An Integrative Analysis of microRNA and mRNA Expression—A Case Study

Background: MicroRNAs are believed to play an important role in gene expression regulation. They have been shown to be involved in cell cycle regulation and cancer. MicroRNA expression profiling became available owing to recent technology advancement. In some studies, both microRNA expression and mRNA expression are measured, which allows an integrated analysis of microRNA and mRNA expression.Results: We demonstrated three aspects of an integrated analysis of microRNA and mRNA expression, through a case study of human cancer data. We showed that (1) microRNA expression efficiently sorts tumors from normal tissues regardless of tumor type, while gene expression does not; (2) many microRNAs are down-regulated in tumors and these microRNAs can be clustered in two ways: microRNAs similarly affected by cancer and microRNAs similarly interacting with genes; (3) taking let-7f as an example, targets genes can be identified and they can be clustered based on their relationship with let-7f expression.Discussion: Our findings in this paper were made using novel applications of existing statistical methods: hierarchical clustering was applied with a new distance measure—the co-clustering frequency—to identify sample clusters that are stable; microRNA-gene correlation profiles were subject to hierarchical clustering to identify microRNAs that similarly interact with genes and hence are likely functionally related; the clustering of regression models method was applied to identify microRNAs similarly related to cancer while adjusting for tissue type and genes similarly related to microRNA while adjusting for disease status. These analytic methods are applicable to interrogate multiple types of -omics data in general

On Comparing the Clustering of Regression Models Method with K-means Clustering

Gene clustering is a common question addressed with microarray data. Previous methods, such as K-means clustering and hierarchical clustering, base gene clustering directly on the observed measurements. A new model-based clustering method, the clustering of regression models (CORM) method, bases the clustering of genes on their relationship to covariates. It explicitly models different sources of variations and bases gene clustering solely on the systematic variation. Both being partitional clustering, CORM is closely related to K-means clustering. In this paper, we discuss the relationship between the two clustering methods in terms of both model formulation and implications on other important aspects of cluster analysis. We show that the two methods can both be considered as solutions to a least squares problem with missing data but they each concern a different type of least squares. We also show that CORM tends to provide stable clusters across samples and is particularly useful if the cluster averages are used as predictors for sample classification. Finally we illustrate the application of CORM to a set of time course data measured on four yeast samples, which has a complicated experimental design and is difficult for K-means to handle

Finding gene clusters for a replicated time course study

BACKGROUND: Finding genes that share similar expression patterns across samples is an important question that is frequently asked in high-throughput microarray studies. Traditional clustering algorithms such as K-means clustering and hierarchical clustering base gene clustering directly on the observed measurements and do not take into account the specific experimental design under which the microarray data were collected. A new model-based clustering method, the clustering of regression models method, takes into account the specific design of the microarray study and bases the clustering on how genes are related to sample covariates. It can find useful gene clusters for studies from complicated study designs such as replicated time course studies. FINDINGS: In this paper, we applied the clustering of regression models method to data from a time course study of yeast on two genotypes, wild type and YOX1 mutant, each with two technical replicates, and compared the clustering results with K-means clustering. We identified gene clusters that have similar expression patterns in wild type yeast, two of which were missed by K-means clustering. We further identified gene clusters whose expression patterns were changed in YOX1 mutant yeast compared to wild type yeast. CONCLUSIONS: The clustering of regression models method can be a valuable tool for identifying genes that are coordinately transcribed by a common mechanism

Spatial-temporal traffic modeling with a fusion graph reconstructed by tensor decomposition

Accurate spatial-temporal traffic flow forecasting is essential for helping traffic managers to take control measures and drivers to choose the optimal travel routes. Recently, graph convolutional networks (GCNs) have been widely used in traffic flow prediction owing to their powerful ability to capture spatial-temporal dependencies. The design of the spatial-temporal graph adjacency matrix is a key to the success of GCNs, and it is still an open question. This paper proposes reconstructing the binary adjacency matrix via tensor decomposition, and a traffic flow forecasting method is proposed. First, we reformulate the spatial-temporal fusion graph adjacency matrix into a three-way adjacency tensor. Then, we reconstructed the adjacency tensor via Tucker decomposition, wherein more informative and global spatial-temporal dependencies are encoded. Finally, a Spatial-temporal Synchronous Graph Convolutional module for localized spatial-temporal correlations learning and a Dilated Convolution module for global correlations learning are assembled to aggregate and learn the comprehensive spatial-temporal dependencies of the road network. Experimental results on four open-access datasets demonstrate that the proposed model outperforms state-of-the-art approaches in terms of the prediction performance and computational cost.Comment: 11 pages, 8 figure

(E)-Benzyl 3-(3-nitro­benzyl­idene)dithio­carbazate

In the title compound, C15H13N3O2S2, the dihedral angle between the aromatic rings is 87.8 (2)°. In the crystal, inversion dimers occur linked by pairs of N—H⋯S hydrogen bonds

Biaxial strain modulated electronic structures of layered two-dimensional MoSiGeN4 Rashba systems

The two-dimensional (2D) MA2Z4 family has received extensive attention in manipulating its electronic structure and achieving intriguing physical properties. However, engineering the electronic properties remains a challenge. Herein, based on first-principles calculations, we systematically investigate the effect of biaxial strains on the electronic structures of 2D Rashba MoSiGeN4 (MSGN), and further explore how the interlayer interactions affect the Rashba spin splitting in such strained layered MSGNs. After applying biaxial strains, the band gap decreases monotonically with increasing tensile strains but increases when the compressive strains are applied. An indirect-direct-indirect band gap transition is induced by applying a moderate compressive strain (< 5%) in the MSGNs. Due to the symmetry breaking and moderate spin-orbit coupling (SOC), the monolayer MSGN possess an isolated Rashba spin splitting (R) near the Fermi level, which could be effectively regulated to the Lifshitz transition (L) by biaxial strain. For instance, a L-R-L transformation of Fermi surface is presented in monolayer and a more complex and changeable L-R-L-R evolution is observed in bilayer and trilayer MSGNs as the biaxial strain vary from -8% to 12%, which actually depend on the appearance, variation, and vanish of the Mexican hat band in the absence of SOC under different strains. The contribution of Mo-dz2 orbital hybridized with N-pz orbital in the highest valence band plays a dominant role on the band evolution under biaxial strains, where the R-L evolution corresponds to the decreased Mo-dz2 orbital contribution. Our study highlights the biaxial strain controllable Rashba spin splitting, in particular the introduction and even the evolution of Lifshitz transition near Fermi surface, which makes the strained MSGNs as promising candidates for future applications in spintronic devices.Comment: 21 pages, 7 figures, supplementary informatio

Some field experience with subsynchronous vibration of centrifugal compressors

A lot of large chemical fertilizer plants producing 1000 ton NH3/day and 1700 ton urea/day were constructed in the 1970's in China. During operation, subsynchronous vibration takes place occasionally in some of the large turbine-compressor sets and has resulted in heavy economic losses. Two cases of subsynchronous vibration are described: Self-excited vibration of the low-pressure (LP) cylinder of one kind of N2-H2 multistage compressor; and Forced subsynchronous vibration of the high-pressure (HP) cylinder of the CO2 compressor

Dynamical encircling exceptional point in largely detuned multimode optomechanical system

Dynamical encircling exceptional point(EP) shows a number of intriguing physical phenomena and its potential applications. To enrich the manipulations of optical systems in experiment, here, we study the dynamical encircling EP, i.e. state transfer process, in largely detuned multimode optomechanical system. The process of state transfer has been investigated with different factors about the location of start point, the orientation and the initial state of the trajectories around the EP in parameter space. Results show that the nonreciprocal and the chiral topological energy transfer between two optical modes are performed successfully by tuning the effective optomechanical coupling in the multimode system with large detuning. Moreover, the factor of evolution speed about system parameters is also discussed. Our work demonstrates the fundamental physics around EP in large detuning domain of multimode optomechanical system and provides an alternative for manipulating of optical modes in non-hermitian system.Comment: 9 pages,7 figure

Enhanced sensing mechanism based on shifting an exceptional point

Non-Hermitian systems associated with exceptional points (EPs) are expected to demonstrate a giant response enhancement for various sensors. The widely investigated enhancement mechanism based on diverging from an EP should destroy the EP and further limits its applications for multiple sensing scenarios in a time sequence. To break the above limit, here we proposed a new enhanced sensing mechanism based on shifting an EP. Different from the mechanism of diverging from an EP, our scheme is an EP non-demolition and the giant enhancement of response is acquired by a slight shift of the EP along the parameter axis induced by perturbation. The new sensing mechanism can promise the most ffective response enhancement for all sensors in the case of multiple sensing in a time sequence. To verify our sensing mechanism, we construct a mass sensor and a gyroscope with concrete physical implementations. Our work will deepen the understanding of EP-based sensing and inspire designing various high sensitivity sensors in different physical systems.Comment: 7 pages, 3 figure