Transcriptomic Analyses of Sexual Dimorphism of the Zebrafish Liver and the Effect of Sex Hormones

10.1371/journal.pone.0053562PLoS ONE81

Comparative analysis of the transcriptome across distant species

The transcriptome is the readout of the genome. Identifying common features in it across distant species can reveal fundamental principles. To this end, the ENCODE and modENCODE consortia have generated large amounts of matched RNA-sequencing data for human, worm and fly. Uniform processing and comprehensive annotation of these data allow comparison across metazoan phyla, extending beyond earlier within-phylum transcriptome comparisons and revealing ancient, conserved features. Specifically, we discover co-expression modules shared across animals, many of which are enriched in developmental genes. Moreover, we use expression patterns to align the stages in worm and fly development and find a novel pairing between worm embryo and fly pupae, in addition to the embryo-to-embryo and larvae-to-larvae pairings. Furthermore, we find that the extent of non-canonical, non-coding transcription is similar in each organism, per base pair. Finally, we find in all three organisms that the gene-expression levels, both coding and non-coding, can be quantitatively predicted from chromatin features at the promoter using a 'universal model' based on a single set of organism-independent parameters

The Value of Environmental Base Flow in Water-Scarce Basins: A Case Study of Wei River Basin, Northwest China

In the perennial river, environmental base flow, associated with environmental flow, is the base flow that should be maintained within the river channel throughout the year, especially in the dry season, to sustain basic ecosystem functions and prevent the shrinkage or discontinuity of a river. The functions of environmental base flow include eco-environmental functions, natural functions, and social functions. In this study, we provided a method based on these functions; this method estimated the function values per unit area, introduced the scarcity coefficient, multiplied by the corresponding water area, and summed over to quantify the value of environmental base flow from 1973 to 2015 in the Wei River Basin, the largest tributary of the Yellow River in Northwest China. We observed that there was a positive correlation between the total value of environmental base flow and its water yield, whereas this outcome was completely different in the benefit per unit discharge of environmental base flow, which was closely associated with the shortage of environmental base flow. This method can thus present the considerable value of environmental base flow in monetary terms in a simple and effective way and lay the foundation for further reasonable protection levels of environmental base flow

Spiral Recurrent Neural Network for Online Learning

Abstract. Autonomous, self * sensor networks require sensor nodes with a certain degree of “intelligence”. An elementary component of such an “intelligence ” is the ability to learn online predicting sensor values. We consider recurrent neural network (RNN) models trained with an extended Kalman filter algorithm based on real time recurrent learning (RTRL) with teacher forcing. We compared the performance of conventional neural network architectures with that of spiral recurrent neural networks (Spiral RNN)- a novel RNN architecture combining a trainable hidden recurrent layer with the “echo state ” property of echo state neural networks (ESN). We found that this novel RNN architecture shows more stable performance and faster convergence.

A Graph-matching based Intra-node Task Assignment Methodology for SMP Clusters

Load distribution for parallel applications on a cluster of Symmetrical Multi-processors(SMP) poses a challenging problem. A cluster of SMPs essentially comprises of a finite number of computing nodes each comprising of 2 or more identical, tightly coupled processing elements, the nodes being connected over a network. While approximate methods of load balancing using standard methods like graph-partitioning, for example, can produce acceptable task assignment across the nodes, they cannot be applied to obtain optimal task assignment on the processors constituting a node. This is because of the complex optimisation involved when one considers the fact that all the processing elements in a node have only one network interface and the node turn-around time is the minimum when the computation and communication activities of the processing elements can be interleaved optimally. A graph-matching based methodology using multi-level refinement is proposed in this paper. Compared to a traditional graph-matching based load balancing algorithm, where worst case (exponential) complexity occurs in practice as the number of task modules increases, this methodology produces optimal assignments within acceptable run time using a multi-level refinement approach and hence can be used for practical applications

Load Balancing for Spatial-Grid-Based Parallel Numeric Simulations on Clusters of SMPs

Load distribution is an essential factor to parallel efficiency of numerical simulations that are based on spatial grids, especially on clusters of symmetric multiprocessors (SMPs). This paper presents a method of mapping spatial grid nodes to processors that combines two load balancing methodologies, graph partitioning and graph matching, to achieve maximum parallel efficiency on SMP clusters. The method has been successfully applied to load distribution in a parallel Computational Fluid Dynamics (CFD) simulation. Test runs on a PC cluster prove the effectiveness of our method. 1