REPORTING BIAS AMONG RADNOMIZED CONTROLLED TRIALS FROM CHINA

Research Question Reporting bias threatens the validity of evidence. This dissertation addressed three types of reporting bias, i.e., primary outcome switching, language bias, and duplicate publication bias among randomized controlled trials (RCTs) from mainland China. Method RCTs that evaluated the efficacy and/or safety of drug interventions and were conducted in mainland China between 2008 and 2014, were retrieved from trial registries, and their corresponding journal articles were identified from both English and Chinese bibliographic databases. First, we evaluated the association between the findings of registered primary outcomes (positive vs. negative) and the switching of registered primary outcomes (registered primary outcomes switched to secondary outcomes in the journal articles vs. registered primary outcomes remained primary in the journal articles). Second, we evaluated the association between the finding of RCTs (positive vs. negative) and the language of corresponding journal articles published subsequently (English vs. Chinese). Third, we evaluated the association between the findings of RCTs (positive vs. negative) and the occurrence of subsequent duplicates. Results When RCTs were prospectively registered, the odds of switching primary outcomes with negative findings were 2.34 (95%CI: 1.03 to 5.33) times the odds of switching primary outcomes with positive findings. When RCTs were retrospectively registered before trial completion, the odds of switching primary outcomes with negative findings were 9.69 (95%CI: 3.62 to 25.93) times the odds of switching primary outcomes with positive findings. Among RCTs registered in bilingual registry, RCTs with positive findings were 3.92 (95%CI: 2.20-7.00) times more likely to be published in English than those with negative findings; among RCTs registered in English registries, RCTs with positive findings were 3.22 (95%CI: 1.34-7.78) times more likely to be published in English than those with negative findings. When the main articles of RCTs were published in Chinese, those with positive findings were 2.48 (95%CI: 1.08 – 5.71) times more likely to have subsequent duplicates than those with negative findings. Conclusion We found evidence supporting the three types of reporting bias among RCTs from mainland China, which may threaten the validity of evidence synthesized by systematic reviews

Measures of Adult Shoulder Function

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/163413/2/acr24230.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163413/1/acr24230_am.pd

REPORTING BIAS AMONG RADNOMIZED CONTROLLED TRIALS FROM CHINA

Research Question Reporting bias threatens the validity of evidence. This dissertation addressed three types of reporting bias, i.e., primary outcome switching, language bias, and duplicate publication bias among randomized controlled trials (RCTs) from mainland China. Method RCTs that evaluated the efficacy and/or safety of drug interventions and were conducted in mainland China between 2008 and 2014, were retrieved from trial registries, and their corresponding journal articles were identified from both English and Chinese bibliographic databases. First, we evaluated the association between the findings of registered primary outcomes (positive vs. negative) and the switching of registered primary outcomes (registered primary outcomes switched to secondary outcomes in the journal articles vs. registered primary outcomes remained primary in the journal articles). Second, we evaluated the association between the finding of RCTs (positive vs. negative) and the language of corresponding journal articles published subsequently (English vs. Chinese). Third, we evaluated the association between the findings of RCTs (positive vs. negative) and the occurrence of subsequent duplicates. Results When RCTs were prospectively registered, the odds of switching primary outcomes with negative findings were 2.34 (95%CI: 1.03 to 5.33) times the odds of switching primary outcomes with positive findings. When RCTs were retrospectively registered before trial completion, the odds of switching primary outcomes with negative findings were 9.69 (95%CI: 3.62 to 25.93) times the odds of switching primary outcomes with positive findings. Among RCTs registered in bilingual registry, RCTs with positive findings were 3.92 (95%CI: 2.20-7.00) times more likely to be published in English than those with negative findings; among RCTs registered in English registries, RCTs with positive findings were 3.22 (95%CI: 1.34-7.78) times more likely to be published in English than those with negative findings. When the main articles of RCTs were published in Chinese, those with positive findings were 2.48 (95%CI: 1.08 – 5.71) times more likely to have subsequent duplicates than those with negative findings. Conclusion We found evidence supporting the three types of reporting bias among RCTs from mainland China, which may threaten the validity of evidence synthesized by systematic reviews

Multispectral LiDAR Point Cloud Segmentation for Land Cover Leveraging Semantic Fusion in Deep Learning Network

Multispectral LiDAR technology can simultaneously acquire spatial geometric data and multispectral wavelength intensity information, which can provide richer attribute features for semantic segmentation of point cloud scenes. However, due to the disordered distribution and huge number of point clouds, it is still a challenging task to accomplish fine-grained semantic segmentation of point clouds from large-scale multispectral LiDAR data. To deal with this situation, we propose a deep learning network that can leverage contextual semantic information to complete the semantic segmentation of large-scale point clouds. In our network, we work on fusing local geometry and feature content based on 3D spatial geometric associativity and embed it into a backbone network. In addition, to cope with the problem of redundant point cloud feature distribution found in the experiment, we designed a data preprocessing with principal component extraction to improve the processing capability of the proposed network on the applied multispectral LiDAR data. Finally, we conduct a series of comparative experiments using multispectral LiDAR point clouds of real land cover in order to objectively evaluate the performance of the proposed method compared with other advanced methods. With the obtained results, we confirm that the proposed method achieves satisfactory results in real point cloud semantic segmentation. Moreover, the quantitative evaluation metrics show that it reaches state-of-the-art

An Enhanced U-Net Approach for Segmentation of Aeroengine Hollow Turbine Blade

The hollow turbine blade plays an important role in the propulsion of the aeroengine. However, due to its complex hollow structure and nickel-based superalloys material property, only industrial computed tomography (ICT) could realize its nondestructive detection with sufficient intuitiveness. The ICT detection precision mainly depends on the segmentation accuracy of target ICT images. However, because the hollow turbine blade is made of special superalloys and contains many small unique structures such as film cooling holes, exhaust edges, etc., the ICT image quality of the hollow turbine blades is often deficient, with artifacts, low contrast, and inhomogeneity scattered around the blade contour, making it hard for traditional mathematical model-based methods to acquire satisfying segmentation precision. Therefore, this paper presents a deep learning-based approach, i.e., the enhanced U-net with multiscale inputs, dense blocks, focal loss function, and residual path in the skip connection to realize the high-precision segmentation of the hollow turbine blade. The experimental results show that our proposed enhanced U-net can achieve better segmentation accuracy for practical turbine blades than conventional U-net and traditional mathematical model-based methods

An Enhanced U-Net Approach for Segmentation of Aeroengine Hollow Turbine Blade

The hollow turbine blade plays an important role in the propulsion of the aeroengine. However, due to its complex hollow structure and nickel-based superalloys material property, only industrial computed tomography (ICT) could realize its nondestructive detection with sufficient intuitiveness. The ICT detection precision mainly depends on the segmentation accuracy of target ICT images. However, because the hollow turbine blade is made of special superalloys and contains many small unique structures such as film cooling holes, exhaust edges, etc., the ICT image quality of the hollow turbine blades is often deficient, with artifacts, low contrast, and inhomogeneity scattered around the blade contour, making it hard for traditional mathematical model-based methods to acquire satisfying segmentation precision. Therefore, this paper presents a deep learning-based approach, i.e., the enhanced U-net with multiscale inputs, dense blocks, focal loss function, and residual path in the skip connection to realize the high-precision segmentation of the hollow turbine blade. The experimental results show that our proposed enhanced U-net can achieve better segmentation accuracy for practical turbine blades than conventional U-net and traditional mathematical model-based methods

Design and Speed-Adaptive Control of a Powered Geared Five-Bar Prosthetic Knee Using BP Neural Network Gait Recognition

To improve the multi-speed adaptability of the powered prosthetic knee, this paper presented a speed-adaptive neural network control based on a powered geared five-bar (GFB) prosthetic knee. The GFB prosthetic knee is actuated via a cylindrical cam-based nonlinear series elastic actuator that can provide the desired actuation for level-ground walking, and its attitude measurement is realized by two inertial sensors and one load cell on the prosthetic knee. To improve the performance of the control system, the motor control and the attitude measurement of the GFB prosthetic knee are run in parallel. The BP neural network uses input data from only the GFB prosthetic knee, and is trained by natural and artificially modified various gait patterns of different able-bodied subjects. To realize the speed-adaptive control, the prosthetic knee speed and gait cycle percentage are identified by the Gaussian mixture model-based gait classifier. Specific knee motion control instructions are generated by matching the neural network predicted gait percentage with the ideal walking gait. Habitual and variable speed level-ground walking experiments are conducted via an able-bodied subject, and the experimental results show that the neural network control system can handle both self-selected walking and variable speed walking with high adaptability

Diet-Related and Gut-Derived Metabolites and Health Outcomes: A Scoping Review

We conducted a scoping review to map available evidence about the health impact of gut microbiota-derived metabolites. We searched PubMed and Embase for studies that assessed the health impact of ten metabolites on any health condition: deoxycholate or deoxycholic acid (DCA), lithocholate or lithocholic acid (LCA), glycolithocholate or glycolithocholic acid, glycodeoxycholate or glycodeoxycholic acid, tryptamine, putrescine, d-alanine, urolithins, N-acetylmannosamine, and phenylacetylglutamine. We identified 352 eligible studies with 168,072 participants. Most (326, 92.6%) were case–control studies, followed by cohort studies (14, 4.0%), clinical trials (8, 2.3%), and cross-sectional studies (6, 1.7%). Most studies assessed the following associations: DCA on hepatobiliary disorders (64 studies, 7976 participants), colorectal cancer (19 studies, 7461 participants), and other digestive disorders (27 studies, 2463 participants); LCA on hepatobiliary disorders (34 studies, 4297 participants), colorectal cancers (14 studies, 4955 participants), and other digestive disorders (26 studies, 2117 participants); putrescine on colorectal cancers (16 studies, 94,399 participants) and cancers excluding colorectal and hepatobiliary cancers (42 studies, 4250 participants). There is a need to conduct more prospective studies, including clinical trials. Moreover, we identified metabolites and conditions for which systemic reviews are warranted to characterize the direction and magnitude of metabolite-disease associations