Original Article The efficacy of mindfulness-based stress reduction vs. standard or usual care in patients with breast cancer: a systematic review and meta-analysis of randomized controlled trials Huiyuan Wu, Fenfen Li, Fenghao Zhang

Nowadays, erasure codes have been widely used in data storage to achieve high fault-tolerance. However, compared with replica-based storage, erasure-coded system may suffer significant performance overhead in encoding, decoding and updating. Traditional updating schemes(e.g. DUM and PUM) use an individual manager node to accomplish the updating. In this paper, we propose two partial-updating schemes (i.e. PUM-P and PDN-P) to improve the small update in erasure coded storage clusters, where both schemes only read a portion of data, including the data blocks to be updated and the parity blocks, and utilize the calculation capacity of the storage nodes. We implement four updating algorithms (DUM, PUM, PUM-P and PDN-P) upon an erasure-coded storage cluster platform, and conduct a set of comparative tests under two real-world workloads with different fault-tolerance parameters. The experimental results shows that PUM-P and PDN-P can speed up the small updating by a factor of up to 1.62 and 2.72 compared with DUM under small update, respectively; and by a factor of up to 1.42 and 2.23 relative to PUM, respectively. We also validate that DUM scheme can achieve better updating performance than the other schemes within the large update scenario.

Point-cloud registration is a fundamental task in computer vision. However, most point clouds are partially overlapping, corrupted by noise and comprised of indistinguishable surfaces, especially for complexly distributed outdoor LiDAR point clouds, which makes registration challenging. In this paper, we propose a multi-scale features-based point cloud registration network named MSPR-Net for large-scale outdoor LiDAR point cloud registration. The main motivation of the proposed MSPR-Net is that the features of two keypoints from a true correspondence must match in different scales. From this point of view, we first utilize a multi-scale backbone to extract the multi-scale features of the keypoints. Next, we propose a bilateral outlier removal strategy to remove the potential outliers in the keypoints based on the multi-scale features. Finally, a coarse-to-fine registration way is applied to exploit the information both in feature and spatial space. Extensive experiments conducted on two large-scale outdoor LiDAR point cloud datasets demonstrate that MSPR-Net achieves state-of-the-art performance.

Recently, self-supervised multi-view stereo (MVS) methods, which are dependent primarily on optimizing networks using photometric consistency, have made clear progress. However, the difference in lighting between different views and reflective objects in the scene can make photometric consistency unreliable. To address this issue, a geometric prior-guided multi-view stereo (GP-MVS) for self-supervised learning is proposed, which exploits the geometric prior from the input data to obtain high-quality depth pseudo-labels. Specifically, two types of pseudo-labels for self-supervised MVS are proposed, based on the structure-from-motion (SfM) and traditional MVS methods. One converts the sparse points of SfM into sparse depth maps and combines the depth maps with spatial smoothness constraints to obtain a sparse prior loss. The other generates initial depth maps for semi-dense depth pseudo-labels using the traditional MVS, and applies a geometric consistency check to filter the wrong depth in the initial depth maps. We conducted extensive experiments on the DTU and Tanks and Temples datasets, which demonstrate that our method achieves state-of-the-art performance compared to existing unsupervised/self-supervised approaches, and even performs on par with traditional and supervised approaches.

This study aims to elucidate the physical evidence of the moulting stages and the characteristics of ecdysteroid levels in the moulting cycle of Neocaridina denticulata sinensis. From hatching to the first complete pleopod development, N. denticulata sinensis moults 14 times at 25°C in the laboratory. The whole process was divided into four stages based on the moulting characteristics during the various intervals. Using the structural changes of the telson and Drach’s classification system, four moulting periods (postmoult, intermoult, premoult, and ecdysis) were recognized. In addition, based on the changes of the setal lumen, internal cone, and the epidermis, the premoult period was further divided into five substages (D₀, D₁, D₂, D₃, and D₄). Despite similar fluctuation patterns in mature males and females, significant differences of haemolymph ecdysteroid titers were revealed in postmoult, intermoult, and premoult. These works will provide an important additional reference for the exploration of the moulting mechanism in crustaceans.

INPLASY202110025 Efficacy and safety of Shen-Ling-Bai-Zhu-San combined with chemotherapy for lung cancer: A protocol for systematic review and meta-analysis Keywords: chemotherapy; protocol; lung cancer; Shen-Ling-Bai-Zhu-San; systematic review. Download PDF – 202110025