scholarly journals A Bayesian approach to phylogeographic clustering

2011 ◽  
Vol 1 (6) ◽  
pp. 909-921 ◽  
Author(s):  
Ioanna Manolopoulou ◽  
Lorenza Legarreta ◽  
Brent C. Emerson ◽  
Steve Brooks ◽  
Simon Tavaré
Keyword(s):  
Mitochondrial Dna ◽  
Bayesian Approach ◽  
Dna Sequences ◽  
Population Distribution ◽  
Computational Cost ◽  
Fixed Number ◽  
Climate Information ◽  
Statistical Framework ◽  
Coalescent Tree ◽  
Fully Bayesian

Phylogeographic methods have attracted a lot of attention in recent years, stressing the need to provide a solid statistical framework for many existing methodologies so as to draw statistically reliable inferences. Here, we take a flexible fully Bayesian approach by reducing the problem to a clustering framework, whereby the population distribution can be explained by a set of migrations, forming geographically stable population clusters. These clusters are such that they are consistent with a fixed number of migrations on the corresponding (unknown) subdivided coalescent tree. Our methods rely upon a clustered population distribution, and allow for inclusion of various covariates (such as phenotype or climate information) at little additional computational cost. We illustrate our methods with an example from weevil mitochondrial DNA sequences from the Iberian peninsula.

scholarly journals Mitochondrial DNA sequence variations in populations of Sardinapilchardus(Walbaum 1792) along the Tunisian coasts

2019 ◽  
Vol 20 (2) ◽  
Author(s):  
ΚARIMA FADHLAOUI-ZID
Keyword(s):  
Genetic Diversity ◽  
Mitochondrial Dna ◽  
Genetic Structure ◽  
Dna Sequences ◽  
Population Distribution ◽  
Genetic Structuring ◽  
Efficient Management ◽  
Sequence Variations ◽  
Haplotypic Diversity ◽  
High Level

Sardine is a fish species of great economic importance to Tunisia. Knowledge of genetic diversity and population distribution is essential for an efficient management and sustainability of any regional fishery. This study aimed to assessing the genetic structure and to specify the stocks of the European sardine (Sardina pilchardus). To this end, 83 specimens were collected from three localities along the Tunisian coast and analysed using mitochondrial DNA sequences. The results of sequence analysis determined the existence of variations in 40 single nucleotide sites within the 307 bp fragment of the cytb gene examined and defined twenty different haplotypes. Genetic diversity, estimated by haplotypic diversity, was high in all samples. Tunisian S.pilchardus samples show some level of genetic structuring. First, genetic differentiation between localities (ΦST estimates) was significant for all comparison. Second, the analysis of molecular variance AMOVA indicates a high level of genetic variation (ΦST = 0.093; P<0.001). The structural patterns identified can be explained largely in relation to the regional oceanographic features. In conclusion, this study provided initial genetic data in making inference of the genetic structure of S. pilchardus along the Tunisian coasts. 

scholarly journals Impact of fertility transmission and other sociodemographic factors on reproductive success and coalescent trees

2012 ◽  
Vol 94 (3) ◽  
pp. 121-131 ◽  
Author(s):  
JEAN-TRISTAN BRANDENBURG ◽  
FRÉDÉRIC AUSTERLITZ ◽  
BRUNO TOUPANCE
Keyword(s):  
Mitochondrial Dna ◽  
Reproductive Success ◽  
Dna Sequences ◽  
Genetic Basis ◽  
Random Mating ◽  
Sociodemographic Factors ◽  
Theoretical Studies ◽  
Intergenerational Correlation ◽  
Coalescent Tree ◽  
Nuclear Loci

SummaryFertility transmission (FT) is a phenomenon with a cultural and/or genetic basis, whereby a positive correlation exists between the number of offspring of an individual and that of his/her parents. Theoretical studies using a haploid individual-based model have shown that FT increases the variance and intergenerational correlation in reproductive success and results in an imbalance in the coalescent tree of sampled genes. This phenomenon has been documented in several demographic studies conducted on the correlation in fertility between generations, or through the reconstruction of the genealogical trees of mitochondrial DNA sequences. However, as mtDNA is a single locus, potentially subject to other forces (e.g. natural selection), it is of interest to extend the theory of FT to nuclear loci. We show that because random mating between individuals leads to a mixing of their fertility profiles, FT in these cases will have less influence on the variance and intergenerational correlation of reproductive success. This, in turn, results in less impact on the shape of the coalescent trees. Nevertheless, in the presence of FT, high heterogeneity in reproductive success and homogamy for family size will increase the imbalance in the coalescent tree. Thus, FT should be easier to detect when occurring in conjunction with these other factors. We also show the utility of analysing different kinds of loci (X-linked, Y-linked, mitochondrial and autosomal) to assess whether FT is matrilineal, patrilineal or biparental. Finally, we demonstrate that the shape of the coalescent tree depends upon population size, in contrast to the classical Kingman's model.

Verbal statistical learning: Prior knowledge impacts word identification but not metacognition

2020 ◽  
Author(s):  
Laetitia Zmuda ◽  
Charlotte Baey ◽  
Paolo Mairano ◽  
Anahita Basirat
Keyword(s):  
Task Performance ◽  
Prior Knowledge ◽  
Statistical Learning ◽  
Cognitive Processes ◽  
Bayesian Approach ◽  
Word Identification ◽  
Artificial Language ◽  
Artificial Languages ◽  
Transitional Probabilities ◽  
Fully Bayesian

It is well-known that individuals can identify novel words in a stream of an artificial language using statistical dependencies. While underlying computations are thought to be similar from one stream to another (e.g. transitional probabilities between syllables), performance are not similar. According to the “linguistic entrenchment” hypothesis, this would be due to the fact that individuals have some prior knowledge regarding co-occurrences of elements in speech which intervene during verbal statistical learning. The focus of previous studies was on task performance. The goal of the current study is to examine the extent to which prior knowledge impacts metacognition (i.e. ability to evaluate one’s own cognitive processes). Participants were exposed to two different artificial languages. Using a fully Bayesian approach, we estimated an unbiased measure of metacognitive efficiency and compared the two languages in terms of task performance and metacognition. While task performance was higher in one of the languages, the metacognitive efficiency was similar in both languages. In addition, a model assuming no correlation between the two languages better accounted for our results compared to a model where correlations were introduced. We discuss the implications of our findings regarding the computations which underlie the interaction between input and prior knowledge during verbal statistical learning.

scholarly journals Evaluation of Mixed Deep Neural Networks for Reverberant Speech Enhancement

Biomimetics ◽  
2019 ◽  
Vol 5 (1) ◽  
pp. 1 ◽  
Author(s):  
Michelle Gutiérrez-Muñoz ◽  
Astryd González-Salazar ◽  
Marvin Coto-Jiménez
Keyword(s):  
Neural Networks ◽  
Short Term Memory ◽  
Computational Cost ◽  
Real Life ◽  
Fixed Number ◽  
Training Procedure ◽  
Statistical Validation ◽  
Significant Drop ◽  
Training Time ◽  
Important Solution

Speech signals are degraded in real-life environments, as a product of background noise or other factors. The processing of such signals for voice recognition and voice analysis systems presents important challenges. One of the conditions that make adverse quality difficult to handle in those systems is reverberation, produced by sound wave reflections that travel from the source to the microphone in multiple directions. To enhance signals in such adverse conditions, several deep learning-based methods have been proposed and proven to be effective. Recently, recurrent neural networks, especially those with long short-term memory (LSTM), have presented surprising results in tasks related to time-dependent processing of signals, such as speech. One of the most challenging aspects of LSTM networks is the high computational cost of the training procedure, which has limited extended experimentation in several cases. In this work, we present a proposal to evaluate the hybrid models of neural networks to learn different reverberation conditions without any previous information. The results show that some combinations of LSTM and perceptron layers produce good results in comparison to those from pure LSTM networks, given a fixed number of layers. The evaluation was made based on quality measurements of the signal’s spectrum, the training time of the networks, and statistical validation of results. In total, 120 artificial neural networks of eight different types were trained and compared. The results help to affirm the fact that hybrid networks represent an important solution for speech signal enhancement, given that reduction in training time is on the order of 30%, in processes that can normally take several days or weeks, depending on the amount of data. The results also present advantages in efficiency, but without a significant drop in quality.

scholarly journals A geostatistical extreme-value framework for fast simulation of natural hazard events

2016 ◽  
Vol 472 (2189) ◽  
pp. 20150855 ◽  
Author(s):  
Benjamin D. Youngman ◽  
David B. Stephenson
Keyword(s):  
Natural Hazard ◽  
Computational Cost ◽  
Additive Model ◽  
Simulation Method ◽  
Value Theory ◽  
Extreme Value ◽  
Wind Gust ◽  
Continuous Space ◽  
Statistical Framework ◽  
Distribution Parameters

We develop a statistical framework for simulating natural hazard events that combines extreme value theory and geostatistics. Robust generalized additive model forms represent generalized Pareto marginal distribution parameters while a Student’s t -process captures spatial dependence and gives a continuous-space framework for natural hazard event simulations. Efficiency of the simulation method allows many years of data (typically over 10 000) to be obtained at relatively little computational cost. This makes the model viable for forming the hazard module of a catastrophe model. We illustrate the framework by simulating maximum wind gusts for European windstorms, which are found to have realistic marginal and spatial properties, and validate well against wind gust measurements.

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