A statistical normalization method and differential expression analysis for RNA-seq data between different species

Background: High-throughput techniques bring novel tools but also statistical challenges to genomic research. Identifying genes with differential expression between different species is an effective way to discover evolutionarily conserved transcriptional responses. To remove systematic variation between different species for a fair comparison, the normalization procedure serves as a crucial pre-processing step that adjusts for the varying sample sequencing depths and other confounding technical effects. Results: In this paper, we propose a scale based normalization (SCBN) method by taking into account the available knowledge of conserved orthologous genes and hypothesis testing framework. Considering the different gene lengths and unmapped genes between different species, we formulate the problem from the perspective of hypothesis testing and search for the optimal scaling factor that minimizes the deviation between the empirical and nominal type I errors. Conclusions: Simulation studies show that the proposed method performs significantly better than the existing competitor in a wide range of settings. An RNA-seq dataset of different species is also analyzed and it coincides with the conclusion that the proposed method outperforms the existing method. For practical applications, we have also developed an R package named "SCBN" and the software is available at http://www.bioconductor.org/packages/devel/bioc/html/SCBN.html

Role of Financial Development in Total Factor Productivity: Evidence from Listed Firms in China

This study empirically investigates the relationship between total factor productivity (TFP) and financial development at the firm-level using listed firms in China over the period of 1999 to 2004. Two main channels through which financial development can affect total factor productivity at the firm level are explored: 1. financial constraints and 2. corporate governance. A nonparametric approach by Good et al. (1996) is chosen to estimate the firm-level TFP. Traditional measures such as size and age are used as proxies for the first channel−financial constraints. Inspired by Hadlock and Pierce (2009), size and age are also chosen as predictor variables to calculate a financial constraint score. For the second channel, two dimensions of the corporate governance are examined: financial structure measured by the debt-to-asset ratio and ownership structure proxied by ownership concentration and ownership category. The paper also evaluates the relationship between a firm's characteristics (capital intensity and export orientation) and its TFP level. Pooled OLS and Fixed Effects estimators are used and the Chow tests are conducted to see whether the role of financial development differs across different types of firms.Findings and Conclusions: This study finds that financial development measured by financial constraints at the firm level is positively associated with TFP: i.e., the easier the access to capital, the higher a firm's TFP. The results show that a high debt-to-asset ratio is associated with a lower firm productivity level, which is consistent with one line of literature. Top ten shareholder concentration has a significant positive relationship, while state-ownership has a significant negative relationship with firm-level TFP.The positive relationship between financial development and total factor productivity at the firm level implies that the sound financial system that China is trying to build will eventually help the country get on a more sustained economic growth path. In addition, indices created for Chinese listed firms using a more comprehensive set of company factors can provide a way to predict which firms will be financially constrained.Department of Economics and Legal Studie

Efficient Aerial Data Collection with UAV in Large-Scale Wireless Sensor Networks

Data collection from deployed sensor networks can be with static sink, ground-based mobile sink, or Unmanned Aerial Vehicle (UAV) based mobile aerial data collector. Considering the large-scale sensor networks and peculiarity of the deployed environments, aerial data collection based on controllable UAV has more advantages. In this paper, we have designed a basic framework for aerial data collection, which includes the following five components: deployment of networks, nodes positioning, anchor points searching, fast path planning for UAV, and data collection from network. We have identified the key challenges in each of them and have proposed efficient solutions. This includes proposal of a Fast Path Planning with Rules (FPPWR) algorithm based on grid division, to increase the efficiency of path planning, while guaranteeing the length of the path to be relatively short. We have designed and implemented a simulation platform for aerial data collection from sensor networks and have validated performance efficiency of the proposed framework based on the following parameters: time consumption of the aerial data collection, flight path distance, and volume of collected data

Development and verification of time-dependent bounding surface model under metro dynamic loads

To study the dynamic characteristics of soft soil foundation under the long-term metro dynamic loads, modified model based on the bounding surface model was presented. The Mesri creep formula was introduced into the bounding surface model, then it could not only consider the effects of time but also could describe the soil’s arbitrary shear stress levels. The modified bounding surface model was derived using the Newton-Raphson method and the secondary development of the model was conducted. Meanwhile, in order to verify the model, the dynamic triaxial tests of the soft soil were conducted by GDS dynamic triaxial equipment and the metro dynamic loads were simulated during dynamic triaxial tests. Then, the numerical simulation of modified bounding surface model was carried out for soft soil and the numerical results were compared with the test results. The results show that the time-dependent bounding surface model provides a more accurate calculation for the dynamic strain, and establishes a theoretical foundation for predicting the settlement of the soft soil

Division Gets Better: Learning Brightness-Aware and Detail-Sensitive Representations for Low-Light Image Enhancement

Low-light image enhancement strives to improve the contrast, adjust the visibility, and restore the distortion in color and texture. Existing methods usually pay more attention to improving the visibility and contrast via increasing the lightness of low-light images, while disregarding the significance of color and texture restoration for high-quality images. Against above issue, we propose a novel luminance and chrominance dual branch network, termed LCDBNet, for low-light image enhancement, which divides low-light image enhancement into two sub-tasks, e.g., luminance adjustment and chrominance restoration. Specifically, LCDBNet is composed of two branches, namely luminance adjustment network (LAN) and chrominance restoration network (CRN). LAN takes responsibility for learning brightness-aware features leveraging long-range dependency and local attention correlation. While CRN concentrates on learning detail-sensitive features via multi-level wavelet decomposition. Finally, a fusion network is designed to blend their learned features to produce visually impressive images. Extensive experiments conducted on seven benchmark datasets validate the effectiveness of our proposed LCDBNet, and the results manifest that LCDBNet achieves superior performance in terms of multiple reference/non-reference quality evaluators compared to other state-of-the-art competitors. Our code and pretrained model will be available.Comment: 14 pages, 16 figure

Identification of viscoplastic parameters and characterization of Lüders behaviour using digital image correlation and the virtual fields method

In this study, tensile loading experiments are performed on notched steel bars at an average applied strain rate of 1s-1. Displacement fields are measured across the specimen by coupling digital image correlation (DIC) with imaging using high speed CCD cameras (4796 fps). Results from the experiments indicate the presence of local strain rates ranging from 0.1 to 10s-1 in the notched specimens. The heterogeneity of the strain rate fields provides suitable conditions for determining simultaneously all the elasto-visco-plastic constitutive parameters governing the material behavior. For that, the whole stress fields are reconstructed in the specimen using the full-field deformation measurements. This reconstruction is repeated with different constitutive parameters until the average stress in the specimen matches the one measured with the load cell response. Perzyna’s model is firstly considered for the reconstruction of stresses but it is shown to be unsuited for providing the drop in the average stress that is systematically detected at the onset of plasticity by the load cell. This drop is attributed to the sudden occurrence of plasticity in the material due to Lüders effect. A modified model for elasto-visco-plasticity taking account of Lüders behavior in the material is considered afterwards. It yields a better agreement between the reconstructed stresses and the load cell response, and a more accurate identification of the parameters driving the visco-plastic model. Eventually, it is shown how to use DIC measurements for replacing the load cell measurements when the transient effects in the test reach the resonance frequency of the load cel