DENSITY PROFILE, VELOCITY ANISOTROPY, AND LINE-OF-SIGHT EXTERNAL CONVERGENCE OF SLACS GRAVITATIONAL LENSES

Data from 58 strong-lensing events surveyed by the Sloan Lens ACS Survey are used to estimate the projected galaxy mass inside their Einstein radii by two independent methods: stellar dynamics and strong gravitational lensing. We perform a joint analysis of these two estimates inside models with up to three degrees of freedom with respect to the lens density profile, stellar velocity anisotropy, and line-of-sight (LOS) external convergence, which incorporates the effect of the large-scale structure on strong lensing. A Bayesian analysis is employed to estimate the model parameters, evaluate their significance, and compare models. We find that the data favor Jaffe`s light profile over Hernquist`s, but that any particular choice between these two does not change the qualitative conclusions with respect to the features of the system that we investigate. The density profile is compatible with an isothermal, being sightly steeper and having an uncertainty in the logarithmic slope of the order of 5% in models that take into account a prior ignorance on anisotropy and external convergence. We identify a considerable degeneracy between the density profile slope and the anisotropy parameter, which largely increases the uncertainties in the estimates of these parameters, but we encounter no evidence in favor of an anisotropic velocity distribution on average for the whole sample. An LOS external convergence following a prior probability distribution given by cosmology has a small effect on the estimation of the lens density profile, but can increase the dispersion of its value by nearly 40%.

EVOLUTIONARY BIOLOGY IN BIODIVERSITY SCIENCE, CONSERVATION, AND POLICY: A CALL TO ACTION

Evolutionary biologists have long endeavored to document how many species exist on Earth, to understand the processes by which biodiversity waxes and wanes, to document and interpret spatial patterns of biodiversity, and to infer evolutionary relationships. Despite the great potential of this knowledge to improve biodiversity science, conservation, and policy, evolutionary biologists have generally devoted limited attention to these broader implications. Likewise, many workers in biodiversity science have underappreciated the fundamental relevance of evolutionary biology. The aim of this article is to summarize and illustrate some ways in which evolutionary biology is directly relevant We do so in the context of four broad areas: (1) discovering and documenting biodiversity, (2) understanding the causes of diversification, (3) evaluating evolutionary responses to human disturbances, and (4) implications for ecological communities, ecosystems, and humans We also introduce bioGENESIS, a new project within DIVERSITAS launched to explore the potential practical contributions of evolutionary biology In addition to fostering the integration of evolutionary thinking into biodiversity science, bioGENESIS provides practical recommendations to policy makers for incorporating evolutionary perspectives into biodiversity agendas and conservation. We solicit your involvement in developing innovative ways of using evolutionary biology to better comprehend and stem the loss of biodiversity.

Advertisement call, vocal activity, and geographic distribution of Brachycephalus hermogenesi (Giaretta and Sawaya, 1998) (Anura, Brachycephalidae)

Brachycephalus hermogenesi is an endemic leaf litter inhabitant of the Atlantic forest of southeastern Brazil, whose original distribution included a restricted area near the boundaries of the States of Sao Paulo and Rio de Janeiro. We were surprised to find out, while conducting herpetofaunal surveys at Estacao Biologica de Boraceia (EBB), that the background forest insect-like sound we have been searching for corresponded to calling individuals of the species. Males call during the day at high densities, hidden under the leaf litter. Individuals do not answer playback, seem to move very infrequently, and seem to ignore nearby calling activity. We gathered data on annual and daily vocal activity of the species at EBB, observing a total of 1,549 calls given by 31 focal individuals in November 2003 and 2005. The call varies from short single note calls to calls composed of groups of two to seven similar notes emitted at regular intervals. We also extend the known distribution of the species southward to the State of Sao Paulo.

Natural History of the Lutz`s Frog Cycloramphus lutzorum Heyer, 1983 (Anura: Cycloramphidae) in the Brazilian Atlantic Forest: Description of the Advertisement Call, Tadpole, and Karyotype

We describe the advertisement call, tadpole, karyotype, and additional information on the natural history of Cycloramphus lutzorum from southern Brazil. Sonograms were generated from digitally recorded calls. Tadpoles were collected in the field for description in the lab, and an adult was collected for karyotyping. Data on seasonal activity were gathered monthly from November 2005 to November 2007. All tadpoles (N = 21), juveniles (N = 18), and adults (N = 52) were found exclusively in streams. Reproduction, as identified by calling frogs, occurred from July through November. Frogs call all day long, but mostly at dusk, from rock crevices inside the stream edges near the splash zone. The call is short and loud, with 11 pulsed notes, of 491-641 ms, with a dominant frequency of 0.98-1.39 kHz. We describe the exotrophic and semiterrestrial tadpoles, always found in constantly humid vertical rock walls in the stream. Tadpoles of C. lutzorum are recognized by differences in labial tooth row formula, eye diameter, body shape, position of nares, and development of tail. Like congeneric species, the karyotype of C. lutzorum comprises 26 metacentric and submetacentric chromosomes. Cycloramphus lutzorum is restricted to and adapted for living in fast flowing streams, many of which are threatened by deforestation, pollution, and habitat loss. Therefore, we recommend the status of C. lutzorum be changed from its current ""Data Deficient"" to ""Near Threatened (NT)"" in the IUCN species red list.

Evolutionary model trees for handling continuous classes in machine learning

Model trees are a particular case of decision trees employed to solve regression problems. They have the advantage of presenting an interpretable output, helping the end-user to get more confidence in the prediction and providing the basis for the end-user to have new insight about the data, confirming or rejecting hypotheses previously formed. Moreover, model trees present an acceptable level of predictive performance in comparison to most techniques used for solving regression problems. Since generating the optimal model tree is an NP-Complete problem, traditional model tree induction algorithms make use of a greedy top-down divide-and-conquer strategy, which may not converge to the global optimal solution. In this paper, we propose a novel algorithm based on the use of the evolutionary algorithms paradigm as an alternate heuristic to generate model trees in order to improve the convergence to globally near-optimal solutions. We call our new approach evolutionary model tree induction (E-Motion). We test its predictive performance using public UCI data sets, and we compare the results to traditional greedy regression/model trees induction algorithms, as well as to other evolutionary approaches. Results show that our method presents a good trade-off between predictive performance and model comprehensibility, which may be crucial in many machine learning applications. (C) 2010 Elsevier Inc. All rights reserved.

Improving ultimate convergence of an augmented Lagrangian method

Optimization methods that employ the classical Powell-Hestenes-Rockafellar augmented Lagrangian are useful tools for solving nonlinear programming problems. Their reputation decreased in the last 10 years due to the comparative success of interior-point Newtonian algorithms, which are asymptotically faster. In this research, a combination of both approaches is evaluated. The idea is to produce a competitive method, being more robust and efficient than its `pure` counterparts for critical problems. Moreover, an additional hybrid algorithm is defined, in which the interior-point method is replaced by the Newtonian resolution of a Karush-Kuhn-Tucker (KKT) system identified by the augmented Lagrangian algorithm. The software used in this work is freely available through the Tango Project web page:http://www.ime.usp.br/similar to egbirgin/tango/.

Density-Profile Processes Describing Biological Signaling Networks: Almost Sure Convergence to Deterministic Trajectories

We introduce jump processes in R(k), called density-profile processes, to model biological signaling networks. Our modeling setup describes the macroscopic evolution of a finite-size spin-flip model with k types of spins with arbitrary number of internal states interacting through a non-reversible stochastic dynamics. We are mostly interested on the multi-dimensional empirical-magnetization vector in the thermodynamic limit, and prove that, within arbitrary finite time-intervals, its path converges almost surely to a deterministic trajectory determined by a first-order (non-linear) differential equation with explicit bounds on the distance between the stochastic and deterministic trajectories. As parameters of the spin-flip dynamics change, the associated dynamical system may go through bifurcations, associated to phase transitions in the statistical mechanical setting. We present a simple example of spin-flip stochastic model, associated to a synthetic biology model known as repressilator, which leads to a dynamical system with Hopf and pitchfork bifurcations. Depending on the parameter values, the magnetization random path can either converge to a unique stable fixed point, converge to one of a pair of stable fixed points, or asymptotically evolve close to a deterministic orbit in Rk. We also discuss a simple signaling pathway related to cancer research, called p53 module.