京都大学 / 新制・課程博士 / 博士(情報学) / 甲第24732号 / 情博第820号 / 新制||情||138(附属図書館) / 京都大学大学院情報学研究科社会情報学専攻 / (主査)教授 緒方 広明, 教授 伊藤 孝行, 准教授 近藤 一晃 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DGAM

京都大学 / 新制・課程博士 / 博士(情報学) / 甲第24733号 / 情博第821号 / 新制||情||138(附属図書館) / 京都大学大学院情報学研究科社会情報学専攻 / (主査)教授 緒方 広明, 教授 伊藤 孝行, 准教授 馬 強 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DGAM

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The Committee was charged with the responsibility for examining the need for change in pharmacy education and the models of leadership that would enable that change to occur across the academy. They also examined the question of faculty wellbeing in a time of change and made several recommendations and suggestions regarding both charges. Building upon the work of the previous Academic Affairs Committee, the 2018-19 AAC encourages the academy to implement new curricular models supporting personalized learning that creates engaged and lifelong learners. This will require transformational leadership and substantial investments in faculty development and new assessment strategies and resources. Recognizing that the magnitude of the recommended change will produce new stress on faculty, the committee identified the need for much additional work on student, faculty and leaders’ wellbeing, noting the limited amount of empirical evidence on pharmacy related to stress and resilience. That said, if faculty and administrators are not able to address personal and community wellbeing, their ability to support their students’ wellbeing will be compromised.

The present study examined the effectiveness of personalized alcohol feedback interventions in three different delivery formats on alcohol use and related negative consequences in a sample of mandated college students referred for alcohol-related violations. Participants were randomized to one of three conditions: an individually-delivered face-to-face intervention, a group-delivered face-to-face intervention, or a web-based electronically-delivered intervention. Given that the current study sought to modify factors associated with alcohol use, analyses were conducted using only those participants who reported alcohol use at the baseline assessment. The final sample resulted in 173 participants, 18-years-of-age and over, and consisted of 57% males (n = 98) who ranged in age from 18 to 25 years, with a mean age of 18.77 (SD = 1.08). The sample distributions in the individual, group, and electronic conditions were 53 (35 males), 72 (41 males), and 48 (22 males), respectively. Self-reported participant race was 82% White, 9% "Other", 4% Black, 4% Asian, and 1% American Indian or Alaska Native, with 91% classifying their ethnicity as Non-Latino/a. Participant class standing consisted of 69% freshmen, 21% sophomores, 6% juniors, and 4% seniors. The type of housing participants reported living in was comprised of 51% on-campus residence hall, 24% off-campus without parents, 20% university-affiliated off-campus, 2% off-campus with parents, 2% "other" type of housing, and 1% who reported living in a fraternity/sorority house. Findings revealed statistically significant reductions in alcohol use for the individually-delivered intervention, and statistically significant reductions in alcohol-related harms for the individually- and electronically-delivered interventions. No statistically significant results were found for the group-delivered intervention. This study is the first randomized clinical trial to compare an empirically supported individually-delivered personalized alcohol feedback intervention with more cost-effective group- and electronically-delivered feedback formats within a single research design. This examination also sought to add to the extant literature on mandated college students by expanding the range of participant drinking habits reported at baseline to include all drinking levels (excluding those meeting criteria for alcohol dependence), not solely those classified as 'heavy drinking,' as is the typical research convention. Additionally, given the potential demand characteristics to underreport illegal and/or illicit behaviors, this is the first study to provide mandated college students with anonymity pre- and post-intervention. Suggestions for future research, limitations of the current investigation, and implications for the development and improvement of personalized feedback interventions and of interventions aimed at mandated college students are also discussed.

Recommender systems are information filtering tools that discover potential matchings between users and items and benefit both parties. This benefit can be considered a social resource that should be equitably allocated across users and items, especially in critical domains such as education and employment. Biases and unfairness in recommendations raise both ethical and legal concerns. In this dissertation, we investigate the concept of unfairness in the context of recommender systems. In particular, we study appropriate unfairness evaluation metrics, examine the relation between bias in recommender models and inequality in the underlying population, as well as propose effective unfairness mitigation approaches. We start with exploring the implication of fairness in recommendation and formulating unfairness evaluation metrics. We focus on the task of rating prediction. We identify the insufficiency of demographic parity for scenarios where the target variable is justifiably dependent on demographic features. Then we propose an alternative set of unfairness metrics that measured based on how much the average predicted ratings deviate from average true ratings. We also reduce these unfairness in matrix factorization (MF) models by explicitly adding them as penalty terms to learning objectives. Next, we target a form of unfairness in matrix factorization models observed as disparate model performance across user groups. We identify four types of biases in the training data that contribute to higher subpopulation error. Then we propose personalized regularization learning (PRL), which learns personalized regularization parameters that directly address the data biases. PRL poses the hyperparameter search problem as a secondary learning task. It enables back-propagation to learn the personalized regularization parameters by leveraging the closed-form solutions of alternating least squares (ALS) to solve MF. Furthermore, the learned parameters are interpretable and provide insights into how fairness is improved. Third, we conduct theoretical analysis on the long-term dynamics of inequality in the underlying population, in terms of the fitting between users and items. We view the task of recommendation as solving a set of classification problems through threshold policies. We mathematically formulate the transition dynamics of user-item fit in one step of recommendation. Then we prove that a system with the formulated dynamics always has at least one equilibrium, and we provide sufficient conditions for the equilibrium to be unique. We also show that, depending on the item category relationships and the recommendation policies, recommendations in one item category can reshape the user-item fit in another item category. To summarize, in this research, we examine different fairness criteria in rating prediction and recommendation, study the dynamic of interactions between recommender systems and users, and propose mitigation methods to promote fairness and equality. / Doctor of Philosophy / Recommender systems are information filtering tools that discover potential matching between users and items. However, a recommender system, if not properly built, may not treat users and items equitably, which raises ethical and legal concerns. In this research, we explore the implication of fairness in the context of recommender systems, study the relation between unfairness in recommender output and inequality in the underlying population, and propose effective unfairness mitigation approaches. We start with finding unfairness metrics appropriate for recommender systems. We focus on the task of rating prediction, which is a crucial step in recommender systems. We propose a set of unfairness metrics measured as the disparity in how much predictions deviate from the ground truth ratings. We also offer a mitigation method to reduce these forms of unfairness in matrix factorization models Next, we look deeper into the factors that contribute to error-based unfairness in matrix factorization models and identify four types of biases that contribute to higher subpopulation error. Then we propose personalized regularization learning (PRL), which is a mitigation strategy that learns personalized regularization parameters to directly addresses data biases. The learned per-user regularization parameters are interpretable and provide insight into how fairness is improved. Third, we conduct a theoretical study on the long-term dynamics of the inequality in the fitting (e.g., interest, qualification, etc.) between users and items. We first mathematically formulate the transition dynamics of user-item fit in one step of recommendation. Then we discuss the existence and uniqueness of system equilibrium as the one-step dynamics repeat. We also show that depending on the relation between item categories and the recommendation policies (unconstrained or fair), recommendations in one item category can reshape the user-item fit in another item category. In summary, we examine different fairness criteria in rating prediction and recommendation, study the dynamics of interactions between recommender systems and users, and propose mitigation methods to promote fairness and equality.

The human gut microbiota contributes to host metabolic processes. Diverse microbial metabolic enzymes can affect therapeutic agents, resulting in chemical modifications that alter drug efficacy and toxicology. These interactions may result in ineffective treatments and dose-limiting side effects, as shown by bacterial modifications of the cardiac drug digoxin and chemotherapy drug irinotecan, respectively. Yet, few drug-microbiome interactions have been characterized. Here, a platform is developed to screen for drug-microbiome interactions, validated by the isolation of a gut bacterium capable of inactivating the antineoplastic drug doxorubicin. Two hundred gut strains isolated from a healthy patient fecal sample were cultured in the presence of antibiotic and antineoplastic drugs to enrich for resistance and possible inactivation. Raoultella planticola was identified for its ability to inactivate doxorubicin anaerobically through whole cell and crude lysate assays. This activity was also observed in other Enterobacteriaceae and resulted in doxorubicin inactivation by the removal of its daunosamine sugar, likely mediated by a molybdopterin-dependent enzyme. Other potential drug-microbiome interactions were identified in this screen and can be analyzed further. This platform enables the identification of drug-microbiome interactions that can be used to study drug pharmacology, improve the efficacy of therapeutic treatments, and advance personalized medicine. / Thesis / Bachelor of Science (BSc) / The collection of microbes in the human intestinal tract, referred to as the gut microbiome, can modify therapeutic agents and change the efficacy of drug treatments. Identifying these interactions between drugs and the microbiome will help the study of drug metabolism, provide explanations for treatment failure, and enable more personalized health care. For this project, a platform was developed to isolate gut bacteria from human fecal samples and characterize bacteria that are capable of inactivating various antibiotics and anticancer drugs. Through this platform, the gut bacterium Raoultella planticola was found to inactivate doxorubicin, a commonly used anticancer drug. These results suggest that doxorubicin may be inactivated in the gut and demonstrates how this platform can be used to identify drug-microbiome interactions.

The world of precision medicine was revolutionized by the discovery of CRISPR-Cas systems. In particular, the capabilities of the programmable nuclease Cas9 and its derivatives have unlocked a world in which applied genome engineering to cure human disease is a reality being pursued in patient clinical trials. Gene editing via the induction of programmable, site-specific double strand breaks (DSBs) has been revolutionary for the precision medicine field. However, there are many safety concerns centered on the induction of DSBs causing potential undesirable on- and off-target consequences, particularly for in vivo CRISPR applications. To circumvent these warranted concerns, many groups have attempted to repurpose recombinases or engineer new fusion systems to perform programmable genome engineering without the induction of DSBs. This dissertation will first highlight the development of recombinases for programmable DNA insertions over the course of decades, including efforts to evolve novel DNA recognition sequences, efforts to tether recombinases to programmable DNA-binding proteins, and the recent discovery of naturally occurring RNA-guided DNA transposition systems. This dissertation will then highlight the development of CRISPR-associated transposases (CASTs) as DSB-independent programmable mammalian gene editing tools capable of integrating large DNA cargos, as well as the future directions that may further enhance CAST activity in human cells. The works in this dissertation detail the initial efforts to engineer and optimize a new class of genome manipulation tools that were previously absent from the gene editing toolkit.

Background Brain biopsy of intracranial lesions is often necessary to determine specific therapy. The cost of the currently used stereotactic rigid frame and optical tracking systems for brain biopsy in dogs is often prohibitive or accuracy is not sufficient for all types of lesion. Objectives To evaluate the application accuracy of an inexpensive magnetic resonance imaging-based personalized, 3D printed brain biopsy device. Animals Twenty-two dog heads from cadavers were separated into 2 groups according to body weight (<15 kg, >20 kg). Methods Experimental study. Two target points in each cadaver head were used (target point 1: caudate nucleus, target point 2: piriform lobe). Comparison between groups was performed using the independent Student's t test or the nonparametric Mann-Whitney U Test. Results The total median target point deviation was 0.83 mm (range 0.09-2.76 mm). The separate median target point deviations for target points 1 and 2 in all dogs were 0.57 mm (range: 0.09-1.25 mm) and 0.85 mm (range: 0.14-2.76 mm), respectively. Conclusion and Clinical Importance This magnetic resonance imaging-based 3D printed stereotactic brain biopsy device achieved an application accuracy that was better than the accuracy of most brain biopsy systems that are currently used in veterinary medicine. The device can be applied to every size and shape of skull and allows precise positioning of brain biopsy needles in dogs.

This thesis researches users’ motivations for using TikTok, their experience with the personalized algorithm and the effect it has on their subjective well-being. The study is of qualitative character containing 19 questionnaire responses and qualitative interviews with five participants that use TikTok to some extent. The choice of participants was made according to convenience sampling and snowball selection where the results have been analyzed through qualitative content analysis. The literature review resulted in three themes, (1) Motivations for using TikTok, (2) TikTok’s personalized algorithm and its effects, (3) TikTok’s effect on users’ well-being. The results show that the motivation is a key part in users’ engagement with TikTok. Users’ motivations can be to seek entertainment, enjoyment, or inspiration for hobbies where the algorithm responds and analyzes the motivations, in turn the algorithm reinforcesthis in users’ consumption patterns which can result in positive or negative consequences. Their well-being can either increase due to being able to fill up time or entertain themselves, but it can also result in feelings of envy or dissatisfaction with their appearance or looks. These conclusions contribute to the body of research related to social media's effect on their users. It shows the crucial need for future investigation and research of user’s motivations, their experience and effect from the personalized algorithm, and the consequences that the use of digital platforms like TikTok can have on their well-being.