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Book ; Online ; E-Book: An introduction to energy diplomacy

Wang, Haiyun / Hazelkorn, Bud / Yin, Ming / Xu, Qinhua

China's perspective

(Research series on the Chinese dream and China's development path)

2022

Author's details	Haiyun Wang, Qinhua Xu ; translated by Ming Yin and Bud Hazelkorn
Series title	Research series on the Chinese dream and China's development path
Keywords	Energy policy ; China
Subject code	327.51
Language	English
Size	1 online resource (261 pages)
Publisher	Springer Nature Singapore Pte Ltd
Publishing place	Singapore
Document type	Book ; Online ; E-Book
Remark	Zugriff für angemeldete ZB MED-Nutzerinnen und -Nutzer
ISBN	981-16-9109-6 ; 981-16-9108-8 ; 978-981-16-9108-9 ; 978-981-16-9109-6
Database	ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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Article: [Effect of Wnt/β-catenin signaling pathway in neural differentiation of human bone marrow mesenchymal stem cells].

Shi, Jianming / Yin, Ming

Zhongguo xiu fu chong jian wai ke za zhi = Zhongguo xiufu chongjian waike zazhi = Chinese journal of reparative and reconstructive surgery

2023 Volume 37, Issue 10, Page(s) 1276–1283

Abstract: Objective: To explore the effect of basic fibroblast growth factor (bFGF), epidermal growth factor (EGF), and the combination of bFGF and EGF in the neural differentiation of human bone marrow mesenchymal stem cells (hBMSCs), and the role of Wnt/β- ... ...

Abstract	Objective: To explore the effect of basic fibroblast growth factor (bFGF), epidermal growth factor (EGF), and the combination of bFGF and EGF in the neural differentiation of human bone marrow mesenchymal stem cells (hBMSCs), and the role of Wnt/β-catenin signaling pathway in this process. Methods: The identified 4th-generation hBMSCs were divided into five groups according to different induction conditions, namely control group (group A), EGF induction group (group B), bFGF induction group (group C), EGF and bFGF combined induction group (group D), and EGF, bFGF, and Dickkopf-related protein 1 (DKK-1) combined induction group (group E). After 7 days of continuous induction, the cell morphology was observed by inverted fluorescence phase contrast microscopy, levels of genes that were related to neural cells [Nestin, neuron-specific enolase (NSE), microtubule-associated protein 2 (MAP-2), and glial fibrillary acidic protein (GFAP)] and key components of the Wnt/β-catenin signaling pathway (β-catenin and Cyclin D1) were detected by RT-PCR, and the levels of proteins that were related to neural cells (Nestin and GFAP) as well as genes that were involved in Wnt/β-catenin signaling pathway [β-catenin, phosphorylation β-catenin (P-β-catenin), Cytoplasmic β-catenin, and Nuclear β-catenin] were explored by cellular immunofluorescence staining and Western blot. Results: When compared to groups A and B, the typical neuro-like cell changes were observed in groups C-E, and most obviously in group D. RT-PCR showed that the relative expressions of Nestin, NSE, and MAP-2 genes in groups C-E, the relative expressions of GFAP gene in groups D and E, the relative expression of NSE gene in group B, the relative expressions of β-catenin gene in groups C and D, and the relative expressions of Cyclin D1 gene in groups B-D significantly increased when compared with group A ( Conclusion: Different from EGF, bFGF can induce neural differentiation of hBMSCs. In addition, EGF can enhance the hBMSCs neural differentiation of bFGF, while the Wnt/β-catenin signaling pathway may play a positive regulatory role in these processes.
MeSH term(s)	Humans ; beta Catenin/metabolism ; Bone Marrow Cells ; Cell Differentiation ; Cells, Cultured ; Epidermal Growth Factor/metabolism ; Mesenchymal Stem Cells ; Wnt Signaling Pathway ; Neurons ; Fibroblast Growth Factor 2/metabolism
Chemical Substances	beta Catenin ; Epidermal Growth Factor (62229-50-9) ; Fibroblast Growth Factor 2 (103107-01-3)
Language	Chinese
Publishing date	2023-10-17
Publishing country	China
Document type	English Abstract ; Journal Article
ZDB-ID	1420212-8
ISSN	1002-1892
ISSN	1002-1892
DOI	10.7507/1002-1892.202306017
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Article: The effect of experiences of fairness on honest behavior: a behavioral and neural study.

Zhang, Chen / Yin, Ming / Wu, Jixia

Frontiers in behavioral neuroscience

2024 Volume 17, Page(s) 1279176

Abstract: Prior studies have investigated the relationship between fairness and honesty. However, the differences in the focus of these studies have rendered cross-comparisons between them challenging and of limited value. Thus, this study explored how fairness ... ...

Abstract	Prior studies have investigated the relationship between fairness and honesty. However, the differences in the focus of these studies have rendered cross-comparisons between them challenging and of limited value. Thus, this study explored how fairness impacts honest decision-making, focusing specifically on the effect of individuals' experiences of fairness on their honest behavior. Experiment 1 explored the influence of different experiences of fairness on honest behavior in an altruistic context. In Experiment 2, we measured event-related potentials to further demonstrate the brain mechanisms of these experiences on altruistic dishonest behavior. In Experiment 1, we found that the reaction time for dishonest behavior was shorter for individuals who had positive unfairness experiences with high altruistic objects compared to low altruistic objects. Individuals who had negative unfairness experiences had shorter reaction times when engaging in dishonest behaviors for the sake of high altruistic objects compared to those with equitable experiences. In Experiment 2, in which there was an opportunity to lie for a highly altruistic object, those with fair experiences had greater N2 volatility and smaller P3 volatility than those with positive unfairness experiences. These findings highlight the value of integrating moral psychology and behavioral economics. Discriminant validity across fairness experiences can help illuminate the different motivations behind moral decisions.
Language	English
Publishing date	2024-01-08
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2452960-6
ISSN	1662-5153
ISSN	1662-5153
DOI	10.3389/fnbeh.2023.1279176
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Article ; Online: Prediction performance of the machine learning model in predicting mortality risk in patients with traumatic brain injuries: a systematic review and meta-analysis.

Wang, Jue / Yin, Ming Jing / Wen, Han Chun

BMC medical informatics and decision making

2023 Volume 23, Issue 1, Page(s) 142

Abstract: Purpose: With the in-depth application of machine learning(ML) in clinical practice, it has been used to predict the mortality risk in patients with traumatic brain injuries(TBI). However, there are disputes over its predictive accuracy. Therefore, we ... ...

Abstract	Purpose: With the in-depth application of machine learning(ML) in clinical practice, it has been used to predict the mortality risk in patients with traumatic brain injuries(TBI). However, there are disputes over its predictive accuracy. Therefore, we implemented this systematic review and meta-analysis, to explore the predictive value of ML for TBI. Methodology: We systematically retrieved literature published in PubMed, Embase.com, Cochrane, and Web of Science as of November 27, 2022. The prediction model risk of bias(ROB) assessment tool (PROBAST) was used to assess the ROB of models and the applicability of reviewed questions. The random-effects model was adopted for the meta-analysis of the C-index and accuracy of ML models, and a bivariate mixed-effects model for the meta-analysis of the sensitivity and specificity. Result: A total of 47 papers were eligible, including 156 model, with 122 newly developed ML models and 34 clinically recommended mature tools. There were 98 ML models predicting the in-hospital mortality in patients with TBI; the pooled C-index, sensitivity, and specificity were 0.86 (95% CI: 0.84, 0.87), 0.79 (95% CI: 0.75, 0.82), and 0.89 (95% CI: 0.86, 0.92), respectively. There were 24 ML models predicting the out-of-hospital mortality; the pooled C-index, sensitivity, and specificity were 0.83 (95% CI: 0.81, 0.85), 0.74 (95% CI: 0.67, 0.81), and 0.75 (95% CI: 0.66, 0.82), respectively. According to multivariate analysis, GCS score, age, CT classification, pupil size/light reflex, glucose, and systolic blood pressure (SBP) exerted the greatest impact on the model performance. Conclusion: According to the systematic review and meta-analysis, ML models are relatively accurate in predicting the mortality of TBI. A single model often outperforms traditional scoring tools, but the pooled accuracy of models is close to that of traditional scoring tools. The key factors related to model performance include the accepted clinical variables of TBI and the use of CT imaging.
MeSH term(s)	Humans ; Brain Injuries, Traumatic/diagnosis ; Sensitivity and Specificity ; Multivariate Analysis ; Hospital Mortality ; Machine Learning
Language	English
Publishing date	2023-07-29
Publishing country	England
Document type	Meta-Analysis ; Systematic Review ; Journal Article
ZDB-ID	2046490-3
ISSN	1472-6947 ; 1472-6947
ISSN (online)	1472-6947
ISSN	1472-6947
DOI	10.1186/s12911-023-02247-8
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Article: Efficacy and preliminary dose studies of ultrasound-guided sclerotherapy with lauromacrogol for the treatment of implanted hepatic tumors in rabbits.

Wang, Shilong / Yin, Ming / Xia, Zhichao

Journal of gastrointestinal oncology

2023 Volume 14, Issue 1, Page(s) 265–277

Abstract: Background: To study the curative effect of sclerotherapy with lauromacrogol on a rabbit VX2 implanted liver tumor model, the effect of ultrasound-guided sclerotherapy with different doses of lauromacrogol in the treatment of the rabbit VX2 implanted ... ...

Abstract	Background: To study the curative effect of sclerotherapy with lauromacrogol on a rabbit VX2 implanted liver tumor model, the effect of ultrasound-guided sclerotherapy with different doses of lauromacrogol in the treatment of the rabbit VX2 implanted liver tumor model and the degree of damage to the surrounding liver tissue was compared and observed. The relationship between the sclerosing effect and drug dose of lauromacrogol in the treatment of liver cancer is preliminarily discussed. Methods: Thirty rabbit models of liver cancer were randomly divided into 5 groups. Control Group A was injected with normal saline, control Group B was injected with absolute ethanol, experimental Group C was injected with lauromacrogol (Z =2.885D); the injection volume in experimental Group D was 1.5-fold higher than that in Group C; and the injection volume in experimental Group E was 2-fold higher than that in Group C. Changes in tumor volume were followed up by ultrasound before and 7 and 14 days after the operation; contrast-enhanced ultrasonography was used to measure the volume of the ablation area and volume rate. For pathological anatomy, hematoxylin and eosin (H&E) staining were used to observe tumor cell necrosis and the degree of damage to surrounding normal liver cells. The expression levels of the apoptosis-related protein cleaved-caspase 3 and the cell proliferation antigen Ki67 were examined by immunohistochemistry. Results: The volumes in Groups A and C increased significantly. The volumes in Groups B, D, and E remained the same or slightly expanded, tumor growth in the three groups was significantly inhibited after sclerotherapy. Contrast-enhanced ultrasonography showed that the ablation volume and ablation volume rate in Groups B, D, and E were similar. Group C were smaller than those in the other treatment groups. Large coagulative necrotic foci were observed in the central area of tumors in Groups B, D, and E. Conclusions: The experiment demonstrates that lauromacrogol can inhibit tumor growth in the rabbit VX2 tumor model and cause VX2 tumor cell apoptosis. The sclerosing effect of the 1.5-fold amount of lauromacrogol is equivalent to that of absolute ethanol, with little effect on normal liver tissue.
Language	English
Publishing date	2023-02-23
Publishing country	China
Document type	Journal Article
ZDB-ID	2594644-4
ISSN	2219-679X ; 2078-6891
ISSN (online)	2219-679X
ISSN	2078-6891
DOI	10.21037/jgo-23-16
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Article: Circuit-specific gene therapy is knocking on the door of Parkinson's disease.

Wang, Yue-Qi / Yin, Ming / Guo, Zhe-Shan

Zoological research

2023 Volume 44, Issue 6, Page(s) 1152–1153

MeSH term(s)	Animals ; Parkinson Disease/genetics ; Parkinson Disease/therapy ; Genetic Therapy
Language	English
Publishing date	2023-11-17
Publishing country	China
Document type	Editorial
ISSN	2095-8137
ISSN	2095-8137
DOI	10.24272/j.issn.2095-8137.2023.353
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Article ; Online: Association between the prognostic nutritional index (PNI) and all-cause mortality in patients with chronic kidney disease.

Yu, Jian-Hong / Chen, Yu / Yin, Ming-Gang

Renal failure

2023 Volume 45, Issue 2, Page(s) 2264393

Abstract: Background: Nutrition and immunity play an important role in many chronic diseases. The prognostic nutritional index (PNI) has been proposed as a comprehensive indicator of an individual's immune and nutritional status. However, there is a lack of ... ...

Abstract	Background: Nutrition and immunity play an important role in many chronic diseases. The prognostic nutritional index (PNI) has been proposed as a comprehensive indicator of an individual's immune and nutritional status. However, there is a lack of evidence regarding the association between the PNI and mortality in patients with chronic kidney disease (CKD). Methods: We used National Health and Nutrition Examination Survey (NHANES) data from 2001-2014 for participants with CKD. Mortality data were obtained from the National Death Index and matched to NHANES participants. Cox proportional hazards models were used to estimate hazard ratios for all-cause mortality. Conclusions: The PNI is a protective factor in patients with CKD, with an L-shaped decrease in all-cause mortality with an increasing PNI.
MeSH term(s)	Humans ; Male ; Middle Aged ; Aged ; Aged, 80 and over ; Female ; Nutrition Assessment ; Nutrition Surveys ; Prognosis ; Retrospective Studies ; Nutritional Status ; Renal Insufficiency, Chronic
Language	English
Publishing date	2023-11-06
Publishing country	England
Document type	Journal Article
ZDB-ID	632949-4
ISSN	1525-6049 ; 0886-022X
ISSN (online)	1525-6049
ISSN	0886-022X
DOI	10.1080/0886022X.2023.2264393
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Book ; Online: Does More Advice Help? The Effects of Second Opinions in AI-Assisted Decision Making

Lu, Zhuoran / Wang, Dakuo / Yin, Ming

2024

Abstract: AI assistance in decision-making has become popular, yet people's inappropriate reliance on AI often leads to unsatisfactory human-AI collaboration performance. In this paper, through three pre-registered, randomized human subject experiments, we explore ...

Abstract	AI assistance in decision-making has become popular, yet people's inappropriate reliance on AI often leads to unsatisfactory human-AI collaboration performance. In this paper, through three pre-registered, randomized human subject experiments, we explore whether and how the provision of {second opinions} may affect decision-makers' behavior and performance in AI-assisted decision-making. We find that if both the AI model's decision recommendation and a second opinion are always presented together, decision-makers reduce their over-reliance on AI while increase their under-reliance on AI, regardless whether the second opinion is generated by a peer or another AI model. However, if decision-makers have the control to decide when to solicit a peer's second opinion, we find that their active solicitations of second opinions have the potential to mitigate over-reliance on AI without inducing increased under-reliance in some cases. We conclude by discussing the implications of our findings for promoting effective human-AI collaborations in decision-making.
Keywords	Computer Science - Human-Computer Interaction ; Computer Science - Artificial Intelligence
Subject code	006
Publishing date	2024-01-13
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Decoding AI's Nudge

Li, Zhuoyan / Lu, Zhuoran / Yin, Ming

A Unified Framework to Predict Human Behavior in AI-assisted Decision Making

2024

Abstract: With the rapid development of AI-based decision aids, different forms of AI assistance have been increasingly integrated into the human decision making processes. To best support humans in decision making, it is essential to quantitatively understand how ...

Abstract	With the rapid development of AI-based decision aids, different forms of AI assistance have been increasingly integrated into the human decision making processes. To best support humans in decision making, it is essential to quantitatively understand how diverse forms of AI assistance influence humans' decision making behavior. To this end, much of the current research focuses on the end-to-end prediction of human behavior using ``black-box'' models, often lacking interpretations of the nuanced ways in which AI assistance impacts the human decision making process. Meanwhile, methods that prioritize the interpretability of human behavior predictions are often tailored for one specific form of AI assistance, making adaptations to other forms of assistance difficult. In this paper, we propose a computational framework that can provide an interpretable characterization of the influence of different forms of AI assistance on decision makers in AI-assisted decision making. By conceptualizing AI assistance as the ``{\em nudge}'' in human decision making processes, our approach centers around modelling how different forms of AI assistance modify humans' strategy in weighing different information in making their decisions. Evaluations on behavior data collected from real human decision makers show that the proposed framework outperforms various baselines in accurately predicting human behavior in AI-assisted decision making. Based on the proposed framework, we further provide insights into how individuals with different cognitive styles are nudged by AI assistance differently. Comment: AAAI 2024
Keywords	Computer Science - Human-Computer Interaction ; Computer Science - Artificial Intelligence
Subject code	006
Publishing date	2024-01-11
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Selective Methylation of Nucleosides via an

Jin, Xiao-Yang / He, Yin-Ming / Hui, Tian-He / Liu, Li / Cheng, Liang

The Journal of organic chemistry

2024 Volume 89, Issue 5, Page(s) 3597–3604

Abstract: A very mild and efficient procedure has been developed for the preparation ... ...

Abstract	A very mild and efficient procedure has been developed for the preparation of
Language	English
Publishing date	2024-02-15
Publishing country	United States
Document type	Journal Article
ZDB-ID	123490-0
ISSN	1520-6904 ; 0022-3263
ISSN (online)	1520-6904
ISSN	0022-3263
DOI	10.1021/acs.joc.3c02578
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