A split-and-transfer flow based entropic centrality.
Ontology highlight
ABSTRACT:
SUBMITTER: Oggier F
PROVIDER: S-EPMC7924417 | biostudies-literature | 2019
REPOSITORIES: biostudies-literature
Publications
A split-and-transfer flow based entropic centrality.
Oggier Frédérique F Phetsouvanh Silivanxay S Datta Anwitaman A
PeerJ. Computer science 20190916
The notion of entropic centrality measures how central a node is in terms of how uncertain the destination of a flow starting at this node is: the more uncertain the destination, the more well connected and thus central the node is deemed. This implicitly assumes that the flow is indivisible, and at every node, the flow is transferred from one edge to another. The contribution of this paper is to propose a split-and-transfer flow model for entropic centrality, where at every node, the flow can a ...[more]
Similar Datasets
Project description:Graph smoothness objectives have achieved great success in semi-supervised learning but have not yet been applied extensively to unsupervised generative models. We define a new class of entropic graph-based posterior regularizers that augment a probabilistic model by encouraging pairs of nearby variables in a regularization graph to have similar posterior distributions. We present a three-way alternating optimization algorithm with closed-form updates for performing inference on this joint model and learning its parameters. This method admits updates linear in the degree of the regularization graph, exhibits monotone convergence, and is easily parallelizable. We are motivated by applications in computational biology in which temporal models such as hidden Markov models are used to learn a human-interpretable representation of genomic data. On a synthetic problem, we show that our method outperforms existing methods for graph-based regularization and a comparable strategy for incorporating long-range interactions using existing methods for approximate inference. Using genome-scale functional genomics data, we integrate genome 3D interaction data into existing models for genome annotation and demonstrate significant improvements in predicting genomic activity.
| S-EPMC11526501 | biostudies-literature
Project description:Paths and cycles are the two pivotal elements in a network. Here, we demonstrate that paths, particularly the shortest ones, are incomplete in information network. However, based on such paths, many network centrality measures are designed. While extensive explorations on paths have been made, modest studies focus on the cycles on measuring network centrality. We study the relationship between the shortest cycle and the shortest path from extensive real-world networks. The results illustrate the incompleteness of the shortest paths on measuring network centrality. Noticing that the shortest cycle is much more robust than the shortest path, we propose two novel cycle-based network centrality measures to address the incompleteness of paths: the shortest cycle closeness centrality (SCC) and the all cycle betweenness centrality (ACC). Notwithstanding we focus on the network centrality problem, our findings on cycles can be applied to explain the incompleteness of paths in applications and could improve the applicability into more scenarios where the paths are employed in network science.
| S-EPMC6079107 | biostudies-literature
Project description:Scapular winging can be a significant source of chronic pain, weakness, and disability of the shoulder. Isolated serratus anterior palsy from long thoracic nerve injury, which is the most common cause of this condition, produces prominent winging and medial malpositioning of the inferior angle of the scapula. In the case of persistent symptoms despite conservative care, treatment options primarily include scapulothoracic fusion and pectoralis major transfer. Outcomes of scapulothoracic fusion are notable for a high complication rate and limited functional improvements. We describe our technique of indirect, split pectoralis major transfer to the inferolateral scapula with allograft tissue augmentation for the surgical treatment of chronic medial winging. This procedure provides dynamic stabilization of the scapula with secure and tension-free tendon transfer. Advantages over alternative treatments include a relatively low complication rate, acceptable cosmesis, and better range of motion. The rationale and technical aspects of this procedure are discussed. Additional clinical studies are warranted to compare outcomes for the direct and indirect split transfer methods.
| S-EPMC5794454 | biostudies-literature
Project description:Herein, we describe the simulation of a novel flow-electrical-split flow thin (Fl-El-SPLITT) separation device and validate it using existing theory and experimentation for the first time using polystyrene particles of 28 and 1000 nm diameters. The fraction of particles exiting selected ports with DC El-SPLITT is predicted with existing theory, but the theory does not include AC fields, nor does it incorporate the use of crossflows. Using DC fields the El-SPLITT simulation and theory calculated transition points result in the same values. These calculated values accurately predict the experimentally obtained transition point using a 50:50 outlet splitting plane (OSP). Relative to actual experimentally obtained transition points, the calculated values lag behind for a 90:10 OSP, and lead ahead for a 10:90 OSP. The simulation explains trends seen in AC testing, and reasonably predicts the fraction of particles exiting each port. As DC current increases, the amount of AC current required to scatter the particles away from the DC-intended port decreases. The simulation also models a crossflow in a SPLITT system with a DC current applied in a direction opposite the crossflow with some success. Long term steady-state testing without crossflows shows a DC voltage dependent loss of particles. At 8 V DC, total recovery of 28 and 1000 nm particles was 70% and 26%, respectively. This work effectively models a new Fl-El-SPLITT system via Matlab simulation by demonstrating key experimental results such as the influence of DC, AC, and crossflows on the SPLITT separation of polystyrene particles.
| S-EPMC8791626 | biostudies-literature
Project description:This paper introduces two new closely related betweenness centrality measures based on the Randomized Shortest Paths (RSP) framework, which fill a gap between traditional network centrality measures based on shortest paths and more recent methods considering random walks or current flows. The framework defines Boltzmann probability distributions over paths of the network which focus on the shortest paths, but also take into account longer paths depending on an inverse temperature parameter. RSP's have previously proven to be useful in defining distance measures on networks. In this work we study their utility in quantifying the importance of the nodes of a network. The proposed RSP betweenness centralities combine, in an optimal way, the ideas of using the shortest and purely random paths for analysing the roles of network nodes, avoiding issues involving these two paradigms. We present the derivations of these measures and how they can be computed in an efficient way. In addition, we show with real world examples the potential of the RSP betweenness centralities in identifying interesting nodes of a network that more traditional methods might fail to notice.
| S-EPMC4738330 | biostudies-other
Project description:Numerous centrality measures have been developed to quantify the importances of nodes in time-independent networks, and many of them can be expressed as the leading eigenvector of some matrix. With the increasing availability of network data that changes in time, it is important to extend such eigenvector-based centrality measures to time-dependent networks. In this paper, we introduce a principled generalization of network centrality measures that is valid for any eigenvector-based centrality. We consider a temporal network with N nodes as a sequence of T layers that describe the network during different time windows, and we couple centrality matrices for the layers into a supra-centrality matrix of size NT × NT whose dominant eigenvector gives the centrality of each node i at each time t. We refer to this eigenvector and its components as a joint centrality, as it reflects the importances of both the node i and the time layer t. We also introduce the concepts of marginal and conditional centralities, which facilitate the study of centrality trajectories over time. We find that the strength of coupling between layers is important for determining multiscale properties of centrality, such as localization phenomena and the time scale of centrality changes. In the strong-coupling regime, we derive expressions for time-averaged centralities, which are given by the zeroth-order terms of a singular perturbation expansion. We also study first-order terms to obtain first-order-mover scores, which concisely describe the magnitude of nodes' centrality changes over time. As examples, we apply our method to three empirical temporal networks: the United States Ph.D. exchange in mathematics, costarring relationships among top-billed actors during the Golden Age of Hollywood, and citations of decisions from the United States Supreme Court.
| S-EPMC5643020 | biostudies-literature
Project description:BackgroundAs in eukaryotes, precursor transfer RNAs in Archaea often contain introns that are removed in tRNA maturation. Two unrelated archaeal species display unique pre-tRNA processing complexity in the form of split tRNA genes, in which two to three segments of tRNAs are transcribed from different loci, then trans-spliced to form a mature tRNA. Another rare type of pre-tRNA, found only in eukaryotic algae, is permuted, where the 3' half is encoded upstream of the 5' half, and must be processed to be functional.ResultsUsing an improved version of the gene-finding program tRNAscan-SE, comparative analyses and experimental verifications, we have now identified four novel trans-spliced tRNA genes, each in a different species of the Desulfurococcales branch of the Archaea: tRNA(Asp(GUC)) in Aeropyrum pernix and Thermosphaera aggregans, and tRNA(Lys(CUU)) in Staphylothermus hellenicus and Staphylothermus marinus. Each of these includes features surprisingly similar to previously studied split tRNAs, yet comparative genomic context analysis and phylogenetic distribution suggest several independent, relatively recent splitting events. Additionally, we identified the first examples of permuted tRNA genes in Archaea: tRNA(iMet(CAU)) and tRNA(Tyr(GUA)) in Thermofilum pendens, which appear to be permuted in the same arrangement seen previously in red alga.ConclusionsOur findings illustrate that split tRNAs are sporadically spread across a major branch of the Archaea, and that permuted tRNAs are a new shared characteristic between archaeal and eukaryotic species. The split tRNA discoveries also provide new clues to their evolutionary history, supporting hypotheses for recent acquisition via viral or other mobile elements.
| S-EPMC3218864 | biostudies-literature
Project description:The combination of upconverting nanoparticles (UCNPs) and immunochromatography has become a widely used and promising new detection technique for point-of-care testing (POCT). However, their low luminescence efficiency, non-specific adsorption, and image noise have always limited their progress toward practical applications. Recently, artificial intelligence (AI) has demonstrated powerful representational learning and generalization capabilities in computer vision. We report for the first time a combination of AI and upconversion nanoparticle-based lateral flow assays (UCNP-LFAs) for the quantitative detection of commercial internet of things (IoT) devices. This universal UCNPs quantitative detection strategy combines high accuracy, sensitivity, and applicability in the field detection environment. By using transfer learning to train AI models in a small self-built database, we not only significantly improved the accuracy and robustness of quantitative detection, but also efficiently solved the actual problems of data scarcity and low computing power of POCT equipment. Then, the trained AI model was deployed in IoT devices, whereby the detection process does not require detailed data preprocessing to achieve real-time inference of quantitative results. We validated the quantitative detection of two detectors using eight transfer learning models on a small dataset. The AI quickly provided ultra-high accuracy prediction results (some models could reach 100% accuracy) even when strong noise was added. Simultaneously, the high flexibility of this strategy promises to be a general quantitative detection method for optical biosensors. We believe that this strategy and device have a scientific significance in revolutionizing the existing POCT technology landscape and providing excellent commercial value in the in vitro diagnostics (IVD) industry.
| S-EPMC11197505 | biostudies-literature
Project description:Numerous computational methods, including evolutionary-based, energy-based, and geometrical-based methods, are utilized to identify cavities inside proteins. Cavity information aids protein function annotation, drug design, poly-pharmacology, and allosteric site investigation. This article introduces "flow transfer algorithm" for rapid and effective identification of diverse protein cavities through multidimensional cavity scan. Initially, it identifies delimiter and susceptible tetrahedra to establish boundary regions and provide seed tetrahedra. Seed tetrahedron faces are precisely scanned using the maximum circle radius to transfer seed flow to neighboring tetrahedra. Seed flow continues until terminated by boundaries or forbidden faces, where a face is forbidden if the estimated maximum circle radius is less or equal to the user-defined maximum circle radius. After a seed scanning, tetrahedra involved in the flow are clustered to locate the cavity. The CRAFT web interface integrates this algorithm for protein cavity identification with enhanced user control. It supports proteins with cofactors, hydrogens, and ligands and provides comprehensive features such as 3D visualization, cavity physicochemical properties, percentage contribution graphs, and highlighted residues for each cavity. CRAFT can be accessed through its web interface at http://pitools.niper.ac.in/CRAFT , complemented by the command version available at https://github.com/PGlab-NIPER/CRAFT/ .Scientific contribution: Flow transfer algorithm is a novel geometric approach for accurate and reliable prediction of diverse protein cavities. This algorithm employs a distinct concept involving maximum circle radius within the 3D Delaunay triangulation to address diverse van der Waals radii while existing methods overlook atom specific van der Waals radii or rely on complex weighted geometric techniques.
| S-EPMC10829215 | biostudies-literature
Project description:We introduce a new concept of hybrid Na-based flow batteries (HNFBs) with a molten Na alloy anode in conjunction with a flowing catholyte separated by a solid Na-ion exchange membrane for grid-scale energy storage. Such HNFBs can operate at ambient temperature, allow catholytes to have multiple electron transfer redox reactions per active ion, offer wide selection of catholyte chemistries with multiple active ions to couple with the highly negative Na alloy anode, and enable the use of both aqueous and non-aqueous catholytes. Further, the molten Na alloy anode permits the decoupled design of power and energy since a large volume of the molten Na alloy can be used with a limited ion-exchange membrane size. In this proof-of-concept study, the feasibility of multi-electron transfer redox reactions per active ion and multiple active ions for catholytes has been demonstrated. The critical barriers to mature this new HNFBs have also been explored.
| S-EPMC4463013 | biostudies-literature