Using spin to understand the formation of LIGO's black holes

With the detection of four candidate binary black hole (BBH) mergers by the Advanced LIGO detectors thus far, it is becoming possible to constrain the properties of the BBH merger population in order to better understand the formation of these systems. Black hole (BH) spin orientations are one of the cleanest discriminators of formation history, with BHs in dynamically formed binaries in dense stellar environments expected to have spins distributed isotropically, in contrast to isolated populations where stellar evolution is expected to induce BH spins preferentially aligned with the orbital angular momentum. In this work we propose a simple, model-agnostic approach to characterizing the spin properties of LIGO's BBH population. Using measurements of the effective spin of the binaries, which is LIGO's best constrained spin parameter, we introduce a simple parameter to quantify the fraction of the population that is isotropically distributed, regardless of the spin magnitude distribution of the population. Once the orientation characteristics of the population have been determined, we show how measurements of effective spin can be used to directly constrain the underlying BH spin magnitude distribution. Although we find that the majority of the current effective spin measurements are too small to be informative, with LIGO's four BBH candidates we find a slight preference for an underlying population with aligned spins over one with isotropic spins (with an odds ratio of 1.1). We argue that it will be possible to distinguish symmetric and anti-symmetric populations at high confidence with tens of additional detections, although mixed populations may take significantly more detections to disentangle. We also derive preliminary spin magnitude distributions for LIGO's black holes, under the assumption of aligned or isotropic populations

IBiSA_Tools: A Computational Toolkit for Ion-Binding State Analysis in Molecular Dynamics Trajectories of Ion Channels

<div><p>Ion conduction mechanisms of ion channels are a long-standing conundrum. Although the molecular dynamics (MD) method has been extensively used to simulate ion conduction dynamics at the atomic level, analysis and interpretation of MD results are not straightforward due to complexity of the dynamics. In our previous reports, we proposed an analytical method called <i>ion-binding state analysis</i> to scrutinize and summarize ion conduction mechanisms by taking advantage of a variety of analytical protocols, <i>e</i>.<i>g</i>., the complex network analysis, sequence alignment, and hierarchical clustering. This approach effectively revealed the ion conduction mechanisms and their dependence on the conditions, <i>i</i>.<i>e</i>., ion concentration and membrane voltage. Here, we present an easy-to-use computational toolkit for ion-binding state analysis, called IBiSA_tools. This toolkit consists of a C++ program and a series of Python and R scripts. From the trajectory file of MD simulations and a structure file, users can generate several images and statistics of ion conduction processes. A complex network named <i>ion-binding state graph</i> is generated in a standard graph format (graph modeling language; GML), which can be visualized by standard network analyzers such as Cytoscape. As a tutorial, a trajectory of a 50 ns MD simulation of the Kv1.2 channel is also distributed with the toolkit. Users can trace the entire process of ion-binding state analysis step by step. The novel method for analysis of ion conduction mechanisms of ion channels can be easily used by means of IBiSA_tools. This software is distributed under an open source license at the following URL: <a href="http://www.ritsumei.ac.jp/~ktkshr/ibisa_tools/" target="_blank">http://www.ritsumei.ac.jp/~ktkshr/ibisa_tools/</a></p></div

An overview of IBiSA_tools.

<p>(A) A summary of the components of IBiSA_tools (dashed rectangle) and their input and output. (B) A schematic image of a definition of the pore axis. Two sets of atoms (the open circles and filled circles) are specified by users. The pore axis is defined as the line from the center of the first set to the center of the second set. (C) An image of the generated figure depicting time courses of ions in coordinates. (D) An image of the generated figure of frequency of ions across the pore axis. (E) An image of the ion-binding state graph. The nodes indicate ion-binding states, and arrows represent the observed transitions between states. (F) An image of classification of ion conduction events. Each string below the dendrogram corresponds to each ion conduction event.</p

Results of the ion-binding state analysis by means of IBiSA_tools.

<p>(A) The trajectory of ions along the pore axis. The horizontal and vertical axes denote time and the pore axis coordinate. A plot in each color corresponds to a trajectory of each ion. (B) Density of the observed frequency of ions along the pore axis. (C) The graph of ion-binding states. Nodes mean the ion-binding states. The color of each node denotes stability of the state (brighter means more stable). Characters in blue beside nodes are single-character representation of the ion-binding state, corresponding to the sequence in panel D. Gray arrows mean the observed transitions between states, whose width indicates the frequency of transition. (D) A classification of ion conduction events. Sequences were defined as a cyclic path in the graph (also see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0167524#pone.0167524.s001" target="_blank">S1 Fig</a>). The dendrogram shows the result of hierarchical clustering based on the sequence alignment.</p

Additional file 1: of Matataki: an ultrafast mRNA quantification method for large-scale reanalysis of RNA-Seq data

Supplementary methods (pseudocode and mapping) and figures. (DOCX 1581 kb

Additional file 1: of De novo profile generation based on sequence context specificity with the long short-term memory network

Figure S1. Learning curves of the LSTM, Figure S2. ROC curves of similarity search for the target (HHBlits) and predictors, Figure S3. Comparison of profile generation time with simulation data, Figure S4. ROC curves of the similarity search for each iterative method, Table S1. Comparison of pAUC values for SCOP classes for SCOP20 test datasets. (PDF 857 kb

Additional file 3: of Matataki: an ultrafast mRNA quantification method for large-scale reanalysis of RNA-Seq data

Table S1. Numbers of indexed k-mer for each transcript. Table S2. List of paralogous genes and number of indexed k-mers. Table S3. List of enriched biological process GO terms in uncovered genes. Table S4. List of enriched molecular function GO terms in uncovered genes. Table S5: Details of the uncovered genes in GENCODE transcripts. (XLSX 3579 kb

Ion Concentration-Dependent Ion Conduction Mechanism of a Voltage-Sensitive Potassium Channel

<div><p>Voltage-sensitive potassium ion channels are essential for life, but the molecular basis of their ion conduction is not well understood. In particular, the impact of ion concentration on ion conduction has not been fully studied. We performed several micro-second molecular dynamics simulations of the pore domain of the Kv1.2 potassium channel in KCl solution at four different ion concentrations, and scrutinized each of the conduction events, based on graphical representations of the simulation trajectories. As a result, we observed that the conduction mechanism switched with different ion concentrations: at high ion concentrations, potassium conduction occurred by Hodgkin and Keynes' knock-on mechanism, where the association of an incoming ion with the channel is tightly coupled with the dissociation of an outgoing ion, in a one-step manner. On the other hand, at low ion concentrations, ions mainly permeated by a two-step association/dissociation mechanism, in which the association and dissociation of ions were not coupled, and occurred in two distinct steps. We also found that this switch was triggered by the facilitated association of an ion from the intracellular side within the channel pore and by the delayed dissociation of the outermost ion, as the ion concentration increased.</p> </div

Enriched-GO-Terms

The list of all enriched GO terms when SCS = 3, 4 and

Example of the correspondence between the conservation-based method modules and the COXPRESdb-based modules.

<p>The three module pairs with the largest numbers of intersecting genes are shown. The list of all similar module pairs is provided in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0132039#pone.0132039.s009" target="_blank">S7 Table</a>.</p