Using ecological niche modelling to infer past, present and future environmental suitability for Leiopelma hochstetteri, an endangered New Zealand native frog

Leiopelma hochstetteri is an endangered New Zealand frog now confined to isolated populations scattered across the North Island. A better understanding of its past, current and predicted future environmental suitability will contribute to its conservation which is in jeopardy due to human activities, feral predators, disease and climate change. Here we use ecological niche modelling with all known occurrence data (N = 1708) and six determinant environmental variables to elucidate current, pre-human and future environmental suitability of this species. Comparison among independent runs, subfossil records and a clamping method allow validation of models. Many areas identified as currently suitable do not host any known populations. This apparent discrepancy could be explained by several non exclusive hypotheses: the areas have not been adequately surveyed and undiscovered populations still remain, the model is over simplistic; the species` sensitivity to fragmentation and small population size; biotic interactions; historical events. An additional outcome is that apparently suitable, but frog-less areas could be targeted for future translocations. Surprisingly, pre-human conditions do not differ markedly highlighting the possibility that the range of the species was broadly fragmented before human arrival. Nevertheless, some populations, particularly on the west of the North Island may have disappeared as a result of human mediated habitat modification. Future conditions are marked with higher temperatures, which are predicted to be favourable to the species. However, such virtual gain in suitable range will probably not benefit the species given the highly fragmented nature of existing habitat and the low dispersal ability of this species. (C) 2010 Elsevier Ltd. All rights reserved.

Induction of neutralizing antibodies in mice immunized with an amino-terminal polypeptide of Streptococcus mutans P1 protein produced by a recombinant Bacillus subtilis strain

The oral pathogen Streptococcus mutans expresses a surface protein, P1, which interacts with the salivary pellicle on the tooth surface or with fluid-phase saliva, resulting in bacterial adhesion or aggregation, respectively. P1 is a target of protective immunity. Its N-terminal region has been associated with adhesion and aggregation functions and contains epitopes recognized by efficacious antibodies. In this study, we used Bacillus subtilis, a gram-positive expression host, to produce a recombinant N-terminal polypeptide of P1 (P1(39-512)) derived from the S. mutans strain UA159. Purified P1(39-512) reacted with an anti-full-length P1 antiserum as well as one raised against intact S. mutans cells, indicating preserved antigenicity. Immunization of mice with soluble and heat-denatured P1(39-512) induced antibodies that reacted specifically with native P1 on the surface of S. mutans cells. The anti-P1(39-512) antiserum was as effective at blocking saliva-mediated aggregation of S. mutans cells and better at blocking bacterial adhesion to saliva-coated plastic surfaces compared with the anti-full-length P1 antiserum. In addition, adsorption of the anti-P1 antiserum with P1(39-512) eliminated its ability to block the adhesion of S. mutans cells to abiotic surfaces. The present results indicate that P1(39-512), expressed and purified from a recombinant B. subtilis strain, maintains important immunological features of the native protein and represents an additional tool for the development of anticaries vaccines.

A complex network approach to text summarization

Automatic summarization of texts is now crucial for several information retrieval tasks owing to the huge amount of information available in digital media, which has increased the demand for simple, language-independent extractive summarization strategies. In this paper, we employ concepts and metrics of complex networks to select sentences for an extractive summary. The graph or network representing one piece of text consists of nodes corresponding to sentences, while edges connect sentences that share common meaningful nouns. Because various metrics could be used, we developed a set of 14 summarizers, generically referred to as CN-Summ, employing network concepts such as node degree, length of shortest paths, d-rings and k-cores. An additional summarizer was created which selects the highest ranked sentences in the 14 systems, as in a voting system. When applied to a corpus of Brazilian Portuguese texts, some CN-Summ versions performed better than summarizers that do not employ deep linguistic knowledge, with results comparable to state-of-the-art summarizers based on expensive linguistic resources. The use of complex networks to represent texts appears therefore as suitable for automatic summarization, consistent with the belief that the metrics of such networks may capture important text features. (c) 2008 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/.