Bootstrap confidence regions for canonical variates: Application to studies of evolutionary differentiation

Theory recently developed to construct confidence regions based on the parametric bootstrap is applied to add inferential information to graphical displays of sample centroids in canonical variate analysis. Problems of morphometric differentiation among subspecies and species are addressed using numerical resampling procedures.

Determination and statistical treatment of short-circuit currents for faults on the lines of networks of 440 kV CESP system

The distribution of short-circuit current is investigated by means of two methods, one direct and the other analytic; both methods consider uniform probability distribution of line faults. In the direct method, the procedure consists of calculating fault currents at equidistant points along the line, starting from one of the end points and considering the other end open. The magnitude of the current is classified according to Brazilian standards (regulation NBR-7118). The analytic method assumes that the distribution of short-circuit currents through the busbar and the distribution of the line length connected to it are known, as well as the independence of values. The method is designed to determine the probability that fault currents through a line will surpass the pre-established magnitude, thus generating frequency distribution curves of short-circuit currents along the lines.

Biology Teachers'Conceptions of the Diversity of Life and the Historical Development of Evolutionary Concepts

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

A survey of evolutionary algorithms for decision-tree induction

This paper presents a survey of evolutionary algorithms that are designed for decision-tree induction. In this context, most of the paper focuses on approaches that evolve decision trees as an alternate heuristics to the traditional top-down divide-and-conquer approach. Additionally, we present some alternative methods that make use of evolutionary algorithms to improve particular components of decision-tree classifiers. The paper's original contributions are the following. First, it provides an up-to-date overview that is fully focused on evolutionary algorithms and decision trees and does not concentrate on any specific evolutionary approach. Second, it provides a taxonomy, which addresses works that evolve decision trees and works that design decision-tree components by the use of evolutionary algorithms. Finally, a number of references are provided that describe applications of evolutionary algorithms for decision-tree induction in different domains. At the end of this paper, we address some important issues and open questions that can be the subject of future research.

A Java-based simulation environment for networks of simplified neuron models

ACR is supported by a research grant from CNPq.

Application of evolutionary algorithms and the boundary element method in the optimization of noise barrier profiles

[EN]This Ph.D. thesis presents a general, robust methodology that may cover any type of 2D acoustic optimization problem. A procedure involving the coupling of Boundary Elements (BE) and Evolutionary Algorithms is proposed for systematic geometric modifications of road barriers that lead to designs with ever-increasing screening performance. Numerical simulations involving single- and multi-objective optimizations of noise barriers of varied nature are included in this document. results disclosed justify the implementation of this methodology by leading to optimal solutions of previously defined topologies that, in general, greatly outperform the acoustic efficiency of classical, widely used barrier designs normally erected near roads.

Synaptic connectivity in micropatterned networks of neuronal cells

The presented thesis describes the formation of functional neuronal networks on an underlying micropattern. Small circuits of interconnected neurons defined by the geometry of the patterned substrate could be observed and were utilised as a model system of reduced complexity for the behaviour of neuronal network formation and activity. The first set of experiments was conducted to investigate aspects of the substrate preparation. Micropatterned substrates were created by microcontact printing of physiological proteins onto polystyrene culture dishes. The substrates displayed a high contrast between the repellant background and the cell attracting pattern, such that neurons seeded onto these surfaces aligned with the stamped structure. Both the patterning process and the cell culture were optimised, yielding highly compliant low-density networks of living neuronal cells. In the second step, cellular physiology of the cells grown on these substrates was investigated by patch-clamp measurements and compared to cells cultivated under control conditions. It could be shown that the growth on a patterned substrate did not result in an impairment of cellular integrity nor that it had an impact on synapse formation or synaptic efficacy. Due to the extremely low-density cell culture that was applied, cellular connectivity through chemical synapses could be observed at the single cell level. Having established that single cells were not negatively affected by the growth on patterned substrates, aspects of network formation were investigated. The formation of physical contact between two cells was analysed through microinjection studies and related to the rate at which functional synaptic contacts formed between two neighbouring cells. Surprisingly, the rate of synapse formation between physically contacting cells was shown to be unaltered in spite of the drastic reduction of potential interaction partners on the micropattern. Additional features of network formation were investigated and found consistent with results reported by other groups: A different rate of synapse formation by excitatory and inhibitory neurons could be reproduced as well as a different rate of frequency-dependent depression at excitatory and inhibitory synapses. Furthermore, regarding simple feedback loops, a significant enrichment of reciprocal connectivity between mixed pairs of excitatory and inhibitory neurons relative to uniform pairs could be demonstrated. This phenomenon has also been described by others in unpatterned cultures [Muller, 1997] and may therefore be a feature underlying neuronal network formation in general. Based on these findings, it can be assumed that inherent features of neuronal behaviour and cellular recognition mechanisms were found in the cultured networks and appear to be undisturbed by patterned growth. At the same time, it was possible to reduce the complexity of the forming networks dramatically in a cell culture on a patterned surface. Thus, features of network architecture and synaptic connectivity could be investigated on the single cell level under highly defined conditions.

Analysis and developments of uncertainty processors for real time flood forecasting

The hydrologic risk (and the hydro-geologic one, closely related to it) is, and has always been, a very relevant issue, due to the severe consequences that may be provoked by a flooding or by waters in general in terms of human and economic losses. Floods are natural phenomena, often catastrophic, and cannot be avoided, but their damages can be reduced if they are predicted sufficiently in advance. For this reason, the flood forecasting plays an essential role in the hydro-geological and hydrological risk prevention. Thanks to the development of sophisticated meteorological, hydrologic and hydraulic models, in recent decades the flood forecasting has made a significant progress, nonetheless, models are imperfect, which means that we are still left with a residual uncertainty on what will actually happen. In this thesis, this type of uncertainty is what will be discussed and analyzed. In operational problems, it is possible to affirm that the ultimate aim of forecasting systems is not to reproduce the river behavior, but this is only a means through which reducing the uncertainty associated to what will happen as a consequence of a precipitation event. In other words, the main objective is to assess whether or not preventive interventions should be adopted and which operational strategy may represent the best option. The main problem for a decision maker is to interpret model results and translate them into an effective intervention strategy. To make this possible, it is necessary to clearly define what is meant by uncertainty, since in the literature confusion is often made on this issue. Therefore, the first objective of this thesis is to clarify this concept, starting with a key question: should be the choice of the intervention strategy to adopt based on the evaluation of the model prediction based on its ability to represent the reality or on the evaluation of what actually will happen on the basis of the information given by the model forecast? Once the previous idea is made unambiguous, the other main concern of this work is to develope a tool that can provide an effective decision support, making possible doing objective and realistic risk evaluations. In particular, such tool should be able to provide an uncertainty assessment as accurate as possible. This means primarily three things: it must be able to correctly combine all the available deterministic forecasts, it must assess the probability distribution of the predicted quantity and it must quantify the flooding probability. Furthermore, given that the time to implement prevention strategies is often limited, the flooding probability will have to be linked to the time of occurrence. For this reason, it is necessary to quantify the flooding probability within a horizon time related to that required to implement the intervention strategy and it is also necessary to assess the probability of the flooding time.

Regulatory networks of Neisseria meningitidis and their implications for pathogenesis

Neisseria meningitidis, the leading cause of bacterial meningitis, can adapt to different host niches during human infection. Both transcriptional and post-transcriptional regulatory networks have been identified as playing a crucial role for bacterial stress responses and virulence. We investigated the N. meningitidis transcriptional landscape both by microarray and by RNA sequencing (RNAseq). Microarray analysis of N. meningitidis grown in the presence or absence of glucose allowed us to identify genes regulated by carbon source availability. In particular, we identified a glucose-responsive hexR-like transcriptional regulator in N. meningitidis. Deletion analysis showed that the hexR gene is accountable for a subset of the glucose-responsive regulation, and in vitro assays with the purified protein showed that HexR binds to the promoters of the central metabolic operons of meningococcus, by targeting a DNA region overlapping putative regulatory sequences. Our results indicate that HexR coordinates the central metabolism of meningococcus in response to the availability of glucose, and N. meningitidis strains lacking the hexR gene are also deficient in establishing successful bacteremia in a mouse model of infection. In parallel, RNAseq analysis of N. meningitidis cultured under standard or iron-limiting in vitro growth conditions allowed us to identify novel small non-coding RNAs (sRNAs) potentially involved in N. meningitidis regulatory networks. Manual curation of the RNAseq data generated a list of 51 sRNAs, 8 of which were validated by Northern blotting. Deletion of selected sRNAs caused attenuation of N. meningitidis infection in a murine model, leading to the identification of the first sRNAs influencing meningococcal bacteraemia. Furthermore, we describe the identification and initial characterization of a novel sRNA unique to meningococcus, closely associated to genes relevant for the intracellular survival of pathogenic Neisseriae. Taken together, our findings could help unravel the regulation of N. meningitidis adaptation to the host environment and its implications for pathogenesis.

Comparisons of sound processors based on osseointegrated implants in patients with conductive or mixed hearing loss

To compare the patient benefit of the Bone-Anchored Hearing Aid (Baha) Divino and the Baha BP100 sound processors.