A genetic study and meta-analysis of the genetic predisposition of prostate cancer in a Chinese population.

Prostate cancer predisposition has been extensively investigated in European populations, but there have been few studies of other ethnic groups. To investigate prostate cancer susceptibility in the under-investigated Chinese population, we performed single-nucleotide polymorphism (SNP) array analysis on a cohort of Chinese cases and controls and then meta-analysis with data from the existing Chinese prostate cancer genome-wide association study (GWAS). Genotyping 211,155 SNPs in 495 cases and 640 controls of Chinese ancestry identified several new suggestive Chinese prostate cancer predisposition loci. However, none of them reached genome-wide significance level either by meta-analysis or replication study. The meta-analysis with the Chinese GWAS data revealed that four 8q24 loci are the main contributors to Chinese prostate cancer risk and the risk alleles from three of them exist at much higher frequencies in Chinese than European populations. We also found that several predisposition loci reported in Western populations have different effect on Chinese men. Therefore, this first extensive single-nucleotide polymorphism study of Chinese prostate cancer in comparison with European population indicates that four loci on 8q24 contribute to a great risk of prostate cancer in a considerable large proportion of Chinese men. Based on those four loci, the top 10% of the population have six- or two-fold prostate cancer risk compared with men of the bottom 10% or median risk respectively, which may facilitate the design of prostate cancer genetic risk screening and prevention in Chinese men. These findings also provide additional insights into the etiology and pathogenesis of prostate cancer.This work was conducted on behalf of the CHIPGECS and The PRACTICAL consortia (see Supplementary Consortia). We acknowledge the contribution of doctors, nurses and postgraduate research students at the CHIPGENCS sample collecting centers. We thank Orchid and Rosetrees for funding support. This work was also supported by National Natural Science foundation of China for funding support to H Zhang (Grant No: 30671793 and 81072377), N Feng (Grant No: 81272831), X Zhang (Grant No: 30572139, 30872924 and 81072095), S Zhao (Grant No: 81072092 and 81328017), Y Yu (Grant No: 81172448) and Program for New Century Excellent Talents in University from Department of Education of China (NCET-08-0223) and the National High Technology Research and Development Program of China (863 Program 2012AA021101) to X Zhang.This is the final version of the article. It first appeared from Impact Journals via http://dx.doi.org/10.18632/oncotarget.725

Generalized Legendre Polynomial Configuration Method for Solving Numerical Solutions of Fractional Pantograph Delay Differential Equations

This paper develops a numerical approach for solving fractional pantograph delay differential equations using generalized Legendre polynomials. These polynomials are derived from generalized Taylor bases, which facilitate the approximation of the underlying analytical solutions, leading to the formulation of numerical solutions. The fractional pantograph delay differential equation is then transformed into a finite set of nonlinear algebraic equations using collocation points. Following this step, Newton's iterative method is applied to the resultant set of nonlinear algebraic equations to compute their numerical solutions. An error analysis for this methodology is subsequently presented, accompanied by numerical examples demonstrating its accuracy and efficiency. Overall, this study contributes a more streamlined and productive tool for determining the numerical solution of fractional differential equations

Mean Remaining Strength Estimation of Multi-State System Based on Nonparametric Bayesian Method

In a stress-strength system, the mean remaining strength is the key to deciding the safety threshold for the system continuing to operate. In this study, a multi-state stress-strength system composed of two-state components is discussed, and the mean remaining strength of the system is studied. Applying a multidimensional signature, the dynamic signature form is established, and the mean remaining strength of the system in different states is deduced. Moreover, the nonparametric Bayesian method is used to estimate the mean remaining strength of the system. The results of Monte Carlo simulation show that the nonparametric Bayesian method can reasonably estimate the mean remaining strength of a multi-state system, and its estimation effect is better than that of the nonparametric estimation method. A practical case based on a fiber strength dataset is presented as an application of the proposed methodology

Inhibitory effect of ultrasonic stimulation on the voltage-dependent potassium currents in rat hippocampal CA1 neurons

Abstract Background Transcranial ultrasonic stimulation is a novel noninvasive tool for neuromodulation, and has high spatial resolution and deep penetration. Although it can increase excitation of neurons, its effects on neuron are poorly understood. This study was to evaluate effect of ultrasonic stimulation (US) on neurons in vitro. In this paper, the effect of US on the excitability and voltage-dependent $$K^{ + }$$ K+ currents of CA1 pyramidal neurons in the rat hippocampus was studied using patch clamp. Results Our results suggest that US increased the spontaneous firing rate and inhibited transient outward potassium current ( \varvec{I}_{\varvec{A}} IA ) and delayed rectifier potassium current ( \varvec{I}_{\varvec{K}} ) IK) . Furthermore, US altered the activation of \varvec{I}_{\varvec{K}} IK channels, inactivation and recovery properties of \varvec{I}_{\varvec{A}} IA channels. After US, the \varvec{I}_{\varvec{K}} IK activation curves significantly moved to the negative voltage direction and increased its slope factor. Moreover, the data showed that US moved the inactivation curve of \varvec{I}_{\varvec{A}} IA to the negative voltage and increased the slope factor. Besides, US delayed the recovery of \varvec{I}_{\varvec{A}} IA channel. Conclusions Our data indicate that US can increase excitation of neurons by inhibiting potassium currents. Different US decreased the voltage sensitivity of \varvec{I}_{\varvec{K}} IK activation differentially. Moreover, the more time is needed for US to make the \varvec{I}_{\varvec{A}} IA channels open again after inactivating. US may play a physiological role by inhibiting voltage-dependent potassium currents in neuromodulation. Our research can provide a theoretical basis for the future clinical application of ultrasound in neuromodulation

Practical Parallel Algorithms for Submodular Maximization Subject to a Knapsack Constraint with Nearly Optimal Adaptivity

Submodular maximization has wide applications in machine learning and data mining, where massive datasets have brought the great need for designing efficient and parallelizable algorithms. One measure of the parallelizability of a submodular maximization algorithm is its adaptivity complexity, which indicates the number of sequential rounds where a polynomial number of queries to the objective function can be executed in parallel. In this paper, we study the problem of non-monotone submodular maximization subject to a knapsack constraint, and propose the first combinatorial algorithm achieving an (8+epsilon)-approximation under O(log n) adaptive complexity, which is optimal up to a factor of O(loglog n). Moreover, under slightly larger adaptivity, we also propose approximation algorithms with nearly optimal query complexity of O(n), while achieving better approximation ratios. We show that our algorithms can also be applied to the special case of submodular maximization subject to a cardinality constraint, and achieve performance bounds comparable with those of state-of-the-art algorithms. Finally, the effectiveness of our approach is demonstrated by extensive experiments on real-world applications

Plant growth and diversity performance after restoration in Carex schmidtii tussock wetlands, Northeast China

Plant performance, which considers plant growth, community composition, and diversity, demonstrated the wetland plants’ restoration efficiency of wetland plants following restoration. Carex tussocks are widespread in temperate freshwater wetlands and streams, well as characterizing with rich biodiversity. However, tussock wetlands sharply shrank and plant performance has changed due to the interaction of long-term drought, and human disturbance (road construction, grazing and mowing). In recent decades, ecological restoration has been widely conducted in degraded tussock wetlands in semi-arid area. A field investigation was done in a restored tussock wetland (R) after restoration for 10 years in order to evaluate efficacy of tussock restoration. Tussock wetlands were chosen as reference wetlands, both natural (N) and degraded (D). In semi-arid zones, the results showed that wetlands were affected by drought and flooding. After 10 years, wetland restoration effectively restored the growth and yield of Carex schmidtii tussocks compared to D, but did not reach to the natural state. The importance value (IV) of C. schmidtii has sharply decreased in R. Xerophyte species (Artemisia integrifolia) have occupied dominant position growth. Furthermore, the IV of other wetland species has dropped through time, and some have even disappeared as a result of drought and flooding. R and N have much lower species richness and Shannon–Wiener index than D. Flooding in August, following a drought, boosted the Simpson index and Evenness index in R and N. Obvious differences in species composi- tion and community structure were found using principal component analysis among N, R, and D. Ecological restoration substantially alleviated wetland degradation in the semi-arid zone, but subsequent hydrological management is required to further promote plant growth and diversity performance

Multiscale Feature Aggregation Capsule Neural Network for Hyperspectral Remote Sensing Image Classification

Models based on capsule neural network (CapsNet), a novel deep learning method, have recently made great achievements in hyperspectral remote sensing image (HSI) classification due to their excellent ability to implicitly model the spatial relationship knowledge embedded in HSIs. However, the number of labeled samples is a common bottleneck in HSI classification, limiting the performance of these deep learning models. To alleviate the problem of limited labeled samples and further explore the potential of CapsNet in the HSI classification field, this study proposes a multiscale feature aggregation capsule neural network (MS-CapsNet) based on CapsNet via the implementation of two branches that simultaneously extract spectral, local spatial, and global spatial features to integrate multiscale features and improve model robustness. Furthermore, because deep features are generally more discriminative than shallow features, two kinds of capsule residual (CapsRES) blocks based on 3D convolutional capsule (3D-ConvCaps) layers and residual connections are proposed to increase the depth of the network and solve the limited labeled sample problem in HSI classification. Moreover, a squeeze-and-excitation (SE) block is introduced in the shallow layers of MS-CapsNet to enhance its feature extraction ability. In addition, a reasonable initialization strategy that transfers parameters from two well-designed, pretrained deep convolutional capsule networks is introduced to help the model find a good set of initializing weight parameters and further improve the HSI classification accuracy of MS-CapsNet. Experimental results on four widely used HSI datasets demonstrate that the proposed method can provide results comparable to those of state-of-the-art methods