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156. Relatively recent evolution of pelage coloration in Colobinae: phylogeny and phylogeography of three closely related langur species.

Author

Zhijin Liu, Boshi Wang, Tilo Nadler, Guangjian Liu, Tao Sun, Chengming Huang, Qihai Zhou, Jiang Zhou, Tengcheng Que, Ziming Wang, Christian Roos, and Ming Li

Subjects
Medicine, Science
Abstract

To understand the evolutionary processes leading to the diversity of Asian colobines, we report here on a phylogenetic, phylogeographical and population genetic analysis of three closely related langurs, Trachypithecus francoisi, T. poliocephalus and T. leucocephalus, which are all characterized by different pelage coloration predominantly on the head and shoulders. Therefore, we sequenced a 395 bp long fragment of the mitochondrial control region from 178 T. francoisi, 54 T. leucocephalus and 19 T. poliocephalus individuals, representing all extant populations of these three species. We found 29 haplotypes in T. francoisi, 12 haplotypes in T. leucocephalus and three haplotypes in T. poliocephalus. T. leucocephalus and T. poliocephalus form monophyletic clades, which are both nested within T. francoisi, and diverged from T. francoisi recently, 0.46-0.27 (T. leucocephalus) and 0.50-0.25 million years ago (T. poliocephalus). Thus, T. francoisi appears as a polyphyletic group, while T. leucocephalus and T. poliocephalus are most likely independent descendents of T. francoisi that are both physically separated from T. francoisi populations by rivers, open sea or larger habitat gaps. Since T. francoisi populations show no variability in pelage coloration, pelage coloration in T. leucocephalus and T. poliocephalus is most likely the result of new genetic mutations after the split from T. francoisi and not of the fixation of different characters derived from an ancestral polymorphism. This case study highlights that morphological changes for example in pelage coloration can occur in isolated populations in relatively short time periods and it provides a solid basis for studies in related species. Nevertheless, to fully understand the evolutionary history of these three langur species, nuclear loci should be investigated as well.

Published
2013
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157. The futile cycling of hexose phosphates could account for the fact that hexokinase exerts a high control on glucose phosphorylation but not on glycolytic rate in transgenic potato (Solanum tuberosum) roots.

Author

Eric Claeyssen, Sonia Dorion, Audrey Clendenning, Jiang Zhou He, Owen Wally, Jingkui Chen, Evgenia L Auslender, Marie-Claude Moisan, Mario Jolicoeur, and Jean Rivoal

Subjects
Medicine, Science
Abstract

The metabolism of potato (Solanum tuberosum) roots constitutively over- and underexpressing hexokinase (HK, EC 2.7.1.1) was examined. An 11-fold variation in HK activity resulted in altered root growth, with antisense roots growing better than sense roots. Quantification of sugars, organic acids and amino acids in transgenic roots demonstrated that the manipulation of HK activity had very little effect on the intracellular pools of these metabolites. However, adenylate and free Pi levels were negatively affected by an increase in HK activity. The flux control coefficient of HK over the phosphorylation of glucose was measured for the first time in plants. Its value varied with HK level. It reached 1.71 at or below normal HK activity value and was much lower (0.32) at very high HK levels. Measurements of glycolytic flux and O(2) uptake rates demonstrated that the differences in glucose phosphorylation did not affect significantly glycolytic and respiratory metabolism. We hypothesized that these results could be explained by the existence of a futile cycle between the pools of hexose-Ps and carbohydrates. This view is supported by several lines of evidence. Firstly, activities of enzymes capable of catalyzing these reactions were detected in roots, including a hexose-P phosphatase. Secondly, metabolic tracer experiments using (14)C-glucose as precursor showed the formation of (14)C-fructose and (14)C-sucrose. We conclude that futile cycling of hexose-P could be partially responsible for the differences in energetic status in roots with high and low HK activity and possibly cause the observed alterations in growth in transgenic roots. The involvement of HK and futile cycles in the control of glucose-6P metabolism is discussed.

Published
2013
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181. Strategic Choices and Order Transformation of Major Power States in East Asia-The Transformation of East Asian Tribute Order in the Yuan and Ming Dynasties as an Example

Author

Yang, Zihuan, Jiang, Zhou, Tang, Yuerong, Striełkowski, Wadim, Editor-in-Chief, Black, Jessica M., Series Editor, Butterfield, Stephen A., Series Editor, Chang, Chi-Cheng, Series Editor, Cheng, Jiuqing, Series Editor, Dumanig, Francisco Perlas, Series Editor, Al-Mabuk, Radhi, Series Editor, Scheper-Hughes, Nancy, Series Editor, Urban, Mathias, Series Editor, Webb, Stephen, Series Editor, Yacob, Shakila, editor, Cicek, Berat, editor, Rak, Joanna, editor, and Ali, Ghaffar, editor

Published
2023
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192. Data-Driven Modeling of Geometry-Adaptive Steady Heat Transfer based on Convolutional Neural Networks: Heat Convection

Author

Peng, Jiang-Zhou, Liu, Xianglei, Aubry, Nadine, Chen, Zhihua, and Wu, Wei-Tao

Subjects
Physics - Applied Physics
Abstract

In this paper, based on neural networks, we develop a data-driven model for extremely fast prediction of steady-state heat convection of a hot object with arbitrary complex geometry in a two-dimensional space. According to the governing equations, the steady-state heat convection is dominated by convection and thermal diffusion terms, thus the distribution of the physical fields would exhibit stronger correlations between adjacent points. Therefore, the proposed neural network model uses Convolutional Neural Network (CNN) layers as the encoder and Deconvolutional Neural Network (DCNN) layers as the decoder. Compared with fully connected (FC) network model, the CNN based model is good for capturing and reconstructing the spatial relationships of low-rank feature spaces, such as edge intersections, parallelism and symmetry. Furthermore, we applied the signed distance function (SDF) as the network input for representing the problem geometry, which contains more information compared to a binary image. For displaying the strong learning and generalization ability of the proposed network model, the training dataset only contains hot objects with simple geometries: triangles, quadrilaterals, pentagons, hexagons and dodecagons; while the validating cases use arbitrary and complex geometries. According to the study, the trained network model can accurately predict the velocity and temperature field of the problems with complex geometries which has never been seen by the network model during the model training; and the prediction speed is four orders faster than the CFD. The ability of the accurate and extremely faster prediction of the network model suggests the potentials of applying such kind of models to the applications of real-time control, optimization, and design in future., Comment: arXiv admin note: substantial text overlap with arXiv:2010.03854

Published
2021

198. Data-Driven Modeling of Geometry-Adaptive Steady Heat Transfer based on Convolutional Neural Networks: Heat Conduction

Author

Peng, Jiang-Zhou, Liu, Xianglei, Aubry, Nadine, Chen, Zhihua, and Wu, Wei-Tao

Subjects
Physics - Applied Physics
Abstract

Numerical simulation of steady-state heat conduction is common for thermal engineering. The simulation process usually involves mathematical formulation, numerical discretization and iteration of discretized ordinary or partial differential equations depending on complexity of problems. In current work, we develop a data-driven model for extremely fast prediction of steady-state heat conduction of a hot object with arbitrary geometry in a two-dimensional space. Mathematically, the steady-state heat conduction can be described by the Laplace's equation, where a heat (spatial) diffusion term dominates the governing equation. As the intensity of the heat diffusion only depends on the gradient of the temperature field, the temperature distribution of the steady-state heat conduction displays strong features of locality. Therefore, in current approach the data-driven model is developed using convolution neural networks (CNNs), which is good at capturing local features (sub-invariant) thus can be treated as numerical discretization in some sense. Furthermore, in our model, a signed distance function (SDF) is proposed to represent the geometry of the problem, which contains more information compared to a binary image. For the training datasets, the hot objects are consisting of five simple geometries: triangles, quadrilaterals, pentagons, hexagons and dodecagons. All the geometries are different in size, shape, orientation and location. After training, the data-driven network model is able to accurately predict steady-state heat conduction of hot objects with complex geometries which has never been seen by the network model; and the prediction speed is three to four orders faster than numerical simulation. According to the outstanding performance of the network model, it is hoped that this approach can serve as a valuable tool for applications of engineering optimization and design in future.

Published
2020

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