176 research outputs found
Eliminating unobserved heterogeneity, using hierarchical models
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Όλ¬Έ(μμ¬) -- μμΈλνκ΅λνμ : κ²½μλν κ²½μνκ³Ό, 2023. 2. κΉμ€λ².Freemium strategies contain both free and premium' options, offering some products or services for free as a sample to encourage paid option sales and expand their user base (Kumar, 2014; Liu et al., 2014; Gu et al., 2018). Distributing basic app downloads for free as a sample and selling paid options, usually through in-app purchases (IAP), has become a prevalent freemium strategy among mobile apps.
This paper empirically analyzes the freemium mobile game users' reaction to the price of add-ons using mobile game transaction data provided by an app store. The observed add-on price in the data is constant over time. Since the add-on information is not included in the dataset (e.g., the characteristics of add-ons, or the quality level of add-ons), the categorical information is limited. There are insufficient game characteristics to capture all the game-level variations, and the add-on price is the only add-on-level variable. The freemium mobile game users can download and experience the games before they purchase add-on options and infer the quality of the games and add-ons. Therefore, it is crucial to include add-on level intercepts to separate the impact of game-level and add-on-level heterogeneity and correctly specify the impact of the add-on price.
First, this research aims to determine mobile game users reactions to the add-on price of apps that use freemium strategies. Second, this study aims to find a categorical intercept that efficiently captures the time-invariant bias. Since the add-on price is time-consistent, including add-on level intercepts in the linear demand models is impossible. This study includes profit-maximizing firm assumptions to obtain a 2-stage model with add-on level intercepts. The categorical heterogeneity can be captured by including fixed or random intercepts. Third, reflecting the multi-level structure of this data is the objective of this paper. The price coefficient can be specified at the genre level. Since the data used in this paper is hierarchical, the Bayesian inference method was implied to improve the understanding of the multi-level structure of the models.ν리미μ(Freemium) μ λ΅μ μλΉμ€λ μ νμ μΌλΆλ₯Ό 무λ£λ‘ μ 곡νκ³ μ λ£ μ΅μ
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Όλ¬Έμ λͺ©μ μ΄λ€. κ°κ²© κ³μλ μ₯λ₯΄ λ³λ‘ μΈ‘μ λ μ μμΌλ©°, λ³Έ λ
Όλ¬Έμμ μ¬μ©ν λ°μ΄ν°λ κ³μΈ΅μ μ΄κΈ° λλ¬Έμ λ² μ΄μ§μ μΆλ‘ λ°©λ²μ μ¬μ©νμ¬ κ³μΈ΅μ ꡬ쑰λ₯Ό μΆμ νμλ€.I. Introduction 1
1.1. Study Background 1
1.2. Research Objectives 3
II. Literature review 5
2.1. Freemium Strategies in the Mobile Game Industry 5
2.2. Sampling Effect in Freemium Apps 8
III. Model 13
3.1. Demand Model 13
3.2. Supply-Side Assumptions 17
IV. Data and Variables 21
4.1. Mobile-Game App Store Data 21
4.2. Independent Variables 22
4.3. Summary Statistics of the Data 24
V. Empirical Analysis with Fixed Effects 28
5.1. Fixed effect Models with Promotion and Download Lag 28
5.2. Comparison of the Fixed Effect Models 33
5.3. Comparison of Models with Popular Games 37
VI. Bayesian Estimation 39
6.1. Bayesian Structure for Genre-Specific Price Coefficients 39
6.2. Sampling the Genre-Specific Price Coefficients 41
VII. Add-on Price Elasticities 42
VIII. Conclusion and Discussion 44
References 47
Abstract in Korean 52
Appendix A 54
Table Index
Table 1. Summary of Relevant Freemium Studies 11
Table 2. Summary Statistics of the Data 26
Table 3. Comparison of Different Fixed Effect Models Non-Purchased 30
Table 4. Fixed Effect Model Comparison Non-Purchase and OnlyPurchased 35
Table 5. Model Comparison with FOC j-level Top Selling Apps 37
Table 6. Posterior of alpha 42
Table 7. Estimated Price Elasticities of the Models 44
Figure Index
Figure 1. Scatter Plot of the Full Data, Including Non-Purchased Observations 27
Figure 2. Correlation Heat Map of the Download and Promotion Variables 33
Figure 3. Scatter Plot of Price and Sales Quantity of Add-ons by Genre 39
Figure 4. Bayesian Structure of Genre-Specific Price Coefficients in Stage 2. 40
Figure 5. Posterior Plot of the Inverse of Genre-specific Price Coefficients 41μ
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ꡬκ°μμ SSRF λ¬Έμ λ₯Ό νμ΄ κ°λ³ μ νΈλ₯Ό ꡬμ±νλ κ°μ ꡬν΄λ΄ μ νΈ λΆλ¦¬λ₯Ό μλ£νλ€. λ³Έ κΈ°λ²μ μ¬λ¬ κ°μ μ΄ νμν κΈ°μ‘΄μ ICA κΈ°λ° μν₯ μ νΈ λΆλ¦¬ λ° YG μν₯ μ νΈ λΆλ¦¬μ λΉν΄ λ μ νν μ νΈλΆλ¦¬ κ²°κ³Όλ₯Ό λ΄λ κ²μ νμΈνμλ€.Recently, research on acoustic signal processing is increasing. This is because meaningful information can be obtained and utilized usefully from acoustic signal processing. Therefore, this paper deals with the acoustic signal processing techniques for sound recorded in the indoor environment.
First, we introduce a method for estimating the location of a sound source under indoor environment where there are high reverberation and lots of noise. In the case of existing methods such as interaural level difference (ILD) based localization, time difference of arrival (TDoA) based localization, and steered response power phase transformation (SRP-PHAT) based localization, the accuracy is lowered when applied under recordings from indoor environment with high reverberation. However in this paper, we define a new cost function that can find an optimal combination of microphone pair which results in highest performance.
The microphone pair with the lowest value of cost function was chosen as an optimal pair, and the source location was estimated with the optimal microphone pair. It was confirmed that the distance error was reduced compared to existing methods.
Next, a technique for recovering the lost sample value from the recorded signal called sketching and stacking with random fork (SSRF) is introduced. In this technique, the target sound source is a superposition of several sinusoidal signals.
It is assumed that there are multiple sound sources in the anechoic chamber, but there is only one microphone. It is trivial that a sinusiodal wave can be transformed into an exponential function based on Euler's formula. If some of the terms of the exponential function follow a geometric sequence, those values can be obtained using SSRF. To solve this problem, the concept of a random fork is newly introduced. Comparing the recovery error based on SSRF with existing methods such as compressive sensing based technique and deep neural network (DNN) based technique, the accuracy of SSRF based signal recovery was higher.
Finally, this paper introduces a blind source separation (BSS) technique for based on the previously introduced SSRF technique. In this technique, as before, it is assumed that the sinusoidal waves are superposed. In addition, while the previous technique assumed a situation where all sinusoidal waves were emitted simultaneously, this technique assumed a situation where different sound sources were separated by different distances from the microphone and arrived at the microphone with different time delays. Under these assumptions, a new BSS method for separating single signals from the mixture based on SSRF is introduced. The SSRF BSS is mainly composed of three steps: estimation of the number of sound sources, estimation of time delay, and signal separation. While the existing BSS methods require information on the source number to be known a priori, SSRF BSS does not require source number. Whereas existing BSS methods can only be applied to signals without time delay, SSRF BSS method has the advantage in that it can be applied to the mixture of signals with different time delays. It was confirmed that SSRF BSS produces more accurate separation results compared to the existing independent component analysis (ICA) BSS and Yu Gang (YG) BSS.1 INTRODUCTION
2 IMPROVING ACOUSTIC LOCALIZATION PERFORMANCE BY FINDING OPTIMAL PAIR OF MICROPHONES BASED ON COST FUNCTION 5
2.1 Motivation 5
2.2 Conventional Acoustic Localization Methods 8
2.2.1 Interaural Level Difference 8
2.2.2 Time Difference of Arrival 12
2.2.3 Steered Response Power Phase Transformation 14
2.3 System Model 17
2.3.1 Experimental Scenarios 17
2.3.2 Definition of Cost Function 18
2.4 Results and Discussion 20
2.5 Summary 22
3 ACOUSTIC SIGNAL RECOVERY BASED ON SKETCHING AND STACKING WITH RANDOM FORK 24
3.1 Motivation 24
3.2 SSRF Signal Model 26
3.2.1 Source Signal Model 26
3.2.2 Sampled Signal Model 26
3.2.3 Corrupted Signal Model 27
3.3 SSRF Problem Statement 28
3.4 SSRF Methodology 28
3.4.1 Geometric Sequential Representation 29
3.4.2 Definition of Random Fork 30
3.4.3 Informative Matrix 31
3.4.4 Data Augmentation 32
3.4.5 Solution of SSRF Problem 33
3.4.6 Reconstruction of Corrupted Samples 37
3.5 Performance Analysis 37
3.5.1 Simulation Set-up 37
3.5.2 Reconstruction Error According to Bernoulli Parameter and Number of Signals 38
3.5.3 Detailed Comparison between SSRF and DNN 40
3.5.4 SSRF Result for Signal with Additive White Gaussian Noise 42
3.6 Summary 43
4 SINGLE CHANNEL ACOUSTIC SOURCE NUMBER ESTIMATION AND BLIND SOURCE SEPARATION BASED ON SKETCHING AND STACKING WITH RANDOM FORK 44
4.1 Motivation 44
4.2 SSRF based BSS System Model 48
4.2.1 Simulation Scenarios 48
4.3 SSRF based BSS Methodology 52
4.3.1 Source Number and ToA Estimation based on SSRF 52
4.3.2 Signal Separation 55
4.4 Results and Discussion 57
4.4.1 Source Number and ToA Estimation Results 57
4.4.2 Separation of the Signal 59
4.5 Summary 61
5 CONCLUSION 64
Abstract (In Korean) 75λ°
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μ κ·λͺ¨μμ μ κ·Ήμ μΈ μ°Έμ¬κ° κ°λ₯νκ² νμμΌλ©°, κΆλ¦¬μ‘°μ λ¨κ³μμ μ°Έμ¬νλ μ§μ£Όλ€μ μΈμμ μΆμμμΌ μ¬κ°λ°μμ κ°μ₯ μ΄λ €μ΄ κ³Όμ μΈ ν μ§μμ κΆμ νμκ³Όμ μ μ½κ² λ§λλ ν¨κ³Όλ₯Ό λ§λ€μλ€. νμ§λ§ μμκ° λ κ° μ§κ΅¬μ μ£Όλ―Όλ€μ κ·Έλ€μ μꡬλ₯Ό μ¬μ
κ³Όμ μ λ°μνκΈ° μ΄λ €μμ‘μΌλ©° μ΄λ μ€νλ € λΆμμ μμΈμ΄ λμλ€. λν μ¬μ
μνμμ κΆν κ°νλ‘ μΈν λΆμμ©μΌλ‘ μΈνμ¬ μ¬κ°λ°λ‘ μΈν ννμ΄ μ§μ μ μΌλ‘ μ§μμ μ£Όλ―Όλ€μκ² λμκ°μ§ λͺ»νλ κ²°κ³Ό λν λ§λ€κ² λμλ€.
μ΄λ¬ν λμ¬μ¬κ°λ°μ¬μ
μ νΉμ§μΌλ‘ μΈνμ¬, μ¬κ°λ°μ΄ μ€νλ μ΄ν κΈκ²©νκ² μ¬μ
μ΄ μ§νλμμ§λ§, κ°μ λλ‘λ³μ μμΉνμ§ μλ μ§κ΅¬λ μ‘΄μΉμ§κ΅¬μ κ°μ΄ μ¬μ
μ±μ΄ λ¨μ΄μ§λ μ§κ΅¬, λλ κ³νκΈ°λ°μμ€μ΄ μμ λ μ§κ΅¬μ κ²½μ° μ¬μ
μ§νμ΄ μ΄λ €μμ§κ² λμλ€. μ΄λ‘ μΈν΄ μ§μ λ΄μ μμΉν λμκΈ°λ°μμ€λ€μ λ―Έμμ±λμ΄ κ³νμ μλλλ‘ μλνμ§ μκ² λλ κ²°κ³Όλ₯Ό λ§λ€μλ€.
λν μλ£μ§κ΅¬μ 건물λ€μ΄ λ‘λΉκ³΅κ°μ λ§λ€κΈ° μν΄ μλ½μμ€ λ° μ§μμμ€μ λλΆλΆ μ§νμ μμΉν νμ μ€νλ € λ―Έμνμ§κ΅¬μ μλ½μμ€μ΄ νΈν©μ λ§μΌλ©° μλ£μ§κ΅¬μ λ°°νμ§μμμ€μ μν μ μννκ² λμ΄ μΌμ μμ€ μ΄μμ κ²½μ μ νλκ³Ό μ΄μ΅μ΄ 보μ₯λμλ€. λ―Έμνμ§κ΅¬μ μλ½μμ€λ€μ λμ΄λλ μΈκ΅¬λ₯Ό μμ©νκΈ° μν΄ νμ§λ¨μμ κ°λ°μ νμΌλ‘ μΈν μμ 건물과 μ’μ 골λͺ©κΈΈλ‘ ꡬμ±λ λ‘μ μμ€μ νκ³λ₯Ό 극볡ν΄μΌλ§ νλλ°, μ΄λ λ―Έμμ±λ λμκΈ°λ°μμ€μ λ°μν μ ν΄κ³΅κ°μ μ μ νκ³ μ μ©νλ νλμΌλ‘ μ΄μ΄μ‘λ€.
λμ¬μ¬κ°λ°μ μ μ§μ μ¬κ°λ°λ°©μμΌλ‘ μΈνμ¬ λ°μν λ―Έμμ±λ λμκΈ°λ°μμ€λ€μ κ³νμ΄ μλμΉ μμλ μ ν΄κ³΅κ°λ€μ λ§λ€μ΄λκ³ , κ·Έ 곡κ°μ μ£Όλ―Όλ€μ λ€μν νμ©λ°©μμ ν΅νμ¬ μ 3곡κ°μ νΉμ§μ λλ©° μ‘΄μ¬νκ³ μλ€. κ·μ λμ§ μκ³ μΌμμ μ΄λ©°, λμμ νμ μΉ¨ν¬νκ³ κ³ μ ν μ§μμ νλλ€μ λ§λλ μ 3곡κ°μ νΉμ±μ μ¬κ°λ°κ³νμ μνμ¬ νμ±λ μλ£μ§κ΅¬μ λμλ₯Ό ꡬμ±νλ©° μνΈλ³΄μμ μΈ μν μ νκ³ μλ€. μλ£μ§κ΅¬μ λ―Έμνμ§κ΅¬μ 곡쑴μνκ° μ₯κΈ°νλκ³ μλ νμ¬μ μν© μμμ λμ¬μ¬κ°λ°μ§μμ μ‘΄μ¬νλ μ 3곡κ°λ€μ νΉμ±μ λμμ μ£Όλ―Ό λͺ¨λκ° μ°Έμ¬νλ μ§μκ°λ₯ν λ€μμ±μ λμλ₯Ό λ§λ€ μ΄μ κ° λ κ²μ΄λ€.μ 1 μ₯ μ λ‘ 1
μ 1 μ μ°κ΅¬μ λ°°κ²½ λ° λͺ©μ 1
μ 2 μ μ°κ΅¬μ λ°©λ² λ° λμ 5
μ 3 μ μ°κ΅¬νλ¦λ 7
μ 2 μ₯ μ 3곡κ°μ κ΄ν μ΄λ‘ μ κ³ μ°° 8
μ 1 μ μ 3κ³΅κ° μ΄λ‘ μ μ κ° 8
1. μ리 λ₯΄νλΈλ₯΄μ μ¬νμ 곡κ°μ΄λ‘ 8
2. μλμλ μμμ μ 3κ³΅κ° 10
μ 2 μ λμμ΄λ‘ μμ μ 3κ³΅κ° 13
1. μ€λλ 건물μ μ€μμ± 13
2. κ³νκ³Ό κ³νμ μ€νμ¬μ΄μ λͺ¨μ 15
3. λμμ λ³νμ μ 3κ³΅κ° 17
μ 3 μ μκ²° 19
μ 3 μ₯ μ¬κ°λ°λ‘ μΈν λ€λμ λ³νκ³Όμ 21
μ 1 μ λμ¬μ¬κ°λ°μ¬μ
μ λμ
κ³Όμ 21
1. μ¬κ°λ° μ΄μ μ μ¬λλ¬Έμ 21
2. μ¬κ°λ° λμ
μ λ°°κ²½ 23
3. μ¬κ°λ°μ λν μ£Όλ―Όλ€κ³Όμ κ°λ± 27
4. μκ²° 28
μ 2 μ λ€λ μ¬κ°λ° κΈ°λ³Έκ³νμ λ³νκ³Όμ 30
1. 1967λ
μ¬κ°λ°μ§κ΅¬κ³ν λ³΄κ³ μ 31
2. 1971λ
μ곡λ λ° λ¬΄κ΅μ§κ΅¬ μ¬κ°λ°κ³ν λ° μ‘°μ¬μ€κ³ 34
3. 1973λ
λ¬΄κ΅ λ° λ€λμ§κ΅¬ μ¬κ°λ°μ¬μ
κΈ°λ³Έκ³ν 37
4. 1976λ
무κ΅, λ€λ λ° μλ¦°μ§κ΅¬ μ¬κ°λ° κΈ°λ³Έκ³ν 39
5. 1978λ
μ΄νμ 무κ΅, λ€λ κΈ°λ³Έκ³ν 43
6. μκ²° 46
μ 3 μ μ¬κ°λ°κ³νμΌλ‘ μΈν λ€λ λμμ‘°μ§μ λ³ν 50
1. μ¬μ
μ μΆμ§μ μ°¨μ μνκ³Όμ 50
2. λ€λ λμμ‘°μ§μ λ³νκ³Όμ 55
3. μ¬κ°λ°κ³ν μνμ κ²°κ³Ό 65
μ 4 μ₯ λ€λμ μ 3κ³΅κ° 69
μ 1 μ μ¬κ°λ° κΈ°λ³Έ κ³νμ λͺ¨μ 69
1. μμ±λμ§ λͺ»ν λμκΈ°λ°μμ€ 70
2. μ‘΄μΉμ§κ΅¬μ λ¬Έμ μ μ§κ΅¬μ ꡬν 72
3. μλ£μ§κ΅¬μ λ―Έμνμ§κ΅¬μ κ΄κ³μ± 76
4. 보ν곡κ°μ΄ λ λ―Έμνμ§κ΅¬μ λλ‘λ€ 79
μ 2 μ λ€λμμ λ°κ²¬λλ μ 3곡κ°λ€ 81
1. μ μ©λλ λμκΈ°λ°μμ€λ€ 81
2. λ―Έμνμ§κ΅¬μ λ€μν μ μ νλ 109
3. μκ²° 122
μ 5 μ₯ λ€λμ λμμ¬μκ³Ό μ 3κ³΅κ° 126
μ 1 μ λ€λμ μμλ λμμ¬μ 126
1. λ―Έμνμ§κ΅¬μ κ°λ° μ ν μν 126
2. 2025 λμνκ²½μ λΉμ¬μ
κΈ°λ³Έκ³ν 130
3. λ¬΄κ΅ λ€λμ λμ¬νλ ₯ νλ‘μ νΈ 134
μ 2 μ μ 3곡κ°μ νμ©λ°©μ 137
1. μΌμμ μΈ λμκ³ν (Interim Plan) 137
2. 곡μ λλ κ³΅κ° (Rentable Spaces) 141
μ 6 μ₯ κ²°λ‘ 146
μ°Έκ³ λ¬Έν 150
Abstract 152Maste
μλ°ν¨μμ μ΄λ§: κ³κ°μ§ ν¨ν¬ν¬μ ν¨ν¬ν¬ λ¬Ένλ₯Ό μ€μ¬μΌλ‘
νμλ
Όλ¬Έ (μμ¬)-- μμΈλνκ΅ λνμ : μ¬ννκ³Ό, 2015. 8. κΉνμ€.λ³Έ μ°κ΅¬λ νκ΅ μ¬νμμ μλ°ν¨μ΄λΌλ κ°μΉκ° μ΄λ»κ² ννλκ³ λμμ μΌλ‘ μΆκ΅¬λλμ§μ λν μ°κ΅¬μ΄λ€. μ΄λ₯Ό ν¨ν¬ν¬ μ‘μ§μ μ΄λ₯Ό λλ¬μΌ λͺ¨μ, μ¬νμ λ΄λ‘ λ±μ ν΅ν΄ λΆμνκ³ μ νμΌλ©° μ°κ΅¬ κ²°κ³Όλ λ€μκ³Ό κ°λ€.
첫째, μ°½μ‘° λμ ν¬νλλμμ μμ
λ ν¨ν¬ν¬ λ¬Ένλ νκ΅μ μμ λ¬Ένμ νλ§ λ¬Ένμ λν μ΄κ΄κ³Ό λ§λ¬Όλ € μΈκΈ°λ₯Ό λμλ€. νκ΅μμ μ΄κΈ°μλ λ
립μΆνλ¬Όμ νλλ‘ κ°λ³μ μΈ μ¨λΌμΈ ꡬ맀μ μκ·λͺ¨ μ±
λ°©μμ ꡬ맀λ₯Ό ν΅ν΄ ν¨ν¬ν¬ μ‘μ§κ° μλ €μ‘μΌλ©° μ΄ν ν¨ν¬ν¬ κ΄λ ¨ λͺ¨μμ΄ μκΈ°κ³ λ―Έλμ΄μ μ‘°λͺ
μ λ°μΌλ©° νμ°λμλ€. μ μΈκ³λ‘ νμ°λ ν¨ν¬ν¬ λ¬Ένλ νμ€ν° λ¬Ένλ‘λ μλ €μ‘λλ° νκ΅μ κ²½μ° μ μ μΈλμ λ¬Ένμ , κ²½μ μ μλ³Έμ λΆμ¬λ‘ μΈν΄ νμ€ν° λ¬Ένκ° λ°λ¬νκΈ°λ μ΄λ €μ μΌλ©° λμ μ°½μμ μΈ μ§μ’
μμ μΌνλ μ§μ₯μΈλ€μ΄λ μ£ΌλΆλ€μ μ€μ¬μΌλ‘ ν¨ν¬ν¬ λ¬Ένκ° νΌμ Έλκ°λ€.
λμ§Έ, μλ°ν μ·¨ν₯κ³Ό μΆμ λ°©μμ λ―Έμ μ·¨ν₯μ κ²°κ³Όμ΄μ μ€λ¦¬μ μ νμ μΌνμ΄κΈ°λ νλ€. μλ°ν μ·¨ν₯μ λ¨μνκ³ μ μ λ μλ¦λ€μ, μ μ°μ§ μμ λ―ν λ©μΌλ‘ λνλλ€. μ΄λ€μ κ³Όν λλμ μ£Όλ κ²μ κ³Όκ°ν 골λΌλ΄κ³ κ°μ₯ νμμ μΈ κ²λ§ λ¨κ²¨ κΉλνκ³ μ°μν λλμ μΆκ΅¬νλ€. μ΄λ μλ±λ°λ± μ μ°μ§ μκ³ νλλ λ©μΌλ‘ 보μ¬μ§λ κ²μ΄ μ€μνλ€. μ΄λ¬ν μλ°ν μ·¨ν₯μ μΌμμμ μΌκ³Ό μ·¨λ―Έμ λΆλΆλͺ
ν κ²½κ³, μμ°κ³Ό κ°κΉμ, μΌμ μ μ¬μ λ‘ λνλκΈ°λ νλ€. μλ°ν¨μ κΈ°μ‘΄μ νλ €ν 볼거리 μμ£Όμ λ―Έμ κ°μΉμ λ°λ κΈλΆλ‘ λνλ μ νμ΄λ©° μ΄λ₯Ό ν΅ν΄ μμ μ μ 체μ±μ κ°ννκ³ λμ€κ³Ό 거리λ₯Ό λλ€. μλ°ν μ·¨ν₯μ λ¨μν λ―Έμ μ νμ λ¬Έμ μμ κ·ΈμΉμ§ μκ³ μμ‘΄μ£Όμμ λν νΌλ‘κ°μμ ννΌνκ³ μ λνλλ€. μμ‘΄μ£Όμλ κ·Ήνμ μν©μμ λͺ©μ¨μ ꡬνκ±°λ 컀λ€λ μ±κ³΅μ μ±μ·¨νλ €λ μμΈλΌκΈ°λ³΄λ€ κ°μΈμ νλ²ν ν볡과 μμ μ μν΄ κ²½μ μν©μ μ§μμ μΌλ‘ μ°Έμ¬νλ λ§μμ΄λ€. ν¨ν¬ν¬ λ¬Ένλ κ²½μ μμμ λννμ§ λͺ»νκ³ μΌμμ μ΄μκ°λ νμμλ€μκ² μ£Όμ²΄κ° λλ κΈ°μ¨μ μ 곡νλ€.
μ
μ§Έ, ν¨ν¬ν¬ λ¬Ένκ° μμ νλ μλ°ν κ΄κ³μ 곡λ체λ μ·¨ν₯κ³Ό μΉλ°ν λΆμκΈ°λ₯Ό 곡μ νλ€. ν¨ν¬ν¬μ‘±λ€μ λ―Έκ΅ ν¨ν¬ν¬ λ³Έμ¬μ μ°κ΄μ λ§Ίκ³ μλ ν¨ν¬ν¬ 곡μ λͺ¨μκ³Ό κ·Έλ°μ λΉκ³΅μ λͺ¨μμ ν΅ν΄ ν¨κ» μμμ λ§λ€κ±°λ μμ¬νλ©΄μ μ΄μΌκΈ°λ₯Ό λλλ€. μ΄λ€μ μμμ κ°κΉμ΄ μ¬λλ€μ ν볡μ μ€μνμ§λ§ μ΄κ²μ΄ ν¨ν¬ν¬μ‘±λ€μ΄ νμμ μμ μλ―Ένμ§λ μλλ€. μ€νλ € μ΄λ€μ μ²μ λ§λλ μ¬λμκ² μ’μ μ¬λ, μ΄λ €μλ μ¬λμ΄ λκ³ μ νλ©° νΈμν λνλ₯Ό νκΈ°λ₯Ό κΈ°λνλ€. λλΆμ΄ μμ§λ¨μ΄ λ€λ₯Έ μμ§λ¨κ³Ό, κ·Έλ¦¬κ³ λ³΄λ€ ν° κ³΅λ체μ μμμ μΌλ‘ μ‘°νλ₯Ό μ΄λ£¨κ³ μ¬νμ λ³νλ₯Ό κ°μ Έμ¬ κ²μ΄λΌλ κΈ°λλ₯Ό κ°μ§κ³ μκΈ°λ νλ€.
λ·μ§Έ, μλ°ν¨μ΄ λνμ μΌλ‘ μλΉλκΈ° μμνλ©΄μ μ€νλ € μλ°ν¨μ μ€μ²μ μ μλκ³ μλ€. ν¨ν¬ν¬ λ¬Ένμ νμ°κ³Ό ν¨κ» νκ²½ λ¬Έμ μ λν κ΄μ¬μ λ°νμΌλ‘ μ¬ν μ΄λμΌλ‘ λ°μ λκ±°λ λλ¦¬κ³ λ¨μν μΆμ λμμ λ°©μμΌλ‘ μλ°ν¨μ΄ μ€μ²λ μ μμ κ²μ΄λΌλ κΈ°λλ λνλ¬λ€. κ·ΈλΌμλ ν¨ν¬ν¬ λ¬Ένμμλ μλ°ν¨μ΄ μ΄λ―Έμ§λ‘μ¨ νκ΅ μ¬νμ μμ‘΄μ£Όμμ κ°λ±μ μΌμΌν€λ κ²μ΄ μλλΌ μ‘°νλ‘μ΄ λͺ¨μ΅μ 보μ΄λ©° κΈ°μ‘΄μ μμ‘΄μ£Όμλ₯Ό μ‘΄μμν€λ νμΌλ‘ μμ©νλ€. μ΄λ νμμλ€μ΄ μλ°ν λ¬Ένμ μμ‘΄μ£ΌμλΌλ νμ€μ΄ μΆ©λν λ μ μλ₯Ό μ ννκΈ°μ λλ λΉμ©μ΄ λͺ¨λ κ°μΈμκ² λΆλ΄λλ©΄μ μλ°ν¨μ μ€μ²ν΄λκ°λ κ²μ΄ μ½μ§ μκΈ° λλ¬Έμ΄λ€. λν μλ°ν¨μ κ°μΉ μμ λμ€ μμμ 곡μ λκ³ μ μΈλλ κ²μΌλ‘ κ°μΈλ€μκ² κ³΅λ체μ μμλΌ μλ€λ λ§μ‘±κ°μ μ£Όλ©΄μ μ€μ λ‘ κ³΅μ μΈ ν΄κ²°μ±
μΌλ‘ μ리μ‘μ§λ λͺ»νκ³ μλ€.
μΈλ λ¬Έν μ¬λλ€μκ² ν¨ν¬ν¬ λ¬Ένλ λμ€ λ¬Ένκ° λμμ§λ§ μΌλ°μΈλ€μκ² ν¨ν¬ν¬ λ¬Ένλ νμ λ¬Ένμ νλλ‘ μΈμλλ€λ μ μμ ν¨ν¬ν¬ λ¬Ένκ° νκ΅ μ¬νμ μλ°ν¨μ μμμ μΌλ§λ λλ¬λ΄μ€ μ μμ κ²μΈμ§ νκ³κ° μ§μ λ μ μλ€. λν κΈΈμ§ μμ κΈ°κ° λμ νλμ μ νμΌλ‘ μ€μ³μ§λκ°λ λ¬Έν νμμ λΆμν¨μ μμ΄ λ³΄λ€ μ²΄κ³μ μΈ λ°©λ²λ‘ μ μ μνμ§ λͺ»ν μ μ΄ μμ½λ€.
μμ‘΄μ£Όμμμ λ²μ΄λκ³ μ νλ μλ°ν¨μμ μ΄λ§μ μΉλ°ν¨κ³Ό μ·¨ν₯μ μ€μ¬μΌλ‘ νλ μμ 곡λ체μμ λ§μ‘±κ°μ μ»κ³ μ€νλ € μμ‘΄μ£Όμμμ μΆκ΅¬ν΄μΌνλ μλ‘μ΄ λ―Έμ κ°μΉλ‘ λ³λͺ¨νλ€. νμ€ μμ μμ‘΄μ£Όμμμ λ²μ΄λκ³ μ νλ κ°μΈλ€μ μλ°ν¨μμ μ΄λ§μ κ°μ§κ³ μμΌλ κ·Έ μ΄λ§μ΄ νμ€κ³Ό μΆ©λνλ©΄μ λ€κ°μ¬ λ―Έλλ₯Ό μ΄λ―Έμ§λ‘ λ§λ€μ΄ 보μ¬μ£Όλ μλ§μΌλ‘ μ νλκ³ μμμ νμ
ν μ μμλ€.β
. μλ‘ 1
1. μ°κ΅¬ λ°°κ²½κ³Ό λ¬Έμ μ κΈ° 1
2. μ ν μ°κ΅¬ κ²ν 6
3. μ£Όμ κ°λ
λ° μ΄λ‘ μ μμ 7
1) μ£Όμ κ°λ
7
β μλ°ν¨ 7
β‘ μ΄λ§ 10
2) μ΄λ‘ μ μμ 11
β μ·¨ν₯κ³Ό μ 체μ±μ μ μΉ 11
β‘ μμ‘΄μ£Όμ 13
β’ νκΈ° κ·Όλμ 곡λ체 15
4. μ°κ΅¬ λμκ³Ό μ°κ΅¬ λ°©λ² 17
1) μ°κ΅¬ λμ 17
2) μ°κ΅¬ λ°©λ² 21
β
‘. ν¨ν¬ν¬ λ¬Ένμ λ°μ 25
1. ν¨ν¬ν¬ λ¬Ένμ μμ
25
1) ν¬νλλμ ν¨ν¬ν¬ λ¬Έν 25
2) νκ΅μΌλ‘μ ν¨ν¬ν¬ λ¬Έν μμ
29
3) ν¨ν¬ν¬μ‘±μ λ°°κ²½ 31
2. ν¨ν¬ν¬ λ¬Ένμ νμ° 40
1) ν¨ν¬ν¬ λͺ¨μμ λ±μ₯κ³Ό λ―Έλμ΄μ μ‘°λͺ
40
2) ν¨ν¬ν¬ λ¬Έν λ΄λ‘ μ κ΅¬μ± 41
β
’. μλ°ν μ·¨ν₯κ³Ό μΆμ λν μ΄λ§ 44
1. μλ°ν μ·¨ν₯ 44
1) κ³Όν¨μ μ μ 45
2) μ μ°μ§ μμ λ© 50
2. μΌμμμ μλ°ν¨μ μ€ν 53
1) μΌκ³Ό μ·¨λ―Έμ λͺ¨νΈν κ²½κ³ 53
2) λμ μ μμ°μ€λ¬μ 57
3) μΌμ μ μ¬μ μ νκ°ν¨ 58
3. μ€λ¦¬μ μ¬λ―Έν 60
1) μ€λ¦¬μ μ νμΌλ‘μμ μ·¨ν₯ 60
2) λ¨λ€λ¦μ μμ©κ³Ό 거리λκΈ°μ νλ 63
4. ν-μμ‘΄μ£Όμλ‘μμ μλ°ν¨ 66
1) μμ‘΄μ£Όμμ λν νΌλ‘κ° 66
2) μΌμ μ ν-μμ‘΄μ£Όμμ μΆκ΅¬ 67
β
£. μλ°ν κ΄κ³μ 곡λ체 νμ±μ λν μ΄λ§ 73
1. λͺ¨μ λΆμ 73
1) 곡μ λͺ¨μ 73
2) λΉκ³΅μ λͺ¨μ 75
2. μΉλ°ν¨κ³Ό μ§μ ν λΆμκΈ°μ μΆκ΅¬ 76
1) μΉλ°ν¨μ νμ± 76
2) μ·¨ν₯μ λ€νΈμν¬ 80
3. μλ°ν κ΄κ³μ 곡λ체μμ μ΄λ§ 81
1) μμμ μ£Όλ³μΈμ ν볡 81
2) μ΄λ¦° μ¬λ λκΈ° λ° λ§λκΈ° 83
3) μμ§λ¨λ€μ μμμ μ‘°νμ λν λ―Ώμ 85
β
€. μλ°ν λμμ μΆκ΅¬ 88
1. λμμ μΆμΌλ‘μ μλ°ν¨μ μΆκ΅¬ 88
1) μ μΉμ λͺ©μ리μ μ¬ν μ΄λμΌλ‘μ λ°μ 88
2) λμμ μΆμ λ°©μμΌλ‘μ μλ°ν¨μ μ€μ² 89
3) κ³ μ λ μ¬λ‘건과 κ°μμ λν κ±°λΆκ° 92
2. μλ°ν¨μ λνν 93
1) ν¨ν¬ν¬ μ€νμΌμ μ ν 93
2) ν¨ν¬ν¬ λ¬Ένμ λν νμν 97
3) κ°λ³μ μΌλ‘ μλΉλλ μλ°ν¨ 98
3. μ μλλ μλ°ν¨μ μ€μ² 100
1) μλ°ν¨μ μ΄λ―Έμ§μ μμ‘΄μ£Όμμ μ‘°ν 100
2) 곡곡μ±μ λ체νλ μλ°ν¨μ 곡λμ± 104
β
₯. κ²°λ‘ 107
μ°Έκ³ λ¬Έν 111
Abstract 121Maste
Induction of WNT inhibitory factor 1 expression by MΓΌllerian inhibiting substance/antiMullerian hormone in the MΓΌllerian duct mesenchyme is linked to MΓΌllerian duct regression.
A key event during mammalian sexual development is regression of the MΓΌllerian ducts (MDs) in the bipotential urogenital ridges (UGRs) of fetal males, which is caused by the expression of MΓΌllerian inhibiting substance (MIS) in the Sertoli cells of the differentiating testes. The paracrine signaling mechanisms involved in MD regression are not completely understood, particularly since the receptor for MIS, MISR2, is expressed in the mesenchyme surrounding the MD, but regression occurs in both the epithelium and mesenchyme. Microarray analysis comparing MIS signaling competent and Misr2 knockout embryonic UGRs was performed to identify secreted factors that might be important for MIS-mediated regression of the MD. A seven-fold increase in the expression of Wif1, an inhibitor of WNT/Ξ²-catenin signaling, was observed in the Misr2-expressing UGRs. Whole mount in situ hybridization of Wif1 revealed a spatial and temporal pattern of expression consistent with Misr2 during the window of MD regression in the mesenchyme surrounding the MD epithelium that was absent in both female UGRs and UGRs knocked out for Misr2. Knockdown of Wif1 expression in male UGRs by Wif1-specific siRNAs beginning on embryonic day 13.5 resulted in MD retention in an organ culture assay, and exposure of female UGRs to added recombinant human MIS induced Wif1 expression in the MD mesenchyme. Knockdown of Wif1 led to increased expression of Ξ²-catenin and its downstream targets TCF1/LEF1 in the MD mesenchyme and to decreased apoptosis, resulting in partial to complete retention of the MD. These results strongly suggest that WIF1 secretion by the MD mesenchyme plays a role in MD regression in fetal males.ope
μ 립μ μκ²μ ν΅ν μ 립μ μ μ§λ¨μ μμ΄μ λΉλ§μ μν₯λΆμ
νμλ
Όλ¬Έ (μμ¬)-- μμΈλνκ΅ λνμ : μνκ³Ό, 2015. 2. κΉμμ
.μλ‘ : λͺ©μ : μ 립μ μκ²λ₯Ό ν΅ν μ 립μ μμ μ§λ¨μ μμ΄μ λΉλ§μ μν₯μ μμ§κΉμ§ λͺ
ννκ² λ°νμ§μ§ μμλ€. λ³Έ μ°κ΅¬μμλ 체μ§λμ§μ (Body mass index, BMI)λ₯Ό μ΄μ©ν΄ λΉλ§μ μ μνκ³ , μ΄λ₯Ό ν΅ν΄ μ 립μ μμ μ§λ¨μ μμ΄μ λΉλ§μ μν₯μ λΆμν΄λ³΄κ³ μ νμλ€.
λμ λ° λ°©λ²: 2008λ
1μλΆν° 2013λ
2μκΉμ§ λ³Έμμμ κ²½μ§μ₯ μ 립μ μκ²μ μν λ°μ 1,213λͺ
μ νμλ₯Ό νν₯μ μΌλ‘ λΆμνμλ€. μ 립μ μκ²μ μ 립μ νΉμ΄νμ μμΉκ° 4ng/ml μ΄μμ΄κ±°λ μ§μ₯μμ§κ²μ¬μμ κ²°μ μ΄ μλ κ²½μ°μ μννμλ€. λΉλ§μ 체μ§λμ§μ 25kg/m2 μ΄μμΌλ‘ μ μνμμΌλ©°, λΉλ§ μ¬λΆμ λ°λΌ νμ κ΅°μ λλκ³ λΉκ΅ λΆμνμλ€. μ‘°μ§κ²μ¬ μ견과 ν¨κ» νμμ λμ΄, μ 립μ νΉμ΄νμμμΉ, μ 립μ ν¬κΈ°, μ§μ₯μμ§κ²μ¬ μ κ²°μ μ 무, λΉλ§ μ¬λΆλ₯Ό μ‘°μ¬νμ¬ λ‘μ§μ€ν± νκ·λΆμμ μννμλ€.
κ²°κ³Ό: μ΄ 1,213λͺ
μ λμμ μ€ 408λͺ
(33.6%)μ΄ λΉλ§μ΄μμΌλ©°, 344λͺ
(28.4%)μμ μ 립μ μμ΄ λ°κ²¬λμκ³ , κ·Έ μ€ 203λͺ
(16.7%)λ κ³ λ±κΈ (high-grade) μ 립μ μμΌλ‘ μ§λ¨λμλ€. λΉλ§ μ¬λΆμ λ°λ₯Έ νμκ΅° λΉκ΅μμ λΉλ§ νμλ λΉλ§μ΄ μλ νμμ λΉν΄ λ μ κ³ (65.5 vs 67.1μΈ, p = 0.003), μ 립μ μ ν¬κΈ°κ° λ μ»ΈμΌλ©° (49.2 vs 42.9 cc, p < 0.001), μ§μ₯μμ§κ²μ¬μμ κ²°μ μ΄ λ μ κ² λνλ¬λ€ (8.1% vs 15.9% p < 0.001). λ€λ³λ λ‘μ§μ€ν± νκ·λΆμμμ λΉλ§ μ¬λΆλ νμμ λμ΄, μ 립μ νΉμ΄νμ μμΉ, μ 립μ ν¬κΈ°, μ μ₯μμ§κ²μ¬μ κ²°μ μ 무μ ν¨κ» μ 립μ μ μ§λ¨μ λν μ μν λ
립μΈμλ‘ λνλ¬μΌλ©°, λΉλ§ νμμμ μ 립μ μμ μ§λ¨ μνλκ° λμμ§λ κ²μΌλ‘ λνλ¬λ€ (OR = 1.446, P = 0.024). μ΄λ¬ν μμμ κ³ λ±κΈ μ 립μ μμ μ§λ¨μ μμ΄μλ λμΌνκ² λνλ¬λ€ (OR = 1.498, P = 0.039).
κ²°λ‘ : λ³Έ μ°κ΅¬μμ λΉλ§ νμλ μ 립μ μ‘°μ§κ²μ¬μμ μ 립μ μμ μ§λ¨ μνλκ° μ μνκ² λμ κ²μΌλ‘ λνλ¬λ€. μΆν μ ν₯μ λ€κΈ°κ΄ μ°κ΅¬λ₯Ό ν΅ν΄ μ 립μ μκ²μ ν΅ν μ 립μ μ μ§λ¨μ μμ΄μ λΉλ§μ μν₯μ λν λ³΄λ€ λ©΄λ°ν λΆμμ΄ νμνκ² λ€.CONTENTS
Abstract. i
Contents . iii
List of tables v
List of figures vi
Introduction. 1
Obesity and health problem. 1
Obesity and prostate cancer. 1
Obesity and prostate cancer in Asia. 1
Patients and Methods. 3
Study Design and prostate biopsy protocol . 3
Clinical parameters and definition of obesity. 3
Statistical analysis 3
Results 4
Patient characteristics. 5
Comparison according to obesity status. 5
Clinical predictors with prostate cancer detection. 5
Additional subgroup analysis. 5
Discussion 7
Difficulty of study due to discrepancy of obesity definition between Asians and Westerns 7
Common clinical findings in Asian and Western biopsy population 7
Obesity and prostate cancer 7
Hypothesis about relationship between obesity and prostate cancer 9
Characteristics of Korean prostate cancer. 10
Limitation and implication 10
Conclusions 12
Ethical standards. 13
References 14
Abstract in Korean 29
List of tables
Table 1. Proposed classification of BMI in adult Asians and Westerns 17
Table 2. Patient characteristics 18
Table 3. Patient characteristics and biopsy outcomes according to BMI 19
Table 4. Multivariate analysis of clinical predictors with overall prostate cancer or high-grade (Gleason score 4+3) prostate cancer detection on prostate biopsy 20
Table 5. Odds ratio of obesity being associated with overall prostate cancer or high-grade (Gleason score 4+3) prostate cancer detection on prostate biopsy. 21
Table 6. Odds ratio of detailed obesity categories being associated with overall prostate cancer or high-grade (Gleason score 4+3) prostate cancer detection on prostate biopsy. 22
Table 7. Odds ratio of obesity being associated with overall prostate cancer or high-grade (Gleason score 4+3) prostate cancer detection on prostate biopsy according to age . 23
Table 8. Odds ratio of obesity being associated with overall prostate cancer or high-grade (Gleason score 4+3) prostate cancer detection on prostate biopsy according to PSA level. 24
Table 9. Patient distribution according to PSA level. 25
List of figures
Figure 1. Patient distribution according to Asian BMI categories 26
Figure 2. Formula for the adjusted PSA value according to the height and weight 27
Figure 3. Hypothetical concept of relationship between obesity and prostate cancer in biopsy population 28Maste
Dehydroascorbic Acid Attenuates Ischemic Brain Edema and Neurotoxicity in Cerebral Ischemia: An in vivo Study
Ischemic stroke results in the diverse phathophysiologies including blood brain barrier (BBB) disruption, brain edema, neuronal cell death, and synaptic loss in brain. Vitamin C has known as the potent anti-oxidant having multiple functions in various organs, as well as in brain. Dehydroascorbic acid (DHA) as the oxidized form of ascorbic acid (AA) acts as a cellular protector against oxidative stress and easily enters into the brain compared to AA. To determine the role of DHA on edema formation, neuronal cell death, and synaptic dysfunction following cerebral ischemia, we investigated the infarct size of ischemic brain tissue and measured the expression of aquaporin 1 (AQP-1) as the water channel protein. We also examined the expression of claudin 5 for confirming the BBB breakdown, and the expression of bcl 2 associated X protein (Bax), caspase-3, inducible nitric oxide synthase (iNOS) for checking the effect of DHA on the neurotoxicity. Finally, we examined postsynaptic density protein-95 (PSD-95) expression to confirm the effect of DHA on synaptic dysfunction following ischemic stroke. Based on our findings, we propose that DHA might alleviate the pathogenesis of ischemic brain injury by attenuating edema, neuronal loss, and by improving synaptic connection.ope
Heat Shock Protein 70 (HSP70) Induction: Chaperonotherapy for Neuroprotection after Brain Injury
The 70 kDa heat shock protein (HSP70) is a stress-inducible protein that has been shown to protect the brain from various nervous system injuries. It allows cells to withstand potentially lethal insults through its chaperone functions. Its chaperone properties can assist in protein folding and prevent protein aggregation following several of these insults. Although its neuroprotective properties have been largely attributed to its chaperone functions, HSP70 may interact directly with proteins involved in cell death and inflammatory pathways following injury. Through the use of mutant animal models, gene transfer, or heat stress, a number of studies have now reported positive outcomes of HSP70 induction. However, these approaches are not practical for clinical translation. Thus, pharmaceutical compounds that can induce HSP70, mostly by inhibiting HSP90, have been investigated as potential therapies to mitigate neurological disease and lead to neuroprotection. This review summarizes the neuroprotective mechanisms of HSP70 and discusses potential ways in which this endogenous therapeutic molecule could be practically induced by pharmacological means to ultimately improve neurological outcomes in acute neurological disease.ope
Bilateral salpingectomy to reduce the risk of ovarian/fallopian/peritoneal cancer in women at average risk: a position statement of the Korean Society of Obstetrics and Gynecology (KSOG)
Based on the current understanding of a preventive effect of bilateral salpingectomy on ovarian/fallopian/peritoneal cancers, the Korean Society of Obstetrics and Gynecology, Korean Society of Gynecologic Endocrinology, Korean Society of Gynecologic Oncology, Korean Society of Maternal Fetal Medicine, and Korean Society for Reproductive Medicine support the following recommendations: β’ Women scheduled for hysterectomy for benign gynecologic disease should be informed that bilateral salpingectomy reduces the risk of ovarian/fallopian/peritoneal cancer, and they should be counseled regarding this procedure at the time of hysterectomy. β’ Although salpingectomy is generally considered as a safe procedure in terms of preserving ovarian reserve, there is a lack of evidences representing its long-term outcomes. Therefore, patients should be informed about the minimal potential of this procedure for decreasing ovarian reserve. β’ Prophylactic salpingectomy during vaginal hysterectomy is favorable in terms of prevention of ovarian/fallopian/peritoneal cancer, although operation-related complications minimally increase with this procedure, compared to the complications associated with vaginal hysterectomy alone. Conversion to open or laparoscopic approach from vaginal approach to perform prophylactic salpingectomy is not recommended. β’ Women who desire permanent sterilization at the time of cesarean delivery could be counseled for prophylactic salpingectomy before surgery on an individual basis.ope
Diagnosis of an indistinct Leydig cell tumor by positron emission tomography-computed tomography
A 51-year-old perimenopausal female patient presented with hirsutism and voice thickening which was started approximately one and a half years ago. Her initial hormone assay revealed elevated plasma testosterone, 5a-dihydrotestosterone, and dehydroepiandrosterone (DHEA) levels and therefore androgen-secreting tumor was first suspected. However, the lesion was inconspicuous on transvaginal sonography, abdominal-pelvic computed tomography (CT) scan, and pelvic magnetic resonance (MRI) imaging. Consequently, 18F-fluorodeoxyglucose (FDG) positron emission tomography-CT was performed, which localized the lesion as a focal FDG uptake within the right adnexa. Total laparoscopic hysterectomy with bilateral salpingo-oophorectomy was performed, and although visible gross mass lesions were not observed intraoperatively, pure Leydig cell tumor was pathologically confirmed within the right ovary. Plasma testosterone, 5a-dihydrotestosterone, and DHEA levels were normalized postoperatively. Clinical signs of virilization were also significantly resolved after 3-months of follow-up.ope
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