Age-related changes in the first order Weiner kernel visual evoked potential

Visual evoked potentials were obtained from 112 subjects (215 eyes) aged 5 to 79 years with fast random stimuli. The stimulus (the presence or absence of the initiation of a reversal) and response (voltage measurements) were used in calculating the first order Wiener kernels to obtain the visual evoked potentials. Five check sizes were used to stimulate each eye. The implicit time of the major negative wave showed a significant increase with age for each check size used. Regression lines for the implicit time vs age data for male subjects were significantly different from those of females. The y-intercepts were lower and the slope of the regression lines were consistently steeper for males. Thus the rate of increase in implicit time was higher in males. The relationship of the amplitude of the major wave to age did not appear to be linear. The data suggested that a second-order polynomial curve might be an appropriate representation of the relationship.     

基于小波支持向量回归的遥感多光谱图像分辨率增强算法

利用小波支持向量回归,实现了遥感多光谱图像分辨率的增强。首先采用非下采样Contourlet变换对低分辨率的多光谱图像和高分辨率的全色图像进行多分辨率分解,再利用小波支持向量回归对分解系数进行学习和预测,获得分辨率初步提高的多光谱图像,最后再与传统的插值方法得到的结果进行融合来实现多光谱图像分辨率增强。实验结果表明:此方法借遥感全色图像的辅助获得丰富的高频细节信息,使得分辨率增强结果无论是最小均方误差还是峰值信噪比都要优于仅依靠原图像本身放大的传统方法以及其他的分辨率增强方法。     

Defining functional distance using manifold embeddings of gene ontology annotations

Although rigorous measures of similarity for sequence and structure are now well established, the problem of defining functional relationships has been particularly daunting. Here, we present several manifold embedding techniques to compute distances between Gene Ontology (GO) functional annotations and consequently estimate functional distances between protein domains. To evaluate accuracy, we correlate the functional distance to the well established measures of sequence, structural, and phylogenetic similarities. Finally, we show that manual classification of structures into folds and superfamilies is mirrored by proximity in the newly defined function space. We show how functional distances place structure-function relationships in biological context resulting in insight into divergent and convergent evolution. The methods and results in this paper can be readily generalized and applied to a wide array of biologically relevant investigations, such as accuracy of annotation transference, the relationship between sequence, structure, and function, or coherence of expression modules.     

Inter-DRG resource dynamics in a prospective payment system: a stochastic kernel approach

This paper empirically investigates the resource distribution dynamics across Diagnosis Related Groups (DRGs) of elective surgery patients, in a continuing Prospective Payment System (PPS). Existing econometric literature has mainly focussed on the impact of PPS on average Length of Stay (LOS) concluding that the average LOS has declined post PPS. There is little literature on the distribution of this decline across DRGs, in a PPS. The present paper helps fill this gap. It models the evolution over time of the empirical distribution of LOS across DRGs. The empirical distributions are estimated using a non parametric “stochastic kernel approach” based on Markov Chain theory. The results for inlier episodes suggest that resource redistribution will increase capacity and expected number of admissions for DRGs having increasing waiting times. In addition, adjustments in relative cost weights are perceived as price signals by hospitals leading to a change in their casemix. The results for high outlier patients reveal that improved quality of care is one of the factors causing reduction in high outlier episodes.

Prediction of Rockfill Materials’ Shear Strength Using Various Kernel Function-Based Regression Models—A Comparative Perspective

The mechanical behavior of the rockfill materials (RFMs) used in a dam’s shell must be evaluated for the safe and cost-effective design of embankment dams. However, the characterization of RFMs with specific reference to shear strength is challenging and costly, as the materials may contain particles larger than 500 mm in diameter. This study explores the potential of various kernel function-based Gaussian process regression (GPR) models to predict the shear strength of RFMs. A total of 165 datasets compiled from the literature were selected to train and test the proposed models. Comparing the developed models based on the GPR method shows that the superlative model was the Pearson universal kernel (PUK) model with an R-squared (R²) of 0.9806, a correlation coefficient (r) of 0.9903, a mean absolute error (MAE) of 0.0646 MPa, a root mean square error (RMSE) of 0.0965 MPa, a relative absolute error (RAE) of 13.0776%, and a root relative squared error (RRSE) of 14.6311% in the training phase, while it performed equally well in the testing phase, with R² = 0.9455, r = 0.9724, MAE = 0.1048 MPa, RMSE = 0.1443 MPa, RAE = 21.8554%, and RRSE = 23.6865%. The prediction results of the GPR-PUK model are found to be more accurate and are in good agreement with the actual shear strength of RFMs, thus verifying the feasibility and effectiveness of the model.     

反相高效液相色谱法测定新生化冲剂中阿魏酸含量

目的：建立测定新生化冲剂中阿魏酸含量的方法。方法：采用反相高效液相色谱(RP HPLC)法，以ODS C18色谱柱分离，以水(水中含1.0%冰醋酸)∶乙腈∶甲醇＝70∶0.6∶30为流动相，流速为1.0 mL·min 1，检测波长320 nm。结果：线性范围5.4～86.4 μg·mL 1，r＝0.998 7，测得平均回收率99.7%，RSD＝0.49%。结论：反相高效液相色谱法高效、快速、灵敏，可作为新生化冲剂质量控制的有效方法。     

Investigation on Amari’s dynamical neural field with global constant inhibition

In this paper, the properties of Amari’s dynamical neural field with global constant inhibition induced by its kernel are investigated. Amari’s dynamical neural field illustrates many neurophysiological phenomena successfully and has been applied to unsupervised learning like data clustering in recent years. In its applications, the stationary solution to Amari’s dynamical neural field plays an important role that the underlying patterns being perceived are usually presented as the excited region in it. However, the type of stationary solution to dynamical neural field with typical kernel is often sensitive to parameters of its kernel that limits its range of application. Different from dynamical neural field with typical kernel that have been discussed a lot, there are few theoretical results on dynamical neural field with global constant inhibitory kernel that has already shown better performance in practice. In this paper, some important results on existence and stability of stationary solution to dynamical neural field with global constant inhibitory kernel are obtained. All of these results show that such kind of dynamical neural field has better potential for missions like data clustering than those with typical kernels, which provide a theoretical basis of its further extensive application.     

GEE‐Based SNP Set Association Test for Continuous and Discrete Traits in Family‐Based Association Studies

Family‐based genetic association studies of related individuals provide opportunities to detect genetic variants that complement studies of unrelated individuals. Most statistical methods for family association studies for common variants are single marker based, which test one SNP a time. In this paper, we consider testing the effect of an SNP set, e.g., SNPs in a gene, in family studies, for both continuous and discrete traits. Specifically, we propose a generalized estimating equations (GEEs) based kernel association test, a variance component based testing method, to test for the association between a phenotype and multiple variants in an SNP set jointly using family samples. The proposed approach allows for both continuous and discrete traits, where the correlation among family members is taken into account through the use of an empirical covariance estimator. We derive the theoretical distribution of the proposed statistic under the null and develop analytical methods to calculate the P‐values. We also propose an efficient resampling method for correcting for small sample size bias in family studies. The proposed method allows for easily incorporating covariates and SNP‐SNP interactions. Simulation studies show that the proposed method properly controls for type I error rates under both random and ascertained sampling schemes in family studies. We demonstrate through simulation studies that our approach has superior performance for association mapping compared to the single marker based minimum P‐value GEE test for an SNP‐set effect over a range of scenarios. We illustrate the application of the proposed method using data from the Cleveland Family GWAS Study.     

柱前衍生化HPLC法测定薏苡非种仁部位中丙氨酸的含量

目的:建立柱前衍生化反相高效液相色谱法测定薏苡非种仁部位中氨基酸含量的方法。方法:采用Shim-pack CLC-ODS(4.6 mm)色谱柱,流动相A为0.1 mol/L乙酸钠溶液(pH=6.5)-乙腈(93∶7),流动相B为乙腈-水(4∶1),梯度洗脱,柱温为40℃,检测波长为254 nm。结果:丙氨酸进样量在0.01296～0.2851μg范围内,线性关系良好,r=0.9998(n=7),平均加样回收率为98.01%,RSD为2.2%。结论:方法准确、可靠,具有良好的重复性和稳定性,可用于薏苡非种仁部位中氨基酸的含量测定。