27 research outputs found
The Effect of Overt/Covert Unsuccessful Retrieval upon Subsequent Learning
μ λ΅μ λ§νκΈ° μ΄λ €μ΄ λ¬Έμ λ₯Ό μ μνμ¬ νλ¦° μΈμΆμ μ λνλ μ¬μ μν(pretesting) μ°κ΅¬λ€μ, νλ¦° μΈμΆμ΄ νμ΅μ μ΄μ§νλ κ²°κ³Όλ₯Ό 보μ¬μ£Όκ³ μλ€. νμ§λ§ νλ¦° μΈμΆ μ€ μ€λ΅μ μ°μΆνλ κ²μ΄ νμ΅μ μ΄λ ν μν₯μ λ―ΈμΉλμ§μ λνμ¬ λ³΄κ³ λ κ²°κ³Όλ€μ μΌκ΄λμ§ μλ€. λ³Έ μ°κ΅¬μμλ λ¨Όμ μ€λ΅ κ°μμ νμ μκ°μ 체κ³μ λ³νμ λ°λ₯Έ νμ΅ ν¨κ³Ό μ°¨μ΄λ₯Ό μμ보μλ€. μ€ν 1, 2μμλ μ°Έκ°μμ μ€λ΅ λ°μμλ₯Ό 1κ°μ 3κ°λ‘ λ€λ₯΄κ² λ³νμμΌ°λλ° μ€λ΅ μ°μΆ μ¦κ°μ λ°λ₯Έ μν μ°¨μ΄κ° λνλμ§ μμλ€. κ·Έλ¬λ μ°Έκ°μμκ² λ΄μ μΈμΆ μ¦, μ€λ΅μ μ°μΆνμ§ μκ³ μ¬μ λ¬Έμ μ κ΄λ ¨λ μ 보λ₯Ό νμλ§ νλλ‘ μ§μν μ€ν 3, 4μμλ νμμ λ§μ΄ ν μλ‘ μνλ ν¨κ» μ¦κ°νμλ€. λ³Έ μ°κ΅¬μμλ λν κΈ°μ‘΄ μ°κ΅¬λ€λ³΄λ€ μ§μ°μκ°μ΄ κΈ΄ κ²½μ°μλ μ¬μ μν ν¨κ³Όκ° λνλλμ§ μμ보μλλ°, λ΄μ νμλ§ νλ κ²½μ° νμ΅ ν¨κ³Όκ° μΌμ£ΌμΌ λ€μλ νμΈλμλ€. κ·Έλ¬λ μΈμ μΌλ‘ μ€λ΅μ μ°μΆν κ²½μ° μ¦μκ²μ¬μμ λνλ¬λ ν¨κ³Όκ° μΌμ£ΌμΌ ν μ§μ°κ²μ¬μμλ μ¬λΌμ‘λ€. μ΄λ¬ν κ²°κ³Όλ€μ μ¬μ μνμμ κ΄λ ¨λ μ 보λ₯Ό λ₯λμ μΌλ‘ νμνλ κ²μ΄ νμ΅μ μ΄μ§νλ€λ νμ μ§ν© μ΄λ‘ (Grimaldi & Karpick, 2012)μ μ§μ§νλ λμμ, μ€λ΅μ μ§μ μ°μΆνλ©΄ μ§μ°κ²μ¬μμ μ λ΅κ³Ό μΈμΆ κ²½μμ μΌμΌμΌ νμ΅μ λ°©ν΄ν μ μμμ μμ¬νλ€.The pretesting effect refers to the enhancement of learning due to unsuccessful retrieval upon being asked a question that is not easily answered, However, the results of research on the effect of overt retrieval on learning, have not been consistent. Therefore, the present study sought to clarify such confusion. We examined whether memory enhancement is affected by the number of wrong answers generated by the examinees and by the duration of retrieval. Four experiments were carried out with college students as participants. In Experiments 1 and 2, we manipulated the number of unsuccessful retrievals to either 1 or 3, and observed that there was no difference in performance. In Experiments 3 and 4, participants were asked to think of possible answers without overt responses. The results showed that the performance was better for those who were asked to think of more answers. The present study also examined whether pretesting effect is found even after a week`s duration. After a week, pretesting effect was observed in case of the covert retrieval group; however it did not last for the overt retrieval group. These results support the search set theory by Grimaladi and Karpicke (2012) which states that active exploration of related material promotes learning. The present study also suggests that overt retrieval brings about retrieval competition and interferes with the retrieval of correct responses, and thus disrupts learning.OAIID:oai:osos.snu.ac.kr:snu2012-01/102/2010001075/3SEQ:3PERF_CD:SNU2012-01EVAL_ITEM_CD:102USER_ID:2010001075ADJUST_YN:YEMP_ID:A078266DEPT_CD:207CITE_RATE:0FILENAME:20211147.pdfDEPT_NM:μ¬λ¦¬νκ³ΌEMAIL:[email protected]_YN:NCONFIRM:
Stabilization of whole-body motions for humanoid robots
Thesis(master`s)--μμΈλνκ΅ λνμ :κΈ°κ³ν곡곡νλΆ,2006.Maste
Systems epidemiological approach for the effect of physical activity on prevention of cardiometabolic diseases
νμλ
Όλ¬Έ(λ°μ¬) -- μμΈλνκ΅λνμ : μκ³Όλν μκ³Όνκ³Ό, 2022.2. μ΅μ§μ½.Background:
A number of studies have shown that physical activity reduces the risk of cardiometabolic diseases including type 2 diabetes, hypertension, and dyslipidemia. However, traditional epidemiology has a major limitation called the βblack boxβ paradigm in that it cannot fully understand complex biological mechanisms. Recently, high-throughput data including genome, metabolome, and microbiome have become available for epidemiological studies as omics techniques have been developed. The availability of these multi-omics biomarkers and the need for understanding biological processes lead to new concepts of systems epidemiology. We aimed to conduct this study following the systems epidemiological approach to understanding the biological process of the effect of physical activity on the prevention of cardiometabolic diseases. First, we examined the indirect effect of clinical parameters as mediators between physical activity and risk of cardiometabolic diseases and visualized the complex relationships between physical activity, various clinical parameters, and risk of cardiometabolic disease through the network. Second, we examined whether the changes in clinical parameters over time differ according to changes in physical activity behavior. Finally, we examined the changes in clinical parameters, metabolites, and the microbiome during physical activity intervention, and visualized integrated relationships at the multi-omics level through a network.
Methods:
The first study included 17,053 subjects aged 40β69 years in the Health Examinees-Gem (HEXA-G) study from 2004 to 2012. Participation or not in regular exercise at baseline and diagnosis of type 2 diabetes, hypertension, and dyslipidemia at follow-up were investigated by questionnaires. Anthropometric measures and laboratory tests from blood were conducted and data on 42 clinical parameters were collected. We examined the mediation effect of clinical parameters using mediation analyses. Clinical parameter networks were constructed based on the significant differential correlations (p < 0.05) between the exercise and non-exercise groups in men and women, respectively.
The second study was conducted using a community-based cohort (Ansan Ansung cohort) from the 3rd wave to the 5th wave. A total of 3,962 men and women aged 40-69 were included and all analyses were performed in men and women, respectively. Participation or not in regular exercise was investigated by questionnaire like as HEXA-G study. According to the combination of regular exercise in each of the 3rd, 4th, and 5th waves (3rd/4th/5th), two groups of no changes in behavior (N/N/N, Y/Y/Y) and 4groups showed changes in behavior were defined. Twenty-three clinical parameters were obtained by anthropometric measurements and laboratory tests from blood. The relative changes (%) in clinical parameters were calculated from the 3rd wave to the 5th wave. The relative changes in clinical parameters according to the patterns and changes in regular exercise behavior were examined by the LSmeans and the general linear model.
The third study included 14 middle-aged women who completed a physical activity intervention. The intervention was conducted with an exercise period of 3 months and a daily life period of 3 months. The amount of objective physical activity was measured by an accelerometer for 2 weeks each during the exercise period and daily life period. Blood collection, fecal collection, measurements of blood pressure, and test of exercise ability were performed three times: at the enrollment, after exercise period, and after daily life period. Glycemic indicators and lipid-related markers were obtained from blood, and the concentration of 208 blood metabolites was measured through targeted metabolomics. Microbiome data was obtained from fecal samples by 16s rRNA sequencing. The difference in biomarkers before and after the intervention period was examined by the Wilcoxon rank-sum test. The integrated relationship between biomarkers in omics level was examined by Spearman correlation coefficient and visualized via the network.
Results:
We observed significant mediators in 14 and 16 of the clinical parameters in men and women, respectively from the first study. Among the mediators, triglyceride level was a noteworthy mediator in decreasing the risk of CMD with exercise, explaining 23.79% in men and 58.20% in women. A group in which TG is linked to low-density lipoprotein (LDL) cholesterol and high-density lipoprotein (HDL) cholesterol was commonly observed in men and women through the clinical parameter network. Body composition-related markers were likely to play major roles in men, while obesity-related markers seemed to be key factors in women.
In the second study, when comparing the group that did not exercise consistently (N/N/N), and the group that did exercise consistently (Y/Y/Y), there was a difference of changes in waist circumference, hip circumference, and fasting blood glucose in men, and changes in 6 body composition-related markers and 2 lipid-related markers showed differences in women. When the regular exercise group changed into the non-exercise behavior, the fasting blood glucose was greater increased in men, and the lipid-related markers were more increased in women. Conversely, when the non-exercise group changed into participating in regular exercise, body fat-related markers, fasting insulin, lipid-related markers were increased less or decreased in men, and body composition-related markers decreased less in women. Noteworthy, the triglycerides increased less when the non-exercise behavior changed into a regular exercise in both men and women.
In the intervention study, physical activity decreased during the daily life period compared with the exercise period. After the exercise period, blood pressure, HbA1c, and LDL cholesterol were reduced, and they tended to increase again after the daily life period. During the exercise period, 40 metabolites showed significant changes, and they were correlated with changes in clinical parameters such as blood pressure, HbA1c, and LDL cholesterol in the network.
Conclusion:
This study used various biomarkers including omics to understand the understanding the biological processes of the effect of physical activity on the prevention of cardiometabolic diseases in terms of systems epidemiology. Moreover, not only the potential mechanisms centered on lipid-related markers but also relationships between clinical parameters and metabolites could be suggested by visualization of the integrated relationship between biomarkers in the network. The network analysis which used in this study can be applied to data obtained from untargeted metabolomics or whole-genome sequencing of the microbiome and contribute to identifying novel biomarkers or suggesting more detailed mechanisms.μ°κ΅¬ λ°°κ²½:
μ 체νλμ΄ μ¬νκ΄ λμ¬μ§ν(cardiometabolic diseases)μ λ°μμνμ κ°μμν¨λ€λ κ²μ μλ§μ μνμ°κ΅¬λ₯Ό ν΅ν΄ μλ €μ Έ μλ€. κ·Έλ¬λ ν΅μμ μΈ μνμ°κ΅¬λ‘λ μ§λ³ μλ°© κΈ°μ μ κ΄μ¬λ μλ¬Όνμ μμΈλ€μ 볡ν©μ μΈ κ΄κ³λ₯Ό μ΄ν΄νκΈ° μ΄λ ΅λ€λ νκ³κ° μλ€. μ΅κ·Ό μνμ°κ΅¬μμλ μ μ 체(genome)λ₯Ό ν¬ν¨ν λμ¬μ²΄(metabolome) λλ μ₯λ΄λ―Έμλ¬Όκ΅°μ μ μ μ λ³΄μΈ λ§μ΄ν¬λ‘λ°μ΄μ΄(microbiome) λ±μ λμ©λ μ€λ―Ήμ€(omics) λ°μ΄ν°λ€μ΄ μ΄μ© κ°λ₯νκ² λ¨μ λ°λΌ, λ€μμ€μ μ¬λ¬ μ체μ§ν λ°μ΄ν°λ€μ μ΄μ©νμ¬, μ΄λ€κ°μ ν΅ν©μ μΈ κ΄κ³λ₯Ό νμ
νκ³ μκ°μ νλ¦μ λ°λ₯Έ λ³νλ₯Ό νμΈνλ μμ€ν
μνμ μ κ·Όμ νμμ±μ΄ μ κΈ°λκ³ μλ€. λ³Έ μ°κ΅¬μμλ μ 체νλμ μ¬νκ΄ λμ¬μ§ν μλ°© ν¨κ³Όμ κ΄λ ¨νμ¬ κΈ°μ‘΄ μνμ°κ΅¬μ νκ³μ μ 극볡νκ³ λ³΅μ‘ν μλ¬Όνμ κΈ°μ μ μ΄ν΄νκΈ° μν΄ μμ€ν
μνμ μ κ·Ό λ°©λ²μΌλ‘ λ€μκ³Ό κ°μ΄ μ°κ΅¬λ₯Ό μννμλ€. 첫 λ²μ§Έλ‘, μ 체νλκ³Ό μ¬νκ΄ λμ¬μ§ν λ°μ μν μ¬μ΄μ 맀κ°μμΈμΌλ‘ μμ©νλ μμμ§νλ€μ κ°μ ν¨κ³Όλ₯Ό νμΈνκ³ , κ·Έλ€ μ¬μ΄μ 볡ν©μ μΈ κ΄κ³λ₯Ό λ€νΈμν¬λ‘ 보μ¬μ£Όκ³ μ νμλ€. λ λ²μ§Έλ‘λ μκ°μ νλ¦μ λ°λ₯Έ μμμ§νλ€μ λ³νκ° μ 체νλμ νν λ³ν μμμ λ°λΌ μ°¨μ΄κ° μλμ§λ₯Ό μ΄ν΄λ³΄κ³ μ νμλ€. λ§μ§λ§μΌλ‘, μ΄λ μ€μ¬μ°κ΅¬λ₯Ό ν΅ν΄ μμμ§νλΏ μλλΌ, λμ¬μ²΄μ λ§μ΄ν¬λ‘λ°μ΄μ΄μ λ³νλ₯Ό νμΈνκ³ multi-omics μμ€μμ ν΅ν©μ μΈ κ΄κ³λ₯Ό λ€νΈμν¬λ₯Ό ν΅ν΄ 보μ¬μ£Όκ³ μ νμλ€.
μ°κ΅¬ λ°©λ²:
첫 λ²μ§Έ μ°κ΅¬λ νκ΅ λμκΈ°λ° μ½νΈνΈ(HEXA-G)μ 40-69μΈ μ±μΈ 17,053λͺ
μ ν¬ν¨νμμΌλ©° λͺ¨λ λΆμμ λ¨λ
κ°κ°μμ μννμλ€. κΈ°λ°μ‘°μ¬μμ κ·μΉμ μΈ μ΄λ μ¬λΆλ μ€λ¬ΈμΌλ‘ μ‘°μ¬νμκ³ , μ 체κ³μΈ‘κ³Ό νμ‘κ²μ¬λ‘ μ»μ΄μ§ μμμ§ν 42κ°λ₯Ό λΆμμ μ΄μ©νμλ€. μ¬νκ΄ λμ¬μ§ν λ°μ μ¬λΆλ₯Ό μ μνκΈ° μν΄ μΆμ μ‘°μ¬μμ μ€λ¬ΈμΌλ‘ μ‘°μ¬λ λΉλ¨, κ³ νμ, μ΄μμ§μ§νμ¦μ μ§λ¨ μ¬λΆμ μ§λ¨ μ°λλ₯Ό μ¬μ©νμλ€. κ·μΉμ μΈ μ΄λκ³Ό μμμ§νκ°μ μ°κ΄μ±μ μΌλ° μ ν λͺ¨λΈμ ν΅ν΄ νκ°νμκ³ , κ·μΉμ μ΄λκ³Ό λμ¬μ§ν λ°μμν κ°μ μ°κ΄μ± λ° μμμ§νλ€κ³Ό λμ¬μ§ν λ°μμν κ°μ μ°κ΄μ±μ μ½μ€λΉλ‘μνλͺ¨λΈμ ν΅ν΄ νμΈνμλ€. 맀κ°λΆμμ ν΅ν΄ κ·μΉμ μΈ μ΄λκ³Ό λμ¬μ§ν λ°μμν μ¬μ΄μ μ°κ΄μ± μΆμ μΉ μ€ μμμ§νλ€μ΄ κ°λ κ°μ ν¨κ³Όλ₯Ό μΆμ νμλ€. μ΄λκ·Έλ£Ήκ³Ό λΉμ΄λκ·Έλ£Ή μ¬μ΄μμ μ μν μ°¨μ΄λ₯Ό 보μ΄λ μ°¨λ³μ μκ΄κ΄κ³(differential correlation)λ₯Ό κΈ°λ°μΌλ‘ μμμ§ν λ€νΈμν¬λ₯Ό ꡬμΆνμλ€.
λ λ²μ§Έ μ°κ΅¬λ νκ΅ μ§μμ¬νκΈ°λ°(μμ° μμ±) μ½νΈνΈ 3κΈ°λΆν° 5κΈ°κΉμ§μ μλ£λ₯Ό μ΄μ©νμλ€. 40-69μΈ μ±μΈ 3,962λͺ
μ ν¬ν¨νμμΌλ©° λΆμμ λ¨λ
κ°κ°μμ μννμλ€. λμκΈ°λ° μ½νΈνΈμμμ λ§μ°¬κ°μ§λ‘ μ€λ¬ΈμΌλ‘ κ·μΉμ μΈ μ΄λ μ¬λΆλ₯Ό μ‘°μ¬νμκ³ , 3κΈ°, 4κΈ°, 5κΈ° κ°κ°μμμ κ·μΉμ μΈ μ΄λ μ¬λΆ μ‘°ν©μ λ°λΌ(3κΈ°/4κΈ°/5κΈ°) μ΄λ νν λ³νκ° μλ 2κ° κ·Έλ£Ή(N/N/N, Y/Y/Y)κ³Ό μ΄λ νν λ³νλ₯Ό λ³΄μΈ 4κ°μ κ·Έλ£Ή(N/N/Y, N/Y/Y, Y/Y/N, Y/N/N)μ μ μ νμλ€. λ³Έ μ°κ΅¬μμλ μ 체κ³μΈ‘κ³Ό νμ‘κ²μ¬λ‘ μ»μ΄μ§ 23κ°μ μμμ§νλ₯Ό μ¬μ©νμκ³ κ° μμμ§νλ€μ λνμ¬ 3κΈ° λλΉ 5κΈ°μμμ μλλ³νλ(%)μ κ³μ°νμλ€. λμ΄λ₯Ό 보μ ν νκ· (LSmeans)κ³Ό μΌλ° μ ν λͺ¨λΈμ ν΅ν΄ κ·μΉμ μ΄λ μ°Έμ¬ νν μμμ λ°λ₯Έ μμμ§ν λ³νλμ μ°¨μ΄λ₯Ό νμΈνμλ€.
μΈ λ²μ§Έ μ°κ΅¬λ μ 체νλ μ€μ¬μ°κ΅¬λ₯Ό μλ£ν 40-69μΈ μ±μΈ μ¬μ± 14λͺ
μ ν¬ν¨νμλ€. μ€μ¬μ°κ΅¬λ 3κ°μμ μ΄λκΈ°κ°κ³Ό 3κ°μμ μΌμμνκΈ°κ°μΌλ‘ μ§ννμμΌλ©°, μ΄λκΈ°κ°κ³Ό μΌμμνκΈ°κ° κ°κ°μμ 2μ£Όμ© κ°μλκ³λ‘ κ°κ΄μ μ 체νλλμ μΈ‘μ νμλ€. μ°κ΅¬ λ±λ‘ μμ , μ΄λκΈ°κ° μ’
λ£ μμ , μΌμμνκΈ°κ° μ’
λ£ μμ μ΄ 3νμ κ±Έμ³ μ±ν, μ±λ³, νμ μΈ‘μ λ° μ΄λ λ₯λ ₯ νκ°κ° μ΄λ£¨μ΄μ‘λ€. νμ‘μΌλ‘λΆν° νλΉ μ§ν, μ§μ§ μ§νλ₯Ό νκ°νκ³ , νμ λμ¬μ²΄ λΆμ(targeted metabolomics)μ ν΅ν΄ 208κ°μ§ νμ€ λμ¬μ²΄μ λλλ₯Ό μΈ‘μ νμλ€. 16s rRNA sequencing λ°©λ²μ ν΅ν΄ λλ³μλ£μ microbiome λ°μ΄ν°λ₯Ό ν보νμλ€. μμ½μ¨ μμ ν© κ²μ μ ν΅ν΄ μ€μ¬ μ νμ μ체μ§ν μ°¨μ΄λ₯Ό κ²μ νμκ³ , μ€νΌμ΄λ§ μκ΄κ³μλ‘ μ체μ§ν λ³νλ€κ°μ κ΄κ³λ₯Ό νμΈνμλ€.
μ°κ΅¬ κ²°κ³Ό:
λμκΈ°λ° μ½νΈνΈμ μμμ§ν κ°μ ν¨κ³Όλ₯Ό νκ°ν μ°κ΅¬μμλ 42κ°μ μμμ§νλ€ μ€ λ¨μμμ 14κ°, μ¬μμμ 16κ°μ μμμ§νκ° κ·μΉμ μ΄λκ³Ό μ¬νκ΄ λμ¬μ§ν λ°μ μνκ°μ μ°κ΄μ±μμ μ μλ―Έν κ°μ ν¨κ³Όλ₯Ό κ°λλ€λ κ²μ νμΈνμλ€. κ·Έ μ€μμλ μ€μ±μ§λ°©(triglyceride (TG))μ΄ λ¨λ
λͺ¨λμμ κ°μ₯ ν° λΆμ¨λ‘ 맀κ°ν¨κ³Όλ₯Ό μ€λͺ
νμλ€(λ¨μ: 23.79%, μ¬μ: 58.20%). λ¨λ
κ°κ°μμ ꡬμΆλ μμμ§ν λ€νΈμν¬λ‘λΆν° triglycerideκ° low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterolκ³Ό μ°κ²°λ κ΅°μ§μ΄ 곡ν΅μ μΌλ‘ κ΄μ°°λμλ€. λ°λ©΄, λ¨μμμλ κ·Όμ‘μ μ€μ¬μΌλ‘ν 체μ±λΆκ΄λ ¨ μ§νλ€μ΄ κ΅°μ§μ μ΄λ£¨μκ³ , μ¬μμμλ 볡λΆμ§λ°©λμ μ€μ¬μΌλ‘ν λΉλ§κ΄λ ¨ μ§νλ€μ΄ μ£Όμ κ΅°μ§μ μ΄λ£¨μλ€.
μ§μμ¬ν μ½νΈνΈμμ μ§νλ μ°κ΅¬μμλ μκ°μ λ°λ₯Έ μ΄λ νν λ³νμ λ°λ₯Έ μμμ§νμ λ³νλ₯Ό νκ°νμλ€. μ΄λμ μ§μμ μΌλ‘ νμ§ μμλ κ·Έλ£Ήκ³Ό μ΄λμ μ§μμ μΌλ‘ νλ κ·Έλ£Ήμ λΉκ΅νμμ λ, λ¨μ±μμλ ν리λλ , μλ©μ΄λλ , 곡볡νλΉ λ³νλμ΄ μ°¨μ΄λ₯Ό 보μκ³ , μ¬μ±μμλ 체μ±λΆκ΄λ ¨ μ§ν 6κ°μ μ§μ§κ΄λ ¨ μ§ν 2κ°κ° μ°¨μ΄λ₯Ό 보μλ€. κΈ°λ°μ‘°μ¬ μμ μμ κ·μΉμ μΈ μ΄λμ νμΌλ μΆμ μ‘°μ¬μμ μ΄λμ μ νλ ννλ‘ λ³νν κ²½μ°, λ¨μ±μμλ 곡볡νλΉμ μ¦κ° μ λκ° λ 컀μ§μ νμΈνμκ³ , μ¬μ±μμλ μ§μ§κ΄λ ¨ μ§νμ μ¦κ°λμ΄ λμ± μ»€μ§λ κ²μΌλ‘ λνλ¬λ€. κΈ°λ°μ‘°μ¬ μμ μμ κ·μΉμ μΈμ΄λμ νμ§ μμμΌλ μΆμ μ‘°μ¬μμ κ·μΉμ μΈ μ΄λμ μ°Έμ¬νλ ννλ‘ λ³ννλ κ²½μ°, λ¨μ±μμλ 체μ§λ°© μ§νλ€κ³Ό 곡볡 μΈμλ¦° μμΉ, μ§μ§ μ§νλ€μ΄ λ μ¦κ°νκ±°λ μ€νλ € κ°μνμκ³ , μ¬μ±μμλ 체μ±λΆ μ§νλ€μ΄ λ κ°μνμλ€. λ¨λ
곡ν΅μ μΌλ‘ μ΄λμ μ°Έμ¬νλ ννλ‘ λ³ννμμ λ μ€μ±μ§λ°©μ΄ λ μ¦κ°νλ κ²μ νμΈνμλ€.
μ€μ¬μ°κ΅¬μμλ κ°κ΄μ μΌλ‘ μΈ‘μ λ μ 체νλλμ΄ μ΄λκΈ°κ°λ³΄λ€ μΌμμνκΈ°κ°μμ μ μνκ² κ°μνμλ€. μ°κ΅¬ λ±λ‘ μμ κ³Ό λΉκ΅νμ¬ μ΄λκΈ°κ° νμ νμκ³Ό νλΉ μ§νμΈ HbA1c, μ§μ§ μ§ν μ€μμλ LDL μ½λ μ€ν
λ‘€μ΄ κ°μνμκ³ , μ΄λ€μ μΌμμν μ΄νμ λ€μ μ¦κ°νλ κ²μΌλ‘ 보μλ€. μ΄λκΈ°κ° λμ 40κ°μ λμ¬μ²΄κ° μ μν λ³νλ₯Ό 보μμΌλ©° μ΄μ€ 6κ°λ μμμ§νμΈ νμ, HbA1c, LDL μ½λ μ€ν
λ‘€μ λ³νμ μκ΄κ΄κ³κ° μμμ λ€νΈμν¬λ₯Ό ν΅ν΄ νμΈνμλ€.
κ²°λ‘ :
λ³Έ μ°κ΅¬λ μ 체νλμ΄ μ¬νκ΄ λμ¬μ§ν μλ°©μ λ―ΈμΉλ μν₯μ κ΄ν κΈ°μ μ μ΄ν΄νκ³ μ, μμ€ν
μνμ μ κ·ΌμΌλ‘μ λ€μν μ체μ§νλ€μ νμ©νμ¬ μ 체νλκ³Όμ μ°κ΄μ±λΏ μλλΌ μ 체νλμ λ³νμ λ°λ₯Έ μ체μ§νμ λ³ν λ° μ체μ§νλ€ μ¬μ΄μ 볡ν©μ μΈ κ΄κ³λ₯Ό νμΈνμλ€. 맀κ°λΆμμ ν΅ν΄ μ 체νλμΌλ‘ μΈν μ¬νκ΄ λμ¬μ§ν μλ°©μ μμ΄ λ¨λ
λͺ¨λ triglycerideκ° κ°μ₯ ν° λ§€κ°ν¨κ³Όλ₯Ό κ°λλ€λ κ²μ νμΈν μ μμκ³ , λ€νΈμν¬ μκ°νλ₯Ό ν΅ν΄ μ§μ§μ§νλ€ μ¬μ΄μ κΈ°μ μ κ΄κ³λ₯Ό 보μ¬μ€ μ μμλ€. μ΄λ νν λ³νμ λ°λ₯Έ μμμ§νμ λ³νμμλ λ¨λ
곡ν΅μ μΌλ‘ triglycerideμ΄ λ³νκ° νμΈλμμΌλ©°, μ€μ¬μ°κ΅¬λ₯Ό ν΅ν΄μλ νμ, λΉλ¨, μ§μ§ κ΄λ ¨ μμμ§νμ λμ¬μ²΄λ€ μ¬μ΄μ μ μ¬μ μΈ κ΄κ³λ₯Ό μ μν μ μμλ€. μμ€ν
μνμ μ κ·Ό λ°©λ²μ λ§μ μ체μ§νλ€κ³Ό κ΄λ ¨ μμΈ μ¬μ΄μ κ΄κ³λ₯Ό ν΅ν©μ μΌλ‘ λΆμνκ³ μκ°ν ν¨μΌλ‘μ¨ λ³΅μ‘ν κ΄κ³μ λν ν΄μμ ν μ μκ² ν΄μ£Όλ©°, κ΄λ ¨λ κΈ°μ μ λν΄ λ λμ μ΄ν΄λ₯Ό ν μ μλλ‘ ν΄μ€λ€. λν λ³Έ μ°κ΅¬μμ μ μλ κΈ°μ λ€μ μ€νμ°κ΅¬μμ μ§μ€μ μΌλ‘ λ€λ£ΈμΌλ‘μ¨ μ 체νλμ΄ κ±΄κ°μ λ―ΈμΉλ μ΄λ‘μ΄ ν¨κ³Όμ λν κΈ°μ μ λ°νκ³ μ€λͺ
νλλ° νκ±Έμ λ λμκ° μ μμ κ²μΌλ‘ κΈ°λνλ€.μ΄λ‘ i
μ½μ΄ λͺ©λ‘ vii
λͺ©μ°¨ x
ν λͺ©λ‘ xii
κ·Έλ¦Ό λͺ©λ‘ xiv
λΆλ‘ λͺ©λ‘ xvi
1. μ λ‘ 1
1.1 μ°κ΅¬ λ°°κ²½ 1
1.2 μ°κ΅¬ λͺ©μ 20
2. μ°κ΅¬ λ°©λ² λ° κ²°κ³Ό 22
2.1. μ 체νλκ³Ό μ¬νκ΄ λμ¬μ§ν λ°μ μνμμμ μμμ§νλ₯Ό ν΅ν 맀κ°ν¨κ³Ό λ° ν΅ν©μ κ΄κ³ νκ° 22
2.1.1 μ°κ΅¬ λ°©λ² 22
2.1.2 μ°κ΅¬ κ²°κ³Ό 34
2.2. μ 체νλ νν λ³ν μμμ λ°λ₯Έ μμμ§ν λ³ν νκ° 45
2.1.1 μ°κ΅¬ λ°©λ² 45
2.1.2 μ°κ΅¬ κ²°κ³Ό 55
2.3. μ΄λ μ€μ¬μ λ°λ₯Έ λ€μ€ μ€λ―Ήμ€ μ체μ§ν λ³ν λ° ν΅ν©μ κ΄κ³ νκ° 68
2.1.1 μ°κ΅¬ λ°©λ² 68
2.1.2 μ°κ΅¬ κ²°κ³Ό 77
3. κ³ μ°° 91
4. κ²°λ‘ 103
μ°Έκ³ λ¬Έν 105
λΆλ‘ 119
Abstract 177λ°
Legal Argument Analysis Using Bayesian Network
μ΄ κΈμ μ΄κ³ μνμμ μ§λ 2013λ
5μ 11μΌ μμΈλνκ΅ λ²νμ°κ΅¬μ μ£Όμ΅ λ, λ§μ, λ²οΌλμΈμ§κ³Όνκ³Ό λ²μ μΈν°νμ΄μ€ νμ λνμμ λ°ννμΌλ©°, κ·Έ ν λ΄μ©μ μΆκ°νμ¬ λ
Όλ¬ΈμΌλ‘ μμ±νμμ.The criminal justice system places importance on identifying substantive truth. However, throughout all ages, uncovering substantive truth has been a difficult process, often plagued by errors and leading to erroneous conclusions. In recent years, both in Korea and abroad, many researchers are gathering information about wrong judgments in actual trials, in order to find ways to reduce them. With this as the backdrop, this study focuses on applying the legal argument analysis using Bayesian network to a few actual legal cases, and examines its applicability in the criminal justice system. The theoretical usefulness of Bayesian analysis has been recognized in various fields; however, its application has been difficult because of the complexity of calculation. Fortunately, recent advances in statistical techniques and development of software such as AgenaRisk have made it easier to apply Bayesian analysis. This study examines three legal cases using AgenaRisk program and ultimately seeks to increase the applicability of Bayesian analysis in the legal system.μ²λ²κ³Ό κ΄λ ¨λ λ² μ λμΈ νμ¬μ¬λ²μ μ°¨μμλ μ¬κ±΄μ λν μ€μ²΄μ μ§μ€ κ·λͺ
μ μ€μνλ€. κ·Έλ°λ° μλ μ§κΈμ΄λ μ€μ²΄μ μ§μ€μ λ°νκΈ°λ μ½μ§ μμ λΏλ§ μλλΌ, μ’
μ’
κ·Έ κ³Όμ μμ μ€λ₯λ₯Ό μΌμΌν€κΈ°λ νκ³ κ²°κ³Όμ μΌλ‘ μλͺ»λ νλ¨μ μ΄λ₯΄κΈ°λ νλ€. κ΅λ΄μΈμμ μ€νμ μ€νλ₯Ό νμΈνκ³ μ νμ λλλ μ°κ΅¬κ° μ΅κ·Ό νλ°νκ² μ΄λ£¨μ΄μ§κ³ μμΌλ©°, μ΄λ₯Ό λ°νμΌλ‘ μ€νμ μ€μ΄κΈ° μν μ€μ§μ λ°©μμ΄ λͺ¨μλκ³ μλ€. μ΄λ¬ν λ°°κ²½μμ, λ³Έ μ°κ΅¬λ μλ―Έ νκ³μμλ λΉκ΅μ μ€λ μ λΆν° νμλμ΄ μμμλ λΆκ΅¬νκ³ κ΅λ΄μμλ μλμ μΌλ‘ μμν λ² μ΄μ§μ λ²μ λ
Όμ¦ λ°©μμ μκ°νκ³ κ΅λ΄μΈμ μ€μ μ¬κ±΄λ€μ λμμΌλ‘ κ·Έ μ μ©κ°λ₯μ±μ μμλ³΄κ³ μ νμλ€. λ² μ΄μ§μ λΆμλ²μ μ΄λ‘ μ μ μ©μ±μ λμ²μμ μΈμ λμμ§λ§, κ³μ°μ 볡μ‘μ± λλ¬Έμ μ λ¬Έκ°λ€μ‘°μ°¨ μ΄ λΆμλ²μ μ μ©νλ κ²μ΄ μ½μ§ μμλ€. μ΅κ·Ό ν΅κ³νμ κΈ°λ²κ³Ό μ΄λ₯Ό μ²λ¦¬νλ μννΈμ¨μ΄μ λ°μ μΌλ‘ κ·Έ μ¬μ©κ°λ₯μ±μ΄ κ·Έ μ΄λ λ보λ€λ μ λ§νλ€. λ³Έ μ°κ΅¬μμλ AgenaRisk νλ‘κ·Έλ¨μ μ΄μ©νμ¬ κ΅λ΄μΈμ μ¬νμ¬λ‘λ₯Ό λΆμνκ³ , κΆκ·Ήμ μΌλ‘ λ²μ λ
Όμ¦μμ λ² μ΄μ§μ λΆμλ²μ μ μ© κ°λ₯μ±μ λμ΄κ³ μ νλ€.λ³Έ μ°κ΅¬λ νκ΅μ°κ΅¬μ¬λ¨ 2013λ
νμ κ°μ΅ν©μ°κ΅¬ μ§μμ¬μ
μ μΌνμΌλ‘ μ§νλμμ
Systems epidemiological approach for the effect of physical activity on prevention of cardiometabolic diseases
Background:
A number of studies have shown that physical activity reduces the risk of cardiometabolic diseases including type 2 diabetes, hypertension, and dyslipidemia. However, traditional epidemiology has a major limitation called the βblack boxβ paradigm in that it cannot fully understand complex biological mechanisms. Recently, high-throughput data including genome, metabolome, and microbiome have become available for epidemiological studies as omics techniques have been developed. The availability of these multi-omics biomarkers and the need for understanding biological processes lead to new concepts of systems epidemiology. We aimed to conduct this study following the systems epidemiological approach to understanding the biological process of the effect of physical activity on the prevention of cardiometabolic diseases. First, we examined the indirect effect of clinical parameters as mediators between physical activity and risk of cardiometabolic diseases and visualized the complex relationships between physical activity, various clinical parameters, and risk of cardiometabolic disease through the network. Second, we examined whether the changes in clinical parameters over time differ according to changes in physical activity behavior. Finally, we examined the changes in clinical parameters, metabolites, and the microbiome during physical activity intervention, and visualized integrated relationships at the multi-omics level through a network.
Methods:
The first study included 17,053 subjects aged 40β69 years in the Health Examinees-Gem (HEXA-G) study from 2004 to 2012. Participation or not in regular exercise at baseline and diagnosis of type 2 diabetes, hypertension, and dyslipidemia at follow-up were investigated by questionnaires. Anthropometric measures and laboratory tests from blood were conducted and data on 42 clinical parameters were collected. We examined the mediation effect of clinical parameters using mediation analyses. Clinical parameter networks were constructed based on the significant differential correlations (p < 0.05) between the exercise and non-exercise groups in men and women, respectively.
The second study was conducted using a community-based cohort (Ansan Ansung cohort) from the 3rd wave to the 5th wave. A total of 3,962 men and women aged 40-69 were included and all analyses were performed in men and women, respectively. Participation or not in regular exercise was investigated by questionnaire like as HEXA-G study. According to the combination of regular exercise in each of the 3rd, 4th, and 5th waves (3rd/4th/5th), two groups of no changes in behavior (N/N/N, Y/Y/Y) and 4groups showed changes in behavior were defined. Twenty-three clinical parameters were obtained by anthropometric measurements and laboratory tests from blood. The relative changes (%) in clinical parameters were calculated from the 3rd wave to the 5th wave. The relative changes in clinical parameters according to the patterns and changes in regular exercise behavior were examined by the LSmeans and the general linear model.
The third study included 14 middle-aged women who completed a physical activity intervention. The intervention was conducted with an exercise period of 3 months and a daily life period of 3 months. The amount of objective physical activity was measured by an accelerometer for 2 weeks each during the exercise period and daily life period. Blood collection, fecal collection, measurements of blood pressure, and test of exercise ability were performed three times: at the enrollment, after exercise period, and after daily life period. Glycemic indicators and lipid-related markers were obtained from blood, and the concentration of 208 blood metabolites was measured through targeted metabolomics. Microbiome data was obtained from fecal samples by 16s rRNA sequencing. The difference in biomarkers before and after the intervention period was examined by the Wilcoxon rank-sum test. The integrated relationship between biomarkers in omics level was examined by Spearman correlation coefficient and visualized via the network.
Results:
We observed significant mediators in 14 and 16 of the clinical parameters in men and women, respectively from the first study. Among the mediators, triglyceride level was a noteworthy mediator in decreasing the risk of CMD with exercise, explaining 23.79% in men and 58.20% in women. A group in which TG is linked to low-density lipoprotein (LDL) cholesterol and high-density lipoprotein (HDL) cholesterol was commonly observed in men and women through the clinical parameter network. Body composition-related markers were likely to play major roles in men, while obesity-related markers seemed to be key factors in women.
In the second study, when comparing the group that did not exercise consistently (N/N/N), and the group that did exercise consistently (Y/Y/Y), there was a difference of changes in waist circumference, hip circumference, and fasting blood glucose in men, and changes in 6 body composition-related markers and 2 lipid-related markers showed differences in women. When the regular exercise group changed into the non-exercise behavior, the fasting blood glucose was greater increased in men, and the lipid-related markers were more increased in women. Conversely, when the non-exercise group changed into participating in regular exercise, body fat-related markers, fasting insulin, lipid-related markers were increased less or decreased in men, and body composition-related markers decreased less in women. Noteworthy, the triglycerides increased less when the non-exercise behavior changed into a regular exercise in both men and women.
In the intervention study, physical activity decreased during the daily life period compared with the exercise period. After the exercise period, blood pressure, HbA1c, and LDL cholesterol were reduced, and they tended to increase again after the daily life period. During the exercise period, 40 metabolites showed significant changes, and they were correlated with changes in clinical parameters such as blood pressure, HbA1c, and LDL cholesterol in the network.
Conclusion:
This study used various biomarkers including omics to understand the understanding the biological processes of the effect of physical activity on the prevention of cardiometabolic diseases in terms of systems epidemiology. Moreover, not only the potential mechanisms centered on lipid-related markers but also relationships between clinical parameters and metabolites could be suggested by visualization of the integrated relationship between biomarkers in the network. The network analysis which used in this study can be applied to data obtained from untargeted metabolomics or whole-genome sequencing of the microbiome and contribute to identifying novel biomarkers or suggesting more detailed mechanisms.μ°κ΅¬ λ°°κ²½:
μ 체νλμ΄ μ¬νκ΄ λμ¬μ§ν(cardiometabolic diseases)μ λ°μμνμ κ°μμν¨λ€λ κ²μ μλ§μ μνμ°κ΅¬λ₯Ό ν΅ν΄ μλ €μ Έ μλ€. κ·Έλ¬λ ν΅μμ μΈ μνμ°κ΅¬λ‘λ μ§λ³ μλ°© κΈ°μ μ κ΄μ¬λ μλ¬Όνμ μμΈλ€μ 볡ν©μ μΈ κ΄κ³λ₯Ό μ΄ν΄νκΈ° μ΄λ ΅λ€λ νκ³κ° μλ€. μ΅κ·Ό μνμ°κ΅¬μμλ μ μ 체(genome)λ₯Ό ν¬ν¨ν λμ¬μ²΄(metabolome) λλ μ₯λ΄λ―Έμλ¬Όκ΅°μ μ μ μ λ³΄μΈ λ§μ΄ν¬λ‘λ°μ΄μ΄(microbiome) λ±μ λμ©λ μ€λ―Ήμ€(omics) λ°μ΄ν°λ€μ΄ μ΄μ© κ°λ₯νκ² λ¨μ λ°λΌ, λ€μμ€μ μ¬λ¬ μ체μ§ν λ°μ΄ν°λ€μ μ΄μ©νμ¬, μ΄λ€κ°μ ν΅ν©μ μΈ κ΄κ³λ₯Ό νμ
νκ³ μκ°μ νλ¦μ λ°λ₯Έ λ³νλ₯Ό νμΈνλ μμ€ν
μνμ μ κ·Όμ νμμ±μ΄ μ κΈ°λκ³ μλ€. λ³Έ μ°κ΅¬μμλ μ 체νλμ μ¬νκ΄ λμ¬μ§ν μλ°© ν¨κ³Όμ κ΄λ ¨νμ¬ κΈ°μ‘΄ μνμ°κ΅¬μ νκ³μ μ 극볡νκ³ λ³΅μ‘ν μλ¬Όνμ κΈ°μ μ μ΄ν΄νκΈ° μν΄ μμ€ν
μνμ μ κ·Ό λ°©λ²μΌλ‘ λ€μκ³Ό κ°μ΄ μ°κ΅¬λ₯Ό μννμλ€. 첫 λ²μ§Έλ‘, μ 체νλκ³Ό μ¬νκ΄ λμ¬μ§ν λ°μ μν μ¬μ΄μ 맀κ°μμΈμΌλ‘ μμ©νλ μμμ§νλ€μ κ°μ ν¨κ³Όλ₯Ό νμΈνκ³ , κ·Έλ€ μ¬μ΄μ 볡ν©μ μΈ κ΄κ³λ₯Ό λ€νΈμν¬λ‘ 보μ¬μ£Όκ³ μ νμλ€. λ λ²μ§Έλ‘λ μκ°μ νλ¦μ λ°λ₯Έ μμμ§νλ€μ λ³νκ° μ 체νλμ νν λ³ν μμμ λ°λΌ μ°¨μ΄κ° μλμ§λ₯Ό μ΄ν΄λ³΄κ³ μ νμλ€. λ§μ§λ§μΌλ‘, μ΄λ μ€μ¬μ°κ΅¬λ₯Ό ν΅ν΄ μμμ§νλΏ μλλΌ, λμ¬μ²΄μ λ§μ΄ν¬λ‘λ°μ΄μ΄μ λ³νλ₯Ό νμΈνκ³ multi-omics μμ€μμ ν΅ν©μ μΈ κ΄κ³λ₯Ό λ€νΈμν¬λ₯Ό ν΅ν΄ 보μ¬μ£Όκ³ μ νμλ€.
μ°κ΅¬ λ°©λ²:
첫 λ²μ§Έ μ°κ΅¬λ νκ΅ λμκΈ°λ° μ½νΈνΈ(HEXA-G)μ 40-69μΈ μ±μΈ 17,053λͺ
μ ν¬ν¨νμμΌλ©° λͺ¨λ λΆμμ λ¨λ
κ°κ°μμ μννμλ€. κΈ°λ°μ‘°μ¬μμ κ·μΉμ μΈ μ΄λ μ¬λΆλ μ€λ¬ΈμΌλ‘ μ‘°μ¬νμκ³ , μ 체κ³μΈ‘κ³Ό νμ‘κ²μ¬λ‘ μ»μ΄μ§ μμμ§ν 42κ°λ₯Ό λΆμμ μ΄μ©νμλ€. μ¬νκ΄ λμ¬μ§ν λ°μ μ¬λΆλ₯Ό μ μνκΈ° μν΄ μΆμ μ‘°μ¬μμ μ€λ¬ΈμΌλ‘ μ‘°μ¬λ λΉλ¨, κ³ νμ, μ΄μμ§μ§νμ¦μ μ§λ¨ μ¬λΆμ μ§λ¨ μ°λλ₯Ό μ¬μ©νμλ€. κ·μΉμ μΈ μ΄λκ³Ό μμμ§νκ°μ μ°κ΄μ±μ μΌλ° μ ν λͺ¨λΈμ ν΅ν΄ νκ°νμκ³ , κ·μΉμ μ΄λκ³Ό λμ¬μ§ν λ°μμν κ°μ μ°κ΄μ± λ° μμμ§νλ€κ³Ό λμ¬μ§ν λ°μμν κ°μ μ°κ΄μ±μ μ½μ€λΉλ‘μνλͺ¨λΈμ ν΅ν΄ νμΈνμλ€. 맀κ°λΆμμ ν΅ν΄ κ·μΉμ μΈ μ΄λκ³Ό λμ¬μ§ν λ°μμν μ¬μ΄μ μ°κ΄μ± μΆμ μΉ μ€ μμμ§νλ€μ΄ κ°λ κ°μ ν¨κ³Όλ₯Ό μΆμ νμλ€. μ΄λκ·Έλ£Ήκ³Ό λΉμ΄λκ·Έλ£Ή μ¬μ΄μμ μ μν μ°¨μ΄λ₯Ό 보μ΄λ μ°¨λ³μ μκ΄κ΄κ³(differential correlation)λ₯Ό κΈ°λ°μΌλ‘ μμμ§ν λ€νΈμν¬λ₯Ό ꡬμΆνμλ€.
λ λ²μ§Έ μ°κ΅¬λ νκ΅ μ§μμ¬νκΈ°λ°(μμ° μμ±) μ½νΈνΈ 3κΈ°λΆν° 5κΈ°κΉμ§μ μλ£λ₯Ό μ΄μ©νμλ€. 40-69μΈ μ±μΈ 3,962λͺ
μ ν¬ν¨νμμΌλ©° λΆμμ λ¨λ
κ°κ°μμ μννμλ€. λμκΈ°λ° μ½νΈνΈμμμ λ§μ°¬κ°μ§λ‘ μ€λ¬ΈμΌλ‘ κ·μΉμ μΈ μ΄λ μ¬λΆλ₯Ό μ‘°μ¬νμκ³ , 3κΈ°, 4κΈ°, 5κΈ° κ°κ°μμμ κ·μΉμ μΈ μ΄λ μ¬λΆ μ‘°ν©μ λ°λΌ(3κΈ°/4κΈ°/5κΈ°) μ΄λ νν λ³νκ° μλ 2κ° κ·Έλ£Ή(N/N/N, Y/Y/Y)κ³Ό μ΄λ νν λ³νλ₯Ό λ³΄μΈ 4κ°μ κ·Έλ£Ή(N/N/Y, N/Y/Y, Y/Y/N, Y/N/N)μ μ μ νμλ€. λ³Έ μ°κ΅¬μμλ μ 체κ³μΈ‘κ³Ό νμ‘κ²μ¬λ‘ μ»μ΄μ§ 23κ°μ μμμ§νλ₯Ό μ¬μ©νμκ³ κ° μμμ§νλ€μ λνμ¬ 3κΈ° λλΉ 5κΈ°μμμ μλλ³νλ(%)μ κ³μ°νμλ€. λμ΄λ₯Ό 보μ ν νκ· (LSmeans)κ³Ό μΌλ° μ ν λͺ¨λΈμ ν΅ν΄ κ·μΉμ μ΄λ μ°Έμ¬ νν μμμ λ°λ₯Έ μμμ§ν λ³νλμ μ°¨μ΄λ₯Ό νμΈνμλ€.
μΈ λ²μ§Έ μ°κ΅¬λ μ 체νλ μ€μ¬μ°κ΅¬λ₯Ό μλ£ν 40-69μΈ μ±μΈ μ¬μ± 14λͺ
μ ν¬ν¨νμλ€. μ€μ¬μ°κ΅¬λ 3κ°μμ μ΄λκΈ°κ°κ³Ό 3κ°μμ μΌμμνκΈ°κ°μΌλ‘ μ§ννμμΌλ©°, μ΄λκΈ°κ°κ³Ό μΌμμνκΈ°κ° κ°κ°μμ 2μ£Όμ© κ°μλκ³λ‘ κ°κ΄μ μ 체νλλμ μΈ‘μ νμλ€. μ°κ΅¬ λ±λ‘ μμ , μ΄λκΈ°κ° μ’
λ£ μμ , μΌμμνκΈ°κ° μ’
λ£ μμ μ΄ 3νμ κ±Έμ³ μ±ν, μ±λ³, νμ μΈ‘μ λ° μ΄λ λ₯λ ₯ νκ°κ° μ΄λ£¨μ΄μ‘λ€. νμ‘μΌλ‘λΆν° νλΉ μ§ν, μ§μ§ μ§νλ₯Ό νκ°νκ³ , νμ λμ¬μ²΄ λΆμ(targeted metabolomics)μ ν΅ν΄ 208κ°μ§ νμ€ λμ¬μ²΄μ λλλ₯Ό μΈ‘μ νμλ€. 16s rRNA sequencing λ°©λ²μ ν΅ν΄ λλ³μλ£μ microbiome λ°μ΄ν°λ₯Ό ν보νμλ€. μμ½μ¨ μμ ν© κ²μ μ ν΅ν΄ μ€μ¬ μ νμ μ체μ§ν μ°¨μ΄λ₯Ό κ²μ νμκ³ , μ€νΌμ΄λ§ μκ΄κ³μλ‘ μ체μ§ν λ³νλ€κ°μ κ΄κ³λ₯Ό νμΈνμλ€.
μ°κ΅¬ κ²°κ³Ό:
λμκΈ°λ° μ½νΈνΈμ μμμ§ν κ°μ ν¨κ³Όλ₯Ό νκ°ν μ°κ΅¬μμλ 42κ°μ μμμ§νλ€ μ€ λ¨μμμ 14κ°, μ¬μμμ 16κ°μ μμμ§νκ° κ·μΉμ μ΄λκ³Ό μ¬νκ΄ λμ¬μ§ν λ°μ μνκ°μ μ°κ΄μ±μμ μ μλ―Έν κ°μ ν¨κ³Όλ₯Ό κ°λλ€λ κ²μ νμΈνμλ€. κ·Έ μ€μμλ μ€μ±μ§λ°©(triglyceride (TG))μ΄ λ¨λ
λͺ¨λμμ κ°μ₯ ν° λΆμ¨λ‘ 맀κ°ν¨κ³Όλ₯Ό μ€λͺ
νμλ€(λ¨μ: 23.79%, μ¬μ: 58.20%). λ¨λ
κ°κ°μμ ꡬμΆλ μμμ§ν λ€νΈμν¬λ‘λΆν° triglycerideκ° low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterolκ³Ό μ°κ²°λ κ΅°μ§μ΄ 곡ν΅μ μΌλ‘ κ΄μ°°λμλ€. λ°λ©΄, λ¨μμμλ κ·Όμ‘μ μ€μ¬μΌλ‘ν 체μ±λΆκ΄λ ¨ μ§νλ€μ΄ κ΅°μ§μ μ΄λ£¨μκ³ , μ¬μμμλ 볡λΆμ§λ°©λμ μ€μ¬μΌλ‘ν λΉλ§κ΄λ ¨ μ§νλ€μ΄ μ£Όμ κ΅°μ§μ μ΄λ£¨μλ€.
μ§μμ¬ν μ½νΈνΈμμ μ§νλ μ°κ΅¬μμλ μκ°μ λ°λ₯Έ μ΄λ νν λ³νμ λ°λ₯Έ μμμ§νμ λ³νλ₯Ό νκ°νμλ€. μ΄λμ μ§μμ μΌλ‘ νμ§ μμλ κ·Έλ£Ήκ³Ό μ΄λμ μ§μμ μΌλ‘ νλ κ·Έλ£Ήμ λΉκ΅νμμ λ, λ¨μ±μμλ ν리λλ , μλ©μ΄λλ , 곡볡νλΉ λ³νλμ΄ μ°¨μ΄λ₯Ό 보μκ³ , μ¬μ±μμλ 체μ±λΆκ΄λ ¨ μ§ν 6κ°μ μ§μ§κ΄λ ¨ μ§ν 2κ°κ° μ°¨μ΄λ₯Ό 보μλ€. κΈ°λ°μ‘°μ¬ μμ μμ κ·μΉμ μΈ μ΄λμ νμΌλ μΆμ μ‘°μ¬μμ μ΄λμ μ νλ ννλ‘ λ³νν κ²½μ°, λ¨μ±μμλ 곡볡νλΉμ μ¦κ° μ λκ° λ 컀μ§μ νμΈνμκ³ , μ¬μ±μμλ μ§μ§κ΄λ ¨ μ§νμ μ¦κ°λμ΄ λμ± μ»€μ§λ κ²μΌλ‘ λνλ¬λ€. κΈ°λ°μ‘°μ¬ μμ μμ κ·μΉμ μΈμ΄λμ νμ§ μμμΌλ μΆμ μ‘°μ¬μμ κ·μΉμ μΈ μ΄λμ μ°Έμ¬νλ ννλ‘ λ³ννλ κ²½μ°, λ¨μ±μμλ 체μ§λ°© μ§νλ€κ³Ό 곡볡 μΈμλ¦° μμΉ, μ§μ§ μ§νλ€μ΄ λ μ¦κ°νκ±°λ μ€νλ € κ°μνμκ³ , μ¬μ±μμλ 체μ±λΆ μ§νλ€μ΄ λ κ°μνμλ€. λ¨λ
곡ν΅μ μΌλ‘ μ΄λμ μ°Έμ¬νλ ννλ‘ λ³ννμμ λ μ€μ±μ§λ°©μ΄ λ μ¦κ°νλ κ²μ νμΈνμλ€.
μ€μ¬μ°κ΅¬μμλ κ°κ΄μ μΌλ‘ μΈ‘μ λ μ 체νλλμ΄ μ΄λκΈ°κ°λ³΄λ€ μΌμμνκΈ°κ°μμ μ μνκ² κ°μνμλ€. μ°κ΅¬ λ±λ‘ μμ κ³Ό λΉκ΅νμ¬ μ΄λκΈ°κ° νμ νμκ³Ό νλΉ μ§νμΈ HbA1c, μ§μ§ μ§ν μ€μμλ LDL μ½λ μ€ν
λ‘€μ΄ κ°μνμκ³ , μ΄λ€μ μΌμμν μ΄νμ λ€μ μ¦κ°νλ κ²μΌλ‘ 보μλ€. μ΄λκΈ°κ° λμ 40κ°μ λμ¬μ²΄κ° μ μν λ³νλ₯Ό 보μμΌλ©° μ΄μ€ 6κ°λ μμμ§νμΈ νμ, HbA1c, LDL μ½λ μ€ν
λ‘€μ λ³νμ μκ΄κ΄κ³κ° μμμ λ€νΈμν¬λ₯Ό ν΅ν΄ νμΈνμλ€.
κ²°λ‘ :
λ³Έ μ°κ΅¬λ μ 체νλμ΄ μ¬νκ΄ λμ¬μ§ν μλ°©μ λ―ΈμΉλ μν₯μ κ΄ν κΈ°μ μ μ΄ν΄νκ³ μ, μμ€ν
μνμ μ κ·ΌμΌλ‘μ λ€μν μ체μ§νλ€μ νμ©νμ¬ μ 체νλκ³Όμ μ°κ΄μ±λΏ μλλΌ μ 체νλμ λ³νμ λ°λ₯Έ μ체μ§νμ λ³ν λ° μ체μ§νλ€ μ¬μ΄μ 볡ν©μ μΈ κ΄κ³λ₯Ό νμΈνμλ€. 맀κ°λΆμμ ν΅ν΄ μ 체νλμΌλ‘ μΈν μ¬νκ΄ λμ¬μ§ν μλ°©μ μμ΄ λ¨λ
λͺ¨λ triglycerideκ° κ°μ₯ ν° λ§€κ°ν¨κ³Όλ₯Ό κ°λλ€λ κ²μ νμΈν μ μμκ³ , λ€νΈμν¬ μκ°νλ₯Ό ν΅ν΄ μ§μ§μ§νλ€ μ¬μ΄μ κΈ°μ μ κ΄κ³λ₯Ό 보μ¬μ€ μ μμλ€. μ΄λ νν λ³νμ λ°λ₯Έ μμμ§νμ λ³νμμλ λ¨λ
곡ν΅μ μΌλ‘ triglycerideμ΄ λ³νκ° νμΈλμμΌλ©°, μ€μ¬μ°κ΅¬λ₯Ό ν΅ν΄μλ νμ, λΉλ¨, μ§μ§ κ΄λ ¨ μμμ§νμ λμ¬μ²΄λ€ μ¬μ΄μ μ μ¬μ μΈ κ΄κ³λ₯Ό μ μν μ μμλ€. μμ€ν
μνμ μ κ·Ό λ°©λ²μ λ§μ μ체μ§νλ€κ³Ό κ΄λ ¨ μμΈ μ¬μ΄μ κ΄κ³λ₯Ό ν΅ν©μ μΌλ‘ λΆμνκ³ μκ°ν ν¨μΌλ‘μ¨ λ³΅μ‘ν κ΄κ³μ λν ν΄μμ ν μ μκ² ν΄μ£Όλ©°, κ΄λ ¨λ κΈ°μ μ λν΄ λ λμ μ΄ν΄λ₯Ό ν μ μλλ‘ ν΄μ€λ€. λν λ³Έ μ°κ΅¬μμ μ μλ κΈ°μ λ€μ μ€νμ°κ΅¬μμ μ§μ€μ μΌλ‘ λ€λ£ΈμΌλ‘μ¨ μ 체νλμ΄ κ±΄κ°μ λ―ΈμΉλ μ΄λ‘μ΄ ν¨κ³Όμ λν κΈ°μ μ λ°νκ³ μ€λͺ
νλλ° νκ±Έμ λ λμκ° μ μμ κ²μΌλ‘ κΈ°λνλ€.μ΄λ‘ i
μ½μ΄ λͺ©λ‘ vii
λͺ©μ°¨ x
ν λͺ©λ‘ xii
κ·Έλ¦Ό λͺ©λ‘ xiv
λΆλ‘ λͺ©λ‘ xvi
1. μ λ‘ 1
1.1 μ°κ΅¬ λ°°κ²½ 1
1.2 μ°κ΅¬ λͺ©μ 20
2. μ°κ΅¬ λ°©λ² λ° κ²°κ³Ό 22
2.1. μ 체νλκ³Ό μ¬νκ΄ λμ¬μ§ν λ°μ μνμμμ μμμ§νλ₯Ό ν΅ν 맀κ°ν¨κ³Ό λ° ν΅ν©μ κ΄κ³ νκ° 22
2.1.1 μ°κ΅¬ λ°©λ² 22
2.1.2 μ°κ΅¬ κ²°κ³Ό 34
2.2. μ 체νλ νν λ³ν μμμ λ°λ₯Έ μμμ§ν λ³ν νκ° 45
2.1.1 μ°κ΅¬ λ°©λ² 45
2.1.2 μ°κ΅¬ κ²°κ³Ό 55
2.3. μ΄λ μ€μ¬μ λ°λ₯Έ λ€μ€ μ€λ―Ήμ€ μ체μ§ν λ³ν λ° ν΅ν©μ κ΄κ³ νκ° 68
2.1.1 μ°κ΅¬ λ°©λ² 68
2.1.2 μ°κ΅¬ κ²°κ³Ό 77
3. κ³ μ°° 91
4. κ²°λ‘ 103
μ°Έκ³ λ¬Έν 105
λΆλ‘ 119
Abstract 177λ°
A Critical Review on Libets Work and Follow-up Studies on Free Will - Implications of Neuroscientific Research on Criminal Law -
μμ μμ§μ μ‘΄μ¬μ λν λ
Όμμ μ² νμμλ λ¬Όλ‘ λ²μ νλ¨μμ μ±
μλ‘ κ³Ό κ΄λ ¨λμ΄ λ²νμμλ λ€λ£¨μ΄μ§κ³ μλ€. μ΄λ€μ μ£Όλ‘ κ°λ
μ λΆμμ΄λ λΉμμ± λ±μ μ μ λ‘ ν μ¬λ³μ λ
Όμμλ€. κ·Έλ°λ° μ΄ μ£Όμ μ λν μ κ²½κ³Όνμ μ°κ΅¬κ° μ§νλλ©΄μ λ
Όμμ μ°Έμ¬νλ μ°κ΅¬μμ λ²μκ° νμ₯λκ³ μλ€. Libet, Gleason, Wright, & Pearl (1983)μ μ°κ΅¬λ κ·Έ μ€μν μλ°μ μ΄λ€. μ΄ μ°κ΅¬μλ€μ λνλκ³Ό μμμ μλκ°μ κ΄λ ¨μ±μ μΈ‘μ ν κ²°κ³Όλ₯Ό λ°νμΌλ‘, μ°λ¦¬μ νλμ λ¬Όλ‘ κ·Έ νλμ νκ³ μ νλ μμμ μλλ κ·Έμ μ ννλ 무μμμ λ νλμμ λΉλ‘―λλ€κ³ μ£Όμ₯νμλ€. λ³Έ κ³ μμλ Libet λ±μ μ°κ΅¬λ₯Ό κ°λ΅ν μ΄ν΄λ³Έ ν, νμ μ°κ΅¬λ₯Ό ν΅ν΄ λ°νμ§ λ¬Έμ μ μ μ μ°¨μμ λ¬Έμ μ Libet λ±μ κ°μ μμμ λ¬Έμ λ‘ λλμ΄ μ΄ν΄λ³΄μλ€. λν Libet λ±μ μ°κ΅¬ κ²°κ³Όλ₯Ό ν¬ν¨νμ¬ μ κ²½κ³Όνμ λ°κ²¬μ λ²μ‘°κ³μμ μμ©νλ λ°©μμ λν λͺ κ°μ§ μ μμ νμλ€.μ΄ λ
Όλ¬Έμ 2010λ
λ SNU Brain Fusion Program(μ κ²½κ³Όνκ³Ό λ² μ°κ΅¬) μ§μμ¬μ
λΉλ₯Ό μ§μλ°μ μ°κ΅¬λμλ€