Molecular discrimination of pouched four-eyed opossums from the Mamirauá Reserve in the Brazilian Amazon

Previous cytochrome B (CytB) mtDNA studies have suggested four species for the opossum genus Philander (four-eyed opossums), three (P. mcilhennyi, P. andersoni and P. opossum) from the Amazon and one (P. frenata) from the Brazilian Atlantic forest. During a faunal survey nine specimens of Philander sp. and four of Didelphis marsupialis were collected in the Mamirauá Sustainable Reserve, Amazonas State, Brazil. Preliminary analyses based on morphology and geographical distributions were not conclusive, suggesting that Philander specimens could belong to either P. andersoni or P. opossum. In order to elucidate the relationship of this taxon to the remaining Amazonian taxa, seven Philander and two Didelphis specimens animals were sequenced for the cytB mtDNA gene and compared to other previously studied taxa. The maximum likelihood (ML), neighbor-Joining (NJ) and maximum parsimony (MP) consensus bootstrap trees depicted six groups: Didelphis., P. frenata, P andersoni, P. mcilhennyi, P.o. opossum and Philander sp. and Philander canus in a common assemblage supported by significant bootstrap values, suggesting that the Philander sp. from Mamiraua in fact belongs to the species Philander canus.

Análise das relações filogenéticas entre as subfamílias de characidae (Ostariophysi: Characoformes) utilizando sequências de DNA mitocondrial e nuclear

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Trichome structure and evolution in Neotropical lianas

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

Morphological and molecular characterization of Clinostomum detruncatum metacercariae infecting Synbranchus marmoratus

A complete morphological description, supported by molecular data, of Clinostomum detruncatum metacercariae is reported in this study. The metacercariae were found infecting Synbranchus marmoratus from the Paraná River, municipality of GuaÃ-ra, Paraná State, Brazil. The taenioidean uterus is the main morphological characteristic of this species. Both Neighbour-Joining (NJ) and Maximum Likelihood (ML) trees showed 2 clear separate clusters grouping together the species from the Palearctic region (Clinostomum complanatum, Clinostomum cutaneum, Clinostomum phalacrocoracis, Clinostomum philippinense) and from the Nearctic/Neotropical regions (Clinostomum marginatum, Clinostomum tataxumui, C. detruncatum, Clinostomum sp. 7). The latter clade is divided into 2 subclades grouping species from North America and Mexico (C. marginatum and C. tataxumui), and from Brazil (C. detruncatum and Clinostomum sp. 7).

Phylogeny of Western Palaearctic long-eared bats (Mammalia, Chiroptera, Plecotus) - a molecular perspective

Die Phylogenie der Westpaläarktischen Langohren (Mammalia, Chiroptera, Plecotus) – eine molekulare Analyse Die Langohren stellen eine Fledermausgattung dar, die fast alle westpaläarktischen Habitate bist zum Polarkreis hin besiedeln und in vielerlei Hinsicht rätselhaft sind. In der Vergangenheit wurden zahlreiche Formen und Varietäten beschrieben. Trotzdem galt für lange Zeit, dass nur zwei Arten in Europa anerkannt wurden. Weitere Arten waren aus Nordafrika, den Kanaren und Asien bekannt, aber auch deren Artstatus wurde vielfach in Frage gestellt. In der vorliegenden Dissertation habe ich mittels molekularer Daten,der partiellen Sequenzierung mitochondrialer Gene (16S rRNA und ND1), sowie der mitochondrialen Kontrollregion, eine molekular Analyse der phylogenetischen Verwandtschaftsverhältnisse innerhalb und zwischen den Linien der westpaläarktischen Langohren durchgeführt. Die besten Substitutionsmodelle wurden berechnet und phylogenetische Bäume mit Hilfe vier verschiedener Methoden konstruiert: dem neighbor joining Verfahren (NJ), dem maximum likelihood Verfahren (ML), dem maximum parsimony Verfahren (MP) und dem Bayesian Verfahren. Sechs Linien der Langohren sind genetisch auf einem Artniveau differenziert: Plecotus auritus, P. austriacus, P. balensis, P. christii, P. sardus, und P. macrobullaris. Im Falle der Arten P. teneriffae, P. kolombatovici und P. begognae ist die alleinige Interpretation der genetischen Daten einzelner mitochondrialer Gene für eine Festlegung des taxonomischen Ranges nicht ausreichend. Ich beschreibe in dieser Dissertation drei neue Taxa: Plecotus sardus, P. kolombatovici gaisleri (=Plecotus teneriffae gaisleri, Benda et al. 2004) and P. macrobullaris alpinus [=Plecotus alpinus, Kiefer & Veith 2002). Morphologische Kennzeichen, insbesondere für die Erkennung im Feld, werden hier dargestellt. Drei der sieben Arten sind polytypisch: P. auritus (eine west- und ein osteuropäische Linie, eine sardische Linie und eine aktuell entdeckte kaukasische Linie, Plecotus kolombatovici (P. k. kolombatovici, P. k. gaisleri und P. k. ssp.) und P. macrobullaris (P. m. macrobullaris und P. m. alpinus). Die Verbreitungsgebiete der meisten Arten werden in dieser Arbeit erstmals ausschließlich anhand genetisch zugeordneter Tiere dargestellt.Die Untersuchung der ökologischen Einnischung der nun anerkannten Formen, insbesondere in Gebieten sympatrischer Verbreitung, bietet ein spannendes und lohnendes Feld für zukünftige Forschungen. Nicht zuletzt muss sich die Entdeckung eines beachtlichen Anteils kryptischer Diversität innerhalb der westpaläarktischen Langohren auch bei der Entwicklung spezieller Artenschutzkonzepte widerspiegeln.

RFID tag localization with virtual multi-antenna systems

In questa tesi si sono valutate le prestazioni di un sistema di localizzazione multi-antenna di tag radio frequency identification (RFID) passivi in ambiente indoor. Il sistema, composto da un reader in movimento che percorre una traiettoria nota, ha come obiettivo localizzare il tag attraverso misure di fase; più precisamente la differenza di fase tra il segnale di interrogazione, emesso dal reader, e il segnale ricevuto riflesso dal tag che è correlato alla distanza tra di essi. Dopo avere eseguito una ricerca sullo stato dell’arte di queste tecniche e aver derivato il criterio maximum likelihood (ML) del sistema si è proceduto a valutarne le prestazioni e come eventuali fattori agissero sul risultato di localizzazione attraverso simulazioni Matlab. Come ultimo passo si è proceduto a effettuare una campagna di misure, testando il sistema in un ambiente reale. Si sono confrontati i risultati di localizzazione di tutti gli algoritmi proposti quando il reader si muove su una traiettoria rettilinea e su una traiettoria angolare, cercando di capire come migliorare i risultati.

Multilevel Latent Class Models with Dirichlet Mixing Distribution

Latent class analysis (LCA) and latent class regression (LCR) are widely used for modeling multivariate categorical outcomes in social sciences and biomedical studies. Standard analyses assume data of different respondents to be mutually independent, excluding application of the methods to familial and other designs in which participants are clustered. In this paper, we develop multilevel latent class model, in which subpopulation mixing probabilities are treated as random effects that vary among clusters according to a common Dirichlet distribution. We apply the Expectation-Maximization (EM) algorithm for model fitting by maximum likelihood (ML). This approach works well, but is computationally intensive when either the number of classes or the cluster size is large. We propose a maximum pairwise likelihood (MPL) approach via a modified EM algorithm for this case. We also show that a simple latent class analysis, combined with robust standard errors, provides another consistent, robust, but less efficient inferential procedure. Simulation studies suggest that the three methods work well in finite samples, and that the MPL estimates often enjoy comparable precision as the ML estimates. We apply our methods to the analysis of comorbid symptoms in the Obsessive Compulsive Disorder study. Our models' random effects structure has more straightforward interpretation than those of competing methods, thus should usefully augment tools available for latent class analysis of multilevel data.

Estimation of the degree of polarization through computational sensing

The degree of polarization of a refected field from active laser illumination can be used for object identifcation and classifcation. The goal of this study is to investigate methods for estimating the degree of polarization for refected fields with active laser illumination, which involves the measurement and processing of two orthogonal field components (complex amplitudes), two orthogonal intensity components, and the total field intensity. We propose to replace interferometric optical apparatuses with a computational approach for estimating the degree of polarization from two orthogonal intensity data and total intensity data. Cramer-Rao bounds for each of the three sensing modalities with various noise models are computed. Algebraic estimators and maximum-likelihood (ML) estimators are proposed. Active-set algorithm and expectation-maximization (EM) algorithm are used to compute ML estimates. The performances of the estimators are compared with each other and with their corresponding Cramer-Rao bounds. Estimators for four-channel polarimeter (intensity interferometer) sensing have a better performance than orthogonal intensities estimators and total intensity estimators. Processing the four intensities data from polarimeter, however, requires complicated optical devices, alignment, and four CCD detectors. It only requires one or two detectors and a computer to process orthogonal intensities data and total intensity data, and the bounds and estimator performances demonstrate that reasonable estimates may still be obtained from orthogonal intensities or total intensity data. Computational sensing is a promising way to estimate the degree of polarization.

Models of DNA sequence evolution

Models of DNA sequence evolution and methods for estimating evolutionary distances are needed for studying the rate and pattern of molecular evolution and for inferring the evolutionary relationships of organisms or genes. In this dissertation, several new models and methods are developed.^ The rate variation among nucleotide sites: To obtain unbiased estimates of evolutionary distances, the rate heterogeneity among nucleotide sites of a gene should be considered. Commonly, it is assumed that the substitution rate varies among sites according to a gamma distribution (gamma model) or, more generally, an invariant+gamma model which includes some invariable sites. A maximum likelihood (ML) approach was developed for estimating the shape parameter of the gamma distribution $(\alpha)$ and/or the proportion of invariable sites $(\theta).$ Computer simulation showed that (1) under the gamma model, $\alpha$ can be well estimated from 3 or 4 sequences if the sequence length is long; and (2) the distance estimate is unbiased and robust against violations of the assumptions of the invariant+gamma model.^ However, this ML method requires a huge amount of computational time and is useful only for less than 6 sequences. Therefore, I developed a fast method for estimating $\alpha,$ which is easy to implement and requires no knowledge of tree. A computer program was developed for estimating $\alpha$ and evolutionary distances, which can handle the number of sequences as large as 30.^ Evolutionary distances under the stationary, time-reversible (SR) model: The SR model is a general model of nucleotide substitution, which assumes (i) stationary nucleotide frequencies and (ii) time-reversibility. It can be extended to SRV model which allows rate variation among sites. I developed a method for estimating the distance under the SR or SRV model, as well as the variance-covariance matrix of distances. Computer simulation showed that the SR method is better than a simpler method when the sequence length $L>1,000$ bp and is robust against deviations from time-reversibility. As expected, when the rate varies among sites, the SRV method is much better than the SR method.^ The evolutionary distances under nonstationary nucleotide frequencies: The statistical properties of the paralinear and LogDet distances under nonstationary nucleotide frequencies were studied. First, I developed formulas for correcting the estimation biases of the paralinear and LogDet distances. The performances of these formulas and the formulas for sampling variances were examined by computer simulation. Second, I developed a method for estimating the variance-covariance matrix of the paralinear distance, so that statistical tests of phylogenies can be conducted when the nucleotide frequencies are nonstationary. Third, a new method for testing the molecular clock hypothesis was developed in the nonstationary case. ^

THEORETICAL STUDIES ON THE METHODS OF RECONSTRUCTING PHYLOGENETIC TREES FROM DNA SEQUENCE DATA (MOLECULAR EVOLUTION, HOMINOID EVOLUTION, COMPUTER SIMULATION)

(1) A mathematical theory for computing the probabilities of various nucleotide configurations is developed, and the probability of obtaining the correct phylogenetic tree (model tree) from sequence data is evaluated for six phylogenetic tree-making methods (UPGMA, distance Wagner method, transformed distance method, Fitch-Margoliash's method, maximum parsimony method, and compatibility method). The number of nucleotides (m*) necessary to obtain the correct tree with a probability of 95% is estimated with special reference to the human, chimpanzee, and gorilla divergence. m* is at least 4,200, but the availability of outgroup species greatly reduces m* for all methods except UPGMA. m* increases if transitions occur more frequently than transversions as in the case of mitochondrial DNA. (2) A new tree-making method called the neighbor-joining method is proposed. This method is applicable either for distance data or character state data. Computer simulation has shown that the neighbor-joining method is generally better than UPGMA, Farris' method, Li's method, and modified Farris method on recovering the true topology when distance data are used. A related method, the simultaneous partitioning method, is also discussed. (3) The maximum likelihood (ML) method for phylogeny reconstruction under the assumption of both constant and varying evolutionary rates is studied, and a new algorithm for obtaining the ML tree is presented. This method gives a tree similar to that obtained by UPGMA when constant evolutionary rate is assumed, whereas it gives a tree similar to that obtained by the maximum parsimony tree and the neighbor-joining method when varying evolutionary rate is assumed. ^

New algorithms for automated phylogenetic reconstruction using artificial intelligence and data mining techniques

Academic and industrial research in the late 90s have brought about an exponential explosion of DNA sequence data. Automated expert systems are being created to help biologists to extract patterns, trends and links from this ever-deepening ocean of information. Two such systems aimed on retrieving and subsequently utilizing phylogenetically relevant information have been developed in this dissertation, the major objective of which was to automate the often difficult and confusing phylogenetic reconstruction process. ^ Popular phylogenetic reconstruction methods, such as distance-based methods, attempt to find an optimal tree topology (that reflects the relationships among related sequences and their evolutionary history) by searching through the topology space. Various compromises between the fast (but incomplete) and exhaustive (but computationally prohibitive) search heuristics have been suggested. An intelligent compromise algorithm that relies on a flexible “beam” search principle from the Artificial Intelligence domain and uses the pre-computed local topology reliability information to adjust the beam search space continuously is described in the second chapter of this dissertation. ^ However, sometimes even a (virtually) complete distance-based method is inferior to the significantly more elaborate (and computationally expensive) maximum likelihood (ML) method. In fact, depending on the nature of the sequence data in question either method might prove to be superior. Therefore, it is difficult (even for an expert) to tell a priori which phylogenetic reconstruction method—distance-based, ML or maybe maximum parsimony (MP)—should be chosen for any particular data set. ^ A number of factors, often hidden, influence the performance of a method. For example, it is generally understood that for a phylogenetically “difficult” data set more sophisticated methods (e.g., ML) tend to be more effective and thus should be chosen. However, it is the interplay of many factors that one needs to consider in order to avoid choosing an inferior method (potentially a costly mistake, both in terms of computational expenses and in terms of reconstruction accuracy.) ^ Chapter III of this dissertation details a phylogenetic reconstruction expert system that selects a superior proper method automatically. It uses a classifier (a Decision Tree-inducing algorithm) to map a new data set to the proper phylogenetic reconstruction method. ^

The optimization principle in phylogenetic analysis tends to give incorrect topologies when the number of nucleotides or amino acids used is small

In the maximum parsimony (MP) and minimum evolution (ME) methods of phylogenetic inference, evolutionary trees are constructed by searching for the topology that shows the minimum number of mutational changes required (M) and the smallest sum of branch lengths (S), respectively, whereas in the maximum likelihood (ML) method the topology showing the highest maximum likelihood (A) of observing a given data set is chosen. However, the theoretical basis of the optimization principle remains unclear. We therefore examined the relationships of M, S, and A for the MP, ME, and ML trees with those for the true tree by using computer simulation. The results show that M and S are generally greater for the true tree than for the MP and ME trees when the number of nucleotides examined (n) is relatively small, whereas A is generally lower for the true tree than for the ML tree. This finding indicates that the optimization principle tends to give incorrect topologies when n is small. To deal with this disturbing property of the optimization principle, we suggest that more attention should be given to testing the statistical reliability of an estimated tree rather than to finding the optimal tree with excessive efforts. When a reliability test is conducted, simplified MP, ME, and ML algorithms such as the neighbor-joining method generally give conclusions about phylogenetic inference very similar to those obtained by the more extensive tree search algorithms.

Estimation of Time-Limited Channel Spectra From Nonuniform Samples

This paper deals with the estimation of a time-invariant channel spectrum from its own nonuniform samples, assuming there is a bound on the channel’s delay spread. Except for this last assumption, this is the basic estimation problem in systems providing channel spectral samples. However, as shown in the paper, the delay spread bound leads us to view the spectrum as a band-limited signal, rather than the Fourier transform of a tapped delay line (TDL). Using this alternative model, a linear estimator is presented that approximately minimizes the expected root-mean-square (RMS) error for a deterministic channel. Its main advantage over the TDL is that it takes into account the spectrum’s smoothness (time width), thus providing a performance improvement. The proposed estimator is compared numerically with the maximum likelihood (ML) estimator based on a TDL model in pilot-assisted channel estimation (PACE) for OFDM.

Monitoring Infants By Automatic Video Processing

This work has, as its objective, the development of non-invasive and low-cost systems for monitoring and automatic diagnosing specific neonatal diseases by means of the analysis of suitable video signals. We focus on monitoring infants potentially at risk of diseases characterized by the presence or absence of rhythmic movements of one or more body parts. Seizures and respiratory diseases are specifically considered, but the approach is general. Seizures are defined as sudden neurological and behavioural alterations. They are age-dependent phenomena and the most common sign of central nervous system dysfunction. Neonatal seizures have onset within the 28th day of life in newborns at term and within the 44th week of conceptional age in preterm infants. Their main causes are hypoxic-ischaemic encephalopathy, intracranial haemorrhage, and sepsis. Studies indicate an incidence rate of neonatal seizures of 0.2% live births, 1.1% for preterm neonates, and 1.3% for infants weighing less than 2500 g at birth. Neonatal seizures can be classified into four main categories: clonic, tonic, myoclonic, and subtle. Seizures in newborns have to be promptly and accurately recognized in order to establish timely treatments that could avoid an increase of the underlying brain damage. Respiratory diseases related to the occurrence of apnoea episodes may be caused by cerebrovascular events. Among the wide range of causes of apnoea, besides seizures, a relevant one is Congenital Central Hypoventilation Syndrome (CCHS) \cite{Healy}. With a reported prevalence of 1 in 200,000 live births, CCHS, formerly known as Ondine's curse, is a rare life-threatening disorder characterized by a failure of the automatic control of breathing, caused by mutations in a gene classified as PHOX2B. CCHS manifests itself, in the neonatal period, with episodes of cyanosis or apnoea, especially during quiet sleep. The reported mortality rates range from 8% to 38% of newborn with genetically confirmed CCHS. Nowadays, CCHS is considered a disorder of autonomic regulation, with related risk of sudden infant death syndrome (SIDS). Currently, the standard method of diagnosis, for both diseases, is based on polysomnography, a set of sensors such as ElectroEncephaloGram (EEG) sensors, ElectroMyoGraphy (EMG) sensors, ElectroCardioGraphy (ECG) sensors, elastic belt sensors, pulse-oximeter and nasal flow-meters. This monitoring system is very expensive, time-consuming, moderately invasive and requires particularly skilled medical personnel, not always available in a Neonatal Intensive Care Unit (NICU). Therefore, automatic, real-time and non-invasive monitoring equipments able to reliably recognize these diseases would be of significant value in the NICU. A very appealing monitoring tool to automatically detect neonatal seizures or breathing disorders may be based on acquiring, through a network of sensors, e.g., a set of video cameras, the movements of the newborn's body (e.g., limbs, chest) and properly processing the relevant signals. An automatic multi-sensor system could be used to permanently monitor every patient in the NICU or specific patients at home. Furthermore, a wire-free technique may be more user-friendly and highly desirable when used with infants, in particular with newborns. This work has focused on a reliable method to estimate the periodicity in pathological movements based on the use of the Maximum Likelihood (ML) criterion. In particular, average differential luminance signals from multiple Red, Green and Blue (RGB) cameras or depth-sensor devices are extracted and the presence or absence of a significant periodicity is analysed in order to detect possible pathological conditions. The efficacy of this monitoring system has been measured on the basis of video recordings provided by the Department of Neurosciences of the University of Parma. Concerning clonic seizures, a kinematic analysis was performed to establish a relationship between neonatal seizures and human inborn pattern of quadrupedal locomotion. Moreover, we have decided to realize simulators able to replicate the symptomatic movements characteristic of the diseases under consideration. The reasons is, essentially, the opportunity to have, at any time, a 'subject' on which to test the continuously evolving detection algorithms. Finally, we have developed a smartphone App, called 'Smartphone based contactless epilepsy detector' (SmartCED), able to detect neonatal clonic seizures and warn the user about the occurrence in real-time.

Classification of oximetry signals using Bayesian neural networks to assist in the detection of obstructive sleep apnoea syndrome

In the present study, multilayer perceptron (MLP) neural networks were applied to help in the diagnosis of obstructive sleep apnoea syndrome (OSAS). Oxygen saturation (SaO2) recordings from nocturnal pulse oximetry were used for this purpose. We performed time and spectral analysis of these signals to extract 14 features related to OSAS. The performance of two different MLP classifiers was compared: maximum likelihood (ML) and Bayesian (BY) MLP networks. A total of 187 subjects suspected of suffering from OSAS took part in the study. Their SaO2 signals were divided into a training set with 74 recordings and a test set with 113 recordings. BY-MLP networks achieved the best performance on the test set with 85.58% accuracy (87.76% sensitivity and 82.39% specificity). These results were substantially better than those provided by ML-MLP networks, which were affected by overfitting and achieved an accuracy of 76.81% (86.42% sensitivity and 62.83% specificity). Our results suggest that the Bayesian framework is preferred to implement our MLP classifiers. The proposed BY-MLP networks could be used for early OSAS detection. They could contribute to overcome the difficulties of nocturnal polysomnography (PSG) and thus reduce the demand for these studies.