Topology identification method of distribution network based on branch active power

Because of low measurement redundancy and frequent switch changes, it is difficult to identify the correct topology structure. In this paper, a topology recognition method of distribution network based on branch active power is proposed. Firstly, branch active power residual algorithm is used to identify the topological structure. The topology obtained by this method has the highest matching degree with the real-time measured data. Then genetic algorithm is used to optimize the inverse recognition of power grid topology. The numerical example shows that the method is reasonable, effective, rapid and simple. It also has good adaptability with a large number of measurement errors.

Logical modeling: Combining manual curation and automated parameterization to predict drug synergies

Treatment with drug combinations carries great promise for personalized therapy. We have previously shown that drug synergies targeting cancer can manually be identified based on a logical framework. We now demonstrate how automated adjustments of model topology and logic equations can greatly reduce the workload traditionally associated with logical model optimization. Our methodology allows the exploration of larger model ensembles that all obey a set of observations. We benchmark synergy predictions against a dataset of 153 targeted drug combinations. We show that well-performing manual models faithfully represent measured biomarker data and that their performance can be outmatched by automated parameterization using a genetic algorithm. The predictive performance of a curated model is strongly affected by simulated curation errors, while data-guided deletion of a small subset of edges can improve prediction quality. With correct topology we find some tolerance to simulated errors in the biomarker calibration data. With our framework we predict the synergy of joint inhibition of PI3K and TAK1, and further substantiate this prediction with observation in cancer cell cultures and in xenograft experiments.

Insertion of plastidic β-barrel proteins into the outer envelopes of plastids involves an intermembrane space intermediate formed with Toc75-V/OEP80

The insertion of organellar membrane proteins with the correct topology requires the following: First, the proteins must contain topogenic signals for translocation across and insertion into the membrane. Second, proteinaceous complexes in the cytoplasm, membrane, and lumen of organelles are required to drive this process. Many complexes required for the intracellular distribution of membrane proteins have been described, but the signals and components required for the insertion of plastidic β-barrel-type proteins into the outer membrane are largely unknown. The discovery of common principles is difficult, as only a few plastidic β-barrel proteins exist. Here, we provide evidence that the plastidic outer envelope β-barrel proteins OEP21, OEP24, and OEP37 from pea (Pisum sativum) and Arabidopsis thaliana contain information defining the topology of the protein. The information required for translocation of pea proteins across the outer envelope membrane is present within the six N-terminal β-strands. This process requires the action of TOC (translocon of the outer chloroplast membrane). After translocation into the intermembrane space, β-barrel proteins interact with TOC75-V, as exemplified by OEP37 and P39, and are integrated into the membrane. The membrane insertion of plastidic β-barrel proteins is affected by mutation of the last β-strand, suggesting that this strand contributes to the insertion signal. These findings shed light on the elements and complexes involved in plastidic β-barrel protein import.

Target-capture phylogenomics provide insights on gene and species tree discordances in Old World treefrogs (Anura: Rhacophoridae)

Genome-scale data have greatly facilitated the resolution of recalcitrant nodes that Sanger-based datasets have been unable to resolve. However, phylogenomic studies continue to use traditional methods such as bootstrapping to estimate branch support; and high bootstrap values are still interpreted as providing strong support for the correct topology. Furthermore, relatively little attention has been given to assessing discordances between gene and species trees, and the underlying processes that produce phylogenetic conflict. We generated novel genomic datasets to characterize and determine the causes of discordance in Old World treefrogs (Family: Rhacophoridae)—a group that is fraught with conflicting and poorly supported topologies among major clades. Additionally, a suite of data filtering strategies and analytical methods were applied to assess their impact on phylogenetic inference. We showed that incomplete lineage sorting was detected at all nodes that exhibited high levels of discordance. Those nodes were also associated with extremely short internal branches. We also clearly demonstrate that bootstrap values do not reflect uncertainty or confidence for the correct topology and, hence, should not be used as a measure of branch support in phylogenomic datasets. Overall, we showed that phylogenetic discordances in Old World treefrogs resulted from incomplete lineage sorting and that species tree inference can be improved using a multi-faceted, total-evidence approach, which uses the most amount of data and considers results from different analytical methods and datasets.

Learning Part Generation and Assembly for Structure-Aware Shape Synthesis

Learning powerful deep generative models for 3D shape synthesis is largely hindered by the difficulty in ensuring plausibility encompassing correct topology and reasonable geometry. Indeed, learning the distribution of plausible 3D shapes seems a daunting task for the holistic approaches, given the significant topological variations of 3D objects even within the same category. Enlightened by the fact that 3D shape structure is characterized as part composition and placement, we propose to model 3D shape variations with a part-aware deep generative network, coined as PAGENet. The network is composed of an array of per-part VAE-GANs, generating semantic parts composing a complete shape, followed by a part assembly module that estimates a transformation for each part to correlate and assemble them into a plausible structure. Through delegating the learning of part composition and part placement into separate networks, the difficulty of modeling structural variations of 3D shapes is greatly reduced. We demonstrate through both qualitative and quantitative evaluations that PAGENet generates 3D shapes with plausible, diverse and detailed structure, and show two applications, i.e., semantic shape segmentation and part-based shape editing.

Multi-state pair-density functional theory

Multi-state Pair-Density Functional Theory (MS-PDFT) gives the correct topology of interacting potential energy surfaces where state-specific calculations fail.

Reorganizing topologies of Steiner trees to accelerate their eliminations

We describe a technique to reorganize topologies of Steiner trees by exchanging neighbors of adjacent Steiner points. We explain how to use the systematic way of building trees, and therefore topologies, to find the correct topology after nodes have been exchanged. Topology reorganizations can be inserted into the enumeration scheme commonly used by exact algorithms for the Euclidean Steiner tree problem in [Formula: see text]-space, providing a method of improvement different than the usual approaches. As an example, we show how topology reorganizations can be used to dynamically change the exploration of the usual branch-and-bound tree when two Steiner points collide during the optimization process. We also turn our attention to the erroneous use of a pre-optimization lower bound in the original algorithm and give an example to confirm its usage is incorrect. In order to provide numerical results on correct solutions, we use planar equilateral points to quickly compute this lower bound, even in dimensions higher than two. Finally, we describe planar twin trees, identical trees yielded by different topologies, whose generalization to higher dimensions could open a new way of building Steiner trees.

ATP-dependent force generation and membrane scission by ESCRT-III and Vps4

The endosomal sorting complexes required for transport (ESCRTs) catalyze reverse-topology scission from the inner face of membrane necks in HIV budding, multivesicular endosome biogenesis, cytokinesis, and other pathways. We encapsulated ESCRT-III subunits Snf7, Vps24, and Vps2 and the AAA+ ATPase (adenosine triphosphatase) Vps4 in giant vesicles from which membrane nanotubes reflecting the correct topology of scission could be pulled. Upon ATP release by photo-uncaging, this system generated forces within the nanotubes that led to membrane scission in a manner dependent upon Vps4 catalytic activity and Vps4 coupling to the ESCRT-III proteins. Imaging of scission revealed Snf7 and Vps4 puncta within nanotubes whose presence followed ATP release, correlated with force generation and nanotube constriction, and preceded scission. These observations directly verify long-standing predictions that ATP-hydrolyzing assemblies of ESCRT-III and Vps4 sever membranes.