Publications by authors named "T Zhou"

7,056 Publications

Effects of Restricted Feeding on Growth Performance, Intestinal Immunity, and Skeletal Muscle Development in New Zealand Rabbits.

Animals (Basel) 2022 Jan 10;12(2). Epub 2022 Jan 10.

College of Animal Science and Technology, Yangzhou University, Yangzhou 225009, China.

This study aimed to explore the effects of different feeding restriction levels on the growth performance, intestinal immunity, and skeletal muscle development of meat rabbits. Additionally, we studied whether complete compensatory growth could be obtained post 2 weeks of restricted feeding, in order to seek a scientific mode of feeding restriction. Each of three groups was exposed to 3 weeks of feeding restriction and 2 weeks of compensatory growth. The 15% feeding restriction showed a negligible effect on the final body-weight of the rabbits ( > 0.05), but significantly reduced the feed-to-weight ratio ( < 0.05); reduced diarrhea and mortality; and increased digestive enzyme activity and antioxidant capacity. However, a 30% feeding-restriction level substantially reduced the growth rate of the rabbits ( < 0.05), impaired skeletal muscle development, and showed no compensatory growth after 2 weeks of nutritional recovery. Additionally, immunoglobulin and antioxidant enzyme synthesis were impaired due to reduced nutritional levels, and levels of pro-inflammatory factors were increased during the compensation period. The IGF1 mRNA expression decreased significantly ( < 0.05), whereas MSTN and FOXO1 expression increased noticeably ( < 0.05). Moreover, protein levels of p-Akt and p-p70 decreased significantly in the 15% feeding restriction group. Overall, the 15% feeding limit unaffected the weight and skeletal muscle development of rabbits, whereas the 30% feeding limit affected the growth and development of skeletal muscle in growing rabbits. The PI3K/Akt signaling pathway is plausibly a mediator of this process.
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http://dx.doi.org/10.3390/ani12020160DOI Listing
January 2022

The Association of Different Sedentary Patterns and Health-Related Physical Fitness in Pre-schoolers.

Front Pediatr 2021 3;9:796417. Epub 2022 Jan 3.

School of Kinesiology, Shanghai University of Sport, Shanghai, China.

The results of sedentary time (ST) and health-related physical fitness (HPF) are not completely consistent and the studies concentrated on pre-schoolers are very limited. We measured ST and ST patterns (ST Bouts time, ST Breaks times) by accelerometer. The health-related physical fitness T-score (HPFT) was calculated by five indexes: height-weight standard score, 20 m shuttle-run test, grip strength, standing long jump and 2 × 10 m shuttle-run test. We included 375 pre-schoolers (211 boys, 164 girls) in the final analysis. The total ST and ST Bouts times negatively correlated with HPFT in pre-schoolers. HPFT reduced by 1.69 and 0.70 points per 10 min increased in total ST and ST Bouts times, respectively. HPFT of the highest quartile group reduced by 9.85 points in total ST, and 10.54 points in ST Bouts time compared with the lowest quartile group. However, the HPFT increased by 0.09 points per 10 times increased in ST Breaks times; the HPFT increased by 16.21 and 15.59 points when moderate to vigorous physical activity (MVPA) replaced total ST and ST Bouts time. HPF negatively correlated with the Total ST and ST Bouts times, but positively correlated with ST Breaks times; and HPF significantly improved when MVPA replaced ST in pre-schoolers.
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http://dx.doi.org/10.3389/fped.2021.796417DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8763387PMC
January 2022

Impact of regular additional endobiliary radiofrequency ablation on survival of patients with advanced extrahepatic cholangiocarcinoma under systemic chemotherapy.

Sci Rep 2022 Jan 19;12(1):1011. Epub 2022 Jan 19.

Department of Internal Medicine I, University Hospital of Bonn, Venusberg-Campus 1, 53127, Bonn, Germany.

Prognosis of patients with advanced extrahepatic cholangiocarcinoma (eCCA) is poor. The current standard first-line treatment is systemic chemotherapy (CT) with gemcitabine and a platinum derivate. Additionally, endobiliary radiofrequency ablation (eRFA) can be applied to treat biliary obstructions. This study aimed to evaluate the additional benefit of scheduled regular eRFA in a real-life patient cohort with advanced extrahepatic cholangiocarcinoma under standard systemic CT. All patients with irresectable eCCA treated at University Hospital Bonn between 2010 and 2020 were eligible for inclusion. Patients were stratified according to treatment: standard CT (n = 26) vs. combination of eRFA with standard CT (n = 40). Overall survival (OS), progression free survival (PFS), feasibility and toxicity were retrospectively analyzed using univariate and multivariate approaches. Combined eRFA and CT resulted in significantly longer median OS (17.3 vs. 8.6 months, p = 0.004) and PFS (12.9 vs. 5.7 months, p = 0.045) compared to the CT only group. While groups did not differ regarding age, sex, tumor stage and chemotherapy treatment regimen, mean MELD was even higher (10.1 vs. 6.7, p = 0.015) in the eRFA + CT group. The survival benefit of concomitant eRFA was more evident in the subgroup with locally advanced tumors. Severe hematological toxicities (CTCAE grades 3 - 5) did not differ significantly between the groups. However, therapy-related cholangitis occurred more often in the combined treatment group (p = 0.031). Combination of eRFA and systemic CT was feasible, well-tolerated and could significantly prolong survival compared to standard CT alone. Thus, eRFA should be considered during therapeutic decision making in advanced eCCA.
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http://dx.doi.org/10.1038/s41598-021-04297-2DOI Listing
January 2022

Automatic Schelling Points Detection from Meshes.

IEEE Trans Vis Comput Graph 2022 Jan 19;PP. Epub 2022 Jan 19.

Mesh Schelling points explain how humans focus on specific regions of a 3D object. They have a large number of important applications in computer graphics and provide valuable information for perceptual psychology studies. However, detecting mesh Schelling points is time-consuming and expensive since the existing techniques are mostly based on participant observation studies. To overcome these limitations, we propose to employ powerful deep learning techniques to detect mesh Schelling points in an automatic manner, free from participant observation studies. Specifically, we utilize the mesh convolution and pooling operations to extract informative features from mesh objects, and then predict the 3D heat map of Schelling points in an end-to-end manner. In addition, we propose a Deep Schelling Network (DS-Net) to automatically detect the Schelling points, including a multi-scale fusion component and a novel region-specific loss function to improve our network for a better regression of heat maps. To the best of our knowledge, DS-Net is the first deep neural network for detecting Schelling points from 3D meshes. We evaluate DS-Net on a mesh Schelling point dataset obtained from participant observation studies. The experimental results demonstrate that DS-Net is capable of detecting mesh Schelling points effectively and outperforms various state-of-the-art mesh saliency methods and deep learning models, both qualitatively and quantitatively.
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http://dx.doi.org/10.1109/TVCG.2022.3144143DOI Listing
January 2022

Rock mass classification prediction model using heuristic algorithms and support vector machines: a case study of Chambishi copper mine.

Sci Rep 2022 Jan 18;12(1):928. Epub 2022 Jan 18.

School of Resources and Safety Engineering, Central South University, Changsha, 410083, Hunan, China.

The rock mass is one of the key parameters in engineering design. Accurate rock mass classification is also essential to ensure operational safety. Over the past decades, various models have been proposed to evaluate and predict rock mass. Among these models, artificial intelligence (AI) based models are becoming more popular due to their outstanding prediction results and generalization ability for multiinfluential factors. In order to develop an easy-to-use rock mass classification model, support vector machine (SVM) techniques are adopted as the basic prediction tools, and three types of optimization algorithms, i.e., particle swarm optimization (PSO), genetic algorithm (GA) and grey wolf optimization (GWO), are implemented to improve the prediction classification and optimize the hyper-parameters. A database was assembled, consisting of 80 sets of real engineering data, involving four influencing factors. The three combined models are compared in accuracy, precision, recall, F value and computational time. The results reveal that among three models, the GWO-SVC-based model shows the best classification performance by training. The accuracy of training and testing sets of GWO-SVC are 90.6250% (58/64) and 93.7500% (15/16), respectively. For Grades I, II, III, IV and V, the precision value is 1, 0.93, 0.90, 0.92, 0.83, the recall value is 1, 1, 0.93, 0.73, 0.83, and the F value is 1, 0.96, 0.92, 0.81, 0.83, respectively. Sensitivity analysis is performed to understand the influence of input parameters on rock mass classification. It shows that the sensitive factor in rock mass quality is the RQD. Finally, the GWO-SVC is employed to assess the quality of rocks from the southeastern ore body of the Chambishi copper mine. Overall, the current study demonstrates the potential of using artificial intelligence methods in rock mass assessment, rendering far better results than the previous reports.
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http://dx.doi.org/10.1038/s41598-022-05027-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8766606PMC
January 2022
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