In this paper, the flexural characteristics of stainless steel (SS) reinforced concrete beams are studied and analyzed. We mainly focus on their crack mode, failure mode, load-deflection curve, and bearing capacity. Six beams with test parameters, including the diameter of reinforcement, the type of the reinforcement, and the stirrup spacing, were tested in 4-point bending. The test results indicate that the failure mode of SS reinforced concrete beam can be divided into three stages: elastic stage, cracking stage, and failure stage. The midspan section deformation of SS reinforced concrete beam conforms to the assumption of plane section. Under the same reinforcement condition, the normal section and the oblique section bearing capacities of the SS reinforced concrete beams are significantly higher than those of the ordinary reinforced concrete beams. In addition, the prediction of cracking moment and bearing capacity calculated by ACI 318-14 and GB 50010-2010 was also evaluated. The calculation results of the two codes were safe and conservative, and GB 50010-2010 provided more accurate prediction of cracking moments. Furthermore, to verify the reliability of the test results, finite element models were established and the analytical results corroborated well with the test results.

This article corrects: Synergistic effect of PCPE1 and sFRP2 on the processing of procollagens via BMP1, Volume 593, Issue 1, 119–127. Article first published 09 November 2018. https://doi.org/10.1002/1873-3468.13291 Dr Daniel S. Greenspan was inadvertently omitted from the list of authors in the original publication. The correct list of authors is as shown above. Dr Daniel S. Greenspan affiliation and contact details are as follows: Department of Cell and Regenerative Biology, University of Wisconsin, Room 4503, WIMRII, 1111 Highland Ave, Madison, WI 53705, dsgreens@wisc.edu

To study the initiation and expansion of the interlayer gap of the China Railway Track System Type II (CRTS-II) ballastless slab track structure under the action of repeated thermal loading as well as the influence of the interlayer gap on the displacement, strain and stiffness of the track structure, a 1/4 scale three-span ballastless slab track simply supported bridge structural system specimen was developed and 18 cycles of extremely thermal loading tests were carried out. Static loading tests were carried out before and after the repeated thermal loading test and the effects of the repeated temperature loading on the mechanical properties of the structural system were analyzed. The test results show that under repeated temperature loading, there is a gap between the track slab and cement emulsified asphalt (CA) mortar near the fixed end section of the beam (close to the shear slots). The interlayer gap gradually expands to the mid-span section in a “stepped” shape in three stages: initiation, expansion and stabilization. Under the same temperature load, the camber of the concrete box beam decreases gradually while that of the track structure increases gradually with the increase of the interlayer gap length. During the three stages of interlayer gap development, the track structure stiffness degrades gradually, and the fastest reduction rate during the expansion stage. At the end of the 18th cycle of thermal loading, a degradation of 14.96% and 2.52% is observed in the stiffness of the track structure and that of the ballastless track-bridge structural system, respectively.

Image degradation caused by bad weather, such as haze, rain, and snow, significantly degrades a vision system's performance, imposing challenges to the autonomous motion of mobile robots. Therefore, it is crucial to restore the degraded images for mobile robots operating outdoors. Spurred by this concern, a framework that restores degraded images and affords simultaneous localization and mapping (SLAM) under multiple bad weather conditions is presented. Specifically, the developed architecture combines the advantages of convolutional neural network features and weather features. It improves the accuracy of identifying the degradation type by developing a weather inference module based on ensemble learning. According to the internal mechanism of image degradation, a degraded image restoration method is proposed utilizing physical models, with a subsequent operation refining the preliminary restored results. To improve our method's adaptability to real scenes, unpaired real-world weather images are introduced into the degradation removal algorithm through generative adversarial networks. The proposed weather persistent assumption combines weather inference and degradation restoration modules in the SLAM system to capture multiple weather conditions, which improves the system's accuracy and running speed. Comprehensive experiments evaluating the developed framework and its components highlight that the proposed weather inference and degraded image restoration methods achieve a highly appealing effect. The final experimental results demonstrate that our framework autonomously identifies weather types, triggers the corresponding restoration, and realizes accurate localization in multiple bad weather conditions. It affords SLAM accuracy under multiple bad weather conditions that is close to raw SLAM in clear weather.

Rationale: Accumulating evidence shows that Rho-GTPase-activating proteins (RhoGAPs) exert suppressive roles in cancer cell proliferation and metastasis. However, no study has systematically investigated the clinical significance of RhoGAPs and analyzed the functions of ARHGAP24 in hepatocellular carcinoma (HCC). Methods: The relationship between RhoGAP expression and HCC prognosis was investigated via using The Cancer Genome Atlas and Gene Expression Omnibus databases. ARHGAP24 expression was detected by reverse transcription-polymerase chain reaction, western blot and immunohistochemistry staining assays. Moreover, in vitro assays including cell counting kit-8, colony formation, wound healing and Transwell assays, and in vivo tumor growth and pulmonary metastases evaluations were conducted to evaluate the biological function of ARHGAP24 in HCC. Liquid chromatography-tandem mass spectrometry, co-immunoprecipitation, GTPase activation, ubiquitination, and luciferase reporter assays and bioinformatics analysis were carried out to gain insights into the mechanisms underlying the tumor-suppressive function of ARHGAP24. Results: ARHGAP24 expression was dramatically decreased in HCC tissues, and low ARHGAP24 expression was an independent poor prognostic indicator for progression-free survival in HCC patients. ARHGAP24 overexpression significantly inhibited cell proliferation, migration and invasion, while knockdown of ARHGAP24 exerted the opposite effects. Through Gene Set Enrichment Analysis (GSEA), we found ARHGAP24 mainly suppressed HCC cell proliferation and invasion by attenuating β-catenin transactivation and blocking β-catenin signaling could effectively abolish the promotional effects of ARHGAP24 knockdown in HCC cells. Notably, GAP-deficient mutant of ARHGAP24 exerted similar inhibitory effects as the wild-type did, indicating suppressive function of ARHGAP24 was independent of its RhoGAP activity. Moreover, we identified pyruvate kinase M2 (PKM2) as a new binding partner of ARHGAP24, which recruited a novel E3 ligase (WWP1) and subsequently promoted PKM2 degradation. WWP1 knockdown significantly reduced the inhibitory function of ARHGAP24, and the C-terminal fragments of ARHGAP24 (amino acids 329 - 430 and 631 - 748) bound directly to WWP1 and PKM2 (amino acids 388 - 531), respectively. Conclusions: Our data indicate that ARHGAP24 may be an independent prognostic indicator for HCC. It is a critical suppressor of HCC that recruits WWP1 for PKM2 degradation. Targeting the ARHGAP24/WWP1/PKM2/β-catenin axis may provide new insights into HCC prevention and treatment.