Association of interleukin 10 rs1800896 polymorphism with susceptibility to breast cancer: a meta-analysis.

Objective: To evaluate the correlation between interleukin 10 (IL-10) -1082A/G polymorphism (rs1800896) and breast cancers by performing a meta-analysis. Methods: The Embase and Medline databases were searched through 1 September 2018 to identify qualified articles. Odds ratios (OR) and corresponding 95% confidence intervals (CIs) were applied to evaluate associations. Results: In total, 14 case-control studies, including 5320 cases and 5727 controls, were analyzed. We detected significant associations between the IL10 -1082 G/G genotype and risk of breast cancer (AA + AG vs. GG: OR = 0.88, 95% CI = 0.80-0.97). Subgroup analyses confirmed a significant association in Caucasian populations (OR = 0.89, 95% CI = 0.80-0.99), in population-based case-control studies (OR = 0.87, 95% CI = 0.78-0.96), and in studies with ≥500 subjects (OR = 0.88, 95% CI = 0.79-0.99) under the recessive model (AA + AG vs. GG). No associations were found in Asian populations. Conclusions: The IL10 -1082A/G polymorphism is associated with an increased risk of breast cancer. The association between IL10 -1082 G/G genotype and increased risk of breast cancer is more significant in Caucasians, in population-based studies, and in larger studies

Direct sampling methods for inverse elastic scattering problems

We consider the inverse elastic scattering of incident plane compressional and shear waves from the knowledge of the far field patterns. Specifically, three direct sampling methods for location and shape reconstruction are proposed using the different component of the far field patterns. Only inner products are involved in the computation, thus the novel sampling methods are very simple and fast to be implemented. With the help of the factorization of the far field operator, we give a lower bound of the proposed indicator functionals for sampling points inside the scatterers. While for the sampling points outside the scatterers, we show that the indicator functionals decay like the Bessel functions as the sampling point goes away from the boundary of the scatterers. We also show that the proposed indicator functionals continuously dependent on the far field patterns, which further implies that the novel sampling methods are extremely stable with respect to data error. For the case when the observation directions are restricted into the limited aperture, we firstly introduce some data retrieval techniques to obtain those data that can not be measured directly and then use the proposed direct sampling methods for location and shape reconstructions. Finally, some numerical simulations in two dimensions are conducted with noisy data, and the results further verify the effectiveness and robustness of the proposed sampling methods, even for multiple multiscale cases and limited-aperture problems

Deep attentive video summarization with distribution consistency learning

This article studies supervised video summarization by formulating it into a sequence-to-sequence learning framework, in which the input and output are sequences of original video frames and their predicted importance scores, respectively. Two critical issues are addressed in this article: short-term contextual attention insufficiency and distribution inconsistency. The former lies in the insufficiency of capturing the short-term contextual attention information within the video sequence itself since the existing approaches focus a lot on the long-term encoder-decoder attention. The latter refers to the distributions of predicted importance score sequence and the ground-truth sequence is inconsistent, which may lead to a suboptimal solution. To better mitigate the first issue, we incorporate a self-attention mechanism in the encoder to highlight the important keyframes in a short-term context. The proposed approach alongside the encoder-decoder attention constitutes our deep attentive models for video summarization. For the second one, we propose a distribution consistency learning method by employing a simple yet effective regularization loss term, which seeks a consistent distribution for the two sequences. Our final approach is dubbed as Attentive and Distribution consistent video Summarization (ADSum). Extensive experiments on benchmark data sets demonstrate the superiority of the proposed ADSum approach against state-of-the-art approaches

Positive solutions of boundary value problems for systems of second-order differential equations with integral boundary condition on the half-line

In this paper, we study the existence of positive solutions of boundary value problems for systems of second-order differential equations with integral boundary condition on the half-line. By using the fixed-point theorem in cones, we show the existence of at least one positive solution with suitable growth conditions imposed on the nonlinear terms. Moreover, the associated integral kernels for the boundary value problems are given