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11. Studies on fatty acids and microbiota characterization of the gastrointestinal tract of Tianzhu white yaks

Author

Chen Shaopeng, Cui Changze, Qi Youpeng, Mi Baohong, Zhang Meixian, Jiao Chenyue, Zhu Chune, Wang Xiangyan, Hu Jiang, Shi Bingang, Ma Xueming, Zhao Zhidong, and Zhang Xiaolan

Subjects
Tianzhu white yak, volatile fatty acids, microbiota, gastrointestinal, function, Microbiology, QR1-502
Abstract

IntroductionThe gut microbiota significantly influences the host’s production performance and health status, with different gastrointestinal tissues exhibiting functional diversity reflected in their microbial diversity.MethodsIn this study, five adult male Tianzhu white yaks (4.5 years old) were selected and fed under the same nutritional conditions. After the feeding experiment, the yaks were slaughtered, and chyme samples were collected from the rumen, abomasum, jejunum, and colon for 16S rRNA full-length sequencing and volatile fatty acid analysis.ResultsThe results showed that the microbial composition and diversity of the rumen and abomasum were similar, with close genetic distances and functional projections. In contrast, the jejunum and colon had distinct microbial compositions and diversity compared to the rumen and abomasum. At the phylum level, the dominant phyla in the rumen, abomasum, and colon were Firmicutes and Bacteroidetes, while in the jejunum, the dominant phyla were Firmicutes and Proteobacteria. The abundance of Firmicutes differed significantly between the jejunum (87.24%) and the rumen (54.67%), abomasum (67.70%), and colon (65.77%). Similarly, Bacteroidetes showed significant differences between the jejunum (2.21%) and the rumen (36.54%), abomasum (23.81%), and colon (28.12%). At the genus level, Rikenellaceae_RC9_gut_group and Christensenellaceae_R-7_group were dominant in both the rumen and abomasum. In the jejunum, Romboutsia and Paeniclostridium were dominant, while Rikenellaceae_RC9_gut_group and UCG-005 were the dominant genera in the colon. At the species level, rumen_bacterium_g_Rikenellaceae_RC9_gut_group and rumen_bacterium_g_Christensenellaceae_R-7_group were dominant in both the rumen and abomasum, while Clostridium_sp._g_Romboutsia and bacterium_g_Paeniclostridium were unique to the jejunum. Ruminococcaceae_bacterium_g_UCG-005 and bacterium_g_Rikenellaceae_RC9_gut_group were unique to the colon. KEGG functional prediction of the microbiota indicated that the dominant functions in the rumen, abomasum, colon, and jejunum were amino acid metabolism, glycan biosynthesis and metabolism, carbohydrate metabolism, and membrane transport, respectively, reflecting the digestive functions of these organs. Volatile fatty acid analysis showed that the concentrations of acetic acid, propionic acid, and butyric acid in the rumen were significantly higher than those in the abomasum, jejunum, and colon (p

Published
2025
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14. Design of Medical Insurance Fund Audit Platform Based on Big Data Analysis

Author

ZHANG, Xiaolan, Zheng, Zheng, Editor-in-Chief, Xi, Zhiyu, Associate Editor, Gong, Siqian, Series Editor, Hong, Wei-Chiang, Series Editor, Mellal, Mohamed Arezki, Series Editor, Narayanan, Ramadas, Series Editor, Nguyen, Quang Ngoc, Series Editor, Ong, Hwai Chyuan, Series Editor, Sun, Zaicheng, Series Editor, Ullah, Sharif, Series Editor, Wu, Junwei, Series Editor, Zhang, Baochang, Series Editor, Zhang, Wei, Series Editor, Zhu, Quanxin, Series Editor, Zheng, Wei, Series Editor, Rauf, Abdul, editor, Zakuan, Norhayati, editor, Sohail, Muhammad Tayyab, editor, and Azmi, Ruzita, editor

Published
2024
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16. Learning an Adaptive Forwarding Strategy for Mobile Wireless Networks: Resource Usage vs. Latency

Author

Manfredi, Victoria, Wolfe, Alicia P., Zhang, Xiaolan, and Wang, Bing

Subjects
Computer Science - Networking and Internet Architecture, Computer Science - Machine Learning
Abstract

Designing effective routing strategies for mobile wireless networks is challenging due to the need to seamlessly adapt routing behavior to spatially diverse and temporally changing network conditions. In this work, we use deep reinforcement learning (DeepRL) to learn a scalable and generalizable single-copy routing strategy for such networks. We make the following contributions: i) we design a reward function that enables the DeepRL agent to explicitly trade-off competing network goals, such as minimizing delay vs. the number of transmissions per packet; ii) we propose a novel set of relational neighborhood, path, and context features to characterize mobile wireless networks and model device mobility independently of a specific network topology; and iii) we use a flexible training approach that allows us to combine data from all packets and devices into a single offline centralized training set to train a single DeepRL agent. To evaluate generalizeability and scalability, we train our DeepRL agent on one mobile network scenario and then test it on other mobile scenarios, varying the number of devices and transmission ranges. Our results show our learned single-copy routing strategy outperforms all other strategies in terms of delay except for the optimal strategy, even on scenarios on which the DeepRL agent was not trained., Comment: 39 pages, 9 figures. Added new features as input to DRL model, added new mobility model to evaluation, updated related work, and updated simulations

Published
2022

23. Effectiveness of Blended Learning on Improving Medical Student's Learning Initiative and Performance in the Physiology Study

Author

Zhang, Xiaolan, Wen, Haixia, Li, Hui, Huang, Yujia, Lv, Chunmei, and Zhu, Hui

Abstract

In recent years, online learning has been widely used than before. However, it depends on student's learning initiative and lacks of teacher-student interaction, which cannot bring desired learning performance. In our physiology teaching practice to "5 + 3" integration medical student, we tried to combine online and classroom learning to form the blended learning. This study assessed the effectiveness of the blended learning on students' learning initiative and performance in the physiology study. It included 180 full-time students from clinical medical specialty across two academic years. These students were divided into the experimental classes receiving blended learning and the control classes receiving traditional learning. We carried out three classroom tests and one questionnaire survey. It found that the students of blended learning who mastered most of the content were as twice as the students of traditional learning. The average accuracy of classroom tests was above 90% from the students of the blended learning, which was higher than the approximate rate of 80% of from the students of the traditional learning. Both the times of preparing lessons and answering questions in class increased in the blended learning practice. In addition, students of blended learning were relatively unaffected by playing with the mobile phones in the class. It found that students acquired more knowledge, performed better in the classroom tests and their learning initiative was excited in the blended learning. This study provides the effect assessment and contributes to improve the teaching effect of the blended learning.

Published
2023
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25. Discovering an Algorithm Actually Learning Restricted Single Occurrence Regular Expression with Interleaving

Author

Wang, Xiaofan and Zhang, Xiaolan

Subjects
Computer Science - Formal Languages and Automata Theory
Abstract

A recent paper proposed an algorithm iSOIRE, which combines single-occurrence automaton (SOA) and maximum independent set (MIS) to learn a subclass single-occurrence regular expressions with interleaving (SOIREs) and claims the learnt expression is SOIRE, which has unrestricted usage for interleaving. However, in reality, the learnt expression still has many restrictions for using interleaving, even does for Kleene-star or interation, i.e, the learnt expression is not an SOIRE, we prove that by examples. In this paper, for the algorithm iSOIRE, we first give the basic notions, then provide analyses about incorrectness, finally present the correct result learnt by iSOIRE. Our theoretical analyses demonstrate that the result derived by iSOIRE belongs to a subclass of SOIREs., Comment: 6 papges

Published
2021

33. Relational Deep Reinforcement Learning for Routing in Wireless Networks

Author

Manfredi, Victoria, Wolfe, Alicia, Wang, Bing, and Zhang, Xiaolan

Subjects
Computer Science - Networking and Internet Architecture, Computer Science - Artificial Intelligence
Abstract

While routing in wireless networks has been studied extensively, existing protocols are typically designed for a specific set of network conditions and so cannot accommodate any drastic changes in those conditions. For instance, protocols designed for connected networks cannot be easily applied to disconnected networks. In this paper, we develop a distributed routing strategy based on deep reinforcement learning that generalizes to diverse traffic patterns, congestion levels, network connectivity, and link dynamics. We make the following key innovations in our design: (i) the use of relational features as inputs to the deep neural network approximating the decision space, which enables our algorithm to generalize to diverse network conditions, (ii) the use of packet-centric decisions to transform the routing problem into an episodic task by viewing packets, rather than wireless devices, as reinforcement learning agents, which provides a natural way to propagate and model rewards accurately during learning, and (iii) the use of extended-time actions to model the time spent by a packet waiting in a queue, which reduces the amount of training data needed and allows the learning algorithm to converge more quickly. We evaluate our routing algorithm using a packet-level simulator and show that the policy our algorithm learns during training is able to generalize to larger and more congested networks, different topologies, and diverse link dynamics. Our algorithm outperforms shortest path and backpressure routing with respect to packets delivered and delay per packet., Comment: 11 pages, 26 figures

Published
2020

37. Single-cell transcriptome analysis indicates fatty acid metabolism-mediated metastasis and immunosuppression in male breast cancer

Author

Sun, Handong, Zhang, Lishen, Wang, Zhonglin, Gu, Danling, Zhu, Mengyan, Cai, Yun, Li, Lu, Tang, Jiaqi, Huang, Bin, Bosco, Bakwatanisa, Li, Ning, Wu, Lingxiang, Wu, Wei, Li, Liangyu, Liang, Yuan, Luo, Lin, Liu, Quanzhong, Zhu, Yanhui, Sun, Jie, Shi, Liang, Xia, Tiansong, Yang, Chuang, Xu, Qitong, Han, Xue, Zhang, Weiming, Liu, Jianxia, Meng, Dong, Shao, Hua, Zheng, Xiangxin, Li, Shuqin, Pan, Hua, Ke, Jing, Jiang, Wenying, Zhang, Xiaolan, Han, Xuedong, Chu, Jian, An, Hongyin, Ge, Juyan, Pan, Chi, Wang, Xiuxing, Li, Kening, Wang, Qianghu, and Ding, Qiang

Published
2023
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49. R2R3 MYB‐dependent auxin signalling regulates trichome formation, and increased trichome density confers spider mite tolerance on tomato

Author

Yuan, Yujin, Xu, Xin, Luo, Yingqing, Gong, Zehao, Hu, Xiaowei, Wu, Mengbo, Liu, Yudong, Yan, Fang, Zhang, Xiaolan, Zhang, Wenfa, Tang, Yuwei, Feng, Bihong, Li, Zhengguo, Jiang, Cai‐Zhong, and Deng, Wei

Subjects
Agricultural Biotechnology, Plant Biology, Agricultural, Veterinary and Food Sciences, Biological Sciences, Animals, Gene Expression Regulation, Plant, Indoleacetic Acids, Solanum lycopersicum, Plant Leaves, Tetranychidae, Trichomes, trichome, auxin response factor, MYB, transcription factor, tomato, Technology, Medical and Health Sciences, Biotechnology, Agricultural biotechnology, Plant biology
Abstract

Unicellular and multicellular tomato trichomes function as mechanical and chemical barriers against herbivores. Auxin treatment increased the formation of II, V and VI type trichomes in tomato leaves. The auxin response factor gene SlARF4, which was highly expressed in II, V and VI type trichomes, positively regulated the auxin-induced formation of II, V and VI type trichomes in the tomato leaves. SlARF4 overexpression plants with high densities of these trichomes exhibited tolerance to spider mites. Two R2R3 MYB genes, SlTHM1 and SlMYB52, were directly targeted and inhibited by SlARF4. SlTHM1 was specifically expressed in II and VI type trichomes and negatively regulated the auxin-induced formation of II and VI type trichomes in the tomato leaves. SlTHM1 down-regulation plants with high densities of II and VI type trichomes also showed tolerance to spider mites. SlMYB52 was specifically expressed in V type trichomes and negatively regulated the auxin-induced formation of V type trichome in the tomato leaves. The regulation of SlARF4 on the formation of II, V and VI type trichomes depended on SlTHM1 and SlMYB52, which directly targeted cyclin gene SlCycB2 and increased its expression. In conclusion, our data indicates that the R2R3 MYB-dependent auxin signalling pathway regulates the formation of II, V and VI type trichomes in tomato leaves. Our study provides an effective method for improving the tolerance of tomato to spider mites.

Published
2021

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