of SHIP164 to VPS13 and ATG2 extends beyond the chorein domain and determined by cryo‐EM that SHIP164, like these two proteins, has an elongated hollow rod‐like structure. We also found that SHIP164 harbors glycerolipids in an aqueous environment and transports them between membrane bilayers in a liposome‐based assay. We further investigated the localization and physiological function of SHIP164 in mammalian cells. We found that endogenous SHIP164 is localized to cation‐independent mannose‐6‐phosphate receptor (MPR)‐positive vesicular structures at the cell periphery and that its over‐expression induces the abnormal formation of dynamic clusters of small 50‐70nm vesicles enriched in these receptors and anchored to endosomes. We also found that SHIP164 interacts with proteins implicated retrograde microtubule‐based transport from endosomes to the Golgi complex: the dynein/dynactin accessory factor LC8 and dynein interactor Rab45/RASEF. Accordingly, in SHIP164 knockout (KO) RPE‐1 cells, the traffic of MPR and Sortilin positive vesicles to the Golgi complex is partially impaired. An additional defect observed in SHIP164 KO cells is the absence of the largest endosomes positive for PI3P and EEA1. In view of the lipid harboring properties of SHIP164 it is tempting to speculate that the latter phenotype may be explained by its lipid transport properties that, as in the case of VPS13 and ATG2, may allow membrane expansion. Direct evidence for such a model is missing, although, we found that SHIP164 positive vesicles are also positive for ATG9, the autophagy factor recently found to function as a scramblase in partnership with ATG2 on autophagosome membranes. An attractive possibility is that SHIP164 may participate in the regulation of endocytic flow by acting in partnership with ATG9 in the control of phospholipids/protein ratio on the membranes of endocytic vesicles.

Low-Energy Clustering Protocol for Query-Based Wireless Sensor Networks

Nowadays, clustering protocols have been widely used to reduce energy consumption in wireless sensor networks (WSNs). However, when existing clustering protocols are used for query-based WSNs, the energy efficiency becomes limited because in such networks, clusters are formed without considering the positional distribution of query targets, and the procedures of node clustering, cluster head selection and inter-cluster routing are performed in a distributed way, which is energy-consuming. In this paper, a centralized clustering protocol, called Low-Energy Dynamic Clustering (LEDC), is developed to enhance the energy efficiency in query-based WSNs. The main contributions of LEDC include: (1) a novel prediction-based scheme is raised to make the centralized network information maintenance energy-efficient and scalable; (2) centralized optimal dynamic cluster formation algorithms are proposed to cluster the query targets in a centralized way, which not only helps the distribution of clusters match with that of query targets but also saves the high cost incurred in the distributed procedures of node clustering, cluster head selection and inter-cluster routing; (3) an energy-aware forwarding strategy is suggested to improve the energy efficiency and balance the energy load among nodes in inter-cluster communication; (4) a parallel processing algorithm is raised to increase the network throughput and reduce the latency of queries. The performance of LEDC is then compared to the latest clustering protocol. Simulation results demonstrate that LEDC considerably saves the energy of nodes and prolongs the network lifetime.

NOVEL APPROACH FOR BEST SIGNAL PROCESSING AND NETWORK STRUCTURE TO IMPROVE CLOUD ERP RELIABILITY AND HIGH SPEED

If the companies face difficulties in using ERP systems because of its complicated integrating and customising functions, the company can consider some other software for processing their data. On the other hand, those who are using ERP system should make precise settings for providing accurate data and timely service. An error-free network structure must be maintained by cloud ERP systems for providing enhanced service. Based on the network structure and best optimization methods, it must focus on nodes, clusters, LANs, and WANs. The novel data pre-processing consists of three core areas, and they are Pattern Recognition, Data Clustering, and Signal Processing. In this paper, Dynamic cluster formation and pattern recognition are given special weightage. For offering high-speed data transactions with data shrinking, Hybrid dynamic clustering algorithm is explained. As there is a shortage of electricity, the priority is given to energy savings by WSN (Wireless Sensor Network). The consumption of energy has decreased by permitting few cluster heads in the network, also known as nodes for communicating with the base station. A simple, effective, and computationally efficient optimization approach known as Particle swarm optimization (PSO) is utilized. With the usage of fitness function every particle poss the fitness value and even their speed it controlled using velocity. These values have been utilized by WSN for rectifying the issues like optimal deployment, clustering, node selection, and data aggregation. Efforts have been made to reduce the energy consumption occurs by the nodes and for extending the life of the network by proposing a PSO-based technique which selects the best nodes as cluster heads and a reselect mechanism for extending the network lifetime.

Cartilage polymers: From viscoelastic solutions to weak gels*

AbstractThe dynamic properties of the polymeric components of cartilage are important for understanding the many biological functions of this biological tissue. Knowledge of the rheology of these materials is also crucial for the development of therapeutic treatments for diseases, such as arthritis, in which cartilage loses its functional physiological properties. In the present work complementary noninvasive techniques, rheology and dynamic light scattering (DLS) are used to probe the structural and dynamic properties of solutions of the principal proteoglycan components of cartilage extracellular matrix at different levels of structural hierarchy (chondroitin sulfate, aggrecan, aggrecan‐hyaluronic acid complex). It is shown that the bottlebrush shaped aggrecan molecules form large, loosely organized polyelectrolyte domains comprised of molecules organized into diffuse branched polymer network structures permeated by a diffuse cloud of counter‐ions. The rheological behaviors of these polymer solutions vary from viscoelastic solutions, consistent with the formation of dynamic polymer clusters in solution, to “weak gel” materials exhibiting approximate power law shear stress relaxation or creep over a wide range of timescales.

Co-condensation between transcription factor and coactivator p300 modulates transcriptional bursting kinetics

The coactivator p300/CREB-binding protein (CBP) regulates genes by facilitating the assembly of transcriptional machinery and by acetylating histones and other factors. However, it remains mostly unclear how both functions of p300 are dynamically coordinated during gene control. Here, we showed that p300 can orchestrate two functions through the formation of dynamic clusters with certain transcription factors (TFs), which is mediated by the interactions between a TF's transactivation domain (TAD) and the intrinsically disordered regions of p300. Co-condensation can enable spatially defined, all-or-none activation of p300's catalytic activity, priming the recruitment of coactivators, including Brd4. We showed that co-condensation can modulate transcriptional initiation rate and burst duration of target genes, underlying nonlinear gene regulatory functions. Such modulation is consistent with how p300 might shape gene bursting kinetics globally. Altogether, these results suggest an intriguing gene regulation mechanism, in which TF and p300 co-condensation contributes to transcriptional bursting regulation and cooperative gene control.

Monoclonal Antibody Aggregation near Silicone Oil-Water Interfaces.

In this work, we study the hydrodynamic behavior of monoclonal antibodies in the presence of silicone oil-water interfaces. We model the antibody molecules using a coarse-grained 24-bead model, where two beads are used to represent each antibody domain. We consider the spatial variation of the antibody polarity in our model as each bead represents a set of hydrophilic or hydrophobic amino acids. We use the dissipative particle dynamics scheme to represent the coarse-grained force field which governs the motion of the beads. In addition, interprotein interactions are modeled using an electrostatic force field. The model parameters are determined by comparing the structure factor against experimental structure factor data ranging from a low concentration regime (10 mg/mL) to a high concentration regime (150 mg/mL). Next, we conduct simulations for a suspension of antibody molecules in the presence of silicone oil-water interfaces. Protein loss from the bulk solution is noticed as the molecules adsorb at the interface. We observe dynamic cluster formation in the solution bulk and at the interface, as the antibody molecules self-associate along their trajectories. We quantify the aggregation using a density clustering algorithm and investigate the effect of the antibody concentration on the diffusivity of the antibody solution, aggregation propensity, and protein loss from the bulk. Our study shows that numerical simulations can be an important tool for understanding the molecular mechanisms driving protein aggregation near hydrophobic interfaces.

Overlapping Attributed Graph Clustering using Mixed strategy games

Unlike a simple network with just nodes and edges in between them, the real-world networks can contain much more, such as a set of attributes associated with every node in the network. These networks opened up a new avenue in community detection called attributed graph clustering (AGC). Furthermore, the clusters in real-world are not usually disjoint, as compared to most of the work that has been carried out in the field of AGC. This raises a need for AGC with fuzzy clusters. In this work, we try to comprehend the problem of attributed graph clustering with the help of a game-theoretic approach called dynamic cluster formation game (DCFG). To address the possibility of fuzzy clusters in a network, we model the problem of AGC as a series of coupled games involving mixed strategies, in contrast to the previous work that was primarily focused on pure strategy equilibrium. We discuss the convergence of the proposed game and the existence of Nash equilibrium at convergence. We also propose a clustering algorithm which uses a game-theoretic approach to partition a network into fuzzy clusters, giving a solution balanced in terms of topology and node attributes. We compare the the results of our work to the state-of-the-art clustering methods available in the literature.

Energy efficient fixed-cluster architecture for wireless sensor networks

Dynamic and frequent re-clustering of nodes along with data aggregation is used to achieve energy-efficient operation in wireless sensor networks. But dynamic cluster formation supports data aggregation only when clusters can be formed using any set of nodes that lie in close proximity to each other. Frequent re-clustering makes network management difficult and adversely affects the use of energy efficient TDMA-based scheduling for data collection within the clusters. To circumvent these issues, a centralized Fixed-Cluster Architecture (FCA) has been proposed in this paper. The proposed scheme leads to a simplified network implementation for smart spaces where it makes more sense to aggregate data that belongs to a cluster of sensors located within the confines of a designated area. A comparative study is done with dynamic clusters formed with a distributive Low Energy Adaptive Clustering Hierarchy (LEACH) and a centralized Harmonic Search Algorithm (HSA). Using uniform cluster size for FCA, the results show that it utilizes the available energy efficiently by providing stability period values that are 56% and 41% more as compared to LEACH and HSA respectively.

Large‐scale Synthesis of Uniform and Shape‐tunable ZnO/Polysiloxane Janus Micromotors Powered by Visible Light and Pure Water

AbstractLarge‐scale synthesis of micromotors powered by visible light and pure water is challenging, yet crucial for practical applications. Herein, a new method for large‐scale synthesis of uniform Janus ZnO micromotors is reported, by partially encapsulating a ZnO microsphere within a polysiloxane microsphere. The resulting micromotors autonomously move under visible light and in pure water at speeds up to 15 μm/s. The shapes of these micromotors are readily tuned by adjusting the chemicals used in the synthesis, and the influence of the shapes on the speeds of the micromotors is quantified. Moreover, interesting collective behaviors, such as the formation of dynamic clusters, emerge from a dense population of these micromotors. This study therefore represents a substantial step forward in building an excellent model system of good quantity, uniformity and tunability for the study of active matter as well as in a variety of practical applications.

UAV-Assisted Emergency Communications in Social IoT: A Dynamic Hypergraph Coloring Approach

In this article, we address the social-awareness property and unmanned-aerial-vehicle (UAV)-assisted information diffusion in emergency scenarios, where UAVs can disseminate alert messages to a set of terrestrial users within their coverage, and then these users can continuously disseminate the received data packets to their socially connected users in a device-to-device (D2D) multicast manner. In this regard, we have to solve both the dynamic cluster formation and spectrum sharing problems in stochastic environments, since both UAVs and terrestrial users may arrive or depart suddenly. For the cluster formation problem, considering that the data rate of a multicast cluster is determined by the member with the worst link condition, we formulate it as a many-to-one matching game and adopt the rotation-swap algorithm to maximize the expected number of users receiving the alerting messages in each time slot. For the dynamic spectrum sharing problem, aiming at eliminating the interference while minimizing the channel switching cost, we propose a dynamic hypergraph coloring approach to model the cumulative interference and maintain the mutual interference at a low level by exploring a small number of vertices, when the graph is dynamically updated, i.e., the insertion/deletion of vertex/edge. Moreover, we prove some crucial properties, including global stability, convergence, and complexity. Finally, simulation results show that our proposed approach can achieve a better tradeoff among the information diffusion speed, channel switch cost, and complexity.

RETRACTED ARTICLE:Optimal coverage along with connectivity maintenance in heterogeneous wireless sensor network

Heterogeneous wireless sensor network (HWSN) is a network which contains sensor nodes with dissimilar capabilities, such as varying energy, sensing range etc. In Heterogeneous wireless sensor network, Coverage along with connectivity is one of the major issues. Coverage with no connectivity is meaningless in wireless sensor networks. In most of the existing work, coverage and connectivity is not provided efficiently for HWSN. The existing methods used in HWSN to attain coverage and connectivity are static and not applied in the dynamic environment. Optimal Coverage and connectivity maintenance not provided. Energy consumption should be concentrated to maximize the network lifetime. Preserving network connectivity at the same time maximizing coverage by using the less number of energy constrained sensor nodes is the most serious problem. To overcome these issues, in this work we put forward a technique called Coverage Scheduling and Power Aware Connectivity (CSPAC). Grouping the sensors into cluster by the strategy of Dynamic cluster formation. Identifying the minimum active set to provide Q-coverage by Artificial Bee Colony Optimization technique and maximize lifetime by mapping Power Aware Scheduling and Battery Discharge Value. Then Optimal Connectivity Management Technique for reachability to provide strong connectivity. By simulation results, we give you an idea that the proposed technique enhances the network coverage and connectivity in HWSN.