Regulation and functional significance of CDC42 alternative splicing in ovarian cancer.

Our previous study found that splicing factor polypyrimidine tract-binding protein 1 (PTBP1) had a role in tumorigenesis but the underlying mechanism remained unclear. In this study, we observed that knockdown of PTBP1 inhibited filopodia formation. Subsequently, we found that PTBP1 regulated the alternative splicing of CDC42, a major regulator of filopodia formation. Two CDC42 variants, CDC42-v1 and CDC42-v2, can be generated through alternative splicing. Knockdown of PTBP1 increased the expression of CDC42-v2. Ectopic expression of individual variants showed that CDC42-v2 suppressed filopodia formation, opposite to the effect of CDC42-v1. Quantitative RT-PCR revealed that CDC42-v2 was expressed at lower levels in ovarian cancer cell lines and ovarian tumor tissues than in normal control cells and tissues. Further, CDC42-v2 was observed to have inhibitory effects on ovarian tumor cell growth, colony formation in soft agar and invasiveness. In contrast, these inhibitory effects were not found with CDC42-v1. Taken together, above results suggest that the role of PTBP1 in tumorigenesis may be partly mediated by its regulation of CDC42 alternative splicing and CDC42-v2 might function as a tumor suppressor

D-OPTIMAL DESIGNS WITH ORDERED CATEGORICAL DATA

Cumulative link models have been widely used for ordered categorical responses. Uniform allocation of experimental units is commonly used in practice, but often suffers from a lack of efficiency. We consider D-optimal designs with ordered categorical responses and cumulative link models. For a predetermined set of design points, we derive the necessary and sufficient conditions for an allocation to be locally D-optimal and develop efficient algorithms for obtaining approximate and exact designs. We prove that the number of support points in a minimally supported design only depends on the number of predictors, which can be much less than the number of parameters in the model. We show that a D-optimal minimally supported allocation in this case is usually not uniform on its support points. In addition, we provide EW D-optimal designs as a highly efficient surrogate to Bayesian D-optimal designs. Both of them can be much more robust than uniform designs

OPTIMAL DESIGNS FOR 2(k) FACTORIAL EXPERIMENTS WITH BINARY RESPONSE.

We consider the problem of obtaining D-optimal designs for factorial experiments with a binary response and k qualitative factors each at two levels. We obtain a characterization of locally D-optimal designs. We then develop efficient numerical techniques to search for locally D-optimal designs. Using prior distributions on the parameters, we investigate EW D-optimal designs that maximize the determinant of the expected information matrix. It turns out that these designs can be obtained easily using our algorithm for locally D-optimal designs and are good surrogates for Bayes D-optimal designs. We also investigate the properties of fractional factorial designs and study robustness with respect to the assumed parameter values of locally D-optimal designs

Using cross-validation in a fast EM algorithm for genomic selection and complex trait prediction

The paper reports on changes to the EM algorithm emBayesB which estimates QTL effects using dense genome-wide SNP marker data. To overcome convergence issues, modifications were made to the original algorithm which included cross-validation for the estimation of model parameters. The modified algorithm called emBayesB_CV was used to analyse a trait simulated on real human genotypes consisting of 294,831 SNP measured on 3925 individuals. Three datasets were simulated for a trait determined by 10, 100 or 1000 additive QTL. The results showed that the modified algorithm emBayesB_CV was not only computationally fast, but also more accurate than GBLUP in predicting breeding value. However prediction accuracy declined as the size of QTL effects decreased due to the result that although emBayesB_CV could accurately locate the chromosomal location of large QTL effects, this was not the case for small QTL effects

Rotation Equivariant Feature Image Pyramid Network for Object Detection in Optical Remote Sensing Imagery

Detection of object is extremely important in various aerial vision-based applications. Over the last few years, the methods based on convolution neural networks have made substantial progress. However, because of the large variety of object scales, densities and arbitrary orientations, the current detectors struggle with extraction of semantically strong feature for small-scale objects by predefined convolution kernel. To address this problem, we propose the rotation equivariant feature image pyramid network (REFIPN), an image pyramid network based on rotation equivariance convolution. The proposed model adopts single-shot detector in parallel with a light-weight image pyramid module to extract representative feature and generate region of interests in an optimization approach. The proposed network extracts features in a wide range of scales and orientations by using novel convolution filters. These features are used to generate vector fields and determine the weight and angle of the highest scoring orientation for all spatial locations on an image. By this approach, the performance for small-sized object detection is enhanced without sacrificing the performance for large-sized object detection. The performance of the proposed model is validated on two commonly used aerial benchmarks and the results show our propose model can achieve state-of-the-art performance with satisfactory efficiency

Multi-patch Feature Pyramid Network for Weakly Supervised Object Detection in Optical Remote Sensing Images

Object detection is a challenging task in remote sensing because objects only occupy a few pixels in the images, and the models are required to simultaneously learn object locations and detection. Even though the established approaches well perform for the objects of regular sizes, they achieve weak performance when analyzing small ones or getting stuck in the local minima (e.g. false object parts). Two possible issues stand in their way. First, the existing methods struggle to perform stably on the detection of small objects because of the complicated background. Second, most of the standard methods used hand-crafted features, and do not work well on the detection of objects parts of which are missing. We here address the above issues and propose a new architecture with a multiple patch feature pyramid network (MPFP-Net). Different from the current models that during training only pursue the most discriminative patches, in MPFP-Net the patches are divided into class-affiliated subsets, in which the patches are related and based on the primary loss function, a sequence of smooth loss functions are determined for the subsets to improve the model for collecting small object parts. To enhance the feature representation for patch selection, we introduce an effective method to regularize the residual values and make the fusion transition layers strictly norm-preserving. The network contains bottom-up and crosswise connections to fuse the features of different scales to achieve better accuracy, compared to several state-of-the-art object detection models. Also, the developed architecture is more efficient than the baselines

Information sharing behavior in social commerce sites: The differences between sellers and non-sellers

The rise of social media encouraged customers to share information more frequently and to larger extent. Previous work primarily focused on how and why customers share information in online social commerce sites. In the current study, we distinguish between the two types of users: sellers and non-sellers in social commerce sites. Drawing on the goal theory, we empirically examine intrinsic and extrinsic benefits as the key direct antecedents, and explore the moderating role of sellers/non-sellers in the relationship between intrinsic and extrinsic benefits and information sharing behavior. Analyzing survey data (n=1170) in the first phase collected from a popular social commerce site, we found that intention to share information among sellers and nonsellers are indeed different. This study can advance the understandings of information sharing literature by revealing the differences between different types of users. The results offer important and interesting insights to IS research and practice

Dual-Tree Complex Wavelet Transform and Twin Support Vector Machine for Pathological Brain Detection

Abstract: (Aim) Classification of brain images as pathological or healthy case is a key pre-clinical step for potential patients. Manual classification is irreproducible and unreliable. In this study, we aim to develop an automatic classification system of brain images in magnetic resonance imaging (MRI). (Method) Three datasets were downloaded from the Internet. Those images are of T2-weighted along axial plane with size of 256 ˆ 256. We utilized an s-level decomposition on the basis of dual-tree complex wavelet transform (DTCWT), in order to obtain 12s “variance and entropy (VE)” features from each subband. Afterwards, we used support vector machine (SVM) and its two variants: the generalized eigenvalue proximal SVM (GEPSVM) and the twin SVM (TSVM), as the classifiers. In all, we proposed three novel approaches: DTCWT + VE + SVM, DTCWT + VE + GEPSVM, and DTCWT + VE + TSVM. (Results) The results showed that our “DTCWT + VE + TSVM” obtained an average accuracy of 99.57%, which was not only better than the two other proposed methods, but also superior to 12 state-of-the-art approaches. In addition, parameter estimation showed the classification accuracy achieved the largest when the decomposition level s was assigned with a value of 1. Further, we used 100 slices from real subjects, and we found our proposed method was superior to human reports from neuroradiologists. (Conclusions) This proposed system is effective and feasible

TIB-Net: Drone Detection Network with Tiny Iterative Backbone

With the widespread application of drone in commercial and industrial fields, drone detection has received increasing attention in public safety and others. However, due to various appearance of small-size drones, changeable and complex environments, and limited memory resources of edge computing devices, drone detection remains a challenging task nowadays. Although deep convolutional neural network (CNN) has shown powerful performance in object detection in recent years, most existing CNN-based methods cannot balance detection performance and model size well. To solve the problem, we develop a drone detection network with tiny iterative backbone named TIB-Net. In this network, we propose a structure called cyclic pathway, which enhances the capability to extract effective features of small object, and integrate it into existing efficient method Extremely Tiny Face Detector (EXTD). This method not only significantly improves the accuracy of drone detection, but also keeps the model size at an acceptable level. Furthermore, we integrate spatial attention module into our network backbone to emphasize information of small object, which can better locate small-size drone and further improve detection performance. In addition, we present massive manual annotations of object bounding boxes for our collected 2860 drone images as a drone benchmark dataset, which is now publicly available. In this work, we conduct a series of experiments on our collected dataset to evaluate TIB-Net, and the result shows that our proposed method achieves mean average precision of 89.2% with model size of 697.0KB, which achieves the state-of-the-art results compared with existing methods

Salient Skin Lesion Segmentation via Dilated Scale-Wise Feature Fusion Network

No description supplie