A maximum likelihood approach to generate hypotheses on the evolution and historical biogeography in the Lower Volga Valley regions (southwest Russia).
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ABSTRACT:
SUBMITTER: Mavrodiev EV
PROVIDER: S-EPMC3434936 | biostudies-literature |
REPOSITORIES: biostudies-literature
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2024-03-20 | GSE261769 | GEO
Project description:Background:The Volga basin is one of the most industrially-developed regions of Russia with a high degree of anthropogenic impact on natural ecosystems. Human influence negatively affects the species diversity and number of animals, including reptiles. There are no endemic species in the reptile fauna of the Volga basin. The herpetofauna of the region makes up 25% of the reptile fauna of Russia (Dunaev and Orlova 2017). We began to study the fauna of reptiles and their distribution in the Volga basin in 1988. Although we registered 20 reptile species in the Volga basin to date, apparently this is not a complete list of species in the region (Bakiev et al. 2004, Bakiev et al. 2009a, Bakiev et al. 2015, Kirillov et al. 2020). The distribution of reptiles in this region is not fully understood. New information:Our dataset contains information on reptile occurrences in the Volga River basin. The dataset is based on original research by the staff of the Laboratory of Herpetology and Toxinology and Laboratory of Population Ecology of the Institute of Ecology of the Volga River basin of the Russian Academy of Sciences and Joint Directorate of the Mordovia State Nature Reserve and National Park "Smolny". A total of 5,086 occurrences of 20 species are published for the first time with georeferencing. Many of these reptiles are listed in regional Red Data Lists. The European Pond Turtle Emys orbicularis (Linnaeus, 1758) is included in the IUCN Red List with the category "Near Threatened".
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Project description:Brain-machine interface decoding algorithms need to be predicated on assumptions that are easily met outside of an experimental setting to enable a practical clinical device. Given present technological limitations, there is a need for decoding algorithms which (a) are not dependent upon a large number of neurons for control, (b) are adaptable to alternative sources of neuronal input such as local field potentials (LFPs), and (c) require only marginal training data for daily calibrations. Moreover, practical algorithms must recognize when the user is not intending to generate a control output and eliminate poor training data. In this paper, we introduce and evaluate a Bayesian maximum-likelihood estimation strategy to address the issues of isolating quality training data and self-paced control. Six animal subjects demonstrate that a multiple state classification task, loosely based on the standard center-out task, can be accomplished with fewer than five engaged neurons while requiring less than ten trials for algorithm training. In addition, untrained animals quickly obtained accurate device control, utilizing LFPs as well as neurons in cingulate cortex, two non-traditional neural inputs.
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Project description:Background:In ichthyological publications from both Russia as a whole, and from the study region, lack of data indicating the actual results of observations in a specific place all result in publication of a generalised analysis. Although our publications contain such data, they are, however, not convenient for users performing global analysis. The main purpose of publishing a database is to make our data available in the global biodiversity system to a wide range of users. Dataset represents a significant addition to the distribution of species in this area. The data can be used to analyse future changes in ichthyofauna, as well as to help the authorities to manage their territory more efficiently.This publication describes a dataset that contains information on fish encounters in the Upper Don basin and the middle Volga (centre of the European part of Russia) over a 30-year period (1990-2020). The dataset contains information on 6400 occurrences of 394341 specimens of 56 species, 99.9% of specimens being identified to the species level. A total of 883 sites were studied, of which 253 were in lentic biotopes (lakes - 121, ponds - 123, backwater - 5, reservoir - 3, pit - 1), 630 were in lotic (rivers - 628, stream - 1, channel - 1). One collecting site has an average of 7.2 species (from 1-21 species per location). Only those species that form self-reproducing populations are given. The dataset is a compilation of data from several working author groups. All observations have precise geo-referencing with the names of water bodies (rivers, lakes etc.). New information:All presented data are published in the form of a database for the first time. Some data form the basis of previously-published works (3998 observations, 62%) and some are published for the first time (2402 observations, 38%). A large amount of data comes from small water bodies that have been neglected by previous researchers.
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Project description:A likelihood-based approach to density modification is developed that can be applied to a wide variety of cases where some information about the electron density at various points in the unit cell is available. The key to the approach consists of developing likelihood functions that represent the probability that a particular value of electron density is consistent with prior expectations for the electron density at that point in the unit cell. These likelihood functions are then combined with likelihood functions based on experimental observations and with others containing any prior knowledge about structure factors to form a combined likelihood function for each structure factor. A simple and general approach to maximizing the combined likelihood function is developed. It is found that this likelihood-based approach yields greater phase improvement in model and real test cases than either conventional solvent flattening and histogram matching or a recent reciprocal-space solvent-flattening procedure [Terwilliger (1999), Acta Cryst. D55, 1863-1871].
| S-EPMC2792768 | biostudies-literature
Project description:Simple Summary Earwigs (Dermaptera) have their highest diversity in the tropical areas of the southern hemisphere, whereas the temperate regions of the northern hemisphere have relatively few species. This pattern has been considered a reflection of their origin in Gondwana, the supercontinent that grouped most of the land masses in today’s southern hemisphere, including Antarctica, South America, Africa and Madagascar. An analysis of the distributions of the major evolutionary lineages of earwigs supports the role of the Gondwanan breakup in determining the current patterns of their diversity, as well the influence of the Indian collision with the Eurasian plate. The dispersal into the Eurasian plate was largely constrained by the Himalayan orogenesis and the presence of colder temperatures. This climatic barrier was probably the most important factor that largely hampered the colonization of North America from South America. Abstract The Dermaptera are an insect order exhibiting their highest diversity in the tropical areas of the southern hemisphere. This pattern has been considered a reflection of a Gondwanan origin. However, this hypothesis has not been tested through analytical methods. In this paper, the world distribution of earwigs was analysed by using the ‘Cladistic Analysis of Distributions and Endemism’ (CADE), a method which groups areas of endemism on the basis of shared distributions and phylogenetic relationships among taxa. In addition, clustering techniques were applied to depict biotic relationships based on similarity indices. Results of CADE support the idea that Gondwanan fragmentation exerted a crucial role in shaping the current distribution of the main clades of earwigs. However, the relationships between India with South East Asia suggested a biotic interchange occurred after the Indian collision with the Eurasian plate. The overall scenario emerging from cluster analyses revealed a strong influence of dispersal events. Overall, the distribution of earwig major clades indicates that their biogeographical history was mainly characterized by vicariance events (led by the break-up of Gondwana) followed by large scale dispersal processes constrained by the Himalayan orogenesis and the presence of colder temperatures, which have largely hampered the colonization of the northern hemisphere.
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Project description:BACKGROUND: Phylogenetic footprinting is the identification of functional regions of DNA by their evolutionary conservation. This is achieved by comparing orthologous regions from multiple species and identifying the DNA regions that have diverged less than neutral DNA. Vestige is a phylogenetic footprinting package built on the PyEvolve toolkit that uses probabilistic molecular evolutionary modelling to represent aspects of sequence evolution, including the conventional divergence measure employed by other footprinting approaches. In addition to measuring the divergence, Vestige allows the expansion of the definition of a phylogenetic footprint to include variation in the distribution of any molecular evolutionary processes. This is achieved by displaying the distribution of model parameters that represent partitions of molecular evolutionary substitutions. Examination of the spatial incidence of these effects across regions of the genome can identify DNA segments that differ in the nature of the evolutionary process. RESULTS: Vestige was applied to a reference dataset of the SCL locus from four species and provided clear identification of the known conserved regions in this dataset. To demonstrate the flexibility to use diverse models of molecular evolution and dissect the nature of the evolutionary process Vestige was used to footprint the Ka/Ks ratio in primate BRCA1 with a codon model of evolution. Two regions of putative adaptive evolution were identified illustrating the ability of Vestige to represent the spatial distribution of distinct molecular evolutionary processes. CONCLUSION: Vestige provides a flexible, open platform for phylogenetic footprinting. Underpinned by the PyEvolve toolkit, Vestige provides a framework for visualising the signatures of evolutionary processes across the genome of numerous organisms simultaneously. By exploiting the maximum-likelihood statistical framework, the complex interplay between mutational processes, DNA repair and selection can be evaluated both spatially (along a sequence alignment) and temporally (for each branch of the tree) providing visual indicators to the attributes and functions of DNA sequences.
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Project description:BACKGROUND:Biological data comprises various topologies or a mixture of forms, which makes its analysis extremely complicated. With this data increasing in a daily basis, the design and development of efficient and accurate statistical methods has become absolutely necessary. Specific analyses, such as those related to genome-wide association studies and multi-omics information, are often aimed at clustering sub-conditions of cancers and other diseases. Hierarchical clustering methods, which can be categorized into agglomerative and divisive, have been widely used in such situations. However, unlike agglomerative methods divisive clustering approaches have consistently proved to be computationally expensive. RESULTS:The proposed clustering algorithm (DRAGON) was verified on mutation and microarray data, and was gauged against standard clustering methods in the literature. Its validation included synthetic and significant biological data. When validated on mixed-lineage leukemia data, DRAGON achieved the highest clustering accuracy with data of four different dimensions. Consequently, DRAGON outperformed previous methods with 3-,4- and 5-dimensional acute leukemia data. When tested on mutation data, DRAGON achieved the best performance with 2-dimensional information. CONCLUSIONS:This work proposes a computationally efficient divisive hierarchical clustering method, which can compete equally with agglomerative approaches. The proposed method turned out to correctly cluster data with distinct topologies. A MATLAB implementation can be extraced from http://www.riken.jp/en/research/labs/ims/med_sci_math/ or http://www.alok-ai-lab.com.
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Project description:Phylogenetics plays a crucial role in the interpretation of genomic data1. Phylogenetic analyses of SARS-CoV-2 genomes have allowed the detailed study of the virus's origins2, of its international3,4 and local4-9 spread, and of the emergence10 and reproductive success11 of new variants, among many applications. These analyses have been enabled by the unparalleled volumes of genome sequence data generated and employed to study and help contain the pandemic12. However, preferred model-based phylogenetic approaches including maximum likelihood and Bayesian methods, mostly based on Felsenstein's 'pruning' algorithm13,14, cannot scale to the size of the datasets from the current pandemic4,15, hampering our understanding of the virus's evolution and transmission16. We present new approaches, based on reworking Felsenstein's algorithm, for likelihood-based phylogenetic analysis of epidemiological genomic datasets at unprecedented scales. We exploit near-certainty regarding ancestral genomes, and the similarities between closely related and densely sampled genomes, to greatly reduce computational demands for memory and time. Combined with new methods for searching amongst candidate evolutionary trees, this results in our MAPLE ('MAximum Parsimonious Likelihood Estimation') software giving better results than popular approaches such as FastTree 217, IQ-TREE 218, RAxML-NG19 and UShER15. Our approach therefore allows complex and accurate probabilistic phylogenetic analyses of millions of microbial genomes, extending the reach of genomic epidemiology. Future epidemiological datasets are likely to be even larger than those currently associated with COVID-19, and other disciplines such as metagenomics and biodiversity science are also generating huge numbers of genome sequences20-22. Our methods will permit continued use of preferred likelihood-based phylogenetic analyses.
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Project description:Maximum likelihood factor analysis of discrete data within the structural equation modeling framework rests on the assumption that the observed discrete responses are manifestations of underlying continuous scores that are normally distributed. As maximizing the likelihood of multivariate response patterns is computationally very intensive, the sum of the log-likelihoods of the bivariate response patterns is maximized instead. Little is yet known about how to assess model fit when the analysis is based on such a pairwise maximum likelihood (PML) of two-way contingency tables. We propose new fit criteria for the PML method and conduct a simulation study to evaluate their performance in model selection. With large sample sizes (500 or more), PML performs as well the robust weighted least squares analysis of polychoric correlations.
| S-EPMC4838635 | biostudies-literature