Towards an Understanding of the Globular Cluster Over--abundance around the Central Giant Elliptical NGC 1399

We investigate the kinematics of a combined sample of 74 globular clusters around NGC 1399. Their high velocity dispersion, increasing with radius, supports their association with the gravitational potential of the galaxy cluster rather than with that of NGC 1399 itself. We find no evidence for rotation in the full sample, although some indication for rotation in the outer regions. The data do not allow us to detect differences between the kinematics of the blue and red sub-populations of globular clusters. A comparison between the globular cluster systems of NGC 1399 and those of NGC 1404 and NGC 1380 indicates that the globular clusters in all three galaxies are likely to have formed via similar mechanisms and at similar epochs. The only property which distinguishes the NGC 1399 globular cluster system from these others is that it is ten times more abundant. We summarize the evidence for associating these excess globulars with the galaxy cluster rather than with NGC 1399 itself, and suggest that the over-abundance can be explained by tidal stripping, at an early epoch, of neighboring galaxies and subsequent accumulation of globulars in the gravitational potential of the galaxy cluster.Comment: AJ accepted (March issue), 27 pages (6 figures included), AAS style, two columns. Also available at http://www.eso.org/~mkissle

SyPRID sampler: A large-volume, high-resolution, autonomous, deep-ocean precision plankton sampling system

AbstractThe current standard for large-volume (thousands of cubic meters) zooplankton sampling in the deep sea is the MOCNESS, a system of multiple opening–closing nets, typically lowered to within 50m of the seabed and towed obliquely to the surface to obtain low-spatial-resolution samples that integrate across 10s of meters of water depth. The SyPRID (Sentry Precision Robotic Impeller Driven) sampler is an innovative, deep-rated (6000m) plankton sampler that partners with the Sentry Autonomous Underwater Vehicle (AUV) to obtain paired, large-volume plankton samples at specified depths and survey lines to within 1.5m of the seabed and with simultaneous collection of sensor data. SyPRID uses a perforated Ultra-High-Molecular-Weight (UHMW) plastic tube to support a fine mesh net within an outer carbon composite tube (tube-within-a-tube design), with an axial flow pump located aft of the capture filter. The pump facilitates flow through the system and reduces or possibly eliminates the bow wave at the mouth opening. The cod end, a hollow truncated cone, is also made of UHMW plastic and includes a collection volume designed to provide an area where zooplankton can collect, out of the high flow region. SyPRID attaches as a saddle-pack to the Sentry vehicle. Sentry itself is configured with a flight control system that enables autonomous survey paths to low altitudes. In its verification deployment at the Blake Ridge Seep (2160m) on the US Atlantic Margin, SyPRID was operated for 6h at an altitude of 5m. It recovered plankton samples, including delicate living larvae, from the near-bottom stratum that is seldom sampled by a typical MOCNESS tow. The prototype SyPRID and its next generations will enable studies of plankton or other particulate distributions associated with localized physico-chemical strata in the water column or above patchy habitats on the seafloor

Using Long-Duration Static Stretch Training to Counteract Strength and Flexibility Deficits in Moderately Trained Participants

Many sports injuries result in surgery and prolonged periods of immobilization, which may lead to significant atrophy accompanied by loss of maximal strength and range of motion and, therefore, a weak-leg/strong-leg ratio (as an imbalance index ∆ ) lower than 1. Consequently, there are common rehabilitation programs that aim to enhance maximal strength, muscle thickness and flexibility; however, the literature demonstrates existing strength imbalances after weeks of rehabilitation. Since no study has previously been conducted to investigate the effects of long-duration static stretch training to treat muscular imbalances, the present research aims to determine the possibility of counteracting imbalances in maximal strength and range of motion. Thirty-nine athletic participants with significant calf muscle imbalances in maximal strength and range of motion were divided into an intervention group (one-hour daily plantar flexors static stretching of the weaker leg for six weeks) and a control group to evaluate the effects on maximal strength and range of motion with extended and bent knee joint. Results show significant increases in maximal strength (d = 0.84–1.61, p < 0.001–0.005) and range of motion (d = 0.92–1.49, p < 0.001–0.002) following six weeks of static stretching. Group * time effects ( p < 0.001–0.004, η² = 0.22–0.55) revealed ∆ changes in the intervention group from 0.87 to 1.03 for maximal strength and from 0.92 to 1.11 in range of motion. The results provide evidence for the use of six weeks of daily, one hour stretching to counteract muscular imbalances. Related research in clinical settings after surgery is suggested

A New Approach to Analyzing Patterns of Collaboration in Co-authorship Networks - Mesoscopic Analysis and Interpretation

This paper focuses on methods to study patterns of collaboration in co-authorship networks at the mesoscopic level. We combine qualitative methods (participant interviews) with quantitative methods (network analysis) and demonstrate the application and value of our approach in a case study comparing three research fields in chemistry. A mesoscopic level of analysis means that in addition to the basic analytic unit of the individual researcher as node in a co-author network, we base our analysis on the observed modular structure of co-author networks. We interpret the clustering of authors into groups as bibliometric footprints of the basic collective units of knowledge production in a research specialty. We find two types of coauthor-linking patterns between author clusters that we interpret as representing two different forms of cooperative behavior, transfer-type connections due to career migrations or one-off services rendered, and stronger, dedicated inter-group collaboration. Hence the generic coauthor network of a research specialty can be understood as the overlay of two distinct types of cooperative networks between groups of authors publishing in a research specialty. We show how our analytic approach exposes field specific differences in the social organization of research.Comment: An earlier version of the paper was presented at ISSI 2009, 14-17 July, Rio de Janeiro, Brazil. Revised version accepted on 2 April 2010 for publication in Scientometrics. Removed part on node-role connectivity profile analysis after finding error in calculation and deciding to postpone analysis

Antimicrobial Resistance of Escherichia coli O26, O103, O111, O128, and O145 from Animals and Humans

Susceptibilities to fourteen antimicrobial agents important in clinical medicine and agriculture were determined for 752 Escherichia coli isolates of serotypes O26, O103, O111, O128, and O145. Strains of these serotypes may cause urinary tract and enteric infections in humans and have been implicated in infections with Shiga toxin–producing E. coli (STEC). Approximately 50% of the 137 isolates from humans were resistant to ampicillin, sulfamethoxazole, cephalothin, tetracycline, or streptomycin, and approximately 25% were resistant to chloramphenicol, trimethoprim-sulfamethoxazole, or amoxicillin-clavulanic acid. Approximately 50% of the 534 isolates from food animals were resistant to sulfamethoxazole, tetracycline, or streptomycin. Of 195 isolates with STEC-related virulence genes, approximately 40% were resistant to sulfamethoxazole, tetracycline, or streptomycin. Findings from this study suggest antimicrobial resistance is widespread among E. coli O26, O103, O111, O128, and O145 inhabiting humans and food animals

Time-Dependent Partition-Free Approach in Resonant Tunneling Systems

An extended Keldysh formalism, well suited to properly take into account the initial correlations, is used in order to deal with the time-dependent current response of a resonant tunneling system. We use a \textit{partition-free} approach by Cini in which the whole system is in equilibrium before an external bias is switched on. No fictitious partitions are used. Besides the steady-state responses one can also calculate physical dynamical responses. In the noninteracting case we clarify under what circumstances a steady-state current develops and compare our result with the one obtained in the partitioned scheme. We prove a Theorem of asymptotic Equivalence between the two schemes for arbitrary time-dependent disturbances. We also show that the steady-state current is independent of the history of the external perturbation (Memory Loss Theorem). In the so called wide-band limit an analytic result for the time-dependent current is obtained. In the interacting case we propose an exact non-equilibrium Green function approach based on Time Dependent Density Functional Theory. The equations are no more difficult than an ordinary Mean Field treatment. We show how the scattering-state scheme by Lang follows from our formulation. An exact formula for the steady-state current of an arbitrary interacting resonant tunneling system is obtained. As an example the time-dependent current response is calculated in the Random Phase Approximation.Comment: final version, 18 pages, 9 figure

An Analytically Solvable Model for Rapid Evolution of Modular Structure

Biological systems often display modularity, in the sense that they can be decomposed into nearly independent subsystems. Recent studies have suggested that modular structure can spontaneously emerge if goals (environments) change over time, such that each new goal shares the same set of sub-problems with previous goals. Such modularly varying goals can also dramatically speed up evolution, relative to evolution under a constant goal. These studies were based on simulations of model systems, such as logic circuits and RNA structure, which are generally not easy to treat analytically. We present, here, a simple model for evolution under modularly varying goals that can be solved analytically. This model helps to understand some of the fundamental mechanisms that lead to rapid emergence of modular structure under modularly varying goals. In particular, the model suggests a mechanism for the dramatic speedup in evolution observed under such temporally varying goals

Automatic diagnosis of the 12-lead ECG using a deep neural network

The role of automatic electrocardiogram (ECG) analysis in clinical practice is limited by the accuracy of existing models. Deep Neural Networks (DNNs) are models composed of stacked transformations that learn tasks by examples. This technology has recently achieved striking success in a variety of task and there are great expectations on how it might improve clinical practice. Here we present a DNN model trained in a dataset with more than 2 million labeled exams analyzed by the Telehealth Network of Minas Gerais and collected under the scope of the CODE (Clinical Outcomes in Digital Electrocardiology) study. The DNN outperform cardiology resident medical doctors in recognizing 6 types of abnormalities in 12-lead ECG recordings, with F1 scores above 80% and specificity over 99%. These results indicate ECG analysis based on DNNs, previously studied in a single-lead setup, generalizes well to 12-lead exams, taking the technology closer to the standard clinical practice

An End to Endless Forms: Epistasis, Phenotype Distribution Bias, and Nonuniform Evolution

Studies of the evolution of development characterize the way in which gene regulatory dynamics during ontogeny constructs and channels phenotypic variation. These studies have identified a number of evolutionary regularities: (1) phenotypes occupy only a small subspace of possible phenotypes, (2) the influence of mutation is not uniform and is often canalized, and (3) a great deal of morphological variation evolved early in the history of multicellular life. An important implication of these studies is that diversity is largely the outcome of the evolution of gene regulation rather than the emergence of new, structural genes. Using a simple model that considers a generic property of developmental maps—the interaction between multiple genetic elements and the nonlinearity of gene interaction in shaping phenotypic traits—we are able to recover many of these empirical regularities. We show that visible phenotypes represent only a small fraction of possibilities. Epistasis ensures that phenotypes are highly clustered in morphospace and that the most frequent phenotypes are the most similar. We perform phylogenetic analyses on an evolving, developmental model and find that species become more alike through time, whereas higher-level grades have a tendency to diverge. Ancestral phenotypes, produced by early developmental programs with a low level of gene interaction, are found to span a significantly greater volume of the total phenotypic space than derived taxa. We suggest that early and late evolution have a different character that we classify into micro- and macroevolutionary configurations. These findings complement the view of development as a key component in the production of endless forms and highlight the crucial role of development in constraining biotic diversity and evolutionary trajectories

Network Archaeology: Uncovering Ancient Networks from Present-day Interactions

Often questions arise about old or extinct networks. What proteins interacted in a long-extinct ancestor species of yeast? Who were the central players in the Last.fm social network 3 years ago? Our ability to answer such questions has been limited by the unavailability of past versions of networks. To overcome these limitations, we propose several algorithms for reconstructing a network's history of growth given only the network as it exists today and a generative model by which the network is believed to have evolved. Our likelihood-based method finds a probable previous state of the network by reversing the forward growth model. This approach retains node identities so that the history of individual nodes can be tracked. We apply these algorithms to uncover older, non-extant biological and social networks believed to have grown via several models, including duplication-mutation with complementarity, forest fire, and preferential attachment. Through experiments on both synthetic and real-world data, we find that our algorithms can estimate node arrival times, identify anchor nodes from which new nodes copy links, and can reveal significant features of networks that have long since disappeared.Comment: 16 pages, 10 figure