Cheng Lin and his life Yin, H M; Li, Y Q
Zhōnghuá yīshĭ zázhì,
2022-Nov-28, Letnik:
52, Številka:
6
Journal Article
Cheng Lin, a famous doctor in the late Ming and early Qing Dynasties, had a great reputation with his medical achievements. According to the prefaces and postscripts in a variety of books and local ...records, he was born earlier than 1616 and died later than 1700 in Huaitang in She Xian. He learned medicine from his uncle Cheng Jingtong when he was young. After that, he learned from the famous doctor Yu Chang. He visited Kaifeng, Hangzhou, Suzhou and Yangzhou, and made friends with many then celebrities, such as Zhou Lianggong, Lin Sihuan and You Tong. He left many medical writings, such as
He was also good at painting and seal cutting. His family, the Cheng's, in Huaitang in Xin'an, had many off-springs who became famous doctors, such as Cheng Jin, Cheng Jie, Cheng Yandao, and Cheng Yingmao, with their medical history continuing up to the present day.
Objectives
Within the large topic of naming disorders, an important and separated chapter belongs to proper names. Defects of proper naming could be a selective linguistic problem. Sometimes, it ...includes names belonging to various kinds of semantically unique entities, but other times, it has been observed for famous people proper names only. According to Bruce and Young’s model, different stages allow to recognize, identify, and name famous people from their faces and voices, subsuming different anatomical pathways, both in right temporal lobe, and their different efficiency in this task. The present study aimed to report the normative data concerning the naming of the same famous people from voice and face.
Subjects and methods
One hundred fifty-three normal subjects underwent a test in which they were requested to name famous people from their face and from their voice. The stimuli belonged to the previously published Famous People Recognition Battery.
Results
The mean percentage score on naming from face was 84.42 ± 12.03% (range 55.26–100%) and the mean percentage score on naming from voice was 66.04 ± 16.81% (range 28.13–100%). The difference observed in performance by face and by voice resulted significant (t|
153
= 15.973;
p
< 0.001). Regression analyses showed that the percentage score obtained on naming from faces was predicted by education, whereas naming from voice was predicted by education and gender.
Discussion
Naming from voice is more difficult than from face, confirming a different difficulty of the two tasks. Education showed high predicting value for faces and less for voices, whereas gender contributed to predict results only for voices.
In September 1957, Francis Crick gave a lecture in which he outlined key ideas about gene function, in particular what he called the central dogma. These ideas still frame how we understand life. ...This essay explores the concepts he developed in this influential lecture, including his prediction that we would study evolution by comparing sequences.
Michelangelo’s unparalleled frescoes in the Sistine Chapel have been traditionally construed to represent the traditional, Roman Catholic interpretation of the Seven Days of Creation in the Book of ...Genesis and the Last Judgement. Indeed, in September 2018, Pope Francis I offered a benediction for the Vatican’s art treasures and for “those who contributed to the Church’s history through art.” A number of studies have suggested that Michelangelo concealed anatomic information about the human brain in his renderings of God and biblical prophets and saints. Was the anatomic content of these images purposeful or coincidental? Was the anatomy intentionally concealed? Is there a common pattern with symbolic connotations in the anatomic representations? Was Michelangelo sending an encoded message? In this study, we analyze the imagery in the Sistine frescoes in light of the literature surrounding Michelangelo’s artistry with a particular interest in the relationship between the certain purported anatomic depictions and details demonstrable using scientific and anatomic techniques available during the High Renaissance.
Research into relationships between victim-generated content, abuse received, and observer characteristics when considering Twitter abuse has been limited to male victims. We evaluated participant ...perceptions of female celebrity victims and abuse received on Twitter. We used a 3 (Initial Tweet Valence; negative, neutral, positive) × 2 (Abuse Volume; low, high) repeated measures design and online survey method. Participants were shown tweets generated by six female celebrities, counterbalanced such that each participant saw each celebrity in one Valence-Volume condition. Stimuli were presented across six 'lists' such that celebrity 'victims' could be rotated across Valence-Volume pairings. Participants rated-per target stimulus-the level of blame attributable to the victim and the perceived severity of the incident. Furthermore, participants were asked to complete a Dark Tetrad scale-measuring their Machiavellianism, Narcissism, Psychopathy, and Sadism. Analyses determined that victim-blaming was influenced by victim Initial Tweet Valence (greater victim-blaming associated with more-negative content) and observer Machiavellianism. Perceived severity was influenced by victim Initial Tweet Valence, Volume of Abuse received, and observer Machiavellianism. Results were consistent with previous research involving male celebrity victims. Further research is needed to understand the contributions of participants' hostile and benevolent sexism, as well as the role of victim attractiveness.
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