Statistical and Biological Evaluation of Different Gene Set Analysis Methods

AbstractGene-set analysis (GSA) methods have been widely used in microarray data analysis. Owing to the unusual characteristics of microarray data, such as multi-dimension, small sample size and complicated relationship between genes, no generally accepted methods have been used to detect differentially expressed gene sets (DEGs) up to now. Our group assessed the statistical performance of some commonly used methods through Monte Carlo simulation combined with the analysis of real-world microarray data sets. Not only did we discover a few novel features of GSA methods during experiences, but also we find that some GSA methods are effective only if genes were assumed to be independent. And we also detected that model-based methods (GlobalTest and PCOT2) performed well when analyzing our simulated data sets in which the inter-gene correlation structure was incorporated into each gene set separately for more reasonable. Through analysis of real-world microarray data, we found GlobalTest is more effective. Then we concluded that GlobalTest is a more effective gene set analysis method, and recommended using it with microarray data analysis

DiffSCI: Zero-Shot Snapshot Compressive Imaging via Iterative Spectral Diffusion Model

This paper endeavors to advance the precision of snapshot compressive imaging (SCI) reconstruction for multispectral image (MSI). To achieve this, we integrate the advantageous attributes of established SCI techniques and an image generative model, propose a novel structured zero-shot diffusion model, dubbed DiffSCI. DiffSCI leverages the structural insights from the deep prior and optimization-based methodologies, complemented by the generative capabilities offered by the contemporary denoising diffusion model. Specifically, firstly, we employ a pre-trained diffusion model, which has been trained on a substantial corpus of RGB images, as the generative denoiser within the Plug-and-Play framework for the first time. This integration allows for the successful completion of SCI reconstruction, especially in the case that current methods struggle to address effectively. Secondly, we systematically account for spectral band correlations and introduce a robust methodology to mitigate wavelength mismatch, thus enabling seamless adaptation of the RGB diffusion model to MSIs. Thirdly, an accelerated algorithm is implemented to expedite the resolution of the data subproblem. This augmentation not only accelerates the convergence rate but also elevates the quality of the reconstruction process. We present extensive testing to show that DiffSCI exhibits discernible performance enhancements over prevailing self-supervised and zero-shot approaches, surpassing even supervised transformer counterparts across both simulated and real datasets. Our code will be available

Structural Optimization Design of Large Wind Turbine Blade considering Aeroelastic Effect

This paper presents a structural optimization design of the realistic large scale wind turbine blade. The mathematical simulations have been compared with experimental data found in the literature. All complicated loads were applied on the blade when it was working, which impacts directly on mixed vibration of the wind rotor, tower, and other components, and this vibration can dramatically affect the service life and performance of wind turbine. The optimized mathematical model of the blade was established in the interaction between aerodynamic and structural conditions. The modal results show that the first six modes are flapwise dominant. Meanwhile, the mechanism relationship was investigated between the blade tip deformation and the load distribution. Finally, resonance cannot occur in the optimized blade, as compared to the natural frequency of the blade. It verified that the optimized model is more appropriate to describe the structure. Additionally, it provided a reference for the structural design of a large wind turbine blade

Bi-nuclear tensor Schatten-p norm minimization for multi-view subspace clustering

Multi-view subspace clustering aims to integrate the complementary information contained in different views to facilitate data representation. Currently, low-rank representation (LRR) serves as a benchmark method. However, we observe that these LRR-based methods would suffer from two issues: limited clustering performance and high computational cost since (1) they usually adopt the nuclear norm with biased estimation to explore the low-rank structures; (2) the singular value decomposition of large-scale matrices is inevitably involved. Moreover, LRR may not achieve low-rank properties in both intra-views and interviews simultaneously. To address the above issues, this paper proposes the Bi-nuclear tensor Schatten-p norm minimization for multi-view subspace clustering (BTMSC). Specifically, BTMSC constructs a third-order tensor from the view dimension to explore the high-order correlation and the subspace structures of multi-view features. The Bi-Nuclear Quasi-Norm (BiN) factorization form of the Schatten-p norm is utilized to factorize the third-order tensor as the product of two small-scale thirdorder tensors, which not only captures the low-rank property of the third-order tensor but also improves the computational efficiency. Finally, an efficient alternating optimization algorithm is designed to solve the BTMSC model. Extensive experiments with ten datasets of texts and images illustrate the performance superiority of the proposed BTMSC method over state-of-the-art methods

Changes in soil chemical properties as affected by pyrogenic organic matter amendment with different intensity and frequency

Pyrogenic organicmatter (PyOM) has long been used as a soil amendment to improve soil physicochemical properties. However, few studies simultaneously investigated both intensities and frequencies of PyOM addition on soil chemical properties of soil base cations, soil pHbuffering capacity (pHBC), and plant availablemicronutrients. In the main food production area of lower Liaohe River Plain in Northeast China, a field manipulation of PyOM addition was initiated in 2013 to examine how the intensities (0, 1%, 3%, and 5% of 0-20 cm soil mass) and frequencies (3% of soil mass applied once versus yearly for 3 years) of PyOM amendment affected soil chemical properties. Higher intensity of PyOM addition significantly increased soil exchangeable Mg (by 24.2%), which was caused by increase of soil pH, soil exchangeable surfaces, and soil organic matter. Plant available Fe, Mn, and Cu were significantly decreased with increasing PyOM addition intensity by up to 39.4%, 50.8%, and 30.0%, respectively, especially under the highest amount of PyOM amendment (5%). This was possibly due to removal of micronutrients with plant biomass or irreversible binding of available micronutrients on PyOM which decreased the extraction efficiency. Under the same amount of PyOM addition (3% in total), higher frequency of PyOM amendment significantly increased soil exchangeable Mg, while lower frequency showed no impact as compared to control plots (CK). Higher frequency of PyOM amendment significantly decreased plant available Mn and Cu as compared to both lower frequency and CK treatments. Both the intensity and frequency of PyOMaddition significantly increased soil pH but showed no influence on soil pHBC. Our results showed that exchangeableMg increased but availableMn and Cu decreasedwith both PyOMamendment intensity and frequency. Even though PyOM amendment could enrich soil base cations, it might cause deficiency of available micronutrients and pose a threat to plant productivity in agroecosystems

Robustness meets low-rankness: unified entropy and tensor learning for multi-view subspace clustering

In this paper, we develop the weighted error entropy-regularized tensor learning method for multi-view subspace clustering (WETMSC), which integrates the noise disturbance removal and subspace structure discovery into one unified framework. Unlike most existing methods which focus only on the affinity matrix learning for the subspace discovery by different optimization models and simply assume that the noise is independent and identically distributed (i.i.d.), our WETMSC method adopts the weighted error entropy to characterize the underlying noise by assuming that noise is independent and piecewise identically distributed (i.p.i.d.). Meanwhile, WETMSC constructs the self-representation tensor by storing all self-representation matrices from the view dimension, preserving high-order correlation of views based on the tensor nuclear norm. To solve the proposed nonconvex optimization method, we design a half-quadratic (HQ) additive optimization technology and iteratively solve all subproblems under the alternating direction method of multipliers framework. Extensive comparison studies with state-of-the-art clustering methods on real-world datasets and synthetic noisy datasets demonstrate the ascendancy of the proposed WETMSC method

Ionic cluster effect in suppression on superconductivity in Ni- and Co-doped YBCO systems

We adopted the x-ray diffraction, oxygen contents, positron annihilation technology as well as simulation methods to investigate systemically YBa₂Cu₃–x(Ni,Co)xO₇–δ (x = 0–0.5). The simulated results show that ions distribute in dispersive form in little doped concentration. As doped concentration increases, ions combine into clusters in the crystal lattice. The calculated results and oxygen contents, together with the impure phases and the local electron density ne, show the ionic cluster effect, which not only causes the local electron density to reach the saturation, but also suppress the superconductivity significantly

Health-related quality of life as measured with EQ-5D among populations with and without specific chronic conditions: A population-based survey in Shaanxi province, China

© 2013 Tan et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Introduction: The aim of this study was to examine health-related quality of life (HRQoL) as measured by EQ-5D and to investigate the influence of chronic conditions and other risk factors on HRQoL based on a distributed sample located in Shaanxi Province, China. Methods: A multi-stage stratified cluster sampling method was performed to select subjects. EQ-5D was employed to measure the HRQoL. The likelihood that individuals with selected chronic diseases would report any problem in the EQ-5D dimensions was calculated and tested relative to that of each of the two reference groups. Multivariable linear regression models were used to investigate factors associated with EQ VAS. Results: The most frequently reported problems involved pain/discomfort (8.8%) and anxiety/depression (7.6%). Nearly half of the respondents who reported problems in any of the five dimensions were chronic patients. Higher EQ VAS scores were associated with the male gender, higher level of education, employment, younger age, an urban area of residence, access to free medical service and higher levels of physical activity. Except for anemia, all the selected chronic diseases were indicative of a negative EQ VAS score. The three leading risk factors were cerebrovascular disease, cancer and mental disease. Increases in age, number of chronic conditions and frequency of physical activity were found to have a gradient effect. Conclusion: The results of the present work add to the volume of knowledge regarding population health status in this area, apart from the known health status using mortality and morbidity data. Medical, policy, social and individual attention should be given to the management of chronic diseases and improvement of HRQoL. Longitudinal studies must be performed to monitor changes in HRQoL and to permit evaluation of the outcomes of chronic disease intervention programs. © 2013 Tan et al.National Nature Science Foundation (No. 8107239