Molecular evidence for the monophyly of East Asian groups of Cyprinidae (Teleostei : Cypriniformes) derived from the nuclear recombination activating gene 2 sequences

The family Cyprinidae is one of the largest families of fishes in the world and a well-known component of the East Asian freshwater fish fauna. However, the phylogenetic relationships among cyprinids are still poorly understood despite much effort paid on the cyprinid molecular phylogenetics. Original nucleotide sequence data of the nuclear recombination activating gene 2 were collected from 109 cyprinid species and four non-cyprinid cypriniform outgroup taxa and used to infer the cyprinid phylogenetic relationships and to estimate node divergence times. Phylogenetic reconstructions using maximum parsimony, maximum likelihood, and Bayesian analysis retrieved the same clades, only branching order within these clades varied slightly between trees. Although the morphological diversity is remarkable, the endemic cyprinid taxa in East Asia emerged as a monophyletic clade referred to as Xenocypridini. The monophyly for the subfamilies including Cyprininae and Leuciscinae, as well as the tribes including Labeonini, Gobionini, Acheilognathini, and Leuciscini, was also well resolved with high nodal support. Analysis of the RAG2 gene supported the following cyprinid molecular phylogeny: the Danioninae is the most basal subfamily within the family Cyprinidae and the Cyprininae is the sister group of the Leuciscinae. The divergence times were estimated for the nodes corresponding to the principal clades within the Cyprinidae. The family Cyprinidae appears to have originated in the mid-Eocene in Asia, with the cladogenic event of the key basal group Danioninae occurring in the early Oligocene (about 31-30 MYA), and the origins of the two subfamilies, Cyprininae and Leuciscinae, occurring in the mid-Oligocene (around 26 MYA). (c) 2006 Elsevier Inc. All rights reserved.The family Cyprinidae is one of the largest families of fishes in the world and a well-known component of the East Asian freshwater fish fauna. However, the phylogenetic relationships among cyprinids are still poorly understood despite much effort paid on the cyprinid molecular phylogenetics. Original nucleotide sequence data of the nuclear recombination activating gene 2 were collected from 109 cyprinid species and four non-cyprinid cypriniform outgroup taxa and used to infer the cyprinid phylogenetic relationships and to estimate node divergence times. Phylogenetic reconstructions using maximum parsimony, maximum likelihood, and Bayesian analysis retrieved the same clades, only branching order within these clades varied slightly between trees. Although the morphological diversity is remarkable, the endemic cyprinid taxa in East Asia emerged as a monophyletic clade referred to as Xenocypridini. The monophyly for the subfamilies including Cyprininae and Leuciscinae, as well as the tribes including Labeonini, Gobionini, Acheilognathini, and Leuciscini, was also well resolved with high nodal support. Analysis of the RAG2 gene supported the following cyprinid molecular phylogeny: the Danioninae is the most basal subfamily within the family Cyprinidae and the Cyprininae is the sister group of the Leuciscinae. The divergence times were estimated for the nodes corresponding to the principal clades within the Cyprinidae. The family Cyprinidae appears to have originated in the mid-Eocene in Asia, with the cladogenic event of the key basal group Danioninae occurring in the early Oligocene (about 31-30 MYA), and the origins of the two subfamilies, Cyprininae and Leuciscinae, occurring in the mid-Oligocene (around 26 MYA). (c) 2006 Elsevier Inc. All rights reserved

The complete mitochondrial genome of the Chinese hook snout carp Opsariichthys bidens (Actinopterygii : Cyprinifonnes) and an altemative pattem of mitogenomic evolution in vertebrate

The complete mitochondrial genome sequence of the Chinese hook snout carp, Opsariichthys bidens, was newly determined using the long and accurate polymerase chain reaction method. The 16,611-nucleotide mitogenome contains 13 protein-coding genes, two rRNA genes (12S, 16S) 22 tRNA genes, and a noncoding control region. We use these data and homologous sequence data from multiple other ostariophysan fishes in a phylogenetic evaluation to test hypothesis pertaining to codon usage pattern of O. bidens mitochondrial protein genes as well as to re-examine the ostariophysan phylogeny. The mitochondrial genome of O. bidens reveals an alternative pattern of vertebrate mitochondrial evolution. For the mitochondrial protein genes of O. bidens, the most frequently used codon generally ends with either A or C, with C preferred over A for most fourfold degenerate codon families; the relative synonymous codon usage of G-ending codons is greatly elevated in all categories. The codon usage pattern of O. bidens mitochondrial protein genes is remarkably different from the general pattern found previously in the relatively closely 9 related zebrafish and most other vertebrate mitochondria. Nucleotide bias at third codon positions is the main cause of codon bias in the mitochondrial protein genes of O. bidens, as it is biased particularly in favor of C over A. Bayesian analysis of 12 concatenated mitochondrial protein sequences for O. bidens and 46 other teleostean taxa supports the monophyly of Cypriniformes and Otophysi and results in a robust estimate of the otophysan phylogeny. (C) 2007 Published by Elsevier B.V.The complete mitochondrial genome sequence of the Chinese hook snout carp, Opsariichthys bidens, was newly determined using the long and accurate polymerase chain reaction method. The 16,611-nucleotide mitogenome contains 13 protein-coding genes, two rRNA genes (12S, 16S) 22 tRNA genes, and a noncoding control region. We use these data and homologous sequence data from multiple other ostariophysan fishes in a phylogenetic evaluation to test hypothesis pertaining to codon usage pattern of O. bidens mitochondrial protein genes as well as to re-examine the ostariophysan phylogeny. The mitochondrial genome of O. bidens reveals an alternative pattern of vertebrate mitochondrial evolution. For the mitochondrial protein genes of O. bidens, the most frequently used codon generally ends with either A or C, with C preferred over A for most fourfold degenerate codon families; the relative synonymous codon usage of G-ending codons is greatly elevated in all categories. The codon usage pattern of O. bidens mitochondrial protein genes is remarkably different from the general pattern found previously in the relatively closely 9 related zebrafish and most other vertebrate mitochondria. Nucleotide bias at third codon positions is the main cause of codon bias in the mitochondrial protein genes of O. bidens, as it is biased particularly in favor of C over A. Bayesian analysis of 12 concatenated mitochondrial protein sequences for O. bidens and 46 other teleostean taxa supports the monophyly of Cypriniformes and Otophysi and results in a robust estimate of the otophysan phylogeny. (C) 2007 Published by Elsevier B.V

The complete mitochondrial genome of Leiocassis crassilabris (Teleostei, Siluriformes: Bagridae)

The Leiocassis crassilabris is an important economic fish in China, and is widely distributed in south China, e.g. Yangtze River, Pearl River, and Min River, so it is a good model to study population genetics and geological changes of these regions. In this study, the complete mitochondrial genome sequence of L. crassilabris has been obtained with PCR. The gene arrangement and composition L. crassilabris of mitochondrial genome sequence are similar to most of the other vertebrates', which contains 13 protein-coding genes, 22 tRNA genes, 2 rRNA genes and a non-coding control region with the total length of 16,530 bp. Except for eight tRNA and ND6 genes, other genes are encoded on heavy-strand (H-strand). Similar to most other vertebrates, the bias of G and C have universality in different region (genes). The complete mitochondrial genome sequence of L. crassilabris would contribute to better understand population genetics, conservation, biogeography, evolution of this lineage.The Leiocassis crassilabris is an important economic fish in China, and is widely distributed in south China, e.g. Yangtze River, Pearl River, and Min River, so it is a good model to study population genetics and geological changes of these regions. In this study, the complete mitochondrial genome sequence of L. crassilabris has been obtained with PCR. The gene arrangement and composition L. crassilabris of mitochondrial genome sequence are similar to most of the other vertebrates', which contains 13 protein-coding genes, 22 tRNA genes, 2 rRNA genes and a non-coding control region with the total length of 16,530 bp. Except for eight tRNA and ND6 genes, other genes are encoded on heavy-strand (H-strand). Similar to most other vertebrates, the bias of G and C have universality in different region (genes). The complete mitochondrial genome sequence of L. crassilabris would contribute to better understand population genetics, conservation, biogeography, evolution of this lineage

Cyprinid phylogeny based on Bayesian and maximum likelihood analyses of partitioned data: implications for Cyprinidae systematics

Cyprinidae is the biggest family of freshwater fish, but the phylogenetic relationships among its higher-level taxa are not yet fully resolved. In this study, we used the nuclear recombination activating gene 2 and the mitochondrial 16S ribosomal RNA and cytochrome b genes to reconstruct cyprinid phylogeny. Our aims were to (i) demonstrate the effects of partitioned phylogenetic analyses on phylogeny reconstruction of cyprinid fishes; (ii) provide new insights into the phylogeny of cyprinids. Our study indicated that unpartitioned strategy was optimal for our analyses; partitioned analyses did not provide better-resolved or -supported estimates of cyprinid phylogeny. Bayesian analyses support the following relationships among the major monophyletic groups within Cyprinidae: (Cyprininae, Labeoninae), ((Acheilognathinae, ((Leuciscinae, Tincinae), Gobioninae)), Xenocyprininae). The placement of Danioninae was poorly resolved. Estimates of divergence dates within the family showed that radiation of the major cyprinid groups occurred during the Late Oligocene through the Late Miocene. Our phylogenetic analyses improved our understanding of the evolutionary history of this important fish family.Cyprinidae is the biggest family of freshwater fish, but the phylogenetic relationships among its higher-level taxa are not yet fully resolved. In this study, we used the nuclear recombination activating gene 2 and the mitochondrial 16S ribosomal RNA and cytochrome b genes to reconstruct cyprinid phylogeny. Our aims were to (i) demonstrate the effects of partitioned phylogenetic analyses on phylogeny reconstruction of cyprinid fishes; (ii) provide new insights into the phylogeny of cyprinids. Our study indicated that unpartitioned strategy was optimal for our analyses; partitioned analyses did not provide better-resolved or -supported estimates of cyprinid phylogeny. Bayesian analyses support the following relationships among the major monophyletic groups within Cyprinidae: (Cyprininae, Labeoninae), ((Acheilognathinae, ((Leuciscinae, Tincinae), Gobioninae)), Xenocyprininae). The placement of Danioninae was poorly resolved. Estimates of divergence dates within the family showed that radiation of the major cyprinid groups occurred during the Late Oligocene through the Late Miocene. Our phylogenetic analyses improved our understanding of the evolutionary history of this important fish family

Accelerated linear algebra compiler for computationally efficient numerical models: Success and potential area of improvement.

The recent dramatic progress in machine learning is partially attributed to the availability of high-performant computers and development tools. The accelerated linear algebra (XLA) compiler is one such tool that automatically optimises array operations (mostly fusion to reduce memory operations) and compiles the optimised operations into high-performant programs specific to target computing platforms. Like machine-learning models, numerical models are often expressed in array operations, and thus their performance can be boosted by XLA. This study is the first of its kind to examine the efficiency of XLA for numerical models, and the efficiency is examined stringently by comparing its performance with that of optimal implementations. Two shared-memory computing platforms are examined-the CPU platform and the GPU platform. To obtain optimal implementations, the computing speed and its optimisation are rigorously studied by considering different workloads and the corresponding computer performance. Two simple equations are found to faithfully modell the computing speed of numerical models with very few easily-measureable parameters. Regarding operation optimisation within XLA, results show that models expressed in low-level operations (e.g., slice, concatenation, and arithmetic operations) are successfully fused while high-level operations (e.g., convolution and roll) are not. Regarding compilation within XLA, results show that for the CPU platform of certain computers and certain simple numerical models on the GPU platform, XLA achieves high efficiency (> 80%) for large problems and acceptable efficiency (10%~80%) for medium-size problems-the gap is from the overhead cost of Python. Unsatisfactory performance is found for the CPU platform of other computers (operations are compiled in a non-optimal way) and for high-dimensional complex models for the GPU platform, where each GPU thread in XLA handles 4 (single precision) or 2 (double precision) output elements-hoping to exploit the high-performant instructions that can read/write 4 or 2 floating-point numbers with one instruction. However, these instructions are rarely used in the generated code for complex models and performance is negatively affected. Therefore, flags should be added to control the compilation for these non-optimal scenarios

Rainfall-Induced Landslides from Initialization to Post-Failure Flows: Stochastic Analysis with Machine Learning

Rainfall-induced landslides represent a severe hazard around the world due to their sudden occurrence, as well as their widespread influence and runout distance. Considering the spatial variability of soil, stochastic analysis is often conducted to give a probability description of the runout. However, rainfall-induced landslides are complex and time-consuming for brute-force Monte Carlo analyses. Therefore, new methods are required to improve the efficiency of stochastic analysis. This paper presents a framework to investigate the influence and runout distance of rainfall-induced landslides with a two-step simulation approach. The complete process, from the initialization of instability to the post-failure flow, is simulated. The rainfall infiltration process and initialization of instability are first solved with a coupled hydro-mechanical finite element model. The post-failure flow is simulated using the coupled Eulerian–Lagrangian method, wherein the soil can flow freely in fixed Eulerian meshes. An equivalent-strength method is used to connect two steps by considering the effective stress of unsaturated soil. A rigorous method has been developed to accurately quantify the influence and runout distance via Eulerian analyses. Several simulations have been produced, using three-dimensional analyses to study the shapes of slopes and using stochastic analysis to consider uncertainty and the spatial variability of soils. It was found that a two-dimensional analysis assuming plain strain is generally conservative and safe in design, but care must be taken to interpret 2D results when the slope is convex in the longitudinal direction. The uncertainty and spatial variability of soils can lead to the statistic of influence and runout distance. The framework of using machine-learning models as surrogate models is effective in stochastic analysis of this problem and can greatly reduce computational effort

Rainfall-Induced Landslides from Initialization to Post-Failure Flows: Stochastic Analysis with Machine Learning

Rainfall-induced landslides represent a severe hazard around the world due to their sudden occurrence, as well as their widespread influence and runout distance. Considering the spatial variability of soil, stochastic analysis is often conducted to give a probability description of the runout. However, rainfall-induced landslides are complex and time-consuming for brute-force Monte Carlo analyses. Therefore, new methods are required to improve the efficiency of stochastic analysis. This paper presents a framework to investigate the influence and runout distance of rainfall-induced landslides with a two-step simulation approach. The complete process, from the initialization of instability to the post-failure flow, is simulated. The rainfall infiltration process and initialization of instability are first solved with a coupled hydro-mechanical finite element model. The post-failure flow is simulated using the coupled Eulerian–Lagrangian method, wherein the soil can flow freely in fixed Eulerian meshes. An equivalent-strength method is used to connect two steps by considering the effective stress of unsaturated soil. A rigorous method has been developed to accurately quantify the influence and runout distance via Eulerian analyses. Several simulations have been produced, using three-dimensional analyses to study the shapes of slopes and using stochastic analysis to consider uncertainty and the spatial variability of soils. It was found that a two-dimensional analysis assuming plain strain is generally conservative and safe in design, but care must be taken to interpret 2D results when the slope is convex in the longitudinal direction. The uncertainty and spatial variability of soils can lead to the statistic of influence and runout distance. The framework of using machine-learning models as surrogate models is effective in stochastic analysis of this problem and can greatly reduce computational effort