137 research outputs found

    Speech-based automatic depression detection via biomarkers identification and artificial intelligence approaches

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    Depression has become one of the most prevalent mental health issues, affecting more than 300 million people all over the world. However, due to factors such as limited medical resources and accessibility to health care, there are still a large number of patients undiagnosed. In addition, the traditional approaches to depression diagnosis have limitations because they are usually time-consuming, and depend on clinical experience that varies across different clinicians. From this perspective, the use of automatic depression detection can make the diagnosis process much faster and more accessible. In this thesis, we present the possibility of using speech for automatic depression detection. This is based on the findings in neuroscience that depressed patients have abnormal cognition mechanisms thus leading to the speech differs from that of healthy people. Therefore, in this thesis, we show two ways of benefiting from automatic depression detection, i.e., identifying speech markers of depression and constructing novel deep learning models to improve detection accuracy. The identification of speech markers tries to capture measurable depression traces left in speech. From this perspective, speech markers such as speech duration, pauses and correlation matrices are proposed. Speech duration and pauses take speech fluency into account, while correlation matrices represent the relationship between acoustic features and aim at capturing psychomotor retardation in depressed patients. Experimental results demonstrate that these proposed markers are effective at improving the performance in recognizing depressed speakers. In addition, such markers show statistically significant differences between depressed patients and non-depressed individuals, which explains the possibility of using these markers for depression detection and further confirms that depression leaves detectable traces in speech. In addition to the above, we propose an attention mechanism, Multi-local Attention (MLA), to emphasize depression-relevant information locally. Then we analyse the effectiveness of MLA on performance and efficiency. According to the experimental results, such a model can significantly improve performance and confidence in the detection while reducing the time required for recognition. Furthermore, we propose Cross-Data Multilevel Attention (CDMA) to emphasize different types of depression-relevant information, i.e., specific to each type of speech and common to both, by using multiple attention mechanisms. Experimental results demonstrate that the proposed model is effective to integrate different types of depression-relevant information in speech, improving the performance significantly for depression detection

    Introduction to Psychology

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    Introduction to Psychology is a modified version of Psychology 2e - OpenStax

    Multilingual markers of depression in remotely collected speech samples: A preliminary analysis

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    Background: Speech contains neuromuscular, physiological and cognitive components, and so is a potential biomarker of mental disorders. Previous studies indicate that speaking rate and pausing are associated with major depressive disorder (MDD). However, results are inconclusive as many studies are small and underpowered and do not include clinical samples. These studies have also been unilingual and use speech collected in controlled settings. If speech markers are to help understand the onset and progress of MDD, we need to uncover markers that are robust to language and establish the strength of associations in real-world data. // Methods: We collected speech data in 585 participants with a history of MDD in the United Kingdom, Spain, and Netherlands as part of the RADAR-MDD study. Participants recorded their speech via smartphones every two weeks for 18โ€ฏmonths. Linear mixed models were used to estimate the strength of specific markers of depression from a set of 28 speech features. // Results: Increased depressive symptoms were associated with speech rate, articulation rate and intensity of speech elicited from a scripted task. These features had consistently stronger effect sizes than pauses. // Limitations: Our findings are derived at the cohort level so may have limited impact on identifying intra-individual speech changes associated with changes in symptom severity. The analysis of features averaged over the entire recording may have underestimated the importance of some features. // Conclusions: Participants with more severe depressive symptoms spoke more slowly and quietly. Our findings are from a real-world, multilingual, clinical dataset so represent a step-change in the usefulness of speech as a digital phenotype of MDD

    Relevance of parental monitoring strategies in explanation of externalising behaviour problems in adolescence: Mediation of parental knowledge

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    A process model of parental monitoring (PM) proposes that PM occurs in two distinct stages: before the adolescent goes out and when they return home. Parental and adolescent responses to monitoring interactions impact on future monitoring episodes. Research suggests that passive PM strategies (e.g. child disclosure) correlate with higher parental knowledge and less behavior problems. Self-reported measures were used on a sample of 507 Belgrade secondary school students (42.1% male) to examine the mediating effect (mediation analysis using JASP) of parental knowledge (the Scale of Parental Monitoring) on the relationship of PM strategies (Child Disclosure, Parental Solicitation and Parental Control) (the Scale of Parental Monitoring) with externalising problems (Aggressive and Rule-Breaking Behaviour) (ASEBA, YSR). The research results show that Parental Knowledge mediate the relation of Child Disclosure and RuleBreaking Behaviour (z = -6.544, p < .001) and Parental Control and Rule-Breaking Behaviour (z =-3.770, p< .001). No direct link between Parental Control and RuleBreaking Behavior, as well as Parental Solicitation and Rule-Breaking Behavior were established. Full mediation of the link between Child Disclosure and Aggressive Behavior by Parental Knowledge is found (total indirect effect z = -4.050, p < .001). The research results were discussed in the context of the relevance of the PM strategies for greater parental knowledge and prevention of externalising problems in adolescence

    Relevance of parental monitoring strategies in explanation of externalising behaviour problems in adolescence: Mediation of parental knowledge

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    A process model of parental monitoring (PM) proposes that PM occurs in two distinct stages: before the adolescent goes out and when they return home. Parental and adolescent responses to monitoring interactions impact on future monitoring episodes. Research suggests that passive PM strategies (e.g. child disclosure) correlate with higher parental knowledge and less behavior problems. Self-reported measures were used on a sample of 507 Belgrade secondary school students (42.1% male) to examine the mediating effect (mediation analysis using JASP) of parental knowledge (the Scale of Parental Monitoring) on the relationship of PM strategies (Child Disclosure, Parental Solicitation and Parental Control) (the Scale of Parental Monitoring) with externalising problems (Aggressive and Rule-Breaking Behaviour) (ASEBA, YSR). The research results show that Parental Knowledge mediate the relation of Child Disclosure and RuleBreaking Behaviour (z = -6.544, p < .001) and Parental Control and Rule-Breaking Behaviour (z =-3.770, p< .001). No direct link between Parental Control and RuleBreaking Behavior, as well as Parental Solicitation and Rule-Breaking Behavior were established. Full mediation of the link between Child Disclosure and Aggressive Behavior by Parental Knowledge is found (total indirect effect z = -4.050, p < .001). The research results were discussed in the context of the relevance of the PM strategies for greater parental knowledge and prevention of externalising problems in adolescence

    Behavior quantification as the missing link between fields: Tools for digital psychiatry and their role in the future of neurobiology

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    The great behavioral heterogeneity observed between individuals with the same psychiatric disorder and even within one individual over time complicates both clinical practice and biomedical research. However, modern technologies are an exciting opportunity to improve behavioral characterization. Existing psychiatry methods that are qualitative or unscalable, such as patient surveys or clinical interviews, can now be collected at a greater capacity and analyzed to produce new quantitative measures. Furthermore, recent capabilities for continuous collection of passive sensor streams, such as phone GPS or smartwatch accelerometer, open avenues of novel questioning that were previously entirely unrealistic. Their temporally dense nature enables a cohesive study of real-time neural and behavioral signals. To develop comprehensive neurobiological models of psychiatric disease, it will be critical to first develop strong methods for behavioral quantification. There is huge potential in what can theoretically be captured by current technologies, but this in itself presents a large computational challenge -- one that will necessitate new data processing tools, new machine learning techniques, and ultimately a shift in how interdisciplinary work is conducted. In my thesis, I detail research projects that take different perspectives on digital psychiatry, subsequently tying ideas together with a concluding discussion on the future of the field. I also provide software infrastructure where relevant, with extensive documentation. Major contributions include scientific arguments and proof of concept results for daily free-form audio journals as an underappreciated psychiatry research datatype, as well as novel stability theorems and pilot empirical success for a proposed multi-area recurrent neural network architecture.Comment: PhD thesis cop

    Bayesian Networks for the robust and unbiased prediction of depression and its symptoms utilizing speech and multimodal data

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    Predicting the presence of major depressive disorder (MDD) using behavioural and cognitive signals is a highly non-trivial task. The heterogeneous clinical profile of MDD means that any given speech, facial expression and/or observed cognitive pattern may be associated with a unique combination of depressive symptoms. Conventional discriminative machine learning models potentially lack the complexity to robustly model this heterogeneity. Bayesian networks, however, may instead be well-suited to such a scenario. These networks are probabilistic graphical models that efficiently describe the joint probability distribution over a set of random variables by explicitly capturing their conditional dependencies. This framework provides further advantages over standard discriminative modelling by offering the possibility to incorporate expert opinion in the graphical structure of the models, generating explainable model predictions, informing about the uncertainty of predictions, and naturally handling missing data. In this study, we apply a Bayesian framework to capture the relationships between depression, depression symptoms, and features derived from speech, facial expression and cognitive game data collected at thymia.Comment: Accepted for publication at Interspeech 202

    Age differences in conspiracy beliefs around Covid-19 pandemic and (dis)trust in the government

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    Objective: Times of societal crisis, such as the COVID-19 pandemic, during which people need to make sense of a chaotic world and to protect their health and lives, according to psychological research, represent suitable ground for the development of conspiracy theories about origins, spread, and treatment of the threat (coronavirus). Although numerous studies have been conducted on this issue since the beginning of the pandemic until today, most of the studies were conducted on the adult population with limited insights into development of the conspiracy beliefs in adolescence or over the lifespan. Objective of this study is precisely to explore how conspiracy beliefs regarding COVID-19 pandemic differentiate between multiple age groups (cross-sectional design), what are their sources and contexts, and how do they relate with the tendency to trust the government. Methodology: Data were gathered through eight focus group discussions with four age groups (11-12, 14-15, 18-19, 30+) in Serbia. Results: Based on critical discourse analysis, this paper identifies the differences in content and the sources of conspiracy thinking and how it relates to trust in the government. Study shows that high distrust in Serbian government is associated with conspiracy beliefs both within youth and adults. However, while among adolescents this finding is exclusively related with their beliefs that ruling structures have financial gain from the pandemic, against the interests of citizens, among adults it is related to the belief that the government (un)intentionally submits to the new global order that is managed by one or more powerful actors who are coordinated in secret action to achieve an outcome that is of public interest, but not public knowledge. Conclusion: The results will be discussed within current socio-political climate in Serbia, as well as the basis for understanding psychological factors which may underlie these tendencies in conspiracy theorizing, such as social identification, collective narcissism, authoritarianism, and social dominance orientation

    ์ฃผ์š” ์šฐ์šธ ์žฅ์• ์˜ ์Œ์„ฑ ๊ธฐ๋ฐ˜ ๋ถ„์„: ์—ฐ์†์ ์ธ ๋ฐœํ™”์˜ ์Œํ–ฅ์  ๋ณ€ํ™”๋ฅผ ์ค‘์‹ฌ์œผ๋กœ

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ์œตํ•ฉ๊ณผํ•™๊ธฐ์ˆ ๋Œ€ํ•™์› ์œตํ•ฉ๊ณผํ•™๋ถ€(๋””์ง€ํ„ธ์ •๋ณด์œตํ•ฉ์ „๊ณต), 2023. 2. ์ด๊ต๊ตฌ.Major depressive disorder (commonly referred to as depression) is a common disorder that affects 3.8% of the world's population. Depression stems from various causes, such as genetics, aging, social factors, and abnormalities in the neurotransmitter system; thus, early detection and monitoring are essential. The human voice is considered a representative biomarker for observing depression; accordingly, several studies have developed an automatic depression diagnosis system based on speech. However, constructing a speech corpus is a challenge, studies focus on adults under 60 years of age, and there are insufficient medical hypotheses based on the clinical findings of psychiatrists, limiting the evolution of the medical diagnostic tool. Moreover, the effect of taking antipsychotic drugs on speech characteristics during the treatment phase is overlooked. Thus, this thesis studies a speech-based automatic depression diagnosis system at the semantic level (sentence). First, to analyze depression among the elderly whose emotional changes do not adequately reflect speech characteristics, it developed the mood-induced sentence to build the elderly depression speech corpus and designed an automatic depression diagnosis system for the elderly. Second, it constructed an extrapyramidal symptom speech corpus to investigate the extrapyramidal symptoms, a typical side effect that can appear from an antipsychotic drug overdose. Accordingly, there is a strong correlation between the antipsychotic dose and speech characteristics. The study paved the way for a comprehensive examination of the automatic diagnosis system for depression.์ฃผ์š” ์šฐ์šธ ์žฅ์•  ์ฆ‰ ํ”ํžˆ ์šฐ์šธ์ฆ์ด๋ผ๊ณ  ์ผ์ปฌ์–ด์ง€๋Š” ๊ธฐ๋ถ„ ์žฅ์• ๋Š” ์ „ ์„ธ๊ณ„์ธ ์ค‘ 3.8%์— ๋‹ฌํ•˜๋Š” ์‚ฌ๋žŒ๋“ค์ด ๊ฒช์€๋ฐ” ์žˆ๋Š” ๋งค์šฐ ํ”ํ•œ ์งˆ๋ณ‘์ด๋‹ค. ์œ ์ „, ๋…ธํ™”, ์‚ฌํšŒ์  ์š”์ธ, ์‹ ๊ฒฝ์ „๋‹ฌ๋ฌผ์งˆ ์ฒด๊ณ„์˜ ์ด์ƒ๋“ฑ ๋‹ค์–‘ํ•œ ์›์ธ์œผ๋กœ ๋ฐœ์ƒํ•˜๋Š” ์šฐ์šธ์ฆ์€ ์กฐ๊ธฐ ๋ฐœ๊ฒฌ ๋ฐ ์ผ์ƒ ์ƒํ™œ์—์„œ์˜ ๊ด€๋ฆฌ๊ฐ€ ๋งค์šฐ ์ค‘์š”ํ•˜๋‹ค๊ณ  ํ•  ์ˆ˜ ์žˆ๋‹ค. ์ธ๊ฐ„์˜ ์Œ์„ฑ์€ ์šฐ์šธ์ฆ์„ ๊ด€์ฐฐํ•˜๊ธฐ์— ๋Œ€ํ‘œ์ ์ธ ๋ฐ”์ด์˜ค๋งˆ์ปค๋กœ ์—ฌ๊ฒจ์ ธ ์™”์œผ๋ฉฐ, ์Œ์„ฑ ๋ฐ์ดํ„ฐ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœํ•œ ์ž๋™ ์šฐ์šธ์ฆ ์ง„๋‹จ ์‹œ์Šคํ…œ ๊ฐœ๋ฐœ์„ ์œ„ํ•œ ์—ฌ๋Ÿฌ ์—ฐ๊ตฌ๋“ค์ด ์ง„ํ–‰๋˜์–ด ์™”๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์Œ์„ฑ ๋ง๋ญ‰์น˜ ๊ตฌ์ถ•์˜ ์–ด๋ ค์›€๊ณผ 60์„ธ ์ดํ•˜์˜ ์„ฑ์ธ๋“ค์—๊ฒŒ ์ดˆ์ ์ด ๋งž์ถ”์–ด์ง„ ์—ฐ๊ตฌ, ์ •์‹ ๊ณผ ์˜์‚ฌ๋“ค์˜ ์ž„์ƒ ์†Œ๊ฒฌ์„ ๋ฐ”ํƒ•์œผ๋กœํ•œ ์˜ํ•™์  ๊ฐ€์„ค ์„ค์ •์˜ ๋ฏธํก๋“ฑ์˜ ํ•œ๊ณ„์ ์„ ๊ฐ€์ง€๊ณ  ์žˆ์œผ๋ฉฐ, ์ด๋Š” ์˜๋ฃŒ ์ง„๋‹จ ๊ธฐ๊ตฌ๋กœ ๋ฐœ์ „ํ•˜๋Š”๋ฐ ํ•œ๊ณ„์ ์ด๋ผ๊ณ  ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋˜ํ•œ, ํ•ญ์ •์‹ ์„ฑ ์•ฝ๋ฌผ์˜ ๋ณต์šฉ์ด ์Œ์„ฑ ํŠน์ง•์— ๋ฏธ์น  ์ˆ˜ ์žˆ๋Š” ์˜ํ–ฅ ๋˜ํ•œ ๊ฐ„๊ณผ๋˜๊ณ  ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์œ„์˜ ํ•œ๊ณ„์ ๋“ค์„ ๋ณด์™„ํ•˜๊ธฐ ์œ„ํ•œ ์˜๋ฏธ๋ก ์  ์ˆ˜์ค€ (๋ฌธ์žฅ ๋‹จ์œ„)์—์„œ์˜ ์Œ์„ฑ ๊ธฐ๋ฐ˜ ์ž๋™ ์šฐ์šธ์ฆ ์ง„๋‹จ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋ฅผ ์‹œํ–‰ํ•˜๊ณ ์ž ํ•œ๋‹ค. ์šฐ์„ ์ ์œผ๋กœ ๊ฐ์ •์˜ ๋ณ€ํ™”๊ฐ€ ์Œ์„ฑ ํŠน์ง•์„ ์ž˜ ๋ฐ˜์˜๋˜์ง€ ์•Š๋Š” ๋…ธ์ธ์ธต์˜ ์šฐ์šธ์ฆ ๋ถ„์„์„ ์œ„ํ•ด ๊ฐ์ • ๋ฐœํ™” ๋ฌธ์žฅ์„ ๊ฐœ๋ฐœํ•˜์—ฌ ๋…ธ์ธ ์šฐ์šธ์ฆ ์Œ์„ฑ ๋ง๋ญ‰์น˜๋ฅผ ๊ตฌ์ถ•ํ•˜๊ณ , ๋ฌธ์žฅ ๋‹จ์œ„์—์„œ์˜ ๊ด€์ฐฐ์„ ํ†ตํ•ด ๋…ธ์ธ ์šฐ์šธ์ฆ ๊ตฐ์—์„œ ๊ฐ์ • ๋ฌธ์žฅ ๋ฐœํ™”๊ฐ€ ๋ฏธ์น˜๋Š” ์˜ํ–ฅ๊ณผ ๊ฐ์ • ์ „์ด๋ฅผ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ์œผ๋ฉฐ, ๋…ธ์ธ์ธต์˜ ์ž๋™ ์šฐ์šธ์ฆ ์ง„๋‹จ ์‹œ์Šคํ…œ์„ ์„ค๊ณ„ํ•˜์˜€๋‹ค. ์ตœ์ข…์ ์œผ๋กœ ํ•ญ์ •์‹ ๋ณ‘ ์•ฝ๋ฌผ์˜ ๊ณผ๋ณต์šฉ์œผ๋กœ ๋‚˜ํƒ€๋‚  ์ˆ˜ ์žˆ๋Š” ๋Œ€ํ‘œ์ ์ธ ๋ถ€์ž‘์šฉ์ธ ์ถ”์ฒด์™ธ๋กœ ์ฆ์ƒ์„ ์กฐ์‚ฌํ•˜๊ธฐ ์œ„ํ•ด ์ถ”์ฒด์™ธ๋กœ ์ฆ์ƒ ์Œ์„ฑ ๋ง๋ญ‰์น˜๋ฅผ ๊ตฌ์ถ•ํ•˜์˜€๊ณ , ํ•ญ์ •์‹ ๋ณ‘ ์•ฝ๋ฌผ์˜ ๋ณต์šฉ๋Ÿ‰๊ณผ ์Œ์„ฑ ํŠน์ง•๊ฐ„์˜ ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ๋ถ„์„ํ•˜์—ฌ ์šฐ์šธ์ฆ์˜ ์น˜๋ฃŒ ๊ณผ์ •์—์„œ ํ•ญ์ •์‹ ๋ณ‘ ์•ฝ๋ฌผ์ด ์Œ์„ฑ์— ๋ฏธ์น  ์ˆ˜ ์žˆ๋Š” ์˜ํ–ฅ์— ๋Œ€ํ•ด์„œ ์กฐ์‚ฌํ•˜์˜€๋‹ค. ์ด๋ฅผ ํ†ตํ•ด ์ฃผ์š” ์šฐ์šธ ์žฅ์• ์˜ ์˜์—ญ์— ๋Œ€ํ•œ ํฌ๊ด„์ ์ธ ์—ฐ๊ตฌ๋ฅผ ์ง„ํ–‰ํ•˜์˜€๋‹ค.Chapter 1 Introduction 1 1.1 Research Motivations 3 1.1.1 Bridging the Gap Between Clinical View and Engineering 3 1.1.2 Limitations of Conventional Depressed Speech Corpora 4 1.1.3 Lack of Studies on Depression Among the Elderly 4 1.1.4 Depression Analysis on Semantic Level 6 1.1.5 How Antipsychotic Drug Affects the Human Voice? 7 1.2 Thesis objectives 9 1.3 Outline of the thesis 10 Chapter 2 Theoretical Background 13 2.1 Clinical View of Major Depressive Disorder 13 2.1.1 Types of Depression 14 2.1.2 Major Causes of Depression 15 2.1.3 Symptoms of Depression 17 2.1.4 Diagnosis of Depression 17 2.2 Objective Diagnostic Markers of Depression 19 2.3 Speech in Mental Disorder 19 2.4 Speech Production and Depression 21 2.5 Automatic Depression Diagnostic System 23 2.5.1 Acoustic Feature Representation 24 2.5.2 Classification / Prediction 27 Chapter 3 Developing Sentences for New Depressed Speech Corpus 31 3.1 Introduction 31 3.2 Building Depressed Speech Corpus 32 3.2.1 Elements of Speech Corpus Production 32 3.2.2 Conventional Depressed Speech Corpora 35 3.2.3 Factors Affecting Depressed Speech Characteristics 39 3.3 Motivations 40 3.3.1 Limitations of Conventional Depressed Speech Corpora 40 3.3.2 Attitude of Subjects to Depression: Masked Depression 43 3.3.3 Emotions in Reading 45 3.3.4 Objectives of this Chapter 45 3.4 Proposed Methods 46 3.4.1 Selection of Words 46 3.4.2 Structure of Sentence 47 3.5 Results 49 3.5.1 Mood-Inducing Sentences (MIS) 49 3.5.2 Neutral Sentences for Extrapyramidal Symptom Analysis 49 3.6 Summary 51 Chapter 4 Screening Depression in The Elderly 52 4.1 Introduction 52 4.2 Korean Elderly Depressive Speech Corpus 55 4.2.1 Participants 55 4.2.2 Recording Procedure 57 4.2.3 Recording Specification 58 4.3 Proposed Methods 59 4.3.1 Voice-based Screening Algorithm for Depression 59 4.3.2 Extraction of Acoustic Features 59 4.3.3 Feature Selection System and Distance Computation 62 4.3.4 Classification and Statistical Analyses 63 4.4 Results 65 4.5 Discussion 69 4.6 Summary 74 Chapter 5 Correlation Analysis of Antipsychotic Dose and Speech Characteristics 75 5.1 Introduction 75 5.2 Korean Extrapyramidal Symptoms Speech Corpus 78 5.2.1 Participants 78 5.2.2 Recording Process 79 5.2.3 Extrapyramidal Symptoms Annotation and Equivalent Dose Calculations 80 5.3 Proposed Methods 81 5.3.1 Acoustic Feature Extraction 81 5.3.2 Speech Characteristics Analysis recording to Eq.dose 83 5.4 Results 83 5.5 Discussion 87 5.6 Summary 90 Chapter 6 Conclusions and Future Work 91 6.1 Conclusions 91 6.2 Future work 95 Bibliography 97 ์ดˆ ๋ก 121๋ฐ•
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