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4. Current status and influencing factors of humanistic care in clinical nursing teaching (临床护理带教人文关怀现状及影响因素分析)

Author

GUO Rui (郭蕊), JU Hui (琚慧), ZHAO Fan (赵璠), ZHANG Yingyu (张瀛予), and LIU Wenjing (刘文静)

Subjects
humanistic care, nursing education, nursing management, clinical nursing teacher, 人文关怀, 护理教育, 护理管理, 护理带教, Nursing, RT1-120
Abstract

Objective To understand the current status of humanistic care in clinical nursing teaching, and analyze its influencing factors. Methods Using the convenient sampling method, a total of 170 clinical nursing teachers were selected. Caring Ability Inventory and the general information questionnaire were used to investigate clinical nursing teachers. Results A total of 170 questionnaires were distributed, and 164 were effectively recovered, with an effective recovery rate of 96. 47%. The humanistic care behavior score of clinical nursing teacher was (100. 90±12. 97). Univariate analysis revealed that the dimension of respectful and connectedness was influenced by marital status, professional titles, years of working and years of clinical teaching(P＜0. 01), dimension of knowledge and skill was influenced by education level and whether having children(P＜0. 01), and dimension of assurance was influenced by marital status and whether having children(P＜0. 01). Multiple linear regression analysis showed that education level and whether having children were influencing factors of teachers’ knowledge and skill in humanistic care practice, which explained 4. 80% of the variation. Conclusion The level of humanistic care behavior in clinical nursing teacher is at a medium level, which is related to marital status. The level of humanistic care behavior in clinical nursing teacher is associated with whether having children, years of working, years of clinical teaching, professional titles, education level and other factors. Nursing managers should take targeted measures to strengthen the cultivation of humanistic care ability in clinical nursing teachers. (目的 了解临床护理带教人文关怀行为现状, 探究影响因素, 为提升临床护理人文关怀水平提供参考。方法 采用方便抽样法, 随机抽取170名临床护理带教老师为调查对象, 使用护理人文关怀量表、一般资料调查表等工具进行问卷调查。结果 共发放170份问卷, 有效回收164份, 有效回收率96. 47%, 临床护理带教人文关怀行为评分为(100. 90±12. 97)分。单因素分析显示, 尊重和联系维度与护士婚姻状况、职称、工作时长、参加带教工作时长有关(P＜0. 01), 知识和技能维度与护士文化水平、有无子女有关(P＜0. 01), 支持和保证维度与护士婚姻状况、有无子女有关(P＜0. 01)。多重线性回归分析显示, 文化水平及有无子女对护理人文关怀能力的知识和技能维度有显著影响, 可解释人文关怀能力知识和技能维度变异的4. 80%。结论 临床护理带教人文关怀行为水平处于中等水平, 与婚姻状况、有无子女、工作时长、带教时长、职称、文化水平等因素相关, 护理管理者应采取针对性的措施加强临床护理带教的人文关怀能力的培养。)

Published
2024
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6. Lowering systolic blood pressure to less than 120 mm Hg versus less than 140 mm Hg in patients with high cardiovascular risk with and without diabetes or previous stroke: an open-label, blinded-outcome, randomised trial

Author

Ai, Xinyue, An, Chun, An, Yuhong, Bai, Shiru, Bai, Xueke, Bi, Jingao, Bin, Xiaoling, Bu, Miaomiao, Bu, Peili, Bu, Wei, Cai, Lvping, Cai, Nana, Cai, Shuhui, Cai, Ting, Cai, Wenjing, Cao, Bin, Cao, Bingbing, Cao, Huaping, Cao, Libo, Cao, Xiancun, Chai, Hui, Chai, Yonggui, Chai, Zhiyong, Chang, Chunduo, Chang, Jianbao, Chang, Shuyue, Chang, Yunling, Chao, Huanhuan, Che, Hang, Che, Qianqiu, Chen, Danlin, Chen, Dongsheng, Chen, Faxiu, Chen, Guang, Chen, Hairong, Chen, Hao, Chen, Huahua, Chen, Huijun, Chen, Jiafu, Chen, Jian, Chen, Jiasen, Chen, Jing, Chen, Jinzi, Chen, Junrong, Chen, lichun, Chen, Lijuan, Chen, Liyuan, Chen, Qun, Chen, Run, Chen, Shaoxing, Chen, Song, Chen, Tieshuang, Chen, Xianghong, Chen, Xiaowu, Chen, Xudong, Chen, Xue, Chen, Xunchun, Chen, Yao, Chen, Yongli, Chen, Yuanyue, Chen, Yuhong, Chen, Yuyi, Chen, Zhangying, Chen, Zhidong, Chen, Zuyi, Cheng, Caiming, Cheng, Jianbin, Cheng, Xiaoxia, Chu, Junjie, Cui, Ruifeng, Cui, Xiaolin, Cui, Xuechen, Cui, Yang, Cui, Zhonghua, Dai, Wanhong, Dai, Xing, Ding, Chunxia, Ding, Huihong, Ding, Qiuhong, Ding, Yaozong, Ding, Yingjie, Dong, Jiajia, Dong, Lei, Dong, Qi, Dong, Yumei, Du, Bing, Du, Hong, Du, Jie, Du, Laijing, Du, Meiling, Du, Qiong, Du, Tianmin, Du, Xue, Duan, Ru, Duan, Xiaojing, Duan, Xiaoting, Fan, Dandan, Fan, Xiaohong, Fan, Xin, Fang, Fang, Fang, Jing, Fang, Xibo, Fang, Yang, Feng, Erke, Feng, Hejin, Feng, Ling, Feng, Rui, Feng, Zhaohui, Fu, Hongmei, Fu, Qiuai, Gao, Haofei, Gao, Li, Gao, Lina, Gao, Liwei, Gao, Lu, Gao, Min, Gao, Qian, Gao, Yan, Gao, Yuan, Ge, Jinzhuo, Geng, Hongxu, Geng, Hui, Geng, Leijun, Geng, Lianqing, Gou, Hongyan, Gu, Qin, Guan, Lili, Guan, Shuo, Guan, Wenchi, Guan, Zheng, Guang, Bin, Guo, Anran, Guo, Changhong, Guo, Gaofeng, Guo, Lizhi, Guo, Qing, Guo, Qiue, Guo, Ying, Guo, Zhihua, Han, Aihong, Han, Meihong, Han, Suhui, Han, Xinru, Han, Yajun, Hao, Feng, Hao, Jingmin, Hao, Shiguo, He, Chuanhui, He, Dejian, He, Mengyuan, He, Miaomiao, He, Shaojuan, He, Wenkai, He, Xiaoyu, He, Yuxiang, Hong, Jige, Hou, Chuanxing, Hou, Jing, Hu, Danli, Hu, Jian, Hu, Jun, Hu, Lingai, Hu, Mengying, Hu, Zhiyuan, Huang, Anhui, Huang, Chunxia, Huang, Haolin, Huang, Jianlan, Huang, Sha, Huang, Siqi, Huang, Weijun, Huang, Wenxiu, Huang, Xinghe, Huang, Xinsheng, Huang, Xinxin, Hui, Jiliang, Hui, Lijun, Hui, Zhongsheng, Huo, Fangjie, Ji, Runqing, Jia, Guojiong, Jia, Hao, Jia, Jingjing, Jia, Jingmei, Jia, Xiaoling, Jiang, Hua, Jiang, Jingcheng, Jiang, Qian, Jiang, Xianyan, Jiang, Xiaoyuan, Jiang, Yanxiang, Jiao, Yunhong, Jie, Liying, Jin, Binbin, Jin, Lingjiao, Jin, Renshu, Jin, Rong, Jin, Xiang, Jin, Xianping, Jin, Yongfan, Jin, Zepu, Jin, Zhenan, Jing, Chengrong, Jing, Jiajie, Jing, Ruiling, Kang, Liping, Kang, Yu, Kong, Jianqiong, Kou, Shijie, Kou, Xianli, Kulaxihan, Lai, Jijia, Lei, Lubi, Li, Baoxiang, Li, Bin, Li, Bing, Li, Chaohui, Li, Cheng, Li, Chunmei, Li, Chunyan, Li, Daqing, Li, Deen, Li, Di, Li, Feng, Li, Guanyi, Li, Haiyang, Li, Hongwei, Li, Jia, Li, Jialin, Li, Jianan, Li, Jianguang, Li, Jiaying, Li, Jing, Li, Jinmei, Li, Lala, Li, Li, Li, Lijun, Li, Liping, Li, Lize, Li, Mingju, Li, Minglan, Li, Mingyan, Li, Na, Li, Nan, Li, Nana, Li, Qiang, Li, Qianru, Li, Rui, Li, Ruihong, Li, Shanshan, Li, Shilin, Li, Si, Li, Suwen, Li, Tongshe, Li, Tongying, Li, Wanke, Li, Wei, Li, Wenbo, Li, Wenjuan, Li, Xi, Li, Xiangxia, Li, Xiao, Li, Xiaohui, Li, Xingyan, Li, Xiujuan, Li, Yan, Li, Yanfang, Li, Yang, Li, Yanxia, Li, Yaona, Li, Yichong, Li, Ying, Li, Yuqing, Li, Zheng, Li, Zhengye, Liang, Chuanliang, Liang, Jihua, Liang, Jin, Liang, Ke, Liang, Linju, Liang, Tingchen, Liang, Xia, Liang, Xianfeng, Liang, Yanli, Liang, Zhenye, Lie, Zhenbang, Lin, Qingfei, Lin, Ruifang, Lin, Xiao, Lin, Zhiqiang, Liu, Aijun, Liu, Chao, Liu, Chunxia, Liu, Cong, Liu, Fang, Liu, Guaiyan, Liu, Hongjun, Liu, Jiamin, Liu, Jiangling, Liu, Jianqi, Liu, Jieyun, Liu, Jihong, Liu, Jing, Liu, Jinsha, Liu, Juan, Liu, Junfang, Liu, Liming, Liu, Lin, Liu, Ling, Liu, Lu, Liu, Qiang, Liu, Qiaoling, Liu, Qiaoxia, Liu, Qiuxia, Liu, Shaobo, Liu, Xiaobao, Liu, Xiaocheng, Liu, Xiaoyuan, Liu, Xinbo, Liu, Xu, Liu, Yang, Liu, Yanhu, Liu, Yanming, Liu, Yaqin, Liu, Yong, Liu, Zhihong, Long, Jing, Lu, Futang, Lu, Huamei, Lu, Jiapeng, Lu, Junhong, Lu, Weibin, Lu, Yanrong, Lu, Yuchun, Luan, Tianwei, Luo, Qingwei, Luo, Qun, Luo, Tian, Luo, Xia, Luo, Yongmei, Lv, Jing, Lv, Jinhai, Lv, Lei, Lv, Lili, Lv, Meng, Ma, Aiqing, Ma, Huaimin, Ma, Huihuang, Ma, Jie, Ma, Jinbao, Ma, Li, Ma, Lingzhen, Ma, Nan, Ma, Qiaojuan, Ma, Shumei, Ma, Tengfei, Ma, Xiange, Ma, Xiaowen, Ma, Yuehua, Mai, Lanxian, Mei, Xiao, Meng, Gen, Miao, Ruichao, Miao, Xue, Miao, Xuyan, Min, Tingting, Mo, Shubing, Morigentu, Nan, Tingyan, Ni, Jinyang, Ni, Shuguo, Nie, Yu, Ning, Benxing, Ning, Xiaowei, Niu, Manman, Niu, Qingying, Niu, Wentang, Niu, Xiaoxia, Ou, Fang, Pan, Biyun, Pan, Chengjie, Pan, Congming, Pan, Jieli, Pan, Xiaowen, Pan, Ziying, Pei, Guangzhong, Pei, Lingyu, Pei, Min, Pei, Yanzhen, Peng, Yinyu, Peng, Yuming, Pu, Zhaokun, Qi, Fengjun, Qi, Liwei, Qi, Meiqiong, Qi, Yan, Qian, Jun, Qin, Lei, Qin, Zhonghua, Qing, Lan, Qiu, Lixia, Qiu, Weiyu, Qiu, Xiaoling, Qu, Yueli, Quan, Minghua, Ren, Dingping, Ren, Hong, Ren, Lingzhi, Ren, Tingting, Ren, Wei, Ren, Yihui, Rong, Yufang, Ruan, Jiahui, Shang, Peiqin, Shao, Minyan, Shao, Xuefeng, Shao, Yuling, Shen, Junrong, Shen, Rui, Sheng, Lin, Shi, Jiangjie, Shi, Xun, Shi, Yanhong, Shi, Yeju, Shi, Yujiao, Shu, Bo, Song, Bingchun, Song, Dan, Song, Jinhui, Song, Jinwang, Song, Jinxian, Song, Wei, Song, Xiaoping, Song, Yawen, Su, He, Su, Qinfeng, Su, Shuhong, Su, Xiaozhou, Sun, Chengxiang, Sun, Fangfang, Sun, Gongping, Sun, Jiangnan, Sun, Mengmeng, Sun, Rongrong, Sun, Shuting, Sun, Songtao, Sun, Ying, Sun, Yongmiao, Sun, Yunhong, Sun, Zhiqiang, Suo, Mengying, Tan, Binghu, Tang, Chunyan, Tang, Zhongli, Tao, Yu, Tian, Changming, Tian, Hongmei, Tian, Jian, Tian, Xiaomin, Wan, Huaibin, Wan, Qin, Wan, Rongjun, Wang, Bobin, Wang, Chao, Wang, Chaoqun, Wang, Chengliang, Wang, Di, Wang, Enfang, Wang, Feng, Wang, Gang, Wang, Guangqiang, Wang, Guixiang, Wang, Haifeng, Wang, Haijun, Wang, Haiyang, Wang, Jianfang, Wang, Jianfeng, Wang, Jing, Wang, Junping, Wang, Junying, Wang, Kang, Wang, Lei, Wang, Lin, Wang, Lize, Wang, Meng, Wang, Pan, Wang, Qi, Wang, Qiong, Wang, Qiuli, Wang, Qiuxue, Wang, Ran, Wang, Shaojin, Wang, Shuai, Wang, Tao, Wang, Tiantian, Wang, Tinghui, Wang, Tongyan, Wang, Wanhong, Wang, Wenjuan, Wang, Wenyan, Wang, Wenying, Wang, Wenzhuan, Wang, Xiaofei, Wang, Xiaoyan, Wang, Xitong, Wang, Xu, Wang, Yan, Wang, Yanfang, Wang, Yang, Wang, Yanping, Wang, Yanying, Wang, Yaoxin, Wang, Yingli, Wang, Yiting, Wang, Yue, Wang, Yumei, Wang, Yuzhuo, Wang, Zhenhua, Wang, Zhifang, Wang, Zhimin, Wei, Chunli, Wei, Lixia, Wei, Pei, Wei, Shuying, Wei, Xiqing, Wen, Hong, Wen, Yun, Wu, Chaoqun, Wu, Hairong, Wu, Lihua, Wu, Lingxiang, Wu, Qi, Wu, Shaorong, Wu, Wenting, Wu, Xueyi, Wu, Yongshuan, Wu, Zhihao, Wu, Zhuying, Wu, Zongyin, Wuhanbilige, Xia, Jun, Xia, Yang, Xiang, Jing, Xiao, Heliu, Xiao, Yaying, Xie, Meiling, Xie, Yinyan, Xin, Huiling, Xing, Jing, Xiu, Guoquan, Xu, Baohua, Xu, Chuangze, Xu, En, Xu, Jian, Xu, Shuli, Xu, Wei, Xu, Wen, Xue, Na, Xue, Tingting, Xue, Wei, Yan, Haiyan, Yan, Xiaofang, Yan, Yanqing, Yang, Bo, Yang, Hui, Yang, Huiyu, Yang, Jinhua, Yang, Kun, Yang, Man, Yang, Mengya, Yang, Ning, Yang, Ping, Yang, Xiajiao, Yang, Xiaomo, Yang, Xin, Yang, Xiujuan, Yang, Xuemei, Yang, Xuming, Yang, Yan, Yang, Yanhua, Yang, Yi, Yang, Yuanyuan, Yang, Zhimei, Yang, Zhiming, Yao, Hui, Yao, Lu, Ye, Jinling, Ye, Wenhua, Yi, Mingjiao, Yi, Shaowei, Yi, Wenyi, Yi, Zhimin, Yin, Guangxia, Yin, Guoyuan, Yu, Guibin, Yu, Hairong, Yu, Huaitao, Yu, Lijie, Yu, Lijun, Yu, Nana, Yu, Qin, Yu, Xinli, Yu, Yi, Yuan, Biao, Zeng, Chunmei, Zhai, Na, Zhai, Xiaojuan, Zhan, Hongju, Zhang, Aizhen, Zhang, Baohua, Zhang, Bin, Zhang, Caizhu, Zhang, Chaoying, Zhang, Chengbo, Zhang, Chunlai, Zhang, Churuo, Zhang, Fan, Zhang, Feiqin, Zhang, Ge, Zhang, Haibo, Zhang, Hailin, Zhang, Hanxue, Zhang, Huaixing, Zhang, Hui, Zhang, Huijuan, Zhang, Jinguo, Zhang, Jingyu, Zhang, Jinyun, Zhang, Jisheng, Zhang, Jun, Zhang, Lei, Zhang, Li, Zhang, Liang, Zhang, Lifeng, Zhang, Lina, Zhang, Liping, Zhang, Min, Zhang, Ping, Zhang, Qiang, Zhang, Rufang, Zhang, Ruifen, Zhang, Shengde, Zhang, Siqi, Zhang, Sufang, Zhang, Tingting, Zhang, Wanyue, Zhang, Weiliang, Zhang, Xiaohan, Zhang, Xiaohong, Zhang, Xiaojuan, Zhang, Xin, Zhang, Xue, Zhang, Xuewei, Zhang, Yachen, Zhang, Yang, Zhang, Yanyan, Zhang, Yaojie, Zhang, Yingyu, Zhang, Yuan, Zhang, Yun, Zhang, Yunfeng, Zhang, Zaozhang, Zhang, Zhichao, Zhao, Baihui, Zhao, Dan, Zhao, Fuxian, Zhao, Guizeng, Zhao, Haijie, Zhao, Honglei, Zhao, Huizhen, Zhao, Jindong, Zhao, Juan, Zhao, Liming, Zhao, Ling, Zhao, Lingshan, Zhao, Qingxia, Zhao, Qiuping, Zhao, Wanchen, Zhao, Wangxiu, Zhao, Weiyi, Zhao, Xiaodi, Zhao, Xiaojing, Zhao, Xiaoli, Zhao, Xiaoyan, Zhao, Xiling, Zhao, Yannan, Zhao, Yiyuan, Zheng, Shuzhen, Zheng, Xin, Zhi, Lixia, Zhong, Hui, Zhong, Qing, Zhong, Xin, Zhong, Yunzhi, Zhou, Jianfeng, Zhou, Jihu, Zhou, Ke, Zhou, Liangliang, Zhou, Ling, Zhou, Na, Zhou, Shengcheng, Zhou, Suyun, Zhou, Tao, Zhou, Wanren, Zhou, Weifeng, Zhou, Weijuan, Zhou, Xiaohong, Zhou, Yunke, Zhou, Yuquan, Zhou, Zhaohai, Zhou, Zhiming, Zhu, Bingpo, Zhu, Jifa, Zhu, Jing, Zhu, Mengnan, Zhu, Youcun, Zong, Dafei, Zuo, Hongbo, and Zuo, Zhaokai

Published
2024
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36. A Many-Objective Evolutionary Algorithm Based on Decomposition and Local Dominance

Author

Zhang, Yingyu, Li, Yuanzhen, Panb, Quan-Ke, and Suganthan, P. N.

Subjects
Computer Science - Neural and Evolutionary Computing
Abstract

Many-objective evolutionary algorithms (MOEAs), especially the decomposition-based MOEAs, have attracted wide attention in recent years. Recent studies show that a well designed combination of the decomposition method and the domination method can improve the performance ,i.e., convergence and diversity, of a MOEA. In this paper, a novel way of combining the decomposition method and the domination method is proposed. More precisely, a set of weight vectors is employed to decompose a given many-objective optimization problem(MaOP), and a hybrid method of the penalty-based boundary intersection function and dominance is proposed to compare local solutions within a subpopulation defined by a weight vector. A MOEA based on the hybrid method is implemented and tested on problems chosen from two famous test suites, i.e., DTLZ and WFG. The experimental results show that our algorithm is very competitive in dealing with MaOPs. Subsequently, our algorithm is extended to solve constraint MaOPs, and the constrained version of our algorithm also shows good performance in terms of convergence and diversity. These reveals that using dominance locally and combining it with the decomposition method can effectively improve the performance of a MOEA., Comment: arXiv admin note: substantial text overlap with arXiv:1803.06282, arXiv:1806.10950

Published
2018

37. A Decomposition-Based Many-Objective Evolutionary Algorithm with Local Iterative Update

Author

Zhang, Yingyu and Zeng, Bing

Subjects
Computer Science - Neural and Evolutionary Computing
Abstract

Existing studies have shown that the conventional multi-objective evolutionary algorithms (MOEAs) based on decomposition may lose the population diversity when solving some many-objective optimization problems. In this paper, a simple decomposition-based MOEA with local iterative update (LIU) is proposed. The LIU strategy has two features that are expected to drive the population to approximate the Pareto Front with good distribution. One is that only the worst solution in the current neighborhood is swapped out by the newly generated offspring, preventing the population from being occupied by copies of a few individuals. The other is that its iterative process helps to assign better solutions to subproblems, which is beneficial to make full use of the similarity of solutions to neighboring subproblems and explore local areas in the search space. In addition, the time complexity of the proposed algorithm is the same as that of MOEA/D, and lower than that of other known MOEAs, since it considers only individuals within the current neighborhood at each update. The algorithm is compared with several of the best MOEAs on problems chosen from two famous test suites DTLZ and WFG. Experimental results demonstrate that only a handful of running instances of the algorithm on DTLZ4 lose their population diversity. What's more, the algorithm wins in most of the test instances in terms of both running time and solution quality, indicating that it is very effective in solving MaOPs., Comment: arXiv admin note: text overlap with arXiv:1803.06282

Published
2018

38. A mullti- or many- objective evolutionary algorithm with global loop update

Author

Zhang, Yingyu, Zeng, Bing, Li, Yuanzhen, and Li, Junqing

Subjects
Computer Science - Neural and Evolutionary Computing
Abstract

Multi- or many-objective evolutionary algorithm- s(MOEAs), especially the decomposition-based MOEAs have been widely concerned in recent years. The decomposition-based MOEAs emphasize convergence and diversity in a simple model and have made a great success in dealing with theoretical and practical multi- or many-objective optimization problems. In this paper, we focus on update strategies of the decomposition- based MOEAs, and their criteria for comparing solutions. Three disadvantages of the decomposition-based MOEAs with local update strategies and several existing criteria for comparing solutions are analyzed and discussed. And a global loop update strategy and two hybrid criteria are suggested. Subsequently, an evolutionary algorithm with the global loop update is implement- ed and compared to several of the best multi- or many-objective optimization algorithms on two famous unconstraint test suites with up to 15 objectives. Experimental results demonstrate that unlike evolutionary algorithms with local update strategies, the population of our algorithm does not degenerate at any generation of its evolution, which guarantees the diversity of the resulting population. In addition, our algorithm wins in most instances of the two test suites, indicating that it is very compet- itive in terms of convergence and diversity. Running results of our algorithm with different criteria for comparing solutions are also compared. Their differences are very significant, indicating that the performance of our algorithm is affected by the criterion it adopts., Comment: 14 pages, 5 figures and 7 tables

Published
2018

40. Exogenous melatonin alleviates sodium chloride stress and increases vegetative growth in Lonicera japonica seedlings via gene regulation.

Author

Song, Cheng, Manzoor, Muhammad Aamir, Ren, Yanshuang, Guo, Jingjing, Zhang, Pengfei, and Zhang, Yingyu

Subjects
CALCIUM-dependent protein kinase, JAPANESE honeysuckle, CHALCONE synthase, ALCOHOL dehydrogenase, REACTIVE oxygen species, MELATONIN
Abstract

Melatonin (Mt) functions as a growth regulator and multifunctional signaling molecule in plants, thereby playing a crucial role in promoting growth and orchestrating protective responses to various abiotic stresses. However, the mechanism whereby exogenous Mt protects Lonicera japonica Thunb. (L. japonica) against salt stress has not been fully elucidated. Therefore, this study aimed to elucidate how exogenous Mt alleviates sodium chloride (NaCl) stress in L. japonica seedlings. Salt-sensitive L. japonica seedlings were treated with an aqueous solution containing 150 mM of NaCl and aqueous solutions containing various concentrations of Mt. The results revealed that treatment of NaCl-stressed L. japonica seedlings with a 60 µM aqueous solution of Mt significantly enhanced vegetative plant growth by scavenging reactive oxygen species and thus reducing oxidative stress. The latter was evidenced by decreases in electrical conductivity and malondialdehyde (MDA) concentrations. Moreover, Mt treatment led to increases in the NaCl-stressed L. japonica seedlings' total chlorophyll content, soluble sugar content, and flavonoid content, demonstrating that Mt treatment improved the seedlings' tolerance of NaCl stress. This was also indicated by the NaCl-stressed L. japonica seedlings exhibiting marked increases in the activities of antioxidant enzymes (superoxide dismutase, peroxidase, catalase, and ascorbate peroxidase) and in photosynthetic functions. Furthermore, Mt treatment of NaCl-stressed L. japonica seedlings increased their expression of phenylalanine ammonia-lyase 1 (PAL1), phenylalanine ammonia-lyase 2 (PAL2), calcium-dependent protein kinase (CPK), cinnamyl alcohol dehydrogenase (CAD), flavanol synthase (FLS), and chalcone synthase (CHS). In conclusion, our results demonstrate that treatment of L. japonica seedlings with a 60 µM aqueous solution of Mt significantly ameliorated the detrimental effects of NaCl stress in the seedlings. Therefore, such treatment has substantial potential for use in safeguarding medicinal plant crops against severe salinity. [ABSTRACT FROM AUTHOR]

Published
2024
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41. Underlying mechanism of Dendrobium huoshanense resistance to lead stress using the quantitative proteomics method.

Author

Song, Cheng, Dai, Jun, Ren, Yanshuang, Manzoor, Muhammad Aamir, and Zhang, Yingyu

Subjects
HOMOLOGOUS recombination, LEAD exposure, POISONS, AMINO acid metabolism, LEAD, TERPENES
Abstract

Lead affects photosynthesis and growth and has serious toxic effects on plants. Here, the differential expressed proteins (DEPs) in D. huoshanense were investigated under different applications of lead acetate solutions. Using label-free quantitative proteomics methods, more than 12,000 peptides and 2,449 proteins were identified. GO and KEGG functional annotations show that these differential proteins mainly participate in carbohydrate metabolism, energy metabolism, amino acid metabolism, translation, protein folding, sorting, and degradation, as well as oxidation and reduction processes. A total of 636 DEPs were identified, and lead could induce the expression of most proteins. KEGG enrichment analysis suggested that proteins involved in processes such as homologous recombination, vitamin B6 metabolism, flavonoid biosynthesis, cellular component organisation or biogenesis, and biological regulation were significantly enriched. Nearly 40 proteins are involved in DNA replication and repair, RNA synthesis, transport, and splicing. The effect of lead stress on D. huoshanense may be achieved through photosynthesis, oxidative phosphorylation, and the production of excess antioxidant substances. The expression of 9 photosynthesis-related proteins and 12 oxidative phosphorylation-related proteins was up-regulated after lead stress. Furthermore, a total of 3 SOD, 12 POD, 3 CAT, and 7 ascorbate-related metabolic enzymes were identified. Under lead stress, almost all key enzymes involved in the synthesis of antioxidant substances are up-regulated, which may facilitate the scavenging of oxygen-free radical scavenging. The expression levels of some key enzymes involved in sugar and glycoside synthesis, the phenylpropanoid synthesis pathway, and the terpene synthesis pathway also increased. More than 30 proteins involved in heavy metal transport were also identified. Expression profiling revealed a significant rise in the expression of the ABC-type multidrug resistance transporter, copper chaperone, and P-type ATPase with exposure to lead stress. Our findings lay the basis for research on the response and resistance of D. huoshanense to heavy metal stress. [ABSTRACT FROM AUTHOR]

Published
2024
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42. The hybrid systems method integrating STAMP and HFACS for the causal analysis of the road traffic accident.

Author

Dong, Chuntong, Zhang, Yingyu, Wang, Ziqi, Liu, Jing, and Zhang, Jie

Subjects
TRAFFIC safety, TRAFFIC accidents, MOTOR vehicle occupants, MOTOR vehicle safety measures, AUTOMOBILE driving, SYSTEMS theory, QUALITY assurance, INDUSTRIAL safety
Abstract

The role of traditional analysis methods in improving complex socio-technical system safety has reached a ceiling, and thus systems theory has been utilised to support the investigations and countermeasures for road traffic accidents. As two widely applied systems accident analysis models, STAMP (systems theoretic accident model and process) and HFACS (human factors analysis and classification system) have their own advantages in accident analysis and safety improvement. Therefore, this study develops a new hybrid systems method integrating STAMP and HFACS for road traffic accident (SH-RTA), which can adopt HFACS to enhance the identification and analysis ability of STAMP for human factors and employ control concepts and elements of STAMP to cement the characteristic of HFACS. To illustrate the applicability of the hybrid method, a case study of '9·22' major road traffic accident in China is thoroughly analysed. Finally, preventive countermeasures and suggestions are presented. Practitioner Summary: This paper proposes a new hybrid systems method integrating STAMP and HFACS for road traffic accident. The new method reveals dysfunctional interactions within the parallel level and across levels, and identifies additional human and organisational factors. The recommendations for preventing road traffic accident are provided from higher levels of system. [ABSTRACT FROM AUTHOR]

Published
2024
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