Minimizing the average distance to a closest leaf in a phylogenetic tree

When performing an analysis on a collection of molecular sequences, it can be convenient to reduce the number of sequences under consideration while maintaining some characteristic of a larger collection of sequences. For example, one may wish to select a subset of high-quality sequences that represent the diversity of a larger collection of sequences. One may also wish to specialize a large database of characterized "reference sequences" to a smaller subset that is as close as possible on average to a collection of "query sequences" of interest. Such a representative subset can be useful whenever one wishes to find a set of reference sequences that is appropriate to use for comparative analysis of environmentally-derived sequences, such as for selecting "reference tree" sequences for phylogenetic placement of metagenomic reads. In this paper we formalize these problems in terms of the minimization of the Average Distance to the Closest Leaf (ADCL) and investigate algorithms to perform the relevant minimization. We show that the greedy algorithm is not effective, show that a variant of the Partitioning Among Medoids (PAM) heuristic gets stuck in local minima, and develop an exact dynamic programming approach. Using this exact program we note that the performance of PAM appears to be good for simulated trees, and is faster than the exact algorithm for small trees. On the other hand, the exact program gives solutions for all numbers of leaves less than or equal to the given desired number of leaves, while PAM only gives a solution for the pre-specified number of leaves. Via application to real data, we show that the ADCL criterion chooses chimeric sequences less often than random subsets, while the maximization of phylogenetic diversity chooses them more often than random. These algorithms have been implemented in publicly available software.Comment: Please contact us with any comments or questions

Focal adhesion kinase and its role in skeletal muscle

Skeletal muscle has a remarkable ability to respond to different physical stresses. Loading muscle through exercise, either anaerobic or aerobic, can lead to increases in muscle size and function while, conversely, the absence of muscle loading stimulates rapid decreases in size and function. A principal mediator of this load-induced change is focal adhesion kinase (FAK), a downstream non-receptor tyrosine kinase that translates the cytoskeletal stress and strain signals transmitted across the cytoplasmic membrane by integrins to activate multiple anti-apoptotic and cell growth pathways. Changes in FAK expression and phosphorylation have been found to correlate to specific developmental states in myoblast differentiation, muscle fiber formation and muscle size in response to loading and unloading. With the capability to regulate costamere formation, hypertrophy and glucose metabolism, FAK is a molecule with diverse functions that are important in regulating muscle cell health

Generating User Stories in Groups

User stories allow customers to easily communicate desired specifications as part of Agile Software Development methods. When elicited from groups instead of individuals, the number of stories generated and the comprehensiveness of the stories is likely to increase. We present a 2 X 2 study design involving group vs. individual user story brainstorming with one or two sentence vs. unlimited user story length

Evidence for potential and inductive convection during intense geomagnetic events using normalized superposed epoch analysis

The relative contribution of storm‐time ring current development by convection driven by either potential or inductive electric fields has remained an unresolved question in geospace research. Studies have been published supporting each side of this debate, including views that ring current buildup is entirely one or the other. This study presents new insights into the relative roles of these storm main phase processes. We perform a superposed epoch study of 97 intense ( Dst Min    Dst Min  > –100 nT) storms using OMNI solar wind and ground‐based data. Instead of using a single reference time for the superpositioning of the events, we choose four reference times and expand or contract each phase of every event to the average length of this phase, creating a normalized timeline for the superposed epoch analysis. Using the bootstrap method, we statistically demonstrate that timeline normalization results in better reproduction of average storm dynamics than conventional methods. Examination of the Dst reveals an inflection point in the intense storm group consistent with two‐step main phase development, which is supported by results for the southward interplanetary magnetic field and various ground‐based magnetic indices. This two‐step main‐phase process is not seen in the moderate storm timeline and data sets. It is determined that the first step of Dst development is due to potential convective drift, during which an initial ring current is formed. The negative feedback of this hot ion population begins to limit further ring current growth. The second step of the main phase, however, is found to be a more even mix of potential and inductive convection. It is hypothesized that this is necessary to achieve intense storm Dst levels because the substorm dipolarizations are effective at breaking through the negative feedback barrier of the existing inner magnetospheric hot ion pressure peak. Key Points Moderate and intense geomagnetic storms Evidence for potential and inductive convection Normalized superposed epoch analysisPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/97285/1/jgra50014.pd

Reconciling taxonomy and phylogenetic inference: formalism and algorithms for describing discord and inferring taxonomic roots

Although taxonomy is often used informally to evaluate the results of phylogenetic inference and find the root of phylogenetic trees, algorithmic methods to do so are lacking. In this paper we formalize these procedures and develop algorithms to solve the relevant problems. In particular, we introduce a new algorithm that solves a "subcoloring" problem for expressing the difference between the taxonomy and phylogeny at a given rank. This algorithm improves upon the current best algorithm in terms of asymptotic complexity for the parameter regime of interest; we also describe a branch-and-bound algorithm that saves orders of magnitude in computation on real data sets. We also develop a formalism and an algorithm for rooting phylogenetic trees according to a taxonomy. All of these algorithms are implemented in freely-available software.Comment: Version submitted to Algorithms for Molecular Biology. A number of fixes from previous versio

Dependence of plasmaspheric morphology on the electric field description during the recovery phase of the 17 April 2002 magnetic storm

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95341/1/jgra17301.pd

Estimation of Plot-Level Burn Severity Using Terrestrial Laser Scanning

Monitoring wildland fire burn severity is important for assessing ecological outcomes of fire and their spatial patterning as well as guiding efforts to mitigate or restore areas where ecological outcomes are negative. Burn severity mapping products are typically created using satellite reflectance data but must be calibrated to field data to derive meaning. The composite burn index (CBI) is the most widely used field-based method used to calibrate satellite-based burn severity data but important limitations of this approach have yet to be resolved. The objective of this study was focused on predicting CBI from point cloud and visible-spectrum camera (RGB) metrics derived from single-scan terrestrial laser scanning (TLS) datasets to determine the viability of TLS data as an alternative approach to estimating burn severity in the field. In our approach, we considered the predictive potential of post-scan-only metrics, differenced pre- and post-scan metrics, RGB metrics, and all three together to predict CBI and evaluated these with candidate algorithms (i.e., linear model, random forest (RF), and support vector machines (SVM) and two evaluation criteria (R-squared and root mean square error (RMSE)). In congruence with the strata-based observations used to calculate CBI, we evaluated the potential approaches at the strata level and at the plot level using 70 TLS and 10 RGB independent variables that we generated from the field data. Machine learning algorithms successfully predicted total plot CBI and strata-specific CBI; however, the accuracy of predictions varied among strata by algorithm. RGB variables improved predictions when used in conjunction with TLS variables, but alone proved a poor predictor of burn severity below the canopy. Although our study was to predict CBI, our results highlight that TLS-based methods for quantifying burn severity can be an improvement over CBI in many ways because TLS is repeatable, quantitative, faster, requires less field-expertise, and is more flexible to phenological variation and biomass change in the understory where prescribed fire effects are most pronounced. We also point out that TLS data can also be leveraged to inform other monitoring needs beyond those specific to wildland fire, representing additional efficiency in using this approach

Evaluating Systematic Dependencies of Type Ia Supernovae: The Influence of Deflagration to Detonation Density

We explore the effects of the deflagration to detonation transition (DDT) density on the production of Ni-56 in thermonuclear supernova explosions (type Ia supernovae). Within the DDT paradigm, the transition density sets the amount of expansion during the deflagration phase of the explosion and therefore the amount of nuclear statistical equilibrium (NSE) material produced. We employ a theoretical framework for a well-controlled statistical study of two-dimensional simulations of thermonuclear supernovae with randomized initial conditions that can, with a particular choice of transition density, produce a similar average and range of Ni-56 masses to those inferred from observations. Within this framework, we utilize a more realistic "simmered" white dwarf progenitor model with a flame model and energetics scheme to calculate the amount of Ni-56 and NSE material synthesized for a suite of simulated explosions in which the transition density is varied in the range 1-3x10^7 g/cc. We find a quadratic dependence of the NSE yield on the log of the transition density, which is determined by the competition between plume rise and stellar expansion. By considering the effect of metallicity on the transition density, we find the NSE yield decreases by 0.055 +/- 0.004 solar masses for a 1 solar metallicity increase evaluated about solar metallicity. For the same change in metallicity, this result translates to a 0.067 +/- 0.004 solar mass decrease in the Ni-56 yield, slightly stronger than that due to the variation in electron fraction from the initial composition. Observations testing the dependence of the yield on metallicity remain somewhat ambiguous, but the dependence we find is comparable to that inferred from some studies.Comment: 15 pages, 13 figures, accepted to ApJ on July 6, 201